From 3cefc5fbd36dc5d375d40db6869a0c84bb1e0dcc Mon Sep 17 00:00:00 2001 From: delamorte Date: Thu, 16 May 2019 15:20:13 +0300 Subject: [PATCH 001/155] Added the initial problem class for the EvoNN algorithm Added an example how to call the algorithm --- pyrvea/Examples/evonn_example.py | 10 +++ pyrvea/Problem/evonn_problem.py | 137 +++++++++++++++++++++++++++++++ 2 files changed, 147 insertions(+) create mode 100644 pyrvea/Examples/evonn_example.py create mode 100644 pyrvea/Problem/evonn_problem.py diff --git a/pyrvea/Examples/evonn_example.py b/pyrvea/Examples/evonn_example.py new file mode 100644 index 0000000..56faff4 --- /dev/null +++ b/pyrvea/Examples/evonn_example.py @@ -0,0 +1,10 @@ +from pyrvea.Problem.evonn_problem import EvoNNProblem +import numpy as np + +input_nodes = 4 +hidden_nodes = 6 +input_data = np.random.uniform(0, 1, size=(hidden_nodes, input_nodes)) + +prob = EvoNNProblem(data_input=input_data, num_input_nodes=4, num_hidden_nodes=6) + +obj_func = prob.objectives() \ No newline at end of file diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py new file mode 100644 index 0000000..7e95ce6 --- /dev/null +++ b/pyrvea/Problem/evonn_problem.py @@ -0,0 +1,137 @@ +import numpy as np + + +class EvoNNProblem: + """Creates an Artificial Neural Network (ANN) for the EvoNN algorithm.""" + + def __init__( + self, + data_input=None, + data_output=None, + name=None, + num_input_nodes=4, + num_hidden_nodes=6, + num_output_nodes=1, + w_low=-5.0, + w_high=5.0, + ): + """ + + Parameters + ---------- + data_input : numpy array + Training data input matrix + data_output : numpy array + Training data desired output + name : str + Name of the sample + num_input_nodes : int + The number of nodes in the input layer + num_hidden_nodes : int + The number of nodes in the hidden layer + num_output_nodes : int + The number of nodes in the output layer + w_low : float + The lower bound for randomly generated weights + w_high : float + The upper bound for randomly generated weights + """ + + self.data_input = data_input + self.data_output = data_output + self.name = name + self.num_input_nodes = num_input_nodes + self.num_hidden_nodes = num_hidden_nodes + self.num_output_nodes = num_output_nodes + self.w_low = w_low + self.w_high = w_high + + if len(self.data_input) > 0: + self.num_input_nodes = np.shape(data_input)[1] + + # self.w_matrix = np.hstack((w_matrix, bias)) + + def objectives(self, decision_variables=None) -> list: + + """ Use this method to calculate objective functions. + + Parameters + ---------- + decision_variables : tuple + Variables from the neural network + + Returns + ------- + obj_func : array + The objective function + + """ + + def init_weight_matrix(): + + # Initialize the weight matrix with random weights within boundaries + # Rows = hidden nodes + # Columns = input nodes + # Last column is for bias + + w_matrix = np.random.uniform( + self.w_low, + self.w_high, + size=(self.num_input_nodes, self.num_hidden_nodes), + ) + + # Initialize bias with a default value of 1 + + bias = np.full((self.num_hidden_nodes, 1), 1) + + return w_matrix, bias + + def feed_forward(w_matrix, bias): + + print(np.shape(w_matrix)) + print(np.shape(self.data_input)) + wi = np.dot(self.data_input, w_matrix) + bias + + return wi + + def activation(weighted_input, a_func): + """ Activation function + + Returns + ------- + The penultimate layer Z before the output + + """ + + def sigmoid(s): + return 1 / (1 + np.exp(-s)) + + if a_func == "sigmoid": + hidden_layer = sigmoid(weighted_input) # activation function + + return hidden_layer + + def minimize_error(activated_function): + """ Optimize the training error + + Parameters + ---------- + activated_function + Output of the activation function + + Returns + ------- + """ + + # llsq() + pass + + def minimize_complexity(): + pass + + w_matrix, bias = init_weight_matrix() + weighted_input = feed_forward(w_matrix, bias) + hidden_layer = activation(weighted_input, "sigmoid") + predicted_output = minimize_error(hidden_layer) + + return self.obj_func(decision_variables) From 72f6cca082233e2c1936f071f284f8ba37e7c82e Mon Sep 17 00:00:00 2001 From: delamorte Date: Fri, 17 May 2019 15:44:03 +0300 Subject: [PATCH 002/155] Added method to calculate and optimize training error Added option to have bias in the same matrix as weight or as its own vector Changed data parameter name to training_data --- pyrvea/Problem/evonn_problem.py | 88 +++++++++++++++++++++------------ 1 file changed, 56 insertions(+), 32 deletions(-) diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index 7e95ce6..2a6c9af 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -1,13 +1,13 @@ import numpy as np - +from scipy.linalg import lstsq class EvoNNProblem: """Creates an Artificial Neural Network (ANN) for the EvoNN algorithm.""" def __init__( self, - data_input=None, - data_output=None, + training_data_input=None, + training_data_output=None, name=None, num_input_nodes=4, num_hidden_nodes=6, @@ -19,10 +19,10 @@ def __init__( Parameters ---------- - data_input : numpy array + training_data_input : numpy array Training data input matrix - data_output : numpy array - Training data desired output + training_data_output : numpy array + Training data expected output name : str Name of the sample num_input_nodes : int @@ -37,8 +37,8 @@ def __init__( The upper bound for randomly generated weights """ - self.data_input = data_input - self.data_output = data_output + self.training_data_input = training_data_input + self.training_data_output = training_data_output self.name = name self.num_input_nodes = num_input_nodes self.num_hidden_nodes = num_hidden_nodes @@ -46,10 +46,8 @@ def __init__( self.w_low = w_low self.w_high = w_high - if len(self.data_input) > 0: - self.num_input_nodes = np.shape(data_input)[1] - - # self.w_matrix = np.hstack((w_matrix, bias)) + if len(self.training_data_input) > 0: + self.num_input_nodes = np.shape(training_data_input)[1] def objectives(self, decision_variables=None) -> list: @@ -77,24 +75,35 @@ def init_weight_matrix(): w_matrix = np.random.uniform( self.w_low, self.w_high, - size=(self.num_input_nodes, self.num_hidden_nodes), + size=(self.num_hidden_nodes, self.num_input_nodes), ) - # Initialize bias with a default value of 1 - bias = np.full((self.num_hidden_nodes, 1), 1) return w_matrix, bias - def feed_forward(w_matrix, bias): + def dot_product(w_matrix, bias): + """ Calculate the dot product of input and weight + bias. + TODO: %timeit whether it's faster to keep bias in its own + vector, or as a column in weight matrix + + Parameters + ---------- + w_matrix + bias + + Returns + ------- + + """ - print(np.shape(w_matrix)) - print(np.shape(self.data_input)) - wi = np.dot(self.data_input, w_matrix) + bias + wi = np.dot(self.training_data_input, w_matrix.transpose()) + bias + # biased_matrix = np.hstack((w_matrix, bias)) + # biased_dot = np.dot(self.data_input, biased_matrix[..., :-1].transpose()) + biased_matrix[:,-1] return wi - def activation(weighted_input, a_func): + def activation(weighted_input, name): """ Activation function Returns @@ -106,12 +115,12 @@ def activation(weighted_input, a_func): def sigmoid(s): return 1 / (1 + np.exp(-s)) - if a_func == "sigmoid": - hidden_layer = sigmoid(weighted_input) # activation function + if name == "sigmoid": + activated_function = sigmoid(weighted_input) # activation function - return hidden_layer + return activated_function - def minimize_error(activated_function): + def optimize_error(activated_function, name): """ Optimize the training error Parameters @@ -122,16 +131,31 @@ def minimize_error(activated_function): Returns ------- """ + if name == "llsq": + w_matrix2 = lstsq(activated_function, self.training_data_output) + predicted_values = np.dot(activated_function, w_matrix2) + + + return w_matrix2, predicted_values + + def calculate_error(predicted_values, name): - # llsq() - pass + if name == "rmse": + return np.sqrt(((self.training_data_output - predicted_values) ** 2).mean()) - def minimize_complexity(): - pass + def calculate_complexity(w_matrix, w_matrix2): + + k = np.nonzero(w_matrix) + np.nonzero(w_matrix2) + + w_matrix, bias = init_weight_matrix() - weighted_input = feed_forward(w_matrix, bias) - hidden_layer = activation(weighted_input, "sigmoid") - predicted_output = minimize_error(hidden_layer) + weighted_input = dot_product(w_matrix, bias) + activated_function = activation(weighted_input, "sigmoid") + w_matrix2, predicted_values = optimize_error(activated_function, "llsq") + training_error = calculate_error(predicted_values, "rmse") + + complexity = calculate_complexity(w_matrix, w_matrix2) + - return self.obj_func(decision_variables) + return self.obj_func(training_error) From 711a1bc1022c46f9d2c9e66385ec1dd6699b64c2 Mon Sep 17 00:00:00 2001 From: delamorte Date: Fri, 17 May 2019 15:46:57 +0300 Subject: [PATCH 003/155] Changed parameter names --- pyrvea/Examples/evonn_example.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/pyrvea/Examples/evonn_example.py b/pyrvea/Examples/evonn_example.py index 56faff4..2c889e3 100644 --- a/pyrvea/Examples/evonn_example.py +++ b/pyrvea/Examples/evonn_example.py @@ -1,10 +1,12 @@ from pyrvea.Problem.evonn_problem import EvoNNProblem + import numpy as np input_nodes = 4 hidden_nodes = 6 -input_data = np.random.uniform(0, 1, size=(hidden_nodes, input_nodes)) +training_data = np.random.uniform(0, 1, size=(hidden_nodes, input_nodes)) + +prob = EvoNNProblem(training_data_input=training_data, num_input_nodes=4, num_hidden_nodes=6) -prob = EvoNNProblem(data_input=input_data, num_input_nodes=4, num_hidden_nodes=6) +obj_func = prob.objectives() -obj_func = prob.objectives() \ No newline at end of file From 2763854d7f8c1775b2fcab6640bf15377882e5cb Mon Sep 17 00:00:00 2001 From: delamorte Date: Wed, 22 May 2019 14:49:28 +0300 Subject: [PATCH 004/155] All the functions added to the Problem class --- pyrvea/Problem/evonn_problem.py | 144 +++++++++++++++++--------------- 1 file changed, 78 insertions(+), 66 deletions(-) diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index 2a6c9af..f28c741 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -1,7 +1,9 @@ import numpy as np from scipy.linalg import lstsq +import timeit +from pyrvea.Problem.baseProblem import baseProblem -class EvoNNProblem: +class EvoNNProblem(): """Creates an Artificial Neural Network (ANN) for the EvoNN algorithm.""" def __init__( @@ -12,9 +14,11 @@ def __init__( num_input_nodes=4, num_hidden_nodes=6, num_output_nodes=1, + num_of_objectives=2, w_low=-5.0, w_high=5.0, ): + """ Parameters @@ -43,8 +47,13 @@ def __init__( self.num_input_nodes = num_input_nodes self.num_hidden_nodes = num_hidden_nodes self.num_output_nodes = num_output_nodes + self.num_of_objectives = num_of_objectives + self.num_of_variables = num_input_nodes self.w_low = w_low self.w_high = w_high + self.lower_limits = w_low + self.upper_limits = w_high + #self.num_of_samples = np.shape(training_data_output)[0] if len(self.training_data_input) > 0: self.num_input_nodes = np.shape(training_data_input)[1] @@ -65,97 +74,100 @@ def objectives(self, decision_variables=None) -> list: """ - def init_weight_matrix(): - - # Initialize the weight matrix with random weights within boundaries - # Rows = hidden nodes - # Columns = input nodes - # Last column is for bias + w_matrix, bias = self.init_weight_matrix() + weighted_input = self.dot_product(w_matrix, bias) + activated_function = self.activation(weighted_input, "sigmoid") + w_matrix2, predicted_values = self.optimize_error(activated_function, "llsq") + training_error = self.loss_function(predicted_values, "rmse") - w_matrix = np.random.uniform( - self.w_low, - self.w_high, - size=(self.num_hidden_nodes, self.num_input_nodes), - ) + complexity = self.calculate_complexity(w_matrix, w_matrix2) + minimized_complexity = self.minimize_complexity(predicted_values, complexity) - bias = np.full((self.num_hidden_nodes, 1), 1) + return self.obj_func(training_error, minimized_complexity) - return w_matrix, bias + def init_weight_matrix(self): - def dot_product(w_matrix, bias): - """ Calculate the dot product of input and weight + bias. - TODO: %timeit whether it's faster to keep bias in its own - vector, or as a column in weight matrix + # Initialize the weight matrix with random weights within boundaries + # Rows = hidden nodes + # Columns = input nodes + # Last column is for bias - Parameters - ---------- - w_matrix - bias + w_matrix = np.random.uniform( + self.w_low, self.w_high, size=(self.num_hidden_nodes, self.num_input_nodes) + ) - Returns - ------- + #bias = np.full((self.num_hidden_nodes, 1), 1) + bias = 1 + return w_matrix, bias - """ + def dot_product(self, w_matrix, bias): + """ Calculate the dot product of input and weight + bias. - wi = np.dot(self.training_data_input, w_matrix.transpose()) + bias - # biased_matrix = np.hstack((w_matrix, bias)) - # biased_dot = np.dot(self.data_input, biased_matrix[..., :-1].transpose()) + biased_matrix[:,-1] + Parameters + ---------- + w_matrix + bias - return wi + Returns + ------- - def activation(weighted_input, name): - """ Activation function + """ - Returns - ------- - The penultimate layer Z before the output + wi = np.dot(self.training_data_input, w_matrix.transpose()) + bias + #biased_matrix = np.hstack((w_matrix, bias)) + #wi = ( + # np.dot(self.training_data_input, biased_matrix[..., :-1].transpose()) + # + biased_matrix[:, -1] + #) - """ + return wi - def sigmoid(s): - return 1 / (1 + np.exp(-s)) + def activation(self, wi, name): + """ Activation function - if name == "sigmoid": - activated_function = sigmoid(weighted_input) # activation function + Returns + ------- + The penultimate layer Z before the output - return activated_function + """ - def optimize_error(activated_function, name): - """ Optimize the training error + if name == "sigmoid": + activated_function = lambda x : 1 / (1 + np.exp(-x)) - Parameters - ---------- - activated_function - Output of the activation function + return activated_function(wi) - Returns - ------- - """ - if name == "llsq": - w_matrix2 = lstsq(activated_function, self.training_data_output) - predicted_values = np.dot(activated_function, w_matrix2) + def optimize_error(self, activated_function, name): + """ Optimize the training error + Parameters + ---------- + activated_function + Output of the activation function - return w_matrix2, predicted_values + Returns + ------- + """ + if name == "llsq": + w_matrix2 = lstsq(activated_function, self.training_data_output) + predicted_values = np.dot(activated_function, w_matrix2) - def calculate_error(predicted_values, name): + return w_matrix2, predicted_values - if name == "rmse": - return np.sqrt(((self.training_data_output - predicted_values) ** 2).mean()) + def loss_function(self, predicted_values, name): - def calculate_complexity(w_matrix, w_matrix2): + if name == "rmse": + return np.sqrt(((self.training_data_output - predicted_values) ** 2).mean()) - k = np.nonzero(w_matrix) + np.nonzero(w_matrix2) + def calculate_complexity(self, w_matrix, w_matrix2): - + k = np.nonzero(w_matrix) + np.nonzero(w_matrix2) - w_matrix, bias = init_weight_matrix() - weighted_input = dot_product(w_matrix, bias) - activated_function = activation(weighted_input, "sigmoid") - w_matrix2, predicted_values = optimize_error(activated_function, "llsq") - training_error = calculate_error(predicted_values, "rmse") + return k - complexity = calculate_complexity(w_matrix, w_matrix2) + def minimize_complexity(self, predicted_values, k): + rss = ((self.training_data_output - predicted_values) ** 2).sum() + aic = 2 * k + self.num_of_samples * np.log(rss/self.num_of_samples) + aicc = aic + (2*k*(k+1)/(self.num_of_samples-k-1)) - return self.obj_func(training_error) + return aicc From 69cf122d1666efa293393f6bd92d041d39d48533 Mon Sep 17 00:00:00 2001 From: delamorte Date: Thu, 23 May 2019 10:47:45 +0300 Subject: [PATCH 005/155] Add files via upload --- .gitignore | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index 9b4ea58..4f111e5 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,4 @@ -results/ +4results/ Backup_results/ # Byte-compiled / optimized / DLL files __pycache__/ @@ -125,4 +125,11 @@ dmypy.json # Pyre type checker .pyre/ -*.json \ No newline at end of file +*.json + +# vsCode settings +.vscode/ + +# Poetry files +poetry.lock +pyproject.toml From 2ec477431dd820f632deae20197ea535ebd123fc Mon Sep 17 00:00:00 2001 From: delamorte Date: Thu, 23 May 2019 10:48:06 +0300 Subject: [PATCH 006/155] Add files via upload --- .gitignore | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index 9b4ea58..4f111e5 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,4 @@ -results/ +4results/ Backup_results/ # Byte-compiled / optimized / DLL files __pycache__/ @@ -125,4 +125,11 @@ dmypy.json # Pyre type checker .pyre/ -*.json \ No newline at end of file +*.json + +# vsCode settings +.vscode/ + +# Poetry files +poetry.lock +pyproject.toml From 3c16b8aad33b7bb3ab27c60642d6d860dda7f726 Mon Sep 17 00:00:00 2001 From: delamorte Date: Thu, 23 May 2019 10:50:06 +0300 Subject: [PATCH 007/155] started working on a population class for EvoNN turned off plotting for debugging population_evonn now can handle 2d matrices --- pyrvea/Examples/evonn_example.py | 22 +- pyrvea/Population/Population.py | 2 +- pyrvea/Population/population_evonn.py | 390 ++++++++++++++++++++++++++ pyrvea/Problem/evonn_problem.py | 31 +- 4 files changed, 426 insertions(+), 19 deletions(-) create mode 100644 pyrvea/Population/population_evonn.py diff --git a/pyrvea/Examples/evonn_example.py b/pyrvea/Examples/evonn_example.py index 2c889e3..fb0608c 100644 --- a/pyrvea/Examples/evonn_example.py +++ b/pyrvea/Examples/evonn_example.py @@ -1,12 +1,24 @@ from pyrvea.Problem.evonn_problem import EvoNNProblem +from pyrvea.Population.population_evonn import PopulationEvoNN +from random import randint, sample import numpy as np -input_nodes = 4 -hidden_nodes = 6 -training_data = np.random.uniform(0, 1, size=(hidden_nodes, input_nodes)) +input_nodes = 5 +hidden_nodes = 4 +num_of_samples = 10 +training_data = np.random.uniform(0, 1, size=(num_of_samples, input_nodes)) +preferred_output = np.ones((num_of_samples, hidden_nodes)) -prob = EvoNNProblem(training_data_input=training_data, num_input_nodes=4, num_hidden_nodes=6) +prob = EvoNNProblem( + name="EvoNN", + training_data_input=training_data, + training_data_output=preferred_output, + num_input_nodes=input_nodes, + num_hidden_nodes=hidden_nodes, +) -obj_func = prob.objectives() +pop = PopulationEvoNN(prob) +selected = sample(range(1, 50), randint(1,50)) +pop.keep(selected) \ No newline at end of file diff --git a/pyrvea/Population/Population.py b/pyrvea/Population/Population.py index 9faa2cf..1f3eee1 100644 --- a/pyrvea/Population/Population.py +++ b/pyrvea/Population/Population.py @@ -26,7 +26,7 @@ def __init__( self, problem: "baseProblem", assign_type: str = "LHSDesign", - plotting: bool = True, + plotting: bool = False, *args ): """Initialize the population. diff --git a/pyrvea/Population/population_evonn.py b/pyrvea/Population/population_evonn.py new file mode 100644 index 0000000..bd711a9 --- /dev/null +++ b/pyrvea/Population/population_evonn.py @@ -0,0 +1,390 @@ +from collections import defaultdict +from collections.abc import Sequence +from random import shuffle +from typing import TYPE_CHECKING + +import numpy as np +import pandas as pd +from pyDOE import lhs +from pygmo import fast_non_dominated_sorting as nds +from pygmo import hypervolume as hv +from pygmo import non_dominated_front_2d as nd2 +from tqdm import tqdm, tqdm_notebook +from pyrvea.OtherTools.plotlyanimate import animate_init_, animate_next_ +from pyrvea.OtherTools.IsNotebook import IsNotebook +from pyrvea.Population.Population import Population +from scipy.stats import bernoulli as bn + +if TYPE_CHECKING: + from pyrvea.Problem.baseProblem import baseProblem + from pyrvea.EAs.baseEA import BaseEA + + +class PopulationEvoNN(): + """Define the population.""" + + def __init__( + self, + problem: "EvoNNProblem", + assign_type: str = "RandomDesign", + plotting: bool = False, + *args + ): + """Initialize the population. + + Parameters + ---------- + problem : baseProblem + An object of the class Problem + assign_type : str, optional + Define the method of creation of population. + If 'assign_type' is 'RandomDesign' the population is generated + randomly. If 'assign_type' is 'LHSDesign', the population is + generated via Latin Hypercube Sampling. If 'assign_type' is + 'custom', the population is imported from file. If assign_type + is 'empty', create blank population. (the default is "RandomAssign") + plotting : bool, optional + (the default is True, which creates the plots) + + """ + """Initialize the population. + + Parameters + ---------- + problem : baseProblem + An object of the class Problem + assign_type : str, optional + Define the method of creation of population. + If 'assign_type' is 'RandomDesign' the population is generated + randomly. If 'assign_type' is 'LHSDesign', the population is + generated via Latin Hypercube Sampling. If 'assign_type' is + 'custom', the population is imported from file. If assign_type + is 'empty', create blank population. (the default is "RandomAssign") + plotting : bool, optional + (the default is True, which creates the plots) + + """ + num_var = problem.num_of_variables + self.lower_limits = np.asarray(problem.lower_limits) + self.upper_limits = np.asarray(problem.upper_limits) + self.hyp = 0 + self.non_dom = 0 + self.problem = problem + self.filename = problem.name + "_" + str(problem.num_of_objectives) + self.plotting = plotting + # These attributes contain the solutions. + self.individuals = np.empty((0, self.problem.num_input_nodes, self.problem.num_hidden_nodes), float) + self.objectives = np.empty((0, self.problem.num_of_objectives), float) + self.fitness = np.empty((0, self.problem.num_of_objectives), float) + self.constraint_violation = np.empty( + (0, self.problem.num_of_constraints), float) + self.archive = pd.DataFrame( + columns=["generation", "decision_variables", "objective_values"] + ) + self.ideal_fitness = np.full((1, self.problem.num_of_objectives), np.inf) + self.worst_fitness = -1 * self.ideal_fitness + + if not assign_type == "empty": + self.create_new_individuals(assign_type) + + def create_new_individuals( + self, design: str = "RandomDesign", pop_size: int = None, decision_variables=None + ): + """Create, evaluate and add new individuals to the population. Initiate Plots. + + The individuals can be created randomly, by LHS design, or can be passed by the + user. + + Parameters + ---------- + design : str, optional + Describe the method of creation of new individuals. + "RandomDesign" creates individuals randomly. + pop_size : int, optional + Number of individuals in the population. If none, some default population + size based on number of objectives is chosen. + decision_variables : numpy array or list, optional + Pass decision variables to be added to the population. + """ + # What is this? + if decision_variables is not None: + pass + + if pop_size is None: + pop_size_options = [50, 105, 120, 126, 132, 112, 156, 90, 275] + pop_size = pop_size_options[self.problem.num_of_objectives - 2] + # Create new individuals + + if design == "RandomDesign": + #individuals = np.random.random((pop_size, self.problem.num_hidden_nodes, self.problem.num_input_nodes+1)) + individuals = np.random.uniform( + self.problem.w_low, self.problem.w_high, size=(pop_size, self.problem.num_input_nodes, self.problem.num_hidden_nodes) + ) + + else: + print("Design not yet supported.") + + self.add(individuals) + + if self.plotting: + self.figure = [] + self.plot_init_() + + def eval_fitness(self): + """ + Calculate fitness based on objective values. Fitness = obj if minimized. + """ + fitness = self.objectives * self.problem.objs + return fitness + + def add(self, new_pop: np.ndarray): + """Evaluate and add individuals to the population. Update ideal and nadir point. + + Parameters + ---------- + new_pop: np.ndarray + Decision variable values for new population. + """ + + if new_pop.shape[0] >= 1: + for i in range(0, new_pop.shape[0]): + self.append_individual(new_pop[i, :, :]) + else: + print("Error while adding new individuals. Check dimensions.") + # print(self.ideal_fitness) + self.update_ideal_and_nadir() + + def keep(self, indices: list): + """Remove individuals from population which are not in "indices". + + Parameters + ---------- + indices: list + Indices of individuals to keep + """ + + new_pop = self.individuals[indices, :, :] + new_obj = self.objectives[indices, :] + new_fitness = self.fitness[indices, :] + new_CV = self.constraint_violation[indices, :] + self.individuals = new_pop + self.objectives = new_obj + length_of_archive = len(self.archive) + if length_of_archive == 0: + gen_count = 0 + else: + gen_count = self.archive["generation"].iloc[-1] + 1 + new_entries = pd.DataFrame( + { + "generation": [gen_count] * len(new_obj), + "decision_variables": new_pop.tolist(), + "objective_values": new_obj.tolist(), + } + ) + self.archive = self.archive.append(new_entries, ignore_index=True) + self.fitness = new_fitness + self.constraint_violation = new_CV + + def append_individual(self, ind: np.ndarray): + """Evaluate and add individual to the population. + + Parameters + ---------- + ind: np.ndarray + """ + self.individuals = np.concatenate((self.individuals, [ind])) + obj, CV, fitness = self.evaluate_individual(ind) + self.objectives = np.vstack((self.objectives, obj)) + self.constraint_violation = np.vstack((self.constraint_violation, CV)) + self.fitness = np.vstack((self.fitness, fitness)) + + def evaluate_individual(self, ind: np.ndarray): + """Evaluate individual. + + Returns objective values, constraint violation, and fitness. + + Parameters + ---------- + ind: np.ndarray + """ + obj = self.problem.objectives(ind) + CV = np.empty((0, self.problem.num_of_constraints), float) + fitness = obj + if self.problem.num_of_constraints: + CV = self.problem.constraints(ind, obj) + fitness = self.eval_fitness(ind, obj, self.problem) + return (obj, CV, fitness) + + def evolve(self, EA: "BaseEA" = None, EA_parameters: dict = None) -> "Population": + """Evolve the population with interruptions. + + Evolves the population based on the EA sent by the user. + + Parameters + ---------- + EA: "BaseEA" + Should be a derivative of BaseEA (Default value = None) + EA_parameters: dict + Contains the parameters needed by EA (Default value = None) + + """ + ################################## + # To determine whether running in console or in notebook. Used for TQDM. + # TQDM will be removed in future generations as number of iterations can vary + if IsNotebook(): + progressbar = tqdm_notebook + else: + progressbar = tqdm + #################################### + # A basic evolution cycle. Will be updated to optimize() in future versions. + ea = EA(self, EA_parameters) + iterations = ea.params["iterations"] + if self.plotting: + self.plot_objectives() # Figure was created in init + for i in progressbar(range(1, iterations), desc="Iteration"): + ea._run_interruption(self) + ea._next_iteration(self) + if self.plotting: + self.plot_objectives() + + def mate(self): + """Conduct crossover and mutation over the population. + + Conduct simulated binary crossover and bounded polunomial mutation. + """ + pop = self.individuals + pop_size, num_var = pop.shape + shuffled_ids = list(range(pop_size)) + shuffle(shuffled_ids) + mating_pop = pop[shuffled_ids] + if pop_size % 2 == 1: + # Maybe it should be pop_size-1? + mating_pop = np.vstack((mating_pop, mating_pop[0])) + pop_size = pop_size + 1 + # The rest closely follows the matlab code. + ProC = 1 + ProM = 1 / num_var + DisC = 30 + DisM = 20 + offspring = np.zeros_like(mating_pop) # empty_like() more efficient? + for i in range(0, pop_size, 2): + beta = np.zeros(num_var) + miu = np.random.rand(num_var) + beta[miu <= 0.5] = (2 * miu[miu <= 0.5]) ** (1 / (DisC + 1)) + beta[miu > 0.5] = (2 - 2 * miu[miu > 0.5]) ** (-1 / (DisC + 1)) + beta = beta * ((-1) ** np.random.randint(0, high=2, size=num_var)) + beta[np.random.rand(num_var) > ProC] = 1 # It was in matlab code + avg = (mating_pop[i] + mating_pop[i + 1]) / 2 + diff = (mating_pop[i] - mating_pop[i + 1]) / 2 + offspring[i] = avg + beta * diff + offspring[i + 1] = avg - beta * diff + min_val = np.ones_like(offspring) * self.lower_limits + max_val = np.ones_like(offspring) * self.upper_limits + k = np.random.random(offspring.shape) + miu = np.random.random(offspring.shape) + temp = np.logical_and((k <= ProM), (miu < 0.5)) + offspring_scaled = (offspring - min_val) / (max_val - min_val) + offspring[temp] = offspring[temp] + ( + (max_val[temp] - min_val[temp]) + * ( + ( + 2 * miu[temp] + + (1 - 2 * miu[temp]) * (1 - offspring_scaled[temp]) ** (DisM + 1) + ) + ** (1 / (DisM + 1)) + - 1 + ) + ) + temp = np.logical_and((k <= ProM), (miu >= 0.5)) + offspring[temp] = offspring[temp] + ( + (max_val[temp] - min_val[temp]) + * ( + 1 + - ( + 2 * (1 - miu[temp]) + + 2 * (miu[temp] - 0.5) * offspring_scaled[temp] ** (DisM + 1) + ) + ** (1 / (DisM + 1)) + ) + ) + offspring[offspring > max_val] = max_val[offspring > max_val] + offspring[offspring < min_val] = min_val[offspring < min_val] + return offspring + + def plot_init_(self): + """Initialize animation objects. Return figure""" + obj = self.objectives + self.figure = animate_init_(obj, self.filename + ".html") + return self.figure + + def plot_objectives(self, iteration: int = None): + """Plot the objective values of individuals in notebook. This is a hack. + + Parameters + ---------- + iteration: int + Iteration count. + """ + obj = self.objectives + self.figure = animate_next_( + obj, self.figure, self.filename + ".html", iteration + ) + + def hypervolume(self, ref_point): + """Calculate hypervolume. Uses package pygmo. Add checks to prevent errors. + + Parameters + ---------- + ref_point + + Returns + ------- + + """ + non_dom = self.non_dom + if not isinstance(ref_point, (Sequence, np.ndarray)): + num_obj = non_dom.shape[1] + ref_point = [ref_point] * num_obj + non_dom = non_dom[np.all(non_dom < ref_point, axis=1), :] + hyp = hv(non_dom) + self.hyp = hyp.compute(ref_point) + return self.hyp + + def non_dominated(self): + """Fix this. check if nd2 and nds mean the same thing""" + obj = self.objectives + num_obj = obj.shape[1] + if num_obj == 2: + non_dom_front = nd2(obj) + else: + non_dom_front = nds(obj) + if isinstance(non_dom_front, tuple): + self.non_dom = self.objectives[non_dom_front[0][0]] + elif isinstance(non_dom_front, np.ndarray): + self.non_dom = self.objectives[non_dom_front] + else: + print("Non Dom error Line 285 in population.py") + return non_dom_front + + def update_ideal_and_nadir(self, new_objective_vals: list = None): + """Updates self.ideal and self.nadir in the fitness space. + + Uses the entire population if new_objective_vals is none. + + Parameters + ---------- + new_objective_vals : list, optional + Objective values for a newly added individual (the default is None, which + calculated the ideal and nadir for the entire population.) + + """ + if new_objective_vals is None: + check_ideal_with = self.fitness + else: + check_ideal_with = new_objective_vals + self.ideal_fitness = np.amin( + np.vstack((self.ideal_fitness, check_ideal_with)), axis=0 + ) + self.worst_fitness = np.amax( + np.vstack((self.worst_fitness, check_ideal_with)), axis=0 + ) diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index f28c741..b36a19a 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -1,5 +1,5 @@ import numpy as np -from scipy.linalg import lstsq +#from scipy.linalg import lstsq import timeit from pyrvea.Problem.baseProblem import baseProblem @@ -15,6 +15,7 @@ def __init__( num_hidden_nodes=6, num_output_nodes=1, num_of_objectives=2, + num_of_constraints=0, w_low=-5.0, w_high=5.0, ): @@ -49,16 +50,18 @@ def __init__( self.num_output_nodes = num_output_nodes self.num_of_objectives = num_of_objectives self.num_of_variables = num_input_nodes + self.num_of_constraints = num_of_constraints self.w_low = w_low self.w_high = w_high self.lower_limits = w_low self.upper_limits = w_high - #self.num_of_samples = np.shape(training_data_output)[0] + self.bias = 1 + self.num_of_samples = np.shape(training_data_output)[0] if len(self.training_data_input) > 0: self.num_input_nodes = np.shape(training_data_input)[1] - def objectives(self, decision_variables=None) -> list: + def objectives(self, decision_variables) -> list: """ Use this method to calculate objective functions. @@ -74,16 +77,18 @@ def objectives(self, decision_variables=None) -> list: """ - w_matrix, bias = self.init_weight_matrix() - weighted_input = self.dot_product(w_matrix, bias) + #w_matrix, bias = self.init_weight_matrix() + weighted_input = self.dot_product(decision_variables) activated_function = self.activation(weighted_input, "sigmoid") w_matrix2, predicted_values = self.optimize_error(activated_function, "llsq") training_error = self.loss_function(predicted_values, "rmse") - complexity = self.calculate_complexity(w_matrix, w_matrix2) + complexity = self.calculate_complexity(decision_variables, w_matrix2) minimized_complexity = self.minimize_complexity(predicted_values, complexity) + obj_func = [] + obj_func.extend([training_error, minimized_complexity]) - return self.obj_func(training_error, minimized_complexity) + return obj_func def init_weight_matrix(self): @@ -100,7 +105,7 @@ def init_weight_matrix(self): bias = 1 return w_matrix, bias - def dot_product(self, w_matrix, bias): + def dot_product(self, w_matrix): """ Calculate the dot product of input and weight + bias. Parameters @@ -113,7 +118,7 @@ def dot_product(self, w_matrix, bias): """ - wi = np.dot(self.training_data_input, w_matrix.transpose()) + bias + wi = np.dot(self.training_data_input, w_matrix) + self.bias #biased_matrix = np.hstack((w_matrix, bias)) #wi = ( # np.dot(self.training_data_input, biased_matrix[..., :-1].transpose()) @@ -148,10 +153,10 @@ def optimize_error(self, activated_function, name): ------- """ if name == "llsq": - w_matrix2 = lstsq(activated_function, self.training_data_output) - predicted_values = np.dot(activated_function, w_matrix2) + w_matrix2 = np.linalg.lstsq(activated_function, self.training_data_output) + predicted_values = np.dot(activated_function, w_matrix2[0]) - return w_matrix2, predicted_values + return w_matrix2[0], predicted_values def loss_function(self, predicted_values, name): @@ -160,7 +165,7 @@ def loss_function(self, predicted_values, name): def calculate_complexity(self, w_matrix, w_matrix2): - k = np.nonzero(w_matrix) + np.nonzero(w_matrix2) + k = np.count_nonzero(w_matrix) + np.count_nonzero(w_matrix2) return k From 3a613c801291c9831a023a9613803569f39170d9 Mon Sep 17 00:00:00 2001 From: delamorte Date: Thu, 23 May 2019 10:52:55 +0300 Subject: [PATCH 008/155] updated gitignore --- .gitignore | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index 9b4ea58..4f111e5 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,4 @@ -results/ +4results/ Backup_results/ # Byte-compiled / optimized / DLL files __pycache__/ @@ -125,4 +125,11 @@ dmypy.json # Pyre type checker .pyre/ -*.json \ No newline at end of file +*.json + +# vsCode settings +.vscode/ + +# Poetry files +poetry.lock +pyproject.toml From 6505b27332464b11189f38f8844c614cc7fe2946 Mon Sep 17 00:00:00 2001 From: delamorte Date: Thu, 23 May 2019 11:33:57 +0300 Subject: [PATCH 009/155] Added 'delete' and 'archive' methods to population_evonn changed new_CV variable to new_cv according to PEP8 --- .vscode/launch.json | 70 -- .vscode/settings.json | 7 - poetry.lock | 1171 ------------------------- pyproject.toml | 38 - pyrvea/Examples/evonn_example.py | 6 +- pyrvea/Population/population_evonn.py | 30 +- 6 files changed, 30 insertions(+), 1292 deletions(-) delete mode 100644 .vscode/launch.json delete mode 100644 .vscode/settings.json delete mode 100644 poetry.lock delete mode 100644 pyproject.toml diff --git a/.vscode/launch.json b/.vscode/launch.json deleted file mode 100644 index 92a2fa2..0000000 --- a/.vscode/launch.json +++ /dev/null @@ -1,70 +0,0 @@ -{ - // Use IntelliSense to learn about possible attributes. - // Hover to view descriptions of existing attributes. - // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 - "version": "0.2.0", - "configurations": [ - { - "name": "Python: Current File (Integrated Terminal)", - "type": "python", - "request": "launch", - "program": "${file}", - "console": "integratedTerminal" - }, - { - "name": "Python: Remote Attach", - "type": "python", - "request": "attach", - "port": 5678, - "host": "localhost", - "pathMappings": [ - { - "localRoot": "${workspaceFolder}", - "remoteRoot": "." - } - ] - }, - { - "name": "Python: Module", - "type": "python", - "request": "launch", - "module": "enter-your-module-name-here", - "console": "integratedTerminal" - }, - { - "name": "Python: Django", - "type": "python", - "request": "launch", - "program": "${workspaceFolder}/manage.py", - "console": "integratedTerminal", - "args": [ - "runserver", - "--noreload", - "--nothreading" - ], - "django": true - }, - { - "name": "Python: Flask", - "type": "python", - "request": "launch", - "module": "flask", - "env": { - "FLASK_APP": "app.py" - }, - "args": [ - "run", - "--no-debugger", - "--no-reload" - ], - "jinja": true - }, - { - "name": "Python: Current File (External Terminal)", - "type": "python", - "request": "launch", - "program": "${file}", - "console": "externalTerminal" - } - ] -} \ No newline at end of file diff --git a/.vscode/settings.json b/.vscode/settings.json deleted file mode 100644 index 36cfe4d..0000000 --- a/.vscode/settings.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - 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"c908c10505904c48081a5415a1e295d8403e353e0c14c42b6d67f8f97fae6616", "c94dd3807c0c0610f7c76f078119f4ea48235a953512752b9175f9f98f5ae2bd", "ce65dee7594a84c466e79d7fb7d3303e7295d16a83c22c7c4037071b059e2c21", "eaa9cfcb221a8a4c2889be6f93da141ac777eb8819f077e1d09fb12d00a09a93", "f3376bc31bad66d46d44b4e6522c5c21976bf9bca4ef5987bb2bf727f4506cbb", "f9202fa138544e13a4ec1a6792c35834250a85958fde1251b6a22e07d1260ae7"] -urllib3 = ["4c291ca23bbb55c76518905869ef34bdd5f0e46af7afe6861e8375643ffee1a0", "9a247273df709c4fedb38c711e44292304f73f39ab01beda9f6b9fc375669ac3"] -vega = ["abb93e4af0f97b4ca7a5034bc1c358b9863ece536b4b9c9707551011b7e65053"] -vega-datasets = ["20d490b417f84607eb5079400f608f2e9c135b7092bee10f6857f6d23136e459", "63d6eaf129ab23a7d0744e54d11dba9e7eecc0974fd9a237cfae9cfdbef3c37a"] -wcwidth = ["3df37372226d6e63e1b1e1eda15c594bca98a22d33a23832a90998faa96bc65e", "f4ebe71925af7b40a864553f761ed559b43544f8f71746c2d756c7fe788ade7c"] -webencodings = ["a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78", "b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"] -widgetsnbextension = ["14b2c65f9940c9a7d3b70adbe713dbd38b5ec69724eebaba034d1036cf3d4740", "fa618be8435447a017fd1bf2c7ae922d0428056cfc7449f7a8641edf76b48265"] -wrapt = ["4aea003270831cceb8a90ff27c4031da6ead7ec1886023b80ce0dfe0adf61533"] diff --git a/pyproject.toml b/pyproject.toml deleted file mode 100644 index 3e93adf..0000000 --- a/pyproject.toml +++ /dev/null @@ -1,38 +0,0 @@ -[tool.poetry] -name = "pyrvea" -version = "0.1.1" -description = "The python version reference vector guided evolutionary algorithm." -authors = ["Bhupinder Saini"] -license = "MPL-2.0" -readme = "README.md" - -[tool.poetry.dependencies] -python = "^3.6" -numpy = "^1.16" -pandas = "^0.24.2" -optproblems = "^1.2" -pygmo = "^2.10" -scipy = "^1.2" -diversipy = "^0.8.0" -pyDOE = "^0.3.8" -plotly = "^3.8" -tqdm = "^4.31" -jupyter = "^1.0" - -[tool.poetry.dev-dependencies] -flake8 = "^3.7" -black = {version = "^18.3-alpha.0",allows-prereleases = true} -sphinx = "^2.0" -recommonmark = "^0.5.0" -sphinx_rtd_theme = "^0.4.3" -pylint = "^2.3" -mypy = "^0.701.0" -altair = "^3.0" -vega = "^2.1" -vega_datasets = "^0.7.0" - - -[build-system] -requires = ["poetry>=0.12"] -build-backend = "poetry.masonry.api" - diff --git a/pyrvea/Examples/evonn_example.py b/pyrvea/Examples/evonn_example.py index fb0608c..bcb5d05 100644 --- a/pyrvea/Examples/evonn_example.py +++ b/pyrvea/Examples/evonn_example.py @@ -20,5 +20,7 @@ pop = PopulationEvoNN(prob) -selected = sample(range(1, 50), randint(1,50)) -pop.keep(selected) \ No newline at end of file +selected = sample(range(1, np.shape(pop.individuals)[0]), randint(1, np.shape(pop.individuals)[0])) +pop.keep(selected) +selected = sample(range(1, np.shape(pop.individuals)[0]), randint(1, np.shape(pop.individuals)[0])) +pop.delete(selected) \ No newline at end of file diff --git a/pyrvea/Population/population_evonn.py b/pyrvea/Population/population_evonn.py index bd711a9..28aacf0 100644 --- a/pyrvea/Population/population_evonn.py +++ b/pyrvea/Population/population_evonn.py @@ -155,7 +155,7 @@ def add(self, new_pop: np.ndarray): self.update_ideal_and_nadir() def keep(self, indices: list): - """Remove individuals from population which are not in "indices". + """Remove individuals from population which are NOT in "indices". Parameters ---------- @@ -166,9 +166,33 @@ def keep(self, indices: list): new_pop = self.individuals[indices, :, :] new_obj = self.objectives[indices, :] new_fitness = self.fitness[indices, :] - new_CV = self.constraint_violation[indices, :] + new_cv = self.constraint_violation[indices, :] + + self.individuals = new_pop + self.objectives = new_obj + self.fitness = new_fitness + self.constraint_violation = new_cv + + def delete(self, indices: list): + """Remove individuals from population which ARE in "indices". + + Parameters + ---------- + indices: list + Indices of individuals to keep + """ + + new_pop = np.delete(self.individuals, indices, axis=0) + new_obj = np.delete(self.objectives, indices, axis=0) + new_fitness = np.delete(self.fitness, indices, axis=0) + new_cv = np.delete(self.constraint_violation, indices, axis=0) + self.individuals = new_pop self.objectives = new_obj + self.fitness = new_fitness + self.constraint_violation = new_cv + + def archive(self, new_pop, new_obj): length_of_archive = len(self.archive) if length_of_archive == 0: gen_count = 0 @@ -182,8 +206,6 @@ def keep(self, indices: list): } ) self.archive = self.archive.append(new_entries, ignore_index=True) - self.fitness = new_fitness - self.constraint_violation = new_CV def append_individual(self, ind: np.ndarray): """Evaluate and add individual to the population. From 6d8fd6dfce7f7c5cf2de85c34b7523891d7391e2 Mon Sep 17 00:00:00 2001 From: delamorte Date: Thu, 23 May 2019 14:44:48 +0300 Subject: [PATCH 010/155] Added comments Fixed function names in evonn_problem --- pyrvea/Examples/evonn_example.py | 2 +- pyrvea/Population/population_evonn.py | 5 ++++- pyrvea/Problem/evonn_problem.py | 17 ++++++++--------- 3 files changed, 13 insertions(+), 11 deletions(-) diff --git a/pyrvea/Examples/evonn_example.py b/pyrvea/Examples/evonn_example.py index bcb5d05..c41b218 100644 --- a/pyrvea/Examples/evonn_example.py +++ b/pyrvea/Examples/evonn_example.py @@ -8,7 +8,7 @@ hidden_nodes = 4 num_of_samples = 10 training_data = np.random.uniform(0, 1, size=(num_of_samples, input_nodes)) -preferred_output = np.ones((num_of_samples, hidden_nodes)) +preferred_output = np.ones(num_of_samples) prob = EvoNNProblem( name="EvoNN", diff --git a/pyrvea/Population/population_evonn.py b/pyrvea/Population/population_evonn.py index 28aacf0..6a47daf 100644 --- a/pyrvea/Population/population_evonn.py +++ b/pyrvea/Population/population_evonn.py @@ -214,7 +214,10 @@ def append_individual(self, ind: np.ndarray): ---------- ind: np.ndarray """ + + # this method seems to work for nd arrays self.individuals = np.concatenate((self.individuals, [ind])) + obj, CV, fitness = self.evaluate_individual(ind) self.objectives = np.vstack((self.objectives, obj)) self.constraint_violation = np.vstack((self.constraint_violation, CV)) @@ -370,7 +373,7 @@ def hypervolume(self, ref_point): non_dom = non_dom[np.all(non_dom < ref_point, axis=1), :] hyp = hv(non_dom) self.hyp = hyp.compute(ref_point) - return self.hyp + return self.hypnp def non_dominated(self): """Fix this. check if nd2 and nds mean the same thing""" diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index b36a19a..e67b0d2 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -84,11 +84,10 @@ def objectives(self, decision_variables) -> list: training_error = self.loss_function(predicted_values, "rmse") complexity = self.calculate_complexity(decision_variables, w_matrix2) - minimized_complexity = self.minimize_complexity(predicted_values, complexity) + corrected_complexity = self.information_criterion(predicted_values, complexity) obj_func = [] - obj_func.extend([training_error, minimized_complexity]) - return obj_func + return obj_func[training_error, corrected_complexity] def init_weight_matrix(self): @@ -127,7 +126,7 @@ def dot_product(self, w_matrix): return wi - def activation(self, wi, name): + def activation(self, wi, name="sigmoid"): """ Activation function Returns @@ -137,11 +136,11 @@ def activation(self, wi, name): """ if name == "sigmoid": - activated_function = lambda x : 1 / (1 + np.exp(-x)) + activated_function = lambda x: 1 / (1 + np.exp(-x)) return activated_function(wi) - def optimize_error(self, activated_function, name): + def optimize_error(self, activated_function, name="llsq"): """ Optimize the training error Parameters @@ -158,7 +157,7 @@ def optimize_error(self, activated_function, name): return w_matrix2[0], predicted_values - def loss_function(self, predicted_values, name): + def loss_function(self, predicted_values, name="rmse"): if name == "rmse": return np.sqrt(((self.training_data_output - predicted_values) ** 2).mean()) @@ -169,8 +168,8 @@ def calculate_complexity(self, w_matrix, w_matrix2): return k - def minimize_complexity(self, predicted_values, k): - + def information_criterion(self, predicted_values, k): + # Information criterion rss = ((self.training_data_output - predicted_values) ** 2).sum() aic = 2 * k + self.num_of_samples * np.log(rss/self.num_of_samples) aicc = aic + (2*k*(k+1)/(self.num_of_samples-k-1)) From 54652279e94d284e42134fc0d35297805eba137d Mon Sep 17 00:00:00 2001 From: delamorte Date: Thu, 23 May 2019 15:37:16 +0300 Subject: [PATCH 011/155] Fixed weight matrix to use bias correctly Set bias to self.bias value (default=1) Updated delete method in population_evonn to use boolean masks. Keep and delete methods can now be combined --- pyrvea/Examples/evonn_example.py | 2 +- pyrvea/Population/population_evonn.py | 40 ++++++++++++++++++--------- pyrvea/Problem/evonn_problem.py | 37 ++++++------------------- 3 files changed, 37 insertions(+), 42 deletions(-) diff --git a/pyrvea/Examples/evonn_example.py b/pyrvea/Examples/evonn_example.py index c41b218..e8b04d7 100644 --- a/pyrvea/Examples/evonn_example.py +++ b/pyrvea/Examples/evonn_example.py @@ -21,6 +21,6 @@ pop = PopulationEvoNN(prob) selected = sample(range(1, np.shape(pop.individuals)[0]), randint(1, np.shape(pop.individuals)[0])) -pop.keep(selected) +#pop.keep(selected) selected = sample(range(1, np.shape(pop.individuals)[0]), randint(1, np.shape(pop.individuals)[0])) pop.delete(selected) \ No newline at end of file diff --git a/pyrvea/Population/population_evonn.py b/pyrvea/Population/population_evonn.py index 6a47daf..23702e6 100644 --- a/pyrvea/Population/population_evonn.py +++ b/pyrvea/Population/population_evonn.py @@ -73,7 +73,7 @@ def __init__( self.filename = problem.name + "_" + str(problem.num_of_objectives) self.plotting = plotting # These attributes contain the solutions. - self.individuals = np.empty((0, self.problem.num_input_nodes, self.problem.num_hidden_nodes), float) + self.individuals = np.empty((0, self.problem.num_input_nodes+1, self.problem.num_hidden_nodes), float) self.objectives = np.empty((0, self.problem.num_of_objectives), float) self.fitness = np.empty((0, self.problem.num_of_objectives), float) self.constraint_violation = np.empty( @@ -88,7 +88,7 @@ def __init__( self.create_new_individuals(assign_type) def create_new_individuals( - self, design: str = "RandomDesign", pop_size: int = None, decision_variables=None + self, design: str = "RandomDesign", pop_size: int = 100, decision_variables=None ): """Create, evaluate and add new individuals to the population. Initiate Plots. @@ -106,19 +106,17 @@ def create_new_individuals( decision_variables : numpy array or list, optional Pass decision variables to be added to the population. """ - # What is this? + if decision_variables is not None: pass - if pop_size is None: - pop_size_options = [50, 105, 120, 126, 132, 112, 156, 90, 275] - pop_size = pop_size_options[self.problem.num_of_objectives - 2] # Create new individuals if design == "RandomDesign": - #individuals = np.random.random((pop_size, self.problem.num_hidden_nodes, self.problem.num_input_nodes+1)) + + # +1 row for bias individuals = np.random.uniform( - self.problem.w_low, self.problem.w_high, size=(pop_size, self.problem.num_input_nodes, self.problem.num_hidden_nodes) + self.problem.w_low, self.problem.w_high, size=(pop_size, self.problem.num_input_nodes+1, self.problem.num_hidden_nodes) ) else: @@ -175,6 +173,8 @@ def keep(self, indices: list): def delete(self, indices: list): """Remove individuals from population which ARE in "indices". + With boolean masks deleted individuals can be obtained with + inverted mask (~mask) Parameters ---------- @@ -182,10 +182,24 @@ def delete(self, indices: list): Indices of individuals to keep """ - new_pop = np.delete(self.individuals, indices, axis=0) - new_obj = np.delete(self.objectives, indices, axis=0) - new_fitness = np.delete(self.fitness, indices, axis=0) - new_cv = np.delete(self.constraint_violation, indices, axis=0) + mask = np.ones(len(self.individuals), dtype=bool) + mask[indices] = False + + # new_pop = np.delete(self.individuals, indices, axis=0) + new_pop = self.individuals[mask, ...] + # deleted_pop = self.individuals[~mask, ...] + + # new_obj = np.delete(self.objectives, indices, axis=0) + new_obj = self.objectives[mask, ...] + # deleted_obj = self.objectives[~mask, ...] + + # new_fitness = np.delete(self.fitness, indices, axis=0) + new_fitness = self.fitness[mask, ...] + # deleted_fitness = self.fitness[~mask, ...] + + # new_cv = np.delete(self.constraint_violation, indices, axis=0) + new_cv = self.constraint_violation[mask, ...] + # deleted_cv = self.constraint_violation[~mask, ...] self.individuals = new_pop self.objectives = new_obj @@ -215,7 +229,7 @@ def append_individual(self, ind: np.ndarray): ind: np.ndarray """ - # this method seems to work for nd arrays + # this method seems to work for n-dimensional arrays self.individuals = np.concatenate((self.individuals, [ind])) obj, CV, fitness = self.evaluate_individual(ind) diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index e67b0d2..707a2ae 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -77,32 +77,16 @@ def objectives(self, decision_variables) -> list: """ - #w_matrix, bias = self.init_weight_matrix() weighted_input = self.dot_product(decision_variables) - activated_function = self.activation(weighted_input, "sigmoid") - w_matrix2, predicted_values = self.optimize_error(activated_function, "llsq") - training_error = self.loss_function(predicted_values, "rmse") + activated_function = self.activation(weighted_input) + w_matrix2, predicted_values = self.optimize_error(activated_function) + training_error = self.loss_function(predicted_values) complexity = self.calculate_complexity(decision_variables, w_matrix2) corrected_complexity = self.information_criterion(predicted_values, complexity) - obj_func = [] + obj_func = [training_error, corrected_complexity] - return obj_func[training_error, corrected_complexity] - - def init_weight_matrix(self): - - # Initialize the weight matrix with random weights within boundaries - # Rows = hidden nodes - # Columns = input nodes - # Last column is for bias - - w_matrix = np.random.uniform( - self.w_low, self.w_high, size=(self.num_hidden_nodes, self.num_input_nodes) - ) - - #bias = np.full((self.num_hidden_nodes, 1), 1) - bias = 1 - return w_matrix, bias + return obj_func def dot_product(self, w_matrix): """ Calculate the dot product of input and weight + bias. @@ -110,19 +94,16 @@ def dot_product(self, w_matrix): Parameters ---------- w_matrix - bias Returns ------- """ + # Init bias value for the first row + w_matrix[0] = self.bias - wi = np.dot(self.training_data_input, w_matrix) + self.bias - #biased_matrix = np.hstack((w_matrix, bias)) - #wi = ( - # np.dot(self.training_data_input, biased_matrix[..., :-1].transpose()) - # + biased_matrix[:, -1] - #) + # Calculate dot product + wi = np.dot(self.training_data_input, w_matrix[1:, :]) + w_matrix[0] return wi From cf199482402e079094c57916acd720928b0a7573 Mon Sep 17 00:00:00 2001 From: delamorte Date: Fri, 24 May 2019 15:01:42 +0300 Subject: [PATCH 012/155] Added crossover and mutation functions to Recombination package --- .gitignore | 1 + pyrvea/Population/Population.py | 1 + .../bounded_polynomial_mutation.py | 38 ++++++++++ .../Recombination/self_adapting_crossover.py | 73 +++++++++++++++++++ .../Recombination/self_adapting_mutation.py | 0 .../simulated_binary_crossover.py | 34 +++++++++ temp.py | 23 ++++++ 7 files changed, 170 insertions(+) create mode 100644 pyrvea/Recombination/bounded_polynomial_mutation.py create mode 100644 pyrvea/Recombination/self_adapting_crossover.py create mode 100644 pyrvea/Recombination/self_adapting_mutation.py create mode 100644 pyrvea/Recombination/simulated_binary_crossover.py create mode 100644 temp.py diff --git a/.gitignore b/.gitignore index 4f111e5..90a862e 100644 --- a/.gitignore +++ b/.gitignore @@ -52,6 +52,7 @@ coverage.xml *.cover .hypothesis/ .pytest_cache/ +temp.py # Translations *.mo diff --git a/pyrvea/Population/Population.py b/pyrvea/Population/Population.py index 1f3eee1..c7ecc4e 100644 --- a/pyrvea/Population/Population.py +++ b/pyrvea/Population/Population.py @@ -265,6 +265,7 @@ def mate(self): diff = (mating_pop[i] - mating_pop[i + 1]) / 2 offspring[i] = avg + beta * diff offspring[i + 1] = avg - beta * diff + min_val = np.ones_like(offspring) * self.lower_limits max_val = np.ones_like(offspring) * self.upper_limits k = np.random.random(offspring.shape) diff --git a/pyrvea/Recombination/bounded_polynomial_mutation.py b/pyrvea/Recombination/bounded_polynomial_mutation.py new file mode 100644 index 0000000..f3ff1a1 --- /dev/null +++ b/pyrvea/Recombination/bounded_polynomial_mutation.py @@ -0,0 +1,38 @@ +import numpy as np + + +def mutation(population, offspring, prob_mut=0.3, dis_mut=20): + + min_val = np.ones_like(offspring) * population.lower_limits + max_val = np.ones_like(offspring) * population.upper_limits + k = np.random.random(offspring.shape) + miu = np.random.random(offspring.shape) + temp = np.logical_and((k <= prob_mut), (miu < 0.5)) + offspring_scaled = (offspring - min_val) / (max_val - min_val) + offspring[temp] = offspring[temp] + ( + (max_val[temp] - min_val[temp]) + * ( + ( + 2 * miu[temp] + + (1 - 2 * miu[temp]) * (1 - offspring_scaled[temp]) ** (dis_mut + 1) + ) + ** (1 / (dis_mut + 1)) + - 1 + ) + ) + temp = np.logical_and((k <= prob_mut), (miu >= 0.5)) + offspring[temp] = offspring[temp] + ( + (max_val[temp] - min_val[temp]) + * ( + 1 + - ( + 2 * (1 - miu[temp]) + + 2 * (miu[temp] - 0.5) * offspring_scaled[temp] ** (dis_mut + 1) + ) + ** (1 / (dis_mut + 1)) + ) + ) + offspring[offspring > max_val] = max_val[offspring > max_val] + offspring[offspring < min_val] = min_val[offspring < min_val] + + return offspring diff --git a/pyrvea/Recombination/self_adapting_crossover.py b/pyrvea/Recombination/self_adapting_crossover.py new file mode 100644 index 0000000..510461c --- /dev/null +++ b/pyrvea/Recombination/self_adapting_crossover.py @@ -0,0 +1,73 @@ +import numpy as np +import random + +# Create w1 and w2 for for testing +individuals = np.random.uniform(-5, 5, size=(50, 6, 4)) +w1 = random.randint(0, 50 - 1) +w2 = random.randint(0, 50 - 1) + + +def crossover(w1, w2, prob_xover=0.8): + + for i in range(np.shape(individuals[w1])[1]): + if random.random() < prob_xover: + tmp = np.copy(individuals[w1][:, i]) + individuals[w1][:, i] = individuals[w2][:, i] + individuals[w2][:, i] = tmp + + return w1, w2 + + +def mutation( + offspring1, offspring2, cur_gen, total_gen, prob_mut=0.3, mut_strength=0.7 +): + + o1 = individuals[offspring1] + o2 = individuals[offspring2] + + indices = [offspring1, offspring2] + mask = np.ones(len(individuals), dtype=bool) + mask[indices] = False + + alternatives = individuals[mask, ...] + + it = np.nditer([o1, o2], flags=["multi_index"], op_flags=["readwrite"]) + for wx1, wx2 in it: + if wx1 != 0 and random.random() < prob_mut: + + select = random.sample(list(alternatives), 2) + + o1[it.multi_index[0], it.multi_index[1]] = wx1 + mut_strength * ( + 1 - cur_gen / total_gen + ) * ( + select[0][it.multi_index[0], it.multi_index[1]] + - select[1][it.multi_index[0], it.multi_index[1]] + ) + + if wx2 != 0 and random.random() < prob_mut: + + select = random.sample(list(alternatives), 2) + + o2[it.multi_index[0], it.multi_index[1]] = wx1 + mut_strength * ( + 1 - cur_gen / total_gen + ) * ( + select[0][it.multi_index[0], it.multi_index[1]] + - select[1][it.multi_index[0], it.multi_index[1]] + ) + + return offspring1, offspring2 + + # print (it.multi_index) + # print(wx1, wx2) + # print(wx1[0], wx2[0]) + + # for j in range (np.shape(offspring1)[1]): + # for k in range (np.shape(offspring1)[0]): + # if offspring1(k, j) != 0 and random.random < prob_mut: + # for ind in individuals: + + +# print(individuals[w1], "\n", individuals[w2], "\n") +offspring1, offspring2 = crossover(w1, w2) +# print(individuals[w1],"\n", individuals[w2], "\n") +mut1, mut2 = mutation(offspring1, offspring2, 1, 10) diff --git a/pyrvea/Recombination/self_adapting_mutation.py b/pyrvea/Recombination/self_adapting_mutation.py new file mode 100644 index 0000000..e69de29 diff --git a/pyrvea/Recombination/simulated_binary_crossover.py b/pyrvea/Recombination/simulated_binary_crossover.py new file mode 100644 index 0000000..a718c0c --- /dev/null +++ b/pyrvea/Recombination/simulated_binary_crossover.py @@ -0,0 +1,34 @@ +import numpy as np +from random import shuffle + + +def crossover(population, prob_xover=1, dis_xover=30): + + pop = population.individuals + pop_size, num_var = pop.shape + shuffled_ids = list(range(pop_size)) + shuffle(shuffled_ids) + mating_pop = pop[shuffled_ids] + + if pop_size % 2 == 1: + # Maybe it should be pop_size-1? + mating_pop = np.vstack((mating_pop, mating_pop[0])) + pop_size = pop_size + 1 + + # The rest closely follows the matlab code. + + offspring = np.zeros_like(mating_pop) # empty_like() more efficient? + + for i in range(0, pop_size, 2): + beta = np.zeros(num_var) + miu = np.random.rand(num_var) + beta[miu <= 0.5] = (2 * miu[miu <= 0.5]) ** (1 / (dis_xover + 1)) + beta[miu > 0.5] = (2 - 2 * miu[miu > 0.5]) ** (-1 / (dis_xover + 1)) + beta = beta * ((-1) ** np.random.randint(0, high=2, size=num_var)) + beta[np.random.rand(num_var) > prob_xover] = 1 # It was in matlab code + avg = (mating_pop[i] + mating_pop[i + 1]) / 2 + diff = (mating_pop[i] - mating_pop[i + 1]) / 2 + offspring[i] = avg + beta * diff + offspring[i + 1] = avg - beta * diff + + return offspring diff --git a/temp.py b/temp.py new file mode 100644 index 0000000..d2f3f17 --- /dev/null +++ b/temp.py @@ -0,0 +1,23 @@ +# Initialize the probabilities for the genetic algorithm +# These should go to the PPGA module +# self.prob_omit_max = 0.99 +# self.prob_omit_min = 0.1 +# self.prob_crossover = 0.8 +# self.prob_mutation = 0.3 +# self.mutation_strength = 0.7 + +# Eliminate some weights / set some weights to zero +# Also in the PPGA module +# flag = bn.rvs(p=1 - self.prob_omit_min, size=np.shape(individuals)) +# random_numbers = np.zeros(np.shape(individuals)) +# individuals[flag == 0] = random_numbers[flag == 0] + +# Initialize Predator population +# predators = np.linspace(0, 1, num=pop_size) + +# Scaling +# Taken from Population.create_new_individuals() +# individuals = ( +# individuals * (self.upper_limits - self.lower_limits) +# + self.lower_limits +# ) From becdc137752eb6a85451392c7c53a361b461ef93 Mon Sep 17 00:00:00 2001 From: delamorte Date: Fri, 24 May 2019 15:16:51 +0300 Subject: [PATCH 013/155] Formatted files Removed debug examples --- .../Recombination/self_adapting_crossover.py | 65 +------------------ .../Recombination/self_adapting_mutation.py | 51 +++++++++++++++ temp.py | 23 ------- 3 files changed, 54 insertions(+), 85 deletions(-) delete mode 100644 temp.py diff --git a/pyrvea/Recombination/self_adapting_crossover.py b/pyrvea/Recombination/self_adapting_crossover.py index 510461c..7faf92a 100644 --- a/pyrvea/Recombination/self_adapting_crossover.py +++ b/pyrvea/Recombination/self_adapting_crossover.py @@ -1,73 +1,14 @@ import numpy as np -import random -# Create w1 and w2 for for testing -individuals = np.random.uniform(-5, 5, size=(50, 6, 4)) -w1 = random.randint(0, 50 - 1) -w2 = random.randint(0, 50 - 1) +def crossover(population, w1, w2, prob_xover=0.8): -def crossover(w1, w2, prob_xover=0.8): + individuals = population.individuals for i in range(np.shape(individuals[w1])[1]): - if random.random() < prob_xover: + if np.random.random() < prob_xover: tmp = np.copy(individuals[w1][:, i]) individuals[w1][:, i] = individuals[w2][:, i] individuals[w2][:, i] = tmp return w1, w2 - - -def mutation( - offspring1, offspring2, cur_gen, total_gen, prob_mut=0.3, mut_strength=0.7 -): - - o1 = individuals[offspring1] - o2 = individuals[offspring2] - - indices = [offspring1, offspring2] - mask = np.ones(len(individuals), dtype=bool) - mask[indices] = False - - alternatives = individuals[mask, ...] - - it = np.nditer([o1, o2], flags=["multi_index"], op_flags=["readwrite"]) - for wx1, wx2 in it: - if wx1 != 0 and random.random() < prob_mut: - - select = random.sample(list(alternatives), 2) - - o1[it.multi_index[0], it.multi_index[1]] = wx1 + mut_strength * ( - 1 - cur_gen / total_gen - ) * ( - select[0][it.multi_index[0], it.multi_index[1]] - - select[1][it.multi_index[0], it.multi_index[1]] - ) - - if wx2 != 0 and random.random() < prob_mut: - - select = random.sample(list(alternatives), 2) - - o2[it.multi_index[0], it.multi_index[1]] = wx1 + mut_strength * ( - 1 - cur_gen / total_gen - ) * ( - select[0][it.multi_index[0], it.multi_index[1]] - - select[1][it.multi_index[0], it.multi_index[1]] - ) - - return offspring1, offspring2 - - # print (it.multi_index) - # print(wx1, wx2) - # print(wx1[0], wx2[0]) - - # for j in range (np.shape(offspring1)[1]): - # for k in range (np.shape(offspring1)[0]): - # if offspring1(k, j) != 0 and random.random < prob_mut: - # for ind in individuals: - - -# print(individuals[w1], "\n", individuals[w2], "\n") -offspring1, offspring2 = crossover(w1, w2) -# print(individuals[w1],"\n", individuals[w2], "\n") -mut1, mut2 = mutation(offspring1, offspring2, 1, 10) diff --git a/pyrvea/Recombination/self_adapting_mutation.py b/pyrvea/Recombination/self_adapting_mutation.py index e69de29..081ead6 100644 --- a/pyrvea/Recombination/self_adapting_mutation.py +++ b/pyrvea/Recombination/self_adapting_mutation.py @@ -0,0 +1,51 @@ +from numpy import np +from random import sample + + +def mutation( + population, + offspring1, + offspring2, + cur_gen, + total_gen, + prob_mut=0.3, + mut_strength=0.7, +): + + individuals = population.individuals + + o1 = individuals[offspring1] + o2 = individuals[offspring2] + + # Could call the delete method from population_evonn class here + indices = [offspring1, offspring2] + mask = np.ones(len(individuals), dtype=bool) + mask[indices] = False + + alternatives = individuals[mask, ...] + + it = np.nditer([o1, o2], flags=["multi_index"], op_flags=["readwrite"]) + for wx1, wx2 in it: + if wx1 != 0 and np.random.random() < prob_mut: + + select = sample(list(alternatives), 2) + + o1[it.multi_index[0], it.multi_index[1]] = wx1 + mut_strength * ( + 1 - cur_gen / total_gen + ) * ( + select[0][it.multi_index[0], it.multi_index[1]] + - select[1][it.multi_index[0], it.multi_index[1]] + ) + + if wx2 != 0 and np.random.random() < prob_mut: + + select = sample(list(alternatives), 2) + + o2[it.multi_index[0], it.multi_index[1]] = wx1 + mut_strength * ( + 1 - cur_gen / total_gen + ) * ( + select[0][it.multi_index[0], it.multi_index[1]] + - select[1][it.multi_index[0], it.multi_index[1]] + ) + + return offspring1, offspring2 diff --git a/temp.py b/temp.py deleted file mode 100644 index d2f3f17..0000000 --- a/temp.py +++ /dev/null @@ -1,23 +0,0 @@ -# Initialize the probabilities for the genetic algorithm -# These should go to the PPGA module -# self.prob_omit_max = 0.99 -# self.prob_omit_min = 0.1 -# self.prob_crossover = 0.8 -# self.prob_mutation = 0.3 -# self.mutation_strength = 0.7 - -# Eliminate some weights / set some weights to zero -# Also in the PPGA module -# flag = bn.rvs(p=1 - self.prob_omit_min, size=np.shape(individuals)) -# random_numbers = np.zeros(np.shape(individuals)) -# individuals[flag == 0] = random_numbers[flag == 0] - -# Initialize Predator population -# predators = np.linspace(0, 1, num=pop_size) - -# Scaling -# Taken from Population.create_new_individuals() -# individuals = ( -# individuals * (self.upper_limits - self.lower_limits) -# + self.lower_limits -# ) From 215bdf15e131493eeed7e9482d3b426103f67672 Mon Sep 17 00:00:00 2001 From: delamorte Date: Fri, 24 May 2019 15:22:55 +0300 Subject: [PATCH 014/155] Added crossover and mutation functions Moved these functions out of Population class --- .../bounded_polynomial_mutation.py | 38 +++++++++++++++++++ .../simulated_binary_crossover.py | 34 +++++++++++++++++ 2 files changed, 72 insertions(+) create mode 100644 pyrvea/Recombination/bounded_polynomial_mutation.py create mode 100644 pyrvea/Recombination/simulated_binary_crossover.py diff --git a/pyrvea/Recombination/bounded_polynomial_mutation.py b/pyrvea/Recombination/bounded_polynomial_mutation.py new file mode 100644 index 0000000..f3ff1a1 --- /dev/null +++ b/pyrvea/Recombination/bounded_polynomial_mutation.py @@ -0,0 +1,38 @@ +import numpy as np + + +def mutation(population, offspring, prob_mut=0.3, dis_mut=20): + + min_val = np.ones_like(offspring) * population.lower_limits + max_val = np.ones_like(offspring) * population.upper_limits + k = np.random.random(offspring.shape) + miu = np.random.random(offspring.shape) + temp = np.logical_and((k <= prob_mut), (miu < 0.5)) + offspring_scaled = (offspring - min_val) / (max_val - min_val) + offspring[temp] = offspring[temp] + ( + (max_val[temp] - min_val[temp]) + * ( + ( + 2 * miu[temp] + + (1 - 2 * miu[temp]) * (1 - offspring_scaled[temp]) ** (dis_mut + 1) + ) + ** (1 / (dis_mut + 1)) + - 1 + ) + ) + temp = np.logical_and((k <= prob_mut), (miu >= 0.5)) + offspring[temp] = offspring[temp] + ( + (max_val[temp] - min_val[temp]) + * ( + 1 + - ( + 2 * (1 - miu[temp]) + + 2 * (miu[temp] - 0.5) * offspring_scaled[temp] ** (dis_mut + 1) + ) + ** (1 / (dis_mut + 1)) + ) + ) + offspring[offspring > max_val] = max_val[offspring > max_val] + offspring[offspring < min_val] = min_val[offspring < min_val] + + return offspring diff --git a/pyrvea/Recombination/simulated_binary_crossover.py b/pyrvea/Recombination/simulated_binary_crossover.py new file mode 100644 index 0000000..a718c0c --- /dev/null +++ b/pyrvea/Recombination/simulated_binary_crossover.py @@ -0,0 +1,34 @@ +import numpy as np +from random import shuffle + + +def crossover(population, prob_xover=1, dis_xover=30): + + pop = population.individuals + pop_size, num_var = pop.shape + shuffled_ids = list(range(pop_size)) + shuffle(shuffled_ids) + mating_pop = pop[shuffled_ids] + + if pop_size % 2 == 1: + # Maybe it should be pop_size-1? + mating_pop = np.vstack((mating_pop, mating_pop[0])) + pop_size = pop_size + 1 + + # The rest closely follows the matlab code. + + offspring = np.zeros_like(mating_pop) # empty_like() more efficient? + + for i in range(0, pop_size, 2): + beta = np.zeros(num_var) + miu = np.random.rand(num_var) + beta[miu <= 0.5] = (2 * miu[miu <= 0.5]) ** (1 / (dis_xover + 1)) + beta[miu > 0.5] = (2 - 2 * miu[miu > 0.5]) ** (-1 / (dis_xover + 1)) + beta = beta * ((-1) ** np.random.randint(0, high=2, size=num_var)) + beta[np.random.rand(num_var) > prob_xover] = 1 # It was in matlab code + avg = (mating_pop[i] + mating_pop[i + 1]) / 2 + diff = (mating_pop[i] - mating_pop[i + 1]) / 2 + offspring[i] = avg + beta * diff + offspring[i + 1] = avg - beta * diff + + return offspring From 0cee2ae3b9f95502b4e27e99f7035b7065e159f0 Mon Sep 17 00:00:00 2001 From: delamorte Date: Fri, 24 May 2019 15:52:58 +0300 Subject: [PATCH 015/155] added nested function to remove repetition --- pyrvea/Examples/evonn_example.py | 11 +++-- .../Recombination/self_adapting_mutation.py | 49 +++++++++---------- 2 files changed, 32 insertions(+), 28 deletions(-) diff --git a/pyrvea/Examples/evonn_example.py b/pyrvea/Examples/evonn_example.py index e8b04d7..105cfcf 100644 --- a/pyrvea/Examples/evonn_example.py +++ b/pyrvea/Examples/evonn_example.py @@ -1,5 +1,7 @@ from pyrvea.Problem.evonn_problem import EvoNNProblem from pyrvea.Population.population_evonn import PopulationEvoNN +from pyrvea.Recombination.self_adapting_crossover import crossover +from pyrvea.Recombination.self_adapting_mutation import mutation from random import randint, sample import numpy as np @@ -19,8 +21,11 @@ ) pop = PopulationEvoNN(prob) +a1 = randint(0, 49) +a2 = randint(0, 49) +mutation(pop, a1, a2) -selected = sample(range(1, np.shape(pop.individuals)[0]), randint(1, np.shape(pop.individuals)[0])) +#selected = sample(range(1, np.shape(pop.individuals)[0]), randint(1, np.shape(pop.individuals)[0])) #pop.keep(selected) -selected = sample(range(1, np.shape(pop.individuals)[0]), randint(1, np.shape(pop.individuals)[0])) -pop.delete(selected) \ No newline at end of file +#selected = sample(range(1, np.shape(pop.individuals)[0]), randint(1, np.shape(pop.individuals)[0])) +#pop.delete(selected) \ No newline at end of file diff --git a/pyrvea/Recombination/self_adapting_mutation.py b/pyrvea/Recombination/self_adapting_mutation.py index 081ead6..f31b287 100644 --- a/pyrvea/Recombination/self_adapting_mutation.py +++ b/pyrvea/Recombination/self_adapting_mutation.py @@ -1,51 +1,50 @@ -from numpy import np +import numpy as np from random import sample def mutation( population, - offspring1, - offspring2, - cur_gen, - total_gen, + offspring1_idx, + offspring2_idx, + cur_gen=1, + total_gen=10, prob_mut=0.3, mut_strength=0.7, ): + def mutate(offspring, wx): + + select = sample(list(alternatives), 2) + + offspring[it.multi_index[0], it.multi_index[1]] = wx + mut_strength * ( + 1 - cur_gen / total_gen + ) * ( + select[0][it.multi_index[0], it.multi_index[1]] + - select[1][it.multi_index[0], it.multi_index[1]] + ) + + return offspring individuals = population.individuals - o1 = individuals[offspring1] - o2 = individuals[offspring2] + o1 = individuals[offspring1_idx] + o2 = individuals[offspring2_idx] # Could call the delete method from population_evonn class here - indices = [offspring1, offspring2] + indices = [offspring1_idx, offspring2_idx] mask = np.ones(len(individuals), dtype=bool) mask[indices] = False alternatives = individuals[mask, ...] it = np.nditer([o1, o2], flags=["multi_index"], op_flags=["readwrite"]) + for wx1, wx2 in it: if wx1 != 0 and np.random.random() < prob_mut: - select = sample(list(alternatives), 2) - - o1[it.multi_index[0], it.multi_index[1]] = wx1 + mut_strength * ( - 1 - cur_gen / total_gen - ) * ( - select[0][it.multi_index[0], it.multi_index[1]] - - select[1][it.multi_index[0], it.multi_index[1]] - ) + o1 = mutate(o1, wx1) if wx2 != 0 and np.random.random() < prob_mut: - select = sample(list(alternatives), 2) - - o2[it.multi_index[0], it.multi_index[1]] = wx1 + mut_strength * ( - 1 - cur_gen / total_gen - ) * ( - select[0][it.multi_index[0], it.multi_index[1]] - - select[1][it.multi_index[0], it.multi_index[1]] - ) + o2 = mutate(o2, wx2) - return offspring1, offspring2 + return offspring1_idx, offspring2_idx From 3d5c03da6c41d98a422bdbdffd490d38dd1cf1c7 Mon Sep 17 00:00:00 2001 From: delamorte Date: Fri, 24 May 2019 16:35:45 +0300 Subject: [PATCH 016/155] Added delete_or_keep method The function takes a parameter a list of indices and "delete" or "keep", and either deletes the indices from the population or keeps them and deletes all others. --- pyrvea/EAs/baseEA.py | 5 ++ pyrvea/Population/Population.py | 119 +++++++++++++++----------------- 2 files changed, 62 insertions(+), 62 deletions(-) diff --git a/pyrvea/EAs/baseEA.py b/pyrvea/EAs/baseEA.py index 6a2a5e8..fb62e5e 100644 --- a/pyrvea/EAs/baseEA.py +++ b/pyrvea/EAs/baseEA.py @@ -1,4 +1,6 @@ from typing import TYPE_CHECKING +import numpy as np +from copy import deepcopy if TYPE_CHECKING: from pyrvea.Population.Population import Population @@ -89,7 +91,10 @@ def _next_gen(self, population: "Population"): offspring = population.mate() population.add(offspring) selected = self.select(population) + tmp = deepcopy(population) population.keep(selected) + tmp.delete_or_keep(selected, "keep") + print("test") def select(self, population) -> list: """Describe a selection mechanism. Return indices of selected diff --git a/pyrvea/Population/Population.py b/pyrvea/Population/Population.py index c7ecc4e..ca449a1 100644 --- a/pyrvea/Population/Population.py +++ b/pyrvea/Population/Population.py @@ -11,6 +11,9 @@ from pygmo import non_dominated_front_2d as nd2 from tqdm import tqdm, tqdm_notebook +from pyrvea.Recombination.bounded_polynomial_mutation import mutation +from pyrvea.Recombination.simulated_binary_crossover import crossover + from pyrvea.OtherTools.plotlyanimate import animate_init_, animate_next_ from pyrvea.OtherTools.IsNotebook import IsNotebook @@ -46,7 +49,7 @@ def __init__( (the default is True, which creates the plots) """ - num_var = problem.num_of_variables + self.num_var = problem.num_of_variables self.lower_limits = np.asarray(problem.lower_limits) self.upper_limits = np.asarray(problem.upper_limits) self.hyp = 0 @@ -55,7 +58,7 @@ def __init__( self.filename = problem.name + "_" + str(problem.num_of_objectives) self.plotting = plotting # These attributes contain the solutions. - self.individuals = np.empty((0, num_var), float) + self.individuals = np.empty((0, self.num_var), float) self.objectives = np.empty((0, self.problem.num_of_objectives), float) self.fitness = np.empty((0, self.problem.num_of_objectives), float) self.constraint_violation = np.empty( @@ -149,7 +152,7 @@ def keep(self, indices: list): indices: list Indices of individuals to keep """ - + indices.sort() new_pop = self.individuals[indices, :] new_obj = self.objectives[indices, :] new_fitness = self.fitness[indices, :] @@ -172,6 +175,52 @@ def keep(self, indices: list): self.fitness = new_fitness self.constraint_violation = new_CV + def delete_or_keep(self, indices: list, delete_or_keep): + """Remove individuals from population which ARE in "indices". + With boolean masks deleted individuals can be obtained with + inverted mask (~mask) + + Parameters + ---------- + indices: list + Indices of individuals to keep + """ + indices.sort() + mask = np.ones(len(self.individuals), dtype=bool) + mask[indices] = False + + # new_pop = np.delete(self.individuals, indices, axis=0) + new_pop = self.individuals[mask, ...] + deleted_pop = self.individuals[~mask, ...] + + # new_obj = np.delete(self.objectives, indices, axis=0) + new_obj = self.objectives[mask, ...] + deleted_obj = self.objectives[~mask, ...] + + # new_fitness = np.delete(self.fitness, indices, axis=0) + new_fitness = self.fitness[mask, ...] + deleted_fitness = self.fitness[~mask, ...] + + if len(self.constraint_violation) > 0: + # new_cv = np.delete(self.constraint_violation, indices, axis=0) + new_cv = self.constraint_violation[mask, ...] + deleted_cv = self.constraint_violation[~mask, ...] + else: + deleted_cv = self.constraint_violation + new_cv = self.constraint_violation + + if delete_or_keep == "delete": + self.individuals = new_pop + self.objectives = new_obj + self.fitness = new_fitness + self.constraint_violation = new_cv + + elif delete_or_keep == "keep": + self.individuals = deleted_pop + self.objectives = deleted_obj + self.fitness = deleted_fitness + self.constraint_violation = deleted_cv + def append_individual(self, ind: np.ndarray): """Evaluate and add individual to the population. @@ -239,65 +288,11 @@ def mate(self): Conduct simulated binary crossover and bounded polunomial mutation. """ - pop = self.individuals - pop_size, num_var = pop.shape - shuffled_ids = list(range(pop_size)) - shuffle(shuffled_ids) - mating_pop = pop[shuffled_ids] - if pop_size % 2 == 1: - # Maybe it should be pop_size-1? - mating_pop = np.vstack((mating_pop, mating_pop[0])) - pop_size = pop_size + 1 - # The rest closely follows the matlab code. - ProC = 1 - ProM = 1 / num_var - DisC = 30 - DisM = 20 - offspring = np.zeros_like(mating_pop) # empty_like() more efficient? - for i in range(0, pop_size, 2): - beta = np.zeros(num_var) - miu = np.random.rand(num_var) - beta[miu <= 0.5] = (2 * miu[miu <= 0.5]) ** (1 / (DisC + 1)) - beta[miu > 0.5] = (2 - 2 * miu[miu > 0.5]) ** (-1 / (DisC + 1)) - beta = beta * ((-1) ** np.random.randint(0, high=2, size=num_var)) - beta[np.random.rand(num_var) > ProC] = 1 # It was in matlab code - avg = (mating_pop[i] + mating_pop[i + 1]) / 2 - diff = (mating_pop[i] - mating_pop[i + 1]) / 2 - offspring[i] = avg + beta * diff - offspring[i + 1] = avg - beta * diff - - min_val = np.ones_like(offspring) * self.lower_limits - max_val = np.ones_like(offspring) * self.upper_limits - k = np.random.random(offspring.shape) - miu = np.random.random(offspring.shape) - temp = np.logical_and((k <= ProM), (miu < 0.5)) - offspring_scaled = (offspring - min_val) / (max_val - min_val) - offspring[temp] = offspring[temp] + ( - (max_val[temp] - min_val[temp]) - * ( - ( - 2 * miu[temp] - + (1 - 2 * miu[temp]) * (1 - offspring_scaled[temp]) ** (DisM + 1) - ) - ** (1 / (DisM + 1)) - - 1 - ) - ) - temp = np.logical_and((k <= ProM), (miu >= 0.5)) - offspring[temp] = offspring[temp] + ( - (max_val[temp] - min_val[temp]) - * ( - 1 - - ( - 2 * (1 - miu[temp]) - + 2 * (miu[temp] - 0.5) * offspring_scaled[temp] ** (DisM + 1) - ) - ** (1 / (DisM + 1)) - ) - ) - offspring[offspring > max_val] = max_val[offspring > max_val] - offspring[offspring < min_val] = min_val[offspring < min_val] - return offspring + offspring = crossover(self) + p = 1 / self.num_var + mut_offspring = mutation(self, offspring, prob_mut=p) + + return mut_offspring def plot_init_(self): """Initialize animation objects. Return figure""" From bd894445497a64707b18632a8c6d5af0673a9a03 Mon Sep 17 00:00:00 2001 From: delamorte Date: Tue, 28 May 2019 13:48:46 +0300 Subject: [PATCH 017/155] added documentation to Recombination functions fixed the Recombination functions to use individuals instead of indices started working on PPGA algorithm --- pyrvea/EAs/PPGA.py | 136 ++++++++++++++++++ pyrvea/EAs/baseEA.py | 89 +++++++++++- pyrvea/Examples/evonn_example.py | 14 +- pyrvea/Population/population_evonn.py | 81 +++-------- pyrvea/Recombination/ppga_crossover.py | 23 +++ pyrvea/Recombination/ppga_mutation.py | 61 ++++++++ .../Recombination/self_adapting_crossover.py | 14 -- .../Recombination/self_adapting_mutation.py | 50 ------- 8 files changed, 336 insertions(+), 132 deletions(-) create mode 100644 pyrvea/EAs/PPGA.py create mode 100644 pyrvea/Recombination/ppga_crossover.py create mode 100644 pyrvea/Recombination/ppga_mutation.py delete mode 100644 pyrvea/Recombination/self_adapting_crossover.py delete mode 100644 pyrvea/Recombination/self_adapting_mutation.py diff --git a/pyrvea/EAs/PPGA.py b/pyrvea/EAs/PPGA.py new file mode 100644 index 0000000..f6d570a --- /dev/null +++ b/pyrvea/EAs/PPGA.py @@ -0,0 +1,136 @@ +from pyrvea.EAs.baseEA import BasePPGA +from random import sample +import numpy as np +from scipy.stats import bernoulli as bn + +class PPGA(BasePPGA): + + def set_params( + self, + population: "Population", + population_size: int = None, + target_pop_size: int = 200, + interact: bool = False, + a_priori_preference: bool = False, + generations_per_iteration: int = 10, + iterations: int = 1, + plotting: bool = True, + prob_omit: float = 0.3, + prob_crossover: float = 0.8, + prob_mutation: float = 0.3, + mut_strength: float = 0.7 + ): + """Set up the parameters. + + Parameters + ---------- + population : Population + Population object + population_size : int + Population Size + interact : bool + bool to enable or disable interaction. Enabled if True + a_priori_preference : bool + similar to interact + generations_per_iteration : int + Number of generations per iteration. + iterations : int + Total Number of iterations. + plotting : bool + Useless really. + prob_crossover : float + Probability of crossover occurring + prob_mutation : float + Probability of mutation occurring + mut_strength : float + Strength of the mutation + prob_omit : float + Probability of deactivating some connections + + Returns + ------- + + """ + + ppgaparams = { + "population": population, + "population_size": population_size, + "target_pop_size": target_pop_size, + "predator_pop_size": 50, + "interact": interact, + "a_priori": a_priori_preference, + "generations": generations_per_iteration, + "iterations": iterations, + "ploton": plotting, + "current_iteration_gen_count": 0, + "current_iteration_count": 0, + "prob_crossover": prob_crossover, + "prob_mutation": prob_mutation, + "mut_strength": mut_strength, + "prob_omit": prob_omit, + "kill_intrvl": 7, + "max_rank": 20 + + } + + lattice = Lattice(60, 60, ppgaparams) + + return ppgaparams + + +class Lattice: + + def __init__(self, size_x, size_y, params): + + self.size_x = size_x + self.size_y = size_y + self.arr = np.zeros((self.size_y, self.size_x)) + self.place_prey(params) + self.place_pred(params) + + def place_prey(self, params): + """Set some of the individual weights to zero, find an empty + position in the lattice and place the prey""" + + # Eliminate some weights + flag = bn.rvs(p=1 - params["prob_omit"], size=np.shape(params["population"].individuals)) + random_numbers = np.zeros(np.shape(params["population"].individuals)) + params["population"].individuals[flag == 0] = random_numbers[flag == 0] + + for i in range(np.shape(params["population"].individuals)[0]): + + x = np.random.randint(self.size_x) + y = np.random.randint(self.size_y) + + while self.arr[x][y] != 0: + x = np.random.randint(self.size_x) + y = np.random.randint(self.size_y) + + # +1 to offset zero index individual + self.arr[y][x] = i+1 + + def place_pred(self, params): + + # Initialize Predator population + + predators = np.linspace(0, 1, num=params["predator_pop_size"]) + + for i in range(np.shape(predators)[0]): + + x = np.random.randint(self.size_x) + y = np.random.randint(self.size_y) + + while self.arr[x][y] != 0: + x = np.random.randint(self.size_x) + y = np.random.randint(self.size_y) + + # +1 to offset zero index individual, set negative number to + # identify predators from prey in the lattice + + self.arr[y][x] = -1*(i+1) + + def move_prey(self): + pass + + def move_pred(self): + pass \ No newline at end of file diff --git a/pyrvea/EAs/baseEA.py b/pyrvea/EAs/baseEA.py index fb62e5e..1a35f79 100644 --- a/pyrvea/EAs/baseEA.py +++ b/pyrvea/EAs/baseEA.py @@ -1,6 +1,4 @@ from typing import TYPE_CHECKING -import numpy as np -from copy import deepcopy if TYPE_CHECKING: from pyrvea.Population.Population import Population @@ -91,10 +89,89 @@ def _next_gen(self, population: "Population"): offspring = population.mate() population.add(offspring) selected = self.select(population) - tmp = deepcopy(population) - population.keep(selected) - tmp.delete_or_keep(selected, "keep") - print("test") + population.delete_or_keep(selected, "keep") + + def select(self, population) -> list: + """Describe a selection mechanism. Return indices of selected + individuals. + + Parameters + ---------- + population : Population + Contains the current population and problem + information. + + Returns + ------- + list + List of indices of individuals to be selected. + """ + pass + + def continue_iteration(self): + """Checks whether the current iteration should be continued or not.""" + return self.params["current_iteration_gen_count"] <= self.params["generations"] + + def continue_evolution(self) -> bool: + """Checks whether the current iteration should be continued or not.""" + pass + + +class BasePPGA(BaseEA): + """The base class for evolutionary algorithms using neural networks.""" + + def __init__(self, population: "Population", EA_parameters: dict = None): + """Initialize the base class, call the method to set up the parameters. + + Parameters + ---------- + population : "Population" + This variable is updated as evolution takes place + EA_parameters : dict + Takes the EA parameters + """ + self.params = self.set_params(population, EA_parameters) + # print("Using BaseDecompositionEA init") + self._next_iteration(population) + + def initialize_lattice(self): + pass + + def _next_iteration(self, population: "Population"): + """Run one iteration of EA. + + One iteration consists of a constant or variable number of + generations. This method leaves EA.params unchanged, except the current + iteration count and gen count. + + Parameters + ---------- + population : "Population" + Contains current population + """ + self.params["current_iteration_gen_count"] = 1 + while self.continue_iteration(): + self._next_gen(population) + self.params["current_iteration_gen_count"] += 1 + self.params["current_iteration_count"] += 1 + + def _next_gen(self, population: "Population"): + """Run one generation of EA. + + This method leaves method.params unchanged. Intended to be used by + next_iteration. + + Parameters + ---------- + population: "Population" + Population object + """ + + ind1, ind2 = self.move_prey() + offspring = population.mate(ind1, ind2) + population.add(offspring) + selected = self.select(population) + population.delete_or_keep(selected, "keep") def select(self, population) -> list: """Describe a selection mechanism. Return indices of selected diff --git a/pyrvea/Examples/evonn_example.py b/pyrvea/Examples/evonn_example.py index 105cfcf..0c25c76 100644 --- a/pyrvea/Examples/evonn_example.py +++ b/pyrvea/Examples/evonn_example.py @@ -1,8 +1,10 @@ from pyrvea.Problem.evonn_problem import EvoNNProblem from pyrvea.Population.population_evonn import PopulationEvoNN -from pyrvea.Recombination.self_adapting_crossover import crossover -from pyrvea.Recombination.self_adapting_mutation import mutation +from pyrvea.Recombination.ppga_crossover import ppga_crossover +from pyrvea.Recombination.ppga_mutation import ppga_mutation +from pyrvea.EAs.PPGA import PPGA from random import randint, sample +import numpy as np import numpy as np @@ -20,10 +22,16 @@ num_hidden_nodes=hidden_nodes, ) + + pop = PopulationEvoNN(prob) + + +p = PPGA(pop) + a1 = randint(0, 49) a2 = randint(0, 49) -mutation(pop, a1, a2) +#mutation(pop, a1, a2) #selected = sample(range(1, np.shape(pop.individuals)[0]), randint(1, np.shape(pop.individuals)[0])) #pop.keep(selected) diff --git a/pyrvea/Population/population_evonn.py b/pyrvea/Population/population_evonn.py index 23702e6..6dd5820 100644 --- a/pyrvea/Population/population_evonn.py +++ b/pyrvea/Population/population_evonn.py @@ -15,6 +15,9 @@ from pyrvea.Population.Population import Population from scipy.stats import bernoulli as bn +from pyrvea.Recombination.ppga_crossover import ppga_crossover +from pyrvea.Recombination.ppga_mutation import ppga_mutation + if TYPE_CHECKING: from pyrvea.Problem.baseProblem import baseProblem from pyrvea.EAs.baseEA import BaseEA @@ -286,68 +289,28 @@ def evolve(self, EA: "BaseEA" = None, EA_parameters: dict = None) -> "Population if self.plotting: self.plot_objectives() - def mate(self): + def mate(self, ind1, ind2): """Conduct crossover and mutation over the population. - Conduct simulated binary crossover and bounded polunomial mutation. """ - pop = self.individuals - pop_size, num_var = pop.shape - shuffled_ids = list(range(pop_size)) - shuffle(shuffled_ids) - mating_pop = pop[shuffled_ids] - if pop_size % 2 == 1: - # Maybe it should be pop_size-1? - mating_pop = np.vstack((mating_pop, mating_pop[0])) - pop_size = pop_size + 1 - # The rest closely follows the matlab code. - ProC = 1 - ProM = 1 / num_var - DisC = 30 - DisM = 20 - offspring = np.zeros_like(mating_pop) # empty_like() more efficient? - for i in range(0, pop_size, 2): - beta = np.zeros(num_var) - miu = np.random.rand(num_var) - beta[miu <= 0.5] = (2 * miu[miu <= 0.5]) ** (1 / (DisC + 1)) - beta[miu > 0.5] = (2 - 2 * miu[miu > 0.5]) ** (-1 / (DisC + 1)) - beta = beta * ((-1) ** np.random.randint(0, high=2, size=num_var)) - beta[np.random.rand(num_var) > ProC] = 1 # It was in matlab code - avg = (mating_pop[i] + mating_pop[i + 1]) / 2 - diff = (mating_pop[i] - mating_pop[i + 1]) / 2 - offspring[i] = avg + beta * diff - offspring[i + 1] = avg - beta * diff - min_val = np.ones_like(offspring) * self.lower_limits - max_val = np.ones_like(offspring) * self.upper_limits - k = np.random.random(offspring.shape) - miu = np.random.random(offspring.shape) - temp = np.logical_and((k <= ProM), (miu < 0.5)) - offspring_scaled = (offspring - min_val) / (max_val - min_val) - offspring[temp] = offspring[temp] + ( - (max_val[temp] - min_val[temp]) - * ( - ( - 2 * miu[temp] - + (1 - 2 * miu[temp]) * (1 - offspring_scaled[temp]) ** (DisM + 1) - ) - ** (1 / (DisM + 1)) - - 1 - ) - ) - temp = np.logical_and((k <= ProM), (miu >= 0.5)) - offspring[temp] = offspring[temp] + ( - (max_val[temp] - min_val[temp]) - * ( - 1 - - ( - 2 * (1 - miu[temp]) - + 2 * (miu[temp] - 0.5) * offspring_scaled[temp] ** (DisM + 1) - ) - ** (1 / (DisM + 1)) - ) - ) - offspring[offspring > max_val] = max_val[offspring > max_val] - offspring[offspring < min_val] = min_val[offspring < min_val] + # Get individuals at indices + w1, w2 = self.individuals[ind1], self.individuals[ind2] + + # Perform crossover + xover_w1, xover_w2 = ppga_crossover(w1, w2) + + # Find randomly two other individuals with current match active for mutation + indices = [xover_w1, xover_w2] + mask = np.ones(len(self.individuals), dtype=bool) + mask[indices] = False + alternatives = self.individuals[mask, ...] + + # Mutate + offspring1, offspring2 = ppga_mutation(xover_w1, xover_w2, alternatives) + + # Add mutated offspring to a list + offspring = np.vstack([offspring1], [offspring2]) + return offspring def plot_init_(self): diff --git a/pyrvea/Recombination/ppga_crossover.py b/pyrvea/Recombination/ppga_crossover.py new file mode 100644 index 0000000..42f7e83 --- /dev/null +++ b/pyrvea/Recombination/ppga_crossover.py @@ -0,0 +1,23 @@ +import numpy as np + + +def ppga_crossover(w1, w2, prob_xover=0.8): + """Randomly exchange nodes between two individuals. + + Parameters + ---------- + w1 : np.ndarray + The first individual + w2 : np.ndarray + The second individual + prob_xover : float + The probability for the crossover + """ + + for i in range(np.shape(w1)[1]): + if np.random.random() < prob_xover: + tmp = np.copy(w1[:, i]) + w1[:, i] = w2[:, i] + w2[:, i] = tmp + + return w1, w2 diff --git a/pyrvea/Recombination/ppga_mutation.py b/pyrvea/Recombination/ppga_mutation.py new file mode 100644 index 0000000..64e0b23 --- /dev/null +++ b/pyrvea/Recombination/ppga_mutation.py @@ -0,0 +1,61 @@ +import numpy as np +from random import sample + + +def ppga_mutation( + alternatives, + w1, + w2, + cur_gen=1, + total_gen=10, + prob_mut=0.3, + mut_strength=0.7, +): + """Randomly mutate two individuals + + Paremeters + ---------- + alternatives : np.ndarray + a list of individuals excluding w1 and w2 + w1 : np.ndarray + first individual to be mutated + w2 : np.ndarray + second individual + cur_gen : int + the number of the current generation + total_gen : int + max number of generations + prob_mut : float + the probability of the mutation happening + mut_strength : float + the strength of the mutation constant + """ + + def mutate(offspring, wx): + """Internal function for the mutation calculation""" + + select = sample(list(alternatives), 2) + + offspring[it.multi_index[0], it.multi_index[1]] = wx + mut_strength * ( + 1 - cur_gen / total_gen + ) * ( + select[0][it.multi_index[0], it.multi_index[1]] + - select[1][it.multi_index[0], it.multi_index[1]] + ) + + return offspring + + # Iterate over both individuals at the same time to avoid nested looping + it = np.nditer([w1, w2], flags=["multi_index"], op_flags=["readwrite"]) + + # wx = individual cells (connections) in the weight matrix + for wx1, wx2 in it: + if wx1 != 0 and np.random.random() < prob_mut: + + w1 = mutate(w1, wx1) + + if wx2 != 0 and np.random.random() < prob_mut: + + w2 = mutate(w2, wx2) + + return w1, w2 diff --git a/pyrvea/Recombination/self_adapting_crossover.py b/pyrvea/Recombination/self_adapting_crossover.py deleted file mode 100644 index 7faf92a..0000000 --- a/pyrvea/Recombination/self_adapting_crossover.py +++ /dev/null @@ -1,14 +0,0 @@ -import numpy as np - - -def crossover(population, w1, w2, prob_xover=0.8): - - individuals = population.individuals - - for i in range(np.shape(individuals[w1])[1]): - if np.random.random() < prob_xover: - tmp = np.copy(individuals[w1][:, i]) - individuals[w1][:, i] = individuals[w2][:, i] - individuals[w2][:, i] = tmp - - return w1, w2 diff --git a/pyrvea/Recombination/self_adapting_mutation.py b/pyrvea/Recombination/self_adapting_mutation.py deleted file mode 100644 index f31b287..0000000 --- a/pyrvea/Recombination/self_adapting_mutation.py +++ /dev/null @@ -1,50 +0,0 @@ -import numpy as np -from random import sample - - -def mutation( - population, - offspring1_idx, - offspring2_idx, - cur_gen=1, - total_gen=10, - prob_mut=0.3, - mut_strength=0.7, -): - def mutate(offspring, wx): - - select = sample(list(alternatives), 2) - - offspring[it.multi_index[0], it.multi_index[1]] = wx + mut_strength * ( - 1 - cur_gen / total_gen - ) * ( - select[0][it.multi_index[0], it.multi_index[1]] - - select[1][it.multi_index[0], it.multi_index[1]] - ) - - return offspring - - individuals = population.individuals - - o1 = individuals[offspring1_idx] - o2 = individuals[offspring2_idx] - - # Could call the delete method from population_evonn class here - indices = [offspring1_idx, offspring2_idx] - mask = np.ones(len(individuals), dtype=bool) - mask[indices] = False - - alternatives = individuals[mask, ...] - - it = np.nditer([o1, o2], flags=["multi_index"], op_flags=["readwrite"]) - - for wx1, wx2 in it: - if wx1 != 0 and np.random.random() < prob_mut: - - o1 = mutate(o1, wx1) - - if wx2 != 0 and np.random.random() < prob_mut: - - o2 = mutate(o2, wx2) - - return offspring1_idx, offspring2_idx From 16c37314c603755a9b74c65e2787d8351ed5839e Mon Sep 17 00:00:00 2001 From: delamorte Date: Wed, 29 May 2019 11:16:52 +0300 Subject: [PATCH 018/155] added move_prey function to PPGA preys and predators are now tracked in a dict within Lattice --- .gitignore | 2 + pyrvea/EAs/PPGA.py | 69 ++++++++++++++++++++++----- pyrvea/EAs/baseEA.py | 5 +- pyrvea/Examples/evonn_example.py | 9 ++-- pyrvea/Population/population_evonn.py | 2 +- 5 files changed, 67 insertions(+), 20 deletions(-) diff --git a/.gitignore b/.gitignore index 90a862e..56fbe02 100644 --- a/.gitignore +++ b/.gitignore @@ -134,3 +134,5 @@ dmypy.json # Poetry files poetry.lock pyproject.toml + +.tests.py \ No newline at end of file diff --git a/pyrvea/EAs/PPGA.py b/pyrvea/EAs/PPGA.py index f6d570a..bdecd67 100644 --- a/pyrvea/EAs/PPGA.py +++ b/pyrvea/EAs/PPGA.py @@ -1,6 +1,7 @@ from pyrvea.EAs.baseEA import BasePPGA from random import sample import numpy as np +from random import randint from scipy.stats import bernoulli as bn class PPGA(BasePPGA): @@ -85,8 +86,11 @@ def __init__(self, size_x, size_y, params): self.size_x = size_x self.size_y = size_y self.arr = np.zeros((self.size_y, self.size_x)) + self.preys = {} + self.predators = {} self.place_prey(params) self.place_pred(params) + self.move_prey() def place_prey(self, params): """Set some of the individual weights to zero, find an empty @@ -99,38 +103,81 @@ def place_prey(self, params): for i in range(np.shape(params["population"].individuals)[0]): - x = np.random.randint(self.size_x) y = np.random.randint(self.size_y) + x = np.random.randint(self.size_x) - while self.arr[x][y] != 0: - x = np.random.randint(self.size_x) + while self.arr[y][x] != 0: y = np.random.randint(self.size_y) + x = np.random.randint(self.size_x) + + # Keep track of preys in a dictionary + self.preys[i] = [y, x] # +1 to offset zero index individual self.arr[y][x] = i+1 def place_pred(self, params): - - # Initialize Predator population + """Initialize the predator population, linearly distributed in [0,1] + and place them in the lattice randomly""" predators = np.linspace(0, 1, num=params["predator_pop_size"]) for i in range(np.shape(predators)[0]): - x = np.random.randint(self.size_x) y = np.random.randint(self.size_y) + x = np.random.randint(self.size_x) - while self.arr[x][y] != 0: - x = np.random.randint(self.size_x) + while self.arr[y][x] != 0: y = np.random.randint(self.size_y) + x = np.random.randint(self.size_x) + + # Keep track of predators in a dictionary + self.predators[i] = [y, x] # +1 to offset zero index individual, set negative number to # identify predators from prey in the lattice - self.arr[y][x] = -1*(i+1) def move_prey(self): - pass + """Find an empty position in the moore neighbourhood for the Prey to move in. + Ensure that the lattice is toroidal.""" + + for prey, pos in self.preys.items(): + + dy = randint(-1, 1) + dx = randint(-1, 1) + dest_y = dy + pos[0] + dest_x = dx + pos[1] + + # Make the lattice toroidal + if dest_x >= self.size_x: + dest_x = 0 + if dest_x < 0: + dest_x = self.size_x - 1 + if dest_y >= self.size_y: + dest_y = 0 + if dest_y < 0: + dest_y = self.size_y - 1 + + while self.arr[dest_y][dest_x] != 0: + dy = randint(-1, 1) + dx = randint(-1, 1) + dest_y = dy + pos[0] + dest_x = dx + pos[1] + + # Move prey, clear previous location + self.arr[dest_y][dest_x] = prey+1 + self.arr[pos[0]][pos[1]] = 0 + + # Update prey location in the dictionary + pos[0], pos[1] = dest_y, dest_x + + # Find mates in the moore neighbourhood + moore = self.arr[pos[0]-1:pos[0]+2, pos[1]-1:pos[1]+2] + mates = moore[moore > prey+1] + mate = np.random.choice(mates, 1) + + return prey def move_pred(self): - pass \ No newline at end of file + pass diff --git a/pyrvea/EAs/baseEA.py b/pyrvea/EAs/baseEA.py index 1a35f79..03a32fb 100644 --- a/pyrvea/EAs/baseEA.py +++ b/pyrvea/EAs/baseEA.py @@ -134,9 +134,6 @@ def __init__(self, population: "Population", EA_parameters: dict = None): # print("Using BaseDecompositionEA init") self._next_iteration(population) - def initialize_lattice(self): - pass - def _next_iteration(self, population: "Population"): """Run one iteration of EA. @@ -167,7 +164,7 @@ def _next_gen(self, population: "Population"): Population object """ - ind1, ind2 = self.move_prey() + ind1, ind2 = self.lattice.move_prey() offspring = population.mate(ind1, ind2) population.add(offspring) selected = self.select(population) diff --git a/pyrvea/Examples/evonn_example.py b/pyrvea/Examples/evonn_example.py index 0c25c76..09b0582 100644 --- a/pyrvea/Examples/evonn_example.py +++ b/pyrvea/Examples/evonn_example.py @@ -5,8 +5,7 @@ from pyrvea.EAs.PPGA import PPGA from random import randint, sample import numpy as np - -import numpy as np +import timeit input_nodes = 5 hidden_nodes = 4 @@ -22,6 +21,8 @@ num_hidden_nodes=hidden_nodes, ) +arr = np.zeros((60,60)) + pop = PopulationEvoNN(prob) @@ -29,8 +30,8 @@ p = PPGA(pop) -a1 = randint(0, 49) -a2 = randint(0, 49) +#a1 = randint(0, 49) +#a2 = randint(0, 49) #mutation(pop, a1, a2) #selected = sample(range(1, np.shape(pop.individuals)[0]), randint(1, np.shape(pop.individuals)[0])) diff --git a/pyrvea/Population/population_evonn.py b/pyrvea/Population/population_evonn.py index 6dd5820..2cb7a39 100644 --- a/pyrvea/Population/population_evonn.py +++ b/pyrvea/Population/population_evonn.py @@ -91,7 +91,7 @@ def __init__( self.create_new_individuals(assign_type) def create_new_individuals( - self, design: str = "RandomDesign", pop_size: int = 100, decision_variables=None + self, design: str = "RandomDesign", pop_size: int = 500, decision_variables=None ): """Create, evaluate and add new individuals to the population. Initiate Plots. From 4806f407179a8421bf5d9db390a54f7dfb3b10ab Mon Sep 17 00:00:00 2001 From: delamorte Date: Wed, 29 May 2019 16:46:32 +0300 Subject: [PATCH 019/155] refactored PPGA class optimized predator and prey placement functions added static methods to check for lattice boundaries and neighbours --- pyrvea/EAs/PPGA.py | 256 ++++++++++++++++++-------- pyrvea/EAs/baseEA.py | 81 +------- pyrvea/Examples/evonn_example.py | 7 +- pyrvea/Population/population_evonn.py | 4 +- pyrvea/tests.py | 33 ++++ 5 files changed, 220 insertions(+), 161 deletions(-) create mode 100644 pyrvea/tests.py diff --git a/pyrvea/EAs/PPGA.py b/pyrvea/EAs/PPGA.py index bdecd67..399f87c 100644 --- a/pyrvea/EAs/PPGA.py +++ b/pyrvea/EAs/PPGA.py @@ -1,10 +1,18 @@ -from pyrvea.EAs.baseEA import BasePPGA +from pyrvea.EAs.baseEA import BaseEA from random import sample import numpy as np -from random import randint +from random import choice, randint from scipy.stats import bernoulli as bn +from pyrvea.Population.population_evonn import Population -class PPGA(BasePPGA): + +class PPGA(BaseEA): + + def __init__(self, population: "Population", ea_parameters: dict = None): + + self.params = self.set_params(population, ea_parameters) + self.lattice = Lattice(60, 60, self.params) + self._next_iteration(population) def set_params( self, @@ -58,6 +66,7 @@ def set_params( "population_size": population_size, "target_pop_size": target_pop_size, "predator_pop_size": 50, + "prey_max_moves": 5, "interact": interact, "a_priori": a_priori_preference, "generations": generations_per_iteration, @@ -74,10 +83,85 @@ def set_params( } - lattice = Lattice(60, 60, ppgaparams) - return ppgaparams + def _next_iteration(self, population: "Population"): + """Run one iteration of EA. + + One iteration consists of a constant or variable number of + generations. This method leaves EA.params unchanged, except the current + iteration count and gen count. + + Parameters + ---------- + population : "Population" + Contains current population + """ + self.params["current_iteration_gen_count"] = 1 + while self.continue_iteration(): + self._next_gen(population) + self.params["current_iteration_gen_count"] += 1 + self.params["current_iteration_count"] += 1 + + def _next_gen(self, population: "Population"): + """Run one generation of EA. + + This method leaves method.params unchanged. Intended to be used by + next_iteration. + + Parameters + ---------- + population: "Population" + Population object + """ + + self.lattice.move_prey() + + for ind in range(population.individuals.shape[0]-1): + + mate_idx = self.lattice.choose_mate(ind) + if not mate_idx: + continue + else: + offspring = population.mate(ind, mate_idx) + + # Try to place the offspring to lattice, add to population if successful + if self.lattice.place_prey(offspring): + population.add(offspring) + + # Kill bad individuals every n generations + if self.params["current_iteration_gen_count"] >= self.params["kill_intrvl"]: + selected = self.select(population) + population.delete_or_keep(selected, "delete") + + # Move predators + self.lattice.move_pred() + + def select(self, population) -> list: + """Describe a selection mechanism. Return indices of selected + individuals. + + Parameters + ---------- + population : Population + Contains the current population and problem + information. + + Returns + ------- + list + List of indices of individuals to be selected. + """ + pass + + def continue_iteration(self): + """Checks whether the current iteration should be continued or not.""" + return self.params["current_iteration_gen_count"] <= self.params["generations"] + + def continue_evolution(self) -> bool: + """Checks whether the current iteration should be continued or not.""" + pass + class Lattice: @@ -88,96 +172,120 @@ def __init__(self, size_x, size_y, params): self.arr = np.zeros((self.size_y, self.size_x)) self.preys = {} self.predators = {} - self.place_prey(params) - self.place_pred(params) - self.move_prey() + self.params = params + self.init_pred() + self.init_prey() + + def init_pred(self): + """Initialize the predator population, linearly distributed in [0,1] + and place them in the lattice randomly""" + + # Initialize the predator population + predators = np.linspace(0, 1, num=self.params["predator_pop_size"]) + + # Take random indices from free (==zero) lattice spaces + free_space = np.transpose(np.nonzero(self.arr == 0)) + indices = sample(free_space.tolist(), predators.shape[0]) + + for i in range(np.shape(predators)[0]): - def place_prey(self, params): + # Keep track of predators in a dictionary + self.predators[i] = [indices[i][0], indices[i][1]] + + # +1 to offset zero index individual, set negative number to + # identify predators from prey in the lattice + self.arr[indices[i][0]][indices[i][1]] = -1*(i+1) + + def init_prey(self): """Set some of the individual weights to zero, find an empty position in the lattice and place the prey""" # Eliminate some weights - flag = bn.rvs(p=1 - params["prob_omit"], size=np.shape(params["population"].individuals)) - random_numbers = np.zeros(np.shape(params["population"].individuals)) - params["population"].individuals[flag == 0] = random_numbers[flag == 0] - - for i in range(np.shape(params["population"].individuals)[0]): + flag = bn.rvs(p=1 - self.params["prob_omit"], size=np.shape(self.params["population"].individuals)) + random_numbers = np.zeros(np.shape(self.params["population"].individuals)) + self.params["population"].individuals[flag == 0] = random_numbers[flag == 0] - y = np.random.randint(self.size_y) - x = np.random.randint(self.size_x) + # Take random indices from free (==zero) lattice spaces + free_space = np.transpose(np.nonzero(self.arr == 0)) + indices = sample(free_space.tolist(), self.params["population"].individuals.shape[0]) - while self.arr[y][x] != 0: - y = np.random.randint(self.size_y) - x = np.random.randint(self.size_x) + for i in range(self.params["population"].individuals.shape[0]): # Keep track of preys in a dictionary - self.preys[i] = [y, x] + self.preys[i] = [indices[i][0], indices[i][1]] # +1 to offset zero index individual - self.arr[y][x] = i+1 + self.arr[indices[i][0]][indices[i][1]] = i+1 - def place_pred(self, params): - """Initialize the predator population, linearly distributed in [0,1] - and place them in the lattice randomly""" + def move_prey(self): + """Find an empty position in the moore neighbourhood for the Prey to move in. + Ensure that the lattice is toroidal.""" - predators = np.linspace(0, 1, num=params["predator_pop_size"]) + for prey, pos in self.preys.items(): + attempts = 0 + while attempts <= self.params["prey_max_moves"]: - for i in range(np.shape(predators)[0]): + neighbours = self.neighbours(self.arr, pos[0], pos[1]) + free_space = np.transpose(np.nonzero(neighbours == 0)) + dy, dx = free_space[randint(0, len(free_space)-1)] - y = np.random.randint(self.size_y) - x = np.random.randint(self.size_x) + dest_y = dy - 1 + pos[0] + dest_x = dx - 1 + pos[1] - while self.arr[y][x] != 0: - y = np.random.randint(self.size_y) - x = np.random.randint(self.size_x) + # Check boundaries of the lattice + if dest_y not in range(self.size_y) or dest_x not in range(self.size_x): + dest_y, dest_x = self.lattice_wrap_idx((dest_y, dest_x), np.shape(self.arr)) - # Keep track of predators in a dictionary - self.predators[i] = [y, x] + # Move prey, clear previous location + self.arr[dest_y][dest_x] = prey+1 + self.arr[pos[0]][pos[1]] = 0 - # +1 to offset zero index individual, set negative number to - # identify predators from prey in the lattice - self.arr[y][x] = -1*(i+1) + # Update prey location in the dictionary + pos[0], pos[1] = dest_y, dest_x - def move_prey(self): - """Find an empty position in the moore neighbourhood for the Prey to move in. - Ensure that the lattice is toroidal.""" + # Find mates in the moore neighbourhood + neighbours = self.neighbours(self.arr, pos[0], pos[1]) + mates = neighbours[neighbours > prey+1] + if len(mates) > 0: + break + else: + attempts += 1 - for prey, pos in self.preys.items(): + def choose_mate(self, ind): - dy = randint(-1, 1) - dx = randint(-1, 1) - dest_y = dy + pos[0] - dest_x = dx + pos[1] - - # Make the lattice toroidal - if dest_x >= self.size_x: - dest_x = 0 - if dest_x < 0: - dest_x = self.size_x - 1 - if dest_y >= self.size_y: - dest_y = 0 - if dest_y < 0: - dest_y = self.size_y - 1 - - while self.arr[dest_y][dest_x] != 0: - dy = randint(-1, 1) - dx = randint(-1, 1) - dest_y = dy + pos[0] - dest_x = dx + pos[1] - - # Move prey, clear previous location - self.arr[dest_y][dest_x] = prey+1 - self.arr[pos[0]][pos[1]] = 0 - - # Update prey location in the dictionary - pos[0], pos[1] = dest_y, dest_x - - # Find mates in the moore neighbourhood - moore = self.arr[pos[0]-1:pos[0]+2, pos[1]-1:pos[1]+2] - mates = moore[moore > prey+1] - mate = np.random.choice(mates, 1) - - return prey + neighbours = self.neighbours(self.arr, self.preys[ind][0], self.preys[ind][1]) + mates = neighbours[neighbours > ind + 1] + if not mates: + return + else: + mate = int(choice(mates)) + return mate def move_pred(self): pass + + @staticmethod + def lattice_wrap_idx(index, lattice_shape): + """Returns periodic lattice index + for a given iterable index + + Parameters + ---------- + index : tuple + one integer for each axis + lattice_shape : tuple + the shape of the lattice to index to + """ + if not hasattr(index, '__iter__'): return index # handle integer slices + if len(index) != len(lattice_shape): return index # must reference a scalar + if any(type(i) == slice for i in index): return index # slices not supported + if len(index) == len(lattice_shape): # periodic indexing of scalars + mod_index = tuple(((i % s + s) % s for i, s in zip(index, lattice_shape))) + return mod_index + raise ValueError('Unexpected index: {}'.format(index)) + + @staticmethod + def neighbours(arr, x, y, n=3): + """Given a 2D-array, returns an nxn array whose "center" element is arr[x,y]""" + arr = np.roll(np.roll(arr, shift=-x + 1, axis=0), shift=-y + 1, axis=1) + return arr[:n, :n] diff --git a/pyrvea/EAs/baseEA.py b/pyrvea/EAs/baseEA.py index 03a32fb..7837bd7 100644 --- a/pyrvea/EAs/baseEA.py +++ b/pyrvea/EAs/baseEA.py @@ -114,83 +114,4 @@ def continue_iteration(self): def continue_evolution(self) -> bool: """Checks whether the current iteration should be continued or not.""" - pass - - -class BasePPGA(BaseEA): - """The base class for evolutionary algorithms using neural networks.""" - - def __init__(self, population: "Population", EA_parameters: dict = None): - """Initialize the base class, call the method to set up the parameters. - - Parameters - ---------- - population : "Population" - This variable is updated as evolution takes place - EA_parameters : dict - Takes the EA parameters - """ - self.params = self.set_params(population, EA_parameters) - # print("Using BaseDecompositionEA init") - self._next_iteration(population) - - def _next_iteration(self, population: "Population"): - """Run one iteration of EA. - - One iteration consists of a constant or variable number of - generations. This method leaves EA.params unchanged, except the current - iteration count and gen count. - - Parameters - ---------- - population : "Population" - Contains current population - """ - self.params["current_iteration_gen_count"] = 1 - while self.continue_iteration(): - self._next_gen(population) - self.params["current_iteration_gen_count"] += 1 - self.params["current_iteration_count"] += 1 - - def _next_gen(self, population: "Population"): - """Run one generation of EA. - - This method leaves method.params unchanged. Intended to be used by - next_iteration. - - Parameters - ---------- - population: "Population" - Population object - """ - - ind1, ind2 = self.lattice.move_prey() - offspring = population.mate(ind1, ind2) - population.add(offspring) - selected = self.select(population) - population.delete_or_keep(selected, "keep") - - def select(self, population) -> list: - """Describe a selection mechanism. Return indices of selected - individuals. - - Parameters - ---------- - population : Population - Contains the current population and problem - information. - - Returns - ------- - list - List of indices of individuals to be selected. - """ - pass - - def continue_iteration(self): - """Checks whether the current iteration should be continued or not.""" - return self.params["current_iteration_gen_count"] <= self.params["generations"] - - def continue_evolution(self) -> bool: - """Checks whether the current iteration should be continued or not.""" - pass + pass \ No newline at end of file diff --git a/pyrvea/Examples/evonn_example.py b/pyrvea/Examples/evonn_example.py index 09b0582..62ca618 100644 --- a/pyrvea/Examples/evonn_example.py +++ b/pyrvea/Examples/evonn_example.py @@ -1,5 +1,5 @@ from pyrvea.Problem.evonn_problem import EvoNNProblem -from pyrvea.Population.population_evonn import PopulationEvoNN +from pyrvea.Population.population_evonn import Population from pyrvea.Recombination.ppga_crossover import ppga_crossover from pyrvea.Recombination.ppga_mutation import ppga_mutation from pyrvea.EAs.PPGA import PPGA @@ -23,10 +23,7 @@ arr = np.zeros((60,60)) - - -pop = PopulationEvoNN(prob) - +pop = Population(prob) p = PPGA(pop) diff --git a/pyrvea/Population/population_evonn.py b/pyrvea/Population/population_evonn.py index 2cb7a39..96ec25c 100644 --- a/pyrvea/Population/population_evonn.py +++ b/pyrvea/Population/population_evonn.py @@ -23,7 +23,7 @@ from pyrvea.EAs.baseEA import BaseEA -class PopulationEvoNN(): +class Population(): """Define the population.""" def __init__( @@ -300,7 +300,7 @@ def mate(self, ind1, ind2): xover_w1, xover_w2 = ppga_crossover(w1, w2) # Find randomly two other individuals with current match active for mutation - indices = [xover_w1, xover_w2] + indices = [ind1, ind2] mask = np.ones(len(self.individuals), dtype=bool) mask[indices] = False alternatives = self.individuals[mask, ...] diff --git a/pyrvea/tests.py b/pyrvea/tests.py new file mode 100644 index 0000000..59a992b --- /dev/null +++ b/pyrvea/tests.py @@ -0,0 +1,33 @@ +import timeit + +mysetup = "import numpy as np \n" \ + "arr = np.zeros((600,600))" +mycode1 = """ +def place_prey1(arr): + for i in range(1, 10000): + + free_space = np.transpose(np.nonzero(arr == 0)) + x, y = free_space[np.random.randint(free_space.shape[0])] + arr[y][x] = i +""" + +mycode2 = """ +def place_prey2(arr): + for i in range(1, 10000): + + x = np.random.randint(arr.shape[1]) + y = np.random.randint(arr.shape[0]) + + while arr[y][x] != 0: + x = np.random.randint(arr.shape[1]) + y = np.random.randint(arr.shape[0]) + + # +1 to offset zero index individual + arr[y][x] = i +""" +mycode3 = """ +def place_prey3(arr): + +""" +print(timeit.timeit(setup=mysetup, stmt=mycode1, number=10000000)) +print(timeit.timeit(setup=mysetup, stmt=mycode2, number=10000000)) \ No newline at end of file From 43c1ee999be97a5c67ca42460bc15a7963870221 Mon Sep 17 00:00:00 2001 From: delamorte Date: Mon, 3 Jun 2019 13:18:00 +0300 Subject: [PATCH 020/155] implemented predator moving and killing fixed bug with offspring placing with wrong indices --- pyrvea/EAs/PPGA.py | 269 ++++++++++++++++++++------ pyrvea/Population/population_evonn.py | 59 +++++- pyrvea/Problem/evonn_problem.py | 17 +- 3 files changed, 273 insertions(+), 72 deletions(-) diff --git a/pyrvea/EAs/PPGA.py b/pyrvea/EAs/PPGA.py index 399f87c..4ea99ad 100644 --- a/pyrvea/EAs/PPGA.py +++ b/pyrvea/EAs/PPGA.py @@ -1,4 +1,5 @@ from pyrvea.EAs.baseEA import BaseEA +from pyrvea.Selection.PPGA_select import ppga_select from random import sample import numpy as np from random import choice, randint @@ -7,7 +8,6 @@ class PPGA(BaseEA): - def __init__(self, population: "Population", ea_parameters: dict = None): self.params = self.set_params(population, ea_parameters) @@ -18,7 +18,7 @@ def set_params( self, population: "Population", population_size: int = None, - target_pop_size: int = 200, + target_pop_size: int = 300, interact: bool = False, a_priori_preference: bool = False, generations_per_iteration: int = 10, @@ -27,7 +27,7 @@ def set_params( prob_omit: float = 0.3, prob_crossover: float = 0.8, prob_mutation: float = 0.3, - mut_strength: float = 0.7 + mut_strength: float = 0.7, ): """Set up the parameters. @@ -67,8 +67,8 @@ def set_params( "target_pop_size": target_pop_size, "predator_pop_size": 50, "prey_max_moves": 5, - "interact": interact, - "a_priori": a_priori_preference, + "predator_max_moves": 5, + "offspring_place_attempts": 10, "generations": generations_per_iteration, "iterations": iterations, "ploton": plotting, @@ -79,8 +79,7 @@ def set_params( "mut_strength": mut_strength, "prob_omit": prob_omit, "kill_intrvl": 7, - "max_rank": 20 - + "max_rank": 20, } return ppgaparams @@ -117,25 +116,41 @@ def _next_gen(self, population: "Population"): self.lattice.move_prey() - for ind in range(population.individuals.shape[0]-1): + offspring = np.empty( + ( + 0, + population.problem.num_input_nodes + 1, + population.problem.num_hidden_nodes, + ), + float, + ) + for ind in range(population.individuals.shape[0]): mate_idx = self.lattice.choose_mate(ind) if not mate_idx: continue else: - offspring = population.mate(ind, mate_idx) + offspring1, offspring2 = population.mate(ind, mate_idx) + offspring = np.concatenate((offspring, [offspring1], [offspring2])) # Try to place the offspring to lattice, add to population if successful - if self.lattice.place_prey(offspring): - population.add(offspring) + placed_indices = self.lattice.place_offspring(offspring.shape[0]) + + # Remove from offsprings the ones that didn't get placed + mask = np.ones(len(offspring), dtype=bool) + mask[placed_indices] = False + offspring_pop = offspring[~mask, ...] + + # Add the successfully placed offspring to the population + population.add(offspring_pop) # Kill bad individuals every n generations - if self.params["current_iteration_gen_count"] >= self.params["kill_intrvl"]: - selected = self.select(population) - population.delete_or_keep(selected, "delete") + # if self.params["current_iteration_gen_count"] >= self.params["kill_intrvl"]: + selected = self.select(population) + # population.delete_or_keep(selected, "delete") # Move predators - self.lattice.move_pred() + self.lattice.move_predator() def select(self, population) -> list: """Describe a selection mechanism. Return indices of selected @@ -152,7 +167,8 @@ def select(self, population) -> list: list List of indices of individuals to be selected. """ - pass + selection = ppga_select(population.fitness) + return selection def continue_iteration(self): """Checks whether the current iteration should be continued or not.""" @@ -164,105 +180,237 @@ def continue_evolution(self) -> bool: class Lattice: - def __init__(self, size_x, size_y, params): self.size_x = size_x self.size_y = size_y - self.arr = np.zeros((self.size_y, self.size_x)) - self.preys = {} - self.predators = {} + self.arr = np.zeros((self.size_y, self.size_x), int) + self.predator_pop = np.empty((0, 1)) + self.predators_loc = [] + self.preys_loc = [] self.params = params - self.init_pred() + self.init_predators() self.init_prey() - def init_pred(self): + def init_predators(self): """Initialize the predator population, linearly distributed in [0,1] and place them in the lattice randomly""" # Initialize the predator population - predators = np.linspace(0, 1, num=self.params["predator_pop_size"]) + self.predator_pop = np.linspace(0, 1, num=self.params["predator_pop_size"]) # Take random indices from free (==zero) lattice spaces free_space = np.transpose(np.nonzero(self.arr == 0)) - indices = sample(free_space.tolist(), predators.shape[0]) + indices = sample(free_space.tolist(), self.predator_pop.shape[0]) - for i in range(np.shape(predators)[0]): + for i in range(self.predator_pop.shape[0]): - # Keep track of predators in a dictionary - self.predators[i] = [indices[i][0], indices[i][1]] + # Keep track of predator locations in a list + self.predators_loc.append([indices[i][0], indices[i][1]]) # +1 to offset zero index individual, set negative number to # identify predators from prey in the lattice - self.arr[indices[i][0]][indices[i][1]] = -1*(i+1) + self.arr[indices[i][0]][indices[i][1]] = int(-1 * (i + 1)) def init_prey(self): """Set some of the individual weights to zero, find an empty position in the lattice and place the prey""" # Eliminate some weights - flag = bn.rvs(p=1 - self.params["prob_omit"], size=np.shape(self.params["population"].individuals)) + flag = bn.rvs( + p=1 - self.params["prob_omit"], + size=np.shape(self.params["population"].individuals), + ) random_numbers = np.zeros(np.shape(self.params["population"].individuals)) self.params["population"].individuals[flag == 0] = random_numbers[flag == 0] # Take random indices from free (==zero) lattice spaces free_space = np.transpose(np.nonzero(self.arr == 0)) - indices = sample(free_space.tolist(), self.params["population"].individuals.shape[0]) + indices = sample( + free_space.tolist(), self.params["population"].individuals.shape[0] + ) for i in range(self.params["population"].individuals.shape[0]): - # Keep track of preys in a dictionary - self.preys[i] = [indices[i][0], indices[i][1]] + # Keep track of preys in a list + self.preys_loc.append([indices[i][0], indices[i][1]]) # +1 to offset zero index individual - self.arr[indices[i][0]][indices[i][1]] = i+1 + self.arr[indices[i][0]][indices[i][1]] = int(i + 1) def move_prey(self): """Find an empty position in the moore neighbourhood for the Prey to move in. Ensure that the lattice is toroidal.""" - for prey, pos in self.preys.items(): - attempts = 0 - while attempts <= self.params["prey_max_moves"]: + for prey, pos in enumerate(self.preys_loc): + + for i in range(self.params["prey_max_moves"]): neighbours = self.neighbours(self.arr, pos[0], pos[1]) - free_space = np.transpose(np.nonzero(neighbours == 0)) - dy, dx = free_space[randint(0, len(free_space)-1)] + + dy = np.random.randint(neighbours.shape[0]) + dx = np.random.randint(neighbours.shape[1]) + + # If neighbouring cell is occupied, skip turn + if neighbours[dy][dx] != 0: + continue dest_y = dy - 1 + pos[0] dest_x = dx - 1 + pos[1] # Check boundaries of the lattice if dest_y not in range(self.size_y) or dest_x not in range(self.size_x): - dest_y, dest_x = self.lattice_wrap_idx((dest_y, dest_x), np.shape(self.arr)) + dest_y, dest_x = self.lattice_wrap_idx( + (dest_y, dest_x), np.shape(self.arr) + ) # Move prey, clear previous location - self.arr[dest_y][dest_x] = prey+1 + self.arr[dest_y][dest_x] = int(prey + 1) self.arr[pos[0]][pos[1]] = 0 - # Update prey location in the dictionary + # Update prey location in the list pos[0], pos[1] = dest_y, dest_x - # Find mates in the moore neighbourhood + def choose_mate(self, ind): + """Choose a mate from moore neighbourhood, return None if no mates + are found.""" + + neighbours = self.neighbours( + self.arr, self.preys_loc[ind][0], self.preys_loc[ind][1] + ) + mates = neighbours[(neighbours > 0) & (neighbours != ind + 1)] + if len(mates) < 1: + return + else: + # -1 for lattice offset + mate = int(choice(mates)) - 1 + return mate + + def place_offspring(self, offspring): + """Try to place the offsprings to the lattice. + + Parameters + ---------- + offspring : int + number of offsprings + + Returns + ------- + list of successfully placed offspring indices + """ + + placed_offspring = [] + + for i in range(offspring): + + y = np.random.randint(self.size_y) + x = np.random.randint(self.size_x) + + for j in range(self.params["offspring_place_attempts"]): + if self.arr[y][x] != 0: + continue + + # Keep track of offspring in a list + if self.arr[y][x] == 0: + # Append the offspring to the list of preys. + # len(self.preys_loc) is the index of the current last prey in the list + self.arr[y][x] = int(len(self.preys_loc)+1) + self.preys_loc.append([y, x]) + placed_offspring.append(i) + + return placed_offspring + + def move_predator(self): + """Find an empty position in the moore neighbourhood for the Predators to move in.""" + + # Calculate predator moves + predator_max_moves = int(( + self.params["population"].individuals.shape[0] + - self.params["target_pop_size"] + ) / self.params["predator_pop_size"]) + + # Track killed preys in list and remove them at the end of the function + to_be_killed = [] + + for predator, pos in enumerate(self.predators_loc): + + for i in range(predator_max_moves): + neighbours = self.neighbours(self.arr, pos[0], pos[1]) - mates = neighbours[neighbours > prey+1] - if len(mates) > 0: + targets = neighbours[neighbours > 0] + + # If preys found in the neighbourhood, calculate their fitness and kill the weakest + if len(targets) > 0: + fitness = [] + for target in targets: + + obj1 = self.params["population"].fitness[target-1][0] + obj2 = self.params["population"].fitness[target-1][1] + + fc = ( + self.predator_pop[predator] * obj1 + + (1 - self.predator_pop[predator]) * obj2 + ) + + fitness.append((fc, target)) + fitness.sort() + weakest_prey = fitness[-1][1] - 1 + + # Kill the weakest prey and move the predator to its place + self.arr[self.preys_loc[weakest_prey][0]][ + self.preys_loc[weakest_prey][1] + ] = -1 * (predator + 1) + + # Set the old predator location to zero + self.arr[pos[0]][pos[1]] = 0 + + # Update predator location in the list of predators + pos[0], pos[1] = ( + self.preys_loc[weakest_prey][0], + self.preys_loc[weakest_prey][1], + ) + + # Set the killed prey as dead in the list of prey locations and end predator turn + to_be_killed.append(weakest_prey) + self.preys_loc[weakest_prey] = None break + else: - attempts += 1 + dy = np.random.randint(neighbours.shape[0]) + dx = np.random.randint(neighbours.shape[1]) - def choose_mate(self, ind): + # If neighbouring cell is occupied by another predator, skip turn + if neighbours[dy][dx] < 0: + continue - neighbours = self.neighbours(self.arr, self.preys[ind][0], self.preys[ind][1]) - mates = neighbours[neighbours > ind + 1] - if not mates: - return - else: - mate = int(choice(mates)) - return mate + dest_y = dy - 1 + pos[0] + dest_x = dx - 1 + pos[1] + + # Check boundaries of the lattice + if dest_y not in range(self.size_y) or dest_x not in range( + self.size_x + ): + dest_y, dest_x = self.lattice_wrap_idx( + (dest_y, dest_x), np.shape(self.arr) + ) + + # Move predator, clear previous location + self.arr[dest_y][dest_x] = -1 * (predator + 1) + self.arr[pos[0]][pos[1]] = 0 + + # Update predator location in the list + pos[0], pos[1] = dest_y, dest_x + + self.params["population"].delete_or_keep(to_be_killed, "delete") + self.update_lattice() + + def update_lattice(self): + updated_preys = [x for x in self.preys_loc if x is not None] + self.preys_loc = updated_preys + + for prey, pos in enumerate(self.preys_loc): + self.arr[pos[0]][pos[1]] = prey - def move_pred(self): - pass @staticmethod def lattice_wrap_idx(index, lattice_shape): @@ -276,13 +424,16 @@ def lattice_wrap_idx(index, lattice_shape): lattice_shape : tuple the shape of the lattice to index to """ - if not hasattr(index, '__iter__'): return index # handle integer slices - if len(index) != len(lattice_shape): return index # must reference a scalar - if any(type(i) == slice for i in index): return index # slices not supported + if not hasattr(index, "__iter__"): + return index # handle integer slices + if len(index) != len(lattice_shape): + return index # must reference a scalar + if any(type(i) == slice for i in index): + return index # slices not supported if len(index) == len(lattice_shape): # periodic indexing of scalars mod_index = tuple(((i % s + s) % s for i, s in zip(index, lattice_shape))) return mod_index - raise ValueError('Unexpected index: {}'.format(index)) + raise ValueError("Unexpected index: {}".format(index)) @staticmethod def neighbours(arr, x, y, n=3): diff --git a/pyrvea/Population/population_evonn.py b/pyrvea/Population/population_evonn.py index 96ec25c..34c79d9 100644 --- a/pyrvea/Population/population_evonn.py +++ b/pyrvea/Population/population_evonn.py @@ -209,6 +209,54 @@ def delete(self, indices: list): self.fitness = new_fitness self.constraint_violation = new_cv + def delete_or_keep(self, indices: list, delete_or_keep): + """Remove individuals from population which ARE in "indices". + With boolean masks deleted individuals can be obtained with + inverted mask (~mask) + + Parameters + ---------- + indices: list + Indices of individuals to keep + delete_or_keep : string + whether to return the deleted individuals or the ones kept + """ + indices.sort() + mask = np.ones(len(self.individuals), dtype=bool) + mask[indices] = False + + # new_pop = np.delete(self.individuals, indices, axis=0) + new_pop = self.individuals[mask, ...] + deleted_pop = self.individuals[~mask, ...] + + # new_obj = np.delete(self.objectives, indices, axis=0) + new_obj = self.objectives[mask, ...] + deleted_obj = self.objectives[~mask, ...] + + # new_fitness = np.delete(self.fitness, indices, axis=0) + new_fitness = self.fitness[mask, ...] + deleted_fitness = self.fitness[~mask, ...] + + if len(self.constraint_violation) > 0: + # new_cv = np.delete(self.constraint_violation, indices, axis=0) + new_cv = self.constraint_violation[mask, ...] + deleted_cv = self.constraint_violation[~mask, ...] + else: + deleted_cv = self.constraint_violation + new_cv = self.constraint_violation + + if delete_or_keep == "delete": + self.individuals = new_pop + self.objectives = new_obj + self.fitness = new_fitness + self.constraint_violation = new_cv + + elif delete_or_keep == "keep": + self.individuals = deleted_pop + self.objectives = deleted_obj + self.fitness = deleted_fitness + self.constraint_violation = deleted_cv + def archive(self, new_pop, new_obj): length_of_archive = len(self.archive) if length_of_archive == 0: @@ -299,19 +347,18 @@ def mate(self, ind1, ind2): # Perform crossover xover_w1, xover_w2 = ppga_crossover(w1, w2) - # Find randomly two other individuals with current match active for mutation + # Find randomly two other individuals with current match active for mutation. + # Make a list of individuals suitable for mutation, exclude the ones to be mutated + # so that they won't mutate with themselves indices = [ind1, ind2] mask = np.ones(len(self.individuals), dtype=bool) mask[indices] = False alternatives = self.individuals[mask, ...] # Mutate - offspring1, offspring2 = ppga_mutation(xover_w1, xover_w2, alternatives) - - # Add mutated offspring to a list - offspring = np.vstack([offspring1], [offspring2]) + offspring1, offspring2 = ppga_mutation(alternatives, xover_w1, xover_w2) - return offspring + return offspring1, offspring2 def plot_init_(self): """Initialize animation objects. Return figure""" diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index 707a2ae..a26e399 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -1,3 +1,4 @@ +from math import log import numpy as np #from scipy.linalg import lstsq import timeit @@ -79,11 +80,11 @@ def objectives(self, decision_variables) -> list: weighted_input = self.dot_product(decision_variables) activated_function = self.activation(weighted_input) - w_matrix2, predicted_values = self.optimize_error(activated_function) + w_matrix2, rss, predicted_values = self.optimize_error(activated_function) training_error = self.loss_function(predicted_values) complexity = self.calculate_complexity(decision_variables, w_matrix2) - corrected_complexity = self.information_criterion(predicted_values, complexity) + corrected_complexity = self.information_criterion(rss, complexity) obj_func = [training_error, corrected_complexity] return obj_func @@ -122,7 +123,9 @@ def activation(self, wi, name="sigmoid"): return activated_function(wi) def optimize_error(self, activated_function, name="llsq"): - """ Optimize the training error + """ Optimize the training error. + + BUG: rss sometimes returned as an empty array? Parameters ---------- @@ -134,9 +137,10 @@ def optimize_error(self, activated_function, name="llsq"): """ if name == "llsq": w_matrix2 = np.linalg.lstsq(activated_function, self.training_data_output) + rss = w_matrix2[1] predicted_values = np.dot(activated_function, w_matrix2[0]) - return w_matrix2[0], predicted_values + return w_matrix2[0], rss, predicted_values def loss_function(self, predicted_values, name="rmse"): @@ -149,10 +153,9 @@ def calculate_complexity(self, w_matrix, w_matrix2): return k - def information_criterion(self, predicted_values, k): + def information_criterion(self, rss, k): # Information criterion - rss = ((self.training_data_output - predicted_values) ** 2).sum() - aic = 2 * k + self.num_of_samples * np.log(rss/self.num_of_samples) + aic = 2 * k + self.num_of_samples * log(rss/self.num_of_samples) aicc = aic + (2*k*(k+1)/(self.num_of_samples-k-1)) return aicc From e55e3d923c0751819591dfdd036c3b74e2184e39 Mon Sep 17 00:00:00 2001 From: delamorte Date: Wed, 5 Jun 2019 09:42:20 +0300 Subject: [PATCH 021/155] preys now move based on probability ignore bias on mutation predator turn now does not end after killing if moves left fixed bugs with offspring placement and indexing --- .gitignore | 3 +- pyrvea/EAs/PPGA.py | 108 ++++++++++++++------------ pyrvea/EAs/baseEA.py | 3 +- pyrvea/Examples/evonn_example.py | 25 +++--- pyrvea/Population/population_evonn.py | 23 ++++-- pyrvea/Problem/evonn_problem.py | 30 +++---- pyrvea/Recombination/ppga_mutation.py | 2 +- 7 files changed, 113 insertions(+), 81 deletions(-) diff --git a/.gitignore b/.gitignore index 56fbe02..0fd2676 100644 --- a/.gitignore +++ b/.gitignore @@ -135,4 +135,5 @@ dmypy.json poetry.lock pyproject.toml -.tests.py \ No newline at end of file +.tests.py +*.csv diff --git a/pyrvea/EAs/PPGA.py b/pyrvea/EAs/PPGA.py index 4ea99ad..fe6cba6 100644 --- a/pyrvea/EAs/PPGA.py +++ b/pyrvea/EAs/PPGA.py @@ -1,9 +1,7 @@ from pyrvea.EAs.baseEA import BaseEA from pyrvea.Selection.PPGA_select import ppga_select -from random import sample +from random import choice, sample import numpy as np -from random import choice, randint -from scipy.stats import bernoulli as bn from pyrvea.Population.population_evonn import Population @@ -24,7 +22,6 @@ def set_params( generations_per_iteration: int = 10, iterations: int = 1, plotting: bool = True, - prob_omit: float = 0.3, prob_crossover: float = 0.8, prob_mutation: float = 0.3, mut_strength: float = 0.7, @@ -67,7 +64,7 @@ def set_params( "target_pop_size": target_pop_size, "predator_pop_size": 50, "prey_max_moves": 5, - "predator_max_moves": 5, + "prob_prey_move": 0.3, "offspring_place_attempts": 10, "generations": generations_per_iteration, "iterations": iterations, @@ -77,8 +74,7 @@ def set_params( "prob_crossover": prob_crossover, "prob_mutation": prob_mutation, "mut_strength": mut_strength, - "prob_omit": prob_omit, - "kill_intrvl": 7, + "kill_intrvl": 5, "max_rank": 20, } @@ -88,6 +84,7 @@ def _next_iteration(self, population: "Population"): """Run one iteration of EA. One iteration consists of a constant or variable number of + generations. This method leaves EA.params unchanged, except the current iteration count and gen count. @@ -145,13 +142,24 @@ def _next_gen(self, population: "Population"): population.add(offspring_pop) # Kill bad individuals every n generations - # if self.params["current_iteration_gen_count"] >= self.params["kill_intrvl"]: - selected = self.select(population) - # population.delete_or_keep(selected, "delete") + if self.params["current_iteration_gen_count"] % self.params["kill_intrvl"] == 0: + selected = self.select(population) + self.lattice.update_lattice(selected) + population.delete_or_keep(selected, "delete") + # Move predators self.lattice.move_predator() + #population.create_archive(population, population.objectives) + + # Place new prey if kill interval condition satisfied + if self.params["current_iteration_gen_count"] % self.params["kill_intrvl"] == 0: + # Create new random individuals to get to preferred population size + old_pop_size = population.individuals.shape[0] + placed_indices = self.lattice.place_offspring(self.params["target_pop_size"] - old_pop_size) + population.create_new_individuals(pop_size=len(placed_indices)) + def select(self, population) -> list: """Describe a selection mechanism. Return indices of selected individuals. @@ -167,7 +175,7 @@ def select(self, population) -> list: list List of indices of individuals to be selected. """ - selection = ppga_select(population.fitness) + selection = ppga_select(population.fitness, self.params["max_rank"]) return selection def continue_iteration(self): @@ -216,14 +224,6 @@ def init_prey(self): """Set some of the individual weights to zero, find an empty position in the lattice and place the prey""" - # Eliminate some weights - flag = bn.rvs( - p=1 - self.params["prob_omit"], - size=np.shape(self.params["population"].individuals), - ) - random_numbers = np.zeros(np.shape(self.params["population"].individuals)) - self.params["population"].individuals[flag == 0] = random_numbers[flag == 0] - # Take random indices from free (==zero) lattice spaces free_space = np.transpose(np.nonzero(self.arr == 0)) indices = sample( @@ -244,32 +244,34 @@ def move_prey(self): for prey, pos in enumerate(self.preys_loc): - for i in range(self.params["prey_max_moves"]): + if np.random.random() < self.params["prob_prey_move"]: - neighbours = self.neighbours(self.arr, pos[0], pos[1]) + for i in range(self.params["prey_max_moves"]): - dy = np.random.randint(neighbours.shape[0]) - dx = np.random.randint(neighbours.shape[1]) + neighbours = self.neighbours(self.arr, pos[0], pos[1]) - # If neighbouring cell is occupied, skip turn - if neighbours[dy][dx] != 0: - continue + dy = np.random.randint(neighbours.shape[0]) + dx = np.random.randint(neighbours.shape[1]) - dest_y = dy - 1 + pos[0] - dest_x = dx - 1 + pos[1] + # If neighbouring cell is occupied, skip turn + if neighbours[dy][dx] != 0: + continue - # Check boundaries of the lattice - if dest_y not in range(self.size_y) or dest_x not in range(self.size_x): - dest_y, dest_x = self.lattice_wrap_idx( - (dest_y, dest_x), np.shape(self.arr) - ) + dest_y = dy - 1 + pos[0] + dest_x = dx - 1 + pos[1] + + # Check boundaries of the lattice + if dest_y not in range(self.size_y) or dest_x not in range(self.size_x): + dest_y, dest_x = self.lattice_wrap_idx( + (dest_y, dest_x), np.shape(self.arr) + ) - # Move prey, clear previous location - self.arr[dest_y][dest_x] = int(prey + 1) - self.arr[pos[0]][pos[1]] = 0 + # Move prey, clear previous location + self.arr[dest_y][dest_x] = int(prey + 1) + self.arr[pos[0]][pos[1]] = 0 - # Update prey location in the list - pos[0], pos[1] = dest_y, dest_x + # Update prey location in the list + pos[0], pos[1] = dest_y, dest_x def choose_mate(self, ind): """Choose a mate from moore neighbourhood, return None if no mates @@ -310,13 +312,15 @@ def place_offspring(self, offspring): if self.arr[y][x] != 0: continue - # Keep track of offspring in a list - if self.arr[y][x] == 0: - # Append the offspring to the list of preys. - # len(self.preys_loc) is the index of the current last prey in the list - self.arr[y][x] = int(len(self.preys_loc)+1) - self.preys_loc.append([y, x]) - placed_offspring.append(i) + else: + + # Keep track of offspring in a list + if self.arr[y][x] == 0: + # Append the offspring to the list of preys. + # len(self.preys_loc) is the index of the current last prey in the list + self.arr[y][x] = int(len(self.preys_loc)+1) + self.preys_loc.append([y, x]) + placed_offspring.append(i) return placed_offspring @@ -373,7 +377,6 @@ def move_predator(self): # Set the killed prey as dead in the list of prey locations and end predator turn to_be_killed.append(weakest_prey) self.preys_loc[weakest_prey] = None - break else: dy = np.random.randint(neighbours.shape[0]) @@ -404,12 +407,21 @@ def move_predator(self): self.params["population"].delete_or_keep(to_be_killed, "delete") self.update_lattice() - def update_lattice(self): + def update_lattice(self, selected=None): + + # Remove selected individuals from the lattice + if selected is not None: + for i in selected: + self.arr[self.preys_loc[i][0]][self.preys_loc[i][1]] = 0 + self.preys_loc[i] = None + + # Update the list of prey locations updated_preys = [x for x in self.preys_loc if x is not None] self.preys_loc = updated_preys + # Update lattice for prey, pos in enumerate(self.preys_loc): - self.arr[pos[0]][pos[1]] = prey + self.arr[pos[0]][pos[1]] = prey+1 @staticmethod diff --git a/pyrvea/EAs/baseEA.py b/pyrvea/EAs/baseEA.py index 7837bd7..110578a 100644 --- a/pyrvea/EAs/baseEA.py +++ b/pyrvea/EAs/baseEA.py @@ -89,7 +89,8 @@ def _next_gen(self, population: "Population"): offspring = population.mate() population.add(offspring) selected = self.select(population) - population.delete_or_keep(selected, "keep") + population.keep(selected) + #population.delete_or_keep(selected, "keep") def select(self, population) -> list: """Describe a selection mechanism. Return indices of selected diff --git a/pyrvea/Examples/evonn_example.py b/pyrvea/Examples/evonn_example.py index 62ca618..5420730 100644 --- a/pyrvea/Examples/evonn_example.py +++ b/pyrvea/Examples/evonn_example.py @@ -1,31 +1,36 @@ +from pygmo import fast_non_dominated_sorting as nds from pyrvea.Problem.evonn_problem import EvoNNProblem from pyrvea.Population.population_evonn import Population from pyrvea.Recombination.ppga_crossover import ppga_crossover from pyrvea.Recombination.ppga_mutation import ppga_mutation from pyrvea.EAs.PPGA import PPGA +from pandas import read_csv from random import randint, sample import numpy as np import timeit -input_nodes = 5 -hidden_nodes = 4 -num_of_samples = 10 -training_data = np.random.uniform(0, 1, size=(num_of_samples, input_nodes)) -preferred_output = np.ones(num_of_samples) +test_data = read_csv("178deb.csv", header=1) + +#training_data = np.random.uniform(0, 1, size=(num_of_samples, input_nodes)) +training_data = test_data.values[:, :-2] +training_data_output = test_data.values[:, 2:] +input_nodes = training_data.shape[1] +#preferred_output = np.random.uniform(0,1,num_of_samples) prob = EvoNNProblem( name="EvoNN", training_data_input=training_data, - training_data_output=preferred_output, + training_data_output=training_data_output, num_input_nodes=input_nodes, - num_hidden_nodes=hidden_nodes, ) -arr = np.zeros((60,60)) +#arr = np.zeros((60,60)) pop = Population(prob) - -p = PPGA(pop) +pop.evolve(PPGA) +non_dom_front = nds(pop.objectives) +print("a") +#p = PPGA(pop) #a1 = randint(0, 49) #a2 = randint(0, 49) diff --git a/pyrvea/Population/population_evonn.py b/pyrvea/Population/population_evonn.py index 34c79d9..1a02bda 100644 --- a/pyrvea/Population/population_evonn.py +++ b/pyrvea/Population/population_evonn.py @@ -114,7 +114,6 @@ def create_new_individuals( pass # Create new individuals - if design == "RandomDesign": # +1 row for bias @@ -122,6 +121,13 @@ def create_new_individuals( self.problem.w_low, self.problem.w_high, size=(pop_size, self.problem.num_input_nodes+1, self.problem.num_hidden_nodes) ) + # Eliminate some weights + flag = bn.rvs(p=1 - self.problem.prob_omit, size=np.shape(individuals)) + + random_numbers = np.zeros(np.shape(individuals)) + individuals[flag == 0] = random_numbers[flag == 0] + + else: print("Design not yet supported.") @@ -209,7 +215,7 @@ def delete(self, indices: list): self.fitness = new_fitness self.constraint_violation = new_cv - def delete_or_keep(self, indices: list, delete_or_keep): + def delete_or_keep(self, indices, delete_or_keep): """Remove individuals from population which ARE in "indices". With boolean masks deleted individuals can be obtained with inverted mask (~mask) @@ -221,7 +227,7 @@ def delete_or_keep(self, indices: list, delete_or_keep): delete_or_keep : string whether to return the deleted individuals or the ones kept """ - indices.sort() + mask = np.ones(len(self.individuals), dtype=bool) mask[indices] = False @@ -257,12 +263,17 @@ def delete_or_keep(self, indices: list, delete_or_keep): self.fitness = deleted_fitness self.constraint_violation = deleted_cv - def archive(self, new_pop, new_obj): + def create_archive(self, new_pop, new_obj): length_of_archive = len(self.archive) + new_pop = self.individuals[:, :, :] + #self.individuals[0].flatten().tolist() + new_obj = self.objectives[:, :] if length_of_archive == 0: gen_count = 0 + else: gen_count = self.archive["generation"].iloc[-1] + 1 + new_entries = pd.DataFrame( { "generation": [gen_count] * len(new_obj), @@ -328,6 +339,8 @@ def evolve(self, EA: "BaseEA" = None, EA_parameters: dict = None) -> "Population #################################### # A basic evolution cycle. Will be updated to optimize() in future versions. ea = EA(self, EA_parameters) + for ind in self.individuals: + self.evaluate_individual(ind) iterations = ea.params["iterations"] if self.plotting: self.plot_objectives() # Figure was created in init @@ -353,7 +366,7 @@ def mate(self, ind1, ind2): indices = [ind1, ind2] mask = np.ones(len(self.individuals), dtype=bool) mask[indices] = False - alternatives = self.individuals[mask, ...] + alternatives = self.individuals[mask, ...][:, 1:, :] # Mutate offspring1, offspring2 = ppga_mutation(alternatives, xover_w1, xover_w2) diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index a26e399..4da58b3 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -13,9 +13,7 @@ def __init__( training_data_output=None, name=None, num_input_nodes=4, - num_hidden_nodes=6, - num_output_nodes=1, - num_of_objectives=2, + num_hidden_nodes=5, num_of_constraints=0, w_low=-5.0, w_high=5.0, @@ -48,16 +46,16 @@ def __init__( self.name = name self.num_input_nodes = num_input_nodes self.num_hidden_nodes = num_hidden_nodes - self.num_output_nodes = num_output_nodes - self.num_of_objectives = num_of_objectives - self.num_of_variables = num_input_nodes + self.num_of_objectives = training_data_output.shape[1] + self.num_of_variables = training_data_input.shape[1] self.num_of_constraints = num_of_constraints self.w_low = w_low self.w_high = w_high self.lower_limits = w_low self.upper_limits = w_high + self.prob_omit = 0.3 self.bias = 1 - self.num_of_samples = np.shape(training_data_output)[0] + self.num_of_samples = training_data_output.shape[0] if len(self.training_data_input) > 0: self.num_input_nodes = np.shape(training_data_input)[1] @@ -83,9 +81,8 @@ def objectives(self, decision_variables) -> list: w_matrix2, rss, predicted_values = self.optimize_error(activated_function) training_error = self.loss_function(predicted_values) - complexity = self.calculate_complexity(decision_variables, w_matrix2) - corrected_complexity = self.information_criterion(rss, complexity) - obj_func = [training_error, corrected_complexity] + complexity = self.calculate_complexity(decision_variables) + obj_func = [training_error, complexity] return obj_func @@ -136,7 +133,7 @@ def optimize_error(self, activated_function, name="llsq"): ------- """ if name == "llsq": - w_matrix2 = np.linalg.lstsq(activated_function, self.training_data_output) + w_matrix2 = np.linalg.lstsq(activated_function, self.training_data_output[:, :-1]) rss = w_matrix2[1] predicted_values = np.dot(activated_function, w_matrix2[0]) @@ -145,16 +142,19 @@ def optimize_error(self, activated_function, name="llsq"): def loss_function(self, predicted_values, name="rmse"): if name == "rmse": - return np.sqrt(((self.training_data_output - predicted_values) ** 2).mean()) + return np.sqrt(((self.training_data_output[:, :-1] - predicted_values) ** 2).mean()) - def calculate_complexity(self, w_matrix, w_matrix2): + def calculate_complexity(self, w_matrix): - k = np.count_nonzero(w_matrix) + np.count_nonzero(w_matrix2) + k = np.count_nonzero(w_matrix[1:, :]) return k - def information_criterion(self, rss, k): + def information_criterion(self, rss, k, w_matrix2, predicted_values): # Information criterion + k = k + np.count_nonzero(w_matrix2) + + rss = ((self.training_data_output[:, :-1] - predicted_values) ** 2).sum() aic = 2 * k + self.num_of_samples * log(rss/self.num_of_samples) aicc = aic + (2*k*(k+1)/(self.num_of_samples-k-1)) diff --git a/pyrvea/Recombination/ppga_mutation.py b/pyrvea/Recombination/ppga_mutation.py index 64e0b23..c3a1606 100644 --- a/pyrvea/Recombination/ppga_mutation.py +++ b/pyrvea/Recombination/ppga_mutation.py @@ -46,7 +46,7 @@ def mutate(offspring, wx): return offspring # Iterate over both individuals at the same time to avoid nested looping - it = np.nditer([w1, w2], flags=["multi_index"], op_flags=["readwrite"]) + it = np.nditer([w1[1:, :], w2[1:, :]], flags=["multi_index"], op_flags=["readwrite"]) # wx = individual cells (connections) in the weight matrix for wx1, wx2 in it: From b599195260e66bd4a1c8074a29985aa365c1b9bd Mon Sep 17 00:00:00 2001 From: delamorte Date: Wed, 5 Jun 2019 09:45:18 +0300 Subject: [PATCH 022/155] added non dominated ranking method --- pyrvea/Selection/PPGA_select.py | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 pyrvea/Selection/PPGA_select.py diff --git a/pyrvea/Selection/PPGA_select.py b/pyrvea/Selection/PPGA_select.py new file mode 100644 index 0000000..161c908 --- /dev/null +++ b/pyrvea/Selection/PPGA_select.py @@ -0,0 +1,11 @@ +from pygmo import fast_non_dominated_sorting as nds +import numpy as np + + +def ppga_select(fitness: list, max_rank): + + # Calculating fronts and ranks + _, _, _, rank = nds(fitness) + to_be_killed = np.nonzero(rank > max_rank) + + return to_be_killed[0] From 5782cf5beb61133be1c57319edff685819d69a6b Mon Sep 17 00:00:00 2001 From: delamorte Date: Thu, 6 Jun 2019 13:55:48 +0300 Subject: [PATCH 023/155] added more test functions --- pyrvea/EAs/PPGA.py | 88 +++++++++++++++--------- pyrvea/EAs/baseEA.py | 11 +++ pyrvea/Examples/evonn_example.py | 42 +++++++++--- pyrvea/Examples/sphere_function_test.py | 75 ++++++++++++++++++++ pyrvea/Examples/zdt_problem_test.py | 91 +++++++++++++++++++++++++ pyrvea/Population/population_evonn.py | 73 ++++++++++++++------ pyrvea/Problem/evonn_problem.py | 36 ++++++---- pyrvea/Recombination/ppga_crossover.py | 6 +- pyrvea/Recombination/ppga_mutation.py | 3 +- 9 files changed, 344 insertions(+), 81 deletions(-) create mode 100644 pyrvea/Examples/sphere_function_test.py create mode 100644 pyrvea/Examples/zdt_problem_test.py diff --git a/pyrvea/EAs/PPGA.py b/pyrvea/EAs/PPGA.py index fe6cba6..59f569d 100644 --- a/pyrvea/EAs/PPGA.py +++ b/pyrvea/EAs/PPGA.py @@ -19,9 +19,9 @@ def set_params( target_pop_size: int = 300, interact: bool = False, a_priori_preference: bool = False, - generations_per_iteration: int = 10, + generations_per_iteration: int = 100, iterations: int = 1, - plotting: bool = True, + plotting: bool = False, prob_crossover: float = 0.8, prob_mutation: float = 0.3, mut_strength: float = 0.7, @@ -63,7 +63,7 @@ def set_params( "population_size": population_size, "target_pop_size": target_pop_size, "predator_pop_size": 50, - "prey_max_moves": 5, + "prey_max_moves": 10, "prob_prey_move": 0.3, "offspring_place_attempts": 10, "generations": generations_per_iteration, @@ -74,7 +74,7 @@ def set_params( "prob_crossover": prob_crossover, "prob_mutation": prob_mutation, "mut_strength": mut_strength, - "kill_intrvl": 5, + "kill_interval": 7, "max_rank": 20, } @@ -96,6 +96,16 @@ def _next_iteration(self, population: "Population"): self.params["current_iteration_gen_count"] = 1 while self.continue_iteration(): self._next_gen(population) + print( + str(self.params["current_iteration_gen_count"]) + + " " + + "population size: " + + str(population.individuals.shape[0]) + + " Min Error: " + + str(np.amin(population.objectives[:,0])) + + " Avg Error: " + + str(np.mean(population.objectives[:, 0])) + ) self.params["current_iteration_gen_count"] += 1 self.params["current_iteration_count"] += 1 @@ -127,7 +137,7 @@ def _next_gen(self, population: "Population"): if not mate_idx: continue else: - offspring1, offspring2 = population.mate(ind, mate_idx) + offspring1, offspring2 = population.mate(ind, mate_idx, self.params) offspring = np.concatenate((offspring, [offspring1], [offspring2])) # Try to place the offspring to lattice, add to population if successful @@ -136,46 +146,59 @@ def _next_gen(self, population: "Population"): # Remove from offsprings the ones that didn't get placed mask = np.ones(len(offspring), dtype=bool) mask[placed_indices] = False - offspring_pop = offspring[~mask, ...] + offspring = offspring[~mask, ...] # Add the successfully placed offspring to the population - population.add(offspring_pop) + population.add(offspring) # Kill bad individuals every n generations - if self.params["current_iteration_gen_count"] % self.params["kill_intrvl"] == 0: - selected = self.select(population) + if ( + self.params["current_iteration_gen_count"] % self.params["kill_interval"] + == 0 + ): + selected = self.select(population, self.params["max_rank"]) self.lattice.update_lattice(selected) population.delete_or_keep(selected, "delete") - # Move predators self.lattice.move_predator() - #population.create_archive(population, population.objectives) + # population.create_archive(population, population.objectives) # Place new prey if kill interval condition satisfied - if self.params["current_iteration_gen_count"] % self.params["kill_intrvl"] == 0: + if ( + self.params["current_iteration_gen_count"] % self.params["kill_interval"] + == 0 + ): # Create new random individuals to get to preferred population size old_pop_size = population.individuals.shape[0] - placed_indices = self.lattice.place_offspring(self.params["target_pop_size"] - old_pop_size) - population.create_new_individuals(pop_size=len(placed_indices)) + if self.params["target_pop_size"] - old_pop_size > 0: + placed_indices = self.lattice.place_offspring( + self.params["target_pop_size"] - old_pop_size + ) + population.create_new_individuals(pop_size=len(placed_indices)) + + if self.params["ploton"]: + population.plot_objectives() - def select(self, population) -> list: - """Describe a selection mechanism. Return indices of selected - individuals. + def select(self, population, max_rank=20) -> list: + """Of the population, individuals lower than max_rank are selected. + Return indices of selected individuals. Parameters ---------- population : Population Contains the current population and problem information. + max_rank : int + Select only individuals lower than max_rank Returns ------- list List of indices of individuals to be selected. """ - selection = ppga_select(population.fitness, self.params["max_rank"]) + selection = ppga_select(population.fitness, max_rank) return selection def continue_iteration(self): @@ -261,7 +284,9 @@ def move_prey(self): dest_x = dx - 1 + pos[1] # Check boundaries of the lattice - if dest_y not in range(self.size_y) or dest_x not in range(self.size_x): + if dest_y not in range(self.size_y) or dest_x not in range( + self.size_x + ): dest_y, dest_x = self.lattice_wrap_idx( (dest_y, dest_x), np.shape(self.arr) ) @@ -318,7 +343,7 @@ def place_offspring(self, offspring): if self.arr[y][x] == 0: # Append the offspring to the list of preys. # len(self.preys_loc) is the index of the current last prey in the list - self.arr[y][x] = int(len(self.preys_loc)+1) + self.arr[y][x] = int(len(self.preys_loc) + 1) self.preys_loc.append([y, x]) placed_offspring.append(i) @@ -328,10 +353,13 @@ def move_predator(self): """Find an empty position in the moore neighbourhood for the Predators to move in.""" # Calculate predator moves - predator_max_moves = int(( - self.params["population"].individuals.shape[0] - - self.params["target_pop_size"] - ) / self.params["predator_pop_size"]) + predator_max_moves = int( + ( + self.params["population"].individuals.shape[0] + - self.params["target_pop_size"] + ) + / self.params["predator_pop_size"] + ) # Track killed preys in list and remove them at the end of the function to_be_killed = [] @@ -348,13 +376,10 @@ def move_predator(self): fitness = [] for target in targets: - obj1 = self.params["population"].fitness[target-1][0] - obj2 = self.params["population"].fitness[target-1][1] + obj1 = self.params["population"].fitness[target - 1][0] + obj2 = self.params["population"].fitness[target - 1][1] - fc = ( - self.predator_pop[predator] * obj1 - + (1 - self.predator_pop[predator]) * obj2 - ) + fc = (self.predator_pop[predator] * obj1 + (1 - self.predator_pop[predator]) * obj2) fitness.append((fc, target)) fitness.sort() @@ -421,8 +446,7 @@ def update_lattice(self, selected=None): # Update lattice for prey, pos in enumerate(self.preys_loc): - self.arr[pos[0]][pos[1]] = prey+1 - + self.arr[pos[0]][pos[1]] = prey + 1 @staticmethod def lattice_wrap_idx(index, lattice_shape): diff --git a/pyrvea/EAs/baseEA.py b/pyrvea/EAs/baseEA.py index 110578a..a481b5d 100644 --- a/pyrvea/EAs/baseEA.py +++ b/pyrvea/EAs/baseEA.py @@ -1,4 +1,5 @@ from typing import TYPE_CHECKING +import numpy as np if TYPE_CHECKING: from pyrvea.Population.Population import Population @@ -72,6 +73,16 @@ def _next_iteration(self, population: "Population"): self.params["current_iteration_gen_count"] = 1 while self.continue_iteration(): self._next_gen(population) + print( + str(self.params["current_iteration_gen_count"]) + + " " + + "population size: " + + str(population.individuals.shape[0]) + + " Min Error: " + + str(np.amin(population.objectives[:,0])) + + " Avg Error: " + + str(np.mean(population.objectives[:, 0])) + ) self.params["current_iteration_gen_count"] += 1 self.params["current_iteration_count"] += 1 diff --git a/pyrvea/Examples/evonn_example.py b/pyrvea/Examples/evonn_example.py index 5420730..e31deca 100644 --- a/pyrvea/Examples/evonn_example.py +++ b/pyrvea/Examples/evonn_example.py @@ -1,35 +1,59 @@ from pygmo import fast_non_dominated_sorting as nds from pyrvea.Problem.evonn_problem import EvoNNProblem from pyrvea.Population.population_evonn import Population +from pyrvea.Problem.testProblem import testProblem from pyrvea.Recombination.ppga_crossover import ppga_crossover from pyrvea.Recombination.ppga_mutation import ppga_mutation from pyrvea.EAs.PPGA import PPGA -from pandas import read_csv +import pandas from random import randint, sample import numpy as np import timeit +import plotly +import plotly.graph_objs as go -test_data = read_csv("178deb.csv", header=1) +test_data = pandas.read_csv("178deb.csv", header=1) #training_data = np.random.uniform(0, 1, size=(num_of_samples, input_nodes)) training_data = test_data.values[:, :-2] -training_data_output = test_data.values[:, 2:] +f1_training_data_output = test_data.values[:, 2] +f2_training_data_output = test_data.values[:, 3] input_nodes = training_data.shape[1] #preferred_output = np.random.uniform(0,1,num_of_samples) -prob = EvoNNProblem( +f1 = EvoNNProblem( name="EvoNN", training_data_input=training_data, - training_data_output=training_data_output, + training_data_output=f1_training_data_output, + num_input_nodes=input_nodes, +) + +f2 = EvoNNProblem( + name="EvoNN", + training_data_input=training_data, + training_data_output=f2_training_data_output, num_input_nodes=input_nodes, ) #arr = np.zeros((60,60)) -pop = Population(prob) -pop.evolve(PPGA) -non_dom_front = nds(pop.objectives) -print("a") +popf1 = Population(f1, plotting=True) +# popf2 = Population(f2) + +model = popf1.evolve(PPGA) +# popf2.evolve(PPGA) + +y_pred = f1.get_prediction(model) + +trace = go.Scatter( + x = y_pred, + y = f1_training_data_output, + mode = 'markers' +) + +data = [trace] + +plotly.offline.plot(data, filename='temp-plot.html', auto_open=True) #p = PPGA(pop) #a1 = randint(0, 49) diff --git a/pyrvea/Examples/sphere_function_test.py b/pyrvea/Examples/sphere_function_test.py new file mode 100644 index 0000000..94b59e5 --- /dev/null +++ b/pyrvea/Examples/sphere_function_test.py @@ -0,0 +1,75 @@ +from pyrvea.Problem.evonn_problem import EvoNNProblem +from pyrvea.Population.population_evonn import Population +from pyrvea.Problem.testProblem import testProblem +from pyrvea.Recombination.ppga_crossover import ppga_crossover +from pyrvea.Recombination.ppga_mutation import ppga_mutation +from pyrvea.EAs.PPGA import PPGA +from pyrvea.EAs.RVEA import RVEA +import pandas +from random import randint, sample +import numpy as np +import cProfile +import timeit +import plotly +import plotly.graph_objs as go +from pyDOE import lhs + + +def objectives(decision_variables): + + # Sphere function + x = np.asarray(decision_variables) + obj_func = sum(x ** 2) + + # Matyas function + # x = np.asarray(decision_variables[0]) + # y = np.asarray(decision_variables[1]) + # obj_func = 0.26 * (x**2 + y**2) - 0.48 * x * y + + return obj_func + + +#rand_training_data_input = np.random.uniform(-10, 10, (150, 2)) +#lin_training_data_input = np.linspace((10, 10), (-10, -10), 149) +lin_training_data_input_temp = np.linspace(10, -10, 149) +x2 = np.zeros_like(lin_training_data_input_temp) +lin_training_data_input = np.vstack((np.hstack((lin_training_data_input_temp,lin_training_data_input_temp)), np.hstack((lin_training_data_input_temp, x2)))).T +#lin_training_data_input = lhs(2, 150) * 20 - 10 +#zero_row = [0,0] +#lin_training_data_input = np.vstack([lin_training_data_input, zero_row]) + +#lin_training_data_input = np.flip(np.sort(lin_training_data_input, axis=0)) + +training_data_output = np.asarray([objectives(x) for x in lin_training_data_input]) + +f1 = EvoNNProblem( + name="linear_sphere_function", + training_data_input=lin_training_data_input, + training_data_output=training_data_output, + num_hidden_nodes=15, +) + + +def plot(f1): + + popf2 = Population(f1, plotting=False) + model = popf2.evolve(RVEA) + y_pred = f1.get_prediction(model) + + trace = go.Scatter(x=y_pred, y=training_data_output, mode="markers") + + data = [trace] + + plotly.offline.plot( + data, + filename=f1.name + + "_num_var" + + str(f1.num_of_variables) + + "_num_nodes" + + str(f1.num_hidden_nodes) + + ".html", + auto_open=True, + ) + + +plot(f1) diff --git a/pyrvea/Examples/zdt_problem_test.py b/pyrvea/Examples/zdt_problem_test.py new file mode 100644 index 0000000..32b4cbb --- /dev/null +++ b/pyrvea/Examples/zdt_problem_test.py @@ -0,0 +1,91 @@ +from pyrvea.Problem.evonn_problem import EvoNNProblem +from pyrvea.Population.population_evonn import Population +from pyrvea.Problem.testProblem import testProblem +from pyrvea.Recombination.ppga_crossover import ppga_crossover +from pyrvea.Recombination.ppga_mutation import ppga_mutation +from pyrvea.EAs.PPGA import PPGA +import pandas +from random import randint, sample +import numpy as np +import cProfile +import timeit +import plotly +import plotly.graph_objs as go + +zdt_problem = testProblem('ZDT2', 30, 2, 0, 1, 0) +training_data_input = np.random.rand(150,30) +training_data_output = np.asarray([zdt_problem.objectives(x) for x in training_data_input]) + +f1_training_data_output = training_data_output[:, 0] +f2_training_data_output = training_data_output[:, 1] + +#preferred_output = np.random.uniform(0,1,num_of_samples) + +def plot(f2): + + popf2 = Population(f2, plotting=True) + model = popf2.evolve(PPGA) + y_pred = f2.get_prediction(model) + + trace = go.Scatter( + x=y_pred, + y=f2_training_data_output, + mode='markers' + ) + + data = [trace] + + plotly.offline.plot(data, filename=f2.name + ".html", auto_open=True) + + +# f1 = EvoNNProblem( +# name="EvoNN", +# training_data_input=training_data_input, +# training_data_output=f1_training_data_output, +# ) + +# f2 = EvoNNProblem( +# name="EvoNN_nodes_10", +# training_data_input=training_data_input, +# training_data_output=f2_training_data_output, +# num_hidden_nodes=10 +# ) +# +# plot(f2) + +f2 = EvoNNProblem( + name="EvoNN_nodes_15", + training_data_input=training_data_input, + training_data_output=f2_training_data_output, + num_hidden_nodes=15 +) + +plot(f2) + +#arr = np.zeros((60,60)) + +#popf1 = Population(f1, plotting=True) +#popf2 = Population(f2, plotting=True) + +#popf1.evolve(PPGA) + +# Profiling + +# pr = cProfile.Profile() +# pr.enable() +# model = popf2.evolve(PPGA) +# pr.disable() +# pr.print_stats(sort="cumtime") + +#non_dom_front = nds(popf1.objectives) +#print("a") +#p = PPGA(pop) + +#a1 = randint(0, 49) +#a2 = randint(0, 49) +#mutation(pop, a1, a2) + +#selected = sample(range(1, np.shape(pop.individuals)[0]), randint(1, np.shape(pop.individuals)[0])) +#pop.keep(selected) +#selected = sample(range(1, np.shape(pop.individuals)[0]), randint(1, np.shape(pop.individuals)[0])) +#pop.delete(selected) \ No newline at end of file diff --git a/pyrvea/Population/population_evonn.py b/pyrvea/Population/population_evonn.py index 1a02bda..7ff3a23 100644 --- a/pyrvea/Population/population_evonn.py +++ b/pyrvea/Population/population_evonn.py @@ -23,7 +23,7 @@ from pyrvea.EAs.baseEA import BaseEA -class Population(): +class Population: """Define the population.""" def __init__( @@ -76,11 +76,14 @@ def __init__( self.filename = problem.name + "_" + str(problem.num_of_objectives) self.plotting = plotting # These attributes contain the solutions. - self.individuals = np.empty((0, self.problem.num_input_nodes+1, self.problem.num_hidden_nodes), float) + self.individuals = np.empty( + (0, self.problem.num_input_nodes + 1, self.problem.num_hidden_nodes), float + ) self.objectives = np.empty((0, self.problem.num_of_objectives), float) self.fitness = np.empty((0, self.problem.num_of_objectives), float) self.constraint_violation = np.empty( - (0, self.problem.num_of_constraints), float) + (0, self.problem.num_of_constraints), float + ) self.archive = pd.DataFrame( columns=["generation", "decision_variables", "objective_values"] ) @@ -89,6 +92,9 @@ def __init__( if not assign_type == "empty": self.create_new_individuals(assign_type) + if self.plotting: + self.figure = [] + self.plot_init_() def create_new_individuals( self, design: str = "RandomDesign", pop_size: int = 500, decision_variables=None @@ -118,7 +124,13 @@ def create_new_individuals( # +1 row for bias individuals = np.random.uniform( - self.problem.w_low, self.problem.w_high, size=(pop_size, self.problem.num_input_nodes+1, self.problem.num_hidden_nodes) + self.problem.w_low, + self.problem.w_high, + size=( + pop_size, + self.problem.num_input_nodes + 1, + self.problem.num_hidden_nodes, + ), ) # Eliminate some weights @@ -127,15 +139,16 @@ def create_new_individuals( random_numbers = np.zeros(np.shape(individuals)) individuals[flag == 0] = random_numbers[flag == 0] + # Set bias + + individuals[:, 0, :] = 1 else: print("Design not yet supported.") self.add(individuals) - if self.plotting: - self.figure = [] - self.plot_init_() + def eval_fitness(self): """ @@ -266,7 +279,7 @@ def delete_or_keep(self, indices, delete_or_keep): def create_archive(self, new_pop, new_obj): length_of_archive = len(self.archive) new_pop = self.individuals[:, :, :] - #self.individuals[0].flatten().tolist() + # self.individuals[0].flatten().tolist() new_obj = self.objectives[:, :] if length_of_archive == 0: gen_count = 0 @@ -339,18 +352,26 @@ def evolve(self, EA: "BaseEA" = None, EA_parameters: dict = None) -> "Population #################################### # A basic evolution cycle. Will be updated to optimize() in future versions. ea = EA(self, EA_parameters) - for ind in self.individuals: - self.evaluate_individual(ind) - iterations = ea.params["iterations"] - if self.plotting: - self.plot_objectives() # Figure was created in init - for i in progressbar(range(1, iterations), desc="Iteration"): - ea._run_interruption(self) - ea._next_iteration(self) - if self.plotting: - self.plot_objectives() - - def mate(self, ind1, ind2): + + # Calculate information criterion for the first front + # and return the model with the lowest value + + lowest_error = np.argmin(self.objectives[:, 0]) + return self.individuals[lowest_error] + # non_dom_front = nds(self.objectives) + # fon_front = non_dom_front[0][0] + # info_c_rank = [] + # for i in fon_front: + # + # if + # info_c = self.problem.information_criterion(self.individuals[i]) + # info_c_rank.append((info_c, i)) + # + # info_c_rank.sort() + + return self.individuals[info_c_rank[0][1]] + + def mate(self, ind1, ind2, params): """Conduct crossover and mutation over the population. """ @@ -358,7 +379,7 @@ def mate(self, ind1, ind2): w1, w2 = self.individuals[ind1], self.individuals[ind2] # Perform crossover - xover_w1, xover_w2 = ppga_crossover(w1, w2) + xover_w1, xover_w2 = ppga_crossover(w1, w2, params["prob_crossover"]) # Find randomly two other individuals with current match active for mutation. # Make a list of individuals suitable for mutation, exclude the ones to be mutated @@ -369,7 +390,15 @@ def mate(self, ind1, ind2): alternatives = self.individuals[mask, ...][:, 1:, :] # Mutate - offspring1, offspring2 = ppga_mutation(alternatives, xover_w1, xover_w2) + offspring1, offspring2 = ppga_mutation( + alternatives, + xover_w1, + xover_w2, + params["current_iteration_gen_count"], + params["generations"], + params["prob_mutation"], + params["mut_strength"] + ) return offspring1, offspring2 diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index 4da58b3..f4a7e11 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -15,6 +15,7 @@ def __init__( num_input_nodes=4, num_hidden_nodes=5, num_of_constraints=0, + num_of_objectives=2, w_low=-5.0, w_high=5.0, ): @@ -46,14 +47,14 @@ def __init__( self.name = name self.num_input_nodes = num_input_nodes self.num_hidden_nodes = num_hidden_nodes - self.num_of_objectives = training_data_output.shape[1] + self.num_of_objectives = num_of_objectives self.num_of_variables = training_data_input.shape[1] self.num_of_constraints = num_of_constraints self.w_low = w_low self.w_high = w_high self.lower_limits = w_low self.upper_limits = w_high - self.prob_omit = 0.3 + self.prob_omit = 0.2 self.bias = 1 self.num_of_samples = training_data_output.shape[0] @@ -78,7 +79,7 @@ def objectives(self, decision_variables) -> list: weighted_input = self.dot_product(decision_variables) activated_function = self.activation(weighted_input) - w_matrix2, rss, predicted_values = self.optimize_error(activated_function) + w_matrix2, rss, predicted_values = self.minimize_error(activated_function) training_error = self.loss_function(predicted_values) complexity = self.calculate_complexity(decision_variables) @@ -97,8 +98,6 @@ def dot_product(self, w_matrix): ------- """ - # Init bias value for the first row - w_matrix[0] = self.bias # Calculate dot product wi = np.dot(self.training_data_input, w_matrix[1:, :]) + w_matrix[0] @@ -119,11 +118,9 @@ def activation(self, wi, name="sigmoid"): return activated_function(wi) - def optimize_error(self, activated_function, name="llsq"): + def minimize_error(self, activated_function, name="llsq"): """ Optimize the training error. - BUG: rss sometimes returned as an empty array? - Parameters ---------- activated_function @@ -133,7 +130,7 @@ def optimize_error(self, activated_function, name="llsq"): ------- """ if name == "llsq": - w_matrix2 = np.linalg.lstsq(activated_function, self.training_data_output[:, :-1]) + w_matrix2 = np.linalg.lstsq(activated_function, self.training_data_output, rcond=None) rss = w_matrix2[1] predicted_values = np.dot(activated_function, w_matrix2[0]) @@ -142,7 +139,7 @@ def optimize_error(self, activated_function, name="llsq"): def loss_function(self, predicted_values, name="rmse"): if name == "rmse": - return np.sqrt(((self.training_data_output[:, :-1] - predicted_values) ** 2).mean()) + return np.sqrt(((self.training_data_output - predicted_values) ** 2).mean()) def calculate_complexity(self, w_matrix): @@ -150,12 +147,23 @@ def calculate_complexity(self, w_matrix): return k - def information_criterion(self, rss, k, w_matrix2, predicted_values): - # Information criterion - k = k + np.count_nonzero(w_matrix2) + def information_criterion(self, w_matrix): + + wi = self.dot_product(w_matrix) + z = self.activation(wi) + w_matrix2, rss, prediction = self.minimize_error(z) + rss = ((self.training_data_output - prediction) ** 2).sum() + k = self.calculate_complexity(w_matrix) + np.count_nonzero(w_matrix2) - rss = ((self.training_data_output[:, :-1] - predicted_values) ** 2).sum() aic = 2 * k + self.num_of_samples * log(rss/self.num_of_samples) aicc = aic + (2*k*(k+1)/(self.num_of_samples-k-1)) return aicc + + def get_prediction(self, decision_variables): + + weighted_input = self.dot_product(decision_variables) + activated_function = self.activation(weighted_input) + w_matrix2, rss, predicted_values = self.minimize_error(activated_function) + + return predicted_values diff --git a/pyrvea/Recombination/ppga_crossover.py b/pyrvea/Recombination/ppga_crossover.py index 42f7e83..c7a8c0a 100644 --- a/pyrvea/Recombination/ppga_crossover.py +++ b/pyrvea/Recombination/ppga_crossover.py @@ -1,7 +1,7 @@ import numpy as np -def ppga_crossover(w1, w2, prob_xover=0.8): +def ppga_crossover(w1, w2, prob_crossover=0.8): """Randomly exchange nodes between two individuals. Parameters @@ -10,12 +10,12 @@ def ppga_crossover(w1, w2, prob_xover=0.8): The first individual w2 : np.ndarray The second individual - prob_xover : float + prob_crossover : float The probability for the crossover """ for i in range(np.shape(w1)[1]): - if np.random.random() < prob_xover: + if np.random.random() < prob_crossover: tmp = np.copy(w1[:, i]) w1[:, i] = w2[:, i] w2[:, i] = tmp diff --git a/pyrvea/Recombination/ppga_mutation.py b/pyrvea/Recombination/ppga_mutation.py index c3a1606..968b7f8 100644 --- a/pyrvea/Recombination/ppga_mutation.py +++ b/pyrvea/Recombination/ppga_mutation.py @@ -36,7 +36,8 @@ def mutate(offspring, wx): select = sample(list(alternatives), 2) - offspring[it.multi_index[0], it.multi_index[1]] = wx + mut_strength * ( + # [it.multi_index[0]+1 to ignore bias + offspring[it.multi_index[0]+1, it.multi_index[1]] = wx + mut_strength * ( 1 - cur_gen / total_gen ) * ( select[0][it.multi_index[0], it.multi_index[1]] From 32264d21a3bc75ad9597aa49f7632aed1d9431bb Mon Sep 17 00:00:00 2001 From: delamorte Date: Fri, 7 Jun 2019 09:40:56 +0300 Subject: [PATCH 024/155] fixed a bug in PPGA mating function, where the parents where also affected by crossover and mutation. PPGA seems to be working as intended now. --- Example.py | 12 ++- pyrvea/EAs/PPGA.py | 81 ++++++++++------ pyrvea/EAs/baseEA.py | 20 +++- pyrvea/Examples/sphere_function_test.py | 32 ++++--- pyrvea/Population/Population.py | 93 +++++++++++++++++-- pyrvea/Population/population_evonn.py | 27 +++--- pyrvea/Problem/evonn_problem.py | 5 +- pyrvea/Recombination/ppga_crossover.py | 10 +- pyrvea/Recombination/ppga_mutation.py | 3 + .../simulated_binary_crossover.py | 2 +- 10 files changed, 211 insertions(+), 74 deletions(-) diff --git a/Example.py b/Example.py index ef66c06..d05fb7c 100644 --- a/Example.py +++ b/Example.py @@ -8,15 +8,19 @@ from pyrvea.EAs.RVEA import RVEA from pyrvea.EAs.NSGAIII import NSGAIII from optproblems import dtlz +import numpy as np class newProblem(baseProblem): """New problem description.""" def objectives(self, decision_variables): - return dtlz.DTLZ3(self.num_of_objectives, self.num_of_variables)( - decision_variables - ) + x = np.asarray(decision_variables) + obj_func = sum(x ** 2) + return obj_func + # return dtlz.DTLZ3(self.num_of_objectives, self.num_of_variables)( + # decision_variables + # ) name = "DTLZ3" @@ -24,7 +28,7 @@ def objectives(self, decision_variables): numobj = 3 numconst = 0 numvar = numobj + k - 1 -problem = newProblem(name, numvar, numobj, numconst) +problem = newProblem(name, 2, 2, numconst) lattice_resolution = 4 population_size = 105 diff --git a/pyrvea/EAs/PPGA.py b/pyrvea/EAs/PPGA.py index 59f569d..5c7461b 100644 --- a/pyrvea/EAs/PPGA.py +++ b/pyrvea/EAs/PPGA.py @@ -96,16 +96,6 @@ def _next_iteration(self, population: "Population"): self.params["current_iteration_gen_count"] = 1 while self.continue_iteration(): self._next_gen(population) - print( - str(self.params["current_iteration_gen_count"]) - + " " - + "population size: " - + str(population.individuals.shape[0]) - + " Min Error: " - + str(np.amin(population.objectives[:,0])) - + " Avg Error: " - + str(np.mean(population.objectives[:, 0])) - ) self.params["current_iteration_gen_count"] += 1 self.params["current_iteration_count"] += 1 @@ -121,6 +111,14 @@ def _next_gen(self, population: "Population"): Population object """ + self.params["predator_max_moves"] = int( + ( + self.params["population"].individuals.shape[0] + - self.params["target_pop_size"] + ) + / self.params["predator_pop_size"] + ) + self.lattice.move_prey() offspring = np.empty( @@ -160,23 +158,46 @@ def _next_gen(self, population: "Population"): self.lattice.update_lattice(selected) population.delete_or_keep(selected, "delete") + print( + str(self.params["current_iteration_gen_count"]) + + " " + + "population size: " + + str(population.individuals.shape[0]) + + " Min Error: " + + str(np.amin(population.objectives[:, 0])) + + " Avg Error: " + + str(np.mean(population.objectives[:, 0])) + ) + # Move predators self.lattice.move_predator() + print( + "AFTER KILL "+ + str(self.params["current_iteration_gen_count"]) + + " " + + "population size: " + + str(population.individuals.shape[0]) + + " Min Error: " + + str(np.amin(population.objectives[:, 0])) + + " Avg Error: " + + str(np.mean(population.objectives[:, 0])) + ) + # population.create_archive(population, population.objectives) - # Place new prey if kill interval condition satisfied - if ( - self.params["current_iteration_gen_count"] % self.params["kill_interval"] - == 0 - ): - # Create new random individuals to get to preferred population size - old_pop_size = population.individuals.shape[0] - if self.params["target_pop_size"] - old_pop_size > 0: - placed_indices = self.lattice.place_offspring( - self.params["target_pop_size"] - old_pop_size - ) - population.create_new_individuals(pop_size=len(placed_indices)) + # # Place new prey if kill interval condition satisfied + # if ( + # self.params["current_iteration_gen_count"] % self.params["kill_interval"] + # == 0 + # ): + # # Create new random individuals to get to preferred population size + # old_pop_size = population.individuals.shape[0] + # if self.params["target_pop_size"] - old_pop_size > 0: + # placed_indices = self.lattice.place_offspring( + # self.params["target_pop_size"] - old_pop_size + # ) + # population.create_new_individuals(pop_size=len(placed_indices), design="EvoNN") if self.params["ploton"]: population.plot_objectives() @@ -353,13 +374,15 @@ def move_predator(self): """Find an empty position in the moore neighbourhood for the Predators to move in.""" # Calculate predator moves - predator_max_moves = int( - ( - self.params["population"].individuals.shape[0] - - self.params["target_pop_size"] - ) - / self.params["predator_pop_size"] - ) + # predator_max_moves = int( + # ( + # self.params["population"].individuals.shape[0] + # - self.params["target_pop_size"] + # ) + # / self.params["predator_pop_size"] + # ) + + predator_max_moves = self.params["predator_max_moves"] # Track killed preys in list and remove them at the end of the function to_be_killed = [] diff --git a/pyrvea/EAs/baseEA.py b/pyrvea/EAs/baseEA.py index a481b5d..6475b7d 100644 --- a/pyrvea/EAs/baseEA.py +++ b/pyrvea/EAs/baseEA.py @@ -1,5 +1,6 @@ from typing import TYPE_CHECKING import numpy as np +import random if TYPE_CHECKING: from pyrvea.Population.Population import Population @@ -97,7 +98,24 @@ def _next_gen(self, population: "Population"): population: "Population" Population object """ - offspring = population.mate() + #offspring = population.mate() + offspring = np.empty( + ( + 0, + population.problem.num_input_nodes + 1, + population.problem.num_hidden_nodes, + ), + float, + ) + for ind in range(population.individuals.shape[0]): + + mate_idx = random.randint(0,population.individuals.shape[0]-1) + if not mate_idx: + continue + else: + offspring1, offspring2 = population.mate(ind, mate_idx, self.params) + offspring = np.concatenate((offspring, [offspring1], [offspring2])) + population.add(offspring) selected = self.select(population) population.keep(selected) diff --git a/pyrvea/Examples/sphere_function_test.py b/pyrvea/Examples/sphere_function_test.py index 94b59e5..191e501 100644 --- a/pyrvea/Examples/sphere_function_test.py +++ b/pyrvea/Examples/sphere_function_test.py @@ -18,42 +18,46 @@ def objectives(decision_variables): # Sphere function - x = np.asarray(decision_variables) - obj_func = sum(x ** 2) + # x = np.asarray(decision_variables) + # obj_func = sum(x ** 2) # Matyas function # x = np.asarray(decision_variables[0]) # y = np.asarray(decision_variables[1]) # obj_func = 0.26 * (x**2 + y**2) - 0.48 * x * y + # Himmelblau's function + x = np.asarray(decision_variables[0]) + y = np.asarray(decision_variables[1]) + + obj_func = (x**2 + y - 11)**2 + (x + y**2 - 7)**2 + return obj_func #rand_training_data_input = np.random.uniform(-10, 10, (150, 2)) -#lin_training_data_input = np.linspace((10, 10), (-10, -10), 149) -lin_training_data_input_temp = np.linspace(10, -10, 149) -x2 = np.zeros_like(lin_training_data_input_temp) -lin_training_data_input = np.vstack((np.hstack((lin_training_data_input_temp,lin_training_data_input_temp)), np.hstack((lin_training_data_input_temp, x2)))).T +lin_training_data_input = np.linspace((-5, -5), (5, 5), 150) +#lin_training_data_input_temp = np.linspace(10, 0, 149) +#x2 = np.zeros_like(lin_training_data_input_temp) +#x2 = np.flip(lin_training_data_input_temp) +#lin_training_data_input = np.vstack((np.hstack((lin_training_data_input_temp,lin_training_data_input_temp)), np.hstack((lin_training_data_input_temp, x2)))).T #lin_training_data_input = lhs(2, 150) * 20 - 10 -#zero_row = [0,0] -#lin_training_data_input = np.vstack([lin_training_data_input, zero_row]) - -#lin_training_data_input = np.flip(np.sort(lin_training_data_input, axis=0)) +#lin_training_data_input[:, 1] = np.flip(lin_training_data_input[:, 1]) training_data_output = np.asarray([objectives(x) for x in lin_training_data_input]) f1 = EvoNNProblem( - name="linear_sphere_function", + name="EvoNN", training_data_input=lin_training_data_input, training_data_output=training_data_output, - num_hidden_nodes=15, + num_hidden_nodes=7, ) def plot(f1): - popf2 = Population(f1, plotting=False) - model = popf2.evolve(RVEA) + popf2 = Population(f1, plotting=False, assign_type="EvoNN") + model = popf2.evolve(PPGA) y_pred = f1.get_prediction(model) trace = go.Scatter(x=y_pred, y=training_data_output, mode="markers") diff --git a/pyrvea/Population/Population.py b/pyrvea/Population/Population.py index ca449a1..64dcca1 100644 --- a/pyrvea/Population/Population.py +++ b/pyrvea/Population/Population.py @@ -9,6 +9,7 @@ from pygmo import fast_non_dominated_sorting as nds from pygmo import hypervolume as hv from pygmo import non_dominated_front_2d as nd2 +from scipy.stats import bernoulli as bn from tqdm import tqdm, tqdm_notebook from pyrvea.Recombination.bounded_polynomial_mutation import mutation @@ -17,6 +18,9 @@ from pyrvea.OtherTools.plotlyanimate import animate_init_, animate_next_ from pyrvea.OtherTools.IsNotebook import IsNotebook +from pyrvea.Recombination.ppga_crossover import ppga_crossover +from pyrvea.Recombination.ppga_mutation import ppga_mutation + if TYPE_CHECKING: from pyrvea.Problem.baseProblem import baseProblem from pyrvea.EAs.baseEA import BaseEA @@ -59,6 +63,10 @@ def __init__( self.plotting = plotting # These attributes contain the solutions. self.individuals = np.empty((0, self.num_var), float) + if problem.name == "EvoNN": + self.individuals = np.empty( + (0, self.problem.num_input_nodes + 1, self.problem.num_hidden_nodes), float + ) self.objectives = np.empty((0, self.problem.num_of_objectives), float) self.fitness = np.empty((0, self.problem.num_of_objectives), float) self.constraint_violation = np.empty( @@ -97,7 +105,9 @@ def create_new_individuals( if pop_size is None: pop_size_options = [50, 105, 120, 126, 132, 112, 156, 90, 275] pop_size = pop_size_options[self.problem.num_of_objectives - 2] + num_var = self.individuals.shape[1] + if design == "RandomDesign": individuals = np.random.random((pop_size, num_var)) # Scaling @@ -112,9 +122,32 @@ def create_new_individuals( individuals * (self.upper_limits - self.lower_limits) + self.lower_limits ) + elif design == "EvoNN": + # +1 row for bias + individuals = np.random.uniform( + self.problem.w_low, + self.problem.w_high, + size=( + pop_size, + self.problem.num_input_nodes + 1, + self.problem.num_hidden_nodes, + ), + ) + + # Eliminate some weights + flag = bn.rvs(p=1 - self.problem.prob_omit, size=np.shape(individuals)) + + random_numbers = np.zeros(np.shape(individuals)) + individuals[flag == 0] = random_numbers[flag == 0] + + # Set bias + individuals[:, 0, :] = 1 + else: print("Design not yet supported.") + self.add(individuals) + if self.plotting: self.figure = [] self.plot_init_() @@ -139,6 +172,9 @@ def add(self, new_pop: np.ndarray): elif new_pop.ndim == 2: for ind in new_pop: self.append_individual(ind) + elif new_pop.ndim == 3: + for i in range(0, new_pop.shape[0]): + self.append_individual(new_pop[i, :, :]) else: print("Error while adding new individuals. Check dimensions.") # print(self.ideal_fitness) @@ -152,7 +188,7 @@ def keep(self, indices: list): indices: list Indices of individuals to keep """ - indices.sort() + new_pop = self.individuals[indices, :] new_obj = self.objectives[indices, :] new_fitness = self.fitness[indices, :] @@ -228,7 +264,7 @@ def append_individual(self, ind: np.ndarray): ---------- ind: np.ndarray """ - self.individuals = np.vstack((self.individuals, ind)) + self.individuals = np.concatenate((self.individuals, [ind])) obj, CV, fitness = self.evaluate_individual(ind) self.objectives = np.vstack((self.objectives, obj)) self.constraint_violation = np.vstack((self.constraint_violation, CV)) @@ -267,6 +303,7 @@ def evolve(self, EA: "BaseEA" = None, EA_parameters: dict = None) -> "Population ################################## # To determine whether running in console or in notebook. Used for TQDM. # TQDM will be removed in future generations as number of iterations can vary + if IsNotebook(): progressbar = tqdm_notebook else: @@ -274,6 +311,20 @@ def evolve(self, EA: "BaseEA" = None, EA_parameters: dict = None) -> "Population #################################### # A basic evolution cycle. Will be updated to optimize() in future versions. ea = EA(self, EA_parameters) + # AicC: + # non_dom_front = nds(self.objectives) + # fon_front = non_dom_front[0][0] + # info_c_rank = [] + # for i in fon_front: + # + # if + # info_c = self.problem.information_criterion(self.individuals[i]) + # info_c_rank.append((info_c, i)) + # + # info_c_rank.sort() + + # return self.individuals[info_c_rank[0][1]] + iterations = ea.params["iterations"] if self.plotting: self.plot_objectives() # Figure was created in init @@ -282,17 +333,45 @@ def evolve(self, EA: "BaseEA" = None, EA_parameters: dict = None) -> "Population ea._next_iteration(self) if self.plotting: self.plot_objectives() + lowest_error = np.argmin(self.objectives[:, 0]) + return self.individuals[lowest_error] - def mate(self): + def mate(self, ind1=None, ind2=None, params=None): """Conduct crossover and mutation over the population. Conduct simulated binary crossover and bounded polunomial mutation. """ - offspring = crossover(self) - p = 1 / self.num_var - mut_offspring = mutation(self, offspring, prob_mut=p) - return mut_offspring + if self.problem.name == "EvoNN": + # Get individuals at indices + w1, w2 = self.individuals[ind1], self.individuals[ind2] + + # Perform crossover + xover_w1, xover_w2 = ppga_crossover(w1, w2) + + # Find randomly two other individuals with current match active for mutation. + # Make a list of individuals suitable for mutation, exclude the ones to be mutated + # so that they won't mutate with themselves + indices = [ind1, ind2] + mask = np.ones(len(self.individuals), dtype=bool) + mask[indices] = False + alternatives = self.individuals[mask, ...][:, 1:, :] + + # Mutate + offspring1, offspring2 = ppga_mutation( + alternatives, + xover_w1, + xover_w2 + ) + + return offspring1, offspring2 + + else: + offspring = crossover(self) + p = 1 / self.num_var + mut_offspring = mutation(self, offspring, prob_mut=p) + + return mut_offspring def plot_init_(self): """Initialize animation objects. Return figure""" diff --git a/pyrvea/Population/population_evonn.py b/pyrvea/Population/population_evonn.py index 7ff3a23..ae4d243 100644 --- a/pyrvea/Population/population_evonn.py +++ b/pyrvea/Population/population_evonn.py @@ -120,7 +120,7 @@ def create_new_individuals( pass # Create new individuals - if design == "RandomDesign": + if design == "EvoNN": # +1 row for bias individuals = np.random.uniform( @@ -356,18 +356,19 @@ def evolve(self, EA: "BaseEA" = None, EA_parameters: dict = None) -> "Population # Calculate information criterion for the first front # and return the model with the lowest value - lowest_error = np.argmin(self.objectives[:, 0]) - return self.individuals[lowest_error] - # non_dom_front = nds(self.objectives) - # fon_front = non_dom_front[0][0] - # info_c_rank = [] - # for i in fon_front: - # - # if - # info_c = self.problem.information_criterion(self.individuals[i]) - # info_c_rank.append((info_c, i)) - # - # info_c_rank.sort() + # lowest_error = np.argmin(self.objectives[:, 0]) + # return self.individuals[lowest_error] + + non_dom_front = nds(self.objectives) + fon_front = non_dom_front[0][0] + info_c_rank = [] + + for i in fon_front: + + info_c = self.problem.information_criterion(self.individuals[i]) + info_c_rank.append((info_c, i)) + + info_c_rank.sort() return self.individuals[info_c_rank[0][1]] diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index f4a7e11..27285c3 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -78,7 +78,7 @@ def objectives(self, decision_variables) -> list: """ weighted_input = self.dot_product(decision_variables) - activated_function = self.activation(weighted_input) + activated_function = self.activation(weighted_input, name="sigmoid") w_matrix2, rss, predicted_values = self.minimize_error(activated_function) training_error = self.loss_function(predicted_values) @@ -116,6 +116,9 @@ def activation(self, wi, name="sigmoid"): if name == "sigmoid": activated_function = lambda x: 1 / (1 + np.exp(-x)) + if name == "relu": + activated_function = lambda x: np.maximum(x, 0) + return activated_function(wi) def minimize_error(self, activated_function, name="llsq"): diff --git a/pyrvea/Recombination/ppga_crossover.py b/pyrvea/Recombination/ppga_crossover.py index c7a8c0a..bcfd471 100644 --- a/pyrvea/Recombination/ppga_crossover.py +++ b/pyrvea/Recombination/ppga_crossover.py @@ -13,11 +13,13 @@ def ppga_crossover(w1, w2, prob_crossover=0.8): prob_crossover : float The probability for the crossover """ + offspring1 = np.copy(w1) + offspring2 = np.copy(w2) for i in range(np.shape(w1)[1]): if np.random.random() < prob_crossover: - tmp = np.copy(w1[:, i]) - w1[:, i] = w2[:, i] - w2[:, i] = tmp + tmp = np.copy(offspring1[:, i]) + offspring1[:, i] = offspring2[:, i] + offspring2[:, i] = tmp - return w1, w2 + return offspring1, offspring2 diff --git a/pyrvea/Recombination/ppga_mutation.py b/pyrvea/Recombination/ppga_mutation.py index 968b7f8..19bb587 100644 --- a/pyrvea/Recombination/ppga_mutation.py +++ b/pyrvea/Recombination/ppga_mutation.py @@ -36,6 +36,9 @@ def mutate(offspring, wx): select = sample(list(alternatives), 2) + # while select[0][it.multi_index[0], it.multi_index[1]] or select[1][it.multi_index[0], it.multi_index[1]] == 0: + # select = sample(list(alternatives), 2) + # [it.multi_index[0]+1 to ignore bias offspring[it.multi_index[0]+1, it.multi_index[1]] = wx + mut_strength * ( 1 - cur_gen / total_gen diff --git a/pyrvea/Recombination/simulated_binary_crossover.py b/pyrvea/Recombination/simulated_binary_crossover.py index a718c0c..eecdc89 100644 --- a/pyrvea/Recombination/simulated_binary_crossover.py +++ b/pyrvea/Recombination/simulated_binary_crossover.py @@ -5,7 +5,7 @@ def crossover(population, prob_xover=1, dis_xover=30): pop = population.individuals - pop_size, num_var = pop.shape + pop_size, num_var = pop.shape[0], pop.shape[1]-1 shuffled_ids = list(range(pop_size)) shuffle(shuffled_ids) mating_pop = pop[shuffled_ids] From e1585464a6d01c553a51f5771a613d0eaf72a6c9 Mon Sep 17 00:00:00 2001 From: delamorte Date: Fri, 7 Jun 2019 15:47:58 +0300 Subject: [PATCH 025/155] optimized the mutation function by inlining a nested function mutate() fixed the mutation function so that zero weights will not be chosen for mutation --- pyrvea/Examples/evonn_example.py | 2 +- pyrvea/Examples/sphere_function_test.py | 96 ++++++++++++++++++++----- pyrvea/Examples/zdt_problem_test.py | 4 +- pyrvea/Population/population_evonn.py | 34 ++++----- pyrvea/Problem/evonn_problem.py | 2 +- pyrvea/Recombination/ppga_mutation.py | 84 ++++++++++++++-------- 6 files changed, 155 insertions(+), 67 deletions(-) diff --git a/pyrvea/Examples/evonn_example.py b/pyrvea/Examples/evonn_example.py index e31deca..a847f58 100644 --- a/pyrvea/Examples/evonn_example.py +++ b/pyrvea/Examples/evonn_example.py @@ -37,7 +37,7 @@ #arr = np.zeros((60,60)) -popf1 = Population(f1, plotting=True) +popf1 = Population(f1, plotting=False) # popf2 = Population(f2) model = popf1.evolve(PPGA) diff --git a/pyrvea/Examples/sphere_function_test.py b/pyrvea/Examples/sphere_function_test.py index 191e501..a30a02a 100644 --- a/pyrvea/Examples/sphere_function_test.py +++ b/pyrvea/Examples/sphere_function_test.py @@ -17,47 +17,109 @@ def objectives(decision_variables): - # Sphere function + # Number of data points = 150, number of generations = 100 + + # Sphere function, --5 <= x <= 5 + # Error close to zero with random data. + # # x = np.asarray(decision_variables) # obj_func = sum(x ** 2) - # Matyas function + # Matyas function, -10 <= x, y <= 10 + # Error close to zero with random data, + # when number of nodes = 20. With less nodes, + # training wasn't as successful. With linear data, + # training wasn't successful. + # # x = np.asarray(decision_variables[0]) # y = np.asarray(decision_variables[1]) # obj_func = 0.26 * (x**2 + y**2) - 0.48 * x * y - # Himmelblau's function + # Himmelblau's function, -5 <= x, y <= 5 + # Error close to zero with random data. + # + # x = np.asarray(decision_variables[0]) + # y = np.asarray(decision_variables[1]) + # obj_func = (x**2 + y - 11)**2 + (x + y**2 - 7)**2 + + # Rastigrin function, -5.12 <= x <= 5.12 + # Didn't work that well with random data + # and 2 variables. + # + # x = np.asarray(decision_variables) + # n = len(x) + # obj_func = 10 * n + sum(x ** 2 - 10 * np.cos(2 * pi * x)) + + # Three-hump camel function, -5 <= x, y <= 5 + # Worked pretty well with random data. + # x = np.asarray(decision_variables[0]) y = np.asarray(decision_variables[1]) + obj_func = 2*x**2 - 1.05 * x**4 + (x**6)/6 + x * y + y**2 + + # Goldstein-Price function, -2 <= x, y <= 2 + # + # with 15 nodes, min. error == 20000 + # with 20 nodes, min. error == 17000 + # with 25 nodes, min. error == 8671 + # + # x = np.asarray(decision_variables[0]) + # y = np.asarray(decision_variables[1]) + # obj_func = (1 + (x + y + 1) ** 2 * (19 - 14 * x + 3 * (x ** 2) - 14 * y + 6 * x * y + 3 * (y ** 2)))\ + # * (30 + (2 * x - 3 * y) ** 2 * (18 - 32 * x + 12 * (x ** 2) + 48 * y - 36 * x * y + 27 * (y ** 2))) - obj_func = (x**2 + y - 11)**2 + (x + y**2 - 7)**2 + # Levi function N.13, -10 <= x, y <= 10 + # + # with random data and 10 nodes, min. error == 27, not very good + # with random data and 15 nodes, min. error == 22, bit better + # x = np.asarray(decision_variables[0]) + # y = np.asarray(decision_variables[1]) + # obj_func = ( + # np.sin(3 * np.pi * x) ** 2 + # + (x - 1) ** 2 * (1 + np.sin(3 * np.pi * y) ** 2) + # + (y - 1) ** 2 * (1 + np.sin(2 * np.pi * y) ** 2) + # ) + + # Schaffer function N. 2, -100 <= x, y <= 100 + # + # Doesn't work with random data + # x = np.asarray(decision_variables[0]) + # y = np.asarray(decision_variables[1]) + # obj_func = 0.5 + (np.sin((x**2 - y**2)**2) - 0.5) / (1 + 0.001*(x**2 + y**2))**2 - return obj_func + # Ackley function, -5 <= x, y <= 5 + # + return obj_func -#rand_training_data_input = np.random.uniform(-10, 10, (150, 2)) -lin_training_data_input = np.linspace((-5, -5), (5, 5), 150) -#lin_training_data_input_temp = np.linspace(10, 0, 149) -#x2 = np.zeros_like(lin_training_data_input_temp) -#x2 = np.flip(lin_training_data_input_temp) -#lin_training_data_input = np.vstack((np.hstack((lin_training_data_input_temp,lin_training_data_input_temp)), np.hstack((lin_training_data_input_temp, x2)))).T -#lin_training_data_input = lhs(2, 150) * 20 - 10 -#lin_training_data_input[:, 1] = np.flip(lin_training_data_input[:, 1]) +np.random.seed(42) +rand_training_data_input = np.random.uniform(-5, 5, (150, 2)) +# lin_training_data_input = np.linspace((-10, -10), (10, 10), 150) +# lin_training_data_input_temp = np.linspace(10, 0, 149) +# x2 = np.zeros_like(lin_training_data_input_temp) +# x2 = np.flip(lin_training_data_input_temp) +# lin_training_data_input = np.vstack((np.hstack((lin_training_data_input_temp,lin_training_data_input_temp)), np.hstack((lin_training_data_input_temp, x2)))).T +# lin_training_data_input = lhs(2, 150) * 20 - 10 +# lin_training_data_input[:, 1] = np.flip(lin_training_data_input[:, 1]) -training_data_output = np.asarray([objectives(x) for x in lin_training_data_input]) +training_data_output = np.asarray([objectives(x) for x in rand_training_data_input]) f1 = EvoNNProblem( name="EvoNN", - training_data_input=lin_training_data_input, + training_data_input=rand_training_data_input, training_data_output=training_data_output, - num_hidden_nodes=7, + num_hidden_nodes=15, ) def plot(f1): - popf2 = Population(f1, plotting=False, assign_type="EvoNN") + popf2 = Population(f1, plotting=False) + pr = cProfile.Profile() + pr.enable() model = popf2.evolve(PPGA) + pr.disable() + pr.print_stats(sort="cumtime") y_pred = f1.get_prediction(model) trace = go.Scatter(x=y_pred, y=training_data_output, mode="markers") diff --git a/pyrvea/Examples/zdt_problem_test.py b/pyrvea/Examples/zdt_problem_test.py index 32b4cbb..51a0add 100644 --- a/pyrvea/Examples/zdt_problem_test.py +++ b/pyrvea/Examples/zdt_problem_test.py @@ -23,7 +23,7 @@ def plot(f2): - popf2 = Population(f2, plotting=True) + popf2 = Population(f2, plotting=False) model = popf2.evolve(PPGA) y_pred = f2.get_prediction(model) @@ -54,7 +54,7 @@ def plot(f2): # plot(f2) f2 = EvoNNProblem( - name="EvoNN_nodes_15", + name="ZDT2_15_nodes", training_data_input=training_data_input, training_data_output=f2_training_data_output, num_hidden_nodes=15 diff --git a/pyrvea/Population/population_evonn.py b/pyrvea/Population/population_evonn.py index ae4d243..d1b40ac 100644 --- a/pyrvea/Population/population_evonn.py +++ b/pyrvea/Population/population_evonn.py @@ -120,7 +120,7 @@ def create_new_individuals( pass # Create new individuals - if design == "EvoNN": + if design == "RandomDesign": # +1 row for bias individuals = np.random.uniform( @@ -353,24 +353,26 @@ def evolve(self, EA: "BaseEA" = None, EA_parameters: dict = None) -> "Population # A basic evolution cycle. Will be updated to optimize() in future versions. ea = EA(self, EA_parameters) - # Calculate information criterion for the first front - # and return the model with the lowest value - - # lowest_error = np.argmin(self.objectives[:, 0]) - # return self.individuals[lowest_error] - - non_dom_front = nds(self.objectives) - fon_front = non_dom_front[0][0] - info_c_rank = [] + # Return the model with the lowest error - for i in fon_front: + lowest_error = np.argmin(self.objectives[:, 0]) + return self.individuals[lowest_error] - info_c = self.problem.information_criterion(self.individuals[i]) - info_c_rank.append((info_c, i)) - - info_c_rank.sort() + # Calculate Akaike information criterion for the first front + # and return the model with the lowest value - return self.individuals[info_c_rank[0][1]] + # non_dom_front = nds(self.objectives) + # fon_front = non_dom_front[0][0] + # info_c_rank = [] + # + # for i in fon_front: + # + # info_c = self.problem.information_criterion(self.individuals[i]) + # info_c_rank.append((info_c, i)) + # + # info_c_rank.sort() + # + # return self.individuals[info_c_rank[0][1]] def mate(self, ind1, ind2, params): """Conduct crossover and mutation over the population. diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index 27285c3..1afe99b 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -155,7 +155,7 @@ def information_criterion(self, w_matrix): wi = self.dot_product(w_matrix) z = self.activation(wi) w_matrix2, rss, prediction = self.minimize_error(z) - rss = ((self.training_data_output - prediction) ** 2).sum() + #rss = ((self.training_data_output - prediction) ** 2).sum() k = self.calculate_complexity(w_matrix) + np.count_nonzero(w_matrix2) aic = 2 * k + self.num_of_samples * log(rss/self.num_of_samples) diff --git a/pyrvea/Recombination/ppga_mutation.py b/pyrvea/Recombination/ppga_mutation.py index 19bb587..0ace47f 100644 --- a/pyrvea/Recombination/ppga_mutation.py +++ b/pyrvea/Recombination/ppga_mutation.py @@ -3,13 +3,7 @@ def ppga_mutation( - alternatives, - w1, - w2, - cur_gen=1, - total_gen=10, - prob_mut=0.3, - mut_strength=0.7, + alternatives, w1, w2, cur_gen=1, total_gen=10, prob_mut=0.3, mut_strength=0.7 ): """Randomly mutate two individuals @@ -31,35 +25,65 @@ def ppga_mutation( the strength of the mutation constant """ - def mutate(offspring, wx): - """Internal function for the mutation calculation""" - - select = sample(list(alternatives), 2) - - # while select[0][it.multi_index[0], it.multi_index[1]] or select[1][it.multi_index[0], it.multi_index[1]] == 0: - # select = sample(list(alternatives), 2) - - # [it.multi_index[0]+1 to ignore bias - offspring[it.multi_index[0]+1, it.multi_index[1]] = wx + mut_strength * ( - 1 - cur_gen / total_gen - ) * ( - select[0][it.multi_index[0], it.multi_index[1]] - - select[1][it.multi_index[0], it.multi_index[1]] - ) - - return offspring - # Iterate over both individuals at the same time to avoid nested looping - it = np.nditer([w1[1:, :], w2[1:, :]], flags=["multi_index"], op_flags=["readwrite"]) + it = np.nditer( + [w1[1:, :], w2[1:, :]], flags=["multi_index"], op_flags=["readwrite"] + ) # wx = individual cells (connections) in the weight matrix for wx1, wx2 in it: - if wx1 != 0 and np.random.random() < prob_mut: + if ( + wx1 != 0 + and np.random.random() < prob_mut + and len( + np.nonzero(alternatives[:, it.multi_index[0], it.multi_index[1]])[0] + ) + >= 2 + ): + + # Randomly select two individuals with current match active (=non-zero) + select = alternatives[ + np.random.choice( + np.nonzero(alternatives[:, it.multi_index[0], it.multi_index[1]])[ + 0 + ], + 2, + ) + ] + + # Mutation function, +1 in w1[it.multi_index[0]+1 is to ignore bias row + w1[it.multi_index[0] + 1, it.multi_index[1]] = wx1 + mut_strength * ( + 1 - cur_gen / total_gen + ) * ( + select[0][it.multi_index[0], it.multi_index[1]] + - select[1][it.multi_index[0], it.multi_index[1]] + ) - w1 = mutate(w1, wx1) + if ( + wx2 != 0 + and np.random.random() < prob_mut + and len( + np.nonzero(alternatives[:, it.multi_index[0], it.multi_index[1]])[0] + ) + >= 2 + ): - if wx2 != 0 and np.random.random() < prob_mut: + # Randomly select two individuals with current match active (=non-zero) + select = alternatives[ + np.random.choice( + np.nonzero(alternatives[:, it.multi_index[0], it.multi_index[1]])[ + 0 + ], + 2, + ) + ] - w2 = mutate(w2, wx2) + # Mutation function, +1 in w2[it.multi_index[0]+1 is to ignore bias row + w2[it.multi_index[0] + 1, it.multi_index[1]] = wx2 + mut_strength * ( + 1 - cur_gen / total_gen + ) * ( + select[0][it.multi_index[0], it.multi_index[1]] + - select[1][it.multi_index[0], it.multi_index[1]] + ) return w1, w2 From 2fc1722de0866aa8f35177df4fb3255dde61979c Mon Sep 17 00:00:00 2001 From: delamorte Date: Mon, 10 Jun 2019 13:12:01 +0300 Subject: [PATCH 026/155] Project file/folder refactoring --- .gitignore | 1 + Example.py | 12 +- pyrvea/EAs/baseEA.py | 28 +-- pyrvea/Examples/evonn_example.py | 66 ------- pyrvea/Examples/sphere_function_test.py | 141 --------------- pyrvea/Examples/zdt_problem_test.py | 91 ---------- pyrvea/Population/Population.py | 14 +- pyrvea/Problem/evonn_problem.py | 2 +- pyrvea/Problem/evonn_test_functions.py | 224 ++++++++++++++++++++++++ pyrvea/Recombination/ppga_mutation.py | 2 +- pyrvea/tests.py | 33 ---- 11 files changed, 242 insertions(+), 372 deletions(-) delete mode 100644 pyrvea/Examples/evonn_example.py delete mode 100644 pyrvea/Examples/sphere_function_test.py delete mode 100644 pyrvea/Examples/zdt_problem_test.py create mode 100644 pyrvea/Problem/evonn_test_functions.py delete mode 100644 pyrvea/tests.py diff --git a/.gitignore b/.gitignore index 0fd2676..b11fc64 100644 --- a/.gitignore +++ b/.gitignore @@ -136,4 +136,5 @@ poetry.lock pyproject.toml .tests.py +Tests/ *.csv diff --git a/Example.py b/Example.py index d05fb7c..ef66c06 100644 --- a/Example.py +++ b/Example.py @@ -8,19 +8,15 @@ from pyrvea.EAs.RVEA import RVEA from pyrvea.EAs.NSGAIII import NSGAIII from optproblems import dtlz -import numpy as np class newProblem(baseProblem): """New problem description.""" def objectives(self, decision_variables): - x = np.asarray(decision_variables) - obj_func = sum(x ** 2) - return obj_func - # return dtlz.DTLZ3(self.num_of_objectives, self.num_of_variables)( - # decision_variables - # ) + return dtlz.DTLZ3(self.num_of_objectives, self.num_of_variables)( + decision_variables + ) name = "DTLZ3" @@ -28,7 +24,7 @@ def objectives(self, decision_variables): numobj = 3 numconst = 0 numvar = numobj + k - 1 -problem = newProblem(name, 2, 2, numconst) +problem = newProblem(name, numvar, numobj, numconst) lattice_resolution = 4 population_size = 105 diff --git a/pyrvea/EAs/baseEA.py b/pyrvea/EAs/baseEA.py index 6475b7d..09af8cb 100644 --- a/pyrvea/EAs/baseEA.py +++ b/pyrvea/EAs/baseEA.py @@ -74,16 +74,6 @@ def _next_iteration(self, population: "Population"): self.params["current_iteration_gen_count"] = 1 while self.continue_iteration(): self._next_gen(population) - print( - str(self.params["current_iteration_gen_count"]) - + " " - + "population size: " - + str(population.individuals.shape[0]) - + " Min Error: " - + str(np.amin(population.objectives[:,0])) - + " Avg Error: " - + str(np.mean(population.objectives[:, 0])) - ) self.params["current_iteration_gen_count"] += 1 self.params["current_iteration_count"] += 1 @@ -98,24 +88,8 @@ def _next_gen(self, population: "Population"): population: "Population" Population object """ - #offspring = population.mate() - offspring = np.empty( - ( - 0, - population.problem.num_input_nodes + 1, - population.problem.num_hidden_nodes, - ), - float, - ) - for ind in range(population.individuals.shape[0]): - - mate_idx = random.randint(0,population.individuals.shape[0]-1) - if not mate_idx: - continue - else: - offspring1, offspring2 = population.mate(ind, mate_idx, self.params) - offspring = np.concatenate((offspring, [offspring1], [offspring2])) + offspring = population.mate() population.add(offspring) selected = self.select(population) population.keep(selected) diff --git a/pyrvea/Examples/evonn_example.py b/pyrvea/Examples/evonn_example.py deleted file mode 100644 index a847f58..0000000 --- a/pyrvea/Examples/evonn_example.py +++ /dev/null @@ -1,66 +0,0 @@ -from pygmo import fast_non_dominated_sorting as nds -from pyrvea.Problem.evonn_problem import EvoNNProblem -from pyrvea.Population.population_evonn import Population -from pyrvea.Problem.testProblem import testProblem -from pyrvea.Recombination.ppga_crossover import ppga_crossover -from pyrvea.Recombination.ppga_mutation import ppga_mutation -from pyrvea.EAs.PPGA import PPGA -import pandas -from random import randint, sample -import numpy as np -import timeit -import plotly -import plotly.graph_objs as go - -test_data = pandas.read_csv("178deb.csv", header=1) - -#training_data = np.random.uniform(0, 1, size=(num_of_samples, input_nodes)) -training_data = test_data.values[:, :-2] -f1_training_data_output = test_data.values[:, 2] -f2_training_data_output = test_data.values[:, 3] -input_nodes = training_data.shape[1] -#preferred_output = np.random.uniform(0,1,num_of_samples) - -f1 = EvoNNProblem( - name="EvoNN", - training_data_input=training_data, - training_data_output=f1_training_data_output, - num_input_nodes=input_nodes, -) - -f2 = EvoNNProblem( - name="EvoNN", - training_data_input=training_data, - training_data_output=f2_training_data_output, - num_input_nodes=input_nodes, -) - -#arr = np.zeros((60,60)) - -popf1 = Population(f1, plotting=False) -# popf2 = Population(f2) - -model = popf1.evolve(PPGA) -# popf2.evolve(PPGA) - -y_pred = f1.get_prediction(model) - -trace = go.Scatter( - x = y_pred, - y = f1_training_data_output, - mode = 'markers' -) - -data = [trace] - -plotly.offline.plot(data, filename='temp-plot.html', auto_open=True) -#p = PPGA(pop) - -#a1 = randint(0, 49) -#a2 = randint(0, 49) -#mutation(pop, a1, a2) - -#selected = sample(range(1, np.shape(pop.individuals)[0]), randint(1, np.shape(pop.individuals)[0])) -#pop.keep(selected) -#selected = sample(range(1, np.shape(pop.individuals)[0]), randint(1, np.shape(pop.individuals)[0])) -#pop.delete(selected) \ No newline at end of file diff --git a/pyrvea/Examples/sphere_function_test.py b/pyrvea/Examples/sphere_function_test.py deleted file mode 100644 index a30a02a..0000000 --- a/pyrvea/Examples/sphere_function_test.py +++ /dev/null @@ -1,141 +0,0 @@ -from pyrvea.Problem.evonn_problem import EvoNNProblem -from pyrvea.Population.population_evonn import Population -from pyrvea.Problem.testProblem import testProblem -from pyrvea.Recombination.ppga_crossover import ppga_crossover -from pyrvea.Recombination.ppga_mutation import ppga_mutation -from pyrvea.EAs.PPGA import PPGA -from pyrvea.EAs.RVEA import RVEA -import pandas -from random import randint, sample -import numpy as np -import cProfile -import timeit -import plotly -import plotly.graph_objs as go -from pyDOE import lhs - - -def objectives(decision_variables): - - # Number of data points = 150, number of generations = 100 - - # Sphere function, --5 <= x <= 5 - # Error close to zero with random data. - # - # x = np.asarray(decision_variables) - # obj_func = sum(x ** 2) - - # Matyas function, -10 <= x, y <= 10 - # Error close to zero with random data, - # when number of nodes = 20. With less nodes, - # training wasn't as successful. With linear data, - # training wasn't successful. - # - # x = np.asarray(decision_variables[0]) - # y = np.asarray(decision_variables[1]) - # obj_func = 0.26 * (x**2 + y**2) - 0.48 * x * y - - # Himmelblau's function, -5 <= x, y <= 5 - # Error close to zero with random data. - # - # x = np.asarray(decision_variables[0]) - # y = np.asarray(decision_variables[1]) - # obj_func = (x**2 + y - 11)**2 + (x + y**2 - 7)**2 - - # Rastigrin function, -5.12 <= x <= 5.12 - # Didn't work that well with random data - # and 2 variables. - # - # x = np.asarray(decision_variables) - # n = len(x) - # obj_func = 10 * n + sum(x ** 2 - 10 * np.cos(2 * pi * x)) - - # Three-hump camel function, -5 <= x, y <= 5 - # Worked pretty well with random data. - # - x = np.asarray(decision_variables[0]) - y = np.asarray(decision_variables[1]) - obj_func = 2*x**2 - 1.05 * x**4 + (x**6)/6 + x * y + y**2 - - # Goldstein-Price function, -2 <= x, y <= 2 - # - # with 15 nodes, min. error == 20000 - # with 20 nodes, min. error == 17000 - # with 25 nodes, min. error == 8671 - # - # x = np.asarray(decision_variables[0]) - # y = np.asarray(decision_variables[1]) - # obj_func = (1 + (x + y + 1) ** 2 * (19 - 14 * x + 3 * (x ** 2) - 14 * y + 6 * x * y + 3 * (y ** 2)))\ - # * (30 + (2 * x - 3 * y) ** 2 * (18 - 32 * x + 12 * (x ** 2) + 48 * y - 36 * x * y + 27 * (y ** 2))) - - # Levi function N.13, -10 <= x, y <= 10 - # - # with random data and 10 nodes, min. error == 27, not very good - # with random data and 15 nodes, min. error == 22, bit better - # x = np.asarray(decision_variables[0]) - # y = np.asarray(decision_variables[1]) - # obj_func = ( - # np.sin(3 * np.pi * x) ** 2 - # + (x - 1) ** 2 * (1 + np.sin(3 * np.pi * y) ** 2) - # + (y - 1) ** 2 * (1 + np.sin(2 * np.pi * y) ** 2) - # ) - - # Schaffer function N. 2, -100 <= x, y <= 100 - # - # Doesn't work with random data - # x = np.asarray(decision_variables[0]) - # y = np.asarray(decision_variables[1]) - # obj_func = 0.5 + (np.sin((x**2 - y**2)**2) - 0.5) / (1 + 0.001*(x**2 + y**2))**2 - - # Ackley function, -5 <= x, y <= 5 - # - - return obj_func - -np.random.seed(42) -rand_training_data_input = np.random.uniform(-5, 5, (150, 2)) -# lin_training_data_input = np.linspace((-10, -10), (10, 10), 150) -# lin_training_data_input_temp = np.linspace(10, 0, 149) -# x2 = np.zeros_like(lin_training_data_input_temp) -# x2 = np.flip(lin_training_data_input_temp) -# lin_training_data_input = np.vstack((np.hstack((lin_training_data_input_temp,lin_training_data_input_temp)), np.hstack((lin_training_data_input_temp, x2)))).T -# lin_training_data_input = lhs(2, 150) * 20 - 10 -# lin_training_data_input[:, 1] = np.flip(lin_training_data_input[:, 1]) - -training_data_output = np.asarray([objectives(x) for x in rand_training_data_input]) - -f1 = EvoNNProblem( - name="EvoNN", - training_data_input=rand_training_data_input, - training_data_output=training_data_output, - num_hidden_nodes=15, -) - - -def plot(f1): - - popf2 = Population(f1, plotting=False) - pr = cProfile.Profile() - pr.enable() - model = popf2.evolve(PPGA) - pr.disable() - pr.print_stats(sort="cumtime") - y_pred = f1.get_prediction(model) - - trace = go.Scatter(x=y_pred, y=training_data_output, mode="markers") - - data = [trace] - - plotly.offline.plot( - data, - filename=f1.name - + "_num_var" - + str(f1.num_of_variables) - + "_num_nodes" - + str(f1.num_hidden_nodes) - + ".html", - auto_open=True, - ) - - -plot(f1) diff --git a/pyrvea/Examples/zdt_problem_test.py b/pyrvea/Examples/zdt_problem_test.py deleted file mode 100644 index 51a0add..0000000 --- a/pyrvea/Examples/zdt_problem_test.py +++ /dev/null @@ -1,91 +0,0 @@ -from pyrvea.Problem.evonn_problem import EvoNNProblem -from pyrvea.Population.population_evonn import Population -from pyrvea.Problem.testProblem import testProblem -from pyrvea.Recombination.ppga_crossover import ppga_crossover -from pyrvea.Recombination.ppga_mutation import ppga_mutation -from pyrvea.EAs.PPGA import PPGA -import pandas -from random import randint, sample -import numpy as np -import cProfile -import timeit -import plotly -import plotly.graph_objs as go - -zdt_problem = testProblem('ZDT2', 30, 2, 0, 1, 0) -training_data_input = np.random.rand(150,30) -training_data_output = np.asarray([zdt_problem.objectives(x) for x in training_data_input]) - -f1_training_data_output = training_data_output[:, 0] -f2_training_data_output = training_data_output[:, 1] - -#preferred_output = np.random.uniform(0,1,num_of_samples) - -def plot(f2): - - popf2 = Population(f2, plotting=False) - model = popf2.evolve(PPGA) - y_pred = f2.get_prediction(model) - - trace = go.Scatter( - x=y_pred, - y=f2_training_data_output, - mode='markers' - ) - - data = [trace] - - plotly.offline.plot(data, filename=f2.name + ".html", auto_open=True) - - -# f1 = EvoNNProblem( -# name="EvoNN", -# training_data_input=training_data_input, -# training_data_output=f1_training_data_output, -# ) - -# f2 = EvoNNProblem( -# name="EvoNN_nodes_10", -# training_data_input=training_data_input, -# training_data_output=f2_training_data_output, -# num_hidden_nodes=10 -# ) -# -# plot(f2) - -f2 = EvoNNProblem( - name="ZDT2_15_nodes", - training_data_input=training_data_input, - training_data_output=f2_training_data_output, - num_hidden_nodes=15 -) - -plot(f2) - -#arr = np.zeros((60,60)) - -#popf1 = Population(f1, plotting=True) -#popf2 = Population(f2, plotting=True) - -#popf1.evolve(PPGA) - -# Profiling - -# pr = cProfile.Profile() -# pr.enable() -# model = popf2.evolve(PPGA) -# pr.disable() -# pr.print_stats(sort="cumtime") - -#non_dom_front = nds(popf1.objectives) -#print("a") -#p = PPGA(pop) - -#a1 = randint(0, 49) -#a2 = randint(0, 49) -#mutation(pop, a1, a2) - -#selected = sample(range(1, np.shape(pop.individuals)[0]), randint(1, np.shape(pop.individuals)[0])) -#pop.keep(selected) -#selected = sample(range(1, np.shape(pop.individuals)[0]), randint(1, np.shape(pop.individuals)[0])) -#pop.delete(selected) \ No newline at end of file diff --git a/pyrvea/Population/Population.py b/pyrvea/Population/Population.py index 64dcca1..c5b3249 100644 --- a/pyrvea/Population/Population.py +++ b/pyrvea/Population/Population.py @@ -148,7 +148,7 @@ def create_new_individuals( self.add(individuals) - if self.plotting: + if self.plotting and design == "EvoNN": self.figure = [] self.plot_init_() @@ -342,12 +342,12 @@ def mate(self, ind1=None, ind2=None, params=None): Conduct simulated binary crossover and bounded polunomial mutation. """ - if self.problem.name == "EvoNN": + if self.individuals.ndim > 2: # Get individuals at indices w1, w2 = self.individuals[ind1], self.individuals[ind2] # Perform crossover - xover_w1, xover_w2 = ppga_crossover(w1, w2) + xover_w1, xover_w2 = ppga_crossover(w1, w2, params["prob_crossover"]) # Find randomly two other individuals with current match active for mutation. # Make a list of individuals suitable for mutation, exclude the ones to be mutated @@ -361,10 +361,16 @@ def mate(self, ind1=None, ind2=None, params=None): offspring1, offspring2 = ppga_mutation( alternatives, xover_w1, - xover_w2 + xover_w2, + params["current_iteration_gen_count"], + params["generations"], + params["prob_mutation"], + params["mut_strength"] ) return offspring1, offspring2 + else: + else: offspring = crossover(self) diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index 1afe99b..0c098cb 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -4,7 +4,7 @@ import timeit from pyrvea.Problem.baseProblem import baseProblem -class EvoNNProblem(): +class EvoNN(): """Creates an Artificial Neural Network (ANN) for the EvoNN algorithm.""" def __init__( diff --git a/pyrvea/Problem/evonn_test_functions.py b/pyrvea/Problem/evonn_test_functions.py new file mode 100644 index 0000000..254a274 --- /dev/null +++ b/pyrvea/Problem/evonn_test_functions.py @@ -0,0 +1,224 @@ +import numpy as np +from pyDOE import lhs +from pyrvea.Problem.baseProblem import baseProblem + + +class EvoNNTestProblem(baseProblem): + + """Test functions for testing the EvoNN/PPGA algorithm. + + Parameters + ---------- + name: str + name of the test function + num_of_variables: int + number of decision variables + num_of_objectives: int + number of objectives + num_of_constraints: int + number of constraints + upper_limits: float + upper boundaries for test data + lower_limits + lower boundaries for test data + """ + + def __init__( + self, + name=None, + num_of_variables=None, + num_of_objectives=None, + num_of_constraints=0, + upper_limits=1, + lower_limits=0, + ): + + super(EvoNNTestProblem, self).__init__( + name, + num_of_variables, + num_of_objectives, + num_of_constraints, + upper_limits, + lower_limits, + ) + + # Define search domain for test functions + self.test_f_limits = { + "Sphere": (-5, 5), + "Matyas": (-10, 10), + "Himmelblau": (-5, 5), + "Rastigrin": (-5.12, 5.12), + "Three-hump camel": (-5, 5), + "Goldstein-Price": (-2, 2), + "LeviN13": (-10, 10), + "Schaffer": (-100, 100), + } + + if self.name in self.test_f_limits.keys(): + self.lower_limits = self.test_f_limits[self.name][0] + self.upper_limits = self.test_f_limits[self.name][1] + + def objectives(self, decision_variables) -> list: + """Use this method to calculate objective functions. + + Parameters + ---------- + decision_variables: np.ndarray + + """ + + if self.name == "Sphere": + # Sphere function, -5 <= x <= 5 + # Error close to zero with random data. + + x = np.asarray(decision_variables) + self.obj_func = sum(x ** 2) + + elif self.name == "Matyas:": + + # Matyas function, -10 <= x, y <= 10 + # Error close to zero with random data, + # when number of nodes = 20. With less nodes, + # training wasn't as successful. With linear data, + # training wasn't successful. + + x = np.asarray(decision_variables[0]) + y = np.asarray(decision_variables[1]) + self.obj_func = 0.26 * (x ** 2 + y ** 2) - 0.48 * x * y + + elif self.name == "Himmelblau": + + # Himmelblau's function, -5 <= x, y <= 5 + # Error close to zero with random data. + + x = np.asarray(decision_variables[0]) + y = np.asarray(decision_variables[1]) + self.obj_func = (x ** 2 + y - 11) ** 2 + (x + y ** 2 - 7) ** 2 + + elif self.name == "Rastigrin": + # Rastigrin function, -5.12 <= x <= 5.12 + # Didn't work that well with random data + # and 2 variables. + + x = np.asarray(decision_variables) + n = len(x) + self.obj_func = 10 * n + sum(x ** 2 - 10 * np.cos(2 * np.pi * x)) + + elif self.name == "Three-hump camel": + # Three-hump camel function, -5 <= x, y <= 5 + # Worked pretty well with random data. + + x = np.asarray(decision_variables[0]) + y = np.asarray(decision_variables[1]) + self.obj_func = 2 * x ** 2 - 1.05 * x ** 4 + (x ** 6) / 6 + x * y + y ** 2 + + elif self.name == "Goldstein-Price": + # Goldstein-Price function, -2 <= x, y <= 2 + # with 15 nodes, min. error == 20000 + # with 20 nodes, min. error == 17000 + # with 25 nodes, min. error == 8671 + x = np.asarray(decision_variables[0]) + y = np.asarray(decision_variables[1]) + self.obj_func = ( + 1 + + (x + y + 1) ** 2 + * (19 - 14 * x + 3 * (x ** 2) - 14 * y + 6 * x * y + 3 * (y ** 2)) + ) * ( + 30 + + (2 * x - 3 * y) ** 2 + * (18 - 32 * x + 12 * (x ** 2) + 48 * y - 36 * x * y + 27 * (y ** 2)) + ) + + elif self.name == "LeviN13": + # Levi function N.13, -10 <= x, y <= 10 + # with random data and 10 nodes, min. error == 27, not very good + # with random data and 15 nodes, min. error == 22, bit better + x = np.asarray(decision_variables[0]) + y = np.asarray(decision_variables[1]) + self.obj_func = ( + np.sin(3 * np.pi * x) ** 2 + + (x - 1) ** 2 * (1 + np.sin(3 * np.pi * y) ** 2) + + (y - 1) ** 2 * (1 + np.sin(2 * np.pi * y) ** 2) + ) + + elif self.name == "Schaffer": + # Schaffer function N. 2, -100 <= x, y <= 100 + # Doesn't work with random data + x = np.asarray(decision_variables[0]) + y = np.asarray(decision_variables[1]) + self.obj_func = ( + 0.5 + + (np.sin((x ** 2 - y ** 2) ** 2) - 0.5) + / (1 + 0.001 * (x ** 2 + y ** 2)) ** 2 + ) + + return self.obj_func + + def create_training_data(self, samples=150, method="random", seed=None): + """Create training data for test functions. + + Parameters + ---------- + samples: int + number of samples + method: str + method to use in data creation. Possible values random, lhs, linear, linear+zeros, linear+reverse. + seed: int + if a number is given, random data will be seeded + """ + + np.random.seed(seed) + + if method == "random": + + training_data_input = np.random.uniform( + self.lower_limits, + self.upper_limits, + (samples, self.num_of_variables), + ) + + elif method == "lhs": + + training_data_input = lhs(self.num_of_variables, samples) * ( + abs(self.upper_limits) + abs(self.lower_limits) + ) - abs(self.upper_limits) + + elif method == "linear": + + np.linspace( + (self.lower_limits, self.lower_limits), + (self.upper_limits, self.upper_limits), + samples, + ) + + elif method == "linear+zeros": + + np.linspace( + (self.lower_limits, self.lower_limits), + (self.upper_limits, self.upper_limits), + samples, + ) + tmp = np.linspace(self.upper_limits, 0, samples) + x2 = np.zeros_like(tmp) + training_data_input = np.vstack( + (np.hstack((tmp, tmp)), np.hstack((tmp, x2))) + ).T + + elif method == "linear+reverse": + + np.linspace( + (self.lower_limits, self.lower_limits), + (self.upper_limits, self.upper_limits), + samples, + ) + tmp = np.linspace(self.upper_limits, 0, samples) + x2 = np.flip(tmp) + training_data_input = np.vstack( + (np.hstack((tmp, tmp)), np.hstack((tmp, x2))) + ).T + + training_data_output = np.asarray( + [self.objectives(x) for x in training_data_input] + ) + + return training_data_input, training_data_output diff --git a/pyrvea/Recombination/ppga_mutation.py b/pyrvea/Recombination/ppga_mutation.py index 0ace47f..61c6c6f 100644 --- a/pyrvea/Recombination/ppga_mutation.py +++ b/pyrvea/Recombination/ppga_mutation.py @@ -5,7 +5,7 @@ def ppga_mutation( alternatives, w1, w2, cur_gen=1, total_gen=10, prob_mut=0.3, mut_strength=0.7 ): - """Randomly mutate two individuals + """Randomly mutate two individuals based on a probability Paremeters ---------- diff --git a/pyrvea/tests.py b/pyrvea/tests.py deleted file mode 100644 index 59a992b..0000000 --- a/pyrvea/tests.py +++ /dev/null @@ -1,33 +0,0 @@ -import timeit - -mysetup = "import numpy as np \n" \ - "arr = np.zeros((600,600))" -mycode1 = """ -def place_prey1(arr): - for i in range(1, 10000): - - free_space = np.transpose(np.nonzero(arr == 0)) - x, y = free_space[np.random.randint(free_space.shape[0])] - arr[y][x] = i -""" - -mycode2 = """ -def place_prey2(arr): - for i in range(1, 10000): - - x = np.random.randint(arr.shape[1]) - y = np.random.randint(arr.shape[0]) - - while arr[y][x] != 0: - x = np.random.randint(arr.shape[1]) - y = np.random.randint(arr.shape[0]) - - # +1 to offset zero index individual - arr[y][x] = i -""" -mycode3 = """ -def place_prey3(arr): - -""" -print(timeit.timeit(setup=mysetup, stmt=mycode1, number=10000000)) -print(timeit.timeit(setup=mysetup, stmt=mycode2, number=10000000)) \ No newline at end of file From 0e0461bacc6e6df6805c255d8cefe0ffd8d67a74 Mon Sep 17 00:00:00 2001 From: delamorte Date: Tue, 11 Jun 2019 14:04:51 +0300 Subject: [PATCH 027/155] Merged Population and population_evonn.py classes --- .gitignore | 4 - Example.py | 4 +- pyrvea/EAs/PPGA.py | 61 +++- pyrvea/EAs/baseEA.py | 2 - pyrvea/Population/Population.py | 51 ++-- pyrvea/Population/population_evonn.py | 2 +- pyrvea/Problem/evonn_problem.py | 268 ++++++++++++------ pyrvea/Problem/evonn_test_functions.py | 26 +- .../simulated_binary_crossover.py | 2 +- 9 files changed, 264 insertions(+), 156 deletions(-) diff --git a/.gitignore b/.gitignore index b11fc64..c9430be 100644 --- a/.gitignore +++ b/.gitignore @@ -131,10 +131,6 @@ dmypy.json # vsCode settings .vscode/ -# Poetry files -poetry.lock -pyproject.toml - .tests.py Tests/ *.csv diff --git a/Example.py b/Example.py index ef66c06..1219615 100644 --- a/Example.py +++ b/Example.py @@ -5,7 +5,9 @@ """ from pyrvea.Population.Population import Population from pyrvea.Problem.baseProblem import baseProblem +from pyrvea.EAs.PPGA import PPGA from pyrvea.EAs.RVEA import RVEA +from pyrvea.EAs.slowRVEA import slowRVEA from pyrvea.EAs.NSGAIII import NSGAIII from optproblems import dtlz @@ -31,7 +33,7 @@ def objectives(self, decision_variables): pop = Population(problem) -pop.evolve(NSGAIII) +pop.evolve(slowRVEA) pop.non_dominated() refpoint = 2 diff --git a/pyrvea/EAs/PPGA.py b/pyrvea/EAs/PPGA.py index 5c7461b..19800e0 100644 --- a/pyrvea/EAs/PPGA.py +++ b/pyrvea/EAs/PPGA.py @@ -6,9 +6,27 @@ class PPGA(BaseEA): - def __init__(self, population: "Population", ea_parameters: dict = None): + """Predatory-Prey algorithm - self.params = self.set_params(population, ea_parameters) + If you have any questions about the code, please contact: + + Bhupinder Saini: bhupinder.s.saini@jyu.fi + + Project researcher at University of Jyväskylä. + + Parameters + ---------- + population : object + The population object + ea_parameters : dict + PPGA specific parameters + + References + ---------- + """ + def __init__(self, population: "Population", EA_parameters: dict = {}): + + self.params = self.set_params(population, **EA_parameters) self.lattice = Lattice(60, 60, self.params) self._next_iteration(population) @@ -17,14 +35,13 @@ def set_params( population: "Population", population_size: int = None, target_pop_size: int = 300, - interact: bool = False, - a_priori_preference: bool = False, - generations_per_iteration: int = 100, + generations_per_iteration: int = 1, iterations: int = 1, plotting: bool = False, + logging: list = [False, None], prob_crossover: float = 0.8, prob_mutation: float = 0.3, - mut_strength: float = 0.7, + mut_strength: float = 0.7 ): """Set up the parameters. @@ -34,24 +51,22 @@ def set_params( Population object population_size : int Population Size - interact : bool - bool to enable or disable interaction. Enabled if True - a_priori_preference : bool - similar to interact + target_pop_size : int + Target population size generations_per_iteration : int Number of generations per iteration. iterations : int Total Number of iterations. plotting : bool Useless really. + logging : list = [bool, logfile] + If true, append parameters to a logfile prob_crossover : float Probability of crossover occurring prob_mutation : float Probability of mutation occurring mut_strength : float Strength of the mutation - prob_omit : float - Probability of deactivating some connections Returns ------- @@ -60,7 +75,7 @@ def set_params( ppgaparams = { "population": population, - "population_size": population_size, + "population_size": population.pop_size, "target_pop_size": target_pop_size, "predator_pop_size": 50, "prey_max_moves": 10, @@ -69,15 +84,33 @@ def set_params( "generations": generations_per_iteration, "iterations": iterations, "ploton": plotting, + "logging": logging, "current_iteration_gen_count": 0, "current_iteration_count": 0, "prob_crossover": prob_crossover, "prob_mutation": prob_mutation, "mut_strength": mut_strength, "kill_interval": 7, - "max_rank": 20, + "max_rank": 20 } + # If logging enabled, write params to file + if ppgaparams["logging"][0]: + if ppgaparams["logging"][1] is None: + # Save params to log file + log_file = open( + population.problem.name + + "_var" + + str(population.problem.num_of_variables) + + "_nodes" + + str(population.problem.num_hidden_nodes) + + ".txt", + "w" + ) + for k, v in ppgaparams.items(): + print(k, v, file=ppgaparams["logging"][1]) + ppgaparams["logging"][1].close() + return ppgaparams def _next_iteration(self, population: "Population"): diff --git a/pyrvea/EAs/baseEA.py b/pyrvea/EAs/baseEA.py index 09af8cb..4edb943 100644 --- a/pyrvea/EAs/baseEA.py +++ b/pyrvea/EAs/baseEA.py @@ -1,6 +1,4 @@ from typing import TYPE_CHECKING -import numpy as np -import random if TYPE_CHECKING: from pyrvea.Population.Population import Population diff --git a/pyrvea/Population/Population.py b/pyrvea/Population/Population.py index c5b3249..84e39ec 100644 --- a/pyrvea/Population/Population.py +++ b/pyrvea/Population/Population.py @@ -33,12 +33,13 @@ def __init__( self, problem: "baseProblem", assign_type: str = "LHSDesign", - plotting: bool = False, + plotting: bool = True, + pop_size = None, *args ): """Initialize the population. - Parameters + Attributes ---------- problem : baseProblem An object of the class Problem @@ -51,6 +52,8 @@ def __init__( is 'empty', create blank population. (the default is "RandomAssign") plotting : bool, optional (the default is True, which creates the plots) + pop_size : int + Population size """ self.num_var = problem.num_of_variables @@ -58,15 +61,12 @@ def __init__( self.upper_limits = np.asarray(problem.upper_limits) self.hyp = 0 self.non_dom = 0 + self.pop_size = pop_size self.problem = problem self.filename = problem.name + "_" + str(problem.num_of_objectives) self.plotting = plotting # These attributes contain the solutions. self.individuals = np.empty((0, self.num_var), float) - if problem.name == "EvoNN": - self.individuals = np.empty( - (0, self.problem.num_input_nodes + 1, self.problem.num_hidden_nodes), float - ) self.objectives = np.empty((0, self.problem.num_of_objectives), float) self.fitness = np.empty((0, self.problem.num_of_objectives), float) self.constraint_violation = np.empty( @@ -78,7 +78,7 @@ def __init__( self.ideal_fitness = np.full((1, self.problem.num_of_objectives), np.inf) self.worst_fitness = -1 * self.ideal_fitness if not assign_type == "empty": - self.create_new_individuals(assign_type) + self.create_new_individuals(assign_type, pop_size=self.pop_size) def create_new_individuals( self, design: str = "LHSDesign", pop_size: int = None, decision_variables=None @@ -94,6 +94,7 @@ def create_new_individuals( Describe the method of creation of new individuals. "RandomDesign" creates individuals randomly. "LHSDesign" creates individuals using Latin hypercube sampling. + "EvoNN" creates Artificial Neural Networks as individuals. pop_size : int, optional Number of individuals in the population. If none, some default population size based on number of objectives is chosen. @@ -123,6 +124,9 @@ def create_new_individuals( + self.lower_limits ) elif design == "EvoNN": + self.individuals = np.empty( + (0, self.problem.num_input_nodes + 1, self.problem.num_hidden_nodes), float + ) # +1 row for bias individuals = np.random.uniform( self.problem.w_low, @@ -148,7 +152,7 @@ def create_new_individuals( self.add(individuals) - if self.plotting and design == "EvoNN": + if self.plotting: self.figure = [] self.plot_init_() @@ -169,12 +173,10 @@ def add(self, new_pop: np.ndarray): """ if new_pop.ndim == 1: self.append_individual(new_pop) - elif new_pop.ndim == 2: - for ind in new_pop: - self.append_individual(ind) - elif new_pop.ndim == 3: + + elif new_pop.ndim >= 2: for i in range(0, new_pop.shape[0]): - self.append_individual(new_pop[i, :, :]) + self.append_individual(new_pop[i, ...]) else: print("Error while adding new individuals. Check dimensions.") # print(self.ideal_fitness) @@ -282,12 +284,14 @@ def evaluate_individual(self, ind: np.ndarray): obj = self.problem.objectives(ind) CV = np.empty((0, self.problem.num_of_constraints), float) fitness = obj + if self.problem.num_of_constraints: CV = self.problem.constraints(ind, obj) fitness = self.eval_fitness(ind, obj, self.problem) + return (obj, CV, fitness) - def evolve(self, EA: "BaseEA" = None, EA_parameters: dict = None) -> "Population": + def evolve(self, EA: "BaseEA" = None, EA_parameters: dict = {}) -> "Population": """Evolve the population with interruptions. Evolves the population based on the EA sent by the user. @@ -311,21 +315,8 @@ def evolve(self, EA: "BaseEA" = None, EA_parameters: dict = None) -> "Population #################################### # A basic evolution cycle. Will be updated to optimize() in future versions. ea = EA(self, EA_parameters) - # AicC: - # non_dom_front = nds(self.objectives) - # fon_front = non_dom_front[0][0] - # info_c_rank = [] - # for i in fon_front: - # - # if - # info_c = self.problem.information_criterion(self.individuals[i]) - # info_c_rank.append((info_c, i)) - # - # info_c_rank.sort() - - # return self.individuals[info_c_rank[0][1]] - iterations = ea.params["iterations"] + if self.plotting: self.plot_objectives() # Figure was created in init for i in progressbar(range(1, iterations), desc="Iteration"): @@ -333,8 +324,6 @@ def evolve(self, EA: "BaseEA" = None, EA_parameters: dict = None) -> "Population ea._next_iteration(self) if self.plotting: self.plot_objectives() - lowest_error = np.argmin(self.objectives[:, 0]) - return self.individuals[lowest_error] def mate(self, ind1=None, ind2=None, params=None): """Conduct crossover and mutation over the population. @@ -369,8 +358,6 @@ def mate(self, ind1=None, ind2=None, params=None): ) return offspring1, offspring2 - else: - else: offspring = crossover(self) diff --git a/pyrvea/Population/population_evonn.py b/pyrvea/Population/population_evonn.py index d1b40ac..026d503 100644 --- a/pyrvea/Population/population_evonn.py +++ b/pyrvea/Population/population_evonn.py @@ -35,7 +35,7 @@ def __init__( ): """Initialize the population. - Parameters + Attributes ---------- problem : baseProblem An object of the class Problem diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index 0c098cb..adc747b 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -1,65 +1,79 @@ -from math import log -import numpy as np -#from scipy.linalg import lstsq -import timeit from pyrvea.Problem.baseProblem import baseProblem - -class EvoNN(): - """Creates an Artificial Neural Network (ANN) for the EvoNN algorithm.""" +import numpy as np +import plotly +import plotly.graph_objs as go + + +class EvoNN(baseProblem): + """Creates an Artificial Neural Network (ANN) for the EvoNN algorithm. + + Attributes + ---------- + name : str + Name of the sample + num_input_nodes : int + The number of nodes in the input layer + num_hidden_nodes : int + The number of nodes in the hidden layer + num_of_objectives : int + The number of objectives + w_low : float + The lower bound for randomly generated weights + w_high : float + The upper bound for randomly generated weights + prob_omit : float + The probability of setting some weights to zero initially + activation_func : str + The function to use for activating the hidden layer + opt_func : str + The function to use for optimizing the upper part of the network + loss_func : str + The loss function to use + """ def __init__( self, - training_data_input=None, - training_data_output=None, name=None, num_input_nodes=4, num_hidden_nodes=5, - num_of_constraints=0, num_of_objectives=2, w_low=-5.0, w_high=5.0, + prob_omit=0.2, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse" ): + super().__init__() - """ - - Parameters - ---------- - training_data_input : numpy array - Training data input matrix - training_data_output : numpy array - Training data expected output - name : str - Name of the sample - num_input_nodes : int - The number of nodes in the input layer - num_hidden_nodes : int - The number of nodes in the hidden layer - num_output_nodes : int - The number of nodes in the output layer - w_low : float - The lower bound for randomly generated weights - w_high : float - The upper bound for randomly generated weights - """ - - self.training_data_input = training_data_input - self.training_data_output = training_data_output self.name = name self.num_input_nodes = num_input_nodes self.num_hidden_nodes = num_hidden_nodes self.num_of_objectives = num_of_objectives - self.num_of_variables = training_data_input.shape[1] - self.num_of_constraints = num_of_constraints self.w_low = w_low self.w_high = w_high - self.lower_limits = w_low - self.upper_limits = w_high - self.prob_omit = 0.2 - self.bias = 1 - self.num_of_samples = training_data_output.shape[0] + self.prob_omit = prob_omit + self.activation_func = activation_func + self.opt_func = opt_func + self.loss_func = loss_func + + def fit(self, training_data, target_values): + """Fit data in EvoNN model. + Parameters + ---------- + training_data : ndarray, shape = (numbers of samples, number of variables) + Training data + target_values : ndarray + Target values + """ + + self.training_data_input = training_data + self.training_data_output = target_values + self.num_of_samples = target_values.shape[0] + self.num_of_variables = training_data.shape[1] if len(self.training_data_input) > 0: - self.num_input_nodes = np.shape(training_data_input)[1] + self.num_input_nodes = np.shape(training_data)[1] def objectives(self, decision_variables) -> list: @@ -67,81 +81,89 @@ def objectives(self, decision_variables) -> list: Parameters ---------- - decision_variables : tuple + decision_variables : ndarray Variables from the neural network Returns ------- - obj_func : array + obj_func : list The objective function """ - weighted_input = self.dot_product(decision_variables) - activated_function = self.activation(weighted_input, name="sigmoid") - w_matrix2, rss, predicted_values = self.minimize_error(activated_function) + activated_layer = self.activation(decision_variables) + w_matrix2, rss, predicted_values = self.minimize_error(activated_layer) training_error = self.loss_function(predicted_values) - complexity = self.calculate_complexity(decision_variables) + obj_func = [training_error, complexity] return obj_func - def dot_product(self, w_matrix): - """ Calculate the dot product of input and weight + bias. + def activation(self, decision_variables): + """ Calculates the dot product and applies the activation function. Parameters ---------- - w_matrix - - Returns - ------- - - """ - - # Calculate dot product - wi = np.dot(self.training_data_input, w_matrix[1:, :]) + w_matrix[0] - - return wi - - def activation(self, wi, name="sigmoid"): - """ Activation function + decision_variables : ndarray + Variables from the neural network + name : str + The activation function to use Returns ------- The penultimate layer Z before the output """ + # Calculate the dot product + wi = ( + np.dot(self.training_data_input, decision_variables[1:, :]) + + decision_variables[0] + ) - if name == "sigmoid": - activated_function = lambda x: 1 / (1 + np.exp(-x)) + if self.activation_func == "sigmoid": + activated_layer = lambda x: 1 / (1 + np.exp(-x)) - if name == "relu": - activated_function = lambda x: np.maximum(x, 0) + if self.activation_func == "relu": + activated_layer = lambda x: np.maximum(x, 0) - return activated_function(wi) + if self.activation_func == "tanh": + activated_layer = lambda x: np.tanh(x) - def minimize_error(self, activated_function, name="llsq"): - """ Optimize the training error. + return activated_layer(wi) + + def minimize_error(self, activated_layer): + """ Minimize the training error. Parameters ---------- - activated_function + activated_layer : ndarray Output of the activation function + name : str + Name of the optimizing algorithm to use Returns ------- + w_matrix[0] : ndarray + The weight matrix of the upper part of the network + rss : float + Sums of residuals + predicted_values : ndarray + The prediction of the model """ - if name == "llsq": - w_matrix2 = np.linalg.lstsq(activated_function, self.training_data_output, rcond=None) + + if self.opt_func == "llsq": + w_matrix2 = np.linalg.lstsq(activated_layer, self.training_data_output, rcond=None) rss = w_matrix2[1] - predicted_values = np.dot(activated_function, w_matrix2[0]) + predicted_values = np.dot(activated_layer, w_matrix2[0]) return w_matrix2[0], rss, predicted_values - def loss_function(self, predicted_values, name="rmse"): + def loss_function(self, predicted_values): - if name == "rmse": + if self.loss_func == "mse": + return ((self.training_data_output - predicted_values) ** 2).mean() + if self.loss_func == "rmse": return np.sqrt(((self.training_data_output - predicted_values) ** 2).mean()) def calculate_complexity(self, w_matrix): @@ -150,23 +172,91 @@ def calculate_complexity(self, w_matrix): return k - def information_criterion(self, w_matrix): + def information_criterion(self, decision_variables): - wi = self.dot_product(w_matrix) - z = self.activation(wi) + z = self.activation(decision_variables) w_matrix2, rss, prediction = self.minimize_error(z) - #rss = ((self.training_data_output - prediction) ** 2).sum() - k = self.calculate_complexity(w_matrix) + np.count_nonzero(w_matrix2) - - aic = 2 * k + self.num_of_samples * log(rss/self.num_of_samples) - aicc = aic + (2*k*(k+1)/(self.num_of_samples-k-1)) + # rss = ((self.training_data_output - prediction) ** 2).sum() + k = self.calculate_complexity(decision_variables) + np.count_nonzero(w_matrix2) + aic = 2 * k + self.num_of_samples * np.log(rss / self.num_of_samples) + aicc = aic + (2 * k * (k + 1) / (self.num_of_samples - k - 1)) return aicc - def get_prediction(self, decision_variables): + def predict(self, decision_variables): - weighted_input = self.dot_product(decision_variables) - activated_function = self.activation(weighted_input) - w_matrix2, rss, predicted_values = self.minimize_error(activated_function) + activated_layer = self.activation(decision_variables) + _, _, predicted_values = self.minimize_error(activated_layer) return predicted_values + + def select(self, pop, non_dom_front, criterion="min_error"): + """ Select target model from the population. + + Parameters + ---------- + pop : obj + The population object + non_dom_front : list + Indices of the models on the non-dominated front + criterion : str + The criterion to use for selecting the model. + Possible values: 'min_error', 'akaike_corrected', 'manual' + + Returns + ------- + The selected model + """ + if criterion == "min_error": + # Return the model with the lowest error + + lowest_error = np.argmin(pop.objectives[:, 0]) + return pop.individuals[lowest_error] + + elif criterion == "akaike_corrected": + + # Calculate Akaike information criterion for the non-dominated front + # and return the model with the lowest value + + info_c_rank = [] + + for i in non_dom_front: + + info_c = self.information_criterion(pop.individuals[i]) + info_c_rank.append((info_c, i)) + + info_c_rank.sort() + + return pop.individuals[info_c_rank[0][1]] + + def single_variance_response(self, model): + + print("number of variables: " + str(self.num_of_variables)) + # pref_val = float( + # input("Please input a value worse than the ideal: ") + # ) + activated_layer = self.activation(model) + _, _, predicted_values = self.minimize_error(activated_layer) + + trace0 = go.Scatter(x=predicted_values, y=self.training_data_output, mode="markers") + trace1 = go.Scatter(x=self.training_data_output, y=self.training_data_output) + data = [trace0, trace1] + plotly.offline.plot( + data, + filename="svr.html", + auto_open=True, + ) + + model[1, :] = model[1] + 1 + + activated_layer = self.activation(model) + _, _, predicted_values = self.minimize_error(activated_layer) + + trace0 = go.Scatter(x=predicted_values, y=self.training_data_output, mode="markers") + trace1 = go.Scatter(x=self.training_data_output, y=self.training_data_output) + data = [trace0, trace1] + plotly.offline.plot( + data, + filename="svr1.html", + auto_open=True, + ) diff --git a/pyrvea/Problem/evonn_test_functions.py b/pyrvea/Problem/evonn_test_functions.py index 254a274..ebc5286 100644 --- a/pyrvea/Problem/evonn_test_functions.py +++ b/pyrvea/Problem/evonn_test_functions.py @@ -7,26 +7,26 @@ class EvoNNTestProblem(baseProblem): """Test functions for testing the EvoNN/PPGA algorithm. - Parameters + Attributes ---------- - name: str + name : str name of the test function - num_of_variables: int + num_of_variables : int number of decision variables - num_of_objectives: int + num_of_objectives : int number of objectives - num_of_constraints: int + num_of_constraints : int number of constraints - upper_limits: float + upper_limits : float upper boundaries for test data - lower_limits + lower_limits : float lower boundaries for test data """ def __init__( self, name=None, - num_of_variables=None, + num_of_variables=2, num_of_objectives=None, num_of_constraints=0, upper_limits=1, @@ -63,10 +63,12 @@ def objectives(self, decision_variables) -> list: Parameters ---------- - decision_variables: np.ndarray + decision_variables : np.ndarray """ + self.num_of_variables = decision_variables.shape[0] + if self.name == "Sphere": # Sphere function, -5 <= x <= 5 # Error close to zero with random data. @@ -159,11 +161,11 @@ def create_training_data(self, samples=150, method="random", seed=None): Parameters ---------- - samples: int + samples : int number of samples - method: str + method : str method to use in data creation. Possible values random, lhs, linear, linear+zeros, linear+reverse. - seed: int + seed : int if a number is given, random data will be seeded """ diff --git a/pyrvea/Recombination/simulated_binary_crossover.py b/pyrvea/Recombination/simulated_binary_crossover.py index eecdc89..a718c0c 100644 --- a/pyrvea/Recombination/simulated_binary_crossover.py +++ b/pyrvea/Recombination/simulated_binary_crossover.py @@ -5,7 +5,7 @@ def crossover(population, prob_xover=1, dis_xover=30): pop = population.individuals - pop_size, num_var = pop.shape[0], pop.shape[1]-1 + pop_size, num_var = pop.shape shuffled_ids = list(range(pop_size)) shuffle(shuffled_ids) mating_pop = pop[shuffled_ids] From f82d3483556c34a3639a8c7ef219beb2036ca8e5 Mon Sep 17 00:00:00 2001 From: delamorte Date: Tue, 11 Jun 2019 14:25:08 +0300 Subject: [PATCH 028/155] Add files via upload --- poetry.lock | 1194 ++++++++++++++++++++++++++++++++++++++++++++++++ pyproject.toml | 39 ++ 2 files changed, 1233 insertions(+) create mode 100644 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["4c291ca23bbb55c76518905869ef34bdd5f0e46af7afe6861e8375643ffee1a0", "9a247273df709c4fedb38c711e44292304f73f39ab01beda9f6b9fc375669ac3"] +vega = ["abb93e4af0f97b4ca7a5034bc1c358b9863ece536b4b9c9707551011b7e65053"] +vega-datasets = ["20d490b417f84607eb5079400f608f2e9c135b7092bee10f6857f6d23136e459", "63d6eaf129ab23a7d0744e54d11dba9e7eecc0974fd9a237cfae9cfdbef3c37a"] +wcwidth = ["3df37372226d6e63e1b1e1eda15c594bca98a22d33a23832a90998faa96bc65e", "f4ebe71925af7b40a864553f761ed559b43544f8f71746c2d756c7fe788ade7c"] +webencodings = ["a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78", "b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"] +widgetsnbextension = ["14b2c65f9940c9a7d3b70adbe713dbd38b5ec69724eebaba034d1036cf3d4740", "fa618be8435447a017fd1bf2c7ae922d0428056cfc7449f7a8641edf76b48265"] +wrapt = ["4aea003270831cceb8a90ff27c4031da6ead7ec1886023b80ce0dfe0adf61533"] diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..4d24d5f --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,39 @@ +[tool.poetry] +name = "pyrvea" +version = "0.1.1" +description = "The python version reference vector guided evolutionary algorithm." +authors = ["Bhupinder Saini"] +license = "MPL-2.0" +readme = "README.md" + +[tool.poetry.dependencies] +python = "^3.6" +numpy = "^1.16" +pandas = "^0.24.2" +optproblems = "^1.2" +pygmo = "^2.10" +scipy = "^1.2" +diversipy = "^0.8.0" +pyDOE = "^0.3.8" +plotly = "^3.8" +tqdm = "^4.31" +jupyter = "^1.0" +scikit-learn = "^0.21.2" + +[tool.poetry.dev-dependencies] +flake8 = "^3.7" +black = {version = "^18.3-alpha.0",allows-prereleases = true} +sphinx = "^2.0" +recommonmark = "^0.5.0" +sphinx_rtd_theme = "^0.4.3" +pylint = "^2.3" +mypy = "^0.701.0" +altair = "^3.0" +vega = "^2.1" +vega_datasets = "^0.7.0" + + +[build-system] +requires = ["poetry>=0.12"] +build-backend = "poetry.masonry.api" + From 39d7b03b7773b207a3c3a3c3a290eeff325b4823 Mon Sep 17 00:00:00 2001 From: delamorte Date: Tue, 11 Jun 2019 14:26:24 +0300 Subject: [PATCH 029/155] Put pycharproject files to .gitignore --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index c9430be..9fec177 100644 --- a/.gitignore +++ b/.gitignore @@ -131,6 +131,9 @@ dmypy.json # vsCode settings .vscode/ +# Pycharm files +.idea/ + .tests.py Tests/ *.csv From c6e9db5dfc5d0a6b935995e3f3b596e44299f42c Mon Sep 17 00:00:00 2001 From: delamorte Date: Tue, 11 Jun 2019 14:37:39 +0300 Subject: [PATCH 030/155] Add files via upload --- pyrvea/Problem/dataproblem.py | 141 ++++++++++++++++++++++++++++++++++ 1 file changed, 141 insertions(+) create mode 100644 pyrvea/Problem/dataproblem.py diff --git a/pyrvea/Problem/dataproblem.py b/pyrvea/Problem/dataproblem.py new file mode 100644 index 0000000..cb01296 --- /dev/null +++ b/pyrvea/Problem/dataproblem.py @@ -0,0 +1,141 @@ +from sklearn.gaussian_process import GaussianProcessRegressor +# from sklearn.gaussian_process.kernels import RBF, WhiteKernel, ConstantKernel as C +from sklearn import preprocessing +from sklearn.model_selection import train_test_split as tts +import numpy as np +from sklearn.metrics import r2_score +from pyrvea.Problem.baseProblem import baseProblem +import pandas as pd +from typing import List + + +class DataProblem(baseProblem): + def __init__( + self, + data: pd.DataFrame = None, + x: List[str] = None, + y: List[str] = None, + minimize: List[bool] = None, + ideal: List[float] = None, + nadir: List[float] = None, + lower_bound: List[float] = None, + upper_bound: List[float] = None, + ): + self.raw_data = data + self.data = data + self.x = x + self.y = y + self.number_of_variables = len(x) + self.number_of_objectives = len(y) + self.number_of_samples = self.data.shape[0] + if minimize is None: + self.minimize = [True] * self.number_of_objectives + else: + assert len(minimize) == self.number_of_objectives + self.minimize = minimize + self.preprocessing_transformations = [] + self.number_of_objectives = len(y) + # These indices define how the training will occur. + # all_indices is the list of indices that can be used. This does not include + # outliers. + self.all_indices = list(range(self.number_of_samples)) + # These should be list of lists. + # The inner list contains the list of indices. + # The outer list, if it contains multiple elements, should lead to multiple + # trained models. Can be used for k-fold validation. + # Train and test indices super list should have the same length. + self.train_indices: List[List] = None + self.test_indices: List[List] = None + self.validation_indices: List[List] = None + + self.models = dict.fromkeys(self.y, []) + self.metrics = [] + # Defining bounds in the decision space + if lower_bound is None: + self.lower_bound = np.min(self.raw_data[x], axis=0) + else: + assert len(lower_bound) == self.num_of_variables + self.lower_bound = lower_bound + if upper_bound is None: + self.upper_bound = np.max(self.raw_data[x], axis=0) + else: + assert len(upper_bound) == self.num_of_variables + self.upper_bound = upper_bound + + def data_scaling(self, data_decision): # Scales the data from 0 to 1 + # Check this range stuff + min_max_scaler = preprocessing.MinMaxScaler( + feature_range=(self.lower_bound, self.upper_bound) + ) + self.processed_data_decision = min_max_scaler.fit_transform(data_decision) + self.preprocessing_transformations.append(min_max_scaler) + + def data_uniform_mapping(self): # Maps the data to uniform distribution + pass + + def outlier_removal(self): # Removes the outliers + pass + + def train_test_split(self, train_percent: float = 0.7): # Split dataset + train_indices, test_indices = tts(self.all_indices, train_percent) + + def train(self, model_type: str = None, objectives: str = None, **kwargs): + if objectives is None: + objectives = self.y + if model_type is None: + model_type = "GPR" + surrogate_model_options = {"GPR": GaussianProcessRegressor} + model_type = surrogate_model_options[model_type] + # Build specific surrogate models + print("Building Surrogate Models ...") + # Fit to data using Maximum Likelihood Estimation of the parameters + for obj in objectives: + print("Building model for " + obj) + for train_indices, train_run in enumerate(self.train_indices): + print("Training run number", train_run, 'of', len(self.train_indices)) + model = model_type(**kwargs) + model.fit( + self.data[self.x][train_indices], self.data[self.y][train_indices] + ) + self.models[obj] = self.models[obj].append(model) + print("Surrogate models build completed.") + + def transform_new_data(self, decision_variables): + decision_variables_transformed = decision_variables + if len(self.preprocessing_transformations) > 0: + for transformation in self.preprocessing_transformations: + decision_variables_transformed = transformation.transfrom( + decision_variables_transformed + ) + return decision_variables_transformed + + def surrogates_predict(self, decision_variables): + y_pred = None + # transforms applied + decision_variables_transformed = self.transform_new_data(decision_variables) + for obj, i in zip(self.obj_names, range(self.number_of_objectives)): + y = self.models[obj].predict( + decision_variables_transformed.reshape(1, self.number_of_variables), + return_std=False, + ) + if y_pred is None: + y_pred = np.asarray(y) + else: + y_pred = np.hstack((y_pred, y)) + return y_pred + + def testing_score(self): # Return R-squared of testing + x, y = self.select_data(self.test_indices) + x = self.transform_new_data(x) + y_pred = None + for i in range(np.shape(x)[0]): + if y_pred is None: + y_pred = np.asarray(self.surrogates_predict(x[i])) + else: + y_pred = np.vstack((y_pred, self.surrogates_predict(x[i]))) + for i in range(np.shape(y)[1]): + self.r_sq.append(r2_score(y[:, i], y_pred[:, i])) + return y_pred + + def retrain_surrogate(self): + pass From b648d756d234e3710b962da2a60b7ec37a243d3c Mon Sep 17 00:00:00 2001 From: delamorte Date: Thu, 13 Jun 2019 12:16:51 +0300 Subject: [PATCH 031/155] created model class to represent trained models added single variable response method added creation of optional logging file fixed parameters passing to PPGA added min-ex to test functions --- poetry.lock | 90 ++++++------ pyrvea/EAs/PPGA.py | 7 +- pyrvea/Population/Population.py | 1 + pyrvea/Problem/dataproblem.py | 7 + pyrvea/Problem/evonn_problem.py | 185 +++++++++++++++++++------ pyrvea/Problem/evonn_test_functions.py | 8 ++ 6 files changed, 208 insertions(+), 90 deletions(-) diff --git a/poetry.lock b/poetry.lock index 2968b66..ef270bd 100644 --- a/poetry.lock +++ b/poetry.lock @@ -12,7 +12,7 @@ description = "Altair: A declarative statistical visualization library for Pytho name = "altair" optional = false python-versions = "*" -version = "3.0.1" +version = "3.1.0" [package.dependencies] entrypoints = "*" @@ -68,10 +68,10 @@ description = "Internationalization utilities" name = "babel" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" -version = "2.6.0" +version = "2.7.0" [package.dependencies] -pytz = ">=0a" +pytz = ">=2015.7" [[package]] category = "main" @@ -137,7 +137,7 @@ description = "Extended pickling support for Python objects" name = "cloudpickle" optional = false python-versions = "*" -version = "0.8.1" +version = "1.2.1" [[package]] category = "main" @@ -154,7 +154,7 @@ description = "Python parser for the CommonMark Markdown spec" name = "commonmark" optional = false python-versions = "*" -version = "0.8.1" +version = "0.9.0" [package.dependencies] future = "*" @@ -247,7 +247,7 @@ description = "IPython Kernel for Jupyter" name = "ipykernel" optional = false python-versions = ">=3.4" -version = "5.1.0" +version = "5.1.1" [package.dependencies] ipython = ">=5.0.0" @@ -308,7 +308,7 @@ description = "A Python utility / library to sort Python imports." name = "isort" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" -version = "4.3.18" +version = "4.3.20" [[package]] category = "main" @@ -416,8 +416,8 @@ category = "dev" description = "A fast and thorough lazy object proxy." name = "lazy-object-proxy" optional = false -python-versions = "*" -version = "1.3.1" +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" +version = "1.4.1" [[package]] category = "main" @@ -527,7 +527,7 @@ description = "NumPy is the fundamental package for array computing with Python. name = "numpy" optional = false python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*" -version = "1.16.3" +version = "1.16.4" [[package]] category = "main" @@ -604,7 +604,7 @@ description = "An open-source, interactive graphing library for Python" name = "plotly" optional = false python-versions = "*" -version = "3.8.1" +version = "3.10.0" [package.dependencies] decorator = ">=4.0.6" @@ -620,7 +620,7 @@ description = "Python client for the Prometheus monitoring system." name = "prometheus-client" optional = false python-versions = "*" -version = "0.6.0" +version = "0.7.0" [[package]] category = "main" @@ -676,8 +676,8 @@ category = "main" description = "Pygments is a syntax highlighting package written in Python." name = "pygments" optional = false -python-versions = "*" -version = "2.3.1" +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +version = "2.4.2" [[package]] category = "main" @@ -719,7 +719,7 @@ description = "Persistent/Functional/Immutable data structures" name = "pyrsistent" optional = false python-versions = "*" -version = "0.15.1" +version = "0.15.2" [package.dependencies] six = "*" @@ -766,7 +766,7 @@ description = "Jupyter Qt console" name = "qtconsole" optional = false python-versions = "*" -version = "4.4.4" +version = "4.5.1" [package.dependencies] ipykernel = ">=4.1" @@ -794,14 +794,14 @@ category = "main" description = "Python HTTP for Humans." name = "requests" optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" -version = "2.21.0" +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +version = "2.22.0" [package.dependencies] certifi = ">=2017.4.17" chardet = ">=3.0.2,<3.1.0" idna = ">=2.5,<2.9" -urllib3 = ">=1.21.1,<1.25" +urllib3 = ">=1.21.1,<1.25.0 || >1.25.0,<1.25.1 || >1.25.1,<1.26" [[package]] category = "main" @@ -832,11 +832,11 @@ category = "main" description = "SciPy: Scientific Library for Python" name = "scipy" optional = false -python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*" -version = "1.2.1" +python-versions = ">=3.5" +version = "1.3.0" [package.dependencies] -numpy = ">=1.8.2" +numpy = ">=1.13.3" [[package]] category = "main" @@ -868,7 +868,7 @@ description = "Python documentation generator" name = "sphinx" optional = false python-versions = ">=3.5" -version = "2.0.1" +version = "2.1.1" [package.dependencies] Jinja2 = ">=2.3" @@ -999,7 +999,7 @@ description = "Fast, Extensible Progress Meter" name = "tqdm" optional = false python-versions = ">=2.6, !=3.0.*, !=3.1.*" -version = "4.31.1" +version = "4.32.1" [[package]] category = "main" @@ -1028,7 +1028,7 @@ description = "HTTP library with thread-safe connection pooling, file post, and name = "urllib3" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, <4" -version = "1.24.2" +version = "1.25.3" [[package]] category = "dev" @@ -1036,7 +1036,7 @@ description = "IPyVega: An IPython/Jupyter widget for Vega 5 and Vega-Lite 3" name = "vega" optional = false python-versions = "*" -version = "2.1.0" +version = "2.2.0" [[package]] category = "dev" @@ -1090,21 +1090,21 @@ python-versions = "^3.6" [metadata.hashes] alabaster = ["446438bdcca0e05bd45ea2de1668c1d9b032e1a9154c2c259092d77031ddd359", "a661d72d58e6ea8a57f7a86e37d86716863ee5e92788398526d58b26a4e4dc02"] -altair = ["63934563a7a7b7186335858206a0b9be6043163b8b54a26cd3b3299a9e5e391f", "65e243afa6da5b746c411890fd7dfd0f187c0f8e581cf3c34b07339712cf6627"] +altair = ["77bf6baee04135cc37a20edd07820163336e2d2bb240804473e80dbefcb4bffd", "f711ca44cc569eaec5ae8eafa610146c18b23a0ba4f7b9900646b28c42c13b9f"] appdirs = ["9e5896d1372858f8dd3344faf4e5014d21849c756c8d5701f78f8a103b372d92", "d8b24664561d0d34ddfaec54636d502d7cea6e29c3eaf68f3df6180863e2166e"] appnope = ["5b26757dc6f79a3b7dc9fab95359328d5747fcb2409d331ea66d0272b90ab2a0", "8b995ffe925347a2138d7ac0fe77155e4311a0ea6d6da4f5128fe4b3cbe5ed71"] astroid = ["6560e1e1749f68c64a4b5dee4e091fce798d2f0d84ebe638cf0e0585a343acf4", "b65db1bbaac9f9f4d190199bb8680af6f6f84fd3769a5ea883df8a91fe68b4c4"] attrs = ["69c0dbf2ed392de1cb5ec704444b08a5ef81680a61cb899dc08127123af36a79", "f0b870f674851ecbfbbbd364d6b5cbdff9dcedbc7f3f5e18a6891057f21fe399"] -babel = ["6778d85147d5d85345c14a26aada5e478ab04e39b078b0745ee6870c2b5cf669", "8cba50f48c529ca3fa18cf81fa9403be176d374ac4d60738b839122dfaaa3d23"] +babel = ["af92e6106cb7c55286b25b38ad7695f8b4efb36a90ba483d7f7a6628c46158ab", "e86135ae101e31e2c8ec20a4e0c5220f4eed12487d5cf3f78be7e98d3a57fc28"] backcall = ["38ecd85be2c1e78f77fd91700c76e14667dc21e2713b63876c0eb901196e01e4", "bbbf4b1e5cd2bdb08f915895b51081c041bac22394fdfcfdfbe9f14b77c08bf2"] black = ["817243426042db1d36617910df579a54f1afd659adb96fc5032fcf4b36209739", "e030a9a28f542debc08acceb273f228ac422798e5215ba2a791a6ddeaaca22a5"] bleach = ["213336e49e102af26d9cde77dd2d0397afabc5a6bf2fed985dc35b5d1e285a16", "3fdf7f77adcf649c9911387df51254b813185e32b2c6619f690b593a617e19fa"] certifi = ["59b7658e26ca9c7339e00f8f4636cdfe59d34fa37b9b04f6f9e9926b3cece1a5", "b26104d6835d1f5e49452a26eb2ff87fe7090b89dfcaee5ea2212697e1e1d7ae"] chardet = ["84ab92ed1c4d4f16916e05906b6b75a6c0fb5db821cc65e70cbd64a3e2a5eaae", "fc323ffcaeaed0e0a02bf4d117757b98aed530d9ed4531e3e15460124c106691"] click = ["2335065e6395b9e67ca716de5f7526736bfa6ceead690adf616d925bdc622b13", "5b94b49521f6456670fdb30cd82a4eca9412788a93fa6dd6df72c94d5a8ff2d7"] -cloudpickle = ["3ea6fd33b7521855a97819b3d645f92d51c8763d3ab5df35197cd8e96c19ba6f", "70b492d5e06b779707c00b334311abcd4ac144c56910068aeeda0079e6244ef0"] +cloudpickle = ["603244e0f552b72a267d47a7d9b347b27a3430f58a0536037a290e7e0e212ecf", "b8ba7e322f2394b9bbbdc1c976e6442c2c02acc784cb9e553cee9186166a6890"] colorama = ["05eed71e2e327246ad6b38c540c4a3117230b19679b875190486ddd2d721422d", "f8ac84de7840f5b9c4e3347b3c1eaa50f7e49c2b07596221daec5edaabbd7c48"] -commonmark = ["9f6dda7876b2bb88dd784440166f4bc8e56cb2b2551264051123bacb0b6c1d8a", "abcbc854e0eae5deaf52ae5e328501b78b4a0758bf98ac8bb792fce993006084"] +commonmark = ["14c3df31e8c9c463377e287b2a1eefaa6019ab97b22dad36e2f32be59d61d68d", "867fc5db078ede373ab811e16b6789e9d033b15ccd7296f370ca52d1ee792ce0"] decorator = ["86156361c50488b84a3f148056ea716ca587df2f0de1d34750d35c21312725de", "f069f3a01830ca754ba5258fde2278454a0b5b79e0d7f5c13b3b97e57d4acff6"] defusedxml = ["6687150770438374ab581bb7a1b327a847dd9c5749e396102de3fad4e8a3ef93", "f684034d135af4c6cbb949b8a4d2ed61634515257a67299e5f940fbaa34377f5"] diversipy = ["6abc959e64821c7122137c4352a7c9d45217a26feb2b4d4830d627b0c82b04d4"] @@ -1114,11 +1114,11 @@ flake8 = ["859996073f341f2670741b51ec1e67a01da142831aa1fdc6242dbf88dffbe661", "a future = ["67045236dcfd6816dc439556d009594abf643e5eb48992e36beac09c2ca659b8"] idna = ["c357b3f628cf53ae2c4c05627ecc484553142ca23264e593d327bcde5e9c3407", "ea8b7f6188e6fa117537c3df7da9fc686d485087abf6ac197f9c46432f7e4a3c"] imagesize = ["3f349de3eb99145973fefb7dbe38554414e5c30abd0c8e4b970a7c9d09f3a1d8", "f3832918bc3c66617f92e35f5d70729187676313caa60c187eb0f28b8fe5e3b5"] -ipykernel = ["0aeb7ec277ac42cc2b59ae3d08b10909b2ec161dc6908096210527162b53675d", "0fc0bf97920d454102168ec2008620066878848fcfca06c22b669696212e292f"] +ipykernel = ["346189536b88859937b5f4848a6fd85d1ad0729f01724a411de5cae9b618819c", "f0e962052718068ad3b1d8bcc703794660858f58803c3798628817f492a8769c"] ipython = ["54c5a8aa1eadd269ac210b96923688ccf01ebb2d0f21c18c3c717909583579a8", "e840810029224b56cd0d9e7719dc3b39cf84d577f8ac686547c8ba7a06eeab26"] ipython-genutils = ["72dd37233799e619666c9f639a9da83c34013a73e8bbc79a7a6348d93c61fab8", "eb2e116e75ecef9d4d228fdc66af54269afa26ab4463042e33785b887c628ba8"] ipywidgets = ["0f2b5cde9f272cb49d52f3f0889fdd1a7ae1e74f37b48dac35a83152780d2b7b", "a3e224f430163f767047ab9a042fc55adbcab0c24bbe6cf9f306c4f89fdf0ba3"] -isort = ["1349c6f7c2a0f7539f5f2ace51a9a8e4a37086ce4de6f78f5f53fb041d0a3cd5", "f09911f6eb114e5592abe635aded8bf3d2c3144ebcfcaf81ee32e7af7b7d1870"] +isort = ["c40744b6bc5162bbb39c1257fe298b7a393861d50978b565f3ccd9cb9de0182a", "f57abacd059dc3bd666258d1efb0377510a89777fda3e3274e3c01f7c03ae22d"] jedi = ["2bb0603e3506f708e792c7f4ad8fc2a7a9d9c2d292a358fbbd58da531695595b", "2c6bcd9545c7d6440951b12b44d373479bf18123a401a52025cf98563fbd826c"] jinja2 = ["065c4f02ebe7f7cf559e49ee5a95fb800a9e4528727aec6f24402a5374c65013", "14dd6caf1527abb21f08f86c784eac40853ba93edb79552aa1e4b8aef1b61c7b"] joblib = ["21e0c34a69ad7fde4f2b1f3402290e9ec46f545f15f1541c582edfe05d87b63a", "315d6b19643ec4afd4c41c671f9f2d65ea9d787da093487a81ead7b0bac94524"] @@ -1127,7 +1127,7 @@ jupyter = ["3e1f86076bbb7c8c207829390305a2b1fe836d471ed54be66a3b8c41e7f46cc7", " jupyter-client = ["b5f9cb06105c1d2d30719db5ffb3ea67da60919fb68deaefa583deccd8813551", "c44411eb1463ed77548bc2d5ec0d744c9b81c4a542d9637c7a52824e2121b987"] jupyter-console = ["308ce876354924fb6c540b41d5d6d08acfc946984bf0c97777c1ddcb42e0b2f5", 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"b283a76a83fe463c9587a2c88003f800e08c3929dfbeba833b78260f9c209785", "c19a7389ab3cd712058a8c3c9ffd8d27a57f3d84b9c91a931f542682bb3d269d", "c3bb4bd2aca82fb498247deeac12265921fe231502a6bc6edea3ee7fe6c40a7a", "c5ea60ece0c0c1c849025bfc541b60a6751b491b6f11dd9ef37ab5b8c9041921", "db61a640ca20f237317d27bc658c1fc54c7581ff7f6502d112922dc285bdabee"] send2trash = ["60001cc07d707fe247c94f74ca6ac0d3255aabcb930529690897ca2a39db28b2", "f1691922577b6fa12821234aeb57599d887c4900b9ca537948d2dac34aea888b"] six = ["3350809f0555b11f552448330d0b52d5f24c91a322ea4a15ef22629740f3761c", "d16a0141ec1a18405cd4ce8b4613101da75da0e9a7aec5bdd4fa804d0e0eba73"] snowballstemmer = ["919f26a68b2c17a7634da993d91339e288964f93c274f1343e3bbbe2096e1128", "9f3bcd3c401c3e862ec0ebe6d2c069ebc012ce142cce209c098ccb5b09136e89"] -sphinx = ["423280646fb37944dd3c85c58fb92a20d745793a9f6c511f59da82fa97cd404b", "de930f42600a4fef993587633984cc5027dedba2464bcf00ddace26b40f8d9ce"] +sphinx = ["15143166e786c7faa76fa990d3b6b38ebffe081ef81cffd1d656b07f3b28a1fa", "5fd62ba64235d77a81554d47ff6b17578171b6dbbc992221e9ebc684898fff59"] sphinx-rtd-theme = ["00cf895504a7895ee433807c62094cf1e95f065843bf3acd17037c3e9a2becd4", "728607e34d60456d736cc7991fd236afb828b21b82f956c5ea75f94c8414040a"] sphinxcontrib-applehelp = ["edaa0ab2b2bc74403149cb0209d6775c96de797dfd5b5e2a71981309efab3897", "fb8dee85af95e5c30c91f10e7eb3c8967308518e0f7488a2828ef7bc191d0d5d"] sphinxcontrib-devhelp = ["6c64b077937330a9128a4da74586e8c2130262f014689b4b89e2d08ee7294a34", "9512ecb00a2b0821a146736b39f7aeb90759834b07e81e8cc23a9c70bacb9981"] @@ -1182,11 +1182,11 @@ testpath = ["46c89ebb683f473ffe2aab0ed9f12581d4d078308a3cb3765d79c6b2317b0109", toml = ["229f81c57791a41d65e399fc06bf0848bab550a9dfd5ed66df18ce5f05e73d5c", "235682dd292d5899d361a811df37e04a8828a5b1da3115886b73cf81ebc9100e", "f1db651f9657708513243e61e6cc67d101a39bad662eaa9b5546f789338e07a3"] toolz = ["929f0a7ea7f61c178bd951bdae93920515d3fbdbafc8e6caf82d752b9b3b31c9"] tornado = ["1174dcb84d08887b55defb2cda1986faeeea715fff189ef3dc44cce99f5fca6b", "2613fab506bd2aedb3722c8c64c17f8f74f4070afed6eea17f20b2115e445aec", "44b82bc1146a24e5b9853d04c142576b4e8fa7a92f2e30bc364a85d1f75c4de2", "457fcbee4df737d2defc181b9073758d73f54a6cfc1f280533ff48831b39f4a8", "49603e1a6e24104961497ad0c07c799aec1caac7400a6762b687e74c8206677d", "8c2f40b99a8153893793559919a355d7b74649a11e59f411b0b0a1793e160bc0", "e1d897889c3b5a829426b7d52828fb37b28bc181cd598624e65c8be40ee3f7fa"] -tqdm = ["d385c95361699e5cf7622485d9b9eae2d4864b21cd5a2374a9c381ffed701021", "e22977e3ebe961f72362f6ddfb9197cc531c9737aaf5f607ef09740c849ecd05"] +tqdm = ["0a860bf2683fdbb4812fe539a6c22ea3f1777843ea985cb8c3807db448a0f7ab", "e288416eecd4df19d12407d0c913cbf77aa8009d7fddb18f632aded3bdbdda6b"] traitlets = ["9c4bd2d267b7153df9152698efb1050a5d84982d3384a37b2c1f7723ba3e7835", "c6cb5e6f57c5a9bdaa40fa71ce7b4af30298fbab9ece9815b5d995ab6217c7d9"] typed-ast = ["132eae51d6ef3ff4a8c47c393a4ef5ebf0d1aecc96880eb5d6c8ceab7017cc9b", "18141c1484ab8784006c839be8b985cfc82a2e9725837b0ecfa0203f71c4e39d", "2baf617f5bbbfe73fd8846463f5aeafc912b5ee247f410700245d68525ec584a", "3d90063f2cbbe39177e9b4d888e45777012652d6110156845b828908c51ae462", "4304b2218b842d610aa1a1d87e1dc9559597969acc62ce717ee4dfeaa44d7eee", "4983ede548ffc3541bae49a82675996497348e55bafd1554dc4e4a5d6eda541a", "5315f4509c1476718a4825f45a203b82d7fdf2a6f5f0c8f166435975b1c9f7d4", "6cdfb1b49d5345f7c2b90d638822d16ba62dc82f7616e9b4caa10b72f3f16649", "7b325f12635598c604690efd7a0197d0b94b7d7778498e76e0710cd582fd1c7a", "8d3b0e3b8626615826f9a626548057c5275a9733512b137984a68ba1598d3d2f", "8f8631160c79f53081bd23446525db0bc4c5616f78d04021e6e434b286493fd7", "912de10965f3dc89da23936f1cc4ed60764f712e5fa603a09dd904f88c996760", "b010c07b975fe853c65d7bbe9d4ac62f1c69086750a574f6292597763781ba18", "c908c10505904c48081a5415a1e295d8403e353e0c14c42b6d67f8f97fae6616", "c94dd3807c0c0610f7c76f078119f4ea48235a953512752b9175f9f98f5ae2bd", "ce65dee7594a84c466e79d7fb7d3303e7295d16a83c22c7c4037071b059e2c21", "eaa9cfcb221a8a4c2889be6f93da141ac777eb8819f077e1d09fb12d00a09a93", "f3376bc31bad66d46d44b4e6522c5c21976bf9bca4ef5987bb2bf727f4506cbb", "f9202fa138544e13a4ec1a6792c35834250a85958fde1251b6a22e07d1260ae7"] -urllib3 = ["4c291ca23bbb55c76518905869ef34bdd5f0e46af7afe6861e8375643ffee1a0", "9a247273df709c4fedb38c711e44292304f73f39ab01beda9f6b9fc375669ac3"] -vega = ["abb93e4af0f97b4ca7a5034bc1c358b9863ece536b4b9c9707551011b7e65053"] +urllib3 = ["b246607a25ac80bedac05c6f282e3cdaf3afb65420fd024ac94435cabe6e18d1", "dbe59173209418ae49d485b87d1681aefa36252ee85884c31346debd19463232"] +vega = ["4d7269756a190edb8e99ce9ce225bc7609d4c124e9fdf89c0a6c6d77ca7ade1d"] vega-datasets = ["20d490b417f84607eb5079400f608f2e9c135b7092bee10f6857f6d23136e459", "63d6eaf129ab23a7d0744e54d11dba9e7eecc0974fd9a237cfae9cfdbef3c37a"] wcwidth = ["3df37372226d6e63e1b1e1eda15c594bca98a22d33a23832a90998faa96bc65e", "f4ebe71925af7b40a864553f761ed559b43544f8f71746c2d756c7fe788ade7c"] webencodings = ["a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78", "b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"] diff --git a/pyrvea/EAs/PPGA.py b/pyrvea/EAs/PPGA.py index 19800e0..71a2592 100644 --- a/pyrvea/EAs/PPGA.py +++ b/pyrvea/EAs/PPGA.py @@ -2,7 +2,7 @@ from pyrvea.Selection.PPGA_select import ppga_select from random import choice, sample import numpy as np -from pyrvea.Population.population_evonn import Population +from pyrvea.Population.Population import Population class PPGA(BaseEA): @@ -107,9 +107,10 @@ def set_params( + ".txt", "w" ) + else: + log_file = ppgaparams["logging"][1] for k, v in ppgaparams.items(): - print(k, v, file=ppgaparams["logging"][1]) - ppgaparams["logging"][1].close() + print(k, v, file=log_file) return ppgaparams diff --git a/pyrvea/Population/Population.py b/pyrvea/Population/Population.py index 84e39ec..1803a8f 100644 --- a/pyrvea/Population/Population.py +++ b/pyrvea/Population/Population.py @@ -66,6 +66,7 @@ def __init__( self.filename = problem.name + "_" + str(problem.num_of_objectives) self.plotting = plotting # These attributes contain the solutions. + self.individuals = np.empty((0, self.num_var), float) self.objectives = np.empty((0, self.problem.num_of_objectives), float) self.fitness = np.empty((0, self.problem.num_of_objectives), float) diff --git a/pyrvea/Problem/dataproblem.py b/pyrvea/Problem/dataproblem.py index cb01296..b1d9861 100644 --- a/pyrvea/Problem/dataproblem.py +++ b/pyrvea/Problem/dataproblem.py @@ -139,3 +139,10 @@ def testing_score(self): # Return R-squared of testing def retrain_surrogate(self): pass + +def main(): + test_data = pd.read_csv("178deb.csv", header=1) + prob = DataProblem(test_data) + prob.train() +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index adc747b..28e56b7 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -33,7 +33,9 @@ class EvoNN(baseProblem): def __init__( self, - name=None, + name, + training_data_input=None, + training_data_output=None, num_input_nodes=4, num_hidden_nodes=5, num_of_objectives=2, @@ -47,6 +49,8 @@ def __init__( super().__init__() self.name = name + self.training_data_input = training_data_input + self.training_data_output = training_data_output self.num_input_nodes = num_input_nodes self.num_hidden_nodes = num_hidden_nodes self.num_of_objectives = num_of_objectives @@ -56,6 +60,12 @@ def __init__( self.activation_func = activation_func self.opt_func = opt_func self.loss_func = loss_func + self.trained_models = [] + + if training_data_input is not None and training_data_output is not None: + self.num_of_samples = training_data_output.shape[0] + self.num_of_variables = training_data_input.shape[1] + self.num_input_nodes = self.num_of_variables def fit(self, training_data, target_values): """Fit data in EvoNN model. @@ -92,7 +102,7 @@ def objectives(self, decision_variables) -> list: """ activated_layer = self.activation(decision_variables) - w_matrix2, rss, predicted_values = self.minimize_error(activated_layer) + _, _, predicted_values = self.minimize_error(activated_layer) training_error = self.loss_function(predicted_values) complexity = self.calculate_complexity(decision_variables) @@ -115,10 +125,11 @@ def activation(self, decision_variables): The penultimate layer Z before the output """ + w1 = decision_variables # Calculate the dot product wi = ( - np.dot(self.training_data_input, decision_variables[1:, :]) - + decision_variables[0] + np.dot(self.training_data_input, w1[1:, :]) + + w1[0] ) if self.activation_func == "sigmoid": @@ -153,11 +164,11 @@ def minimize_error(self, activated_layer): """ if self.opt_func == "llsq": - w_matrix2 = np.linalg.lstsq(activated_layer, self.training_data_output, rcond=None) - rss = w_matrix2[1] - predicted_values = np.dot(activated_layer, w_matrix2[0]) + w2 = np.linalg.lstsq(activated_layer, self.training_data_output, rcond=None) + rss = w2[1] + predicted_values = np.dot(activated_layer, w2[0]) - return w_matrix2[0], rss, predicted_values + return w2[0], rss, predicted_values def loss_function(self, predicted_values): @@ -183,13 +194,6 @@ def information_criterion(self, decision_variables): return aicc - def predict(self, decision_variables): - - activated_layer = self.activation(decision_variables) - _, _, predicted_values = self.minimize_error(activated_layer) - - return predicted_values - def select(self, pop, non_dom_front, criterion="min_error"): """ Select target model from the population. @@ -207,11 +211,12 @@ def select(self, pop, non_dom_front, criterion="min_error"): ------- The selected model """ + model = None if criterion == "min_error": # Return the model with the lowest error lowest_error = np.argmin(pop.objectives[:, 0]) - return pop.individuals[lowest_error] + model = TrainedModel(name=self.name+"_model", w1=pop.individuals[lowest_error]) elif criterion == "akaike_corrected": @@ -227,36 +232,132 @@ def select(self, pop, non_dom_front, criterion="min_error"): info_c_rank.sort() - return pop.individuals[info_c_rank[0][1]] + model = TrainedModel(name=self.name+"_model", w1=pop.individuals[info_c_rank[0][1]]) - def single_variance_response(self, model): + self.trained_models.append(model) - print("number of variables: " + str(self.num_of_variables)) - # pref_val = float( - # input("Please input a value worse than the ideal: ") - # ) - activated_layer = self.activation(model) - _, _, predicted_values = self.minimize_error(activated_layer) + return model - trace0 = go.Scatter(x=predicted_values, y=self.training_data_output, mode="markers") - trace1 = go.Scatter(x=self.training_data_output, y=self.training_data_output) - data = [trace0, trace1] - plotly.offline.plot( - data, - filename="svr.html", - auto_open=True, - ) - model[1, :] = model[1] + 1 +class TrainedModel(EvoNN): - activated_layer = self.activation(model) - _, _, predicted_values = self.minimize_error(activated_layer) + def __init__(self, name, w1, w2=None, y_pred=None, svr=None): + super().__init__(name) + self.name = name + self.w1 = w1 + self.w2 = w2 + self.y_pred = y_pred + self.svr = svr + + def init_upper_part(self): + + activated_layer = super().activation(self.w1) + self.w2, _, self.y_pred = self.minimize_error(activated_layer) + + def fit(self, training_data, target_values): + """Fit data in EvoNN model. + + Parameters + ---------- + training_data : ndarray, shape = (numbers of samples, number of variables) + Training data + target_values : ndarray + Target values + """ + + self.training_data_input = training_data + self.training_data_output = target_values + self.num_of_samples = target_values.shape[0] + self.num_of_variables = training_data.shape[1] + if len(self.training_data_input) > 0: + self.num_input_nodes = np.shape(training_data)[1] + + self.init_upper_part() - trace0 = go.Scatter(x=predicted_values, y=self.training_data_output, mode="markers") - trace1 = go.Scatter(x=self.training_data_output, y=self.training_data_output) - data = [trace0, trace1] - plotly.offline.plot( - data, - filename="svr1.html", - auto_open=True, + def predict(self, decision_variables): + + activated_layer = self.activation(decision_variables) + out = np.dot(activated_layer, self.w2) + + return out + + def activation(self, decision_variables): + """ Calculates the dot product and applies the activation function. + + Parameters + ---------- + decision_variables : ndarray + Variables from the neural network + name : str + The activation function to use + + Returns + ------- + The penultimate layer Z before the output + + """ + wi = ( + np.dot(decision_variables, self.w1[1:, :]) + + self.w1[0] ) + + if self.activation_func == "sigmoid": + activated_layer = lambda x: 1 / (1 + np.exp(-x)) + + if self.activation_func == "relu": + activated_layer = lambda x: np.maximum(x, 0) + + if self.activation_func == "tanh": + activated_layer = lambda x: np.tanh(x) + + return activated_layer(wi) + + def single_variable_response(self, ploton=False, log=None): + + trend = np.loadtxt("trend") + trend = trend[0:self.num_of_samples] + avg = np.ones((1, self.num_input_nodes))*(0+1)/2 + svr = np.empty((0, 2)) + + for i in range(self.num_input_nodes): + variables = np.ones((len(trend), 1))*avg + variables[:, i] = trend + + out = self.predict(variables) + + if min(out) == max(out): + out = 0.5 * np.ones(out.size) + else: + out = (out - min(out)) / (max(out) - min(out)) + + if ploton: + trace0 = go.Scatter(x=np.arange(len(variables[:, 1])), y=variables[:, i], name="input") + trace1 = go.Scatter(x=np.arange(len(variables[:, 1])), y=out, name="output") + data = [trace0, trace1] + plotly.offline.plot( + data, + filename="x"+str(i+1)+"_response.html", + auto_open=True, + ) + + p = np.diff(out) + q = np.diff(trend) + r = np.multiply(p, q) + r_max = max(r) + r_min = min(r) + if r_max <= 0 and r_min <= 0: + response = -1 + s = "inverse" + elif r_max >= 0 and r_min >= 0: + response = 1 + s = "direct" + elif r_max == 0 and r_min == 0: + response = 0 + s = "nil" + elif r_min < 0 < r_max: + response = 2 + s = "mixed" + + print("x" + str(i + 1) + " response: " + str(response) + " " + s, file=log) + svr = np.vstack((svr, ["x"+str(i+1), s])) + self.svr = svr diff --git a/pyrvea/Problem/evonn_test_functions.py b/pyrvea/Problem/evonn_test_functions.py index ebc5286..a79851a 100644 --- a/pyrvea/Problem/evonn_test_functions.py +++ b/pyrvea/Problem/evonn_test_functions.py @@ -52,6 +52,8 @@ def __init__( "Goldstein-Price": (-2, 2), "LeviN13": (-10, 10), "Schaffer": (-100, 100), + "min-ex_f1": (0, 1), + "min-ex_f2": (0, 5) } if self.name in self.test_f_limits.keys(): @@ -154,6 +156,12 @@ def objectives(self, decision_variables) -> list: / (1 + 0.001 * (x ** 2 + y ** 2)) ** 2 ) + elif self.name == "min-ex": + x1 = decision_variables[1] + x2 = decision_variables[2] + + self.obj_func = [x1, (1 + x2) / x1] + return self.obj_func def create_training_data(self, samples=150, method="random", seed=None): From 02cf91b20b9a3ec975fc7afa0318e7a30dc1a881 Mon Sep 17 00:00:00 2001 From: delamorte Date: Thu, 13 Jun 2019 16:35:21 +0300 Subject: [PATCH 032/155] poetry add Black PPGA now uses iterations correctly plots for PPGA are created every iteration, instead of every gen removed debug modifications from dataproblem.py --- poetry.lock | 10 +++---- pyproject.toml | 1 + pyrvea/EAs/PPGA.py | 23 ++++++++++++--- pyrvea/Problem/dataproblem.py | 9 +----- pyrvea/Problem/evonn_problem.py | 50 ++++++++++++++++----------------- 5 files changed, 50 insertions(+), 43 deletions(-) diff --git a/poetry.lock b/poetry.lock index ef270bd..830e25a 100644 --- a/poetry.lock +++ b/poetry.lock @@ -24,7 +24,7 @@ six = "*" toolz = "*" [[package]] -category = "dev" +category = "main" description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." name = "appdirs" optional = false @@ -82,7 +82,7 @@ python-versions = "*" version = "0.1.0" [[package]] -category = "dev" +category = "main" description = "The uncompromising code formatter." name = "black" optional = false @@ -124,7 +124,7 @@ python-versions = "*" version = "3.0.4" [[package]] -category = "dev" +category = "main" description = "Composable command line interface toolkit" name = "click" optional = false @@ -970,7 +970,7 @@ python-versions = "*" version = "0.4.2" [[package]] -category = "dev" +category = "main" description = "Python Library for Tom's Obvious, Minimal Language" name = "toml" optional = false @@ -1085,7 +1085,7 @@ python-versions = "*" version = "1.11.1" [metadata] -content-hash = "1bb3ed42f916d52387721b4ce9e6fcb34194f1ead2e40131d20377dcc896d33f" +content-hash = "a5559602953991f5e0af8fad0a547a857102db5daf691718fdaa9f3cc162a782" python-versions = "^3.6" [metadata.hashes] diff --git a/pyproject.toml b/pyproject.toml index 4d24d5f..9c2b2ac 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -19,6 +19,7 @@ plotly = "^3.8" tqdm = "^4.31" jupyter = "^1.0" scikit-learn = "^0.21.2" +black = {version = "^18.3-alpha.0",allows-prereleases = true} [tool.poetry.dev-dependencies] flake8 = "^3.7" diff --git a/pyrvea/EAs/PPGA.py b/pyrvea/EAs/PPGA.py index 71a2592..799770f 100644 --- a/pyrvea/EAs/PPGA.py +++ b/pyrvea/EAs/PPGA.py @@ -35,9 +35,10 @@ def set_params( population: "Population", population_size: int = None, target_pop_size: int = 300, - generations_per_iteration: int = 1, + generations_per_iteration: int = 10, iterations: int = 1, - plotting: bool = False, + interact: bool = False, + plotting: bool = True, logging: list = [False, None], prob_crossover: float = 0.8, prob_mutation: float = 0.3, @@ -83,7 +84,8 @@ def set_params( "offspring_place_attempts": 10, "generations": generations_per_iteration, "iterations": iterations, - "ploton": plotting, + "interact": interact, + "plotting": plotting, "logging": logging, "current_iteration_gen_count": 0, "current_iteration_count": 0, @@ -233,7 +235,20 @@ def _next_gen(self, population: "Population"): # ) # population.create_new_individuals(pop_size=len(placed_indices), design="EvoNN") - if self.params["ploton"]: + + def _run_interruption(self, population: "Population"): + """Run the interruption phase of PPGA. + + Use this phase to make changes to PPGA.params or other objects. + + Parameters + ---------- + population : Population + """ + if self.params["interact"]: + pass + + if self.params["plotting"]: population.plot_objectives() def select(self, population, max_rank=20) -> list: diff --git a/pyrvea/Problem/dataproblem.py b/pyrvea/Problem/dataproblem.py index b1d9861..2b62280 100644 --- a/pyrvea/Problem/dataproblem.py +++ b/pyrvea/Problem/dataproblem.py @@ -138,11 +138,4 @@ def testing_score(self): # Return R-squared of testing return y_pred def retrain_surrogate(self): - pass - -def main(): - test_data = pd.read_csv("178deb.csv", header=1) - prob = DataProblem(test_data) - prob.train() -if __name__ == '__main__': - main() \ No newline at end of file + pass \ No newline at end of file diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index 28e56b7..ead4395 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -34,8 +34,8 @@ class EvoNN(baseProblem): def __init__( self, name, - training_data_input=None, - training_data_output=None, + X_train=None, + y_train=None, num_input_nodes=4, num_hidden_nodes=5, num_of_objectives=2, @@ -49,8 +49,8 @@ def __init__( super().__init__() self.name = name - self.training_data_input = training_data_input - self.training_data_output = training_data_output + self.X_train = X_train + self.y_train = y_train self.num_input_nodes = num_input_nodes self.num_hidden_nodes = num_hidden_nodes self.num_of_objectives = num_of_objectives @@ -62,9 +62,9 @@ def __init__( self.loss_func = loss_func self.trained_models = [] - if training_data_input is not None and training_data_output is not None: - self.num_of_samples = training_data_output.shape[0] - self.num_of_variables = training_data_input.shape[1] + if X_train is not None and y_train is not None: + self.num_of_samples = y_train.shape[0] + self.num_of_variables = y_train.shape[1] self.num_input_nodes = self.num_of_variables def fit(self, training_data, target_values): @@ -78,12 +78,11 @@ def fit(self, training_data, target_values): Target values """ - self.training_data_input = training_data - self.training_data_output = target_values + self.X_train = training_data + self.y_train = target_values self.num_of_samples = target_values.shape[0] self.num_of_variables = training_data.shape[1] - if len(self.training_data_input) > 0: - self.num_input_nodes = np.shape(training_data)[1] + self.num_input_nodes = self.num_of_variables def objectives(self, decision_variables) -> list: @@ -128,7 +127,7 @@ def activation(self, decision_variables): w1 = decision_variables # Calculate the dot product wi = ( - np.dot(self.training_data_input, w1[1:, :]) + np.dot(self.X_train, w1[1:, :]) + w1[0] ) @@ -164,18 +163,17 @@ def minimize_error(self, activated_layer): """ if self.opt_func == "llsq": - w2 = np.linalg.lstsq(activated_layer, self.training_data_output, rcond=None) + w2 = np.linalg.lstsq(activated_layer, self.y_train, rcond=None) rss = w2[1] predicted_values = np.dot(activated_layer, w2[0]) - - return w2[0], rss, predicted_values + return w2[0], rss, predicted_values def loss_function(self, predicted_values): if self.loss_func == "mse": - return ((self.training_data_output - predicted_values) ** 2).mean() + return ((self.y_train - predicted_values) ** 2).mean() if self.loss_func == "rmse": - return np.sqrt(((self.training_data_output - predicted_values) ** 2).mean()) + return np.sqrt(((self.y_train - predicted_values) ** 2).mean()) def calculate_complexity(self, w_matrix): @@ -187,7 +185,7 @@ def information_criterion(self, decision_variables): z = self.activation(decision_variables) w_matrix2, rss, prediction = self.minimize_error(z) - # rss = ((self.training_data_output - prediction) ** 2).sum() + # rss = ((self.y_train - prediction) ** 2).sum() k = self.calculate_complexity(decision_variables) + np.count_nonzero(w_matrix2) aic = 2 * k + self.num_of_samples * np.log(rss / self.num_of_samples) aicc = aic + (2 * k * (k + 1) / (self.num_of_samples - k - 1)) @@ -216,7 +214,7 @@ def select(self, pop, non_dom_front, criterion="min_error"): # Return the model with the lowest error lowest_error = np.argmin(pop.objectives[:, 0]) - model = TrainedModel(name=self.name+"_model", w1=pop.individuals[lowest_error]) + model = EvoNNModel(name=self.name+"_model", w1=pop.individuals[lowest_error]) elif criterion == "akaike_corrected": @@ -232,14 +230,14 @@ def select(self, pop, non_dom_front, criterion="min_error"): info_c_rank.sort() - model = TrainedModel(name=self.name+"_model", w1=pop.individuals[info_c_rank[0][1]]) + model = EvoNNModel(name=self.name+"_model", w1=pop.individuals[info_c_rank[0][1]]) self.trained_models.append(model) return model -class TrainedModel(EvoNN): +class EvoNNModel(EvoNN): def __init__(self, name, w1, w2=None, y_pred=None, svr=None): super().__init__(name) @@ -265,11 +263,11 @@ def fit(self, training_data, target_values): Target values """ - self.training_data_input = training_data - self.training_data_output = target_values - self.num_of_samples = target_values.shape[0] - self.num_of_variables = training_data.shape[1] - if len(self.training_data_input) > 0: + self.X_train = training_data.values + self.y_train = target_values.values + self.num_of_samples = self.y_train.shape[0] + self.num_of_variables = self.X_train.shape[1] + if len(self.X_train) > 0: self.num_input_nodes = np.shape(training_data)[1] self.init_upper_part() From ed0347cae2f68c55dd59d775046ecc16f45e2532 Mon Sep 17 00:00:00 2001 From: delamorte Date: Mon, 17 Jun 2019 14:58:40 +0300 Subject: [PATCH 033/155] added example file Refactored code to use EvoNN and EvoNNModel objects correctly logging now correcly passed as parameter --- evonn_example.py | 20 +++ pyrvea/EAs/PPGA.py | 31 ++--- pyrvea/Problem/evonn_problem.py | 218 ++++++++++++++++++++------------ 3 files changed, 172 insertions(+), 97 deletions(-) create mode 100644 evonn_example.py diff --git a/evonn_example.py b/evonn_example.py new file mode 100644 index 0000000..1044d40 --- /dev/null +++ b/evonn_example.py @@ -0,0 +1,20 @@ +from pyrvea.Problem.evonn_test_functions import EvoNNTestProblem +from pyrvea.Problem.evonn_problem import EvoNNModel + +test_prob = EvoNNTestProblem("Three-hump camel") +training_data_input, training_data_output = test_prob.create_training_data( + samples=150, method="random" +) + +model_f1 = EvoNNModel(name="EvoNN_three_hump_camel") +model_f1.set_params( + num_hidden_nodes=15, + pop_size=500, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_f1.fit(training_data_input, training_data_output) \ No newline at end of file diff --git a/pyrvea/EAs/PPGA.py b/pyrvea/EAs/PPGA.py index 799770f..234da6b 100644 --- a/pyrvea/EAs/PPGA.py +++ b/pyrvea/EAs/PPGA.py @@ -36,10 +36,10 @@ def set_params( population_size: int = None, target_pop_size: int = 300, generations_per_iteration: int = 10, - iterations: int = 1, + iterations: int = 10, interact: bool = False, plotting: bool = True, - logging: list = [False, None], + logging: list = False, prob_crossover: float = 0.8, prob_mutation: float = 0.3, mut_strength: float = 0.7 @@ -97,20 +97,17 @@ def set_params( } # If logging enabled, write params to file - if ppgaparams["logging"][0]: - if ppgaparams["logging"][1] is None: - # Save params to log file - log_file = open( - population.problem.name - + "_var" - + str(population.problem.num_of_variables) - + "_nodes" - + str(population.problem.num_hidden_nodes) - + ".txt", - "w" - ) - else: - log_file = ppgaparams["logging"][1] + if ppgaparams["logging"]: + # Save params to log file + log_file = open( + population.problem.name + + "_var" + + str(population.problem.num_of_variables) + + "_nodes" + + str(population.problem.num_hidden_nodes) + + ".log", + "w" + ) for k, v in ppgaparams.items(): print(k, v, file=log_file) @@ -248,7 +245,7 @@ def _run_interruption(self, population: "Population"): if self.params["interact"]: pass - if self.params["plotting"]: + if population.plotting: population.plot_objectives() def select(self, population, max_rank=20) -> list: diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index ead4395..67bbdc2 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -1,4 +1,6 @@ from pyrvea.Problem.baseProblem import baseProblem +from pyrvea.Population.Population import Population +from pyrvea.EAs.PPGA import PPGA import numpy as np import plotly import plotly.graph_objs as go @@ -42,9 +44,7 @@ def __init__( w_low=-5.0, w_high=5.0, prob_omit=0.2, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse" + params=None, ): super().__init__() @@ -57,15 +57,7 @@ def __init__( self.w_low = w_low self.w_high = w_high self.prob_omit = prob_omit - self.activation_func = activation_func - self.opt_func = opt_func - self.loss_func = loss_func - self.trained_models = [] - - if X_train is not None and y_train is not None: - self.num_of_samples = y_train.shape[0] - self.num_of_variables = y_train.shape[1] - self.num_input_nodes = self.num_of_variables + self.params = params def fit(self, training_data, target_values): """Fit data in EvoNN model. @@ -83,6 +75,24 @@ def fit(self, training_data, target_values): self.num_of_samples = target_values.shape[0] self.num_of_variables = training_data.shape[1] self.num_input_nodes = self.num_of_variables + self.num_hidden_nodes = self.params["num_hidden_nodes"] + + def train(self, model): + + pop = Population(self, assign_type="EvoNN", pop_size=500, plotting=False) + pop.evolve( + PPGA, + { + "logging": self.params["logging"], + "iterations": 10, + "generations_per_iteration": 10 + } + ) + + non_dom_front = pop.non_dominated() + model.w1 = self.select(pop, non_dom_front, self.params["criterion"]) + activated_layer = self.activation(model.w1) + model.w2, _, model.y_pred = self.minimize_error(activated_layer) def objectives(self, decision_variables) -> list: @@ -126,18 +136,15 @@ def activation(self, decision_variables): """ w1 = decision_variables # Calculate the dot product - wi = ( - np.dot(self.X_train, w1[1:, :]) - + w1[0] - ) + wi = np.dot(self.X_train, w1[1:, :]) + w1[0] - if self.activation_func == "sigmoid": + if self.params["activation_func"] == "sigmoid": activated_layer = lambda x: 1 / (1 + np.exp(-x)) - if self.activation_func == "relu": + if self.params["activation_func"] == "relu": activated_layer = lambda x: np.maximum(x, 0) - if self.activation_func == "tanh": + if self.params["activation_func"] == "tanh": activated_layer = lambda x: np.tanh(x) return activated_layer(wi) @@ -162,7 +169,7 @@ def minimize_error(self, activated_layer): The prediction of the model """ - if self.opt_func == "llsq": + if self.params["opt_func"] == "llsq": w2 = np.linalg.lstsq(activated_layer, self.y_train, rcond=None) rss = w2[1] predicted_values = np.dot(activated_layer, w2[0]) @@ -170,9 +177,9 @@ def minimize_error(self, activated_layer): def loss_function(self, predicted_values): - if self.loss_func == "mse": + if self.params["loss_func"] == "mse": return ((self.y_train - predicted_values) ** 2).mean() - if self.loss_func == "rmse": + if self.params["loss_func"]: return np.sqrt(((self.y_train - predicted_values) ** 2).mean()) def calculate_complexity(self, w_matrix): @@ -214,7 +221,7 @@ def select(self, pop, non_dom_front, criterion="min_error"): # Return the model with the lowest error lowest_error = np.argmin(pop.objectives[:, 0]) - model = EvoNNModel(name=self.name+"_model", w1=pop.individuals[lowest_error]) + model = pop.individuals[lowest_error] elif criterion == "akaike_corrected": @@ -230,27 +237,71 @@ def select(self, pop, non_dom_front, criterion="min_error"): info_c_rank.sort() - model = EvoNNModel(name=self.name+"_model", w1=pop.individuals[info_c_rank[0][1]]) - - self.trained_models.append(model) + model = pop.individuals[info_c_rank[0][1]] return model + def create_logfile(self): + + # Save params to log file + log_file = open( + self.name + + "_var" + + str(self.num_of_variables) + + "_nodes" + + str(self.num_hidden_nodes) + + ".log", + "a", + ) + print( + "samples: " + + str(self.num_of_samples) + + "\n" + + "variables: " + + str(self.num_of_variables) + + "\n" + + "nodes: " + + str(self.num_hidden_nodes) + + "\n" + + "activation: " + + self.params["activation_func"] + + "\n" + + "opt func: " + + self.params["opt_func"] + + "\n" + + "loss func: " + + self.params["loss_func"], + file=log_file, + ) + return log_file + + def create_plot(self, model): + + trace0 = go.Scatter(x=model.y_pred, y=self.y_train, mode="markers") + trace1 = go.Scatter(x=self.y_train, y=self.y_train) + data = [trace0, trace1] + plotly.offline.plot( + data, + filename=self.name + + "_var" + + str(self.num_of_variables) + + "_nodes" + + str(self.num_hidden_nodes) + + ".html", + auto_open=True, + ) + class EvoNNModel(EvoNN): - - def __init__(self, name, w1, w2=None, y_pred=None, svr=None): + def __init__(self, name, w1=None, w2=None, y_pred=None, svr=None): super().__init__(name) self.name = name self.w1 = w1 self.w2 = w2 self.y_pred = y_pred self.svr = svr - - def init_upper_part(self): - - activated_layer = super().activation(self.w1) - self.w2, _, self.y_pred = self.minimize_error(activated_layer) + self.log = None + self.set_params() def fit(self, training_data, target_values): """Fit data in EvoNN model. @@ -262,63 +313,68 @@ def fit(self, training_data, target_values): target_values : ndarray Target values """ - - self.X_train = training_data.values - self.y_train = target_values.values - self.num_of_samples = self.y_train.shape[0] - self.num_of_variables = self.X_train.shape[1] - if len(self.X_train) > 0: - self.num_input_nodes = np.shape(training_data)[1] - - self.init_upper_part() + f1 = EvoNN(name=self.name, params=self.params) + f1.fit(training_data, target_values) + f1.train(self) + if f1.params["logging"]: + self.log = f1.create_logfile() + if f1.params["plotting"]: + f1.create_plot(self) + self.num_of_samples = f1.num_of_samples + self.single_variable_response(ploton=True, log=self.log) def predict(self, decision_variables): - activated_layer = self.activation(decision_variables) - out = np.dot(activated_layer, self.w2) - - return out - - def activation(self, decision_variables): - """ Calculates the dot product and applies the activation function. - - Parameters - ---------- - decision_variables : ndarray - Variables from the neural network - name : str - The activation function to use + wi = np.dot(decision_variables, self.w1[1:, :]) + self.w1[0] - Returns - ------- - The penultimate layer Z before the output - - """ - wi = ( - np.dot(decision_variables, self.w1[1:, :]) - + self.w1[0] - ) - - if self.activation_func == "sigmoid": + if self.params["activation_func"] == "sigmoid": activated_layer = lambda x: 1 / (1 + np.exp(-x)) - if self.activation_func == "relu": + if self.params["activation_func"] == "relu": activated_layer = lambda x: np.maximum(x, 0) - if self.activation_func == "tanh": + if self.params["activation_func"] == "tanh": activated_layer = lambda x: np.tanh(x) - return activated_layer(wi) + out = np.dot(activated_layer(wi), self.w2) + + return out + + def set_params( + self, + name=None, + num_hidden_nodes=15, + pop_size=500, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=False, + plotting=False, + ): + params = { + "name": name, + "num_hidden_nodes": num_hidden_nodes, + "pop_size": pop_size, + "activation_func": activation_func, + "opt_func": opt_func, + "loss_func": loss_func, + "criterion": criterion, + "logging": logging, + "plotting": plotting, + } + + self.params = params def single_variable_response(self, ploton=False, log=None): trend = np.loadtxt("trend") - trend = trend[0:self.num_of_samples] - avg = np.ones((1, self.num_input_nodes))*(0+1)/2 + trend = trend[0: self.num_of_samples] + avg = np.ones((1, self.w1[1:].shape[0])) * (0 + 1) / 2 svr = np.empty((0, 2)) - for i in range(self.num_input_nodes): - variables = np.ones((len(trend), 1))*avg + for i in range(self.w1[1:].shape[0]): + variables = np.ones((len(trend), 1)) * avg variables[:, i] = trend out = self.predict(variables) @@ -329,13 +385,15 @@ def single_variable_response(self, ploton=False, log=None): out = (out - min(out)) / (max(out) - min(out)) if ploton: - trace0 = go.Scatter(x=np.arange(len(variables[:, 1])), y=variables[:, i], name="input") - trace1 = go.Scatter(x=np.arange(len(variables[:, 1])), y=out, name="output") + trace0 = go.Scatter( + x=np.arange(len(variables[:, 1])), y=variables[:, i], name="input" + ) + trace1 = go.Scatter( + x=np.arange(len(variables[:, 1])), y=out, name="output" + ) data = [trace0, trace1] plotly.offline.plot( - data, - filename="x"+str(i+1)+"_response.html", - auto_open=True, + data, filename="x" + str(i + 1) + "_response.html", auto_open=True ) p = np.diff(out) @@ -357,5 +415,5 @@ def single_variable_response(self, ploton=False, log=None): s = "mixed" print("x" + str(i + 1) + " response: " + str(response) + " " + s, file=log) - svr = np.vstack((svr, ["x"+str(i+1), s])) + svr = np.vstack((svr, ["x" + str(i + 1), s])) self.svr = svr From c30f9605ac52023dceba3dc0a847ee3d6caddbfe Mon Sep 17 00:00:00 2001 From: delamorte Date: Mon, 17 Jun 2019 15:07:51 +0300 Subject: [PATCH 034/155] began work on EvoDN2 --- pyrvea/Population/Population.py | 37 ++++++++++++++++++++++++++++++--- 1 file changed, 34 insertions(+), 3 deletions(-) diff --git a/pyrvea/Population/Population.py b/pyrvea/Population/Population.py index 1803a8f..dc49b50 100644 --- a/pyrvea/Population/Population.py +++ b/pyrvea/Population/Population.py @@ -34,7 +34,7 @@ def __init__( problem: "baseProblem", assign_type: str = "LHSDesign", plotting: bool = True, - pop_size = None, + pop_size=None, *args ): """Initialize the population. @@ -126,7 +126,8 @@ def create_new_individuals( ) elif design == "EvoNN": self.individuals = np.empty( - (0, self.problem.num_input_nodes + 1, self.problem.num_hidden_nodes), float + (0, self.problem.num_input_nodes + 1, self.problem.num_hidden_nodes), + float, ) # +1 row for bias individuals = np.random.uniform( @@ -148,6 +149,36 @@ def create_new_individuals( # Set bias individuals[:, 0, :] = 1 + elif design == "EvoDN2": + self.individuals = np.empty( + ( + self.problem.num_layers, + 0, + self.problem.num_input_nodes + 1, + self.problem.num_hidden_nodes, + ) + ) + # +1 row for bias + individuals = np.random.uniform( + self.problem.w_low, + self.problem.w_high, + size=( + self.problem.num_layers, + pop_size, + self.problem.num_input_nodes + 1, + self.problem.num_hidden_nodes, + ), + ) + + # Eliminate some weights + flag = bn.rvs(p=1 - self.problem.prob_omit, size=np.shape(individuals)) + + random_numbers = np.zeros(np.shape(individuals)) + individuals[flag == 0] = random_numbers[flag == 0] + + # Set bias + individuals[:, :, 0, :] = 1 + else: print("Design not yet supported.") @@ -355,7 +386,7 @@ def mate(self, ind1=None, ind2=None, params=None): params["current_iteration_gen_count"], params["generations"], params["prob_mutation"], - params["mut_strength"] + params["mut_strength"], ) return offspring1, offspring2 From baf6ed09ab58367f23bdaada76a9807147d16344 Mon Sep 17 00:00:00 2001 From: delamorte Date: Wed, 19 Jun 2019 14:02:34 +0300 Subject: [PATCH 035/155] population class can now handle deep neural networks made a class for deep neural networks added example for EvoDN2 added a more efficient crossover method for EvoDN2 added PPGA parameters "crossover_type" and "mutation_type" --- evodn2_example.py | 26 + evonn_example.py | 3 +- pyrvea/EAs/PPGA.py | 4 + pyrvea/Population/Population.py | 34 +- pyrvea/Problem/evodn2_problem.py | 487 ++++++++++++++++++ .../Recombination/ppga_parallel_crossover.py | 44 ++ 6 files changed, 573 insertions(+), 25 deletions(-) create mode 100644 evodn2_example.py create mode 100644 pyrvea/Problem/evodn2_problem.py create mode 100644 pyrvea/Recombination/ppga_parallel_crossover.py diff --git a/evodn2_example.py b/evodn2_example.py new file mode 100644 index 0000000..cca8240 --- /dev/null +++ b/evodn2_example.py @@ -0,0 +1,26 @@ +from pyrvea.Problem.evonn_test_functions import EvoNNTestProblem +from pyrvea.Problem.evonn_problem import EvoNNModel +from pyrvea.Problem.evodn2_problem import EvoDN2Model + +# test_prob = EvoNNTestProblem("Three-hump camel") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=150, method="random" +# ) + +test_prob = EvoNNTestProblem("Sphere", num_of_variables=10) +training_data_input, training_data_output = test_prob.create_training_data( + samples=150, method="random" +) + +model_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_f1.set_params( + num_hidden_nodes=15, + pop_size=500, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_f1.fit(training_data_input, training_data_output) diff --git a/evonn_example.py b/evonn_example.py index 1044d40..6db930c 100644 --- a/evonn_example.py +++ b/evonn_example.py @@ -1,12 +1,13 @@ from pyrvea.Problem.evonn_test_functions import EvoNNTestProblem from pyrvea.Problem.evonn_problem import EvoNNModel +from pyrvea.Problem.evodn2_problem import EvoDN2Model test_prob = EvoNNTestProblem("Three-hump camel") training_data_input, training_data_output = test_prob.create_training_data( samples=150, method="random" ) -model_f1 = EvoNNModel(name="EvoNN_three_hump_camel") +model_f1 = EvoDN2Model(name="EvoDN2_three_hump_camel") model_f1.set_params( num_hidden_nodes=15, pop_size=500, diff --git a/pyrvea/EAs/PPGA.py b/pyrvea/EAs/PPGA.py index 234da6b..6bdb86f 100644 --- a/pyrvea/EAs/PPGA.py +++ b/pyrvea/EAs/PPGA.py @@ -40,6 +40,8 @@ def set_params( interact: bool = False, plotting: bool = True, logging: list = False, + crossover_type: str = None, + mutation_type: str = None, prob_crossover: float = 0.8, prob_mutation: float = 0.3, mut_strength: float = 0.7 @@ -89,6 +91,8 @@ def set_params( "logging": logging, "current_iteration_gen_count": 0, "current_iteration_count": 0, + "crossover_type": crossover_type, + "mutation_type": mutation_type, "prob_crossover": prob_crossover, "prob_mutation": prob_mutation, "mut_strength": mut_strength, diff --git a/pyrvea/Population/Population.py b/pyrvea/Population/Population.py index dc49b50..f7a3c19 100644 --- a/pyrvea/Population/Population.py +++ b/pyrvea/Population/Population.py @@ -3,6 +3,7 @@ from random import shuffle from typing import TYPE_CHECKING +import random import numpy as np import pandas as pd from pyDOE import lhs @@ -19,6 +20,7 @@ from pyrvea.OtherTools.IsNotebook import IsNotebook from pyrvea.Recombination.ppga_crossover import ppga_crossover +from pyrvea.Recombination.ppga_parallel_crossover import ppga_parallel_crossover from pyrvea.Recombination.ppga_mutation import ppga_mutation if TYPE_CHECKING: @@ -125,6 +127,7 @@ def create_new_individuals( + self.lower_limits ) elif design == "EvoNN": + # The population could perhaps be created inside the problem class in EvoNN cases? self.individuals = np.empty( (0, self.problem.num_input_nodes + 1, self.problem.num_hidden_nodes), float, @@ -152,32 +155,12 @@ def create_new_individuals( elif design == "EvoDN2": self.individuals = np.empty( ( - self.problem.num_layers, 0, - self.problem.num_input_nodes + 1, - self.problem.num_hidden_nodes, + self.problem.subnets[0] ) ) - # +1 row for bias - individuals = np.random.uniform( - self.problem.w_low, - self.problem.w_high, - size=( - self.problem.num_layers, - pop_size, - self.problem.num_input_nodes + 1, - self.problem.num_hidden_nodes, - ), - ) - - # Eliminate some weights - flag = bn.rvs(p=1 - self.problem.prob_omit, size=np.shape(individuals)) - random_numbers = np.zeros(np.shape(individuals)) - individuals[flag == 0] = random_numbers[flag == 0] - - # Set bias - individuals[:, :, 0, :] = 1 + individuals = self.problem.create_population() else: print("Design not yet supported.") @@ -363,12 +346,15 @@ def mate(self, ind1=None, ind2=None, params=None): Conduct simulated binary crossover and bounded polunomial mutation. """ - if self.individuals.ndim > 2: + if self.individuals.ndim >= 2: # Get individuals at indices w1, w2 = self.individuals[ind1], self.individuals[ind2] # Perform crossover - xover_w1, xover_w2 = ppga_crossover(w1, w2, params["prob_crossover"]) + if params["crossover_type"] == "short": + xover_w1, xover_w2 = ppga_parallel_crossover(w1, w2, params["prob_crossover"]) + else: + xover_w1, xover_w2 = ppga_crossover(w1, w2, params["prob_crossover"]) # Find randomly two other individuals with current match active for mutation. # Make a list of individuals suitable for mutation, exclude the ones to be mutated diff --git a/pyrvea/Problem/evodn2_problem.py b/pyrvea/Problem/evodn2_problem.py new file mode 100644 index 0000000..c7032ba --- /dev/null +++ b/pyrvea/Problem/evodn2_problem.py @@ -0,0 +1,487 @@ +from pyrvea.Problem.baseProblem import baseProblem +from pyrvea.Population.Population import Population +from pyrvea.EAs.PPGA import PPGA +import numpy as np +import plotly +import plotly.graph_objs as go +import random +from math import ceil + + +class EvoDN2(baseProblem): + """Creates an Artificial Neural Network (ANN) for the EvoNN algorithm. + + Attributes + ---------- + name : str + Name of the sample + num_input_nodes : int + The number of nodes in the input layer + num_hidden_nodes : int + The number of nodes in the hidden layer + num_of_objectives : int + The number of objectives + w_low : float + The lower bound for randomly generated weights + w_high : float + The upper bound for randomly generated weights + prob_omit : float + The probability of setting some weights to zero initially + activation_func : str + The function to use for activating the hidden layer + opt_func : str + The function to use for optimizing the upper part of the network + loss_func : str + The loss function to use + """ + + def __init__( + self, + name, + X_train=None, + y_train=None, + num_input_nodes=4, + num_hidden_nodes=5, + num_of_objectives=2, + w_low=-5.0, + w_high=5.0, + prob_omit=0.2, + subnets=[], + params=None, + ): + super().__init__() + + self.name = name + self.X_train = X_train + self.y_train = y_train + self.num_input_nodes = num_input_nodes + self.num_hidden_nodes = num_hidden_nodes + self.num_of_objectives = num_of_objectives + self.w_low = w_low + self.w_high = w_high + self.prob_omit = prob_omit + self.params = params + self.subnets = [4, 4] + self.max_nodes = 3 + + def fit(self, training_data, target_values): + """Fit data in EvoNN model. + + Parameters + ---------- + training_data : ndarray, shape = (numbers of samples, number of variables) + Training data + target_values : ndarray + Target values + """ + + self.X_train = training_data + self.y_train = target_values + self.num_of_samples = target_values.shape[0] + self.num_of_variables = training_data.shape[1] + + # Create random subsets of decision variables for each subnet + self.subsets = [] + for i in range(self.subnets[0]): + n = random.randint(1, self.X_train.shape[1]) + self.subsets.append(random.sample(range(self.X_train.shape[1]), n)) + + # Ensure that each decision variable is used as an input in at least one subnet + for n in list(range(self.X_train.shape[1])): + if not any(n in k for k in self.subsets): + self.subsets[random.randint(0, self.subnets[0]-1)].append(n) + + def create_population(self): + + individuals = [] + for i in range(self.params["pop_size"]): + nets = [] + for j in range(self.subnets[0]): + + layers = [] + num_layers = np.random.randint(1, self.subnets[1]) + in_nodes = len(self.subsets[j]) + + for k in range(num_layers): + out_nodes = random.randint(1, self.max_nodes) + net = np.random.uniform( + self.w_low, + self.w_high, + size=( + in_nodes, + out_nodes + ) + ) + # Randomly set some weights to zero + zeros = np.random.choice(np.arange(net.size), ceil(net.size * self.prob_omit)) + net.ravel()[zeros] = 0 + + # Add bias + net = np.insert(net, 0, 1, axis=0) + in_nodes = out_nodes + layers.append(net) + nets.append(layers) + + individuals.append(nets) + + individuals = np.asarray(individuals) + + return individuals + + def train(self, model): + + pop = Population(self, assign_type="EvoDN2", pop_size=500, plotting=False) + pop.evolve( + PPGA, + { + "logging": self.params["logging"], + "iterations": 10, + "generations_per_iteration": 10, + "crossover_type": "short", + "mutation_type": "short" + } + ) + + non_dom_front = pop.non_dominated() + model.w1 = self.select(pop, non_dom_front, self.params["criterion"]) + activated_layer = self.activation(model.w1) + model.w2, _, model.y_pred = self.minimize_error(activated_layer) + + def objectives(self, decision_variables) -> list: + + """ Use this method to calculate objective functions. + + Parameters + ---------- + decision_variables : ndarray + Variables from the neural network + + Returns + ------- + obj_func : list + The objective function + + """ + + # Shuffle variables + # random.shuffle(x) + # for i in x: + # random.shuffle(i) + # print(x) + + end_net, complexity = self.activation(decision_variables) + _, _, predicted_values = self.minimize_error(end_net) + training_error = self.loss_function(predicted_values) + + obj_func = [training_error, complexity] + + return obj_func + + def activation(self, decision_variables): + """ Calculates the dot product and applies the activation function. + + Parameters + ---------- + decision_variables : ndarray + Variables from the neural network + name : str + The activation function to use + + Returns + ------- + The penultimate layer Z before the output + + """ + subnet_cmplx = [] + end_net = np.empty((self.num_of_samples, 0)) + for i in range(self.subnets[0]): + + subnet = decision_variables[i] + in_nodes = self.X_train[:, self.subsets[i]] + cnet = np.abs(subnet[0][1:, :]) + for layer in range(len(subnet)): + # Calculate the dot product + out = np.dot(in_nodes, subnet[layer][1:, :]) + subnet[layer][0] + if layer > 0: + cnet = np.dot(cnet, np.abs(subnet[layer][1:, :])) + + if self.params["activation_func"] == "sigmoid": + activated_layer = lambda x: 1 / (1 + np.exp(-x)) + + if self.params["activation_func"] == "relu": + activated_layer = lambda x: np.maximum(x, 0) + + if self.params["activation_func"] == "tanh": + activated_layer = lambda x: np.tanh(x) + + in_nodes = activated_layer(out) + + subnet_cmplx.append(cnet) + end_net = np.hstack((end_net, in_nodes)) + + complexity = np.sum([np.sum(x) for x in subnet_cmplx]) + + return end_net, complexity + + def minimize_error(self, activated_layer): + """ Minimize the training error. + + Parameters + ---------- + activated_layer : ndarray + Output of the activation function + name : str + Name of the optimizing algorithm to use + + Returns + ------- + w_matrix[0] : ndarray + The weight matrix of the upper part of the network + rss : float + Sums of residuals + predicted_values : ndarray + The prediction of the model + """ + + if self.params["opt_func"] == "llsq": + w2 = np.linalg.lstsq(activated_layer, self.y_train, rcond=None) + rss = w2[1] + predicted_values = np.dot(activated_layer, w2[0]) + return w2[0], rss, predicted_values + + def loss_function(self, predicted_values): + + if self.params["loss_func"] == "mse": + return ((self.y_train - predicted_values) ** 2).mean() + if self.params["loss_func"]: + return np.sqrt(((self.y_train - predicted_values) ** 2).mean()) + + def information_criterion(self, decision_variables): + + z = self.activation(decision_variables) + w_matrix2, rss, prediction = self.minimize_error(z) + # rss = ((self.y_train - prediction) ** 2).sum() + k = self.calculate_complexity(decision_variables) + np.count_nonzero(w_matrix2) + aic = 2 * k + self.num_of_samples * np.log(rss / self.num_of_samples) + aicc = aic + (2 * k * (k + 1) / (self.num_of_samples - k - 1)) + + return aicc + + def select(self, pop, non_dom_front, criterion="min_error"): + """ Select target model from the population. + + Parameters + ---------- + pop : obj + The population object + non_dom_front : list + Indices of the models on the non-dominated front + criterion : str + The criterion to use for selecting the model. + Possible values: 'min_error', 'akaike_corrected', 'manual' + + Returns + ------- + The selected model + """ + model = None + if criterion == "min_error": + # Return the model with the lowest error + + lowest_error = np.argmin(pop.objectives[:, 0]) + model = pop.individuals[lowest_error] + + elif criterion == "akaike_corrected": + + # Calculate Akaike information criterion for the non-dominated front + # and return the model with the lowest value + + info_c_rank = [] + + for i in non_dom_front: + + info_c = self.information_criterion(pop.individuals[i]) + info_c_rank.append((info_c, i)) + + info_c_rank.sort() + + model = pop.individuals[info_c_rank[0][1]] + + return model + + def create_logfile(self): + + # Save params to log file + log_file = open( + self.name + + "_var" + + str(self.num_of_variables) + + "_nodes" + + str(self.num_hidden_nodes) + + ".log", + "a", + ) + print( + "samples: " + + str(self.num_of_samples) + + "\n" + + "variables: " + + str(self.num_of_variables) + + "\n" + + "nodes: " + + str(self.num_hidden_nodes) + + "\n" + + "activation: " + + self.params["activation_func"] + + "\n" + + "opt func: " + + self.params["opt_func"] + + "\n" + + "loss func: " + + self.params["loss_func"], + file=log_file, + ) + return log_file + + def create_plot(self, model): + + trace0 = go.Scatter(x=model.y_pred, y=self.y_train, mode="markers") + trace1 = go.Scatter(x=self.y_train, y=self.y_train) + data = [trace0, trace1] + plotly.offline.plot( + data, + filename=self.name + + "_var" + + str(self.num_of_variables) + + "_nodes" + + str(self.num_hidden_nodes) + + ".html", + auto_open=True, + ) + + +class EvoDN2Model(EvoDN2): + def __init__(self, name, w1=None, w2=None, y_pred=None, svr=None): + super().__init__(name) + self.name = name + self.w1 = w1 + self.w2 = w2 + self.y_pred = y_pred + self.svr = svr + self.log = None + self.set_params() + + def fit(self, training_data, target_values): + """Fit data in EvoNN model. + + Parameters + ---------- + training_data : ndarray, shape = (numbers of samples, number of variables) + Training data + target_values : ndarray + Target values + """ + f1 = EvoDN2(name=self.name, params=self.params) + f1.fit(training_data, target_values) + f1.train(self) + if f1.params["logging"]: + self.log = f1.create_logfile() + if f1.params["plotting"]: + f1.create_plot(self) + self.num_of_samples = f1.num_of_samples + self.single_variable_response(ploton=True, log=self.log) + + def predict(self, decision_variables): + + wi = np.dot(decision_variables, self.w1[1:, :]) + self.w1[0] + + if self.params["activation_func"] == "sigmoid": + activated_layer = lambda x: 1 / (1 + np.exp(-x)) + + if self.params["activation_func"] == "relu": + activated_layer = lambda x: np.maximum(x, 0) + + if self.params["activation_func"] == "tanh": + activated_layer = lambda x: np.tanh(x) + + out = np.dot(activated_layer(wi), self.w2) + + return out + + def set_params( + self, + name=None, + num_hidden_nodes=15, + pop_size=500, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=False, + plotting=False, + ): + params = { + "name": name, + "num_hidden_nodes": num_hidden_nodes, + "pop_size": pop_size, + "activation_func": activation_func, + "opt_func": opt_func, + "loss_func": loss_func, + "criterion": criterion, + "logging": logging, + "plotting": plotting, + } + + self.params = params + + def single_variable_response(self, ploton=False, log=None): + + trend = np.loadtxt("trend") + trend = trend[0: self.num_of_samples] + avg = np.ones((1, self.w1[1:].shape[0])) * (0 + 1) / 2 + svr = np.empty((0, 2)) + + for i in range(self.w1[1:].shape[0]): + variables = np.ones((len(trend), 1)) * avg + variables[:, i] = trend + + out = self.predict(variables) + + if min(out) == max(out): + out = 0.5 * np.ones(out.size) + else: + out = (out - min(out)) / (max(out) - min(out)) + + if ploton: + trace0 = go.Scatter( + x=np.arange(len(variables[:, 1])), y=variables[:, i], name="input" + ) + trace1 = go.Scatter( + x=np.arange(len(variables[:, 1])), y=out, name="output" + ) + data = [trace0, trace1] + plotly.offline.plot( + data, filename="x" + str(i + 1) + "_response.html", auto_open=True + ) + + p = np.diff(out) + q = np.diff(trend) + r = np.multiply(p, q) + r_max = max(r) + r_min = min(r) + if r_max <= 0 and r_min <= 0: + response = -1 + s = "inverse" + elif r_max >= 0 and r_min >= 0: + response = 1 + s = "direct" + elif r_max == 0 and r_min == 0: + response = 0 + s = "nil" + elif r_min < 0 < r_max: + response = 2 + s = "mixed" + + print("x" + str(i + 1) + " response: " + str(response) + " " + s, file=log) + svr = np.vstack((svr, ["x" + str(i + 1), s])) + self.svr = svr diff --git a/pyrvea/Recombination/ppga_parallel_crossover.py b/pyrvea/Recombination/ppga_parallel_crossover.py new file mode 100644 index 0000000..7634ab1 --- /dev/null +++ b/pyrvea/Recombination/ppga_parallel_crossover.py @@ -0,0 +1,44 @@ +import numpy as np +from copy import deepcopy +from random import sample + + +def ppga_parallel_crossover(parent1, parent2, prob_crossover=0.8): + """Randomly exchange connections between two individuals. + + Parameters + ---------- + parent1 : np.ndarray + The first individual + parent2 : np.ndarray + The second individual + prob_crossover : float + The probability for the crossover + """ + + offspring1 = deepcopy(parent1) + offspring2 = deepcopy(parent2) + + for subnet in range(len(offspring1)): + + sub1 = offspring1[subnet] + sub2 = offspring2[subnet] + + if len(sub1) <= len(sub2): + r = len(sub1) + else: + r = len(sub2) + + for layer in range(r): + + if sub1[layer].size <= sub2[layer].size: + connections = sub1[layer][1:, :].size + else: + connections = sub2[layer][1:, :].size + + exchange = sample(range(connections), np.random.binomial(connections, prob_crossover)) + tmp = deepcopy(sub1[layer]) + sub1[layer][1:, :].ravel()[exchange] = sub2[layer][1:, :].ravel()[exchange] + sub2[layer][1:, :].ravel()[exchange] = tmp[1:, :].ravel()[exchange] + + return offspring1, offspring2 From 0b70d446e1e7025a52e85719849f828326a0fc04 Mon Sep 17 00:00:00 2001 From: delamorte Date: Wed, 19 Jun 2019 14:41:46 +0300 Subject: [PATCH 036/155] implemented a faster way of randomly killing connections --- evonn_example.py | 24 +++++++++++++++++++++--- pyrvea/Population/Population.py | 16 +++++++--------- pyrvea/Problem/evodn2_problem.py | 4 ++-- pyrvea/Problem/evonn_problem.py | 2 +- 4 files changed, 31 insertions(+), 15 deletions(-) diff --git a/evonn_example.py b/evonn_example.py index 6db930c..77cbd35 100644 --- a/evonn_example.py +++ b/evonn_example.py @@ -2,12 +2,30 @@ from pyrvea.Problem.evonn_problem import EvoNNModel from pyrvea.Problem.evodn2_problem import EvoDN2Model -test_prob = EvoNNTestProblem("Three-hump camel") +# test_prob = EvoNNTestProblem("Three-hump camel") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=150, method="random" +# ) +# +# model_f1 = EvoNNModel(name="EvoDN2_three_hump_camel") +# model_f1.set_params( +# num_hidden_nodes=15, +# pop_size=500, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# model_f1.fit(training_data_input, training_data_output) + +test_prob = EvoNNTestProblem("Sphere", num_of_variables=10) training_data_input, training_data_output = test_prob.create_training_data( samples=150, method="random" ) -model_f1 = EvoDN2Model(name="EvoDN2_three_hump_camel") +model_f1 = EvoNNModel(name="EvoNN_" + test_prob.name) model_f1.set_params( num_hidden_nodes=15, pop_size=500, @@ -18,4 +36,4 @@ logging=True, plotting=True) -model_f1.fit(training_data_input, training_data_output) \ No newline at end of file +model_f1.fit(training_data_input, training_data_output) diff --git a/pyrvea/Population/Population.py b/pyrvea/Population/Population.py index f7a3c19..e164466 100644 --- a/pyrvea/Population/Population.py +++ b/pyrvea/Population/Population.py @@ -2,7 +2,7 @@ from collections.abc import Sequence from random import shuffle from typing import TYPE_CHECKING - +import timeit import random import numpy as np import pandas as pd @@ -22,7 +22,7 @@ from pyrvea.Recombination.ppga_crossover import ppga_crossover from pyrvea.Recombination.ppga_parallel_crossover import ppga_parallel_crossover from pyrvea.Recombination.ppga_mutation import ppga_mutation - +from math import ceil if TYPE_CHECKING: from pyrvea.Problem.baseProblem import baseProblem from pyrvea.EAs.baseEA import BaseEA @@ -138,19 +138,17 @@ def create_new_individuals( self.problem.w_high, size=( pop_size, - self.problem.num_input_nodes + 1, + self.problem.num_input_nodes+1, self.problem.num_hidden_nodes, ), ) - # Eliminate some weights - flag = bn.rvs(p=1 - self.problem.prob_omit, size=np.shape(individuals)) - - random_numbers = np.zeros(np.shape(individuals)) - individuals[flag == 0] = random_numbers[flag == 0] + # Randomly set some weights to zero + zeros = np.random.choice(np.arange(individuals.size), ceil(individuals.size * self.problem.prob_omit)) + individuals.ravel()[zeros] = 0 # Set bias - individuals[:, 0, :] = 1 + individuals = np.insert(individuals, 0, 1, axis=1) elif design == "EvoDN2": self.individuals = np.empty( diff --git a/pyrvea/Problem/evodn2_problem.py b/pyrvea/Problem/evodn2_problem.py index c7032ba..4f5cbec 100644 --- a/pyrvea/Problem/evodn2_problem.py +++ b/pyrvea/Problem/evodn2_problem.py @@ -46,7 +46,6 @@ def __init__( w_low=-5.0, w_high=5.0, prob_omit=0.2, - subnets=[], params=None, ): super().__init__() @@ -61,6 +60,7 @@ def __init__( self.w_high = w_high self.prob_omit = prob_omit self.params = params + # [num of subnets, max num of layers] self.subnets = [4, 4] self.max_nodes = 3 @@ -130,7 +130,7 @@ def create_population(self): def train(self, model): - pop = Population(self, assign_type="EvoDN2", pop_size=500, plotting=False) + pop = Population(self, assign_type="EvoDN2", pop_size=self.params["pop_size"], plotting=False) pop.evolve( PPGA, { diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index 67bbdc2..9f6cbd8 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -79,7 +79,7 @@ def fit(self, training_data, target_values): def train(self, model): - pop = Population(self, assign_type="EvoNN", pop_size=500, plotting=False) + pop = Population(self, assign_type="EvoNN", pop_size=self.params["pop_size"], plotting=False) pop.evolve( PPGA, { From 4cb136964aedbd6bb2185da7490e882db040bfda Mon Sep 17 00:00:00 2001 From: delamorte Date: Wed, 19 Jun 2019 14:54:15 +0300 Subject: [PATCH 037/155] EvoNN population now created in evonn_problem class --- pyrvea/Population/Population.py | 29 ++++------------------------- pyrvea/Problem/evonn_problem.py | 22 ++++++++++++++++++++++ 2 files changed, 26 insertions(+), 25 deletions(-) diff --git a/pyrvea/Population/Population.py b/pyrvea/Population/Population.py index e164466..0bf34a5 100644 --- a/pyrvea/Population/Population.py +++ b/pyrvea/Population/Population.py @@ -1,16 +1,14 @@ from collections import defaultdict from collections.abc import Sequence -from random import shuffle + from typing import TYPE_CHECKING -import timeit -import random import numpy as np import pandas as pd from pyDOE import lhs from pygmo import fast_non_dominated_sorting as nds from pygmo import hypervolume as hv from pygmo import non_dominated_front_2d as nd2 -from scipy.stats import bernoulli as bn + from tqdm import tqdm, tqdm_notebook from pyrvea.Recombination.bounded_polynomial_mutation import mutation @@ -127,28 +125,9 @@ def create_new_individuals( + self.lower_limits ) elif design == "EvoNN": - # The population could perhaps be created inside the problem class in EvoNN cases? - self.individuals = np.empty( - (0, self.problem.num_input_nodes + 1, self.problem.num_hidden_nodes), - float, - ) - # +1 row for bias - individuals = np.random.uniform( - self.problem.w_low, - self.problem.w_high, - size=( - pop_size, - self.problem.num_input_nodes+1, - self.problem.num_hidden_nodes, - ), - ) - - # Randomly set some weights to zero - zeros = np.random.choice(np.arange(individuals.size), ceil(individuals.size * self.problem.prob_omit)) - individuals.ravel()[zeros] = 0 - # Set bias - individuals = np.insert(individuals, 0, 1, axis=1) + individuals = self.problem.create_population() + self.individuals = np.empty((0, individuals.shape[1], individuals.shape[2])) elif design == "EvoDN2": self.individuals = np.empty( diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index 9f6cbd8..9f188d0 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -1,6 +1,7 @@ from pyrvea.Problem.baseProblem import baseProblem from pyrvea.Population.Population import Population from pyrvea.EAs.PPGA import PPGA +from math import ceil import numpy as np import plotly import plotly.graph_objs as go @@ -77,6 +78,27 @@ def fit(self, training_data, target_values): self.num_input_nodes = self.num_of_variables self.num_hidden_nodes = self.params["num_hidden_nodes"] + def create_population(self): + + individuals = np.random.uniform( + self.w_low, + self.w_high, + size=( + self.params["pop_size"], + self.num_input_nodes, + self.num_hidden_nodes, + ), + ) + + # Randomly set some weights to zero + zeros = np.random.choice(np.arange(individuals.size), ceil(individuals.size * self.prob_omit)) + individuals.ravel()[zeros] = 0 + + # Set bias + individuals = np.insert(individuals, 0, 1, axis=1) + + return individuals + def train(self, model): pop = Population(self, assign_type="EvoNN", pop_size=self.params["pop_size"], plotting=False) From 481a975d114eee23790b22263f2f72e1f194694a Mon Sep 17 00:00:00 2001 From: delamorte Date: Thu, 20 Jun 2019 15:39:02 +0300 Subject: [PATCH 038/155] EvoDN2 algorithm completed (needs testing) added mutation function for evodn2 added svr for evodn2 --- evodn2_example.py | 18 +- evonn_example.py | 42 +-- pyrvea/EAs/PPGA.py | 33 ++- pyrvea/Population/Population.py | 56 ++-- pyrvea/Problem/evodn2_problem.py | 89 +++--- pyrvea/Problem/evonn_problem.py | 27 +- pyrvea/Recombination/evodn2_xover_mut.py | 96 +++++++ .../Recombination/ppga_parallel_crossover.py | 44 --- trend | 255 ++++++++++++++++++ 9 files changed, 500 insertions(+), 160 deletions(-) create mode 100644 pyrvea/Recombination/evodn2_xover_mut.py delete mode 100644 pyrvea/Recombination/ppga_parallel_crossover.py create mode 100644 trend diff --git a/evodn2_example.py b/evodn2_example.py index cca8240..fbfdd56 100644 --- a/evodn2_example.py +++ b/evodn2_example.py @@ -7,20 +7,32 @@ # samples=150, method="random" # ) -test_prob = EvoNNTestProblem("Sphere", num_of_variables=10) +test_prob = EvoNNTestProblem("Three-hump camel") training_data_input, training_data_output = test_prob.create_training_data( samples=150, method="random" ) model_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name) model_f1.set_params( - num_hidden_nodes=15, pop_size=500, activation_func="sigmoid", opt_func="llsq", loss_func="rmse", - criterion="akaike_corrected", + criterion="min_error", logging=True, plotting=True) model_f1.fit(training_data_input, training_data_output) + +model_f2 = EvoNNModel(name="EvoNN_" + test_prob.name) +model_f2.set_params( + num_nodes=15, + pop_size=500, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_f2.fit(training_data_input, training_data_output) \ No newline at end of file diff --git a/evonn_example.py b/evonn_example.py index 77cbd35..e617438 100644 --- a/evonn_example.py +++ b/evonn_example.py @@ -2,32 +2,14 @@ from pyrvea.Problem.evonn_problem import EvoNNModel from pyrvea.Problem.evodn2_problem import EvoDN2Model -# test_prob = EvoNNTestProblem("Three-hump camel") -# training_data_input, training_data_output = test_prob.create_training_data( -# samples=150, method="random" -# ) -# -# model_f1 = EvoNNModel(name="EvoDN2_three_hump_camel") -# model_f1.set_params( -# num_hidden_nodes=15, -# pop_size=500, -# activation_func="sigmoid", -# opt_func="llsq", -# loss_func="rmse", -# criterion="akaike_corrected", -# logging=True, -# plotting=True) -# -# model_f1.fit(training_data_input, training_data_output) - -test_prob = EvoNNTestProblem("Sphere", num_of_variables=10) +test_prob = EvoNNTestProblem("Three-hump camel") training_data_input, training_data_output = test_prob.create_training_data( samples=150, method="random" ) -model_f1 = EvoNNModel(name="EvoNN_" + test_prob.name) +model_f1 = EvoNNModel(name="EvoDN2_three_hump_camel") model_f1.set_params( - num_hidden_nodes=15, + num_nodes=15, pop_size=500, activation_func="sigmoid", opt_func="llsq", @@ -37,3 +19,21 @@ plotting=True) model_f1.fit(training_data_input, training_data_output) + +# test_prob = EvoNNTestProblem("Sphere", num_of_variables=10) +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=150, method="random" +# ) +# +# model_f1 = EvoNNModel(name="EvoNN_" + test_prob.name) +# model_f1.set_params( +# num_nodes=15, +# pop_size=500, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# model_f1.fit(training_data_input, training_data_output) diff --git a/pyrvea/EAs/PPGA.py b/pyrvea/EAs/PPGA.py index 6bdb86f..aca2576 100644 --- a/pyrvea/EAs/PPGA.py +++ b/pyrvea/EAs/PPGA.py @@ -108,7 +108,7 @@ def set_params( + "_var" + str(population.problem.num_of_variables) + "_nodes" - + str(population.problem.num_hidden_nodes) + + str(population.problem.num_nodes) + ".log", "w" ) @@ -148,6 +148,7 @@ def _next_gen(self, population: "Population"): Population object """ + # Predator max moves for gen self.params["predator_max_moves"] = int( ( self.params["population"].individuals.shape[0] @@ -156,16 +157,30 @@ def _next_gen(self, population: "Population"): / self.params["predator_pop_size"] ) + # Move prey self.lattice.move_prey() - offspring = np.empty( - ( - 0, - population.problem.num_input_nodes + 1, - population.problem.num_hidden_nodes, - ), - float, - ) + # Choose and create offspring + if population.problem.__class__.__name__ == "EvoNN": + + offspring = np.empty( + ( + 0, + population.problem.num_input_nodes + 1, + population.problem.num_nodes, + ), + float, + ) + elif population.problem.__class__.__name__ == "EvoDN2": + # # Calculate standard deviation of pop + # y = [] + # for x in population.individuals.ravel(): + # y.append(np.concatenate(x, axis=None)) + # std_dev = -1 ** (np.random.randint(1, 100)) * np.std(np.concatenate(np.array(y))) ** 2 + # self.params["std_dev"] = std_dev + + offspring = np.empty((0, population.problem.subnets[0])) + for ind in range(population.individuals.shape[0]): mate_idx = self.lattice.choose_mate(ind) diff --git a/pyrvea/Population/Population.py b/pyrvea/Population/Population.py index 0bf34a5..3c5b4e6 100644 --- a/pyrvea/Population/Population.py +++ b/pyrvea/Population/Population.py @@ -18,9 +18,10 @@ from pyrvea.OtherTools.IsNotebook import IsNotebook from pyrvea.Recombination.ppga_crossover import ppga_crossover -from pyrvea.Recombination.ppga_parallel_crossover import ppga_parallel_crossover +from pyrvea.Recombination.evodn2_xover_mut import evodn2_xover_mut from pyrvea.Recombination.ppga_mutation import ppga_mutation from math import ceil + if TYPE_CHECKING: from pyrvea.Problem.baseProblem import baseProblem from pyrvea.EAs.baseEA import BaseEA @@ -130,12 +131,7 @@ def create_new_individuals( self.individuals = np.empty((0, individuals.shape[1], individuals.shape[2])) elif design == "EvoDN2": - self.individuals = np.empty( - ( - 0, - self.problem.subnets[0] - ) - ) + self.individuals = np.empty((0, self.problem.subnets[0])) individuals = self.problem.create_population() @@ -329,28 +325,36 @@ def mate(self, ind1=None, ind2=None, params=None): # Perform crossover if params["crossover_type"] == "short": - xover_w1, xover_w2 = ppga_parallel_crossover(w1, w2, params["prob_crossover"]) + offspring1, offspring2 = evodn2_xover_mut( + w1, + w2, + self.individuals, + params["prob_crossover"], + params["prob_mutation"], + params["mut_strength"], + params["current_iteration_gen_count"], + params["generations"] + ) else: xover_w1, xover_w2 = ppga_crossover(w1, w2, params["prob_crossover"]) - # Find randomly two other individuals with current match active for mutation. - # Make a list of individuals suitable for mutation, exclude the ones to be mutated - # so that they won't mutate with themselves - indices = [ind1, ind2] - mask = np.ones(len(self.individuals), dtype=bool) - mask[indices] = False - alternatives = self.individuals[mask, ...][:, 1:, :] - - # Mutate - offspring1, offspring2 = ppga_mutation( - alternatives, - xover_w1, - xover_w2, - params["current_iteration_gen_count"], - params["generations"], - params["prob_mutation"], - params["mut_strength"], - ) + # Make a list of individuals suitable for mutation, exclude the ones to be mutated + # so that they won't mutate with themselves + indices = [ind1, ind2] + mask = np.ones(len(self.individuals), dtype=bool) + mask[indices] = False + alternatives = self.individuals[mask, ...][:, 1:, :] + + # Mutate + offspring1, offspring2 = ppga_mutation( + alternatives, + xover_w1, + xover_w2, + params["current_iteration_gen_count"], + params["generations"], + params["prob_mutation"], + params["mut_strength"], + ) return offspring1, offspring2 diff --git a/pyrvea/Problem/evodn2_problem.py b/pyrvea/Problem/evodn2_problem.py index 4f5cbec..0bb44e9 100644 --- a/pyrvea/Problem/evodn2_problem.py +++ b/pyrvea/Problem/evodn2_problem.py @@ -15,10 +15,8 @@ class EvoDN2(baseProblem): ---------- name : str Name of the sample - num_input_nodes : int - The number of nodes in the input layer - num_hidden_nodes : int - The number of nodes in the hidden layer + num_nodes : int + The maximum number of nodes in the hidden layer num_of_objectives : int The number of objectives w_low : float @@ -40,8 +38,6 @@ def __init__( name, X_train=None, y_train=None, - num_input_nodes=4, - num_hidden_nodes=5, num_of_objectives=2, w_low=-5.0, w_high=5.0, @@ -53,16 +49,14 @@ def __init__( self.name = name self.X_train = X_train self.y_train = y_train - self.num_input_nodes = num_input_nodes - self.num_hidden_nodes = num_hidden_nodes self.num_of_objectives = num_of_objectives self.w_low = w_low self.w_high = w_high self.prob_omit = prob_omit self.params = params # [num of subnets, max num of layers] - self.subnets = [4, 4] - self.max_nodes = 3 + self.subnets = [3, 8] + self.num_nodes = 8 def fit(self, training_data, target_values): """Fit data in EvoNN model. @@ -91,6 +85,8 @@ def fit(self, training_data, target_values): if not any(n in k for k in self.subsets): self.subsets[random.randint(0, self.subnets[0]-1)].append(n) + return self.subsets + def create_population(self): individuals = [] @@ -103,7 +99,7 @@ def create_population(self): in_nodes = len(self.subsets[j]) for k in range(num_layers): - out_nodes = random.randint(1, self.max_nodes) + out_nodes = random.randint(1, self.num_nodes) net = np.random.uniform( self.w_low, self.w_high, @@ -135,17 +131,17 @@ def train(self, model): PPGA, { "logging": self.params["logging"], - "iterations": 10, - "generations_per_iteration": 10, + "iterations": 1, + "generations_per_iteration": 1, "crossover_type": "short", "mutation_type": "short" } ) non_dom_front = pop.non_dominated() - model.w1 = self.select(pop, non_dom_front, self.params["criterion"]) - activated_layer = self.activation(model.w1) - model.w2, _, model.y_pred = self.minimize_error(activated_layer) + model.w1, model.fitness = self.select(pop, non_dom_front, self.params["criterion"]) + model.end_net, complexity = self.activation(model.w1) + model.w2, _, model.y_pred = self.minimize_error(model.end_net) def objectives(self, decision_variables) -> list: @@ -194,7 +190,7 @@ def activation(self, decision_variables): """ subnet_cmplx = [] end_net = np.empty((self.num_of_samples, 0)) - for i in range(self.subnets[0]): + for i in range(decision_variables.shape[0]): subnet = decision_variables[i] in_nodes = self.X_train[:, self.subsets[i]] @@ -256,17 +252,6 @@ def loss_function(self, predicted_values): if self.params["loss_func"]: return np.sqrt(((self.y_train - predicted_values) ** 2).mean()) - def information_criterion(self, decision_variables): - - z = self.activation(decision_variables) - w_matrix2, rss, prediction = self.minimize_error(z) - # rss = ((self.y_train - prediction) ** 2).sum() - k = self.calculate_complexity(decision_variables) + np.count_nonzero(w_matrix2) - aic = 2 * k + self.num_of_samples * np.log(rss / self.num_of_samples) - aicc = aic + (2 * k * (k + 1) / (self.num_of_samples - k - 1)) - - return aicc - def select(self, pop, non_dom_front, criterion="min_error"): """ Select target model from the population. @@ -285,11 +270,13 @@ def select(self, pop, non_dom_front, criterion="min_error"): The selected model """ model = None + fitness = None if criterion == "min_error": # Return the model with the lowest error lowest_error = np.argmin(pop.objectives[:, 0]) model = pop.individuals[lowest_error] + fitness = pop.fitness[lowest_error] elif criterion == "akaike_corrected": @@ -306,8 +293,9 @@ def select(self, pop, non_dom_front, criterion="min_error"): info_c_rank.sort() model = pop.individuals[info_c_rank[0][1]] + fitness = pop.fitness[info_c_rank[0][1]] - return model + return model, fitness def create_logfile(self): @@ -317,7 +305,7 @@ def create_logfile(self): + "_var" + str(self.num_of_variables) + "_nodes" - + str(self.num_hidden_nodes) + + str(self.num_nodes) + ".log", "a", ) @@ -329,7 +317,7 @@ def create_logfile(self): + str(self.num_of_variables) + "\n" + "nodes: " - + str(self.num_hidden_nodes) + + str(self.num_nodes) + "\n" + "activation: " + self.params["activation_func"] @@ -354,7 +342,7 @@ def create_plot(self, model): + "_var" + str(self.num_of_variables) + "_nodes" - + str(self.num_hidden_nodes) + + str(self.num_nodes) + ".html", auto_open=True, ) @@ -382,7 +370,8 @@ def fit(self, training_data, target_values): Target values """ f1 = EvoDN2(name=self.name, params=self.params) - f1.fit(training_data, target_values) + self.subsets = f1.fit(training_data, target_values) + self.num_of_variables = f1.num_of_variables f1.train(self) if f1.params["logging"]: self.log = f1.create_logfile() @@ -393,25 +382,36 @@ def fit(self, training_data, target_values): def predict(self, decision_variables): - wi = np.dot(decision_variables, self.w1[1:, :]) + self.w1[0] + in_end = np.empty((self.num_of_samples, 0)) + for i in range(self.w1.size): - if self.params["activation_func"] == "sigmoid": - activated_layer = lambda x: 1 / (1 + np.exp(-x)) + subnet = self.w1[i] + in_nodes = decision_variables[:, self.subsets[i]] - if self.params["activation_func"] == "relu": - activated_layer = lambda x: np.maximum(x, 0) + for layer in range(len(subnet)): + # Calculate the dot product + out = np.dot(in_nodes, subnet[layer][1:, :]) + subnet[layer][0] + + if self.params["activation_func"] == "sigmoid": + activated_layer = lambda x: 1 / (1 + np.exp(-x)) + + if self.params["activation_func"] == "relu": + activated_layer = lambda x: np.maximum(x, 0) + + if self.params["activation_func"] == "tanh": + activated_layer = lambda x: np.tanh(x) + + in_nodes = activated_layer(out) - if self.params["activation_func"] == "tanh": - activated_layer = lambda x: np.tanh(x) + in_end = np.hstack((in_end, in_nodes)) - out = np.dot(activated_layer(wi), self.w2) + result = np.dot(in_end, self.w2) - return out + return result def set_params( self, name=None, - num_hidden_nodes=15, pop_size=500, activation_func="sigmoid", opt_func="llsq", @@ -422,7 +422,6 @@ def set_params( ): params = { "name": name, - "num_hidden_nodes": num_hidden_nodes, "pop_size": pop_size, "activation_func": activation_func, "opt_func": opt_func, @@ -438,7 +437,7 @@ def single_variable_response(self, ploton=False, log=None): trend = np.loadtxt("trend") trend = trend[0: self.num_of_samples] - avg = np.ones((1, self.w1[1:].shape[0])) * (0 + 1) / 2 + avg = np.ones((1, self.num_of_variables)) * (np.finfo(float).eps + 1) / 2 svr = np.empty((0, 2)) for i in range(self.w1[1:].shape[0]): diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index 9f188d0..790ced2 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -16,7 +16,7 @@ class EvoNN(baseProblem): Name of the sample num_input_nodes : int The number of nodes in the input layer - num_hidden_nodes : int + num_nodes : int The number of nodes in the hidden layer num_of_objectives : int The number of objectives @@ -40,7 +40,7 @@ def __init__( X_train=None, y_train=None, num_input_nodes=4, - num_hidden_nodes=5, + num_nodes=5, num_of_objectives=2, w_low=-5.0, w_high=5.0, @@ -53,7 +53,7 @@ def __init__( self.X_train = X_train self.y_train = y_train self.num_input_nodes = num_input_nodes - self.num_hidden_nodes = num_hidden_nodes + self.num_nodes = num_nodes self.num_of_objectives = num_of_objectives self.w_low = w_low self.w_high = w_high @@ -76,7 +76,7 @@ def fit(self, training_data, target_values): self.num_of_samples = target_values.shape[0] self.num_of_variables = training_data.shape[1] self.num_input_nodes = self.num_of_variables - self.num_hidden_nodes = self.params["num_hidden_nodes"] + self.num_nodes = self.params["num_nodes"] def create_population(self): @@ -86,7 +86,7 @@ def create_population(self): size=( self.params["pop_size"], self.num_input_nodes, - self.num_hidden_nodes, + self.num_nodes, ), ) @@ -112,7 +112,7 @@ def train(self, model): ) non_dom_front = pop.non_dominated() - model.w1 = self.select(pop, non_dom_front, self.params["criterion"]) + model.w1, model.fitness = self.select(pop, non_dom_front, self.params["criterion"]) activated_layer = self.activation(model.w1) model.w2, _, model.y_pred = self.minimize_error(activated_layer) @@ -239,11 +239,13 @@ def select(self, pop, non_dom_front, criterion="min_error"): The selected model """ model = None + fitness = None if criterion == "min_error": # Return the model with the lowest error lowest_error = np.argmin(pop.objectives[:, 0]) model = pop.individuals[lowest_error] + fitness = pop.fitness[lowest_error] elif criterion == "akaike_corrected": @@ -260,8 +262,9 @@ def select(self, pop, non_dom_front, criterion="min_error"): info_c_rank.sort() model = pop.individuals[info_c_rank[0][1]] + fitness = pop.fitness[info_c_rank[0][1]] - return model + return model, fitness def create_logfile(self): @@ -271,7 +274,7 @@ def create_logfile(self): + "_var" + str(self.num_of_variables) + "_nodes" - + str(self.num_hidden_nodes) + + str(self.num_nodes) + ".log", "a", ) @@ -283,7 +286,7 @@ def create_logfile(self): + str(self.num_of_variables) + "\n" + "nodes: " - + str(self.num_hidden_nodes) + + str(self.num_nodes) + "\n" + "activation: " + self.params["activation_func"] @@ -308,7 +311,7 @@ def create_plot(self, model): + "_var" + str(self.num_of_variables) + "_nodes" - + str(self.num_hidden_nodes) + + str(self.num_nodes) + ".html", auto_open=True, ) @@ -365,7 +368,7 @@ def predict(self, decision_variables): def set_params( self, name=None, - num_hidden_nodes=15, + num_nodes=15, pop_size=500, activation_func="sigmoid", opt_func="llsq", @@ -376,7 +379,7 @@ def set_params( ): params = { "name": name, - "num_hidden_nodes": num_hidden_nodes, + "num_nodes": num_nodes, "pop_size": pop_size, "activation_func": activation_func, "opt_func": opt_func, diff --git a/pyrvea/Recombination/evodn2_xover_mut.py b/pyrvea/Recombination/evodn2_xover_mut.py new file mode 100644 index 0000000..f681862 --- /dev/null +++ b/pyrvea/Recombination/evodn2_xover_mut.py @@ -0,0 +1,96 @@ +import numpy as np +from copy import deepcopy +from random import sample + + +def evodn2_xover_mut( + parent1, + parent2, + individuals, + prob_crossover=0.8, + prob_mut=0.3, + mut_strength=0.7, + std_dev=1, + cur_gen=1, + total_gen=10 +): + """ Perform simultaneous crossover and mutation over two individuals. + + Parameters + ---------- + parent1 : ndarray + The first individual + parent2 : ndarray + The second individual + individuals : ndarray + All individuals to choose mutation partner from + prob_crossover : float + The probability for crossover + prob_mut : float + The probability for mutation + mut_strength : float + Mutation alfa parameter + std_dev : float + Standard deviation of population + cur_gen : int + Current generation + total_gen : int + Total generations + """ + + offspring1 = deepcopy(parent1) + offspring2 = deepcopy(parent2) + + for subnet in range(len(offspring1)): + + sub1 = offspring1[subnet] + sub2 = offspring2[subnet] + sub3 = individuals[np.random.randint(0, individuals.shape[0] - 1)][subnet] + sub4 = individuals[np.random.randint(0, individuals.shape[0] - 1)][subnet] + + r = min(len(sub1), len(sub2)) + + for layer in range(r): + + connections = min(sub1[layer][1:, :].size, sub2[layer][1:, :].size) + + # Crossover + exchange = sample( + range(connections), np.random.binomial(connections, prob_crossover) + ) + tmp = deepcopy(sub1[layer]) + sub1[layer][1:, :].ravel()[exchange] = sub2[layer][1:, :].ravel()[exchange] + sub2[layer][1:, :].ravel()[exchange] = tmp[1:, :].ravel()[exchange] + + try: + # Mutate first individual + connections = min( + sub1[layer][1:, :].size, + sub2[layer][1:, :].size, + sub3[layer][1:, :].size, + sub4[layer][1:, :].size, + ) + mutate = sample( + range(connections), np.random.binomial(connections, prob_mut) + ) + sub1[layer][1:, :].ravel()[mutate] = sub1[layer][1:, :].ravel()[ + mutate + ] + mut_strength * std_dev * (1 - cur_gen / total_gen) * ( + sub3[layer][1:, :].ravel()[mutate] + - sub4[layer][1:, :].ravel()[mutate] + ) + + # Mutate second individual + mutate = sample( + range(connections), np.random.binomial(connections, prob_mut) + ) + sub2[layer][1:, :].ravel()[mutate] = sub2[layer][1:, :].ravel()[ + mutate + ] + mut_strength * std_dev * (1 - cur_gen / total_gen) * ( + sub3[layer][1:, :].ravel()[mutate] + - sub4[layer][1:, :].ravel()[mutate] + ) + except IndexError: + break + + return offspring1, offspring2 diff --git a/pyrvea/Recombination/ppga_parallel_crossover.py b/pyrvea/Recombination/ppga_parallel_crossover.py deleted file mode 100644 index 7634ab1..0000000 --- a/pyrvea/Recombination/ppga_parallel_crossover.py +++ /dev/null @@ -1,44 +0,0 @@ -import numpy as np -from copy import deepcopy -from random import sample - - -def ppga_parallel_crossover(parent1, parent2, prob_crossover=0.8): - """Randomly exchange connections between two individuals. - - Parameters - ---------- - parent1 : np.ndarray - The first individual - parent2 : np.ndarray - The second individual - prob_crossover : float - The probability for the crossover - """ - - offspring1 = deepcopy(parent1) - offspring2 = deepcopy(parent2) - - for subnet in range(len(offspring1)): - - sub1 = offspring1[subnet] - sub2 = offspring2[subnet] - - if len(sub1) <= len(sub2): - r = len(sub1) - else: - r = len(sub2) - - for layer in range(r): - - if sub1[layer].size <= sub2[layer].size: - connections = sub1[layer][1:, :].size - else: - connections = sub2[layer][1:, :].size - - exchange = sample(range(connections), np.random.binomial(connections, prob_crossover)) - tmp = deepcopy(sub1[layer]) - sub1[layer][1:, :].ravel()[exchange] = sub2[layer][1:, :].ravel()[exchange] - sub2[layer][1:, :].ravel()[exchange] = tmp[1:, :].ravel()[exchange] - - return offspring1, offspring2 diff --git a/trend b/trend new file mode 100644 index 0000000..930db53 --- /dev/null +++ b/trend @@ -0,0 +1,255 @@ + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 1.00000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.00000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.75000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.25000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 1.00000 + 1.00000 + 1.00000 + 1.00000 + 1.00000 + 1.00000 + 1.00000 + 1.00000 + 1.00000 + 1.00000 + 1.00000 + 1.00000 + 1.00000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.00000 + 0.00000 + 0.00000 + 0.00000 + 0.00000 + 0.00000 + 0.00000 + 0.00000 + 0.00000 + 0.00000 + 0.00000 + 0.00000 + 0.00000 + 0.00000 + 0.00000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.75000 + 0.75000 + 0.75000 + 0.75000 + 0.75000 + 0.75000 + 0.75000 + 0.75000 + 0.75000 + 0.75000 + 0.75000 + 0.75000 + 0.75000 + 0.75000 + 0.75000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.25000 + 0.25000 + 0.25000 + 0.25000 + 0.25000 + 0.25000 + 0.25000 + 0.25000 + 0.25000 + 0.25000 + 0.25000 + 0.25000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.60000 + 0.60000 + 0.60000 + 0.60000 + 0.60000 + 0.60000 + 0.60000 + 0.60000 + 0.60000 + 0.80000 + 0.80000 + 0.80000 + 0.80000 + 0.80000 + 0.80000 + 0.80000 + 0.80000 + 0.80000 + 0.80000 + 1.00000 + 1.00000 + 1.00000 + 1.00000 + 1.00000 + 1.00000 + 1.00000 + 1.00000 + 1.00000 + 1.00000 + 1.00000 + 1.00000 + 1.00000 + 1.00000 + 1.00000 + 1.00000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.30000 + 0.30000 + 0.30000 + 0.30000 + 0.30000 + 0.30000 + 0.30000 + 0.10000 + 0.10000 + 0.10000 + 0.10000 + 0.10000 + 0.10000 + 0.10000 + 0.10000 + 0.10000 + 0.10000 + 0.10000 + 0.10000 + 0.00000 + 0.00000 + 0.00000 + 0.00000 + 0.00000 + 0.00000 + 0.00000 + 0.00000 + 0.00000 + 0.00000 + 0.00000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 + 0.50000 From 973d1be2786d6a46838766421652e19341945939 Mon Sep 17 00:00:00 2001 From: delamorte Date: Mon, 24 Jun 2019 13:58:49 +0300 Subject: [PATCH 039/155] added some documentation --- evodn2_example.py | 27 +++-- pyrvea/Problem/evodn2_problem.py | 132 +++++++++++++----------- pyrvea/Problem/evonn_problem.py | 133 +++++++++++++++++-------- pyrvea/Problem/evonn_test_functions.py | 2 +- 4 files changed, 187 insertions(+), 107 deletions(-) diff --git a/evodn2_example.py b/evodn2_example.py index fbfdd56..51d282d 100644 --- a/evodn2_example.py +++ b/evodn2_example.py @@ -1,20 +1,28 @@ from pyrvea.Problem.evonn_test_functions import EvoNNTestProblem from pyrvea.Problem.evonn_problem import EvoNNModel from pyrvea.Problem.evodn2_problem import EvoDN2Model +from pyrvea.Problem.testProblem import testProblem +import numpy as np # test_prob = EvoNNTestProblem("Three-hump camel") # training_data_input, training_data_output = test_prob.create_training_data( # samples=150, method="random" # ) -test_prob = EvoNNTestProblem("Three-hump camel") +# test_prob = testProblem('ZDT2', 30, 2, 0, 1, 0) +# training_data_input = np.random.rand(150,30) +# training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) + +test_prob = EvoNNTestProblem("Goldstein-Price") training_data_input, training_data_output = test_prob.create_training_data( - samples=150, method="random" + samples=250, method="random" ) -model_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_f1.set_params( +model_evodn = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn.set_params( pop_size=500, + subnets=(4, 8), + num_nodes=10, activation_func="sigmoid", opt_func="llsq", loss_func="rmse", @@ -22,11 +30,12 @@ logging=True, plotting=True) -model_f1.fit(training_data_input, training_data_output) -model_f2 = EvoNNModel(name="EvoNN_" + test_prob.name) -model_f2.set_params( - num_nodes=15, +model_evodn.fit(training_data_input, training_data_output) + +model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +model_evonn.set_params( + num_nodes=20, pop_size=500, activation_func="sigmoid", opt_func="llsq", @@ -35,4 +44,4 @@ logging=True, plotting=True) -model_f2.fit(training_data_input, training_data_output) \ No newline at end of file +model_evonn.fit(training_data_input, training_data_output) diff --git a/pyrvea/Problem/evodn2_problem.py b/pyrvea/Problem/evodn2_problem.py index 0bb44e9..997b0a6 100644 --- a/pyrvea/Problem/evodn2_problem.py +++ b/pyrvea/Problem/evodn2_problem.py @@ -9,14 +9,12 @@ class EvoDN2(baseProblem): - """Creates an Artificial Neural Network (ANN) for the EvoNN algorithm. + """Creates an Artificial Neural Network (ANN) for the EvoDN2 algorithm. - Attributes + Parameters ---------- name : str Name of the sample - num_nodes : int - The maximum number of nodes in the hidden layer num_of_objectives : int The number of objectives w_low : float @@ -25,12 +23,7 @@ class EvoDN2(baseProblem): The upper bound for randomly generated weights prob_omit : float The probability of setting some weights to zero initially - activation_func : str - The function to use for activating the hidden layer - opt_func : str - The function to use for optimizing the upper part of the network - loss_func : str - The loss function to use + params : """ def __init__( @@ -41,7 +34,7 @@ def __init__( num_of_objectives=2, w_low=-5.0, w_high=5.0, - prob_omit=0.2, + prob_omit=0.3, params=None, ): super().__init__() @@ -54,9 +47,6 @@ def __init__( self.w_high = w_high self.prob_omit = prob_omit self.params = params - # [num of subnets, max num of layers] - self.subnets = [3, 8] - self.num_nodes = 8 def fit(self, training_data, target_values): """Fit data in EvoNN model. @@ -73,6 +63,8 @@ def fit(self, training_data, target_values): self.y_train = target_values self.num_of_samples = target_values.shape[0] self.num_of_variables = training_data.shape[1] + self.subnets = self.params["subnets"] + self.num_nodes = self.params["num_nodes"] # Create random subsets of decision variables for each subnet self.subsets = [] @@ -131,8 +123,8 @@ def train(self, model): PPGA, { "logging": self.params["logging"], - "iterations": 1, - "generations_per_iteration": 1, + "iterations": 10, + "generations_per_iteration": 10, "crossover_type": "short", "mutation_type": "short" } @@ -159,12 +151,6 @@ def objectives(self, decision_variables) -> list: """ - # Shuffle variables - # random.shuffle(x) - # for i in x: - # random.shuffle(i) - # print(x) - end_net, complexity = self.activation(decision_variables) _, _, predicted_values = self.minimize_error(end_net) training_error = self.loss_function(predicted_values) @@ -249,7 +235,7 @@ def loss_function(self, predicted_values): if self.params["loss_func"] == "mse": return ((self.y_train - predicted_values) ** 2).mean() - if self.params["loss_func"]: + if self.params["loss_func"] == "rmse": return np.sqrt(((self.y_train - predicted_values) ** 2).mean()) def select(self, pop, non_dom_front, criterion="min_error"): @@ -349,6 +335,7 @@ def create_plot(self, model): class EvoDN2Model(EvoDN2): + """""" def __init__(self, name, w1=None, w2=None, y_pred=None, svr=None): super().__init__(name) self.name = name @@ -359,6 +346,59 @@ def __init__(self, name, w1=None, w2=None, y_pred=None, svr=None): self.log = None self.set_params() + def set_params( + self, + name=None, + pop_size=500, + subnets=(3, 8), + num_nodes=5, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=False, + plotting=False, + ): + """ Set parameters for EvoDN2 model. + + Parameters + ---------- + name : str + Name of the problem. + pop_size : int + Population size. + subnets : tuple + Structure of the subnets for the model, shape=(num of subnets, max num of layers) + num_nodes : int + Maximum number of nodes per layer. + activation_func : str + Function to use for activation. + opt_func : str + Function to use for optimizing the final layer of the model. + loss_func : str + The loss function to use. + criterion : str + The criterion to use for selecting the model. + logging : bool + True to create a logfile, False otherwise. + plotting : bool + True to create a plot, False otherwise. + """ + params = { + "name": name, + "pop_size": pop_size, + "subnets": subnets, + "num_nodes": num_nodes, + "activation_func": activation_func, + "opt_func": opt_func, + "loss_func": loss_func, + "criterion": criterion, + "logging": logging, + "plotting": plotting, + } + + self.params = params + def fit(self, training_data, target_values): """Fit data in EvoNN model. @@ -369,16 +409,16 @@ def fit(self, training_data, target_values): target_values : ndarray Target values """ - f1 = EvoDN2(name=self.name, params=self.params) - self.subsets = f1.fit(training_data, target_values) - self.num_of_variables = f1.num_of_variables - f1.train(self) - if f1.params["logging"]: - self.log = f1.create_logfile() - if f1.params["plotting"]: - f1.create_plot(self) - self.num_of_samples = f1.num_of_samples - self.single_variable_response(ploton=True, log=self.log) + prob = EvoDN2(name=self.name, params=self.params) + self.subsets = prob.fit(training_data, target_values) + self.num_of_variables = prob.num_of_variables + prob.train(self) + if prob.params["logging"]: + self.log = prob.create_logfile() + if prob.params["plotting"]: + prob.create_plot(self) + self.num_of_samples = prob.num_of_samples + self.single_variable_response(ploton=False, log=self.log) def predict(self, decision_variables): @@ -409,30 +449,6 @@ def predict(self, decision_variables): return result - def set_params( - self, - name=None, - pop_size=500, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=False, - plotting=False, - ): - params = { - "name": name, - "pop_size": pop_size, - "activation_func": activation_func, - "opt_func": opt_func, - "loss_func": loss_func, - "criterion": criterion, - "logging": logging, - "plotting": plotting, - } - - self.params = params - def single_variable_response(self, ploton=False, log=None): trend = np.loadtxt("trend") @@ -440,7 +456,7 @@ def single_variable_response(self, ploton=False, log=None): avg = np.ones((1, self.num_of_variables)) * (np.finfo(float).eps + 1) / 2 svr = np.empty((0, 2)) - for i in range(self.w1[1:].shape[0]): + for i in range(self.num_of_variables): variables = np.ones((len(trend), 1)) * avg variables[:, i] = trend diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index 790ced2..6904796 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -8,12 +8,20 @@ class EvoNN(baseProblem): - """Creates an Artificial Neural Network (ANN) for the EvoNN algorithm. + """Creates Artificial Neural Network (ANN) models for the EvoNN algorithm. - Attributes + These models contain only one hidden node layer. The lower part of the network + is optimized by a genetic algorithm, and the upper part is optimized by Linear Least Square + algorithm by default. + + Parameters ---------- name : str Name of the sample + X_train : ndarray + Training data input + y_train : ndarray + Training data target values num_input_nodes : int The number of nodes in the input layer num_nodes : int @@ -26,12 +34,8 @@ class EvoNN(baseProblem): The upper bound for randomly generated weights prob_omit : float The probability of setting some weights to zero initially - activation_func : str - The function to use for activating the hidden layer - opt_func : str - The function to use for optimizing the upper part of the network - loss_func : str - The loss function to use + params : dict + Parameters for model training """ def __init__( @@ -44,7 +48,7 @@ def __init__( num_of_objectives=2, w_low=-5.0, w_high=5.0, - prob_omit=0.2, + prob_omit=0.3, params=None, ): super().__init__() @@ -79,19 +83,27 @@ def fit(self, training_data, target_values): self.num_nodes = self.params["num_nodes"] def create_population(self): + """Create a population of neural networks for the EvoNN problem. + + Individuals are 2d arrays representing the weight matrices of the NNs. + One extra row is added for bias. Individuals are then stacked together + to form the population. + Returns + ------- + individuals : ndarray + The population for EvoNN + """ individuals = np.random.uniform( self.w_low, self.w_high, - size=( - self.params["pop_size"], - self.num_input_nodes, - self.num_nodes, - ), + size=(self.params["pop_size"], self.num_input_nodes, self.num_nodes), ) # Randomly set some weights to zero - zeros = np.random.choice(np.arange(individuals.size), ceil(individuals.size * self.prob_omit)) + zeros = np.random.choice( + np.arange(individuals.size), ceil(individuals.size * self.prob_omit) + ) individuals.ravel()[zeros] = 0 # Set bias @@ -100,19 +112,30 @@ def create_population(self): return individuals def train(self, model): + """Trains the networks and selects the best model from the non dominated front. - pop = Population(self, assign_type="EvoNN", pop_size=self.params["pop_size"], plotting=False) + Parameters + ---------- + model : :obj: + The model to be chosen. + """ + pop = Population( + self, assign_type="EvoNN", pop_size=self.params["pop_size"], plotting=False + ) pop.evolve( PPGA, { "logging": self.params["logging"], "iterations": 10, - "generations_per_iteration": 10 - } + "generations_per_iteration": 10, + }, ) non_dom_front = pop.non_dominated() - model.w1, model.fitness = self.select(pop, non_dom_front, self.params["criterion"]) + model.w1, model.fitness = self.select( + pop, non_dom_front, self.params["criterion"] + ) + activated_layer = self.activation(model.w1) model.w2, _, model.y_pred = self.minimize_error(activated_layer) @@ -148,16 +171,14 @@ def activation(self, decision_variables): ---------- decision_variables : ndarray Variables from the neural network - name : str - The activation function to use Returns ------- - The penultimate layer Z before the output + The penultimate layer before the output """ w1 = decision_variables - # Calculate the dot product + # Calculate the dot product + bias wi = np.dot(self.X_train, w1[1:, :]) + w1[0] if self.params["activation_func"] == "sigmoid": @@ -178,15 +199,13 @@ def minimize_error(self, activated_layer): ---------- activated_layer : ndarray Output of the activation function - name : str - Name of the optimizing algorithm to use Returns ------- - w_matrix[0] : ndarray + w2[0] : ndarray The weight matrix of the upper part of the network rss : float - Sums of residuals + Sum of residuals predicted_values : ndarray The prediction of the model """ @@ -198,20 +217,34 @@ def minimize_error(self, activated_layer): return w2[0], rss, predicted_values def loss_function(self, predicted_values): + """Calculate the error between prediction and target values.""" if self.params["loss_func"] == "mse": return ((self.y_train - predicted_values) ** 2).mean() - if self.params["loss_func"]: + if self.params["loss_func"] == "rmse": return np.sqrt(((self.y_train - predicted_values) ** 2).mean()) def calculate_complexity(self, w_matrix): + """Calculate the complexity of the model. + + Returns + ------- + The number of non-zero connections in the lower part of the network. + """ k = np.count_nonzero(w_matrix[1:, :]) return k def information_criterion(self, decision_variables): + """Calculate the information criterion. + + Currently supports Akaike and corrected Akaike Information Criterion. + Returns + ------- + Corrected Akaike Information Criterion by default + """ z = self.activation(decision_variables) w_matrix2, rss, prediction = self.minimize_error(z) # rss = ((self.y_train - prediction) ** 2).sum() @@ -232,7 +265,7 @@ def select(self, pop, non_dom_front, criterion="min_error"): Indices of the models on the non-dominated front criterion : str The criterion to use for selecting the model. - Possible values: 'min_error', 'akaike_corrected', 'manual' + Possible values: 'min_error', 'akaike_corrected' Returns ------- @@ -267,6 +300,12 @@ def select(self, pop, non_dom_front, criterion="min_error"): return model, fitness def create_logfile(self): + """Create a log file containing the parameters for training the model and the GA. + + Returns + ------- + An external log file + """ # Save params to log file log_file = open( @@ -301,6 +340,12 @@ def create_logfile(self): return log_file def create_plot(self, model): + """Creates and shows a plot for the model. + + Parameters + ---------- + The model to create the plot for. + """ trace0 = go.Scatter(x=model.y_pred, y=self.y_train, mode="markers") trace1 = go.Scatter(x=self.y_train, y=self.y_train) @@ -318,6 +363,16 @@ def create_plot(self, model): class EvoNNModel(EvoNN): + """The class for the surrogate model. + + Parameters + ---------- + name : str + Name of the problem + w1 : ndarray + The weight matrix of the lower par + + """ def __init__(self, name, w1=None, w2=None, y_pred=None, svr=None): super().__init__(name) self.name = name @@ -338,15 +393,15 @@ def fit(self, training_data, target_values): target_values : ndarray Target values """ - f1 = EvoNN(name=self.name, params=self.params) - f1.fit(training_data, target_values) - f1.train(self) - if f1.params["logging"]: - self.log = f1.create_logfile() - if f1.params["plotting"]: - f1.create_plot(self) - self.num_of_samples = f1.num_of_samples - self.single_variable_response(ploton=True, log=self.log) + prob = EvoNN(name=self.name, params=self.params) + prob.fit(training_data, target_values) + prob.train(self) + if prob.params["logging"]: + self.log = prob.create_logfile() + if prob.params["plotting"]: + prob.create_plot(self) + self.num_of_samples = prob.num_of_samples + self.single_variable_response(ploton=False, log=self.log) def predict(self, decision_variables): @@ -394,7 +449,7 @@ def set_params( def single_variable_response(self, ploton=False, log=None): trend = np.loadtxt("trend") - trend = trend[0: self.num_of_samples] + trend = trend[0 : self.num_of_samples] avg = np.ones((1, self.w1[1:].shape[0])) * (0 + 1) / 2 svr = np.empty((0, 2)) diff --git a/pyrvea/Problem/evonn_test_functions.py b/pyrvea/Problem/evonn_test_functions.py index a79851a..55d4bf3 100644 --- a/pyrvea/Problem/evonn_test_functions.py +++ b/pyrvea/Problem/evonn_test_functions.py @@ -78,7 +78,7 @@ def objectives(self, decision_variables) -> list: x = np.asarray(decision_variables) self.obj_func = sum(x ** 2) - elif self.name == "Matyas:": + elif self.name == "Matyas": # Matyas function, -10 <= x, y <= 10 # Error close to zero with random data, From 380022943363843a864c4cbbc3fe782dec837944 Mon Sep 17 00:00:00 2001 From: delamorte Date: Tue, 25 Jun 2019 14:20:47 +0300 Subject: [PATCH 040/155] added more tests improved logging and plotting names --- Example.py | 2 +- evodn2_example.py | 112 +++++++++++++++++++++++-- pyrvea/EAs/PPGA.py | 60 +++++++++---- pyrvea/Problem/evodn2_problem.py | 57 +++++++++++-- pyrvea/Problem/evonn_problem.py | 42 ++++++++-- pyrvea/Problem/evonn_test_functions.py | 15 +++- 6 files changed, 248 insertions(+), 40 deletions(-) diff --git a/Example.py b/Example.py index 1219615..b45836e 100644 --- a/Example.py +++ b/Example.py @@ -33,7 +33,7 @@ def objectives(self, decision_variables): pop = Population(problem) -pop.evolve(slowRVEA) +pop.evolve(RVEA) pop.non_dominated() refpoint = 2 diff --git a/evodn2_example.py b/evodn2_example.py index 51d282d..7bf4546 100644 --- a/evodn2_example.py +++ b/evodn2_example.py @@ -13,15 +13,15 @@ # training_data_input = np.random.rand(150,30) # training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) -test_prob = EvoNNTestProblem("Goldstein-Price") +test_prob = EvoNNTestProblem("Sphere") training_data_input, training_data_output = test_prob.create_training_data( samples=250, method="random" ) -model_evodn = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn.set_params( +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( pop_size=500, - subnets=(4, 8), + subnets=(6, 10), num_nodes=10, activation_func="sigmoid", opt_func="llsq", @@ -30,9 +30,6 @@ logging=True, plotting=True) - -model_evodn.fit(training_data_input, training_data_output) - model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) model_evonn.set_params( num_nodes=20, @@ -44,4 +41,105 @@ logging=True, plotting=True) +model_evodn2.fit(training_data_input, training_data_output) model_evonn.fit(training_data_input, training_data_output) + +# test_prob = EvoNNTestProblem("Matyas") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random" +# ) +# +# model_evodn2.fit(training_data_input, training_data_output) +# model_evonn.fit(training_data_input, training_data_output) +# +# test_prob = EvoNNTestProblem("Himmelblau") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random" +# ) +# +# model_evodn2.fit(training_data_input, training_data_output) +# model_evonn.fit(training_data_input, training_data_output) +# +# test_prob = EvoNNTestProblem("Rastigrin", num_of_variables=2) +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random" +# ) +# +# model_evodn2.fit(training_data_input, training_data_output) +# model_evonn.fit(training_data_input, training_data_output) +# +# test_prob = EvoNNTestProblem("Three-hump camel") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random" +# ) +# +# model_evodn2.fit(training_data_input, training_data_output) +# model_evonn.fit(training_data_input, training_data_output) +# +# test_prob = EvoNNTestProblem("Goldstein-Price") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random" +# ) +# +# model_evodn2.fit(training_data_input, training_data_output) +# model_evonn.fit(training_data_input, training_data_output) +# +# test_prob = EvoNNTestProblem("LeviN13") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random" +# ) +# +# model_evodn2.fit(training_data_input, training_data_output) +# model_evonn.fit(training_data_input, training_data_output) +# +# test_prob = EvoNNTestProblem("SchafferN2") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random" +# ) +# +# model_evodn2.fit(training_data_input, training_data_output) +# model_evonn.fit(training_data_input, training_data_output) +# +# # ZDT 1 & 2 +test_prob = testProblem('ZDT6', 10, 2, 0, 1, 0) +training_data_input = np.random.rand(250, 10) +training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) + +f1_training_data_output = training_data_output[:, 0] +f2_training_data_output = training_data_output[:, 1] + +model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") +model_evodn_f1.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") +model_evodn_f2.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) +model_evodn_f1.fit(training_data_input, f1_training_data_output) +model_evodn_f2.fit(training_data_input, f2_training_data_output) +# +# test_prob = testProblem('ZDT2', 30, 2, 0, 1, 0) +# training_data_input = np.random.rand(250, 30) +# training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) +# +# f1_training_data_output = training_data_output[:, 0] +# f2_training_data_output = training_data_output[:, 1] +# +# model_evodn_f1.fit(training_data_input, f1_training_data_output) +# model_evodn_f2.fit(training_data_input, f2_training_data_output) diff --git a/pyrvea/EAs/PPGA.py b/pyrvea/EAs/PPGA.py index aca2576..decc651 100644 --- a/pyrvea/EAs/PPGA.py +++ b/pyrvea/EAs/PPGA.py @@ -24,6 +24,7 @@ class PPGA(BaseEA): References ---------- """ + def __init__(self, population: "Population", EA_parameters: dict = {}): self.params = self.set_params(population, **EA_parameters) @@ -40,11 +41,12 @@ def set_params( interact: bool = False, plotting: bool = True, logging: list = False, + logfile = None, crossover_type: str = None, mutation_type: str = None, prob_crossover: float = 0.8, prob_mutation: float = 0.3, - mut_strength: float = 0.7 + mut_strength: float = 0.7, ): """Set up the parameters. @@ -89,6 +91,7 @@ def set_params( "interact": interact, "plotting": plotting, "logging": logging, + "logfile": logfile, "current_iteration_gen_count": 0, "current_iteration_count": 0, "crossover_type": crossover_type, @@ -97,23 +100,13 @@ def set_params( "prob_mutation": prob_mutation, "mut_strength": mut_strength, "kill_interval": 7, - "max_rank": 20 + "max_rank": 20, } # If logging enabled, write params to file if ppgaparams["logging"]: - # Save params to log file - log_file = open( - population.problem.name - + "_var" - + str(population.problem.num_of_variables) - + "_nodes" - + str(population.problem.num_nodes) - + ".log", - "w" - ) for k, v in ppgaparams.items(): - print(k, v, file=log_file) + print(k, v, file=ppgaparams["logfile"]) return ppgaparams @@ -225,8 +218,8 @@ def _next_gen(self, population: "Population"): self.lattice.move_predator() print( - "AFTER KILL "+ - str(self.params["current_iteration_gen_count"]) + "AFTER KILL " + + str(self.params["current_iteration_gen_count"]) + " " + "population size: " + str(population.individuals.shape[0]) @@ -236,6 +229,19 @@ def _next_gen(self, population: "Population"): + str(np.mean(population.objectives[:, 0])) ) + if ( + self.params["current_iteration_count"] == self.params["iterations"] - 1 + and self.params["current_iteration_gen_count"] + == self.params["generations"] + ): + print( + "last gen min error: " + + str(np.amin(population.objectives[:, 0])) + + " avg error: " + + str(np.mean(population.objectives[:, 0])), + file=self.params["logfile"], + ) + # population.create_archive(population, population.objectives) # # Place new prey if kill interval condition satisfied @@ -251,7 +257,6 @@ def _next_gen(self, population: "Population"): # ) # population.create_new_individuals(pop_size=len(placed_indices), design="EvoNN") - def _run_interruption(self, population: "Population"): """Run the interruption phase of PPGA. @@ -467,7 +472,10 @@ def move_predator(self): obj1 = self.params["population"].fitness[target - 1][0] obj2 = self.params["population"].fitness[target - 1][1] - fc = (self.predator_pop[predator] * obj1 + (1 - self.predator_pop[predator]) * obj2) + fc = ( + self.predator_pop[predator] * obj1 + + (1 - self.predator_pop[predator]) * obj2 + ) fitness.append((fc, target)) fitness.sort() @@ -561,6 +569,22 @@ def lattice_wrap_idx(index, lattice_shape): @staticmethod def neighbours(arr, x, y, n=3): - """Given a 2D-array, returns an nxn array whose "center" element is arr[x,y]""" + """Given a 2D-array, returns an nxn array whose "center" element is arr[x,y] + + Parameters + ---------- + arr : ndarray + A 2D-array where to get the neighbouring cells + x : int + X coordinate for the center element + y : int + Y coordinate for the center element + n : int + Radius of the neighbourhood + + Returns + ------- + The neighbouring cells of x, y in radius n. Defaults to Moore neighbourhood (n=3) + """ arr = np.roll(np.roll(arr, shift=-x + 1, axis=0), shift=-y + 1, axis=1) return arr[:n, :n] diff --git a/pyrvea/Problem/evodn2_problem.py b/pyrvea/Problem/evodn2_problem.py index 997b0a6..30ca7f1 100644 --- a/pyrvea/Problem/evodn2_problem.py +++ b/pyrvea/Problem/evodn2_problem.py @@ -34,7 +34,7 @@ def __init__( num_of_objectives=2, w_low=-5.0, w_high=5.0, - prob_omit=0.3, + prob_omit=0.2, params=None, ): super().__init__() @@ -123,6 +123,7 @@ def train(self, model): PPGA, { "logging": self.params["logging"], + "logfile": model.log, "iterations": 10, "generations_per_iteration": 10, "crossover_type": "short", @@ -291,6 +292,8 @@ def create_logfile(self): + "_var" + str(self.num_of_variables) + "_nodes" + + str(self.subnets[0]) + "_" + + str(self.subnets[1]) + "_" + str(self.num_nodes) + ".log", "a", @@ -302,6 +305,12 @@ def create_logfile(self): + "variables: " + str(self.num_of_variables) + "\n" + + "number of subnets: " + + str(self.subnets[0]) + + "\n" + + "max number of layers: " + + str(self.subnets[1]) + + "\n" + "nodes: " + str(self.num_nodes) + "\n" @@ -328,6 +337,8 @@ def create_plot(self, model): + "_var" + str(self.num_of_variables) + "_nodes" + + str(self.subnets[0]) + "_" + + str(self.subnets[1]) + "_" + str(self.num_nodes) + ".html", auto_open=True, @@ -335,14 +346,27 @@ def create_plot(self, model): class EvoDN2Model(EvoDN2): - """""" - def __init__(self, name, w1=None, w2=None, y_pred=None, svr=None): + """Class for the surrogate model. + + Parameters + ---------- + name : str + Name of the problem + w1 : ndarray + The weight matrix of the lower part of the network + w2 : ndarray + The weight matrix of the upper part of the network + y_pred : ndarray + Prediction of the model + + """ + def __init__(self, name, w1=None, w2=None, y_pred=None): super().__init__(name) self.name = name self.w1 = w1 self.w2 = w2 self.y_pred = y_pred - self.svr = svr + self.svr = None self.log = None self.set_params() @@ -350,8 +374,8 @@ def set_params( self, name=None, pop_size=500, - subnets=(3, 8), - num_nodes=5, + subnets=(4, 8), + num_nodes=10, activation_func="sigmoid", opt_func="llsq", loss_func="rmse", @@ -412,14 +436,33 @@ def fit(self, training_data, target_values): prob = EvoDN2(name=self.name, params=self.params) self.subsets = prob.fit(training_data, target_values) self.num_of_variables = prob.num_of_variables - prob.train(self) if prob.params["logging"]: self.log = prob.create_logfile() + prob.train(self) if prob.params["plotting"]: prob.create_plot(self) self.num_of_samples = prob.num_of_samples self.single_variable_response(ploton=False, log=self.log) + def opt(self): + + pop = Population(self, assign_type="EvoDN2", pop_size=self.params["pop_size"], plotting=False) + pop.evolve( + PPGA, + { + "logging": self.params["logging"], + "iterations": 10, + "generations_per_iteration": 10, + "crossover_type": "short", + "mutation_type": "short" + } + ) + + non_dom_front = pop.non_dominated() + self.w1, self.fitness = self.select(pop, non_dom_front, self.params["criterion"]) + self.end_net, complexity = self.activation(self.w1) + self.w2, _, self.y_pred = self.minimize_error(self.end_net) + def predict(self, decision_variables): in_end = np.empty((self.num_of_samples, 0)) diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index 6904796..4cdc045 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -126,6 +126,7 @@ def train(self, model): PPGA, { "logging": self.params["logging"], + "logfile": model.log, "iterations": 10, "generations_per_iteration": 10, }, @@ -370,16 +371,20 @@ class EvoNNModel(EvoNN): name : str Name of the problem w1 : ndarray - The weight matrix of the lower par + The weight matrix of the lower part of the network + w2 : ndarray + The weight matrix of the upper part of the network + y_pred : ndarray + Prediction of the model """ - def __init__(self, name, w1=None, w2=None, y_pred=None, svr=None): + def __init__(self, name, w1=None, w2=None, y_pred=None): super().__init__(name) self.name = name self.w1 = w1 self.w2 = w2 self.y_pred = y_pred - self.svr = svr + self.svr = None self.log = None self.set_params() @@ -395,9 +400,9 @@ def fit(self, training_data, target_values): """ prob = EvoNN(name=self.name, params=self.params) prob.fit(training_data, target_values) - prob.train(self) if prob.params["logging"]: self.log = prob.create_logfile() + prob.train(self) if prob.params["plotting"]: prob.create_plot(self) self.num_of_samples = prob.num_of_samples @@ -423,8 +428,8 @@ def predict(self, decision_variables): def set_params( self, name=None, - num_nodes=15, pop_size=500, + num_nodes=15, activation_func="sigmoid", opt_func="llsq", loss_func="rmse", @@ -432,10 +437,34 @@ def set_params( logging=False, plotting=False, ): + + """ Set parameters for EvoNN model. + + Parameters + ---------- + name : str + Name of the problem. + pop_size : int + Population size. + num_nodes : int + Maximum number of nodes per layer. + activation_func : str + Function to use for activation. + opt_func : str + Function to use for optimizing the final layer of the model. + loss_func : str + The loss function to use. + criterion : str + The criterion to use for selecting the model. + logging : bool + True to create a logfile, False otherwise. + plotting : bool + True to create a plot, False otherwise. + """ params = { "name": name, - "num_nodes": num_nodes, "pop_size": pop_size, + "num_nodes": num_nodes, "activation_func": activation_func, "opt_func": opt_func, "loss_func": loss_func, @@ -447,6 +476,7 @@ def set_params( self.params = params def single_variable_response(self, ploton=False, log=None): + """Get the model's response to a single variable.""" trend = np.loadtxt("trend") trend = trend[0 : self.num_of_samples] diff --git a/pyrvea/Problem/evonn_test_functions.py b/pyrvea/Problem/evonn_test_functions.py index 55d4bf3..0bb5e19 100644 --- a/pyrvea/Problem/evonn_test_functions.py +++ b/pyrvea/Problem/evonn_test_functions.py @@ -1,6 +1,7 @@ import numpy as np from pyDOE import lhs from pyrvea.Problem.baseProblem import baseProblem +from sklearn.preprocessing import minmax_scale class EvoNNTestProblem(baseProblem): @@ -121,6 +122,8 @@ def objectives(self, decision_variables) -> list: # with 15 nodes, min. error == 20000 # with 20 nodes, min. error == 17000 # with 25 nodes, min. error == 8671 + # EvoDN2 performs better here even with + # lesser amount of nets/layers/nodes x = np.asarray(decision_variables[0]) y = np.asarray(decision_variables[1]) self.obj_func = ( @@ -145,7 +148,7 @@ def objectives(self, decision_variables) -> list: + (y - 1) ** 2 * (1 + np.sin(2 * np.pi * y) ** 2) ) - elif self.name == "Schaffer": + elif self.name == "SchafferN2": # Schaffer function N. 2, -100 <= x, y <= 100 # Doesn't work with random data x = np.asarray(decision_variables[0]) @@ -156,6 +159,12 @@ def objectives(self, decision_variables) -> list: / (1 + 0.001 * (x ** 2 + y ** 2)) ** 2 ) + elif self.name == "McCormick": + # McCormick function, -1.5 <= x <= 4, -3 <= y <= 4 + x = np.asarray(decision_variables[0]) + y = np.asarray(decision_variables[1]) + self.obj_func = np.sin(x + y) + (x - y)**2 - 1.5 * x + 2.5 * y + 1 + elif self.name == "min-ex": x1 = decision_variables[1] x2 = decision_variables[2] @@ -187,6 +196,10 @@ def create_training_data(self, samples=150, method="random", seed=None): (samples, self.num_of_variables), ) + if self.name == "McCormick": + training_data_input[:, 0] = minmax_scale(training_data_input[:, 0], feature_range=(-1.5, 4)) + training_data_input[:, 1] = minmax_scale(training_data_input[:, 1], feature_range=(-3, 4)) + elif method == "lhs": training_data_input = lhs(self.num_of_variables, samples) * ( From 96e86665572387bfb363c6d9abb42db2aff36254 Mon Sep 17 00:00:00 2001 From: delamorte Date: Tue, 25 Jun 2019 16:42:05 +0300 Subject: [PATCH 041/155] new tests comparing evoNN & evoDN with other machine learning tools --- evodn2_vs_mlp.py | 991 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 991 insertions(+) create mode 100644 evodn2_vs_mlp.py diff --git a/evodn2_vs_mlp.py b/evodn2_vs_mlp.py new file mode 100644 index 0000000..81f40ce --- /dev/null +++ b/evodn2_vs_mlp.py @@ -0,0 +1,991 @@ +import plotly +import plotly.graph_objs as go +from pyrvea.Problem.evonn_test_functions import EvoNNTestProblem +from pyrvea.Problem.evonn_problem import EvoNNModel +from pyrvea.Problem.evodn2_problem import EvoDN2Model +from pyrvea.Problem.testProblem import testProblem +from sklearn.neural_network import MLPRegressor +from sklearn.gaussian_process import GaussianProcessRegressor +import numpy as np + +test_prob = EvoNNTestProblem("Sphere", num_of_variables=5) +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random", seed=30 +) + +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +model_evonn.set_params( + num_nodes=20, + pop_size=500, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) +model_evonn.fit(training_data_input, training_data_output) + +mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +mlp_reg.fit(training_data_input, training_data_output) +mlp_reg_y_pred = mlp_reg.predict(training_data_input) + +trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") +trace1 = go.Scatter(x=training_data_output, y=training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="MLP Regressor " + test_prob.name + + ".html", + auto_open=True, +) + +gpr = GaussianProcessRegressor() +gpr.fit(training_data_input, training_data_output) +gpr_y_pred = gpr.predict(training_data_input) + +trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") +trace1 = go.Scatter(x=training_data_output, y=training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="Gaussian Process Regressor " + test_prob.name + + ".html", + auto_open=True, +) + +test_prob = EvoNNTestProblem("Matyas") +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random", seed=31 +) + +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +model_evonn.set_params( + num_nodes=20, + pop_size=500, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) +model_evonn.fit(training_data_input, training_data_output) + +mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +mlp_reg.fit(training_data_input, training_data_output) +mlp_reg_y_pred = mlp_reg.predict(training_data_input) + +trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") +trace1 = go.Scatter(x=training_data_output, y=training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="MLP Regressor " + test_prob.name + + ".html", + auto_open=True, +) + +gpr = GaussianProcessRegressor() +gpr.fit(training_data_input, training_data_output) +gpr_y_pred = gpr.predict(training_data_input) + +trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") +trace1 = go.Scatter(x=training_data_output, y=training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="Gaussian Process Regressor " + test_prob.name + + ".html", + auto_open=True, +) + +test_prob = EvoNNTestProblem("Himmelblau") +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random", seed=32 +) + +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +model_evonn.set_params( + num_nodes=20, + pop_size=500, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) +model_evonn.fit(training_data_input, training_data_output) + +mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +mlp_reg.fit(training_data_input, training_data_output) +mlp_reg_y_pred = mlp_reg.predict(training_data_input) + +trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") +trace1 = go.Scatter(x=training_data_output, y=training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="MLP Regressor " + test_prob.name + + ".html", + auto_open=True, +) + +gpr = GaussianProcessRegressor() +gpr.fit(training_data_input, training_data_output) +gpr_y_pred = gpr.predict(training_data_input) + +trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") +trace1 = go.Scatter(x=training_data_output, y=training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="Gaussian Process Regressor " + test_prob.name + + ".html", + auto_open=True, +) + +test_prob = EvoNNTestProblem("Rastigrin", num_of_variables=2) +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random", seed=33 +) + +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +model_evonn.set_params( + num_nodes=20, + pop_size=500, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) +model_evonn.fit(training_data_input, training_data_output) + +mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +mlp_reg.fit(training_data_input, training_data_output) +mlp_reg_y_pred = mlp_reg.predict(training_data_input) + +trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") +trace1 = go.Scatter(x=training_data_output, y=training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="MLP Regressor " + test_prob.name + + ".html", + auto_open=True, +) + +gpr = GaussianProcessRegressor() +gpr.fit(training_data_input, training_data_output) +gpr_y_pred = gpr.predict(training_data_input) + +trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") +trace1 = go.Scatter(x=training_data_output, y=training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="Gaussian Process Regressor " + test_prob.name + + ".html", + auto_open=True, +) + +test_prob = EvoNNTestProblem("Three-hump camel") +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random", seed=34 +) + +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +model_evonn.set_params( + num_nodes=20, + pop_size=500, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) +model_evonn.fit(training_data_input, training_data_output) + +mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +mlp_reg.fit(training_data_input, training_data_output) +mlp_reg_y_pred = mlp_reg.predict(training_data_input) + +trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") +trace1 = go.Scatter(x=training_data_output, y=training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="MLP Regressor " + test_prob.name + + ".html", + auto_open=True, +) + +gpr = GaussianProcessRegressor() +gpr.fit(training_data_input, training_data_output) +gpr_y_pred = gpr.predict(training_data_input) + +trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") +trace1 = go.Scatter(x=training_data_output, y=training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="Gaussian Process Regressor " + test_prob.name + + ".html", + auto_open=True, +) + +test_prob = EvoNNTestProblem("Goldstein-Price") +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random", seed=35 +) + +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +model_evonn.set_params( + num_nodes=20, + pop_size=500, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) +model_evonn.fit(training_data_input, training_data_output) + +mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +mlp_reg.fit(training_data_input, training_data_output) +mlp_reg_y_pred = mlp_reg.predict(training_data_input) + +trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") +trace1 = go.Scatter(x=training_data_output, y=training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="MLP Regressor " + test_prob.name + + ".html", + auto_open=True, +) + +gpr = GaussianProcessRegressor() +gpr.fit(training_data_input, training_data_output) +gpr_y_pred = gpr.predict(training_data_input) + +trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") +trace1 = go.Scatter(x=training_data_output, y=training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="Gaussian Process Regressor " + test_prob.name + + ".html", + auto_open=True, +) + +test_prob = EvoNNTestProblem("LeviN13") +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random", seed=36 +) + +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +model_evonn.set_params( + num_nodes=20, + pop_size=500, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) +model_evonn.fit(training_data_input, training_data_output) + +mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +mlp_reg.fit(training_data_input, training_data_output) +mlp_reg_y_pred = mlp_reg.predict(training_data_input) + +trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") +trace1 = go.Scatter(x=training_data_output, y=training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="MLP Regressor " + test_prob.name + + ".html", + auto_open=True, +) + +gpr = GaussianProcessRegressor() +gpr.fit(training_data_input, training_data_output) +gpr_y_pred = gpr.predict(training_data_input) + +trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") +trace1 = go.Scatter(x=training_data_output, y=training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="Gaussian Process Regressor " + test_prob.name + + ".html", + auto_open=True, +) + +test_prob = EvoNNTestProblem("SchafferN2") +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random", seed=37 +) + +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +model_evonn.set_params( + num_nodes=20, + pop_size=500, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) +model_evonn.fit(training_data_input, training_data_output) + +mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +mlp_reg.fit(training_data_input, training_data_output) +mlp_reg_y_pred = mlp_reg.predict(training_data_input) + +trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") +trace1 = go.Scatter(x=training_data_output, y=training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="MLP Regressor " + test_prob.name + + ".html", + auto_open=True, +) + +gpr = GaussianProcessRegressor() +gpr.fit(training_data_input, training_data_output) +gpr_y_pred = gpr.predict(training_data_input) + +trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") +trace1 = go.Scatter(x=training_data_output, y=training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="Gaussian Process Regressor " + test_prob.name + + ".html", + auto_open=True, +) + +test_prob = EvoNNTestProblem("McCormick") +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random", seed=38 +) + +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +model_evonn.set_params( + num_nodes=20, + pop_size=500, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) +model_evonn.fit(training_data_input, training_data_output) + +mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +mlp_reg.fit(training_data_input, training_data_output) +mlp_reg_y_pred = mlp_reg.predict(training_data_input) + +trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") +trace1 = go.Scatter(x=training_data_output, y=training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="MLP Regressor " + test_prob.name + + ".html", + auto_open=True, +) + +gpr = GaussianProcessRegressor() +gpr.fit(training_data_input, training_data_output) +gpr_y_pred = gpr.predict(training_data_input) + +trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") +trace1 = go.Scatter(x=training_data_output, y=training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="Gaussian Process Regressor " + test_prob.name + + ".html", + auto_open=True, +) + +# --------------------------------------------------------------- +# +# ZDT 1 +test_prob = testProblem('ZDT1', 30, 2, 0, 1, 0) +np.random.seed(1) +training_data_input = np.random.rand(250, 30) +training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) + +f1_training_data_output = training_data_output[:, 0] +f2_training_data_output = training_data_output[:, 1] + +model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") +model_evodn_f1.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") +model_evodn_f2.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) +model_evodn_f1.fit(training_data_input, f1_training_data_output) +model_evodn_f2.fit(training_data_input, f2_training_data_output) + +mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +mlp_reg.fit(training_data_input, f1_training_data_output) +mlp_reg_y_pred = mlp_reg.predict(training_data_input) + +trace0 = go.Scatter(x=mlp_reg_y_pred, y=f1_training_data_output, mode="markers") +trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="MLP Regressor " + test_prob.name + "_f1" + + ".html", + auto_open=True, +) + +mlp_reg.fit(training_data_input, f2_training_data_output) +mlp_reg_y_pred = mlp_reg.predict(training_data_input) + +trace0 = go.Scatter(x=mlp_reg_y_pred, y=f2_training_data_output, mode="markers") +trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="MLP Regressor " + test_prob.name + "_f2" + + ".html", + auto_open=True, +) + +gpr = GaussianProcessRegressor() +gpr.fit(training_data_input, f1_training_data_output) +gpr_y_pred = gpr.predict(training_data_input) + +trace0 = go.Scatter(x=gpr_y_pred, y=f1_training_data_output, mode="markers") +trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="GPR Regressor " + test_prob.name + "_f1" + + ".html", + auto_open=True, +) + +gpr.fit(training_data_input, f2_training_data_output) +gpr_y_pred = gpr.predict(training_data_input) + +trace0 = go.Scatter(x=gpr_y_pred, y=f2_training_data_output, mode="markers") +trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="GPR Regressor " + test_prob.name + "_f2" + + ".html", + auto_open=True, +) + +# ZDT2 + +test_prob = testProblem('ZDT2', 30, 2, 0, 1, 0) +np.random.seed(2) +training_data_input = np.random.rand(250, 30) +training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) + +f1_training_data_output = training_data_output[:, 0] +f2_training_data_output = training_data_output[:, 1] + +model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") +model_evodn_f1.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") +model_evodn_f2.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) +model_evodn_f1.fit(training_data_input, f1_training_data_output) +model_evodn_f2.fit(training_data_input, f2_training_data_output) + +mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +mlp_reg.fit(training_data_input, f1_training_data_output) +mlp_reg_y_pred = mlp_reg.predict(training_data_input) + +trace0 = go.Scatter(x=mlp_reg_y_pred, y=f1_training_data_output, mode="markers") +trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="MLP Regressor " + test_prob.name + "_f1" + + ".html", + auto_open=True, +) + +mlp_reg.fit(training_data_input, f2_training_data_output) +mlp_reg_y_pred = mlp_reg.predict(training_data_input) + +trace0 = go.Scatter(x=mlp_reg_y_pred, y=f2_training_data_output, mode="markers") +trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="MLP Regressor " + test_prob.name + "_f2" + + ".html", + auto_open=True, +) + +gpr = GaussianProcessRegressor() +gpr.fit(training_data_input, f1_training_data_output) +gpr_y_pred = gpr.predict(training_data_input) + +trace0 = go.Scatter(x=gpr_y_pred, y=f1_training_data_output, mode="markers") +trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="GPR Regressor " + test_prob.name + "_f1" + + ".html", + auto_open=True, +) + +gpr.fit(training_data_input, f2_training_data_output) +gpr_y_pred = gpr.predict(training_data_input) + +trace0 = go.Scatter(x=gpr_y_pred, y=f2_training_data_output, mode="markers") +trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="GPR Regressor " + test_prob.name + "_f2" + + ".html", + auto_open=True, +) + +# ZDT3 + +test_prob = testProblem('ZDT3', 30, 2, 0, 1, 0) +np.random.seed(3) +training_data_input = np.random.rand(250, 30) +training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) + +f1_training_data_output = training_data_output[:, 0] +f2_training_data_output = training_data_output[:, 1] + +model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") +model_evodn_f1.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") +model_evodn_f2.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) +model_evodn_f1.fit(training_data_input, f1_training_data_output) +model_evodn_f2.fit(training_data_input, f2_training_data_output) + +mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +mlp_reg.fit(training_data_input, f1_training_data_output) +mlp_reg_y_pred = mlp_reg.predict(training_data_input) + +trace0 = go.Scatter(x=mlp_reg_y_pred, y=f1_training_data_output, mode="markers") +trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="MLP Regressor " + test_prob.name + "_f1" + + ".html", + auto_open=True, +) + +mlp_reg.fit(training_data_input, f2_training_data_output) +mlp_reg_y_pred = mlp_reg.predict(training_data_input) + +trace0 = go.Scatter(x=mlp_reg_y_pred, y=f2_training_data_output, mode="markers") +trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="MLP Regressor " + test_prob.name + "_f2" + + ".html", + auto_open=True, +) + +gpr = GaussianProcessRegressor() +gpr.fit(training_data_input, f1_training_data_output) +gpr_y_pred = gpr.predict(training_data_input) + +trace0 = go.Scatter(x=gpr_y_pred, y=f1_training_data_output, mode="markers") +trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="GPR Regressor " + test_prob.name + "_f1" + + ".html", + auto_open=True, +) + +gpr.fit(training_data_input, f2_training_data_output) +gpr_y_pred = gpr.predict(training_data_input) + +trace0 = go.Scatter(x=gpr_y_pred, y=f2_training_data_output, mode="markers") +trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="GPR Regressor " + test_prob.name + "_f2" + + ".html", + auto_open=True, +) + +# ZDT 4 + +test_prob = testProblem('ZDT4', 10, 2, 0, 1, 0) +np.random.seed(4) +training_data_input = np.random.rand(250, 10) +training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) + +f1_training_data_output = training_data_output[:, 0] +f2_training_data_output = training_data_output[:, 1] + +model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") +model_evodn_f1.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") +model_evodn_f2.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) +model_evodn_f1.fit(training_data_input, f1_training_data_output) +model_evodn_f2.fit(training_data_input, f2_training_data_output) + +mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +mlp_reg.fit(training_data_input, f1_training_data_output) +mlp_reg_y_pred = mlp_reg.predict(training_data_input) + +trace0 = go.Scatter(x=mlp_reg_y_pred, y=f1_training_data_output, mode="markers") +trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="MLP Regressor " + test_prob.name + "_f1" + + ".html", + auto_open=True, +) + +mlp_reg.fit(training_data_input, f2_training_data_output) +mlp_reg_y_pred = mlp_reg.predict(training_data_input) + +trace0 = go.Scatter(x=mlp_reg_y_pred, y=f2_training_data_output, mode="markers") +trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="MLP Regressor " + test_prob.name + "_f2" + + ".html", + auto_open=True, +) + +gpr = GaussianProcessRegressor() +gpr.fit(training_data_input, f1_training_data_output) +gpr_y_pred = gpr.predict(training_data_input) + +trace0 = go.Scatter(x=gpr_y_pred, y=f1_training_data_output, mode="markers") +trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="GPR Regressor " + test_prob.name + "_f1" + + ".html", + auto_open=True, +) + +gpr.fit(training_data_input, f2_training_data_output) +gpr_y_pred = gpr.predict(training_data_input) + +trace0 = go.Scatter(x=gpr_y_pred, y=f2_training_data_output, mode="markers") +trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="GPR Regressor " + test_prob.name + "_f2" + + ".html", + auto_open=True, +) + +# ZDT 6 + +test_prob = testProblem('ZDT6', 10, 2, 0, 1, 0) +np.random.seed(6) +training_data_input = np.random.rand(250, 10) +training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) + +f1_training_data_output = training_data_output[:, 0] +f2_training_data_output = training_data_output[:, 1] + +model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") +model_evodn_f1.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") +model_evodn_f2.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) +model_evodn_f1.fit(training_data_input, f1_training_data_output) +model_evodn_f2.fit(training_data_input, f2_training_data_output) + +mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +mlp_reg.fit(training_data_input, f1_training_data_output) +mlp_reg_y_pred = mlp_reg.predict(training_data_input) + +trace0 = go.Scatter(x=mlp_reg_y_pred, y=f1_training_data_output, mode="markers") +trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="MLP Regressor " + test_prob.name + "_f1" + + ".html", + auto_open=True, +) + +mlp_reg.fit(training_data_input, f2_training_data_output) +mlp_reg_y_pred = mlp_reg.predict(training_data_input) + +trace0 = go.Scatter(x=mlp_reg_y_pred, y=f2_training_data_output, mode="markers") +trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="MLP Regressor " + test_prob.name + "_f2" + + ".html", + auto_open=True, +) + +gpr = GaussianProcessRegressor() +gpr.fit(training_data_input, f1_training_data_output) +gpr_y_pred = gpr.predict(training_data_input) + +trace0 = go.Scatter(x=gpr_y_pred, y=f1_training_data_output, mode="markers") +trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="GPR Regressor " + test_prob.name + "_f1" + + ".html", + auto_open=True, +) + +gpr.fit(training_data_input, f2_training_data_output) +gpr_y_pred = gpr.predict(training_data_input) + +trace0 = go.Scatter(x=gpr_y_pred, y=f2_training_data_output, mode="markers") +trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) +data = [trace0, trace1] +plotly.offline.plot( + data, + filename="GPR Regressor " + test_prob.name + "_f2" + + ".html", + auto_open=True, +) \ No newline at end of file From d2f78e1f8f3776317da37437186aaf27a246f3f0 Mon Sep 17 00:00:00 2001 From: delamorte Date: Wed, 26 Jun 2019 09:42:15 +0300 Subject: [PATCH 042/155] more tests --- evodn2_vs_mlp.py | 2 +- pyrvea/Problem/evodn2_problem.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/evodn2_vs_mlp.py b/evodn2_vs_mlp.py index 81f40ce..b7bad27 100644 --- a/evodn2_vs_mlp.py +++ b/evodn2_vs_mlp.py @@ -8,7 +8,7 @@ from sklearn.gaussian_process import GaussianProcessRegressor import numpy as np -test_prob = EvoNNTestProblem("Sphere", num_of_variables=5) +test_prob = EvoNNTestProblem("Sphere", num_of_variables=3) training_data_input, training_data_output = test_prob.create_training_data( samples=250, method="random", seed=30 ) diff --git a/pyrvea/Problem/evodn2_problem.py b/pyrvea/Problem/evodn2_problem.py index 30ca7f1..49f496c 100644 --- a/pyrvea/Problem/evodn2_problem.py +++ b/pyrvea/Problem/evodn2_problem.py @@ -34,7 +34,7 @@ def __init__( num_of_objectives=2, w_low=-5.0, w_high=5.0, - prob_omit=0.2, + prob_omit=0.3, params=None, ): super().__init__() From 0d8208793aa02b252fa971e05345ccf7c9c1ca36 Mon Sep 17 00:00:00 2001 From: delamorte Date: Thu, 27 Jun 2019 12:58:18 +0300 Subject: [PATCH 043/155] added new mutation method --- evodn2_example.py | 165 ++++++++++++++---- pyrvea/EAs/PPGA.py | 19 +- pyrvea/Population/Population.py | 18 +- pyrvea/Problem/evodn2_problem.py | 9 +- pyrvea/Problem/evonn_problem.py | 2 +- pyrvea/Problem/evonn_test_functions.py | 2 +- pyrvea/Recombination/evodn2_xover_mut.py | 38 ++-- .../evodn2_xover_mut_gaussian.py | 149 ++++++++++++++++ 8 files changed, 326 insertions(+), 76 deletions(-) create mode 100644 pyrvea/Recombination/evodn2_xover_mut_gaussian.py diff --git a/evodn2_example.py b/evodn2_example.py index 7bf4546..57899aa 100644 --- a/evodn2_example.py +++ b/evodn2_example.py @@ -13,7 +13,7 @@ # training_data_input = np.random.rand(150,30) # training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) -test_prob = EvoNNTestProblem("Sphere") +test_prob = EvoNNTestProblem("Sphere", num_of_variables=3) training_data_input, training_data_output = test_prob.create_training_data( samples=250, method="random" ) @@ -42,67 +42,158 @@ plotting=True) model_evodn2.fit(training_data_input, training_data_output) -model_evonn.fit(training_data_input, training_data_output) +#model_evonn.fit(training_data_input, training_data_output) -# test_prob = EvoNNTestProblem("Matyas") -# training_data_input, training_data_output = test_prob.create_training_data( -# samples=250, method="random" -# ) -# -# model_evodn2.fit(training_data_input, training_data_output) -# model_evonn.fit(training_data_input, training_data_output) +test_prob = EvoNNTestProblem("Matyas") +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random" +) + +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) + +#model_evonn.fit(training_data_input, training_data_output) # -# test_prob = EvoNNTestProblem("Himmelblau") -# training_data_input, training_data_output = test_prob.create_training_data( -# samples=250, method="random" -# ) +test_prob = EvoNNTestProblem("Himmelblau") +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random" +) + +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) # # model_evodn2.fit(training_data_input, training_data_output) # model_evonn.fit(training_data_input, training_data_output) # -# test_prob = EvoNNTestProblem("Rastigrin", num_of_variables=2) -# training_data_input, training_data_output = test_prob.create_training_data( -# samples=250, method="random" -# ) +test_prob = EvoNNTestProblem("Rastigrin", num_of_variables=2) +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random" +) + +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) # # model_evodn2.fit(training_data_input, training_data_output) # model_evonn.fit(training_data_input, training_data_output) # -# test_prob = EvoNNTestProblem("Three-hump camel") -# training_data_input, training_data_output = test_prob.create_training_data( -# samples=250, method="random" -# ) +test_prob = EvoNNTestProblem("Three-hump camel") +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random" +) +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) # # model_evodn2.fit(training_data_input, training_data_output) # model_evonn.fit(training_data_input, training_data_output) # -# test_prob = EvoNNTestProblem("Goldstein-Price") -# training_data_input, training_data_output = test_prob.create_training_data( -# samples=250, method="random" -# ) -# +test_prob = EvoNNTestProblem("Goldstein-Price") +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random" +) +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) # model_evodn2.fit(training_data_input, training_data_output) # model_evonn.fit(training_data_input, training_data_output) # -# test_prob = EvoNNTestProblem("LeviN13") -# training_data_input, training_data_output = test_prob.create_training_data( -# samples=250, method="random" -# ) +test_prob = EvoNNTestProblem("LeviN13") +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random" +) +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) # # model_evodn2.fit(training_data_input, training_data_output) # model_evonn.fit(training_data_input, training_data_output) # -# test_prob = EvoNNTestProblem("SchafferN2") -# training_data_input, training_data_output = test_prob.create_training_data( -# samples=250, method="random" -# ) -# +test_prob = EvoNNTestProblem("SchafferN2") +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random" +) +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnets=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) # model_evodn2.fit(training_data_input, training_data_output) # model_evonn.fit(training_data_input, training_data_output) # # # ZDT 1 & 2 -test_prob = testProblem('ZDT6', 10, 2, 0, 1, 0) -training_data_input = np.random.rand(250, 10) +test_prob = testProblem('ZDT2', 30, 2, 0, 1, 0) +training_data_input = np.random.rand(250, 30) training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) f1_training_data_output = training_data_output[:, 0] diff --git a/pyrvea/EAs/PPGA.py b/pyrvea/EAs/PPGA.py index decc651..d8f8619 100644 --- a/pyrvea/EAs/PPGA.py +++ b/pyrvea/EAs/PPGA.py @@ -41,7 +41,7 @@ def set_params( interact: bool = False, plotting: bool = True, logging: list = False, - logfile = None, + logfile=None, crossover_type: str = None, mutation_type: str = None, prob_crossover: float = 0.8, @@ -88,11 +88,13 @@ def set_params( "offspring_place_attempts": 10, "generations": generations_per_iteration, "iterations": iterations, + "total_generations": iterations * generations_per_iteration, "interact": interact, "plotting": plotting, "logging": logging, "logfile": logfile, "current_iteration_gen_count": 0, + "current_total_gen_count": 1, "current_iteration_count": 0, "crossover_type": crossover_type, "mutation_type": mutation_type, @@ -127,6 +129,7 @@ def _next_iteration(self, population: "Population"): while self.continue_iteration(): self._next_gen(population) self.params["current_iteration_gen_count"] += 1 + self.params["current_total_gen_count"] += 1 self.params["current_iteration_count"] += 1 def _next_gen(self, population: "Population"): @@ -165,12 +168,11 @@ def _next_gen(self, population: "Population"): float, ) elif population.problem.__class__.__name__ == "EvoDN2": - # # Calculate standard deviation of pop - # y = [] - # for x in population.individuals.ravel(): - # y.append(np.concatenate(x, axis=None)) - # std_dev = -1 ** (np.random.randint(1, 100)) * np.std(np.concatenate(np.array(y))) ** 2 - # self.params["std_dev"] = std_dev + self.params["std_dev"] = (population.problem.w_high / 3) * ( + 1 + - self.params["current_total_gen_count"] + / self.params["total_generations"] + ) offspring = np.empty((0, population.problem.subnets[0])) @@ -231,8 +233,7 @@ def _next_gen(self, population: "Population"): if ( self.params["current_iteration_count"] == self.params["iterations"] - 1 - and self.params["current_iteration_gen_count"] - == self.params["generations"] + and self.params["current_iteration_gen_count"] == self.params["generations"] ): print( "last gen min error: " diff --git a/pyrvea/Population/Population.py b/pyrvea/Population/Population.py index 3c5b4e6..abd30bc 100644 --- a/pyrvea/Population/Population.py +++ b/pyrvea/Population/Population.py @@ -19,6 +19,7 @@ from pyrvea.Recombination.ppga_crossover import ppga_crossover from pyrvea.Recombination.evodn2_xover_mut import evodn2_xover_mut +from pyrvea.Recombination.evodn2_xover_mut_gaussian import evodn2_xover_mut_gaussian from pyrvea.Recombination.ppga_mutation import ppga_mutation from math import ceil @@ -316,7 +317,7 @@ def evolve(self, EA: "BaseEA" = None, EA_parameters: dict = {}) -> "Population": def mate(self, ind1=None, ind2=None, params=None): """Conduct crossover and mutation over the population. - Conduct simulated binary crossover and bounded polunomial mutation. + Conduct simulated binary crossover and bounded polynomial mutation. """ if self.individuals.ndim >= 2: @@ -325,15 +326,16 @@ def mate(self, ind1=None, ind2=None, params=None): # Perform crossover if params["crossover_type"] == "short": - offspring1, offspring2 = evodn2_xover_mut( + offspring1, offspring2 = evodn2_xover_mut_gaussian( w1, w2, self.individuals, params["prob_crossover"], - params["prob_mutation"], - params["mut_strength"], - params["current_iteration_gen_count"], - params["generations"] + 0.3, + 0.7, + params["current_total_gen_count"], + params["total_generations"], + params["std_dev"] ) else: xover_w1, xover_w2 = ppga_crossover(w1, w2, params["prob_crossover"]) @@ -350,8 +352,8 @@ def mate(self, ind1=None, ind2=None, params=None): alternatives, xover_w1, xover_w2, - params["current_iteration_gen_count"], - params["generations"], + params["current_total_gen_count"], + params["total_generations"], params["prob_mutation"], params["mut_strength"], ) diff --git a/pyrvea/Problem/evodn2_problem.py b/pyrvea/Problem/evodn2_problem.py index 49f496c..e24dd09 100644 --- a/pyrvea/Problem/evodn2_problem.py +++ b/pyrvea/Problem/evodn2_problem.py @@ -81,6 +81,11 @@ def fit(self, training_data, target_values): def create_population(self): + no_nodes = [] + for i in range(self.subnets[1]): + n = random.randint(1, self.num_nodes) + no_nodes.append(n) + individuals = [] for i in range(self.params["pop_size"]): nets = [] @@ -91,7 +96,7 @@ def create_population(self): in_nodes = len(self.subsets[j]) for k in range(num_layers): - out_nodes = random.randint(1, self.num_nodes) + out_nodes = no_nodes[k] net = np.random.uniform( self.w_low, self.w_high, @@ -101,7 +106,7 @@ def create_population(self): ) ) # Randomly set some weights to zero - zeros = np.random.choice(np.arange(net.size), ceil(net.size * self.prob_omit)) + zeros = np.random.choice(np.arange(net.size), ceil(net.size * self.prob_omit), replace=False) net.ravel()[zeros] = 0 # Add bias diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index 4cdc045..61c88e5 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -48,7 +48,7 @@ def __init__( num_of_objectives=2, w_low=-5.0, w_high=5.0, - prob_omit=0.3, + prob_omit=0.2, params=None, ): super().__init__() diff --git a/pyrvea/Problem/evonn_test_functions.py b/pyrvea/Problem/evonn_test_functions.py index 0bb5e19..5be166f 100644 --- a/pyrvea/Problem/evonn_test_functions.py +++ b/pyrvea/Problem/evonn_test_functions.py @@ -52,7 +52,7 @@ def __init__( "Three-hump camel": (-5, 5), "Goldstein-Price": (-2, 2), "LeviN13": (-10, 10), - "Schaffer": (-100, 100), + "SchafferN2": (-100, 100), "min-ex_f1": (0, 1), "min-ex_f2": (0, 5) } diff --git a/pyrvea/Recombination/evodn2_xover_mut.py b/pyrvea/Recombination/evodn2_xover_mut.py index f681862..d02a665 100644 --- a/pyrvea/Recombination/evodn2_xover_mut.py +++ b/pyrvea/Recombination/evodn2_xover_mut.py @@ -10,9 +10,8 @@ def evodn2_xover_mut( prob_crossover=0.8, prob_mut=0.3, mut_strength=0.7, - std_dev=1, cur_gen=1, - total_gen=10 + total_gen=10, ): """ Perform simultaneous crossover and mutation over two individuals. @@ -30,8 +29,6 @@ def evodn2_xover_mut( The probability for mutation mut_strength : float Mutation alfa parameter - std_dev : float - Standard deviation of population cur_gen : int Current generation total_gen : int @@ -64,33 +61,38 @@ def evodn2_xover_mut( try: # Mutate first individual - connections = min( - sub1[layer][1:, :].size, - sub2[layer][1:, :].size, - sub3[layer][1:, :].size, - sub4[layer][1:, :].size, - ) - mutate = sample( - range(connections), np.random.binomial(connections, prob_mut) - ) + connections = sub1[layer][1:, :].size + mutate = sample(range(connections), np.random.binomial(connections, prob_mut)) + sub1[layer][1:, :].ravel()[mutate] = sub1[layer][1:, :].ravel()[ mutate - ] + mut_strength * std_dev * (1 - cur_gen / total_gen) * ( + ] + mut_strength * (1 - cur_gen / total_gen) * ( sub3[layer][1:, :].ravel()[mutate] - sub4[layer][1:, :].ravel()[mutate] ) # Mutate second individual - mutate = sample( - range(connections), np.random.binomial(connections, prob_mut) - ) + connections = sub2[layer][1:, :].size + mutate = sample(range(connections), np.random.binomial(connections, prob_mut)) + sub2[layer][1:, :].ravel()[mutate] = sub2[layer][1:, :].ravel()[ mutate - ] + mut_strength * std_dev * (1 - cur_gen / total_gen) * ( + ] + mut_strength * (1 - cur_gen / total_gen) * ( sub3[layer][1:, :].ravel()[mutate] - sub4[layer][1:, :].ravel()[mutate] ) except IndexError: + + # If mutation partner had less layers than the offspring, randomly mutate the rest of the layers + # for l in range(layer, r): + # mutate = sample( + # range(connections), np.random.binomial(connections, prob_mut) + # ) + # sub1[layer][1:, :].ravel()[mutate] = sub1[layer][1:, :].ravel()[ + # mutate + # ] * np.random.uniform( + # 0.9, 1.0, sub1[layer][1:, :].ravel()[mutate].shape + # ) break return offspring1, offspring2 diff --git a/pyrvea/Recombination/evodn2_xover_mut_gaussian.py b/pyrvea/Recombination/evodn2_xover_mut_gaussian.py new file mode 100644 index 0000000..0854cec --- /dev/null +++ b/pyrvea/Recombination/evodn2_xover_mut_gaussian.py @@ -0,0 +1,149 @@ +import numpy as np +from copy import deepcopy +from random import sample +from math import ceil + + +def evodn2_xover_mut_gaussian( + parent1, + parent2, + individuals, + prob_crossover=0.8, + prob_mut=0.3, + mut_strength=0.7, + cur_gen=1, + total_gen=10, + std_dev=1.0, +): + """ Perform simultaneous crossover and mutation over two individuals. + + Parameters + ---------- + parent1 : ndarray + The first individual + parent2 : ndarray + The second individual + individuals : ndarray + All individuals to choose mutation partner from + prob_crossover : float + The probability for crossover + prob_mut : float + The probability for mutation + mut_strength : float + Mutation alfa parameter + cur_gen : int + Current generation + total_gen : int + Total generations + std_dev : float + Standard deviation + """ + + offspring1 = deepcopy(parent1) + offspring2 = deepcopy(parent2) + + for subnet in range(len(offspring1)): + + sub1 = offspring1[subnet] + sub2 = offspring2[subnet] + + for layer in range(min(len(sub1), len(sub2))): + + connections = min(sub1[layer][1:, :].size, sub2[layer][1:, :].size) + + # Crossover + exchange = sample( + range(connections), np.random.binomial(connections, prob_crossover) + ) + tmp = deepcopy(sub1[layer]) + sub1[layer][1:, :].ravel()[exchange] = sub2[layer][1:, :].ravel()[exchange] + sub2[layer][1:, :].ravel()[exchange] = tmp[1:, :].ravel()[exchange] + + # mut_val = np.random.normal(0, std_dev, connections) + # + # mut = np.random.choice( + # np.arange(connections), + # np.random.binomial(connections, prob_mut), + # replace=False, + # ) + # + # sub1[layer][1:, :].ravel()[mut] *= mut_val[mut] + # + # mut = np.random.choice( + # np.arange(connections), + # np.random.binomial(connections, prob_mut), + # replace=False, + # ) + # sub2[layer][1:, :].ravel()[mut] *= mut_val[mut] + + # for layer in range(max(len(sub1), len(sub2))): + # + # it = np.nditer( + # [sub1[layer][1:, :], sub2[layer][1:, :]], op_flags=["readwrite"] + # ) + # + # for mut_layer in it.itviews: + # + # connections = mut_layer.size + # + # mut_val = np.random.normal(0, std_dev, connections) + # + # mut = np.random.choice(np.arange(mut_layer.size), ceil(mut_layer.size * prob_mut), replace=False) + # mut_layer[mut] *= mut_val[mut] + + # THIS WORKS THE BEST FOR NOW + for layer in sub1: + + connections = layer[1:, :].size + + mut_val = np.random.normal(0, std_dev, connections) + + mut = np.random.choice(connections, np.random.binomial(connections, prob_mut), replace=False) + layer[1:, :].ravel()[mut] *= mut_val[mut] + + for layer in sub2: + + connections = layer[1:, :].size + + mut_val = np.random.normal(0, std_dev, connections) + + mut = np.random.choice(connections, np.random.binomial(connections, prob_mut), replace=False) + layer[1:, :].ravel()[mut] *= mut_val[mut] + + # try: + # # Mutate first individual + # connections = sub1[layer][1:, :].size + # mutate = sample(range(connections), int(connections*prob_mut)) + # + # sub1[layer][1:, :].ravel()[mutate] = sub1[layer][1:, :].ravel()[ + # mutate + # ] + mut_strength * (1 - cur_gen / total_gen) * ( + # sub3[layer][1:, :].ravel()[mutate] + # - sub4[layer][1:, :].ravel()[mutate] + # ) + # + # # Mutate second individual + # connections = sub2[layer][1:, :].size + # mutate = sample(range(connections), int(connections*prob_mut)) + # + # sub2[layer][1:, :].ravel()[mutate] = sub2[layer][1:, :].ravel()[ + # mutate + # ] + mut_strength * (1 - cur_gen / total_gen) * ( + # sub3[layer][1:, :].ravel()[mutate] + # - sub4[layer][1:, :].ravel()[mutate] + # ) + # except IndexError: + # + # # If mutation partner had less layers than the offspring, randomly mutate the rest of the layers + # # for l in range(layer, r): + # # mutate = sample( + # # range(connections), np.random.binomial(connections, prob_mut) + # # ) + # # sub1[layer][1:, :].ravel()[mutate] = sub1[layer][1:, :].ravel()[ + # # mutate + # # ] * np.random.uniform( + # # 0.9, 1.0, sub1[layer][1:, :].ravel()[mutate].shape + # # ) + # break + + return offspring1, offspring2 From e9fa954ff4cbaacd041d44d4d6101434ac81abb1 Mon Sep 17 00:00:00 2001 From: delamorte Date: Thu, 27 Jun 2019 15:57:17 +0300 Subject: [PATCH 044/155] fixed calculation error in gaussian mutation optimized gaussian mutation function optimizations in activation func in evodn2_problem --- evodn2_example.py | 6 +- pyrvea/EAs/PPGA.py | 4 +- pyrvea/Population/Population.py | 2 +- pyrvea/Problem/evodn2_problem.py | 53 +++++++-- pyrvea/Recombination/evodn2_xover_mut.py | 49 ++++---- .../evodn2_xover_mut_gaussian.py | 109 ++++++++---------- 6 files changed, 121 insertions(+), 102 deletions(-) diff --git a/evodn2_example.py b/evodn2_example.py index 57899aa..0dfff79 100644 --- a/evodn2_example.py +++ b/evodn2_example.py @@ -13,7 +13,7 @@ # training_data_input = np.random.rand(150,30) # training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) -test_prob = EvoNNTestProblem("Sphere", num_of_variables=3) +test_prob = EvoNNTestProblem("Matyas") training_data_input, training_data_output = test_prob.create_training_data( samples=250, method="random" ) @@ -188,9 +188,7 @@ plotting=True) model_evodn2.fit(training_data_input, training_data_output) -# model_evodn2.fit(training_data_input, training_data_output) -# model_evonn.fit(training_data_input, training_data_output) -# + # # ZDT 1 & 2 test_prob = testProblem('ZDT2', 30, 2, 0, 1, 0) training_data_input = np.random.rand(250, 30) diff --git a/pyrvea/EAs/PPGA.py b/pyrvea/EAs/PPGA.py index d8f8619..7e5987b 100644 --- a/pyrvea/EAs/PPGA.py +++ b/pyrvea/EAs/PPGA.py @@ -168,7 +168,7 @@ def _next_gen(self, population: "Population"): float, ) elif population.problem.__class__.__name__ == "EvoDN2": - self.params["std_dev"] = (population.problem.w_high / 3) * ( + self.params["std_dev"] = (5 / 3) * ( 1 - self.params["current_total_gen_count"] / self.params["total_generations"] @@ -214,6 +214,8 @@ def _next_gen(self, population: "Population"): + str(np.amin(population.objectives[:, 0])) + " Avg Error: " + str(np.mean(population.objectives[:, 0])) + + " Str dev: " + + str(self.params["std_dev"]) ) # Move predators diff --git a/pyrvea/Population/Population.py b/pyrvea/Population/Population.py index abd30bc..65cebac 100644 --- a/pyrvea/Population/Population.py +++ b/pyrvea/Population/Population.py @@ -331,7 +331,7 @@ def mate(self, ind1=None, ind2=None, params=None): w2, self.individuals, params["prob_crossover"], - 0.3, + params["prob_mutation"], 0.7, params["current_total_gen_count"], params["total_generations"], diff --git a/pyrvea/Problem/evodn2_problem.py b/pyrvea/Problem/evodn2_problem.py index e24dd09..fd162fb 100644 --- a/pyrvea/Problem/evodn2_problem.py +++ b/pyrvea/Problem/evodn2_problem.py @@ -81,6 +81,41 @@ def fit(self, training_data, target_values): def create_population(self): + # Random num of nodes & layers + # individuals = [] + # for i in range(self.params["pop_size"]): + # nets = [] + # for j in range(self.subnets[0]): + # + # layers = [] + # num_layers = np.random.randint(1, self.subnets[1]) + # in_nodes = len(self.subsets[j]) + # + # for k in range(num_layers): + # out_nodes = random.randint(1, self.num_nodes) + # net = np.random.uniform( + # self.w_low, + # self.w_high, + # size=( + # in_nodes, + # out_nodes + # ) + # ) + # # Randomly set some weights to zero + # zeros = np.random.choice(np.arange(net.size), ceil(net.size * self.prob_omit)) + # net.ravel()[zeros] = 0 + # + # # Add bias + # net = np.insert(net, 0, 1, axis=0) + # in_nodes = out_nodes + # layers.append(net) + # nets.append(layers) + # + # individuals.append(nets) + # + # individuals = np.asarray(individuals) + # Fixed connections + no_nodes = [] for i in range(self.subnets[1]): n = random.randint(1, self.num_nodes) @@ -182,19 +217,18 @@ def activation(self, decision_variables): """ subnet_cmplx = [] end_net = np.empty((self.num_of_samples, 0)) - for i in range(decision_variables.shape[0]): + for i, subnet in enumerate(decision_variables): - subnet = decision_variables[i] in_nodes = self.X_train[:, self.subsets[i]] cnet = np.abs(subnet[0][1:, :]) - for layer in range(len(subnet)): + for j, layer in enumerate(subnet): # Calculate the dot product - out = np.dot(in_nodes, subnet[layer][1:, :]) + subnet[layer][0] - if layer > 0: - cnet = np.dot(cnet, np.abs(subnet[layer][1:, :])) + out = np.dot(in_nodes, layer[1:, :] + layer[0]) + if j > 0: + cnet = np.dot(cnet, np.abs(layer[1:, :])) if self.params["activation_func"] == "sigmoid": - activated_layer = lambda x: 1 / (1 + np.exp(-x)) + activated_layer = lambda x: 1.0 / (1.0 + np.exp(-x)) if self.params["activation_func"] == "relu": activated_layer = lambda x: np.maximum(x, 0) @@ -349,7 +383,6 @@ def create_plot(self, model): auto_open=True, ) - class EvoDN2Model(EvoDN2): """Class for the surrogate model. @@ -481,7 +514,7 @@ def predict(self, decision_variables): out = np.dot(in_nodes, subnet[layer][1:, :]) + subnet[layer][0] if self.params["activation_func"] == "sigmoid": - activated_layer = lambda x: 1 / (1 + np.exp(-x)) + activated_layer = lambda x: 1.0 / (1.0 + np.exp(-x)) if self.params["activation_func"] == "relu": activated_layer = lambda x: np.maximum(x, 0) @@ -547,4 +580,4 @@ def single_variable_response(self, ploton=False, log=None): print("x" + str(i + 1) + " response: " + str(response) + " " + s, file=log) svr = np.vstack((svr, ["x" + str(i + 1), s])) - self.svr = svr + self.svr = svr \ No newline at end of file diff --git a/pyrvea/Recombination/evodn2_xover_mut.py b/pyrvea/Recombination/evodn2_xover_mut.py index d02a665..97c4e97 100644 --- a/pyrvea/Recombination/evodn2_xover_mut.py +++ b/pyrvea/Recombination/evodn2_xover_mut.py @@ -45,54 +45,49 @@ def evodn2_xover_mut( sub3 = individuals[np.random.randint(0, individuals.shape[0] - 1)][subnet] sub4 = individuals[np.random.randint(0, individuals.shape[0] - 1)][subnet] - r = min(len(sub1), len(sub2)) + r = max(len(sub1), len(sub2)) for layer in range(r): - connections = min(sub1[layer][1:, :].size, sub2[layer][1:, :].size) + try: + connections = min(sub1[layer][1:, :].size, sub2[layer][1:, :].size) + + # Crossover + exchange = np.random.choice(connections, np.random.binomial(connections, prob_crossover), replace=False) - # Crossover - exchange = sample( - range(connections), np.random.binomial(connections, prob_crossover) - ) - tmp = deepcopy(sub1[layer]) - sub1[layer][1:, :].ravel()[exchange] = sub2[layer][1:, :].ravel()[exchange] - sub2[layer][1:, :].ravel()[exchange] = tmp[1:, :].ravel()[exchange] + tmp = deepcopy(sub1[layer]) + sub1[layer][1:, :].ravel()[exchange] = sub2[layer][1:, :].ravel()[exchange] + sub2[layer][1:, :].ravel()[exchange] = tmp[1:, :].ravel()[exchange] + + except IndexError: + pass try: # Mutate first individual connections = sub1[layer][1:, :].size - mutate = sample(range(connections), np.random.binomial(connections, prob_mut)) + mutate = np.random.choice(connections, np.random.binomial(connections, prob_mut), replace=False) sub1[layer][1:, :].ravel()[mutate] = sub1[layer][1:, :].ravel()[ mutate ] + mut_strength * (1 - cur_gen / total_gen) * ( - sub3[layer][1:, :].ravel()[mutate] - - sub4[layer][1:, :].ravel()[mutate] + np.copy(sub3[layer][1:, :].ravel()[mutate]) + - np.copy(sub4[layer][1:, :].ravel()[mutate]) ) + except IndexError: + pass + try: # Mutate second individual connections = sub2[layer][1:, :].size - mutate = sample(range(connections), np.random.binomial(connections, prob_mut)) + mutate = np.random.choice(connections, np.random.binomial(connections, prob_mut), replace=False) sub2[layer][1:, :].ravel()[mutate] = sub2[layer][1:, :].ravel()[ mutate ] + mut_strength * (1 - cur_gen / total_gen) * ( - sub3[layer][1:, :].ravel()[mutate] - - sub4[layer][1:, :].ravel()[mutate] + np.copy(sub3[layer][1:, :].ravel()[mutate]) + - np.copy(sub4[layer][1:, :].ravel()[mutate]) ) except IndexError: - - # If mutation partner had less layers than the offspring, randomly mutate the rest of the layers - # for l in range(layer, r): - # mutate = sample( - # range(connections), np.random.binomial(connections, prob_mut) - # ) - # sub1[layer][1:, :].ravel()[mutate] = sub1[layer][1:, :].ravel()[ - # mutate - # ] * np.random.uniform( - # 0.9, 1.0, sub1[layer][1:, :].ravel()[mutate].shape - # ) - break + continue return offspring1, offspring2 diff --git a/pyrvea/Recombination/evodn2_xover_mut_gaussian.py b/pyrvea/Recombination/evodn2_xover_mut_gaussian.py index 0854cec..86a9914 100644 --- a/pyrvea/Recombination/evodn2_xover_mut_gaussian.py +++ b/pyrvea/Recombination/evodn2_xover_mut_gaussian.py @@ -2,7 +2,7 @@ from copy import deepcopy from random import sample from math import ceil - +from timeit import default_timer as timer def evodn2_xover_mut_gaussian( parent1, @@ -47,17 +47,42 @@ def evodn2_xover_mut_gaussian( sub1 = offspring1[subnet] sub2 = offspring2[subnet] - for layer in range(min(len(sub1), len(sub2))): + for layer in range(max(len(sub1), len(sub2))): + + try: + connections = min(sub1[layer][1:, :].size, sub2[layer][1:, :].size) + + # Crossover + exchange = np.random.choice(connections, np.random.binomial(connections, prob_crossover), replace=False) + tmp = np.copy(sub1[layer]) + sub1[layer][1:, :].ravel()[exchange] = sub2[layer][1:, :].ravel()[exchange] + sub2[layer][1:, :].ravel()[exchange] = tmp[1:, :].ravel()[exchange] + + except IndexError: + pass + + try: + connections = sub1[layer][1:, :].size + + mut_val = np.random.normal(0, std_dev, connections) + + mut = np.random.choice(connections, np.random.binomial(connections, prob_mut), replace=False) + sub1[layer][1:, :].ravel()[mut] += sub1[layer][1:, :].ravel()[mut] * mut_val[mut] - connections = min(sub1[layer][1:, :].size, sub2[layer][1:, :].size) + except IndexError: + pass + + try: + connections = sub2[layer][1:, :].size + + mut_val = np.random.normal(0, std_dev, connections) + + mut = np.random.choice(connections, np.random.binomial(connections, prob_mut), replace=False) + sub2[layer][1:, :].ravel()[mut] += sub2[layer][1:, :].ravel()[mut] * mut_val[mut] + + except IndexError: + continue - # Crossover - exchange = sample( - range(connections), np.random.binomial(connections, prob_crossover) - ) - tmp = deepcopy(sub1[layer]) - sub1[layer][1:, :].ravel()[exchange] = sub2[layer][1:, :].ravel()[exchange] - sub2[layer][1:, :].ravel()[exchange] = tmp[1:, :].ravel()[exchange] # mut_val = np.random.normal(0, std_dev, connections) # @@ -92,58 +117,24 @@ def evodn2_xover_mut_gaussian( # mut_layer[mut] *= mut_val[mut] # THIS WORKS THE BEST FOR NOW - for layer in sub1: - - connections = layer[1:, :].size - - mut_val = np.random.normal(0, std_dev, connections) - - mut = np.random.choice(connections, np.random.binomial(connections, prob_mut), replace=False) - layer[1:, :].ravel()[mut] *= mut_val[mut] - - for layer in sub2: - - connections = layer[1:, :].size - - mut_val = np.random.normal(0, std_dev, connections) - - mut = np.random.choice(connections, np.random.binomial(connections, prob_mut), replace=False) - layer[1:, :].ravel()[mut] *= mut_val[mut] - - # try: - # # Mutate first individual - # connections = sub1[layer][1:, :].size - # mutate = sample(range(connections), int(connections*prob_mut)) + # for layer in sub1: # - # sub1[layer][1:, :].ravel()[mutate] = sub1[layer][1:, :].ravel()[ - # mutate - # ] + mut_strength * (1 - cur_gen / total_gen) * ( - # sub3[layer][1:, :].ravel()[mutate] - # - sub4[layer][1:, :].ravel()[mutate] - # ) + # connections = layer[1:, :].size # - # # Mutate second individual - # connections = sub2[layer][1:, :].size - # mutate = sample(range(connections), int(connections*prob_mut)) + # mut_val = np.random.normal(0, std_dev, connections) # - # sub2[layer][1:, :].ravel()[mutate] = sub2[layer][1:, :].ravel()[ - # mutate - # ] + mut_strength * (1 - cur_gen / total_gen) * ( - # sub3[layer][1:, :].ravel()[mutate] - # - sub4[layer][1:, :].ravel()[mutate] - # ) - # except IndexError: + # mut = np.random.choice(connections, np.random.binomial(connections, prob_mut), replace=False) + # layer[1:, :].ravel()[mut] *= mut_val[mut] + # + # for layer in sub2: + # + # connections = layer[1:, :].size # - # # If mutation partner had less layers than the offspring, randomly mutate the rest of the layers - # # for l in range(layer, r): - # # mutate = sample( - # # range(connections), np.random.binomial(connections, prob_mut) - # # ) - # # sub1[layer][1:, :].ravel()[mutate] = sub1[layer][1:, :].ravel()[ - # # mutate - # # ] * np.random.uniform( - # # 0.9, 1.0, sub1[layer][1:, :].ravel()[mutate].shape - # # ) - # break + # mut_val = np.random.normal(0, std_dev, connections) + # + # mut = np.random.choice(connections, np.random.binomial(connections, prob_mut), replace=False) + # layer[1:, :].ravel()[mut] *= mut_val[mut] + + return offspring1, offspring2 From 8d3a918d67911f734ba23ec1325add0e00e67e48 Mon Sep 17 00:00:00 2001 From: delamorte Date: Fri, 28 Jun 2019 09:46:07 +0300 Subject: [PATCH 045/155] changed number of nodes back to random changed probability of killing weights initially to 0.2 from 0.3 reverted optimizations in evodn2_problem --- evodn2_example.py | 2 +- evodn2_vs_mlp.py | 1450 +++++++++++----------- pyrvea/Problem/evodn2_problem.py | 78 +- pyrvea/Recombination/evodn2_xover_mut.py | 66 +- 4 files changed, 772 insertions(+), 824 deletions(-) diff --git a/evodn2_example.py b/evodn2_example.py index 0dfff79..9f9b3cd 100644 --- a/evodn2_example.py +++ b/evodn2_example.py @@ -15,7 +15,7 @@ test_prob = EvoNNTestProblem("Matyas") training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random" + samples=250, method="random", seed=31 ) model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) diff --git a/evodn2_vs_mlp.py b/evodn2_vs_mlp.py index b7bad27..16746ab 100644 --- a/evodn2_vs_mlp.py +++ b/evodn2_vs_mlp.py @@ -8,419 +8,419 @@ from sklearn.gaussian_process import GaussianProcessRegressor import numpy as np -test_prob = EvoNNTestProblem("Sphere", num_of_variables=3) -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random", seed=30 -) - -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) -model_evonn.set_params( - num_nodes=20, - pop_size=500, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - -model_evodn2.fit(training_data_input, training_data_output) -model_evonn.fit(training_data_input, training_data_output) - -mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) -mlp_reg.fit(training_data_input, training_data_output) -mlp_reg_y_pred = mlp_reg.predict(training_data_input) - -trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") -trace1 = go.Scatter(x=training_data_output, y=training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="MLP Regressor " + test_prob.name - + ".html", - auto_open=True, -) - -gpr = GaussianProcessRegressor() -gpr.fit(training_data_input, training_data_output) -gpr_y_pred = gpr.predict(training_data_input) - -trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") -trace1 = go.Scatter(x=training_data_output, y=training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="Gaussian Process Regressor " + test_prob.name - + ".html", - auto_open=True, -) - -test_prob = EvoNNTestProblem("Matyas") -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random", seed=31 -) - -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) -model_evonn.set_params( - num_nodes=20, - pop_size=500, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - -model_evodn2.fit(training_data_input, training_data_output) -model_evonn.fit(training_data_input, training_data_output) - -mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) -mlp_reg.fit(training_data_input, training_data_output) -mlp_reg_y_pred = mlp_reg.predict(training_data_input) - -trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") -trace1 = go.Scatter(x=training_data_output, y=training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="MLP Regressor " + test_prob.name - + ".html", - auto_open=True, -) - -gpr = GaussianProcessRegressor() -gpr.fit(training_data_input, training_data_output) -gpr_y_pred = gpr.predict(training_data_input) - -trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") -trace1 = go.Scatter(x=training_data_output, y=training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="Gaussian Process Regressor " + test_prob.name - + ".html", - auto_open=True, -) - -test_prob = EvoNNTestProblem("Himmelblau") -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random", seed=32 -) - -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) -model_evonn.set_params( - num_nodes=20, - pop_size=500, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - -model_evodn2.fit(training_data_input, training_data_output) -model_evonn.fit(training_data_input, training_data_output) - -mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) -mlp_reg.fit(training_data_input, training_data_output) -mlp_reg_y_pred = mlp_reg.predict(training_data_input) - -trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") -trace1 = go.Scatter(x=training_data_output, y=training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="MLP Regressor " + test_prob.name - + ".html", - auto_open=True, -) - -gpr = GaussianProcessRegressor() -gpr.fit(training_data_input, training_data_output) -gpr_y_pred = gpr.predict(training_data_input) - -trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") -trace1 = go.Scatter(x=training_data_output, y=training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="Gaussian Process Regressor " + test_prob.name - + ".html", - auto_open=True, -) - -test_prob = EvoNNTestProblem("Rastigrin", num_of_variables=2) -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random", seed=33 -) - -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) -model_evonn.set_params( - num_nodes=20, - pop_size=500, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - -model_evodn2.fit(training_data_input, training_data_output) -model_evonn.fit(training_data_input, training_data_output) - -mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) -mlp_reg.fit(training_data_input, training_data_output) -mlp_reg_y_pred = mlp_reg.predict(training_data_input) - -trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") -trace1 = go.Scatter(x=training_data_output, y=training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="MLP Regressor " + test_prob.name - + ".html", - auto_open=True, -) - -gpr = GaussianProcessRegressor() -gpr.fit(training_data_input, training_data_output) -gpr_y_pred = gpr.predict(training_data_input) - -trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") -trace1 = go.Scatter(x=training_data_output, y=training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="Gaussian Process Regressor " + test_prob.name - + ".html", - auto_open=True, -) - -test_prob = EvoNNTestProblem("Three-hump camel") -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random", seed=34 -) - -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) -model_evonn.set_params( - num_nodes=20, - pop_size=500, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - -model_evodn2.fit(training_data_input, training_data_output) -model_evonn.fit(training_data_input, training_data_output) - -mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) -mlp_reg.fit(training_data_input, training_data_output) -mlp_reg_y_pred = mlp_reg.predict(training_data_input) - -trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") -trace1 = go.Scatter(x=training_data_output, y=training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="MLP Regressor " + test_prob.name - + ".html", - auto_open=True, -) - -gpr = GaussianProcessRegressor() -gpr.fit(training_data_input, training_data_output) -gpr_y_pred = gpr.predict(training_data_input) - -trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") -trace1 = go.Scatter(x=training_data_output, y=training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="Gaussian Process Regressor " + test_prob.name - + ".html", - auto_open=True, -) - -test_prob = EvoNNTestProblem("Goldstein-Price") -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random", seed=35 -) - -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) -model_evonn.set_params( - num_nodes=20, - pop_size=500, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - -model_evodn2.fit(training_data_input, training_data_output) -model_evonn.fit(training_data_input, training_data_output) - -mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) -mlp_reg.fit(training_data_input, training_data_output) -mlp_reg_y_pred = mlp_reg.predict(training_data_input) - -trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") -trace1 = go.Scatter(x=training_data_output, y=training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="MLP Regressor " + test_prob.name - + ".html", - auto_open=True, -) - -gpr = GaussianProcessRegressor() -gpr.fit(training_data_input, training_data_output) -gpr_y_pred = gpr.predict(training_data_input) - -trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") -trace1 = go.Scatter(x=training_data_output, y=training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="Gaussian Process Regressor " + test_prob.name - + ".html", - auto_open=True, -) - -test_prob = EvoNNTestProblem("LeviN13") -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random", seed=36 -) - -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) -model_evonn.set_params( - num_nodes=20, - pop_size=500, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - -model_evodn2.fit(training_data_input, training_data_output) -model_evonn.fit(training_data_input, training_data_output) - -mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) -mlp_reg.fit(training_data_input, training_data_output) -mlp_reg_y_pred = mlp_reg.predict(training_data_input) - -trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") -trace1 = go.Scatter(x=training_data_output, y=training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="MLP Regressor " + test_prob.name - + ".html", - auto_open=True, -) - -gpr = GaussianProcessRegressor() -gpr.fit(training_data_input, training_data_output) -gpr_y_pred = gpr.predict(training_data_input) - -trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") -trace1 = go.Scatter(x=training_data_output, y=training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="Gaussian Process Regressor " + test_prob.name - + ".html", - auto_open=True, -) - +# test_prob = EvoNNTestProblem("Sphere", num_of_variables=3) +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random", seed=30 +# ) + +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnets=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +# model_evonn.set_params( +# num_nodes=20, +# pop_size=500, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# model_evodn2.fit(training_data_input, training_data_output) +# #model_evonn.fit(training_data_input, training_data_output) +# +# # mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +# # mlp_reg.fit(training_data_input, training_data_output) +# # mlp_reg_y_pred = mlp_reg.predict(training_data_input) +# # +# # trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") +# # trace1 = go.Scatter(x=training_data_output, y=training_data_output) +# # data = [trace0, trace1] +# # plotly.offline.plot( +# # data, +# # filename="MLP Regressor " + test_prob.name +# # + ".html", +# # auto_open=True, +# # ) +# # +# # gpr = GaussianProcessRegressor() +# # gpr.fit(training_data_input, training_data_output) +# # gpr_y_pred = gpr.predict(training_data_input) +# # +# # trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") +# # trace1 = go.Scatter(x=training_data_output, y=training_data_output) +# # data = [trace0, trace1] +# # plotly.offline.plot( +# # data, +# # filename="Gaussian Process Regressor " + test_prob.name +# # + ".html", +# # auto_open=True, +# # ) +# +# test_prob = EvoNNTestProblem("Matyas") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random", seed=31 +# ) +# +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnets=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +# model_evonn.set_params( +# num_nodes=20, +# pop_size=500, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# model_evodn2.fit(training_data_input, training_data_output) +# # model_evonn.fit(training_data_input, training_data_output) +# # +# # mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +# # mlp_reg.fit(training_data_input, training_data_output) +# # mlp_reg_y_pred = mlp_reg.predict(training_data_input) +# # +# # trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") +# # trace1 = go.Scatter(x=training_data_output, y=training_data_output) +# # data = [trace0, trace1] +# # plotly.offline.plot( +# # data, +# # filename="MLP Regressor " + test_prob.name +# # + ".html", +# # auto_open=True, +# # ) +# # +# # gpr = GaussianProcessRegressor() +# # gpr.fit(training_data_input, training_data_output) +# # gpr_y_pred = gpr.predict(training_data_input) +# # +# # trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") +# # trace1 = go.Scatter(x=training_data_output, y=training_data_output) +# # data = [trace0, trace1] +# # plotly.offline.plot( +# # data, +# # filename="Gaussian Process Regressor " + test_prob.name +# # + ".html", +# # auto_open=True, +# # ) +# +# test_prob = EvoNNTestProblem("Himmelblau") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random", seed=32 +# ) +# +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnets=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +# model_evonn.set_params( +# num_nodes=20, +# pop_size=500, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# model_evodn2.fit(training_data_input, training_data_output) +# # model_evonn.fit(training_data_input, training_data_output) +# # +# # mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +# # mlp_reg.fit(training_data_input, training_data_output) +# # mlp_reg_y_pred = mlp_reg.predict(training_data_input) +# # +# # trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") +# # trace1 = go.Scatter(x=training_data_output, y=training_data_output) +# # data = [trace0, trace1] +# # plotly.offline.plot( +# # data, +# # filename="MLP Regressor " + test_prob.name +# # + ".html", +# # auto_open=True, +# # ) +# # +# # gpr = GaussianProcessRegressor() +# # gpr.fit(training_data_input, training_data_output) +# # gpr_y_pred = gpr.predict(training_data_input) +# # +# # trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") +# # trace1 = go.Scatter(x=training_data_output, y=training_data_output) +# # data = [trace0, trace1] +# # plotly.offline.plot( +# # data, +# # filename="Gaussian Process Regressor " + test_prob.name +# # + ".html", +# # auto_open=True, +# # ) +# +# test_prob = EvoNNTestProblem("Rastigrin", num_of_variables=2) +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random", seed=33 +# ) +# +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnets=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +# model_evonn.set_params( +# num_nodes=20, +# pop_size=500, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# model_evodn2.fit(training_data_input, training_data_output) +# # model_evonn.fit(training_data_input, training_data_output) +# # +# # mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +# # mlp_reg.fit(training_data_input, training_data_output) +# # mlp_reg_y_pred = mlp_reg.predict(training_data_input) +# # +# # trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") +# # trace1 = go.Scatter(x=training_data_output, y=training_data_output) +# # data = [trace0, trace1] +# # plotly.offline.plot( +# # data, +# # filename="MLP Regressor " + test_prob.name +# # + ".html", +# # auto_open=True, +# # ) +# # +# # gpr = GaussianProcessRegressor() +# # gpr.fit(training_data_input, training_data_output) +# # gpr_y_pred = gpr.predict(training_data_input) +# # +# # trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") +# # trace1 = go.Scatter(x=training_data_output, y=training_data_output) +# # data = [trace0, trace1] +# # plotly.offline.plot( +# # data, +# # filename="Gaussian Process Regressor " + test_prob.name +# # + ".html", +# # auto_open=True, +# # ) +# +# test_prob = EvoNNTestProblem("Three-hump camel") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random", seed=34 +# ) +# +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnets=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +# model_evonn.set_params( +# num_nodes=20, +# pop_size=500, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# model_evodn2.fit(training_data_input, training_data_output) +# # model_evonn.fit(training_data_input, training_data_output) +# # +# # mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +# # mlp_reg.fit(training_data_input, training_data_output) +# # mlp_reg_y_pred = mlp_reg.predict(training_data_input) +# # +# # trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") +# # trace1 = go.Scatter(x=training_data_output, y=training_data_output) +# # data = [trace0, trace1] +# # plotly.offline.plot( +# # data, +# # filename="MLP Regressor " + test_prob.name +# # + ".html", +# # auto_open=True, +# # ) +# # +# # gpr = GaussianProcessRegressor() +# # gpr.fit(training_data_input, training_data_output) +# # gpr_y_pred = gpr.predict(training_data_input) +# # +# # trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") +# # trace1 = go.Scatter(x=training_data_output, y=training_data_output) +# # data = [trace0, trace1] +# # plotly.offline.plot( +# # data, +# # filename="Gaussian Process Regressor " + test_prob.name +# # + ".html", +# # auto_open=True, +# # ) +# +# test_prob = EvoNNTestProblem("Goldstein-Price") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random", seed=35 +# ) +# +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnets=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +# model_evonn.set_params( +# num_nodes=20, +# pop_size=500, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# model_evodn2.fit(training_data_input, training_data_output) +# # model_evonn.fit(training_data_input, training_data_output) +# # +# # mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +# # mlp_reg.fit(training_data_input, training_data_output) +# # mlp_reg_y_pred = mlp_reg.predict(training_data_input) +# # +# # trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") +# # trace1 = go.Scatter(x=training_data_output, y=training_data_output) +# # data = [trace0, trace1] +# # plotly.offline.plot( +# # data, +# # filename="MLP Regressor " + test_prob.name +# # + ".html", +# # auto_open=True, +# # ) +# # +# # gpr = GaussianProcessRegressor() +# # gpr.fit(training_data_input, training_data_output) +# # gpr_y_pred = gpr.predict(training_data_input) +# # +# # trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") +# # trace1 = go.Scatter(x=training_data_output, y=training_data_output) +# # data = [trace0, trace1] +# # plotly.offline.plot( +# # data, +# # filename="Gaussian Process Regressor " + test_prob.name +# # + ".html", +# # auto_open=True, +# # ) +# +# test_prob = EvoNNTestProblem("LeviN13") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random", seed=36 +# ) +# +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnets=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +# model_evonn.set_params( +# num_nodes=20, +# pop_size=500, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# model_evodn2.fit(training_data_input, training_data_output) +# # model_evonn.fit(training_data_input, training_data_output) +# # +# # mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +# # mlp_reg.fit(training_data_input, training_data_output) +# # mlp_reg_y_pred = mlp_reg.predict(training_data_input) +# # +# # trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") +# # trace1 = go.Scatter(x=training_data_output, y=training_data_output) +# # data = [trace0, trace1] +# # plotly.offline.plot( +# # data, +# # filename="MLP Regressor " + test_prob.name +# # + ".html", +# # auto_open=True, +# # ) +# # +# # gpr = GaussianProcessRegressor() +# # gpr.fit(training_data_input, training_data_output) +# # gpr_y_pred = gpr.predict(training_data_input) +# # +# # trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") +# # trace1 = go.Scatter(x=training_data_output, y=training_data_output) +# # data = [trace0, trace1] +# # plotly.offline.plot( +# # data, +# # filename="Gaussian Process Regressor " + test_prob.name +# # + ".html", +# # auto_open=True, +# # ) +# test_prob = EvoNNTestProblem("SchafferN2") training_data_input, training_data_output = test_prob.create_training_data( samples=250, method="random", seed=37 @@ -449,8 +449,8 @@ logging=True, plotting=True) -model_evodn2.fit(training_data_input, training_data_output) -model_evonn.fit(training_data_input, training_data_output) +# model_evodn2.fit(training_data_input, training_data_output) +#model_evonn.fit(training_data_input, training_data_output) mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) mlp_reg.fit(training_data_input, training_data_output) @@ -497,47 +497,47 @@ logging=True, plotting=True) -model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) -model_evonn.set_params( - num_nodes=20, - pop_size=500, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - -model_evodn2.fit(training_data_input, training_data_output) -model_evonn.fit(training_data_input, training_data_output) - -mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) -mlp_reg.fit(training_data_input, training_data_output) -mlp_reg_y_pred = mlp_reg.predict(training_data_input) - -trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") -trace1 = go.Scatter(x=training_data_output, y=training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="MLP Regressor " + test_prob.name - + ".html", - auto_open=True, -) - -gpr = GaussianProcessRegressor() -gpr.fit(training_data_input, training_data_output) -gpr_y_pred = gpr.predict(training_data_input) +model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +model_evonn.set_params( + num_nodes=20, + pop_size=500, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) -trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") -trace1 = go.Scatter(x=training_data_output, y=training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="Gaussian Process Regressor " + test_prob.name - + ".html", - auto_open=True, -) +model_evodn2.fit(training_data_input, training_data_output) +# model_evonn.fit(training_data_input, training_data_output) +# +# mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +# mlp_reg.fit(training_data_input, training_data_output) +# mlp_reg_y_pred = mlp_reg.predict(training_data_input) +# +# trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") +# trace1 = go.Scatter(x=training_data_output, y=training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="MLP Regressor " + test_prob.name +# + ".html", +# auto_open=True, +# ) +# +# gpr = GaussianProcessRegressor() +# gpr.fit(training_data_input, training_data_output) +# gpr_y_pred = gpr.predict(training_data_input) +# +# trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") +# trace1 = go.Scatter(x=training_data_output, y=training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="Gaussian Process Regressor " + test_prob.name +# + ".html", +# auto_open=True, +# ) # --------------------------------------------------------------- # @@ -575,60 +575,60 @@ plotting=True) model_evodn_f1.fit(training_data_input, f1_training_data_output) model_evodn_f2.fit(training_data_input, f2_training_data_output) - -mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) -mlp_reg.fit(training_data_input, f1_training_data_output) -mlp_reg_y_pred = mlp_reg.predict(training_data_input) - -trace0 = go.Scatter(x=mlp_reg_y_pred, y=f1_training_data_output, mode="markers") -trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="MLP Regressor " + test_prob.name + "_f1" - + ".html", - auto_open=True, -) - -mlp_reg.fit(training_data_input, f2_training_data_output) -mlp_reg_y_pred = mlp_reg.predict(training_data_input) - -trace0 = go.Scatter(x=mlp_reg_y_pred, y=f2_training_data_output, mode="markers") -trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="MLP Regressor " + test_prob.name + "_f2" - + ".html", - auto_open=True, -) - -gpr = GaussianProcessRegressor() -gpr.fit(training_data_input, f1_training_data_output) -gpr_y_pred = gpr.predict(training_data_input) - -trace0 = go.Scatter(x=gpr_y_pred, y=f1_training_data_output, mode="markers") -trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="GPR Regressor " + test_prob.name + "_f1" - + ".html", - auto_open=True, -) - -gpr.fit(training_data_input, f2_training_data_output) -gpr_y_pred = gpr.predict(training_data_input) - -trace0 = go.Scatter(x=gpr_y_pred, y=f2_training_data_output, mode="markers") -trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="GPR Regressor " + test_prob.name + "_f2" - + ".html", - auto_open=True, -) +# +# mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +# mlp_reg.fit(training_data_input, f1_training_data_output) +# mlp_reg_y_pred = mlp_reg.predict(training_data_input) +# +# trace0 = go.Scatter(x=mlp_reg_y_pred, y=f1_training_data_output, mode="markers") +# trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="MLP Regressor " + test_prob.name + "_f1" +# + ".html", +# auto_open=True, +# ) +# +# mlp_reg.fit(training_data_input, f2_training_data_output) +# mlp_reg_y_pred = mlp_reg.predict(training_data_input) +# +# trace0 = go.Scatter(x=mlp_reg_y_pred, y=f2_training_data_output, mode="markers") +# trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="MLP Regressor " + test_prob.name + "_f2" +# + ".html", +# auto_open=True, +# ) +# +# gpr = GaussianProcessRegressor() +# gpr.fit(training_data_input, f1_training_data_output) +# gpr_y_pred = gpr.predict(training_data_input) +# +# trace0 = go.Scatter(x=gpr_y_pred, y=f1_training_data_output, mode="markers") +# trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="GPR Regressor " + test_prob.name + "_f1" +# + ".html", +# auto_open=True, +# ) +# +# gpr.fit(training_data_input, f2_training_data_output) +# gpr_y_pred = gpr.predict(training_data_input) +# +# trace0 = go.Scatter(x=gpr_y_pred, y=f2_training_data_output, mode="markers") +# trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="GPR Regressor " + test_prob.name + "_f2" +# + ".html", +# auto_open=True, +# ) # ZDT2 @@ -665,60 +665,60 @@ plotting=True) model_evodn_f1.fit(training_data_input, f1_training_data_output) model_evodn_f2.fit(training_data_input, f2_training_data_output) - -mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) -mlp_reg.fit(training_data_input, f1_training_data_output) -mlp_reg_y_pred = mlp_reg.predict(training_data_input) - -trace0 = go.Scatter(x=mlp_reg_y_pred, y=f1_training_data_output, mode="markers") -trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="MLP Regressor " + test_prob.name + "_f1" - + ".html", - auto_open=True, -) - -mlp_reg.fit(training_data_input, f2_training_data_output) -mlp_reg_y_pred = mlp_reg.predict(training_data_input) - -trace0 = go.Scatter(x=mlp_reg_y_pred, y=f2_training_data_output, mode="markers") -trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="MLP Regressor " + test_prob.name + "_f2" - + ".html", - auto_open=True, -) - -gpr = GaussianProcessRegressor() -gpr.fit(training_data_input, f1_training_data_output) -gpr_y_pred = gpr.predict(training_data_input) - -trace0 = go.Scatter(x=gpr_y_pred, y=f1_training_data_output, mode="markers") -trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="GPR Regressor " + test_prob.name + "_f1" - + ".html", - auto_open=True, -) - -gpr.fit(training_data_input, f2_training_data_output) -gpr_y_pred = gpr.predict(training_data_input) - -trace0 = go.Scatter(x=gpr_y_pred, y=f2_training_data_output, mode="markers") -trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="GPR Regressor " + test_prob.name + "_f2" - + ".html", - auto_open=True, -) +# +# mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +# mlp_reg.fit(training_data_input, f1_training_data_output) +# mlp_reg_y_pred = mlp_reg.predict(training_data_input) +# +# trace0 = go.Scatter(x=mlp_reg_y_pred, y=f1_training_data_output, mode="markers") +# trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="MLP Regressor " + test_prob.name + "_f1" +# + ".html", +# auto_open=True, +# ) +# +# mlp_reg.fit(training_data_input, f2_training_data_output) +# mlp_reg_y_pred = mlp_reg.predict(training_data_input) +# +# trace0 = go.Scatter(x=mlp_reg_y_pred, y=f2_training_data_output, mode="markers") +# trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="MLP Regressor " + test_prob.name + "_f2" +# + ".html", +# auto_open=True, +# ) +# +# gpr = GaussianProcessRegressor() +# gpr.fit(training_data_input, f1_training_data_output) +# gpr_y_pred = gpr.predict(training_data_input) +# +# trace0 = go.Scatter(x=gpr_y_pred, y=f1_training_data_output, mode="markers") +# trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="GPR Regressor " + test_prob.name + "_f1" +# + ".html", +# auto_open=True, +# ) +# +# gpr.fit(training_data_input, f2_training_data_output) +# gpr_y_pred = gpr.predict(training_data_input) +# +# trace0 = go.Scatter(x=gpr_y_pred, y=f2_training_data_output, mode="markers") +# trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="GPR Regressor " + test_prob.name + "_f2" +# + ".html", +# auto_open=True, +# ) # ZDT3 @@ -755,60 +755,60 @@ plotting=True) model_evodn_f1.fit(training_data_input, f1_training_data_output) model_evodn_f2.fit(training_data_input, f2_training_data_output) - -mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) -mlp_reg.fit(training_data_input, f1_training_data_output) -mlp_reg_y_pred = mlp_reg.predict(training_data_input) - -trace0 = go.Scatter(x=mlp_reg_y_pred, y=f1_training_data_output, mode="markers") -trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="MLP Regressor " + test_prob.name + "_f1" - + ".html", - auto_open=True, -) - -mlp_reg.fit(training_data_input, f2_training_data_output) -mlp_reg_y_pred = mlp_reg.predict(training_data_input) - -trace0 = go.Scatter(x=mlp_reg_y_pred, y=f2_training_data_output, mode="markers") -trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="MLP Regressor " + test_prob.name + "_f2" - + ".html", - auto_open=True, -) - -gpr = GaussianProcessRegressor() -gpr.fit(training_data_input, f1_training_data_output) -gpr_y_pred = gpr.predict(training_data_input) - -trace0 = go.Scatter(x=gpr_y_pred, y=f1_training_data_output, mode="markers") -trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="GPR Regressor " + test_prob.name + "_f1" - + ".html", - auto_open=True, -) - -gpr.fit(training_data_input, f2_training_data_output) -gpr_y_pred = gpr.predict(training_data_input) - -trace0 = go.Scatter(x=gpr_y_pred, y=f2_training_data_output, mode="markers") -trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="GPR Regressor " + test_prob.name + "_f2" - + ".html", - auto_open=True, -) +# +# mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +# mlp_reg.fit(training_data_input, f1_training_data_output) +# mlp_reg_y_pred = mlp_reg.predict(training_data_input) +# +# trace0 = go.Scatter(x=mlp_reg_y_pred, y=f1_training_data_output, mode="markers") +# trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="MLP Regressor " + test_prob.name + "_f1" +# + ".html", +# auto_open=True, +# ) +# +# mlp_reg.fit(training_data_input, f2_training_data_output) +# mlp_reg_y_pred = mlp_reg.predict(training_data_input) +# +# trace0 = go.Scatter(x=mlp_reg_y_pred, y=f2_training_data_output, mode="markers") +# trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="MLP Regressor " + test_prob.name + "_f2" +# + ".html", +# auto_open=True, +# ) +# +# gpr = GaussianProcessRegressor() +# gpr.fit(training_data_input, f1_training_data_output) +# gpr_y_pred = gpr.predict(training_data_input) +# +# trace0 = go.Scatter(x=gpr_y_pred, y=f1_training_data_output, mode="markers") +# trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="GPR Regressor " + test_prob.name + "_f1" +# + ".html", +# auto_open=True, +# ) +# +# gpr.fit(training_data_input, f2_training_data_output) +# gpr_y_pred = gpr.predict(training_data_input) +# +# trace0 = go.Scatter(x=gpr_y_pred, y=f2_training_data_output, mode="markers") +# trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="GPR Regressor " + test_prob.name + "_f2" +# + ".html", +# auto_open=True, +# ) # ZDT 4 @@ -845,60 +845,60 @@ plotting=True) model_evodn_f1.fit(training_data_input, f1_training_data_output) model_evodn_f2.fit(training_data_input, f2_training_data_output) - -mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) -mlp_reg.fit(training_data_input, f1_training_data_output) -mlp_reg_y_pred = mlp_reg.predict(training_data_input) - -trace0 = go.Scatter(x=mlp_reg_y_pred, y=f1_training_data_output, mode="markers") -trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="MLP Regressor " + test_prob.name + "_f1" - + ".html", - auto_open=True, -) - -mlp_reg.fit(training_data_input, f2_training_data_output) -mlp_reg_y_pred = mlp_reg.predict(training_data_input) - -trace0 = go.Scatter(x=mlp_reg_y_pred, y=f2_training_data_output, mode="markers") -trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="MLP Regressor " + test_prob.name + "_f2" - + ".html", - auto_open=True, -) - -gpr = GaussianProcessRegressor() -gpr.fit(training_data_input, f1_training_data_output) -gpr_y_pred = gpr.predict(training_data_input) - -trace0 = go.Scatter(x=gpr_y_pred, y=f1_training_data_output, mode="markers") -trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="GPR Regressor " + test_prob.name + "_f1" - + ".html", - auto_open=True, -) - -gpr.fit(training_data_input, f2_training_data_output) -gpr_y_pred = gpr.predict(training_data_input) - -trace0 = go.Scatter(x=gpr_y_pred, y=f2_training_data_output, mode="markers") -trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="GPR Regressor " + test_prob.name + "_f2" - + ".html", - auto_open=True, -) +# +# mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +# mlp_reg.fit(training_data_input, f1_training_data_output) +# mlp_reg_y_pred = mlp_reg.predict(training_data_input) +# +# trace0 = go.Scatter(x=mlp_reg_y_pred, y=f1_training_data_output, mode="markers") +# trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="MLP Regressor " + test_prob.name + "_f1" +# + ".html", +# auto_open=True, +# ) +# +# mlp_reg.fit(training_data_input, f2_training_data_output) +# mlp_reg_y_pred = mlp_reg.predict(training_data_input) +# +# trace0 = go.Scatter(x=mlp_reg_y_pred, y=f2_training_data_output, mode="markers") +# trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="MLP Regressor " + test_prob.name + "_f2" +# + ".html", +# auto_open=True, +# ) +# +# gpr = GaussianProcessRegressor() +# gpr.fit(training_data_input, f1_training_data_output) +# gpr_y_pred = gpr.predict(training_data_input) +# +# trace0 = go.Scatter(x=gpr_y_pred, y=f1_training_data_output, mode="markers") +# trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="GPR Regressor " + test_prob.name + "_f1" +# + ".html", +# auto_open=True, +# ) +# +# gpr.fit(training_data_input, f2_training_data_output) +# gpr_y_pred = gpr.predict(training_data_input) +# +# trace0 = go.Scatter(x=gpr_y_pred, y=f2_training_data_output, mode="markers") +# trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="GPR Regressor " + test_prob.name + "_f2" +# + ".html", +# auto_open=True, +# ) # ZDT 6 @@ -935,57 +935,57 @@ plotting=True) model_evodn_f1.fit(training_data_input, f1_training_data_output) model_evodn_f2.fit(training_data_input, f2_training_data_output) - -mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) -mlp_reg.fit(training_data_input, f1_training_data_output) -mlp_reg_y_pred = mlp_reg.predict(training_data_input) - -trace0 = go.Scatter(x=mlp_reg_y_pred, y=f1_training_data_output, mode="markers") -trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="MLP Regressor " + test_prob.name + "_f1" - + ".html", - auto_open=True, -) - -mlp_reg.fit(training_data_input, f2_training_data_output) -mlp_reg_y_pred = mlp_reg.predict(training_data_input) - -trace0 = go.Scatter(x=mlp_reg_y_pred, y=f2_training_data_output, mode="markers") -trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="MLP Regressor " + test_prob.name + "_f2" - + ".html", - auto_open=True, -) - -gpr = GaussianProcessRegressor() -gpr.fit(training_data_input, f1_training_data_output) -gpr_y_pred = gpr.predict(training_data_input) - -trace0 = go.Scatter(x=gpr_y_pred, y=f1_training_data_output, mode="markers") -trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="GPR Regressor " + test_prob.name + "_f1" - + ".html", - auto_open=True, -) - -gpr.fit(training_data_input, f2_training_data_output) -gpr_y_pred = gpr.predict(training_data_input) - -trace0 = go.Scatter(x=gpr_y_pred, y=f2_training_data_output, mode="markers") -trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="GPR Regressor " + test_prob.name + "_f2" - + ".html", - auto_open=True, -) \ No newline at end of file +# +# mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +# mlp_reg.fit(training_data_input, f1_training_data_output) +# mlp_reg_y_pred = mlp_reg.predict(training_data_input) +# +# trace0 = go.Scatter(x=mlp_reg_y_pred, y=f1_training_data_output, mode="markers") +# trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="MLP Regressor " + test_prob.name + "_f1" +# + ".html", +# auto_open=True, +# ) +# +# mlp_reg.fit(training_data_input, f2_training_data_output) +# mlp_reg_y_pred = mlp_reg.predict(training_data_input) +# +# trace0 = go.Scatter(x=mlp_reg_y_pred, y=f2_training_data_output, mode="markers") +# trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="MLP Regressor " + test_prob.name + "_f2" +# + ".html", +# auto_open=True, +# ) +# +# gpr = GaussianProcessRegressor() +# gpr.fit(training_data_input, f1_training_data_output) +# gpr_y_pred = gpr.predict(training_data_input) +# +# trace0 = go.Scatter(x=gpr_y_pred, y=f1_training_data_output, mode="markers") +# trace1 = go.Scatter(x=f1_training_data_output, y=f1_training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="GPR Regressor " + test_prob.name + "_f1" +# + ".html", +# auto_open=True, +# ) +# +# gpr.fit(training_data_input, f2_training_data_output) +# gpr_y_pred = gpr.predict(training_data_input) +# +# trace0 = go.Scatter(x=gpr_y_pred, y=f2_training_data_output, mode="markers") +# trace1 = go.Scatter(x=f2_training_data_output, y=f2_training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="GPR Regressor " + test_prob.name + "_f2" +# + ".html", +# auto_open=True, +# ) \ No newline at end of file diff --git a/pyrvea/Problem/evodn2_problem.py b/pyrvea/Problem/evodn2_problem.py index fd162fb..da9f3be 100644 --- a/pyrvea/Problem/evodn2_problem.py +++ b/pyrvea/Problem/evodn2_problem.py @@ -10,7 +10,6 @@ class EvoDN2(baseProblem): """Creates an Artificial Neural Network (ANN) for the EvoDN2 algorithm. - Parameters ---------- name : str @@ -34,7 +33,7 @@ def __init__( num_of_objectives=2, w_low=-5.0, w_high=5.0, - prob_omit=0.3, + prob_omit=0.2, params=None, ): super().__init__() @@ -50,7 +49,6 @@ def __init__( def fit(self, training_data, target_values): """Fit data in EvoNN model. - Parameters ---------- training_data : ndarray, shape = (numbers of samples, number of variables) @@ -81,46 +79,6 @@ def fit(self, training_data, target_values): def create_population(self): - # Random num of nodes & layers - # individuals = [] - # for i in range(self.params["pop_size"]): - # nets = [] - # for j in range(self.subnets[0]): - # - # layers = [] - # num_layers = np.random.randint(1, self.subnets[1]) - # in_nodes = len(self.subsets[j]) - # - # for k in range(num_layers): - # out_nodes = random.randint(1, self.num_nodes) - # net = np.random.uniform( - # self.w_low, - # self.w_high, - # size=( - # in_nodes, - # out_nodes - # ) - # ) - # # Randomly set some weights to zero - # zeros = np.random.choice(np.arange(net.size), ceil(net.size * self.prob_omit)) - # net.ravel()[zeros] = 0 - # - # # Add bias - # net = np.insert(net, 0, 1, axis=0) - # in_nodes = out_nodes - # layers.append(net) - # nets.append(layers) - # - # individuals.append(nets) - # - # individuals = np.asarray(individuals) - # Fixed connections - - no_nodes = [] - for i in range(self.subnets[1]): - n = random.randint(1, self.num_nodes) - no_nodes.append(n) - individuals = [] for i in range(self.params["pop_size"]): nets = [] @@ -131,7 +89,7 @@ def create_population(self): in_nodes = len(self.subsets[j]) for k in range(num_layers): - out_nodes = no_nodes[k] + out_nodes = random.randint(1, self.num_nodes) net = np.random.uniform( self.w_low, self.w_high, @@ -141,7 +99,7 @@ def create_population(self): ) ) # Randomly set some weights to zero - zeros = np.random.choice(np.arange(net.size), ceil(net.size * self.prob_omit), replace=False) + zeros = np.random.choice(np.arange(net.size), ceil(net.size * self.prob_omit)) net.ravel()[zeros] = 0 # Add bias @@ -179,17 +137,14 @@ def train(self, model): def objectives(self, decision_variables) -> list: """ Use this method to calculate objective functions. - Parameters ---------- decision_variables : ndarray Variables from the neural network - Returns ------- obj_func : list The objective function - """ end_net, complexity = self.activation(decision_variables) @@ -202,33 +157,31 @@ def objectives(self, decision_variables) -> list: def activation(self, decision_variables): """ Calculates the dot product and applies the activation function. - Parameters ---------- decision_variables : ndarray Variables from the neural network name : str The activation function to use - Returns ------- The penultimate layer Z before the output - """ subnet_cmplx = [] end_net = np.empty((self.num_of_samples, 0)) - for i, subnet in enumerate(decision_variables): + for i in range(decision_variables.shape[0]): + subnet = decision_variables[i] in_nodes = self.X_train[:, self.subsets[i]] cnet = np.abs(subnet[0][1:, :]) - for j, layer in enumerate(subnet): + for layer in range(len(subnet)): # Calculate the dot product - out = np.dot(in_nodes, layer[1:, :] + layer[0]) - if j > 0: - cnet = np.dot(cnet, np.abs(layer[1:, :])) + out = np.dot(in_nodes, subnet[layer][1:, :]) + subnet[layer][0] + if layer > 0: + cnet = np.dot(cnet, np.abs(subnet[layer][1:, :])) if self.params["activation_func"] == "sigmoid": - activated_layer = lambda x: 1.0 / (1.0 + np.exp(-x)) + activated_layer = lambda x: 1 / (1 + np.exp(-x)) if self.params["activation_func"] == "relu": activated_layer = lambda x: np.maximum(x, 0) @@ -247,14 +200,12 @@ def activation(self, decision_variables): def minimize_error(self, activated_layer): """ Minimize the training error. - Parameters ---------- activated_layer : ndarray Output of the activation function name : str Name of the optimizing algorithm to use - Returns ------- w_matrix[0] : ndarray @@ -280,7 +231,6 @@ def loss_function(self, predicted_values): def select(self, pop, non_dom_front, criterion="min_error"): """ Select target model from the population. - Parameters ---------- pop : obj @@ -290,7 +240,6 @@ def select(self, pop, non_dom_front, criterion="min_error"): criterion : str The criterion to use for selecting the model. Possible values: 'min_error', 'akaike_corrected', 'manual' - Returns ------- The selected model @@ -383,9 +332,9 @@ def create_plot(self, model): auto_open=True, ) + class EvoDN2Model(EvoDN2): """Class for the surrogate model. - Parameters ---------- name : str @@ -396,7 +345,6 @@ class EvoDN2Model(EvoDN2): The weight matrix of the upper part of the network y_pred : ndarray Prediction of the model - """ def __init__(self, name, w1=None, w2=None, y_pred=None): super().__init__(name) @@ -422,7 +370,6 @@ def set_params( plotting=False, ): """ Set parameters for EvoDN2 model. - Parameters ---------- name : str @@ -463,7 +410,6 @@ def set_params( def fit(self, training_data, target_values): """Fit data in EvoNN model. - Parameters ---------- training_data : ndarray, shape = (numbers of samples, number of variables) @@ -514,7 +460,7 @@ def predict(self, decision_variables): out = np.dot(in_nodes, subnet[layer][1:, :]) + subnet[layer][0] if self.params["activation_func"] == "sigmoid": - activated_layer = lambda x: 1.0 / (1.0 + np.exp(-x)) + activated_layer = lambda x: 1 / (1 + np.exp(-x)) if self.params["activation_func"] == "relu": activated_layer = lambda x: np.maximum(x, 0) diff --git a/pyrvea/Recombination/evodn2_xover_mut.py b/pyrvea/Recombination/evodn2_xover_mut.py index 97c4e97..3db90ed 100644 --- a/pyrvea/Recombination/evodn2_xover_mut.py +++ b/pyrvea/Recombination/evodn2_xover_mut.py @@ -45,49 +45,51 @@ def evodn2_xover_mut( sub3 = individuals[np.random.randint(0, individuals.shape[0] - 1)][subnet] sub4 = individuals[np.random.randint(0, individuals.shape[0] - 1)][subnet] - r = max(len(sub1), len(sub2)) + r = min(len(sub1), len(sub2)) for layer in range(r): - try: - connections = min(sub1[layer][1:, :].size, sub2[layer][1:, :].size) - - # Crossover - exchange = np.random.choice(connections, np.random.binomial(connections, prob_crossover), replace=False) + connections = min(sub1[layer][1:, :].size, sub2[layer][1:, :].size) - tmp = deepcopy(sub1[layer]) - sub1[layer][1:, :].ravel()[exchange] = sub2[layer][1:, :].ravel()[exchange] - sub2[layer][1:, :].ravel()[exchange] = tmp[1:, :].ravel()[exchange] - - except IndexError: - pass + # Crossover + exchange = sample( + range(connections), np.random.binomial(connections, prob_crossover) + ) + tmp = deepcopy(sub1[layer]) + sub1[layer][1:, :].ravel()[exchange] = sub2[layer][1:, :].ravel()[exchange] + sub2[layer][1:, :].ravel()[exchange] = tmp[1:, :].ravel()[exchange] try: # Mutate first individual - connections = sub1[layer][1:, :].size - mutate = np.random.choice(connections, np.random.binomial(connections, prob_mut), replace=False) - - sub1[layer][1:, :].ravel()[mutate] = sub1[layer][1:, :].ravel()[ - mutate - ] + mut_strength * (1 - cur_gen / total_gen) * ( - np.copy(sub3[layer][1:, :].ravel()[mutate]) - - np.copy(sub4[layer][1:, :].ravel()[mutate]) + connections = min( + sub1[layer][1:, :].size, + sub2[layer][1:, :].size, + sub3[layer][1:, :].size, + sub4[layer][1:, :].size, ) - except IndexError: - pass + mutate = sample( + range(connections), np.random.binomial(connections, prob_mut) + ) + sub1[layer][1:, :].ravel()[mutate] = sub1[layer][1:, :].ravel()[ + mutate + ] + mut_strength * 0 * (1 - cur_gen / total_gen) * ( + sub3[layer][1:, :].ravel()[mutate] + - sub4[layer][1:, :].ravel()[mutate] + ) - try: # Mutate second individual - connections = sub2[layer][1:, :].size - mutate = np.random.choice(connections, np.random.binomial(connections, prob_mut), replace=False) - - sub2[layer][1:, :].ravel()[mutate] = sub2[layer][1:, :].ravel()[ - mutate - ] + mut_strength * (1 - cur_gen / total_gen) * ( - np.copy(sub3[layer][1:, :].ravel()[mutate]) - - np.copy(sub4[layer][1:, :].ravel()[mutate]) + mutate = sample( + range(connections), np.random.binomial(connections, prob_mut) ) + sub2[layer][1:, :].ravel()[mutate] = sub2[layer][1:, :].ravel()[ + mutate + ] + mut_strength * 0 * (1 - cur_gen / total_gen) * ( + sub3[layer][1:, :].ravel()[mutate] + - sub4[layer][1:, :].ravel()[mutate] + ) except IndexError: - continue + + + break return offspring1, offspring2 From b5ac22cc8904b65776eb500ebdef208d25855a46 Mon Sep 17 00:00:00 2001 From: delamorte Date: Fri, 28 Jun 2019 13:52:27 +0300 Subject: [PATCH 046/155] remade optimization in activation func evodn2_problem activation functions now in static method made variable names better in evodn2_model evodn2_model predict method now works correctly model single variable response now works correctly prob_omit is now a paramater that can be passed when creating model cleaned unused code --- evodn2_example.py | 340 ++++++++++-------- evodn2_vs_mlp.py | 334 ++++++++--------- pyrvea/EAs/PPGA.py | 4 +- pyrvea/Population/Population.py | 2 +- pyrvea/Problem/evodn2_problem.py | 278 +++++++------- .../evodn2_xover_mut_gaussian.py | 54 --- 6 files changed, 475 insertions(+), 537 deletions(-) diff --git a/evodn2_example.py b/evodn2_example.py index 9f9b3cd..205ccc2 100644 --- a/evodn2_example.py +++ b/evodn2_example.py @@ -13,22 +13,27 @@ # training_data_input = np.random.rand(150,30) # training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) -test_prob = EvoNNTestProblem("Matyas") +test_prob = EvoNNTestProblem("Sphere") training_data_input, training_data_output = test_prob.create_training_data( samples=250, method="random", seed=31 ) -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnet_struct=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evodn2.fit(training_data_input, training_data_output) +# y = model_evodn2.predict(training_data_input) +# model_evodn2.plot(y) +# model_evodn2.single_variable_response() model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) model_evonn.set_params( @@ -41,155 +46,188 @@ logging=True, plotting=True) -model_evodn2.fit(training_data_input, training_data_output) -#model_evonn.fit(training_data_input, training_data_output) - -test_prob = EvoNNTestProblem("Matyas") -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random" -) +model_evonn.fit(training_data_input, training_data_output) -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evodn2.fit(training_data_input, training_data_output) - -#model_evonn.fit(training_data_input, training_data_output) # -test_prob = EvoNNTestProblem("Himmelblau") -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random" -) - -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evodn2.fit(training_data_input, training_data_output) +# test_prob = EvoNNTestProblem("Matyas") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random" +# ) +# +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnet_struct=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) # # model_evodn2.fit(training_data_input, training_data_output) -# model_evonn.fit(training_data_input, training_data_output) # -test_prob = EvoNNTestProblem("Rastigrin", num_of_variables=2) -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random" -) - -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evodn2.fit(training_data_input, training_data_output) +# #model_evonn.fit(training_data_input, training_data_output) +# # +# test_prob = EvoNNTestProblem("Himmelblau") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random" +# ) +# +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnet_struct=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) # # model_evodn2.fit(training_data_input, training_data_output) -# model_evonn.fit(training_data_input, training_data_output) +# # +# # model_evodn2.fit(training_data_input, training_data_output) +# # model_evonn.fit(training_data_input, training_data_output) +# # +# test_prob = EvoNNTestProblem("Rastigrin", num_of_variables=2) +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random" +# ) # -test_prob = EvoNNTestProblem("Three-hump camel") -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random" -) -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evodn2.fit(training_data_input, training_data_output) +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnet_struct=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) # # model_evodn2.fit(training_data_input, training_data_output) -# model_evonn.fit(training_data_input, training_data_output) +# # +# # model_evodn2.fit(training_data_input, training_data_output) +# # model_evonn.fit(training_data_input, training_data_output) +# # +# test_prob = EvoNNTestProblem("Three-hump camel") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random" +# ) +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnet_struct=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) # -test_prob = EvoNNTestProblem("Goldstein-Price") -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random" -) -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evodn2.fit(training_data_input, training_data_output) # model_evodn2.fit(training_data_input, training_data_output) -# model_evonn.fit(training_data_input, training_data_output) +# # +# # model_evodn2.fit(training_data_input, training_data_output) +# # model_evonn.fit(training_data_input, training_data_output) +# # +# test_prob = EvoNNTestProblem("Goldstein-Price") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random" +# ) +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnet_struct=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) # -test_prob = EvoNNTestProblem("LeviN13") -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random" -) -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evodn2.fit(training_data_input, training_data_output) +# model_evodn2.fit(training_data_input, training_data_output) +# # model_evodn2.fit(training_data_input, training_data_output) +# # model_evonn.fit(training_data_input, training_data_output) +# # +# test_prob = EvoNNTestProblem("LeviN13") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random" +# ) +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnet_struct=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) # # model_evodn2.fit(training_data_input, training_data_output) -# model_evonn.fit(training_data_input, training_data_output) +# # +# # model_evodn2.fit(training_data_input, training_data_output) +# # model_evonn.fit(training_data_input, training_data_output) +# # +# test_prob = EvoNNTestProblem("SchafferN2") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random" +# ) +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnet_struct=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) # -test_prob = EvoNNTestProblem("SchafferN2") -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random" -) -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evodn2.fit(training_data_input, training_data_output) +# model_evodn2.fit(training_data_input, training_data_output) +# +# # # ZDT 1 & 2 +# test_prob = testProblem('ZDT1', 30, 2, 0, 1, 0) +# training_data_input = np.random.rand(250, 30) +# training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) +# +# f1_training_data_output = training_data_output[:, 0] +# f2_training_data_output = training_data_output[:, 1] +# +# model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") +# model_evodn_f1.set_params( +# pop_size=500, +# subnet_struct=(8, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") +# model_evodn_f2.set_params( +# pop_size=500, +# subnet_struct=(8, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# model_evodn_f1.fit(training_data_input, f1_training_data_output) +# model_evodn_f2.fit(training_data_input, f2_training_data_output) -# # ZDT 1 & 2 test_prob = testProblem('ZDT2', 30, 2, 0, 1, 0) training_data_input = np.random.rand(250, 30) training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) @@ -200,7 +238,7 @@ model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") model_evodn_f1.set_params( pop_size=500, - subnets=(6, 10), + subnet_struct=(8, 10), num_nodes=10, activation_func="sigmoid", opt_func="llsq", @@ -212,7 +250,7 @@ model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") model_evodn_f2.set_params( pop_size=500, - subnets=(6, 10), + subnet_struct=(8, 10), num_nodes=10, activation_func="sigmoid", opt_func="llsq", @@ -222,13 +260,3 @@ plotting=True) model_evodn_f1.fit(training_data_input, f1_training_data_output) model_evodn_f2.fit(training_data_input, f2_training_data_output) -# -# test_prob = testProblem('ZDT2', 30, 2, 0, 1, 0) -# training_data_input = np.random.rand(250, 30) -# training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) -# -# f1_training_data_output = training_data_output[:, 0] -# f2_training_data_output = training_data_output[:, 1] -# -# model_evodn_f1.fit(training_data_input, f1_training_data_output) -# model_evodn_f2.fit(training_data_input, f2_training_data_output) diff --git a/evodn2_vs_mlp.py b/evodn2_vs_mlp.py index 16746ab..52bd6cb 100644 --- a/evodn2_vs_mlp.py +++ b/evodn2_vs_mlp.py @@ -429,7 +429,7 @@ model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) model_evodn2.set_params( pop_size=500, - subnets=(6, 10), + subnet_struct=(6, 10), num_nodes=10, activation_func="sigmoid", opt_func="llsq", @@ -488,7 +488,7 @@ model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) model_evodn2.set_params( pop_size=500, - subnets=(6, 10), + subnet_struct=(6, 10), num_nodes=10, activation_func="sigmoid", opt_func="llsq", @@ -542,39 +542,39 @@ # --------------------------------------------------------------- # # ZDT 1 -test_prob = testProblem('ZDT1', 30, 2, 0, 1, 0) -np.random.seed(1) -training_data_input = np.random.rand(250, 30) -training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) - -f1_training_data_output = training_data_output[:, 0] -f2_training_data_output = training_data_output[:, 1] - -model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") -model_evodn_f1.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") -model_evodn_f2.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) -model_evodn_f1.fit(training_data_input, f1_training_data_output) -model_evodn_f2.fit(training_data_input, f2_training_data_output) +# test_prob = testProblem('ZDT1', 30, 2, 0, 1, 0) +# np.random.seed(1) +# training_data_input = np.random.rand(250, 30) +# training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) +# +# f1_training_data_output = training_data_output[:, 0] +# f2_training_data_output = training_data_output[:, 1] +# +# model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") +# model_evodn_f1.set_params( +# pop_size=500, +# subnets=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") +# model_evodn_f2.set_params( +# pop_size=500, +# subnets=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# model_evodn_f1.fit(training_data_input, f1_training_data_output) +# model_evodn_f2.fit(training_data_input, f2_training_data_output) # # mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) # mlp_reg.fit(training_data_input, f1_training_data_output) @@ -632,39 +632,39 @@ # ZDT2 -test_prob = testProblem('ZDT2', 30, 2, 0, 1, 0) -np.random.seed(2) -training_data_input = np.random.rand(250, 30) -training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) - -f1_training_data_output = training_data_output[:, 0] -f2_training_data_output = training_data_output[:, 1] - -model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") -model_evodn_f1.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") -model_evodn_f2.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) -model_evodn_f1.fit(training_data_input, f1_training_data_output) -model_evodn_f2.fit(training_data_input, f2_training_data_output) +# test_prob = testProblem('ZDT2', 30, 2, 0, 1, 0) +# np.random.seed(2) +# training_data_input = np.random.rand(250, 30) +# training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) +# +# f1_training_data_output = training_data_output[:, 0] +# f2_training_data_output = training_data_output[:, 1] +# +# model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") +# model_evodn_f1.set_params( +# pop_size=500, +# subnets=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") +# model_evodn_f2.set_params( +# pop_size=500, +# subnets=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# model_evodn_f1.fit(training_data_input, f1_training_data_output) +# model_evodn_f2.fit(training_data_input, f2_training_data_output) # # mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) # mlp_reg.fit(training_data_input, f1_training_data_output) @@ -722,39 +722,39 @@ # ZDT3 -test_prob = testProblem('ZDT3', 30, 2, 0, 1, 0) -np.random.seed(3) -training_data_input = np.random.rand(250, 30) -training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) - -f1_training_data_output = training_data_output[:, 0] -f2_training_data_output = training_data_output[:, 1] - -model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") -model_evodn_f1.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") -model_evodn_f2.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) -model_evodn_f1.fit(training_data_input, f1_training_data_output) -model_evodn_f2.fit(training_data_input, f2_training_data_output) +# test_prob = testProblem('ZDT3', 30, 2, 0, 1, 0) +# np.random.seed(3) +# training_data_input = np.random.rand(250, 30) +# training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) +# +# f1_training_data_output = training_data_output[:, 0] +# f2_training_data_output = training_data_output[:, 1] +# +# model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") +# model_evodn_f1.set_params( +# pop_size=500, +# subnets=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") +# model_evodn_f2.set_params( +# pop_size=500, +# subnets=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# model_evodn_f1.fit(training_data_input, f1_training_data_output) +# model_evodn_f2.fit(training_data_input, f2_training_data_output) # # mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) # mlp_reg.fit(training_data_input, f1_training_data_output) @@ -812,39 +812,39 @@ # ZDT 4 -test_prob = testProblem('ZDT4', 10, 2, 0, 1, 0) -np.random.seed(4) -training_data_input = np.random.rand(250, 10) -training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) - -f1_training_data_output = training_data_output[:, 0] -f2_training_data_output = training_data_output[:, 1] - -model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") -model_evodn_f1.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") -model_evodn_f2.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) -model_evodn_f1.fit(training_data_input, f1_training_data_output) -model_evodn_f2.fit(training_data_input, f2_training_data_output) +# test_prob = testProblem('ZDT4', 10, 2, 0, 1, 0) +# np.random.seed(4) +# training_data_input = np.random.rand(250, 10) +# training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) +# +# f1_training_data_output = training_data_output[:, 0] +# f2_training_data_output = training_data_output[:, 1] +# +# model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") +# model_evodn_f1.set_params( +# pop_size=500, +# subnets=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") +# model_evodn_f2.set_params( +# pop_size=500, +# subnets=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# model_evodn_f1.fit(training_data_input, f1_training_data_output) +# model_evodn_f2.fit(training_data_input, f2_training_data_output) # # mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) # mlp_reg.fit(training_data_input, f1_training_data_output) @@ -902,39 +902,39 @@ # ZDT 6 -test_prob = testProblem('ZDT6', 10, 2, 0, 1, 0) -np.random.seed(6) -training_data_input = np.random.rand(250, 10) -training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) - -f1_training_data_output = training_data_output[:, 0] -f2_training_data_output = training_data_output[:, 1] - -model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") -model_evodn_f1.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") -model_evodn_f2.set_params( - pop_size=500, - subnets=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) -model_evodn_f1.fit(training_data_input, f1_training_data_output) -model_evodn_f2.fit(training_data_input, f2_training_data_output) +# test_prob = testProblem('ZDT6', 10, 2, 0, 1, 0) +# np.random.seed(6) +# training_data_input = np.random.rand(250, 10) +# training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) +# +# f1_training_data_output = training_data_output[:, 0] +# f2_training_data_output = training_data_output[:, 1] +# +# model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") +# model_evodn_f1.set_params( +# pop_size=500, +# subnets=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") +# model_evodn_f2.set_params( +# pop_size=500, +# subnets=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# model_evodn_f1.fit(training_data_input, f1_training_data_output) +# model_evodn_f2.fit(training_data_input, f2_training_data_output) # # mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) # mlp_reg.fit(training_data_input, f1_training_data_output) diff --git a/pyrvea/EAs/PPGA.py b/pyrvea/EAs/PPGA.py index 7e5987b..b460808 100644 --- a/pyrvea/EAs/PPGA.py +++ b/pyrvea/EAs/PPGA.py @@ -174,7 +174,7 @@ def _next_gen(self, population: "Population"): / self.params["total_generations"] ) - offspring = np.empty((0, population.problem.subnets[0])) + offspring = np.empty((0, population.problem.subnet_struct[0])) for ind in range(population.individuals.shape[0]): @@ -214,8 +214,6 @@ def _next_gen(self, population: "Population"): + str(np.amin(population.objectives[:, 0])) + " Avg Error: " + str(np.mean(population.objectives[:, 0])) - + " Str dev: " - + str(self.params["std_dev"]) ) # Move predators diff --git a/pyrvea/Population/Population.py b/pyrvea/Population/Population.py index 65cebac..98990bc 100644 --- a/pyrvea/Population/Population.py +++ b/pyrvea/Population/Population.py @@ -132,7 +132,7 @@ def create_new_individuals( self.individuals = np.empty((0, individuals.shape[1], individuals.shape[2])) elif design == "EvoDN2": - self.individuals = np.empty((0, self.problem.subnets[0])) + self.individuals = np.empty((0, self.problem.subnet_struct[0])) individuals = self.problem.create_population() diff --git a/pyrvea/Problem/evodn2_problem.py b/pyrvea/Problem/evodn2_problem.py index da9f3be..243a6ef 100644 --- a/pyrvea/Problem/evodn2_problem.py +++ b/pyrvea/Problem/evodn2_problem.py @@ -1,6 +1,7 @@ from pyrvea.Problem.baseProblem import baseProblem from pyrvea.Population.Population import Population from pyrvea.EAs.PPGA import PPGA +from scipy.special import expit import numpy as np import plotly import plotly.graph_objs as go @@ -20,8 +21,6 @@ class EvoDN2(baseProblem): The lower bound for randomly generated weights w_high : float The upper bound for randomly generated weights - prob_omit : float - The probability of setting some weights to zero initially params : """ @@ -33,7 +32,6 @@ def __init__( num_of_objectives=2, w_low=-5.0, w_high=5.0, - prob_omit=0.2, params=None, ): super().__init__() @@ -44,7 +42,6 @@ def __init__( self.num_of_objectives = num_of_objectives self.w_low = w_low self.w_high = w_high - self.prob_omit = prob_omit self.params = params def fit(self, training_data, target_values): @@ -61,45 +58,66 @@ def fit(self, training_data, target_values): self.y_train = target_values self.num_of_samples = target_values.shape[0] self.num_of_variables = training_data.shape[1] - self.subnets = self.params["subnets"] + self.subnet_struct = self.params["subnet_struct"] self.num_nodes = self.params["num_nodes"] # Create random subsets of decision variables for each subnet self.subsets = [] - for i in range(self.subnets[0]): + for i in range(self.subnet_struct[0]): n = random.randint(1, self.X_train.shape[1]) self.subsets.append(random.sample(range(self.X_train.shape[1]), n)) # Ensure that each decision variable is used as an input in at least one subnet for n in list(range(self.X_train.shape[1])): if not any(n in k for k in self.subsets): - self.subsets[random.randint(0, self.subnets[0]-1)].append(n) + self.subsets[random.randint(0, self.subnet_struct[0] - 1)].append(n) return self.subsets + def train(self, model): + + pop = Population( + self, assign_type="EvoDN2", pop_size=self.params["pop_size"], plotting=False + ) + pop.evolve( + PPGA, + { + "logging": self.params["logging"], + "logfile": model.log, + "iterations": 10, + "generations_per_iteration": 10, + "crossover_type": "short", + "mutation_type": "short", + }, + ) + + non_dom_front = pop.non_dominated() + model.subnets, model.fitness = self.select( + pop, non_dom_front, self.params["criterion"] + ) + model.non_linear_layer, _ = self.activation(model.subnets) + model.linear_layer, _ = self.minimize_error(model.non_linear_layer) + def create_population(self): individuals = [] for i in range(self.params["pop_size"]): nets = [] - for j in range(self.subnets[0]): + for j in range(self.subnet_struct[0]): layers = [] - num_layers = np.random.randint(1, self.subnets[1]) + num_layers = np.random.randint(1, self.subnet_struct[1]) in_nodes = len(self.subsets[j]) for k in range(num_layers): out_nodes = random.randint(1, self.num_nodes) net = np.random.uniform( - self.w_low, - self.w_high, - size=( - in_nodes, - out_nodes - ) + self.w_low, self.w_high, size=(in_nodes, out_nodes) ) # Randomly set some weights to zero - zeros = np.random.choice(np.arange(net.size), ceil(net.size * self.prob_omit)) + zeros = np.random.choice( + np.arange(net.size), ceil(net.size * self.params["prob_omit"]) + ) net.ravel()[zeros] = 0 # Add bias @@ -114,26 +132,6 @@ def create_population(self): return individuals - def train(self, model): - - pop = Population(self, assign_type="EvoDN2", pop_size=self.params["pop_size"], plotting=False) - pop.evolve( - PPGA, - { - "logging": self.params["logging"], - "logfile": model.log, - "iterations": 10, - "generations_per_iteration": 10, - "crossover_type": "short", - "mutation_type": "short" - } - ) - - non_dom_front = pop.non_dominated() - model.w1, model.fitness = self.select(pop, non_dom_front, self.params["criterion"]) - model.end_net, complexity = self.activation(model.w1) - model.w2, _, model.y_pred = self.minimize_error(model.end_net) - def objectives(self, decision_variables) -> list: """ Use this method to calculate objective functions. @@ -147,8 +145,8 @@ def objectives(self, decision_variables) -> list: The objective function """ - end_net, complexity = self.activation(decision_variables) - _, _, predicted_values = self.minimize_error(end_net) + non_linear_layer, complexity = self.activation(decision_variables) + _, predicted_values = self.minimize_error(non_linear_layer) training_error = self.loss_function(predicted_values) obj_func = [training_error, complexity] @@ -160,43 +158,38 @@ def activation(self, decision_variables): Parameters ---------- decision_variables : ndarray - Variables from the neural network - name : str - The activation function to use + Array of all subnets in the current neural network + Returns ------- - The penultimate layer Z before the output + non_linear_layer : ndarray + The final non-linear layer before the output + complexity : float + The complexity of the neural network """ - subnet_cmplx = [] - end_net = np.empty((self.num_of_samples, 0)) - for i in range(decision_variables.shape[0]): + network_complexity = [] + non_linear_layer = np.empty((self.num_of_samples, 0)) - subnet = decision_variables[i] - in_nodes = self.X_train[:, self.subsets[i]] - cnet = np.abs(subnet[0][1:, :]) - for layer in range(len(subnet)): - # Calculate the dot product - out = np.dot(in_nodes, subnet[layer][1:, :]) + subnet[layer][0] - if layer > 0: - cnet = np.dot(cnet, np.abs(subnet[layer][1:, :])) + for i, subnet in enumerate(decision_variables): - if self.params["activation_func"] == "sigmoid": - activated_layer = lambda x: 1 / (1 + np.exp(-x)) + # Get the input variables for the first layer + in_nodes = self.X_train[:, self.subsets[i]] + subnet_complexity = 1 - if self.params["activation_func"] == "relu": - activated_layer = lambda x: np.maximum(x, 0) + for layer in subnet: + # Calculate the dot product + bias + out = np.dot(in_nodes, layer[1:, :]) + layer[0] - if self.params["activation_func"] == "tanh": - activated_layer = lambda x: np.tanh(x) + subnet_complexity = np.dot(subnet_complexity, np.abs(layer[1:, :])) - in_nodes = activated_layer(out) + in_nodes = self.activate(self.params["activation_func"], out) - subnet_cmplx.append(cnet) - end_net = np.hstack((end_net, in_nodes)) + network_complexity.append(np.sum(subnet_complexity)) + non_linear_layer = np.hstack((non_linear_layer, in_nodes)) - complexity = np.sum([np.sum(x) for x in subnet_cmplx]) + complexity = np.sum(network_complexity) - return end_net, complexity + return non_linear_layer, complexity def minimize_error(self, activated_layer): """ Minimize the training error. @@ -204,8 +197,6 @@ def minimize_error(self, activated_layer): ---------- activated_layer : ndarray Output of the activation function - name : str - Name of the optimizing algorithm to use Returns ------- w_matrix[0] : ndarray @@ -218,9 +209,8 @@ def minimize_error(self, activated_layer): if self.params["opt_func"] == "llsq": w2 = np.linalg.lstsq(activated_layer, self.y_train, rcond=None) - rss = w2[1] predicted_values = np.dot(activated_layer, w2[0]) - return w2[0], rss, predicted_values + return w2[0], predicted_values def loss_function(self, predicted_values): @@ -253,23 +243,6 @@ def select(self, pop, non_dom_front, criterion="min_error"): model = pop.individuals[lowest_error] fitness = pop.fitness[lowest_error] - elif criterion == "akaike_corrected": - - # Calculate Akaike information criterion for the non-dominated front - # and return the model with the lowest value - - info_c_rank = [] - - for i in non_dom_front: - - info_c = self.information_criterion(pop.individuals[i]) - info_c_rank.append((info_c, i)) - - info_c_rank.sort() - - model = pop.individuals[info_c_rank[0][1]] - fitness = pop.fitness[info_c_rank[0][1]] - return model, fitness def create_logfile(self): @@ -280,8 +253,10 @@ def create_logfile(self): + "_var" + str(self.num_of_variables) + "_nodes" - + str(self.subnets[0]) + "_" - + str(self.subnets[1]) + "_" + + str(self.subnet_struct[0]) + + "_" + + str(self.subnet_struct[1]) + + "_" + str(self.num_nodes) + ".log", "a", @@ -294,12 +269,12 @@ def create_logfile(self): + str(self.num_of_variables) + "\n" + "number of subnets: " - + str(self.subnets[0]) + + str(self.subnet_struct[0]) + "\n" + "max number of layers: " - + str(self.subnets[1]) + + str(self.subnet_struct[1]) + "\n" - + "nodes: " + + "max nodes: " + str(self.num_nodes) + "\n" + "activation: " @@ -314,23 +289,16 @@ def create_logfile(self): ) return log_file - def create_plot(self, model): + @staticmethod + def activate(name, x): + if name == "sigmoid": + return expit(x) - trace0 = go.Scatter(x=model.y_pred, y=self.y_train, mode="markers") - trace1 = go.Scatter(x=self.y_train, y=self.y_train) - data = [trace0, trace1] - plotly.offline.plot( - data, - filename=self.name - + "_var" - + str(self.num_of_variables) - + "_nodes" - + str(self.subnets[0]) + "_" - + str(self.subnets[1]) + "_" - + str(self.num_nodes) - + ".html", - auto_open=True, - ) + if name == "relu": + return np.maximum(x, 0) + + if name == "tanh": + return np.tanh(x) class EvoDN2Model(EvoDN2): @@ -339,18 +307,22 @@ class EvoDN2Model(EvoDN2): ---------- name : str Name of the problem - w1 : ndarray - The weight matrix of the lower part of the network - w2 : ndarray - The weight matrix of the upper part of the network + subnets : ndarray + The subnets of the model + linear_layer : ndarray + The final optimized layer of the network y_pred : ndarray Prediction of the model """ - def __init__(self, name, w1=None, w2=None, y_pred=None): + + def __init__(self, name, subnets=None, linear_layer=None, y_pred=None): super().__init__(name) self.name = name - self.w1 = w1 - self.w2 = w2 + self.subnets = subnets + self.subsets = None + self.fitness = None + self.non_linear_layer = None + self.linear_layer = linear_layer self.y_pred = y_pred self.svr = None self.log = None @@ -360,8 +332,9 @@ def set_params( self, name=None, pop_size=500, - subnets=(4, 8), + subnet_struct=(4, 8), num_nodes=10, + prob_omit=0.2, activation_func="sigmoid", opt_func="llsq", loss_func="rmse", @@ -376,10 +349,12 @@ def set_params( Name of the problem. pop_size : int Population size. - subnets : tuple + subnet_struct : tuple Structure of the subnets for the model, shape=(num of subnets, max num of layers) num_nodes : int Maximum number of nodes per layer. + prob_omit : float + Probability of setting some weights to zero initially. activation_func : str Function to use for activation. opt_func : str @@ -396,8 +371,9 @@ def set_params( params = { "name": name, "pop_size": pop_size, - "subnets": subnets, + "subnet_struct": subnet_struct, "num_nodes": num_nodes, + "prob_omit": prob_omit, "activation_func": activation_func, "opt_func": opt_func, "loss_func": loss_func, @@ -420,66 +396,54 @@ def fit(self, training_data, target_values): prob = EvoDN2(name=self.name, params=self.params) self.subsets = prob.fit(training_data, target_values) self.num_of_variables = prob.num_of_variables - if prob.params["logging"]: + if self.params["logging"]: self.log = prob.create_logfile() + prob.train(self) - if prob.params["plotting"]: - prob.create_plot(self) - self.num_of_samples = prob.num_of_samples - self.single_variable_response(ploton=False, log=self.log) - def opt(self): + def plot(self, prediction): - pop = Population(self, assign_type="EvoDN2", pop_size=self.params["pop_size"], plotting=False) - pop.evolve( - PPGA, - { - "logging": self.params["logging"], - "iterations": 10, - "generations_per_iteration": 10, - "crossover_type": "short", - "mutation_type": "short" - } + trace0 = go.Scatter(x=prediction, y=self.y_train, mode="markers") + trace1 = go.Scatter(x=self.y_train, y=self.y_train) + data = [trace0, trace1] + plotly.offline.plot( + data, + filename=self.name + + "_var" + + str(self.num_of_variables) + + "_nodes" + + str(self.params["subnet_struct"][0]) + + "_" + + str(self.params["subnet_struct"][1]) + + "_" + + str(self.params["num_nodes"]) + + ".html", + auto_open=True, ) - non_dom_front = pop.non_dominated() - self.w1, self.fitness = self.select(pop, non_dom_front, self.params["criterion"]) - self.end_net, complexity = self.activation(self.w1) - self.w2, _, self.y_pred = self.minimize_error(self.end_net) - def predict(self, decision_variables): - in_end = np.empty((self.num_of_samples, 0)) - for i in range(self.w1.size): + in_end = np.empty((decision_variables.shape[0], 0)) - subnet = self.w1[i] - in_nodes = decision_variables[:, self.subsets[i]] + for i, subnet in enumerate(self.subnets): - for layer in range(len(subnet)): - # Calculate the dot product - out = np.dot(in_nodes, subnet[layer][1:, :]) + subnet[layer][0] - - if self.params["activation_func"] == "sigmoid": - activated_layer = lambda x: 1 / (1 + np.exp(-x)) + in_nodes = decision_variables[:, self.subsets[i]] - if self.params["activation_func"] == "relu": - activated_layer = lambda x: np.maximum(x, 0) + for layer in subnet: - if self.params["activation_func"] == "tanh": - activated_layer = lambda x: np.tanh(x) + out = np.dot(in_nodes, layer[1:, :]) + layer[0] - in_nodes = activated_layer(out) + in_nodes = self.activate(self.params["activation_func"], out) in_end = np.hstack((in_end, in_nodes)) - result = np.dot(in_end, self.w2) + y = np.dot(in_end, self.linear_layer) - return result + return y def single_variable_response(self, ploton=False, log=None): trend = np.loadtxt("trend") - trend = trend[0: self.num_of_samples] avg = np.ones((1, self.num_of_variables)) * (np.finfo(float).eps + 1) / 2 svr = np.empty((0, 2)) @@ -511,6 +475,8 @@ def single_variable_response(self, ploton=False, log=None): r = np.multiply(p, q) r_max = max(r) r_min = min(r) + response = None + s = None if r_max <= 0 and r_min <= 0: response = -1 s = "inverse" @@ -526,4 +492,4 @@ def single_variable_response(self, ploton=False, log=None): print("x" + str(i + 1) + " response: " + str(response) + " " + s, file=log) svr = np.vstack((svr, ["x" + str(i + 1), s])) - self.svr = svr \ No newline at end of file + self.svr = svr diff --git a/pyrvea/Recombination/evodn2_xover_mut_gaussian.py b/pyrvea/Recombination/evodn2_xover_mut_gaussian.py index 86a9914..162a7f6 100644 --- a/pyrvea/Recombination/evodn2_xover_mut_gaussian.py +++ b/pyrvea/Recombination/evodn2_xover_mut_gaussian.py @@ -83,58 +83,4 @@ def evodn2_xover_mut_gaussian( except IndexError: continue - - # mut_val = np.random.normal(0, std_dev, connections) - # - # mut = np.random.choice( - # np.arange(connections), - # np.random.binomial(connections, prob_mut), - # replace=False, - # ) - # - # sub1[layer][1:, :].ravel()[mut] *= mut_val[mut] - # - # mut = np.random.choice( - # np.arange(connections), - # np.random.binomial(connections, prob_mut), - # replace=False, - # ) - # sub2[layer][1:, :].ravel()[mut] *= mut_val[mut] - - # for layer in range(max(len(sub1), len(sub2))): - # - # it = np.nditer( - # [sub1[layer][1:, :], sub2[layer][1:, :]], op_flags=["readwrite"] - # ) - # - # for mut_layer in it.itviews: - # - # connections = mut_layer.size - # - # mut_val = np.random.normal(0, std_dev, connections) - # - # mut = np.random.choice(np.arange(mut_layer.size), ceil(mut_layer.size * prob_mut), replace=False) - # mut_layer[mut] *= mut_val[mut] - - # THIS WORKS THE BEST FOR NOW - # for layer in sub1: - # - # connections = layer[1:, :].size - # - # mut_val = np.random.normal(0, std_dev, connections) - # - # mut = np.random.choice(connections, np.random.binomial(connections, prob_mut), replace=False) - # layer[1:, :].ravel()[mut] *= mut_val[mut] - # - # for layer in sub2: - # - # connections = layer[1:, :].size - # - # mut_val = np.random.normal(0, std_dev, connections) - # - # mut = np.random.choice(connections, np.random.binomial(connections, prob_mut), replace=False) - # layer[1:, :].ravel()[mut] *= mut_val[mut] - - - return offspring1, offspring2 From 71409e28e1ae0cd08a9675e7c937ee254ba2769b Mon Sep 17 00:00:00 2001 From: delamorte Date: Fri, 28 Jun 2019 15:34:10 +0300 Subject: [PATCH 047/155] copied the changes and optimizations from evodn2 to evonn --- evodn2_example.py | 74 +++++++++------ pyrvea/Problem/evodn2_problem.py | 14 +-- pyrvea/Problem/evonn_problem.py | 152 +++++++++++++++---------------- 3 files changed, 126 insertions(+), 114 deletions(-) diff --git a/evodn2_example.py b/evodn2_example.py index 205ccc2..8529910 100644 --- a/evodn2_example.py +++ b/evodn2_example.py @@ -13,16 +13,16 @@ # training_data_input = np.random.rand(150,30) # training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) -test_prob = EvoNNTestProblem("Sphere") -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random", seed=31 -) - +# test_prob = EvoNNTestProblem("Matyas") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random", seed=31 +# ) +# # model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) # model_evodn2.set_params( # pop_size=500, -# subnet_struct=(6, 10), -# num_nodes=10, +# subnet_struct=(4, 4), +# num_nodes=4, # activation_func="sigmoid", # opt_func="llsq", # loss_func="rmse", @@ -32,21 +32,22 @@ # # model_evodn2.fit(training_data_input, training_data_output) # y = model_evodn2.predict(training_data_input) -# model_evodn2.plot(y) -# model_evodn2.single_variable_response() - -model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) -model_evonn.set_params( - num_nodes=20, - pop_size=500, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - -model_evonn.fit(training_data_input, training_data_output) +# model_evodn2.plot(y, training_data_output) +# +# model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +# model_evonn.set_params( +# num_nodes=20, +# pop_size=500, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# model_evonn.fit(training_data_input, training_data_output) +# y = model_evonn.predict(training_data_input) +# model_evonn.plot(y, training_data_output) # # test_prob = EvoNNTestProblem("Matyas") @@ -228,7 +229,7 @@ # model_evodn_f1.fit(training_data_input, f1_training_data_output) # model_evodn_f2.fit(training_data_input, f2_training_data_output) -test_prob = testProblem('ZDT2', 30, 2, 0, 1, 0) +test_prob = testProblem('ZDT1', 30, 2, 0, 1, 0) training_data_input = np.random.rand(250, 30) training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) @@ -238,8 +239,8 @@ model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") model_evodn_f1.set_params( pop_size=500, - subnet_struct=(8, 10), - num_nodes=10, + subnet_struct=(6, 8), + num_nodes=8, activation_func="sigmoid", opt_func="llsq", loss_func="rmse", @@ -250,13 +251,30 @@ model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") model_evodn_f2.set_params( pop_size=500, - subnet_struct=(8, 10), - num_nodes=10, + subnet_struct=(6, 8), + num_nodes=8, activation_func="sigmoid", opt_func="llsq", loss_func="rmse", criterion="min_error", logging=True, plotting=True) -model_evodn_f1.fit(training_data_input, f1_training_data_output) +#model_evodn_f1.fit(training_data_input, f1_training_data_output) model_evodn_f2.fit(training_data_input, f2_training_data_output) +y = model_evodn_f2.predict(training_data_input) +model_evodn_f2.plot(y, f2_training_data_output) + +model_evonn_f2 = EvoNNModel(name="EvoNN_" + test_prob.name) +model_evonn_f2.set_params( + num_nodes=20, + pop_size=500, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_evonn_f2.fit(training_data_input, training_data_output) +y = model_evonn_f2.predict(training_data_input) +model_evonn_f2.plot(y, f2_training_data_output) diff --git a/pyrvea/Problem/evodn2_problem.py b/pyrvea/Problem/evodn2_problem.py index 243a6ef..39ca3b0 100644 --- a/pyrvea/Problem/evodn2_problem.py +++ b/pyrvea/Problem/evodn2_problem.py @@ -401,10 +401,12 @@ def fit(self, training_data, target_values): prob.train(self) - def plot(self, prediction): + self.single_variable_response(ploton=False, log=self.log) - trace0 = go.Scatter(x=prediction, y=self.y_train, mode="markers") - trace1 = go.Scatter(x=self.y_train, y=self.y_train) + def plot(self, prediction, target): + + trace0 = go.Scatter(x=prediction, y=target, mode="markers") + trace1 = go.Scatter(x=target, y=target) data = [trace0, trace1] plotly.offline.plot( data, @@ -423,7 +425,7 @@ def plot(self, prediction): def predict(self, decision_variables): - in_end = np.empty((decision_variables.shape[0], 0)) + non_linear_layer = np.empty((decision_variables.shape[0], 0)) for i, subnet in enumerate(self.subnets): @@ -435,9 +437,9 @@ def predict(self, decision_variables): in_nodes = self.activate(self.params["activation_func"], out) - in_end = np.hstack((in_end, in_nodes)) + non_linear_layer = np.hstack((non_linear_layer, in_nodes)) - y = np.dot(in_end, self.linear_layer) + y = np.dot(non_linear_layer, self.linear_layer) return y diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index 61c88e5..ac7616c 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -2,6 +2,7 @@ from pyrvea.Population.Population import Population from pyrvea.EAs.PPGA import PPGA from math import ceil +from scipy.special import expit import numpy as np import plotly import plotly.graph_objs as go @@ -32,8 +33,6 @@ class EvoNN(baseProblem): The lower bound for randomly generated weights w_high : float The upper bound for randomly generated weights - prob_omit : float - The probability of setting some weights to zero initially params : dict Parameters for model training """ @@ -48,7 +47,6 @@ def __init__( num_of_objectives=2, w_low=-5.0, w_high=5.0, - prob_omit=0.2, params=None, ): super().__init__() @@ -61,7 +59,6 @@ def __init__( self.num_of_objectives = num_of_objectives self.w_low = w_low self.w_high = w_high - self.prob_omit = prob_omit self.params = params def fit(self, training_data, target_values): @@ -102,7 +99,7 @@ def create_population(self): # Randomly set some weights to zero zeros = np.random.choice( - np.arange(individuals.size), ceil(individuals.size * self.prob_omit) + np.arange(individuals.size), ceil(individuals.size * self.params["prob_omit"]) ) individuals.ravel()[zeros] = 0 @@ -133,12 +130,12 @@ def train(self, model): ) non_dom_front = pop.non_dominated() - model.w1, model.fitness = self.select( + model.w_matrix, model.fitness = self.select( pop, non_dom_front, self.params["criterion"] ) - activated_layer = self.activation(model.w1) - model.w2, _, model.y_pred = self.minimize_error(activated_layer) + non_linear_layer = self.activation(model.w_matrix) + model.linear_layer, _, _ = self.minimize_error(non_linear_layer) def objectives(self, decision_variables) -> list: @@ -156,8 +153,8 @@ def objectives(self, decision_variables) -> list: """ - activated_layer = self.activation(decision_variables) - _, _, predicted_values = self.minimize_error(activated_layer) + non_linear_layer = self.activation(decision_variables) + _, _, predicted_values = self.minimize_error(non_linear_layer) training_error = self.loss_function(predicted_values) complexity = self.calculate_complexity(decision_variables) @@ -165,40 +162,30 @@ def objectives(self, decision_variables) -> list: return obj_func - def activation(self, decision_variables): + def activation(self, w_matrix): """ Calculates the dot product and applies the activation function. Parameters ---------- - decision_variables : ndarray - Variables from the neural network + w_matrix : ndarray + Weight matrix of the neural network Returns ------- - The penultimate layer before the output + The final non-linear layer before the output """ - w1 = decision_variables # Calculate the dot product + bias - wi = np.dot(self.X_train, w1[1:, :]) + w1[0] - - if self.params["activation_func"] == "sigmoid": - activated_layer = lambda x: 1 / (1 + np.exp(-x)) - - if self.params["activation_func"] == "relu": - activated_layer = lambda x: np.maximum(x, 0) - - if self.params["activation_func"] == "tanh": - activated_layer = lambda x: np.tanh(x) + out = np.dot(self.X_train, w_matrix[1:, :]) + w_matrix[0] - return activated_layer(wi) + return self.activate(self.params["activation_func"], out) - def minimize_error(self, activated_layer): + def minimize_error(self, non_linear_layer): """ Minimize the training error. Parameters ---------- - activated_layer : ndarray + non_linear_layer : ndarray Output of the activation function Returns @@ -212,10 +199,11 @@ def minimize_error(self, activated_layer): """ if self.params["opt_func"] == "llsq": - w2 = np.linalg.lstsq(activated_layer, self.y_train, rcond=None) - rss = w2[1] - predicted_values = np.dot(activated_layer, w2[0]) - return w2[0], rss, predicted_values + linear_solution = np.linalg.lstsq(non_linear_layer, self.y_train, rcond=None) + linear_layer = linear_solution[0] + rss = linear_solution[1] + predicted_values = np.dot(non_linear_layer, linear_layer) + return linear_layer, rss, predicted_values def loss_function(self, predicted_values): """Calculate the error between prediction and target values.""" @@ -246,10 +234,10 @@ def information_criterion(self, decision_variables): ------- Corrected Akaike Information Criterion by default """ - z = self.activation(decision_variables) - w_matrix2, rss, prediction = self.minimize_error(z) + non_linear_layer = self.activation(decision_variables) + linear_layer, rss, _ = self.minimize_error(non_linear_layer) # rss = ((self.y_train - prediction) ** 2).sum() - k = self.calculate_complexity(decision_variables) + np.count_nonzero(w_matrix2) + k = self.calculate_complexity(decision_variables) + np.count_nonzero(linear_layer) aic = 2 * k + self.num_of_samples * np.log(rss / self.num_of_samples) aicc = aic + (2 * k * (k + 1) / (self.num_of_samples - k - 1)) @@ -340,27 +328,16 @@ def create_logfile(self): ) return log_file - def create_plot(self, model): - """Creates and shows a plot for the model. + @staticmethod + def activate(name, x): + if name == "sigmoid": + return expit(x) - Parameters - ---------- - The model to create the plot for. - """ + if name == "relu": + return np.maximum(x, 0) - trace0 = go.Scatter(x=model.y_pred, y=self.y_train, mode="markers") - trace1 = go.Scatter(x=self.y_train, y=self.y_train) - data = [trace0, trace1] - plotly.offline.plot( - data, - filename=self.name - + "_var" - + str(self.num_of_variables) - + "_nodes" - + str(self.num_nodes) - + ".html", - auto_open=True, - ) + if name == "tanh": + return np.tanh(x) class EvoNNModel(EvoNN): @@ -370,20 +347,17 @@ class EvoNNModel(EvoNN): ---------- name : str Name of the problem - w1 : ndarray + w_matrix : ndarray The weight matrix of the lower part of the network - w2 : ndarray - The weight matrix of the upper part of the network - y_pred : ndarray - Prediction of the model + linear_layer : ndarray + The linear layer of the upper part of the network """ - def __init__(self, name, w1=None, w2=None, y_pred=None): + def __init__(self, name, w_matrix=None, linear_layer=None): super().__init__(name) self.name = name - self.w1 = w1 - self.w2 = w2 - self.y_pred = y_pred + self.w_matrix = w_matrix + self.linear_layer = linear_layer self.svr = None self.log = None self.set_params() @@ -403,33 +377,25 @@ def fit(self, training_data, target_values): if prob.params["logging"]: self.log = prob.create_logfile() prob.train(self) - if prob.params["plotting"]: - prob.create_plot(self) - self.num_of_samples = prob.num_of_samples + self.single_variable_response(ploton=False, log=self.log) def predict(self, decision_variables): - wi = np.dot(decision_variables, self.w1[1:, :]) + self.w1[0] + out = np.dot(decision_variables, self.w_matrix[1:, :]) + self.w_matrix[0] - if self.params["activation_func"] == "sigmoid": - activated_layer = lambda x: 1 / (1 + np.exp(-x)) + non_linear_layer = self.activate(self.params["activation_func"], out) - if self.params["activation_func"] == "relu": - activated_layer = lambda x: np.maximum(x, 0) + y = np.dot(non_linear_layer, self.linear_layer) - if self.params["activation_func"] == "tanh": - activated_layer = lambda x: np.tanh(x) - - out = np.dot(activated_layer(wi), self.w2) - - return out + return y def set_params( self, name=None, pop_size=500, num_nodes=15, + prob_omit=0.2, activation_func="sigmoid", opt_func="llsq", loss_func="rmse", @@ -448,6 +414,8 @@ def set_params( Population size. num_nodes : int Maximum number of nodes per layer. + prob_omit : float + Probability of setting some weights to zero initially. activation_func : str Function to use for activation. opt_func : str @@ -465,6 +433,7 @@ def set_params( "name": name, "pop_size": pop_size, "num_nodes": num_nodes, + "prob_omit": prob_omit, "activation_func": activation_func, "opt_func": opt_func, "loss_func": loss_func, @@ -475,15 +444,36 @@ def set_params( self.params = params + def plot(self, prediction, target): + """Creates and shows a plot for the model. + + Parameters + ---------- + The model to create the plot for. + """ + + trace0 = go.Scatter(x=prediction, y=target, mode="markers") + trace1 = go.Scatter(x=target, y=target) + data = [trace0, trace1] + plotly.offline.plot( + data, + filename=self.name + + "_var" + + str(self.num_of_variables) + + "_nodes" + + str(self.num_nodes) + + ".html", + auto_open=True, + ) + def single_variable_response(self, ploton=False, log=None): """Get the model's response to a single variable.""" trend = np.loadtxt("trend") - trend = trend[0 : self.num_of_samples] - avg = np.ones((1, self.w1[1:].shape[0])) * (0 + 1) / 2 + avg = np.ones((1, self.w_matrix[1:].shape[0])) * (0 + 1) / 2 svr = np.empty((0, 2)) - for i in range(self.w1[1:].shape[0]): + for i in range(self.w_matrix[1:].shape[0]): variables = np.ones((len(trend), 1)) * avg variables[:, i] = trend @@ -511,6 +501,8 @@ def single_variable_response(self, ploton=False, log=None): r = np.multiply(p, q) r_max = max(r) r_min = min(r) + response = None + s = None if r_max <= 0 and r_min <= 0: response = -1 s = "inverse" From 24b3dbb21a930acd001d44eeb93800e89519e2be Mon Sep 17 00:00:00 2001 From: delamorte Date: Mon, 1 Jul 2019 16:33:24 +0300 Subject: [PATCH 048/155] Modified Population class to be more universal crossover and mutation methods set at population init all recombination functions now take the same parameters added gaussian mutation method to evonn renamed recombination methods removed unnecessary init file edited recombination functions to use the new structure removed unnecessary if check from population.add --- Example.py | 6 +- evodn2_example.py | 172 ++++++------ evodn2_vs_mlp.py | 257 ++++++++++-------- pyrvea/EAs/PPGA.py | 24 +- pyrvea/EAs/RVEA.py | 4 + pyrvea/EAs/baseEA.py | 5 +- pyrvea/Population/Population.py | 102 +++---- pyrvea/Population/population_evonn.py | 2 +- pyrvea/Problem/evodn2_problem.py | 15 +- pyrvea/Problem/evonn_problem.py | 19 +- pyrvea/Recombination/__init__.py | 0 .../bounded_polynomial_mutation.py | 12 +- .../evodn2_xover_mut_gaussian.py | 28 +- pyrvea/Recombination/evonn_mut_gaussian.py | 111 ++++++++ pyrvea/Recombination/ppga_crossover.py | 35 ++- ..._mutation.py => self_adapting_mutation.py} | 22 +- .../simulated_binary_crossover.py | 9 +- 17 files changed, 505 insertions(+), 318 deletions(-) delete mode 100644 pyrvea/Recombination/__init__.py create mode 100644 pyrvea/Recombination/evonn_mut_gaussian.py rename pyrvea/Recombination/{ppga_mutation.py => self_adapting_mutation.py} (78%) diff --git a/Example.py b/Example.py index b45836e..7ef1e9f 100644 --- a/Example.py +++ b/Example.py @@ -31,7 +31,11 @@ def objectives(self, decision_variables): lattice_resolution = 4 population_size = 105 -pop = Population(problem) +pop = Population( + problem, + crossover_type="simulated_binary_crossover", + mutation_type="bounded_polynomial_mutation", +) pop.evolve(RVEA) diff --git a/evodn2_example.py b/evodn2_example.py index 8529910..d3262c4 100644 --- a/evodn2_example.py +++ b/evodn2_example.py @@ -13,41 +13,43 @@ # training_data_input = np.random.rand(150,30) # training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) -# test_prob = EvoNNTestProblem("Matyas") -# training_data_input, training_data_output = test_prob.create_training_data( -# samples=250, method="random", seed=31 -# ) -# -# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -# model_evodn2.set_params( -# pop_size=500, -# subnet_struct=(4, 4), -# num_nodes=4, -# activation_func="sigmoid", -# opt_func="llsq", -# loss_func="rmse", -# criterion="min_error", -# logging=True, -# plotting=True) -# -# model_evodn2.fit(training_data_input, training_data_output) -# y = model_evodn2.predict(training_data_input) -# model_evodn2.plot(y, training_data_output) -# -# model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) -# model_evonn.set_params( -# num_nodes=20, -# pop_size=500, -# activation_func="sigmoid", -# opt_func="llsq", -# loss_func="rmse", -# criterion="akaike_corrected", -# logging=True, -# plotting=True) -# -# model_evonn.fit(training_data_input, training_data_output) -# y = model_evonn.predict(training_data_input) -# model_evonn.plot(y, training_data_output) +test_prob = EvoNNTestProblem("Goldstein-Price") +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random", seed=31 +) + +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnet_struct=(6, 10), + num_nodes=10, + prob_omit=0.2, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) +y = model_evodn2.predict(training_data_input) +model_evodn2.plot(y, training_data_output) + +model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +model_evonn.set_params( + num_nodes=20, + pop_size=500, + prob_omit=0.2, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_evonn.fit(training_data_input, training_data_output) +y = model_evonn.predict(training_data_input) +model_evonn.plot(y, training_data_output) # # test_prob = EvoNNTestProblem("Matyas") @@ -229,52 +231,54 @@ # model_evodn_f1.fit(training_data_input, f1_training_data_output) # model_evodn_f2.fit(training_data_input, f2_training_data_output) -test_prob = testProblem('ZDT1', 30, 2, 0, 1, 0) -training_data_input = np.random.rand(250, 30) -training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) - -f1_training_data_output = training_data_output[:, 0] -f2_training_data_output = training_data_output[:, 1] - -model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") -model_evodn_f1.set_params( - pop_size=500, - subnet_struct=(6, 8), - num_nodes=8, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") -model_evodn_f2.set_params( - pop_size=500, - subnet_struct=(6, 8), - num_nodes=8, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) -#model_evodn_f1.fit(training_data_input, f1_training_data_output) -model_evodn_f2.fit(training_data_input, f2_training_data_output) -y = model_evodn_f2.predict(training_data_input) -model_evodn_f2.plot(y, f2_training_data_output) - -model_evonn_f2 = EvoNNModel(name="EvoNN_" + test_prob.name) -model_evonn_f2.set_params( - num_nodes=20, - pop_size=500, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - -model_evonn_f2.fit(training_data_input, training_data_output) -y = model_evonn_f2.predict(training_data_input) -model_evonn_f2.plot(y, f2_training_data_output) +# test_prob = testProblem('ZDT1', 30, 2, 0, 1, 0) +# np.random.seed(6) +# training_data_input = np.random.rand(250, 30) +# training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) +# +# f1_training_data_output = training_data_output[:, 0] +# f2_training_data_output = training_data_output[:, 1] +# +# model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") +# model_evodn_f1.set_params( +# pop_size=500, +# subnet_struct=(6, 8), +# num_nodes=8, +# prob_omit=0.2, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") +# model_evodn_f2.set_params( +# pop_size=500, +# subnet_struct=(6, 8), +# num_nodes=8, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# #model_evodn_f1.fit(training_data_input, f1_training_data_output) +# model_evodn_f2.fit(training_data_input, f2_training_data_output) +# y = model_evodn_f2.predict(training_data_input) +# model_evodn_f2.plot(y, f2_training_data_output) +# +# model_evonn_f2 = EvoNNModel(name="EvoNN_" + test_prob.name) +# model_evonn_f2.set_params( +# num_nodes=20, +# pop_size=500, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# model_evonn_f2.fit(training_data_input, training_data_output) +# y = model_evonn_f2.predict(training_data_input) +# model_evonn_f2.plot(y, f2_training_data_output) diff --git a/evodn2_vs_mlp.py b/evodn2_vs_mlp.py index 52bd6cb..fe6d0de 100644 --- a/evodn2_vs_mlp.py +++ b/evodn2_vs_mlp.py @@ -189,23 +189,24 @@ # training_data_input, training_data_output = test_prob.create_training_data( # samples=250, method="random", seed=33 # ) -# -# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -# model_evodn2.set_params( -# pop_size=500, -# subnets=(6, 10), -# num_nodes=10, -# activation_func="sigmoid", -# opt_func="llsq", -# loss_func="rmse", -# criterion="min_error", -# logging=True, -# plotting=True) -# +# # +# # model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# # model_evodn2.set_params( +# # pop_size=500, +# # subnets=(6, 10), +# # num_nodes=10, +# # activation_func="sigmoid", +# # opt_func="llsq", +# # loss_func="rmse", +# # criterion="min_error", +# # logging=True, +# # plotting=True) +# # # model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) # model_evonn.set_params( # num_nodes=20, # pop_size=500, +# prob_omit=0.2, # activation_func="sigmoid", # opt_func="llsq", # loss_func="rmse", @@ -213,6 +214,10 @@ # logging=True, # plotting=True) # +# model_evonn.fit(training_data_input, training_data_output) +# y = model_evonn.predict(training_data_input) +# model_evonn.plot(y, training_data_output) + # model_evodn2.fit(training_data_input, training_data_output) # # model_evonn.fit(training_data_input, training_data_output) # # @@ -421,94 +426,94 @@ # # auto_open=True, # # ) # -test_prob = EvoNNTestProblem("SchafferN2") -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random", seed=37 -) - -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnet_struct=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) -model_evonn.set_params( - num_nodes=20, - pop_size=500, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - +# test_prob = EvoNNTestProblem("SchafferN2") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random", seed=37 +# ) +# +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnet_struct=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +# model_evonn.set_params( +# num_nodes=20, +# pop_size=500, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# # model_evodn2.fit(training_data_input, training_data_output) +# #model_evonn.fit(training_data_input, training_data_output) +# +# mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) +# mlp_reg.fit(training_data_input, training_data_output) +# mlp_reg_y_pred = mlp_reg.predict(training_data_input) +# +# trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") +# trace1 = go.Scatter(x=training_data_output, y=training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="MLP Regressor " + test_prob.name +# + ".html", +# auto_open=True, +# ) +# +# gpr = GaussianProcessRegressor() +# gpr.fit(training_data_input, training_data_output) +# gpr_y_pred = gpr.predict(training_data_input) +# +# trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") +# trace1 = go.Scatter(x=training_data_output, y=training_data_output) +# data = [trace0, trace1] +# plotly.offline.plot( +# data, +# filename="Gaussian Process Regressor " + test_prob.name +# + ".html", +# auto_open=True, +# ) +# +# test_prob = EvoNNTestProblem("McCormick") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random", seed=38 +# ) +# +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnet_struct=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +# model_evonn.set_params( +# num_nodes=20, +# pop_size=500, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# # model_evodn2.fit(training_data_input, training_data_output) -#model_evonn.fit(training_data_input, training_data_output) - -mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) -mlp_reg.fit(training_data_input, training_data_output) -mlp_reg_y_pred = mlp_reg.predict(training_data_input) - -trace0 = go.Scatter(x=mlp_reg_y_pred, y=training_data_output, mode="markers") -trace1 = go.Scatter(x=training_data_output, y=training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="MLP Regressor " + test_prob.name - + ".html", - auto_open=True, -) - -gpr = GaussianProcessRegressor() -gpr.fit(training_data_input, training_data_output) -gpr_y_pred = gpr.predict(training_data_input) - -trace0 = go.Scatter(x=gpr_y_pred, y=training_data_output, mode="markers") -trace1 = go.Scatter(x=training_data_output, y=training_data_output) -data = [trace0, trace1] -plotly.offline.plot( - data, - filename="Gaussian Process Regressor " + test_prob.name - + ".html", - auto_open=True, -) - -test_prob = EvoNNTestProblem("McCormick") -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random", seed=38 -) - -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnet_struct=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) -model_evonn.set_params( - num_nodes=20, - pop_size=500, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - -model_evodn2.fit(training_data_input, training_data_output) # model_evonn.fit(training_data_input, training_data_output) # # mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) @@ -632,28 +637,33 @@ # ZDT2 -# test_prob = testProblem('ZDT2', 30, 2, 0, 1, 0) -# np.random.seed(2) -# training_data_input = np.random.rand(250, 30) -# training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) -# -# f1_training_data_output = training_data_output[:, 0] -# f2_training_data_output = training_data_output[:, 1] +test_prob = testProblem('ZDT2', 30, 2, 0, 1, 0) +np.random.seed(2) +training_data_input = np.random.rand(250, 30) +training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) + +f1_training_data_output = training_data_output[:, 0] +f2_training_data_output = training_data_output[:, 1] # -# model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") -# model_evodn_f1.set_params( +# model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name + "_f2") +# model_evonn.set_params( +# num_nodes=20, # pop_size=500, -# subnets=(6, 10), -# num_nodes=10, +# prob_omit=0.2, # activation_func="sigmoid", # opt_func="llsq", # loss_func="rmse", -# criterion="min_error", +# criterion="akaike_corrected", # logging=True, # plotting=True) # -# model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") -# model_evodn_f2.set_params( +# model_evonn.fit(training_data_input, f2_training_data_output) +# y = model_evonn.predict(training_data_input) +# model_evonn.plot(y, f2_training_data_output) + +# +# model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") +# model_evodn_f1.set_params( # pop_size=500, # subnets=(6, 10), # num_nodes=10, @@ -663,8 +673,23 @@ # criterion="min_error", # logging=True, # plotting=True) -# model_evodn_f1.fit(training_data_input, f1_training_data_output) -# model_evodn_f2.fit(training_data_input, f2_training_data_output) +# +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") +model_evodn2.set_params( + pop_size=500, + subnet_struct=(6, 10), + num_nodes=10, + prob_omit=0.2, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, f2_training_data_output) +y = model_evodn2.predict(training_data_input) +model_evodn2.plot(y, f2_training_data_output) # # mlp_reg = MLPRegressor(max_iter=10000, n_iter_no_change=100) # mlp_reg.fit(training_data_input, f1_training_data_output) diff --git a/pyrvea/EAs/PPGA.py b/pyrvea/EAs/PPGA.py index b460808..342edd6 100644 --- a/pyrvea/EAs/PPGA.py +++ b/pyrvea/EAs/PPGA.py @@ -29,7 +29,7 @@ def __init__(self, population: "Population", EA_parameters: dict = {}): self.params = self.set_params(population, **EA_parameters) self.lattice = Lattice(60, 60, self.params) - self._next_iteration(population) + #self._next_iteration(population) def set_params( self, @@ -94,10 +94,8 @@ def set_params( "logging": logging, "logfile": logfile, "current_iteration_gen_count": 0, - "current_total_gen_count": 1, + "current_total_gen_count": 0, "current_iteration_count": 0, - "crossover_type": crossover_type, - "mutation_type": mutation_type, "prob_crossover": prob_crossover, "prob_mutation": prob_mutation, "mut_strength": mut_strength, @@ -158,7 +156,11 @@ def _next_gen(self, population: "Population"): # Choose and create offspring if population.problem.__class__.__name__ == "EvoNN": - + self.params["std_dev"] = (5 / 3) * ( + 1 + - self.params["current_total_gen_count"] + / self.params["total_generations"] + ) offspring = np.empty( ( 0, @@ -173,17 +175,17 @@ def _next_gen(self, population: "Population"): - self.params["current_total_gen_count"] / self.params["total_generations"] ) - offspring = np.empty((0, population.problem.subnet_struct[0])) - for ind in range(population.individuals.shape[0]): mate_idx = self.lattice.choose_mate(ind) if not mate_idx: continue else: - offspring1, offspring2 = population.mate(ind, mate_idx, self.params) - offspring = np.concatenate((offspring, [offspring1], [offspring2])) + mating_pop = [ind, mate_idx] + offspring = np.concatenate((offspring, population.mate(mating_pop, self.params))) + + #offspring = np.concatenate((offspring, [offspring1], [offspring2])) # Try to place the offspring to lattice, add to population if successful placed_indices = self.lattice.place_offspring(offspring.shape[0]) @@ -206,7 +208,7 @@ def _next_gen(self, population: "Population"): population.delete_or_keep(selected, "delete") print( - str(self.params["current_iteration_gen_count"]) + str(self.params["current_total_gen_count"]) + " " + "population size: " + str(population.individuals.shape[0]) @@ -221,7 +223,7 @@ def _next_gen(self, population: "Population"): print( "AFTER KILL " - + str(self.params["current_iteration_gen_count"]) + + str(self.params["current_total_gen_count"]) + " " + "population size: " + str(population.individuals.shape[0]) diff --git a/pyrvea/EAs/RVEA.py b/pyrvea/EAs/RVEA.py index e07981b..dd85419 100644 --- a/pyrvea/EAs/RVEA.py +++ b/pyrvea/EAs/RVEA.py @@ -98,6 +98,10 @@ def set_params( "reference_vectors": ReferenceVectors( lattice_resolution, population.problem.num_of_objectives ), + "prob_mut": 1 / population.num_var, + "dis_mut": 20, + "prob_xover": 1, + "dis_xover": 30 } return rveaparams diff --git a/pyrvea/EAs/baseEA.py b/pyrvea/EAs/baseEA.py index 4edb943..0790d89 100644 --- a/pyrvea/EAs/baseEA.py +++ b/pyrvea/EAs/baseEA.py @@ -87,11 +87,10 @@ def _next_gen(self, population: "Population"): Population object """ - offspring = population.mate() + offspring = population.mate(params=self.params) population.add(offspring) selected = self.select(population) - population.keep(selected) - #population.delete_or_keep(selected, "keep") + population.delete_or_keep(selected, "keep") def select(self, population) -> list: """Describe a selection mechanism. Return indices of selected diff --git a/pyrvea/Population/Population.py b/pyrvea/Population/Population.py index 98990bc..931a881 100644 --- a/pyrvea/Population/Population.py +++ b/pyrvea/Population/Population.py @@ -11,16 +11,25 @@ from tqdm import tqdm, tqdm_notebook -from pyrvea.Recombination.bounded_polynomial_mutation import mutation -from pyrvea.Recombination.simulated_binary_crossover import crossover +# from pyrvea.Recombination.bounded_polynomial_mutation import mutation as rvea_mutation +# from pyrvea.Recombination.simulated_binary_crossover import crossover as rvea_crossover from pyrvea.OtherTools.plotlyanimate import animate_init_, animate_next_ from pyrvea.OtherTools.IsNotebook import IsNotebook - -from pyrvea.Recombination.ppga_crossover import ppga_crossover +from pyrvea.Recombination import ( + evodn2_xover_mut_gaussian, + evonn_mut_gaussian, + ppga_crossover, + self_adapting_mutation, + bounded_polynomial_mutation, + simulated_binary_crossover, +) + +# from pyrvea.Recombination.ppga_crossover import ppga_crossover from pyrvea.Recombination.evodn2_xover_mut import evodn2_xover_mut -from pyrvea.Recombination.evodn2_xover_mut_gaussian import evodn2_xover_mut_gaussian -from pyrvea.Recombination.ppga_mutation import ppga_mutation + +# from pyrvea.Recombination.evodn2_xover_mut_gaussian import evodn2_xover_mut_gaussian +# from pyrvea.Recombination.ppga_mutation import ppga_mutation from math import ceil if TYPE_CHECKING: @@ -37,6 +46,8 @@ def __init__( assign_type: str = "LHSDesign", plotting: bool = True, pop_size=None, + crossover_type=None, + mutation_type=None, *args ): """Initialize the population. @@ -64,11 +75,22 @@ def __init__( self.hyp = 0 self.non_dom = 0 self.pop_size = pop_size + self.recombination_funcs = { + "DNN_gaussian_xover+mut": evodn2_xover_mut_gaussian, + "evonn_gaussian" : evonn_mut_gaussian, + "EvoNN_xover": ppga_crossover, + "EvoNN_mut": self_adapting_mutation, + "bounded_polynomial_mutation": bounded_polynomial_mutation, + "simulated_binary_crossover": simulated_binary_crossover, + } + self.crossover_type = crossover_type + self.mutation_type = mutation_type + self.crossover = self.recombination_funcs[crossover_type] + self.mutation = self.recombination_funcs[mutation_type] self.problem = problem self.filename = problem.name + "_" + str(problem.num_of_objectives) self.plotting = plotting # These attributes contain the solutions. - self.individuals = np.empty((0, self.num_var), float) self.objectives = np.empty((0, self.problem.num_of_objectives), float) self.fitness = np.empty((0, self.problem.num_of_objectives), float) @@ -160,14 +182,9 @@ def add(self, new_pop: np.ndarray): new_pop: np.ndarray Decision variable values for new population. """ - if new_pop.ndim == 1: - self.append_individual(new_pop) + for i in range(len(new_pop)): + self.append_individual(new_pop[i]) - elif new_pop.ndim >= 2: - for i in range(0, new_pop.shape[0]): - self.append_individual(new_pop[i, ...]) - else: - print("Error while adding new individuals. Check dimensions.") # print(self.ideal_fitness) self.update_ideal_and_nadir() @@ -308,64 +325,25 @@ def evolve(self, EA: "BaseEA" = None, EA_parameters: dict = {}) -> "Population": if self.plotting: self.plot_objectives() # Figure was created in init - for i in progressbar(range(1, iterations), desc="Iteration"): + for i in progressbar(range(0, iterations), desc="Iteration"): ea._run_interruption(self) ea._next_iteration(self) if self.plotting: self.plot_objectives() - def mate(self, ind1=None, ind2=None, params=None): + def mate(self, mating_pop=None, params=None): """Conduct crossover and mutation over the population. - Conduct simulated binary crossover and bounded polynomial mutation. """ - if self.individuals.ndim >= 2: - # Get individuals at indices - w1, w2 = self.individuals[ind1], self.individuals[ind2] - - # Perform crossover - if params["crossover_type"] == "short": - offspring1, offspring2 = evodn2_xover_mut_gaussian( - w1, - w2, - self.individuals, - params["prob_crossover"], - params["prob_mutation"], - 0.7, - params["current_total_gen_count"], - params["total_generations"], - params["std_dev"] - ) - else: - xover_w1, xover_w2 = ppga_crossover(w1, w2, params["prob_crossover"]) - - # Make a list of individuals suitable for mutation, exclude the ones to be mutated - # so that they won't mutate with themselves - indices = [ind1, ind2] - mask = np.ones(len(self.individuals), dtype=bool) - mask[indices] = False - alternatives = self.individuals[mask, ...][:, 1:, :] - - # Mutate - offspring1, offspring2 = ppga_mutation( - alternatives, - xover_w1, - xover_w2, - params["current_total_gen_count"], - params["total_generations"], - params["prob_mutation"], - params["mut_strength"], - ) - - return offspring1, offspring2 - - else: - offspring = crossover(self) - p = 1 / self.num_var - mut_offspring = mutation(self, offspring, prob_mut=p) + offspring = self.crossover.mate(mating_pop, self.individuals, params) + if ( + not self.crossover_type == self.mutation_type + and self.mutation_type is not None + ): + self.mutation.mutate(offspring, self.individuals, params, self.lower_limits, self.upper_limits) - return mut_offspring + return offspring def plot_init_(self): """Initialize animation objects. Return figure""" diff --git a/pyrvea/Population/population_evonn.py b/pyrvea/Population/population_evonn.py index 026d503..5b07fe7 100644 --- a/pyrvea/Population/population_evonn.py +++ b/pyrvea/Population/population_evonn.py @@ -16,7 +16,7 @@ from scipy.stats import bernoulli as bn from pyrvea.Recombination.ppga_crossover import ppga_crossover -from pyrvea.Recombination.ppga_mutation import ppga_mutation +from pyrvea.Recombination.self_adapting_mutation import ppga_mutation if TYPE_CHECKING: from pyrvea.Problem.baseProblem import baseProblem diff --git a/pyrvea/Problem/evodn2_problem.py b/pyrvea/Problem/evodn2_problem.py index 39ca3b0..945c712 100644 --- a/pyrvea/Problem/evodn2_problem.py +++ b/pyrvea/Problem/evodn2_problem.py @@ -77,7 +77,12 @@ def fit(self, training_data, target_values): def train(self, model): pop = Population( - self, assign_type="EvoDN2", pop_size=self.params["pop_size"], plotting=False + self, + assign_type="EvoDN2", + pop_size=self.params["pop_size"], + crossover_type="DNN_gaussian_xover+mut", + mutation_type="DNN_gaussian_xover+mut", + plotting=False, ) pop.evolve( PPGA, @@ -85,10 +90,8 @@ def train(self, model): "logging": self.params["logging"], "logfile": model.log, "iterations": 10, - "generations_per_iteration": 10, - "crossover_type": "short", - "mutation_type": "short", - }, + "generations_per_iteration": 10 + } ) non_dom_front = pop.non_dominated() @@ -128,8 +131,6 @@ def create_population(self): individuals.append(nets) - individuals = np.asarray(individuals) - return individuals def objectives(self, decision_variables) -> list: diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index ac7616c..fd6ba67 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -99,7 +99,8 @@ def create_population(self): # Randomly set some weights to zero zeros = np.random.choice( - np.arange(individuals.size), ceil(individuals.size * self.params["prob_omit"]) + np.arange(individuals.size), + ceil(individuals.size * self.params["prob_omit"]), ) individuals.ravel()[zeros] = 0 @@ -117,7 +118,12 @@ def train(self, model): The model to be chosen. """ pop = Population( - self, assign_type="EvoNN", pop_size=self.params["pop_size"], plotting=False + self, + assign_type="EvoNN", + pop_size=self.params["pop_size"], + plotting=False, + crossover_type="EvoNN_xover", + mutation_type="evonn_gaussian", ) pop.evolve( PPGA, @@ -199,7 +205,9 @@ def minimize_error(self, non_linear_layer): """ if self.params["opt_func"] == "llsq": - linear_solution = np.linalg.lstsq(non_linear_layer, self.y_train, rcond=None) + linear_solution = np.linalg.lstsq( + non_linear_layer, self.y_train, rcond=None + ) linear_layer = linear_solution[0] rss = linear_solution[1] predicted_values = np.dot(non_linear_layer, linear_layer) @@ -237,7 +245,9 @@ def information_criterion(self, decision_variables): non_linear_layer = self.activation(decision_variables) linear_layer, rss, _ = self.minimize_error(non_linear_layer) # rss = ((self.y_train - prediction) ** 2).sum() - k = self.calculate_complexity(decision_variables) + np.count_nonzero(linear_layer) + k = self.calculate_complexity(decision_variables) + np.count_nonzero( + linear_layer + ) aic = 2 * k + self.num_of_samples * np.log(rss / self.num_of_samples) aicc = aic + (2 * k * (k + 1) / (self.num_of_samples - k - 1)) @@ -353,6 +363,7 @@ class EvoNNModel(EvoNN): The linear layer of the upper part of the network """ + def __init__(self, name, w_matrix=None, linear_layer=None): super().__init__(name) self.name = name diff --git a/pyrvea/Recombination/__init__.py b/pyrvea/Recombination/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/pyrvea/Recombination/bounded_polynomial_mutation.py b/pyrvea/Recombination/bounded_polynomial_mutation.py index f3ff1a1..b7d3a9d 100644 --- a/pyrvea/Recombination/bounded_polynomial_mutation.py +++ b/pyrvea/Recombination/bounded_polynomial_mutation.py @@ -1,10 +1,16 @@ import numpy as np -def mutation(population, offspring, prob_mut=0.3, dis_mut=20): +def mutate(offspring, individuals, params, lower_limits, upper_limits, prob_mut=0.3, dis_mut=20): - min_val = np.ones_like(offspring) * population.lower_limits - max_val = np.ones_like(offspring) * population.upper_limits + try: + prob_mut = params["prob_mut"] + dis_mut = params["dis_mut"] + except: + pass + + min_val = np.ones_like(offspring) * lower_limits + max_val = np.ones_like(offspring) * upper_limits k = np.random.random(offspring.shape) miu = np.random.random(offspring.shape) temp = np.logical_and((k <= prob_mut), (miu < 0.5)) diff --git a/pyrvea/Recombination/evodn2_xover_mut_gaussian.py b/pyrvea/Recombination/evodn2_xover_mut_gaussian.py index 162a7f6..97e23d2 100644 --- a/pyrvea/Recombination/evodn2_xover_mut_gaussian.py +++ b/pyrvea/Recombination/evodn2_xover_mut_gaussian.py @@ -4,16 +4,11 @@ from math import ceil from timeit import default_timer as timer -def evodn2_xover_mut_gaussian( - parent1, - parent2, + +def mate( + mating_pop, individuals, - prob_crossover=0.8, - prob_mut=0.3, - mut_strength=0.7, - cur_gen=1, - total_gen=10, - std_dev=1.0, + params ): """ Perform simultaneous crossover and mutation over two individuals. @@ -27,7 +22,7 @@ def evodn2_xover_mut_gaussian( All individuals to choose mutation partner from prob_crossover : float The probability for crossover - prob_mut : float + prob_mutation : float The probability for mutation mut_strength : float Mutation alfa parameter @@ -39,8 +34,14 @@ def evodn2_xover_mut_gaussian( Standard deviation """ + parent1, parent2 = individuals[mating_pop[0]], individuals[mating_pop[1]] offspring1 = deepcopy(parent1) offspring2 = deepcopy(parent2) + offspring = np.empty((0, len(offspring1))) + + prob_crossover = params["prob_crossover"] + prob_mutation = params["prob_mutation"] + std_dev = params["std_dev"] for subnet in range(len(offspring1)): @@ -66,7 +67,7 @@ def evodn2_xover_mut_gaussian( mut_val = np.random.normal(0, std_dev, connections) - mut = np.random.choice(connections, np.random.binomial(connections, prob_mut), replace=False) + mut = np.random.choice(connections, np.random.binomial(connections, prob_mutation), replace=False) sub1[layer][1:, :].ravel()[mut] += sub1[layer][1:, :].ravel()[mut] * mut_val[mut] except IndexError: @@ -77,10 +78,11 @@ def evodn2_xover_mut_gaussian( mut_val = np.random.normal(0, std_dev, connections) - mut = np.random.choice(connections, np.random.binomial(connections, prob_mut), replace=False) + mut = np.random.choice(connections, np.random.binomial(connections, prob_mutation), replace=False) sub2[layer][1:, :].ravel()[mut] += sub2[layer][1:, :].ravel()[mut] * mut_val[mut] except IndexError: continue - return offspring1, offspring2 + offspring = np.concatenate((offspring, [offspring1], [offspring2])) + return offspring diff --git a/pyrvea/Recombination/evonn_mut_gaussian.py b/pyrvea/Recombination/evonn_mut_gaussian.py new file mode 100644 index 0000000..a1c9be1 --- /dev/null +++ b/pyrvea/Recombination/evonn_mut_gaussian.py @@ -0,0 +1,111 @@ +import numpy as np +from copy import deepcopy +from random import sample +from math import ceil +from timeit import default_timer as timer + + +def mutate( + mating_pop, + individuals, + params, + lower_limits=None, + upper_limits=None +): + """ Perform simultaneous crossover and mutation over two individuals. + + Parameters + ---------- + parent1 : ndarray + The first individual + parent2 : ndarray + The second individual + individuals : ndarray + All individuals to choose mutation partner from + prob_crossover : float + The probability for crossover + prob_mutation : float + The probability for mutation + mut_strength : float + Mutation alfa parameter + cur_gen : int + Current generation + total_gen : int + Total generations + std_dev : float + Standard deviation + """ + + parent1, parent2 = mating_pop[0], mating_pop[1] + offspring1 = np.copy(parent1) + offspring2 = np.copy(parent2) + offspring = np.empty( + ( + 0, + offspring1.shape[0], + offspring1.shape[1], + ), + float, + ) + + cur_gen = params["current_total_gen_count"] + total_gen = params["total_generations"] + prob_crossover = params["prob_crossover"] + prob_mutation = params["prob_mutation"] + std_dev = params["std_dev"] + + connections = offspring1[1:, :].size + + # Method 1: Gaussian + # Take a random number of connections based on probability and mutate based on + # standard deviation, calculated once per generation + # VERY FAST + # + mut_val = np.random.normal(0, std_dev, connections) + + mut = np.random.choice(connections, np.random.binomial(connections, prob_mutation), replace=False) + offspring1[1:, :].ravel()[mut] += offspring1[1:, :].ravel()[mut] * mut_val[mut] + + mut_val = np.random.normal(0, std_dev, connections) + + mut = np.random.choice(connections, np.random.binomial(connections, prob_mutation), replace=False) + offspring2[1:, :].ravel()[mut] += offspring2[1:, :].ravel()[mut] * mut_val[mut] + + # Method 2 + # Choose two random individuals and a random number of connections, + # mutate offspring based on current gen and connections of two randomly chosen individuals + # + # alternatives = individuals[:, 1:, :] + # + # # Randomly select two individuals with current match active (=non-zero) + # select = alternatives[ + # np.random.choice( + # np.nonzero(alternatives)[ + # 0 + # ], + # 2, + # ) + # ] + # + # mut = np.random.choice(connections, np.random.binomial(connections, prob_mutation), replace=False) + # offspring1[1:, :].ravel()[mut] = offspring1[1:, :].ravel()[mut] + params["mut_strength"] * ( + # 1 - cur_gen / total_gen + # ) * (select[1].ravel()[mut] - select[0].ravel()[mut]) + # + # select = alternatives[ + # np.random.choice( + # np.nonzero(alternatives)[ + # 0 + # ], + # 2, + # ) + # ] + # + # mut = np.random.choice(connections, np.random.binomial(connections, prob_mutation), replace=False) + # offspring2[1:, :].ravel()[mut] = offspring2[1:, :].ravel()[mut] + params["mut_strength"] * ( + # 1 - cur_gen / total_gen + # ) * (select[1].ravel()[mut] - select[0].ravel()[mut]) + + + offspring = np.concatenate((offspring, [offspring1], [offspring2])) + return offspring diff --git a/pyrvea/Recombination/ppga_crossover.py b/pyrvea/Recombination/ppga_crossover.py index bcfd471..b31179a 100644 --- a/pyrvea/Recombination/ppga_crossover.py +++ b/pyrvea/Recombination/ppga_crossover.py @@ -1,7 +1,7 @@ import numpy as np -def ppga_crossover(w1, w2, prob_crossover=0.8): +def mate(mating_pop, individuals, params): """Randomly exchange nodes between two individuals. Parameters @@ -13,13 +13,38 @@ def ppga_crossover(w1, w2, prob_crossover=0.8): prob_crossover : float The probability for the crossover """ - offspring1 = np.copy(w1) - offspring2 = np.copy(w2) - for i in range(np.shape(w1)[1]): + prob_crossover = params["prob_crossover"] + offspring1 = np.copy(individuals[mating_pop[0]]) + offspring2 = np.copy(individuals[mating_pop[1]]) + offspring = np.empty( + ( + 0, + offspring1.shape[0], + offspring1.shape[1], + ), + float, + ) + + # Crossover + + # Method 1 + # Take a random number of connections based on probability and swap them + # + # connections = offspring1[1:, :].size + # exchange = np.random.choice(connections, np.random.binomial(connections, prob_crossover), replace=False) + # tmp = np.copy(offspring1) + # offspring1[1:, :].ravel()[exchange] = offspring2[1:, :].ravel()[exchange] + # offspring2[1:, :].ravel()[exchange] = tmp[1:, :].ravel()[exchange] + + # Method 2 + # Take random nodes based on probability and swap them + # + for i in range(offspring1.shape[1]): if np.random.random() < prob_crossover: tmp = np.copy(offspring1[:, i]) offspring1[:, i] = offspring2[:, i] offspring2[:, i] = tmp - return offspring1, offspring2 + offspring = np.concatenate((offspring, [offspring1], [offspring2])) + return offspring diff --git a/pyrvea/Recombination/ppga_mutation.py b/pyrvea/Recombination/self_adapting_mutation.py similarity index 78% rename from pyrvea/Recombination/ppga_mutation.py rename to pyrvea/Recombination/self_adapting_mutation.py index 61c6c6f..ea25b88 100644 --- a/pyrvea/Recombination/ppga_mutation.py +++ b/pyrvea/Recombination/self_adapting_mutation.py @@ -2,8 +2,8 @@ from random import sample -def ppga_mutation( - alternatives, w1, w2, cur_gen=1, total_gen=10, prob_mut=0.3, mut_strength=0.7 +def mutate( + offspring, individuals, params, lower_limits=None, upper_limits=None ): """Randomly mutate two individuals based on a probability @@ -25,9 +25,19 @@ def ppga_mutation( the strength of the mutation constant """ + cur_gen = params["current_iteration_gen_count"] + total_gen = params["generations"] + prob_mut = params["prob_mutation"] + mut_strength = params["mut_strength"] + + alternatives = individuals[:, 1:, :] + + mut_offspring1 = offspring[0] + mut_offspring2 = offspring[1] + # Iterate over both individuals at the same time to avoid nested looping it = np.nditer( - [w1[1:, :], w2[1:, :]], flags=["multi_index"], op_flags=["readwrite"] + [mut_offspring1[1:, :], mut_offspring2[1:, :]], flags=["multi_index"], op_flags=["readwrite"] ) # wx = individual cells (connections) in the weight matrix @@ -52,7 +62,7 @@ def ppga_mutation( ] # Mutation function, +1 in w1[it.multi_index[0]+1 is to ignore bias row - w1[it.multi_index[0] + 1, it.multi_index[1]] = wx1 + mut_strength * ( + mut_offspring1[it.multi_index[0] + 1, it.multi_index[1]] = wx1 + mut_strength * ( 1 - cur_gen / total_gen ) * ( select[0][it.multi_index[0], it.multi_index[1]] @@ -79,11 +89,11 @@ def ppga_mutation( ] # Mutation function, +1 in w2[it.multi_index[0]+1 is to ignore bias row - w2[it.multi_index[0] + 1, it.multi_index[1]] = wx2 + mut_strength * ( + mut_offspring2[it.multi_index[0] + 1, it.multi_index[1]] = wx2 + mut_strength * ( 1 - cur_gen / total_gen ) * ( select[0][it.multi_index[0], it.multi_index[1]] - select[1][it.multi_index[0], it.multi_index[1]] ) - return w1, w2 + return mut_offspring1, mut_offspring2 diff --git a/pyrvea/Recombination/simulated_binary_crossover.py b/pyrvea/Recombination/simulated_binary_crossover.py index a718c0c..46a13e0 100644 --- a/pyrvea/Recombination/simulated_binary_crossover.py +++ b/pyrvea/Recombination/simulated_binary_crossover.py @@ -2,9 +2,14 @@ from random import shuffle -def crossover(population, prob_xover=1, dis_xover=30): +def mate(mating_pop, pop, params, prob_xover=1, dis_xover=30): + + try: + prob_xover = params["prob_xover"] + dis_xover = params["dis_xover"] + except: + pass - pop = population.individuals pop_size, num_var = pop.shape shuffled_ids = list(range(pop_size)) shuffle(shuffled_ids) From b9597b8bc4774120c378e5dd2d13eb3c21245d61 Mon Sep 17 00:00:00 2001 From: delamorte Date: Mon, 1 Jul 2019 16:47:27 +0300 Subject: [PATCH 049/155] Edited tests --- evodn2_example.py | 650 +++++++++++++++++++++++++++++----------------- 1 file changed, 412 insertions(+), 238 deletions(-) diff --git a/evodn2_example.py b/evodn2_example.py index d3262c4..ae192ff 100644 --- a/evodn2_example.py +++ b/evodn2_example.py @@ -4,20 +4,281 @@ from pyrvea.Problem.testProblem import testProblem import numpy as np -# test_prob = EvoNNTestProblem("Three-hump camel") -# training_data_input, training_data_output = test_prob.create_training_data( -# samples=150, method="random" -# ) -# test_prob = testProblem('ZDT2', 30, 2, 0, 1, 0) -# training_data_input = np.random.rand(150,30) -# training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) +test_prob = EvoNNTestProblem("Sphere", num_of_variables=3) +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random", seed=31 +) + +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnet_struct=(6, 10), + num_nodes=10, + prob_omit=0.2, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) +y = model_evodn2.predict(training_data_input) +model_evodn2.plot(y, training_data_output) + +model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +model_evonn.set_params( + num_nodes=20, + pop_size=500, + prob_omit=0.2, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_evonn.fit(training_data_input, training_data_output) +y = model_evonn.predict(training_data_input) +model_evonn.plot(y, training_data_output) + + +test_prob = EvoNNTestProblem("Matyas") +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random", seed=31 +) + +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnet_struct=(6, 10), + num_nodes=10, + prob_omit=0.2, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) +y = model_evodn2.predict(training_data_input) +model_evodn2.plot(y, training_data_output) + +model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +model_evonn.set_params( + num_nodes=20, + pop_size=500, + prob_omit=0.2, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_evonn.fit(training_data_input, training_data_output) +y = model_evonn.predict(training_data_input) +model_evonn.plot(y, training_data_output) + +test_prob = EvoNNTestProblem("Himmelblau") +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random", seed=31 +) + +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnet_struct=(6, 10), + num_nodes=10, + prob_omit=0.2, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) +y = model_evodn2.predict(training_data_input) +model_evodn2.plot(y, training_data_output) + +model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +model_evonn.set_params( + num_nodes=20, + pop_size=500, + prob_omit=0.2, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_evonn.fit(training_data_input, training_data_output) +y = model_evonn.predict(training_data_input) +model_evonn.plot(y, training_data_output) + + +test_prob = EvoNNTestProblem("Rastigrin", num_of_variables=2) +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random", seed=31 +) + +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnet_struct=(6, 10), + num_nodes=10, + prob_omit=0.2, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) +y = model_evodn2.predict(training_data_input) +model_evodn2.plot(y, training_data_output) + +model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +model_evonn.set_params( + num_nodes=20, + pop_size=500, + prob_omit=0.2, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_evonn.fit(training_data_input, training_data_output) +y = model_evonn.predict(training_data_input) +model_evonn.plot(y, training_data_output) + + +test_prob = EvoNNTestProblem("Three-hump camel") +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random", seed=31 +) + +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnet_struct=(6, 10), + num_nodes=10, + prob_omit=0.2, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) +y = model_evodn2.predict(training_data_input) +model_evodn2.plot(y, training_data_output) + +model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +model_evonn.set_params( + num_nodes=20, + pop_size=500, + prob_omit=0.2, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_evonn.fit(training_data_input, training_data_output) +y = model_evonn.predict(training_data_input) +model_evonn.plot(y, training_data_output) + test_prob = EvoNNTestProblem("Goldstein-Price") training_data_input, training_data_output = test_prob.create_training_data( samples=250, method="random", seed=31 ) +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnet_struct=(6, 10), + num_nodes=10, + prob_omit=0.2, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) +y = model_evodn2.predict(training_data_input) +model_evodn2.plot(y, training_data_output) + +model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +model_evonn.set_params( + num_nodes=20, + pop_size=500, + prob_omit=0.2, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_evonn.fit(training_data_input, training_data_output) +y = model_evonn.predict(training_data_input) +model_evonn.plot(y, training_data_output) + + +test_prob = EvoNNTestProblem("LeviN13") +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random", seed=31 +) +model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +model_evodn2.set_params( + pop_size=500, + subnet_struct=(6, 10), + num_nodes=10, + prob_omit=0.2, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn2.fit(training_data_input, training_data_output) +y = model_evodn2.predict(training_data_input) +model_evodn2.plot(y, training_data_output) + +model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +model_evonn.set_params( + num_nodes=20, + pop_size=500, + prob_omit=0.2, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) +model_evonn.fit(training_data_input, training_data_output) +y = model_evonn.predict(training_data_input) +model_evonn.plot(y, training_data_output) + + +test_prob = EvoNNTestProblem("SchafferN2") +training_data_input, training_data_output = test_prob.create_training_data( + samples=250, method="random", seed=31 +) model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) model_evodn2.set_params( pop_size=500, @@ -51,234 +312,147 @@ y = model_evonn.predict(training_data_input) model_evonn.plot(y, training_data_output) -# -# test_prob = EvoNNTestProblem("Matyas") -# training_data_input, training_data_output = test_prob.create_training_data( -# samples=250, method="random" -# ) -# -# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -# model_evodn2.set_params( -# pop_size=500, -# subnet_struct=(6, 10), -# num_nodes=10, -# activation_func="sigmoid", -# opt_func="llsq", -# loss_func="rmse", -# criterion="min_error", -# logging=True, -# plotting=True) -# -# model_evodn2.fit(training_data_input, training_data_output) -# -# #model_evonn.fit(training_data_input, training_data_output) -# # -# test_prob = EvoNNTestProblem("Himmelblau") -# training_data_input, training_data_output = test_prob.create_training_data( -# samples=250, method="random" -# ) -# -# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -# model_evodn2.set_params( -# pop_size=500, -# subnet_struct=(6, 10), -# num_nodes=10, -# activation_func="sigmoid", -# opt_func="llsq", -# loss_func="rmse", -# criterion="min_error", -# logging=True, -# plotting=True) -# -# model_evodn2.fit(training_data_input, training_data_output) -# # -# # model_evodn2.fit(training_data_input, training_data_output) -# # model_evonn.fit(training_data_input, training_data_output) -# # -# test_prob = EvoNNTestProblem("Rastigrin", num_of_variables=2) -# training_data_input, training_data_output = test_prob.create_training_data( -# samples=250, method="random" -# ) -# -# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -# model_evodn2.set_params( -# pop_size=500, -# subnet_struct=(6, 10), -# num_nodes=10, -# activation_func="sigmoid", -# opt_func="llsq", -# loss_func="rmse", -# criterion="min_error", -# logging=True, -# plotting=True) -# -# model_evodn2.fit(training_data_input, training_data_output) -# # -# # model_evodn2.fit(training_data_input, training_data_output) -# # model_evonn.fit(training_data_input, training_data_output) -# # -# test_prob = EvoNNTestProblem("Three-hump camel") -# training_data_input, training_data_output = test_prob.create_training_data( -# samples=250, method="random" -# ) -# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -# model_evodn2.set_params( -# pop_size=500, -# subnet_struct=(6, 10), -# num_nodes=10, -# activation_func="sigmoid", -# opt_func="llsq", -# loss_func="rmse", -# criterion="min_error", -# logging=True, -# plotting=True) -# -# model_evodn2.fit(training_data_input, training_data_output) -# # -# # model_evodn2.fit(training_data_input, training_data_output) -# # model_evonn.fit(training_data_input, training_data_output) -# # -# test_prob = EvoNNTestProblem("Goldstein-Price") -# training_data_input, training_data_output = test_prob.create_training_data( -# samples=250, method="random" -# ) -# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -# model_evodn2.set_params( -# pop_size=500, -# subnet_struct=(6, 10), -# num_nodes=10, -# activation_func="sigmoid", -# opt_func="llsq", -# loss_func="rmse", -# criterion="min_error", -# logging=True, -# plotting=True) -# -# model_evodn2.fit(training_data_input, training_data_output) -# # model_evodn2.fit(training_data_input, training_data_output) -# # model_evonn.fit(training_data_input, training_data_output) -# # -# test_prob = EvoNNTestProblem("LeviN13") -# training_data_input, training_data_output = test_prob.create_training_data( -# samples=250, method="random" -# ) -# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -# model_evodn2.set_params( -# pop_size=500, -# subnet_struct=(6, 10), -# num_nodes=10, -# activation_func="sigmoid", -# opt_func="llsq", -# loss_func="rmse", -# criterion="min_error", -# logging=True, -# plotting=True) -# -# model_evodn2.fit(training_data_input, training_data_output) -# # -# # model_evodn2.fit(training_data_input, training_data_output) -# # model_evonn.fit(training_data_input, training_data_output) -# # -# test_prob = EvoNNTestProblem("SchafferN2") -# training_data_input, training_data_output = test_prob.create_training_data( -# samples=250, method="random" -# ) -# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -# model_evodn2.set_params( -# pop_size=500, -# subnet_struct=(6, 10), -# num_nodes=10, -# activation_func="sigmoid", -# opt_func="llsq", -# loss_func="rmse", -# criterion="min_error", -# logging=True, -# plotting=True) -# -# model_evodn2.fit(training_data_input, training_data_output) -# -# # # ZDT 1 & 2 -# test_prob = testProblem('ZDT1', 30, 2, 0, 1, 0) -# training_data_input = np.random.rand(250, 30) -# training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) -# -# f1_training_data_output = training_data_output[:, 0] -# f2_training_data_output = training_data_output[:, 1] -# -# model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") -# model_evodn_f1.set_params( -# pop_size=500, -# subnet_struct=(8, 10), -# num_nodes=10, -# activation_func="sigmoid", -# opt_func="llsq", -# loss_func="rmse", -# criterion="min_error", -# logging=True, -# plotting=True) -# -# model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") -# model_evodn_f2.set_params( -# pop_size=500, -# subnet_struct=(8, 10), -# num_nodes=10, -# activation_func="sigmoid", -# opt_func="llsq", -# loss_func="rmse", -# criterion="min_error", -# logging=True, -# plotting=True) -# model_evodn_f1.fit(training_data_input, f1_training_data_output) -# model_evodn_f2.fit(training_data_input, f2_training_data_output) - -# test_prob = testProblem('ZDT1', 30, 2, 0, 1, 0) -# np.random.seed(6) -# training_data_input = np.random.rand(250, 30) -# training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) -# -# f1_training_data_output = training_data_output[:, 0] -# f2_training_data_output = training_data_output[:, 1] -# -# model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") -# model_evodn_f1.set_params( -# pop_size=500, -# subnet_struct=(6, 8), -# num_nodes=8, -# prob_omit=0.2, -# activation_func="sigmoid", -# opt_func="llsq", -# loss_func="rmse", -# criterion="min_error", -# logging=True, -# plotting=True) -# -# model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") -# model_evodn_f2.set_params( -# pop_size=500, -# subnet_struct=(6, 8), -# num_nodes=8, -# activation_func="sigmoid", -# opt_func="llsq", -# loss_func="rmse", -# criterion="min_error", -# logging=True, -# plotting=True) -# #model_evodn_f1.fit(training_data_input, f1_training_data_output) -# model_evodn_f2.fit(training_data_input, f2_training_data_output) -# y = model_evodn_f2.predict(training_data_input) -# model_evodn_f2.plot(y, f2_training_data_output) -# -# model_evonn_f2 = EvoNNModel(name="EvoNN_" + test_prob.name) -# model_evonn_f2.set_params( -# num_nodes=20, -# pop_size=500, -# activation_func="sigmoid", -# opt_func="llsq", -# loss_func="rmse", -# criterion="akaike_corrected", -# logging=True, -# plotting=True) -# -# model_evonn_f2.fit(training_data_input, training_data_output) -# y = model_evonn_f2.predict(training_data_input) -# model_evonn_f2.plot(y, f2_training_data_output) +# # ZDT 1 & 2 +test_prob = testProblem('ZDT1', 30, 2, 0, 1, 0) +np.random.seed(31) +training_data_input = np.random.rand(250, 30) +training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) + +f1_training_data_output = training_data_output[:, 0] +f2_training_data_output = training_data_output[:, 1] + +model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") +model_evodn_f1.set_params( + pop_size=500, + subnet_struct=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") +model_evodn_f2.set_params( + pop_size=500, + subnet_struct=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) +model_evodn_f1.fit(training_data_input, f1_training_data_output) +y = model_evodn_f1.predict(training_data_input) +model_evodn_f1.plot(y, f1_training_data_output) + +model_evodn_f2.fit(training_data_input, f2_training_data_output) +y = model_evodn_f2.predict(training_data_input) +model_evodn_f2.plot(y, f2_training_data_output) + +model_evonn_f1 = EvoNNModel(name="EvoNN_" + test_prob.name + "_f1") +model_evonn_f1.set_params( + num_nodes=20, + pop_size=500, + prob_omit=0.2, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_evonn_f2 = EvoNNModel(name="EvoNN_" + test_prob.name + "_f2") +model_evonn_f2.set_params( + num_nodes=20, + pop_size=500, + prob_omit=0.2, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_evonn_f1.fit(training_data_input, f1_training_data_output) +y = model_evonn_f1.predict(training_data_input) +model_evonn_f1.plot(y, f1_training_data_output) + +model_evonn_f2.fit(training_data_input, f2_training_data_output) +y = model_evonn_f2.predict(training_data_input) +model_evonn_f2.plot(y, f2_training_data_output) + +test_prob = testProblem('ZDT2', 30, 2, 0, 1, 0) +np.random.seed(31) +training_data_input = np.random.rand(250, 30) +training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) + +f1_training_data_output = training_data_output[:, 0] +f2_training_data_output = training_data_output[:, 1] + +model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") +model_evodn_f1.set_params( + pop_size=500, + subnet_struct=(6, 10), + num_nodes=10, + prob_omit=0.2, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) + +model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") +model_evodn_f2.set_params( + pop_size=500, + subnet_struct=(6, 10), + num_nodes=10, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="min_error", + logging=True, + plotting=True) +model_evodn_f1.fit(training_data_input, f1_training_data_output) +y = model_evodn_f1.predict(training_data_input) +model_evodn_f1.plot(y, f1_training_data_output) + +model_evodn_f2.fit(training_data_input, f2_training_data_output) +y = model_evodn_f2.predict(training_data_input) +model_evodn_f2.plot(y, f2_training_data_output) + + +model_evonn_f1 = EvoNNModel(name="EvoNN_" + test_prob.name + "_f1") +model_evonn_f1.set_params( + num_nodes=20, + pop_size=500, + prob_omit=0.2, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_evonn_f2 = EvoNNModel(name="EvoNN_" + test_prob.name + "_f2") +model_evonn_f2.set_params( + num_nodes=20, + pop_size=500, + prob_omit=0.2, + activation_func="sigmoid", + opt_func="llsq", + loss_func="rmse", + criterion="akaike_corrected", + logging=True, + plotting=True) + +model_evonn_f1.fit(training_data_input, f1_training_data_output) +y = model_evonn_f1.predict(training_data_input) +model_evonn_f1.plot(y, f1_training_data_output) + +model_evonn_f2.fit(training_data_input, f2_training_data_output) +y = model_evonn_f2.predict(training_data_input) +model_evonn_f2.plot(y, f2_training_data_output) From eacbb182cebbfbadddab5b55c77121581dbcc211 Mon Sep 17 00:00:00 2001 From: delamorte Date: Tue, 2 Jul 2019 10:39:20 +0300 Subject: [PATCH 050/155] Individuals in population are now a list of any objects or arrays Removed unnecessary __init__ files Edited recombination functions to handle new individuals format --- evodn2_example.py | 10 ++-- pyrvea/EAs/PPGA.py | 49 +++++++----------- pyrvea/EAs/__init__.py | 0 pyrvea/OtherTools/__init__.py | 0 pyrvea/Population/Population.py | 51 ++++++++++--------- pyrvea/Population/__init__.py | 0 pyrvea/Problem/__init__.py | 0 pyrvea/Problem/evodn2_problem.py | 2 +- pyrvea/Problem/evonn_problem.py | 2 +- .../bounded_polynomial_mutation.py | 6 +-- .../evodn2_xover_mut_gaussian.py | 3 +- pyrvea/Recombination/evonn_mut_gaussian.py | 14 +---- pyrvea/Recombination/ppga_crossover.py | 10 +--- .../Recombination/self_adapting_mutation.py | 5 +- .../simulated_binary_crossover.py | 3 +- 15 files changed, 59 insertions(+), 96 deletions(-) delete mode 100644 pyrvea/EAs/__init__.py delete mode 100644 pyrvea/OtherTools/__init__.py delete mode 100644 pyrvea/Population/__init__.py delete mode 100644 pyrvea/Problem/__init__.py diff --git a/evodn2_example.py b/evodn2_example.py index ae192ff..e802943 100644 --- a/evodn2_example.py +++ b/evodn2_example.py @@ -13,8 +13,8 @@ model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) model_evodn2.set_params( pop_size=500, - subnet_struct=(6, 10), - num_nodes=10, + subnet_struct=(4, 6), + num_nodes=6, prob_omit=0.2, activation_func="sigmoid", opt_func="llsq", @@ -23,9 +23,9 @@ logging=True, plotting=True) -model_evodn2.fit(training_data_input, training_data_output) -y = model_evodn2.predict(training_data_input) -model_evodn2.plot(y, training_data_output) +# model_evodn2.fit(training_data_input, training_data_output) +# y = model_evodn2.predict(training_data_input) +# model_evodn2.plot(y, training_data_output) model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) model_evonn.set_params( diff --git a/pyrvea/EAs/PPGA.py b/pyrvea/EAs/PPGA.py index 342edd6..ca828e0 100644 --- a/pyrvea/EAs/PPGA.py +++ b/pyrvea/EAs/PPGA.py @@ -145,7 +145,7 @@ def _next_gen(self, population: "Population"): # Predator max moves for gen self.params["predator_max_moves"] = int( ( - self.params["population"].individuals.shape[0] + len(self.params["population"].individuals) - self.params["target_pop_size"] ) / self.params["predator_pop_size"] @@ -155,45 +155,31 @@ def _next_gen(self, population: "Population"): self.lattice.move_prey() # Choose and create offspring - if population.problem.__class__.__name__ == "EvoNN": - self.params["std_dev"] = (5 / 3) * ( - 1 - - self.params["current_total_gen_count"] - / self.params["total_generations"] - ) - offspring = np.empty( - ( - 0, - population.problem.num_input_nodes + 1, - population.problem.num_nodes, - ), - float, - ) - elif population.problem.__class__.__name__ == "EvoDN2": - self.params["std_dev"] = (5 / 3) * ( + offspring = [] + + # Calculate standard deviation + self.params["std_dev"] = (4 / 3) * ( 1 - self.params["current_total_gen_count"] / self.params["total_generations"] - ) - offspring = np.empty((0, population.problem.subnet_struct[0])) - for ind in range(population.individuals.shape[0]): + ) + + for ind in range(len(population.individuals)): mate_idx = self.lattice.choose_mate(ind) if not mate_idx: continue else: mating_pop = [ind, mate_idx] - offspring = np.concatenate((offspring, population.mate(mating_pop, self.params))) - - #offspring = np.concatenate((offspring, [offspring1], [offspring2])) + offspring += population.mate(mating_pop, self.params) # Try to place the offspring to lattice, add to population if successful - placed_indices = self.lattice.place_offspring(offspring.shape[0]) + placed_indices = self.lattice.place_offspring(len(offspring)) # Remove from offsprings the ones that didn't get placed mask = np.ones(len(offspring), dtype=bool) mask[placed_indices] = False - offspring = offspring[~mask, ...] + offspring = np.array(offspring)[~mask] # Add the successfully placed offspring to the population population.add(offspring) @@ -211,7 +197,7 @@ def _next_gen(self, population: "Population"): str(self.params["current_total_gen_count"]) + " " + "population size: " - + str(population.individuals.shape[0]) + + str(len(population.individuals)) + " Min Error: " + str(np.amin(population.objectives[:, 0])) + " Avg Error: " @@ -226,7 +212,7 @@ def _next_gen(self, population: "Population"): + str(self.params["current_total_gen_count"]) + " " + "population size: " - + str(population.individuals.shape[0]) + + str(len(population.individuals)) + " Min Error: " + str(np.amin(population.objectives[:, 0])) + " Avg Error: " @@ -338,16 +324,15 @@ def init_predators(self): self.arr[indices[i][0]][indices[i][1]] = int(-1 * (i + 1)) def init_prey(self): - """Set some of the individual weights to zero, find an empty - position in the lattice and place the prey""" + """Find an empty position in the lattice and place the prey""" # Take random indices from free (==zero) lattice spaces free_space = np.transpose(np.nonzero(self.arr == 0)) indices = sample( - free_space.tolist(), self.params["population"].individuals.shape[0] + free_space.tolist(), len(self.params["population"].individuals) ) - for i in range(self.params["population"].individuals.shape[0]): + for i in range(len(self.params["population"].individuals)): # Keep track of preys in a list self.preys_loc.append([indices[i][0], indices[i][1]]) @@ -449,7 +434,7 @@ def move_predator(self): # Calculate predator moves # predator_max_moves = int( # ( - # self.params["population"].individuals.shape[0] + # len(self.params["population"].individuals) # - self.params["target_pop_size"] # ) # / self.params["predator_pop_size"] diff --git a/pyrvea/EAs/__init__.py b/pyrvea/EAs/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/pyrvea/OtherTools/__init__.py b/pyrvea/OtherTools/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/pyrvea/Population/Population.py b/pyrvea/Population/Population.py index 931a881..e78d9d1 100644 --- a/pyrvea/Population/Population.py +++ b/pyrvea/Population/Population.py @@ -77,9 +77,9 @@ def __init__( self.pop_size = pop_size self.recombination_funcs = { "DNN_gaussian_xover+mut": evodn2_xover_mut_gaussian, - "evonn_gaussian" : evonn_mut_gaussian, + "2d_gaussian" : evonn_mut_gaussian, "EvoNN_xover": ppga_crossover, - "EvoNN_mut": self_adapting_mutation, + "self_adapting": self_adapting_mutation, "bounded_polynomial_mutation": bounded_polynomial_mutation, "simulated_binary_crossover": simulated_binary_crossover, } @@ -91,7 +91,7 @@ def __init__( self.filename = problem.name + "_" + str(problem.num_of_objectives) self.plotting = plotting # These attributes contain the solutions. - self.individuals = np.empty((0, self.num_var), float) + self.individuals = [] self.objectives = np.empty((0, self.problem.num_of_objectives), float) self.fitness = np.empty((0, self.problem.num_of_objectives), float) self.constraint_violation = np.empty( @@ -132,7 +132,7 @@ def create_new_individuals( pop_size_options = [50, 105, 120, 126, 132, 112, 156, 90, 275] pop_size = pop_size_options[self.problem.num_of_objectives - 2] - num_var = self.individuals.shape[1] + num_var = self.num_var if design == "RandomDesign": individuals = np.random.random((pop_size, num_var)) @@ -150,12 +150,13 @@ def create_new_individuals( ) elif design == "EvoNN": + self.individuals = [] individuals = self.problem.create_population() - self.individuals = np.empty((0, individuals.shape[1], individuals.shape[2])) - elif design == "EvoDN2": - self.individuals = np.empty((0, self.problem.subnet_struct[0])) + elif design == "EvoDN2": + #self.individuals = np.empty((0, self.problem.subnet_struct[0])) + self.individuals = [] individuals = self.problem.create_population() else: @@ -219,48 +220,49 @@ def keep(self, indices: list): self.fitness = new_fitness self.constraint_violation = new_CV - def delete_or_keep(self, indices: list, delete_or_keep): - """Remove individuals from population which ARE in "indices". - With boolean masks deleted individuals can be obtained with - inverted mask (~mask) + def delete_or_keep(self, indices, delete_or_keep="delete"): + """Remove from population individuals which are in indices, or + keep them and remove all others. Parameters ---------- - indices: list + indices: list or ndarray Indices of individuals to keep + delete_or_keep: str + Whether to delete indices from current population, or keep them and delete others """ - indices.sort() + mask = np.ones(len(self.individuals), dtype=bool) mask[indices] = False # new_pop = np.delete(self.individuals, indices, axis=0) - new_pop = self.individuals[mask, ...] - deleted_pop = self.individuals[~mask, ...] + new_pop = np.array(self.individuals)[mask] + deleted_pop = np.array(self.individuals)[~mask] # new_obj = np.delete(self.objectives, indices, axis=0) - new_obj = self.objectives[mask, ...] - deleted_obj = self.objectives[~mask, ...] + new_obj = self.objectives[mask] + deleted_obj = self.objectives[~mask] # new_fitness = np.delete(self.fitness, indices, axis=0) - new_fitness = self.fitness[mask, ...] - deleted_fitness = self.fitness[~mask, ...] + new_fitness = self.fitness[mask] + deleted_fitness = self.fitness[~mask] if len(self.constraint_violation) > 0: # new_cv = np.delete(self.constraint_violation, indices, axis=0) - new_cv = self.constraint_violation[mask, ...] - deleted_cv = self.constraint_violation[~mask, ...] + new_cv = self.constraint_violation[mask] + deleted_cv = self.constraint_violation[~mask] else: deleted_cv = self.constraint_violation new_cv = self.constraint_violation if delete_or_keep == "delete": - self.individuals = new_pop + self.individuals = new_pop.tolist() self.objectives = new_obj self.fitness = new_fitness self.constraint_violation = new_cv elif delete_or_keep == "keep": - self.individuals = deleted_pop + self.individuals = deleted_pop.tolist() self.objectives = deleted_obj self.fitness = deleted_fitness self.constraint_violation = deleted_cv @@ -272,7 +274,8 @@ def append_individual(self, ind: np.ndarray): ---------- ind: np.ndarray """ - self.individuals = np.concatenate((self.individuals, [ind])) + + self.individuals.append(ind) obj, CV, fitness = self.evaluate_individual(ind) self.objectives = np.vstack((self.objectives, obj)) self.constraint_violation = np.vstack((self.constraint_violation, CV)) diff --git a/pyrvea/Population/__init__.py b/pyrvea/Population/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/pyrvea/Problem/__init__.py b/pyrvea/Problem/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/pyrvea/Problem/evodn2_problem.py b/pyrvea/Problem/evodn2_problem.py index 945c712..b225769 100644 --- a/pyrvea/Problem/evodn2_problem.py +++ b/pyrvea/Problem/evodn2_problem.py @@ -113,7 +113,7 @@ def create_population(self): in_nodes = len(self.subsets[j]) for k in range(num_layers): - out_nodes = random.randint(1, self.num_nodes) + out_nodes = random.randint(2, self.num_nodes) net = np.random.uniform( self.w_low, self.w_high, size=(in_nodes, out_nodes) ) diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index fd6ba67..b5d4e88 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -123,7 +123,7 @@ def train(self, model): pop_size=self.params["pop_size"], plotting=False, crossover_type="EvoNN_xover", - mutation_type="evonn_gaussian", + mutation_type="2d_gaussian", ) pop.evolve( PPGA, diff --git a/pyrvea/Recombination/bounded_polynomial_mutation.py b/pyrvea/Recombination/bounded_polynomial_mutation.py index b7d3a9d..cb2ab12 100644 --- a/pyrvea/Recombination/bounded_polynomial_mutation.py +++ b/pyrvea/Recombination/bounded_polynomial_mutation.py @@ -6,7 +6,7 @@ def mutate(offspring, individuals, params, lower_limits, upper_limits, prob_mut= try: prob_mut = params["prob_mut"] dis_mut = params["dis_mut"] - except: + except ValueError: pass min_val = np.ones_like(offspring) * lower_limits @@ -39,6 +39,4 @@ def mutate(offspring, individuals, params, lower_limits, upper_limits, prob_mut= ) ) offspring[offspring > max_val] = max_val[offspring > max_val] - offspring[offspring < min_val] = min_val[offspring < min_val] - - return offspring + offspring[offspring < min_val] = min_val[offspring < min_val] \ No newline at end of file diff --git a/pyrvea/Recombination/evodn2_xover_mut_gaussian.py b/pyrvea/Recombination/evodn2_xover_mut_gaussian.py index 97e23d2..1a83577 100644 --- a/pyrvea/Recombination/evodn2_xover_mut_gaussian.py +++ b/pyrvea/Recombination/evodn2_xover_mut_gaussian.py @@ -37,7 +37,6 @@ def mate( parent1, parent2 = individuals[mating_pop[0]], individuals[mating_pop[1]] offspring1 = deepcopy(parent1) offspring2 = deepcopy(parent2) - offspring = np.empty((0, len(offspring1))) prob_crossover = params["prob_crossover"] prob_mutation = params["prob_mutation"] @@ -84,5 +83,5 @@ def mate( except IndexError: continue - offspring = np.concatenate((offspring, [offspring1], [offspring2])) + offspring = [offspring1, offspring2] return offspring diff --git a/pyrvea/Recombination/evonn_mut_gaussian.py b/pyrvea/Recombination/evonn_mut_gaussian.py index a1c9be1..7eb2bad 100644 --- a/pyrvea/Recombination/evonn_mut_gaussian.py +++ b/pyrvea/Recombination/evonn_mut_gaussian.py @@ -39,14 +39,6 @@ def mutate( parent1, parent2 = mating_pop[0], mating_pop[1] offspring1 = np.copy(parent1) offspring2 = np.copy(parent2) - offspring = np.empty( - ( - 0, - offspring1.shape[0], - offspring1.shape[1], - ), - float, - ) cur_gen = params["current_total_gen_count"] total_gen = params["total_generations"] @@ -104,8 +96,4 @@ def mutate( # mut = np.random.choice(connections, np.random.binomial(connections, prob_mutation), replace=False) # offspring2[1:, :].ravel()[mut] = offspring2[1:, :].ravel()[mut] + params["mut_strength"] * ( # 1 - cur_gen / total_gen - # ) * (select[1].ravel()[mut] - select[0].ravel()[mut]) - - - offspring = np.concatenate((offspring, [offspring1], [offspring2])) - return offspring + # ) * (select[1].ravel()[mut] - select[0].ravel()[mut]) \ No newline at end of file diff --git a/pyrvea/Recombination/ppga_crossover.py b/pyrvea/Recombination/ppga_crossover.py index b31179a..08d41a1 100644 --- a/pyrvea/Recombination/ppga_crossover.py +++ b/pyrvea/Recombination/ppga_crossover.py @@ -17,14 +17,6 @@ def mate(mating_pop, individuals, params): prob_crossover = params["prob_crossover"] offspring1 = np.copy(individuals[mating_pop[0]]) offspring2 = np.copy(individuals[mating_pop[1]]) - offspring = np.empty( - ( - 0, - offspring1.shape[0], - offspring1.shape[1], - ), - float, - ) # Crossover @@ -46,5 +38,5 @@ def mate(mating_pop, individuals, params): offspring1[:, i] = offspring2[:, i] offspring2[:, i] = tmp - offspring = np.concatenate((offspring, [offspring1], [offspring2])) + offspring = [offspring1, offspring2] return offspring diff --git a/pyrvea/Recombination/self_adapting_mutation.py b/pyrvea/Recombination/self_adapting_mutation.py index ea25b88..4e05a03 100644 --- a/pyrvea/Recombination/self_adapting_mutation.py +++ b/pyrvea/Recombination/self_adapting_mutation.py @@ -30,8 +30,7 @@ def mutate( prob_mut = params["prob_mutation"] mut_strength = params["mut_strength"] - alternatives = individuals[:, 1:, :] - + alternatives = np.array(individuals)[:, 1:, :] mut_offspring1 = offspring[0] mut_offspring2 = offspring[1] @@ -95,5 +94,3 @@ def mutate( select[0][it.multi_index[0], it.multi_index[1]] - select[1][it.multi_index[0], it.multi_index[1]] ) - - return mut_offspring1, mut_offspring2 diff --git a/pyrvea/Recombination/simulated_binary_crossover.py b/pyrvea/Recombination/simulated_binary_crossover.py index 46a13e0..85a56a3 100644 --- a/pyrvea/Recombination/simulated_binary_crossover.py +++ b/pyrvea/Recombination/simulated_binary_crossover.py @@ -7,9 +7,10 @@ def mate(mating_pop, pop, params, prob_xover=1, dis_xover=30): try: prob_xover = params["prob_xover"] dis_xover = params["dis_xover"] - except: + except ValueError: pass + pop = np.array(pop) pop_size, num_var = pop.shape shuffled_ids = list(range(pop_size)) shuffle(shuffled_ids) From e9ac8cf9530b3a7ca8bdf6b31e75590c7ba43e43 Mon Sep 17 00:00:00 2001 From: delamorte Date: Tue, 2 Jul 2019 10:52:53 +0300 Subject: [PATCH 051/155] Fixed exception type --- Example.py | 2 +- pyrvea/Recombination/bounded_polynomial_mutation.py | 2 +- pyrvea/Recombination/simulated_binary_crossover.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/Example.py b/Example.py index 7ef1e9f..f770b24 100644 --- a/Example.py +++ b/Example.py @@ -37,7 +37,7 @@ def objectives(self, decision_variables): mutation_type="bounded_polynomial_mutation", ) -pop.evolve(RVEA) +pop.evolve(NSGAIII) pop.non_dominated() refpoint = 2 diff --git a/pyrvea/Recombination/bounded_polynomial_mutation.py b/pyrvea/Recombination/bounded_polynomial_mutation.py index cb2ab12..cc1a7b6 100644 --- a/pyrvea/Recombination/bounded_polynomial_mutation.py +++ b/pyrvea/Recombination/bounded_polynomial_mutation.py @@ -6,7 +6,7 @@ def mutate(offspring, individuals, params, lower_limits, upper_limits, prob_mut= try: prob_mut = params["prob_mut"] dis_mut = params["dis_mut"] - except ValueError: + except KeyError: pass min_val = np.ones_like(offspring) * lower_limits diff --git a/pyrvea/Recombination/simulated_binary_crossover.py b/pyrvea/Recombination/simulated_binary_crossover.py index 85a56a3..1f9f200 100644 --- a/pyrvea/Recombination/simulated_binary_crossover.py +++ b/pyrvea/Recombination/simulated_binary_crossover.py @@ -7,7 +7,7 @@ def mate(mating_pop, pop, params, prob_xover=1, dis_xover=30): try: prob_xover = params["prob_xover"] dis_xover = params["dis_xover"] - except ValueError: + except KeyError: pass pop = np.array(pop) From 5ad80f21118bbb58f6e00680da74cbdb94f2794e Mon Sep 17 00:00:00 2001 From: delamorte Date: Tue, 2 Jul 2019 12:31:31 +0300 Subject: [PATCH 052/155] Individuals now created in separate file removed old unused population class (has been merged) --- Example.py | 2 +- evodn2_example.py | 756 +++++++++++---------- pyrvea/Population/Population.py | 74 +- pyrvea/Population/create_individuals.py | 115 ++++ pyrvea/Population/population_evonn.py | 484 ------------- pyrvea/Problem/evodn2_problem.py | 32 - pyrvea/Problem/evonn_problem.py | 30 - pyrvea/Recombination/evonn_mut_gaussian.py | 4 +- 8 files changed, 502 insertions(+), 995 deletions(-) create mode 100644 pyrvea/Population/create_individuals.py delete mode 100644 pyrvea/Population/population_evonn.py diff --git a/Example.py b/Example.py index f770b24..7ef1e9f 100644 --- a/Example.py +++ b/Example.py @@ -37,7 +37,7 @@ def objectives(self, decision_variables): mutation_type="bounded_polynomial_mutation", ) -pop.evolve(NSGAIII) +pop.evolve(RVEA) pop.non_dominated() refpoint = 2 diff --git a/evodn2_example.py b/evodn2_example.py index e802943..c145e6e 100644 --- a/evodn2_example.py +++ b/evodn2_example.py @@ -2,8 +2,9 @@ from pyrvea.Problem.evonn_problem import EvoNNModel from pyrvea.Problem.evodn2_problem import EvoDN2Model from pyrvea.Problem.testProblem import testProblem +from pyrvea.Problem.dataproblem import DataProblem import numpy as np - +import pandas as pd test_prob = EvoNNTestProblem("Sphere", num_of_variables=3) training_data_input, training_data_output = test_prob.create_training_data( @@ -42,277 +43,277 @@ model_evonn.fit(training_data_input, training_data_output) y = model_evonn.predict(training_data_input) model_evonn.plot(y, training_data_output) - - -test_prob = EvoNNTestProblem("Matyas") -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random", seed=31 -) - -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnet_struct=(6, 10), - num_nodes=10, - prob_omit=0.2, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evodn2.fit(training_data_input, training_data_output) -y = model_evodn2.predict(training_data_input) -model_evodn2.plot(y, training_data_output) - -model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) -model_evonn.set_params( - num_nodes=20, - pop_size=500, - prob_omit=0.2, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - -model_evonn.fit(training_data_input, training_data_output) -y = model_evonn.predict(training_data_input) -model_evonn.plot(y, training_data_output) - -test_prob = EvoNNTestProblem("Himmelblau") -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random", seed=31 -) - -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnet_struct=(6, 10), - num_nodes=10, - prob_omit=0.2, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evodn2.fit(training_data_input, training_data_output) -y = model_evodn2.predict(training_data_input) -model_evodn2.plot(y, training_data_output) - -model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) -model_evonn.set_params( - num_nodes=20, - pop_size=500, - prob_omit=0.2, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - -model_evonn.fit(training_data_input, training_data_output) -y = model_evonn.predict(training_data_input) -model_evonn.plot(y, training_data_output) - - -test_prob = EvoNNTestProblem("Rastigrin", num_of_variables=2) -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random", seed=31 -) - -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnet_struct=(6, 10), - num_nodes=10, - prob_omit=0.2, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evodn2.fit(training_data_input, training_data_output) -y = model_evodn2.predict(training_data_input) -model_evodn2.plot(y, training_data_output) - -model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) -model_evonn.set_params( - num_nodes=20, - pop_size=500, - prob_omit=0.2, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - -model_evonn.fit(training_data_input, training_data_output) -y = model_evonn.predict(training_data_input) -model_evonn.plot(y, training_data_output) - - -test_prob = EvoNNTestProblem("Three-hump camel") -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random", seed=31 -) - -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnet_struct=(6, 10), - num_nodes=10, - prob_omit=0.2, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evodn2.fit(training_data_input, training_data_output) -y = model_evodn2.predict(training_data_input) -model_evodn2.plot(y, training_data_output) - -model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) -model_evonn.set_params( - num_nodes=20, - pop_size=500, - prob_omit=0.2, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - -model_evonn.fit(training_data_input, training_data_output) -y = model_evonn.predict(training_data_input) -model_evonn.plot(y, training_data_output) - - -test_prob = EvoNNTestProblem("Goldstein-Price") -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random", seed=31 -) -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnet_struct=(6, 10), - num_nodes=10, - prob_omit=0.2, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evodn2.fit(training_data_input, training_data_output) -y = model_evodn2.predict(training_data_input) -model_evodn2.plot(y, training_data_output) - -model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) -model_evonn.set_params( - num_nodes=20, - pop_size=500, - prob_omit=0.2, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - -model_evonn.fit(training_data_input, training_data_output) -y = model_evonn.predict(training_data_input) -model_evonn.plot(y, training_data_output) - - -test_prob = EvoNNTestProblem("LeviN13") -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random", seed=31 -) -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnet_struct=(6, 10), - num_nodes=10, - prob_omit=0.2, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evodn2.fit(training_data_input, training_data_output) -y = model_evodn2.predict(training_data_input) -model_evodn2.plot(y, training_data_output) - -model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) -model_evonn.set_params( - num_nodes=20, - pop_size=500, - prob_omit=0.2, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - -model_evonn.fit(training_data_input, training_data_output) -y = model_evonn.predict(training_data_input) -model_evonn.plot(y, training_data_output) - - -test_prob = EvoNNTestProblem("SchafferN2") -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random", seed=31 -) -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnet_struct=(6, 10), - num_nodes=10, - prob_omit=0.2, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evodn2.fit(training_data_input, training_data_output) -y = model_evodn2.predict(training_data_input) -model_evodn2.plot(y, training_data_output) - -model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) -model_evonn.set_params( - num_nodes=20, - pop_size=500, - prob_omit=0.2, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - -model_evonn.fit(training_data_input, training_data_output) -y = model_evonn.predict(training_data_input) -model_evonn.plot(y, training_data_output) - -# # ZDT 1 & 2 +# +# +# test_prob = EvoNNTestProblem("Matyas") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random", seed=31 +# ) +# +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnet_struct=(6, 10), +# num_nodes=10, +# prob_omit=0.2, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evodn2.fit(training_data_input, training_data_output) +# y = model_evodn2.predict(training_data_input) +# model_evodn2.plot(y, training_data_output) +# +# model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +# model_evonn.set_params( +# num_nodes=20, +# pop_size=500, +# prob_omit=0.2, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# model_evonn.fit(training_data_input, training_data_output) +# y = model_evonn.predict(training_data_input) +# model_evonn.plot(y, training_data_output) +# +# test_prob = EvoNNTestProblem("Himmelblau") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random", seed=31 +# ) +# +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnet_struct=(6, 10), +# num_nodes=10, +# prob_omit=0.2, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evodn2.fit(training_data_input, training_data_output) +# y = model_evodn2.predict(training_data_input) +# model_evodn2.plot(y, training_data_output) +# +# model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +# model_evonn.set_params( +# num_nodes=20, +# pop_size=500, +# prob_omit=0.2, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# model_evonn.fit(training_data_input, training_data_output) +# y = model_evonn.predict(training_data_input) +# model_evonn.plot(y, training_data_output) +# +# +# test_prob = EvoNNTestProblem("Rastigrin", num_of_variables=2) +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random", seed=31 +# ) +# +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnet_struct=(6, 10), +# num_nodes=10, +# prob_omit=0.2, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evodn2.fit(training_data_input, training_data_output) +# y = model_evodn2.predict(training_data_input) +# model_evodn2.plot(y, training_data_output) +# +# model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +# model_evonn.set_params( +# num_nodes=20, +# pop_size=500, +# prob_omit=0.2, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# model_evonn.fit(training_data_input, training_data_output) +# y = model_evonn.predict(training_data_input) +# model_evonn.plot(y, training_data_output) +# +# +# test_prob = EvoNNTestProblem("Three-hump camel") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random", seed=31 +# ) +# +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnet_struct=(6, 10), +# num_nodes=10, +# prob_omit=0.2, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evodn2.fit(training_data_input, training_data_output) +# y = model_evodn2.predict(training_data_input) +# model_evodn2.plot(y, training_data_output) +# +# model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +# model_evonn.set_params( +# num_nodes=20, +# pop_size=500, +# prob_omit=0.2, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# model_evonn.fit(training_data_input, training_data_output) +# y = model_evonn.predict(training_data_input) +# model_evonn.plot(y, training_data_output) +# +# +# test_prob = EvoNNTestProblem("Goldstein-Price") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random", seed=31 +# ) +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnet_struct=(6, 10), +# num_nodes=10, +# prob_omit=0.2, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evodn2.fit(training_data_input, training_data_output) +# y = model_evodn2.predict(training_data_input) +# model_evodn2.plot(y, training_data_output) +# +# model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +# model_evonn.set_params( +# num_nodes=20, +# pop_size=500, +# prob_omit=0.2, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# model_evonn.fit(training_data_input, training_data_output) +# y = model_evonn.predict(training_data_input) +# model_evonn.plot(y, training_data_output) +# +# +# test_prob = EvoNNTestProblem("LeviN13") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random", seed=31 +# ) +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnet_struct=(6, 10), +# num_nodes=10, +# prob_omit=0.2, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evodn2.fit(training_data_input, training_data_output) +# y = model_evodn2.predict(training_data_input) +# model_evodn2.plot(y, training_data_output) +# +# model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +# model_evonn.set_params( +# num_nodes=20, +# pop_size=500, +# prob_omit=0.2, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# model_evonn.fit(training_data_input, training_data_output) +# y = model_evonn.predict(training_data_input) +# model_evonn.plot(y, training_data_output) +# +# +# test_prob = EvoNNTestProblem("SchafferN2") +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random", seed=31 +# ) +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnet_struct=(6, 10), +# num_nodes=10, +# prob_omit=0.2, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evodn2.fit(training_data_input, training_data_output) +# y = model_evodn2.predict(training_data_input) +# model_evodn2.plot(y, training_data_output) +# +# model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +# model_evonn.set_params( +# num_nodes=20, +# pop_size=500, +# prob_omit=0.2, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# model_evonn.fit(training_data_input, training_data_output) +# y = model_evonn.predict(training_data_input) +# model_evonn.plot(y, training_data_output) +# +# # # ZDT 1 & 2 test_prob = testProblem('ZDT1', 30, 2, 0, 1, 0) np.random.seed(31) training_data_input = np.random.rand(250, 30) @@ -321,83 +322,14 @@ f1_training_data_output = training_data_output[:, 0] f2_training_data_output = training_data_output[:, 1] -model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") -model_evodn_f1.set_params( - pop_size=500, - subnet_struct=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) - -model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") -model_evodn_f2.set_params( - pop_size=500, - subnet_struct=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) -model_evodn_f1.fit(training_data_input, f1_training_data_output) -y = model_evodn_f1.predict(training_data_input) -model_evodn_f1.plot(y, f1_training_data_output) - -model_evodn_f2.fit(training_data_input, f2_training_data_output) -y = model_evodn_f2.predict(training_data_input) -model_evodn_f2.plot(y, f2_training_data_output) - -model_evonn_f1 = EvoNNModel(name="EvoNN_" + test_prob.name + "_f1") -model_evonn_f1.set_params( - num_nodes=20, - pop_size=500, - prob_omit=0.2, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - -model_evonn_f2 = EvoNNModel(name="EvoNN_" + test_prob.name + "_f2") -model_evonn_f2.set_params( - num_nodes=20, - pop_size=500, - prob_omit=0.2, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - -model_evonn_f1.fit(training_data_input, f1_training_data_output) -y = model_evonn_f1.predict(training_data_input) -model_evonn_f1.plot(y, f1_training_data_output) - -model_evonn_f2.fit(training_data_input, f2_training_data_output) -y = model_evonn_f2.predict(training_data_input) -model_evonn_f2.plot(y, f2_training_data_output) - -test_prob = testProblem('ZDT2', 30, 2, 0, 1, 0) -np.random.seed(31) -training_data_input = np.random.rand(250, 30) -training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) - -f1_training_data_output = training_data_output[:, 0] -f2_training_data_output = training_data_output[:, 1] +# dataset = pd.DataFrame.from_records(training_data_input) +# problem = DataProblem(data=dataset) model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") model_evodn_f1.set_params( pop_size=500, subnet_struct=(6, 10), num_nodes=10, - prob_omit=0.2, activation_func="sigmoid", opt_func="llsq", loss_func="rmse", @@ -423,36 +355,108 @@ model_evodn_f2.fit(training_data_input, f2_training_data_output) y = model_evodn_f2.predict(training_data_input) model_evodn_f2.plot(y, f2_training_data_output) - - -model_evonn_f1 = EvoNNModel(name="EvoNN_" + test_prob.name + "_f1") -model_evonn_f1.set_params( - num_nodes=20, - pop_size=500, - prob_omit=0.2, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - -model_evonn_f2 = EvoNNModel(name="EvoNN_" + test_prob.name + "_f2") -model_evonn_f2.set_params( - num_nodes=20, - pop_size=500, - prob_omit=0.2, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - -model_evonn_f1.fit(training_data_input, f1_training_data_output) -y = model_evonn_f1.predict(training_data_input) -model_evonn_f1.plot(y, f1_training_data_output) - -model_evonn_f2.fit(training_data_input, f2_training_data_output) -y = model_evonn_f2.predict(training_data_input) -model_evonn_f2.plot(y, f2_training_data_output) +# +# model_evonn_f1 = EvoNNModel(name="EvoNN_" + test_prob.name + "_f1") +# model_evonn_f1.set_params( +# num_nodes=20, +# pop_size=500, +# prob_omit=0.2, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# model_evonn_f2 = EvoNNModel(name="EvoNN_" + test_prob.name + "_f2") +# model_evonn_f2.set_params( +# num_nodes=20, +# pop_size=500, +# prob_omit=0.2, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# model_evonn_f1.fit(training_data_input, f1_training_data_output) +# y = model_evonn_f1.predict(training_data_input) +# model_evonn_f1.plot(y, f1_training_data_output) +# +# model_evonn_f2.fit(training_data_input, f2_training_data_output) +# y = model_evonn_f2.predict(training_data_input) +# model_evonn_f2.plot(y, f2_training_data_output) +# +# test_prob = testProblem('ZDT2', 30, 2, 0, 1, 0) +# np.random.seed(31) +# training_data_input = np.random.rand(250, 30) +# training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) +# +# f1_training_data_output = training_data_output[:, 0] +# f2_training_data_output = training_data_output[:, 1] +# +# model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") +# model_evodn_f1.set_params( +# pop_size=500, +# subnet_struct=(6, 10), +# num_nodes=10, +# prob_omit=0.2, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") +# model_evodn_f2.set_params( +# pop_size=500, +# subnet_struct=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# model_evodn_f1.fit(training_data_input, f1_training_data_output) +# y = model_evodn_f1.predict(training_data_input) +# model_evodn_f1.plot(y, f1_training_data_output) +# +# model_evodn_f2.fit(training_data_input, f2_training_data_output) +# y = model_evodn_f2.predict(training_data_input) +# model_evodn_f2.plot(y, f2_training_data_output) +# +# +# model_evonn_f1 = EvoNNModel(name="EvoNN_" + test_prob.name + "_f1") +# model_evonn_f1.set_params( +# num_nodes=20, +# pop_size=500, +# prob_omit=0.2, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# model_evonn_f2 = EvoNNModel(name="EvoNN_" + test_prob.name + "_f2") +# model_evonn_f2.set_params( +# num_nodes=20, +# pop_size=500, +# prob_omit=0.2, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# model_evonn_f1.fit(training_data_input, f1_training_data_output) +# y = model_evonn_f1.predict(training_data_input) +# model_evonn_f1.plot(y, f1_training_data_output) +# +# model_evonn_f2.fit(training_data_input, f2_training_data_output) +# y = model_evonn_f2.predict(training_data_input) +# model_evonn_f2.plot(y, f2_training_data_output) diff --git a/pyrvea/Population/Population.py b/pyrvea/Population/Population.py index e78d9d1..3b6de2f 100644 --- a/pyrvea/Population/Population.py +++ b/pyrvea/Population/Population.py @@ -11,8 +11,7 @@ from tqdm import tqdm, tqdm_notebook -# from pyrvea.Recombination.bounded_polynomial_mutation import mutation as rvea_mutation -# from pyrvea.Recombination.simulated_binary_crossover import crossover as rvea_crossover +from pyrvea.Population.create_individuals import create_new_individuals from pyrvea.OtherTools.plotlyanimate import animate_init_, animate_next_ from pyrvea.OtherTools.IsNotebook import IsNotebook @@ -25,13 +24,6 @@ simulated_binary_crossover, ) -# from pyrvea.Recombination.ppga_crossover import ppga_crossover -from pyrvea.Recombination.evodn2_xover_mut import evodn2_xover_mut - -# from pyrvea.Recombination.evodn2_xover_mut_gaussian import evodn2_xover_mut_gaussian -# from pyrvea.Recombination.ppga_mutation import ppga_mutation -from math import ceil - if TYPE_CHECKING: from pyrvea.Problem.baseProblem import baseProblem from pyrvea.EAs.baseEA import BaseEA @@ -77,7 +69,7 @@ def __init__( self.pop_size = pop_size self.recombination_funcs = { "DNN_gaussian_xover+mut": evodn2_xover_mut_gaussian, - "2d_gaussian" : evonn_mut_gaussian, + "2d_gaussian": evonn_mut_gaussian, "EvoNN_xover": ppga_crossover, "self_adapting": self_adapting_mutation, "bounded_polynomial_mutation": bounded_polynomial_mutation, @@ -103,66 +95,8 @@ def __init__( self.ideal_fitness = np.full((1, self.problem.num_of_objectives), np.inf) self.worst_fitness = -1 * self.ideal_fitness if not assign_type == "empty": - self.create_new_individuals(assign_type, pop_size=self.pop_size) - - def create_new_individuals( - self, design: str = "LHSDesign", pop_size: int = None, decision_variables=None - ): - """Create, evaluate and add new individuals to the population. Initiate Plots. - - The individuals can be created randomly, by LHS design, or can be passed by the - user. - - Parameters - ---------- - design : str, optional - Describe the method of creation of new individuals. - "RandomDesign" creates individuals randomly. - "LHSDesign" creates individuals using Latin hypercube sampling. - "EvoNN" creates Artificial Neural Networks as individuals. - pop_size : int, optional - Number of individuals in the population. If none, some default population - size based on number of objectives is chosen. - decision_variables : numpy array or list, optional - Pass decision variables to be added to the population. - """ - if decision_variables is not None: - pass - if pop_size is None: - pop_size_options = [50, 105, 120, 126, 132, 112, 156, 90, 275] - pop_size = pop_size_options[self.problem.num_of_objectives - 2] - - num_var = self.num_var - - if design == "RandomDesign": - individuals = np.random.random((pop_size, num_var)) - # Scaling - individuals = ( - individuals * (self.upper_limits - self.lower_limits) - + self.lower_limits - ) - elif design == "LHSDesign": - individuals = lhs(num_var, samples=pop_size) - # Scaling - individuals = ( - individuals * (self.upper_limits - self.lower_limits) - + self.lower_limits - ) - elif design == "EvoNN": - - self.individuals = [] - individuals = self.problem.create_population() - - - elif design == "EvoDN2": - #self.individuals = np.empty((0, self.problem.subnet_struct[0])) - self.individuals = [] - individuals = self.problem.create_population() - - else: - print("Design not yet supported.") - - self.add(individuals) + individuals = create_new_individuals(assign_type, problem, pop_size=self.pop_size) + self.add(individuals) if self.plotting: self.figure = [] diff --git a/pyrvea/Population/create_individuals.py b/pyrvea/Population/create_individuals.py new file mode 100644 index 0000000..43b2e19 --- /dev/null +++ b/pyrvea/Population/create_individuals.py @@ -0,0 +1,115 @@ +import numpy as np +import random +from math import ceil +from pyDOE import lhs + + +def create_new_individuals(design, problem, pop_size=None): + """Create, evaluate and add new individuals to the population. Initiate Plots. + + The individuals can be created randomly, by LHS design, or can be passed by the + user. + + Parameters + ---------- + design : str, optional + Describe the method of creation of new individuals. + "RandomDesign" creates individuals randomly. + "LHSDesign" creates individuals using Latin hypercube sampling. + "EvoNN" creates Artificial Neural Networks as individuals. + "EvoDN2" creates Deep Neural Networks. + problem : baseProblem + An object of the class Problem + pop_size : int, optional + Number of individuals in the population. If none, some default population + size based on number of objectives is chosen. + num_var : int + Number of variables + """ + + if pop_size is None: + pop_size_options = [50, 105, 120, 126, 132, 112, 156, 90, 275] + pop_size = pop_size_options[problem.num_of_objectives - 2] + + if design == "RandomDesign": + lower_limits = np.asarray(problem.lower_limits) + upper_limits = np.asarray(problem.upper_limits) + individuals = np.random.random((pop_size, problem.num_of_variables)) + # Scaling + individuals = ( + individuals * (upper_limits - lower_limits) + + lower_limits + ) + + return individuals + + elif design == "LHSDesign": + lower_limits = np.asarray(problem.lower_limits) + upper_limits = np.asarray(problem.upper_limits) + individuals = lhs(problem.num_of_variables, samples=pop_size) + # Scaling + individuals = ( + individuals * (upper_limits - lower_limits) + + lower_limits + ) + + return individuals + + elif design == "EvoNN": + + """Create a population of neural networks for the EvoNN problem. + + Individuals are 2d arrays representing the weight matrices of the NNs. + One extra row is added for bias. + + """ + individuals = np.random.uniform( + problem.w_low, + problem.w_high, + size=(problem.params["pop_size"], problem.num_input_nodes, problem.num_nodes), + ) + + # Randomly set some weights to zero + zeros = np.random.choice( + np.arange(individuals.size), + ceil(individuals.size * problem.params["prob_omit"]), + ) + individuals.ravel()[zeros] = 0 + + # Set bias + individuals = np.insert(individuals, 0, 1, axis=1) + + return individuals + + elif design == "EvoDN2": + + individuals = [] + for i in range(problem.params["pop_size"]): + nets = [] + for j in range(problem.subnet_struct[0]): + + layers = [] + num_layers = np.random.randint(1, problem.subnet_struct[1]) + in_nodes = len(problem.subsets[j]) + + for k in range(num_layers): + out_nodes = random.randint(2, problem.num_nodes) + net = np.random.uniform( + problem.w_low, problem.w_high, size=(in_nodes, out_nodes) + ) + # Randomly set some weights to zero + zeros = np.random.choice( + np.arange(net.size), ceil(net.size * problem.params["prob_omit"]) + ) + net.ravel()[zeros] = 0 + + # Add bias + net = np.insert(net, 0, 1, axis=0) + in_nodes = out_nodes + layers.append(net) + + nets.append(layers) + + individuals.append(nets) + + return individuals diff --git a/pyrvea/Population/population_evonn.py b/pyrvea/Population/population_evonn.py deleted file mode 100644 index 5b07fe7..0000000 --- a/pyrvea/Population/population_evonn.py +++ /dev/null @@ -1,484 +0,0 @@ -from collections import defaultdict -from collections.abc import Sequence -from random import shuffle -from typing import TYPE_CHECKING - -import numpy as np -import pandas as pd -from pyDOE import lhs -from pygmo import fast_non_dominated_sorting as nds -from pygmo import hypervolume as hv -from pygmo import non_dominated_front_2d as nd2 -from tqdm import tqdm, tqdm_notebook -from pyrvea.OtherTools.plotlyanimate import animate_init_, animate_next_ -from pyrvea.OtherTools.IsNotebook import IsNotebook -from pyrvea.Population.Population import Population -from scipy.stats import bernoulli as bn - -from pyrvea.Recombination.ppga_crossover import ppga_crossover -from pyrvea.Recombination.self_adapting_mutation import ppga_mutation - -if TYPE_CHECKING: - from pyrvea.Problem.baseProblem import baseProblem - from pyrvea.EAs.baseEA import BaseEA - - -class Population: - """Define the population.""" - - def __init__( - self, - problem: "EvoNNProblem", - assign_type: str = "RandomDesign", - plotting: bool = False, - *args - ): - """Initialize the population. - - Attributes - ---------- - problem : baseProblem - An object of the class Problem - assign_type : str, optional - Define the method of creation of population. - If 'assign_type' is 'RandomDesign' the population is generated - randomly. If 'assign_type' is 'LHSDesign', the population is - generated via Latin Hypercube Sampling. If 'assign_type' is - 'custom', the population is imported from file. If assign_type - is 'empty', create blank population. (the default is "RandomAssign") - plotting : bool, optional - (the default is True, which creates the plots) - - """ - """Initialize the population. - - Parameters - ---------- - problem : baseProblem - An object of the class Problem - assign_type : str, optional - Define the method of creation of population. - If 'assign_type' is 'RandomDesign' the population is generated - randomly. If 'assign_type' is 'LHSDesign', the population is - generated via Latin Hypercube Sampling. If 'assign_type' is - 'custom', the population is imported from file. If assign_type - is 'empty', create blank population. (the default is "RandomAssign") - plotting : bool, optional - (the default is True, which creates the plots) - - """ - num_var = problem.num_of_variables - self.lower_limits = np.asarray(problem.lower_limits) - self.upper_limits = np.asarray(problem.upper_limits) - self.hyp = 0 - self.non_dom = 0 - self.problem = problem - self.filename = problem.name + "_" + str(problem.num_of_objectives) - self.plotting = plotting - # These attributes contain the solutions. - self.individuals = np.empty( - (0, self.problem.num_input_nodes + 1, self.problem.num_hidden_nodes), float - ) - self.objectives = np.empty((0, self.problem.num_of_objectives), float) - self.fitness = np.empty((0, self.problem.num_of_objectives), float) - self.constraint_violation = np.empty( - (0, self.problem.num_of_constraints), float - ) - self.archive = pd.DataFrame( - columns=["generation", "decision_variables", "objective_values"] - ) - self.ideal_fitness = np.full((1, self.problem.num_of_objectives), np.inf) - self.worst_fitness = -1 * self.ideal_fitness - - if not assign_type == "empty": - self.create_new_individuals(assign_type) - if self.plotting: - self.figure = [] - self.plot_init_() - - def create_new_individuals( - self, design: str = "RandomDesign", pop_size: int = 500, decision_variables=None - ): - """Create, evaluate and add new individuals to the population. Initiate Plots. - - The individuals can be created randomly, by LHS design, or can be passed by the - user. - - Parameters - ---------- - design : str, optional - Describe the method of creation of new individuals. - "RandomDesign" creates individuals randomly. - pop_size : int, optional - Number of individuals in the population. If none, some default population - size based on number of objectives is chosen. - decision_variables : numpy array or list, optional - Pass decision variables to be added to the population. - """ - - if decision_variables is not None: - pass - - # Create new individuals - if design == "RandomDesign": - - # +1 row for bias - individuals = np.random.uniform( - self.problem.w_low, - self.problem.w_high, - size=( - pop_size, - self.problem.num_input_nodes + 1, - self.problem.num_hidden_nodes, - ), - ) - - # Eliminate some weights - flag = bn.rvs(p=1 - self.problem.prob_omit, size=np.shape(individuals)) - - random_numbers = np.zeros(np.shape(individuals)) - individuals[flag == 0] = random_numbers[flag == 0] - - # Set bias - - individuals[:, 0, :] = 1 - - else: - print("Design not yet supported.") - - self.add(individuals) - - - - def eval_fitness(self): - """ - Calculate fitness based on objective values. Fitness = obj if minimized. - """ - fitness = self.objectives * self.problem.objs - return fitness - - def add(self, new_pop: np.ndarray): - """Evaluate and add individuals to the population. Update ideal and nadir point. - - Parameters - ---------- - new_pop: np.ndarray - Decision variable values for new population. - """ - - if new_pop.shape[0] >= 1: - for i in range(0, new_pop.shape[0]): - self.append_individual(new_pop[i, :, :]) - else: - print("Error while adding new individuals. Check dimensions.") - # print(self.ideal_fitness) - self.update_ideal_and_nadir() - - def keep(self, indices: list): - """Remove individuals from population which are NOT in "indices". - - Parameters - ---------- - indices: list - Indices of individuals to keep - """ - - new_pop = self.individuals[indices, :, :] - new_obj = self.objectives[indices, :] - new_fitness = self.fitness[indices, :] - new_cv = self.constraint_violation[indices, :] - - self.individuals = new_pop - self.objectives = new_obj - self.fitness = new_fitness - self.constraint_violation = new_cv - - def delete(self, indices: list): - """Remove individuals from population which ARE in "indices". - With boolean masks deleted individuals can be obtained with - inverted mask (~mask) - - Parameters - ---------- - indices: list - Indices of individuals to keep - """ - - mask = np.ones(len(self.individuals), dtype=bool) - mask[indices] = False - - # new_pop = np.delete(self.individuals, indices, axis=0) - new_pop = self.individuals[mask, ...] - # deleted_pop = self.individuals[~mask, ...] - - # new_obj = np.delete(self.objectives, indices, axis=0) - new_obj = self.objectives[mask, ...] - # deleted_obj = self.objectives[~mask, ...] - - # new_fitness = np.delete(self.fitness, indices, axis=0) - new_fitness = self.fitness[mask, ...] - # deleted_fitness = self.fitness[~mask, ...] - - # new_cv = np.delete(self.constraint_violation, indices, axis=0) - new_cv = self.constraint_violation[mask, ...] - # deleted_cv = self.constraint_violation[~mask, ...] - - self.individuals = new_pop - self.objectives = new_obj - self.fitness = new_fitness - self.constraint_violation = new_cv - - def delete_or_keep(self, indices, delete_or_keep): - """Remove individuals from population which ARE in "indices". - With boolean masks deleted individuals can be obtained with - inverted mask (~mask) - - Parameters - ---------- - indices: list - Indices of individuals to keep - delete_or_keep : string - whether to return the deleted individuals or the ones kept - """ - - mask = np.ones(len(self.individuals), dtype=bool) - mask[indices] = False - - # new_pop = np.delete(self.individuals, indices, axis=0) - new_pop = self.individuals[mask, ...] - deleted_pop = self.individuals[~mask, ...] - - # new_obj = np.delete(self.objectives, indices, axis=0) - new_obj = self.objectives[mask, ...] - deleted_obj = self.objectives[~mask, ...] - - # new_fitness = np.delete(self.fitness, indices, axis=0) - new_fitness = self.fitness[mask, ...] - deleted_fitness = self.fitness[~mask, ...] - - if len(self.constraint_violation) > 0: - # new_cv = np.delete(self.constraint_violation, indices, axis=0) - new_cv = self.constraint_violation[mask, ...] - deleted_cv = self.constraint_violation[~mask, ...] - else: - deleted_cv = self.constraint_violation - new_cv = self.constraint_violation - - if delete_or_keep == "delete": - self.individuals = new_pop - self.objectives = new_obj - self.fitness = new_fitness - self.constraint_violation = new_cv - - elif delete_or_keep == "keep": - self.individuals = deleted_pop - self.objectives = deleted_obj - self.fitness = deleted_fitness - self.constraint_violation = deleted_cv - - def create_archive(self, new_pop, new_obj): - length_of_archive = len(self.archive) - new_pop = self.individuals[:, :, :] - # self.individuals[0].flatten().tolist() - new_obj = self.objectives[:, :] - if length_of_archive == 0: - gen_count = 0 - - else: - gen_count = self.archive["generation"].iloc[-1] + 1 - - new_entries = pd.DataFrame( - { - "generation": [gen_count] * len(new_obj), - "decision_variables": new_pop.tolist(), - "objective_values": new_obj.tolist(), - } - ) - self.archive = self.archive.append(new_entries, ignore_index=True) - - def append_individual(self, ind: np.ndarray): - """Evaluate and add individual to the population. - - Parameters - ---------- - ind: np.ndarray - """ - - # this method seems to work for n-dimensional arrays - self.individuals = np.concatenate((self.individuals, [ind])) - - obj, CV, fitness = self.evaluate_individual(ind) - self.objectives = np.vstack((self.objectives, obj)) - self.constraint_violation = np.vstack((self.constraint_violation, CV)) - self.fitness = np.vstack((self.fitness, fitness)) - - def evaluate_individual(self, ind: np.ndarray): - """Evaluate individual. - - Returns objective values, constraint violation, and fitness. - - Parameters - ---------- - ind: np.ndarray - """ - obj = self.problem.objectives(ind) - CV = np.empty((0, self.problem.num_of_constraints), float) - fitness = obj - if self.problem.num_of_constraints: - CV = self.problem.constraints(ind, obj) - fitness = self.eval_fitness(ind, obj, self.problem) - return (obj, CV, fitness) - - def evolve(self, EA: "BaseEA" = None, EA_parameters: dict = None) -> "Population": - """Evolve the population with interruptions. - - Evolves the population based on the EA sent by the user. - - Parameters - ---------- - EA: "BaseEA" - Should be a derivative of BaseEA (Default value = None) - EA_parameters: dict - Contains the parameters needed by EA (Default value = None) - - """ - ################################## - # To determine whether running in console or in notebook. Used for TQDM. - # TQDM will be removed in future generations as number of iterations can vary - if IsNotebook(): - progressbar = tqdm_notebook - else: - progressbar = tqdm - #################################### - # A basic evolution cycle. Will be updated to optimize() in future versions. - ea = EA(self, EA_parameters) - - # Return the model with the lowest error - - lowest_error = np.argmin(self.objectives[:, 0]) - return self.individuals[lowest_error] - - # Calculate Akaike information criterion for the first front - # and return the model with the lowest value - - # non_dom_front = nds(self.objectives) - # fon_front = non_dom_front[0][0] - # info_c_rank = [] - # - # for i in fon_front: - # - # info_c = self.problem.information_criterion(self.individuals[i]) - # info_c_rank.append((info_c, i)) - # - # info_c_rank.sort() - # - # return self.individuals[info_c_rank[0][1]] - - def mate(self, ind1, ind2, params): - """Conduct crossover and mutation over the population. - - """ - # Get individuals at indices - w1, w2 = self.individuals[ind1], self.individuals[ind2] - - # Perform crossover - xover_w1, xover_w2 = ppga_crossover(w1, w2, params["prob_crossover"]) - - # Find randomly two other individuals with current match active for mutation. - # Make a list of individuals suitable for mutation, exclude the ones to be mutated - # so that they won't mutate with themselves - indices = [ind1, ind2] - mask = np.ones(len(self.individuals), dtype=bool) - mask[indices] = False - alternatives = self.individuals[mask, ...][:, 1:, :] - - # Mutate - offspring1, offspring2 = ppga_mutation( - alternatives, - xover_w1, - xover_w2, - params["current_iteration_gen_count"], - params["generations"], - params["prob_mutation"], - params["mut_strength"] - ) - - return offspring1, offspring2 - - def plot_init_(self): - """Initialize animation objects. Return figure""" - obj = self.objectives - self.figure = animate_init_(obj, self.filename + ".html") - return self.figure - - def plot_objectives(self, iteration: int = None): - """Plot the objective values of individuals in notebook. This is a hack. - - Parameters - ---------- - iteration: int - Iteration count. - """ - obj = self.objectives - self.figure = animate_next_( - obj, self.figure, self.filename + ".html", iteration - ) - - def hypervolume(self, ref_point): - """Calculate hypervolume. Uses package pygmo. Add checks to prevent errors. - - Parameters - ---------- - ref_point - - Returns - ------- - - """ - non_dom = self.non_dom - if not isinstance(ref_point, (Sequence, np.ndarray)): - num_obj = non_dom.shape[1] - ref_point = [ref_point] * num_obj - non_dom = non_dom[np.all(non_dom < ref_point, axis=1), :] - hyp = hv(non_dom) - self.hyp = hyp.compute(ref_point) - return self.hypnp - - def non_dominated(self): - """Fix this. check if nd2 and nds mean the same thing""" - obj = self.objectives - num_obj = obj.shape[1] - if num_obj == 2: - non_dom_front = nd2(obj) - else: - non_dom_front = nds(obj) - if isinstance(non_dom_front, tuple): - self.non_dom = self.objectives[non_dom_front[0][0]] - elif isinstance(non_dom_front, np.ndarray): - self.non_dom = self.objectives[non_dom_front] - else: - print("Non Dom error Line 285 in population.py") - return non_dom_front - - def update_ideal_and_nadir(self, new_objective_vals: list = None): - """Updates self.ideal and self.nadir in the fitness space. - - Uses the entire population if new_objective_vals is none. - - Parameters - ---------- - new_objective_vals : list, optional - Objective values for a newly added individual (the default is None, which - calculated the ideal and nadir for the entire population.) - - """ - if new_objective_vals is None: - check_ideal_with = self.fitness - else: - check_ideal_with = new_objective_vals - self.ideal_fitness = np.amin( - np.vstack((self.ideal_fitness, check_ideal_with)), axis=0 - ) - self.worst_fitness = np.amax( - np.vstack((self.worst_fitness, check_ideal_with)), axis=0 - ) diff --git a/pyrvea/Problem/evodn2_problem.py b/pyrvea/Problem/evodn2_problem.py index b225769..34991c8 100644 --- a/pyrvea/Problem/evodn2_problem.py +++ b/pyrvea/Problem/evodn2_problem.py @@ -101,38 +101,6 @@ def train(self, model): model.non_linear_layer, _ = self.activation(model.subnets) model.linear_layer, _ = self.minimize_error(model.non_linear_layer) - def create_population(self): - - individuals = [] - for i in range(self.params["pop_size"]): - nets = [] - for j in range(self.subnet_struct[0]): - - layers = [] - num_layers = np.random.randint(1, self.subnet_struct[1]) - in_nodes = len(self.subsets[j]) - - for k in range(num_layers): - out_nodes = random.randint(2, self.num_nodes) - net = np.random.uniform( - self.w_low, self.w_high, size=(in_nodes, out_nodes) - ) - # Randomly set some weights to zero - zeros = np.random.choice( - np.arange(net.size), ceil(net.size * self.params["prob_omit"]) - ) - net.ravel()[zeros] = 0 - - # Add bias - net = np.insert(net, 0, 1, axis=0) - in_nodes = out_nodes - layers.append(net) - nets.append(layers) - - individuals.append(nets) - - return individuals - def objectives(self, decision_variables) -> list: """ Use this method to calculate objective functions. diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index b5d4e88..c3c5b15 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -79,36 +79,6 @@ def fit(self, training_data, target_values): self.num_input_nodes = self.num_of_variables self.num_nodes = self.params["num_nodes"] - def create_population(self): - """Create a population of neural networks for the EvoNN problem. - - Individuals are 2d arrays representing the weight matrices of the NNs. - One extra row is added for bias. Individuals are then stacked together - to form the population. - - Returns - ------- - individuals : ndarray - The population for EvoNN - """ - individuals = np.random.uniform( - self.w_low, - self.w_high, - size=(self.params["pop_size"], self.num_input_nodes, self.num_nodes), - ) - - # Randomly set some weights to zero - zeros = np.random.choice( - np.arange(individuals.size), - ceil(individuals.size * self.params["prob_omit"]), - ) - individuals.ravel()[zeros] = 0 - - # Set bias - individuals = np.insert(individuals, 0, 1, axis=1) - - return individuals - def train(self, model): """Trains the networks and selects the best model from the non dominated front. diff --git a/pyrvea/Recombination/evonn_mut_gaussian.py b/pyrvea/Recombination/evonn_mut_gaussian.py index 7eb2bad..2e786a8 100644 --- a/pyrvea/Recombination/evonn_mut_gaussian.py +++ b/pyrvea/Recombination/evonn_mut_gaussian.py @@ -67,7 +67,7 @@ def mutate( # Choose two random individuals and a random number of connections, # mutate offspring based on current gen and connections of two randomly chosen individuals # - # alternatives = individuals[:, 1:, :] + # alternatives = np.array(individuals)[:, 1:, :] # # # Randomly select two individuals with current match active (=non-zero) # select = alternatives[ @@ -96,4 +96,4 @@ def mutate( # mut = np.random.choice(connections, np.random.binomial(connections, prob_mutation), replace=False) # offspring2[1:, :].ravel()[mut] = offspring2[1:, :].ravel()[mut] + params["mut_strength"] * ( # 1 - cur_gen / total_gen - # ) * (select[1].ravel()[mut] - select[0].ravel()[mut]) \ No newline at end of file + # ) * (select[1].ravel()[mut] - select[0].ravel()[mut]) From 91bdb9e79973adf4d85ce84586ef0d93287d5042 Mon Sep 17 00:00:00 2001 From: delamorte Date: Wed, 3 Jul 2019 15:50:01 +0300 Subject: [PATCH 053/155] dataproblem class now handles training of the models optimization works using dataproblem class population now has recombination types as attributes --- evodn2_example.py | 201 ++++++++++++++------- poetry.lock | 44 ++++- pyproject.toml | 1 + pyrvea/EAs/PPGA.py | 83 +++------ pyrvea/Population/Population.py | 110 +++++------ pyrvea/Population/create_individuals.py | 22 ++- pyrvea/Problem/dataproblem.py | 106 +++++++---- pyrvea/Problem/evodn2_problem.py | 59 ++++-- pyrvea/Problem/evonn_problem.py | 27 ++- pyrvea/Problem/evonn_test_functions.py | 4 +- pyrvea/Recombination/evonn_mut_gaussian.py | 14 +- 11 files changed, 411 insertions(+), 260 deletions(-) diff --git a/evodn2_example.py b/evodn2_example.py index c145e6e..142cad5 100644 --- a/evodn2_example.py +++ b/evodn2_example.py @@ -3,46 +3,55 @@ from pyrvea.Problem.evodn2_problem import EvoDN2Model from pyrvea.Problem.testProblem import testProblem from pyrvea.Problem.dataproblem import DataProblem +from pyrvea.Population.Population import Population +from pyrvea.EAs.PPGA import PPGA +from pyrvea.EAs.RVEA import RVEA import numpy as np import pandas as pd +import matplotlib +import plotly +import plotly.graph_objs as go -test_prob = EvoNNTestProblem("Sphere", num_of_variables=3) -training_data_input, training_data_output = test_prob.create_training_data( - samples=250, method="random", seed=31 -) +matplotlib.use("WebAgg") +import matplotlib.pyplot as plt -model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) -model_evodn2.set_params( - pop_size=500, - subnet_struct=(4, 6), - num_nodes=6, - prob_omit=0.2, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) +# test_prob = EvoNNTestProblem("Sphere", num_of_variables=3) +# training_data_input, training_data_output = test_prob.create_training_data( +# samples=250, method="random", seed=31 +# ) +# +# model_evodn2 = EvoDN2Model(name="EvoDN2_" + test_prob.name) +# model_evodn2.set_params( +# pop_size=500, +# subnet_struct=(4, 6), +# num_nodes=6, +# prob_omit=0.2, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) # model_evodn2.fit(training_data_input, training_data_output) # y = model_evodn2.predict(training_data_input) # model_evodn2.plot(y, training_data_output) -model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) -model_evonn.set_params( - num_nodes=20, - pop_size=500, - prob_omit=0.2, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="akaike_corrected", - logging=True, - plotting=True) - -model_evonn.fit(training_data_input, training_data_output) -y = model_evonn.predict(training_data_input) -model_evonn.plot(y, training_data_output) +# model_evonn = EvoNNModel(name="EvoNN_" + test_prob.name) +# model_evonn.set_params( +# num_nodes=20, +# pop_size=500, +# prob_omit=0.2, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="akaike_corrected", +# logging=True, +# plotting=True) +# +# model_evonn.fit(training_data_input, training_data_output) +# y = model_evonn.predict(training_data_input) +# model_evonn.plot(y, training_data_output) # # # test_prob = EvoNNTestProblem("Matyas") @@ -314,47 +323,111 @@ # model_evonn.plot(y, training_data_output) # # # # ZDT 1 & 2 -test_prob = testProblem('ZDT1', 30, 2, 0, 1, 0) +test_prob = testProblem("ZDT1", 30, 2, 0, 1, 0) np.random.seed(31) training_data_input = np.random.rand(250, 30) -training_data_output = np.asarray([test_prob.objectives(x) for x in training_data_input]) - +training_data_output = np.asarray( + [test_prob.objectives(x) for x in training_data_input] +) +data = np.hstack((training_data_input, training_data_output)) f1_training_data_output = training_data_output[:, 0] f2_training_data_output = training_data_output[:, 1] -# dataset = pd.DataFrame.from_records(training_data_input) -# problem = DataProblem(data=dataset) +dataset = pd.DataFrame.from_records(data) +dataset.columns = np.arange(1, 33) +x = np.arange(1, 31) +y = np.arange(31, 33) +problem = DataProblem(data=dataset, x=x, y=y) +problem.train_test_split() +problem.train(model_type="EvoDN2") + +pop = Population( + problem, + pop_size=100, + assign_type="LHSDesign", + crossover_type="simulated_binary_crossover", + mutation_type="bounded_polynomial_mutation", +) + +pop.evolve( + PPGA, + { + "prob_prey_move": 0.5, + "opt": True, + "kill_interval": 4, + "iterations": 10, + "generations_per_iteration": 10, + } +) + +ndf = pop.non_dominated() +pareto = pop.objectives[ndf] +pareto_pop = np.asarray(pop.individuals)[ndf].tolist() + +for x in pareto_pop: + for i, y in enumerate(x): + x[i] = "x"+str(i+1) + ": " + str(y) + "
" -model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") -model_evodn_f1.set_params( - pop_size=500, - subnet_struct=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) +trace0 = go.Scatter(x=pop.objectives[:, 0], y=pop.objectives[:, 1], mode="markers") +trace1 = go.Scatter(x=pareto[:, 0], y=pareto[:, 1], text=pareto_pop, hoverinfo='text', mode="markers+lines") +data = [trace0, trace1] +layout = go.Layout( + xaxis=dict( + title='f1' + ), + yaxis=dict( + title='f2', + ), +) +plotly.offline.plot( + data, + filename=problem.model_type_name + test_prob.name + "pareto" + + ".html", + auto_open=True, +) -model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") -model_evodn_f2.set_params( - pop_size=500, - subnet_struct=(6, 10), - num_nodes=10, - activation_func="sigmoid", - opt_func="llsq", - loss_func="rmse", - criterion="min_error", - logging=True, - plotting=True) -model_evodn_f1.fit(training_data_input, f1_training_data_output) -y = model_evodn_f1.predict(training_data_input) -model_evodn_f1.plot(y, f1_training_data_output) +# f1_all = pop.objectives[:, 0] +# f2_all = pop.objectives[:, 1] +# f1_pareto = pareto[:, 0] +# f2_pareto = pareto[:, 1] +# +# plt.scatter(f1_all, f2_all) +# plt.plot(f1_pareto, f2_pareto, color='r') +# plt.xlabel('f1') +# plt.ylabel('f2') +# plt.show() -model_evodn_f2.fit(training_data_input, f2_training_data_output) -y = model_evodn_f2.predict(training_data_input) -model_evodn_f2.plot(y, f2_training_data_output) + +# model_evodn_f1 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f1") +# model_evodn_f1.set_params( +# pop_size=500, +# subnet_struct=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# +# model_evodn_f2 = EvoDN2Model(name="EvoDN2_" + test_prob.name + "_f2") +# model_evodn_f2.set_params( +# pop_size=500, +# subnet_struct=(6, 10), +# num_nodes=10, +# activation_func="sigmoid", +# opt_func="llsq", +# loss_func="rmse", +# criterion="min_error", +# logging=True, +# plotting=True) +# model_evodn_f1.fit(training_data_input, f1_training_data_output) +# y = model_evodn_f1.predict(training_data_input) +# model_evodn_f1.plot(y, f1_training_data_output) +# +# model_evodn_f2.fit(training_data_input, f2_training_data_output) +# y = model_evodn_f2.predict(training_data_input) +# model_evodn_f2.plot(y, f2_training_data_output) # # model_evonn_f1 = EvoNNModel(name="EvoNN_" + test_prob.name + "_f1") # model_evonn_f1.set_params( diff --git a/poetry.lock b/poetry.lock index 830e25a..71a21e7 100644 --- a/poetry.lock +++ b/poetry.lock @@ -159,6 +159,17 @@ version = "0.9.0" [package.dependencies] future = "*" +[[package]] +category = "main" +description = "Composable style cycles" +name = "cycler" +optional = false +python-versions = "*" +version = "0.10.0" + +[package.dependencies] +six = "*" + [[package]] category = "main" description = "Better living through Python with decorators" @@ -411,6 +422,17 @@ version = "4.4.0" [package.dependencies] traitlets = "*" +[[package]] +category = "main" +description = "A fast implementation of the Cassowary constraint solver" +name = "kiwisolver" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" +version = "1.1.0" + +[package.dependencies] +setuptools = "*" + [[package]] category = "dev" description = "A fast and thorough lazy object proxy." @@ -427,6 +449,21 @@ optional = false python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*" version = "1.1.1" +[[package]] +category = "main" +description = "Python plotting package" +name = "matplotlib" +optional = false +python-versions = ">=3.6" +version = "3.1.1" + +[package.dependencies] +cycler = ">=0.10" +kiwisolver = ">=1.0.1" +numpy = ">=1.11" +pyparsing = ">=2.0.1,<2.0.4 || >2.0.4,<2.1.2 || >2.1.2,<2.1.6 || >2.1.6" +python-dateutil = ">=2.1" + [[package]] category = "dev" description = "McCabe checker, plugin for flake8" @@ -706,7 +743,7 @@ isort = ">=4.2.5,<5" mccabe = ">=0.6,<0.7" [[package]] -category = "dev" +category = "main" description = "Python parsing module" name = "pyparsing" optional = false @@ -1085,7 +1122,7 @@ python-versions = "*" version = "1.11.1" [metadata] -content-hash = "a5559602953991f5e0af8fad0a547a857102db5daf691718fdaa9f3cc162a782" +content-hash = "80917cff72be302f18a58e8cacc0b07fedef5669fcdee4c70d2242a2ccb5d49f" python-versions = "^3.6" [metadata.hashes] @@ -1105,6 +1142,7 @@ click = ["2335065e6395b9e67ca716de5f7526736bfa6ceead690adf616d925bdc622b13", "5b cloudpickle = ["603244e0f552b72a267d47a7d9b347b27a3430f58a0536037a290e7e0e212ecf", "b8ba7e322f2394b9bbbdc1c976e6442c2c02acc784cb9e553cee9186166a6890"] colorama = ["05eed71e2e327246ad6b38c540c4a3117230b19679b875190486ddd2d721422d", "f8ac84de7840f5b9c4e3347b3c1eaa50f7e49c2b07596221daec5edaabbd7c48"] commonmark = ["14c3df31e8c9c463377e287b2a1eefaa6019ab97b22dad36e2f32be59d61d68d", "867fc5db078ede373ab811e16b6789e9d033b15ccd7296f370ca52d1ee792ce0"] +cycler = ["1d8a5ae1ff6c5cf9b93e8811e581232ad8920aeec647c37316ceac982b08cb2d", "cd7b2d1018258d7247a71425e9f26463dfb444d411c39569972f4ce586b0c9d8"] decorator = ["86156361c50488b84a3f148056ea716ca587df2f0de1d34750d35c21312725de", "f069f3a01830ca754ba5258fde2278454a0b5b79e0d7f5c13b3b97e57d4acff6"] defusedxml = ["6687150770438374ab581bb7a1b327a847dd9c5749e396102de3fad4e8a3ef93", "f684034d135af4c6cbb949b8a4d2ed61634515257a67299e5f940fbaa34377f5"] diversipy = ["6abc959e64821c7122137c4352a7c9d45217a26feb2b4d4830d627b0c82b04d4"] @@ -1127,8 +1165,10 @@ jupyter = ["3e1f86076bbb7c8c207829390305a2b1fe836d471ed54be66a3b8c41e7f46cc7", " jupyter-client = ["b5f9cb06105c1d2d30719db5ffb3ea67da60919fb68deaefa583deccd8813551", "c44411eb1463ed77548bc2d5ec0d744c9b81c4a542d9637c7a52824e2121b987"] jupyter-console = ["308ce876354924fb6c540b41d5d6d08acfc946984bf0c97777c1ddcb42e0b2f5", "cc80a97a5c389cbd30252ffb5ce7cefd4b66bde98219edd16bf5cb6f84bb3568"] jupyter-core = ["927d713ffa616ea11972534411544589976b2493fc7e09ad946e010aa7eb9970", "ba70754aa680300306c699790128f6fbd8c306ee5927976cbe48adacf240c0b7"] +kiwisolver = ["05b5b061e09f60f56244adc885c4a7867da25ca387376b02c1efc29cc16bcd0f", "26f4fbd6f5e1dabff70a9ba0d2c4bd30761086454aa30dddc5b52764ee4852b7", "3b2378ad387f49cbb328205bda569b9f87288d6bc1bf4cd683c34523a2341efe", "400599c0fe58d21522cae0e8b22318e09d9729451b17ee61ba8e1e7c0346565c", "47b8cb81a7d18dbaf4fed6a61c3cecdb5adec7b4ac292bddb0d016d57e8507d5", "53eaed412477c836e1b9522c19858a8557d6e595077830146182225613b11a75", "58e626e1f7dfbb620d08d457325a4cdac65d1809680009f46bf41eaf74ad0187", "5a52e1b006bfa5be04fe4debbcdd2688432a9af4b207a3f429c74ad625022641", "5c7ca4e449ac9f99b3b9d4693debb1d6d237d1542dd6a56b3305fe8a9620f883", "682e54f0ce8f45981878756d7203fd01e188cc6c8b2c5e2cf03675390b4534d5", "79bfb2f0bd7cbf9ea256612c9523367e5ec51d7cd616ae20ca2c90f575d839a2", "7f4dd50874177d2bb060d74769210f3bce1af87a8c7cf5b37d032ebf94f0aca3", "8944a16020c07b682df861207b7e0efcd2f46c7488619cb55f65882279119389", "8aa7009437640beb2768bfd06da049bad0df85f47ff18426261acecd1cf00897", "939f36f21a8c571686eb491acfffa9c7f1ac345087281b412d63ea39ca14ec4a", "9733b7f64bd9f807832d673355f79703f81f0b3e52bfce420fc00d8cb28c6a6c", "a02f6c3e229d0b7220bd74600e9351e18bc0c361b05f29adae0d10599ae0e326", "a0c0a9f06872330d0dd31b45607197caab3c22777600e88031bfe66799e70bb0", "acc4df99308111585121db217681f1ce0eecb48d3a828a2f9bbf9773f4937e9e", "b64916959e4ae0ac78af7c3e8cef4becee0c0e9694ad477b4c6b3a536de6a544", "d3fcf0819dc3fea58be1fd1ca390851bdb719a549850e708ed858503ff25d995", "d52e3b1868a4e8fd18b5cb15055c76820df514e26aa84cc02f593d99fef6707f", "db1a5d3cc4ae943d674718d6c47d2d82488ddd94b93b9e12d24aabdbfe48caee", "e3a21a720791712ed721c7b95d433e036134de6f18c77dbe96119eaf7aa08004", "e8bf074363ce2babeb4764d94f8e65efd22e6a7c74860a4f05a6947afc020ff2", "f16814a4a96dc04bf1da7d53ee8d5b1d6decfc1a92a63349bb15d37b6a263dd9", "f2b22153870ca5cf2ab9c940d7bc38e8e9089fa0f7e5856ea195e1cf4ff43d5a", "f790f8b3dff3d53453de6a7b7ddd173d2e020fb160baff578d578065b108a05f"] lazy-object-proxy = ["159a745e61422217881c4de71f9eafd9d703b93af95618635849fe469a283661", "23f63c0821cc96a23332e45dfaa83266feff8adc72b9bcaef86c202af765244f", "3b11be575475db2e8a6e11215f5aa95b9ec14de658628776e10d96fa0b4dac13", "3f447aff8bc61ca8b42b73304f6a44fa0d915487de144652816f950a3f1ab821", "4ba73f6089cd9b9478bc0a4fa807b47dbdb8fad1d8f31a0f0a5dbf26a4527a71", "4f53eadd9932055eac465bd3ca1bd610e4d7141e1278012bd1f28646aebc1d0e", "64483bd7154580158ea90de5b8e5e6fc29a16a9b4db24f10193f0c1ae3f9d1ea", "6f72d42b0d04bfee2397aa1862262654b56922c20a9bb66bb76b6f0e5e4f9229", "7c7f1ec07b227bdc561299fa2328e85000f90179a2f44ea30579d38e037cb3d4", "7c8b1ba1e15c10b13cad4171cfa77f5bb5ec2580abc5a353907780805ebe158e", "8559b94b823f85342e10d3d9ca4ba5478168e1ac5658a8a2f18c991ba9c52c20", "a262c7dfb046f00e12a2bdd1bafaed2408114a89ac414b0af8755c696eb3fc16", "acce4e3267610c4fdb6632b3886fe3f2f7dd641158a843cf6b6a68e4ce81477b", "be089bb6b83fac7f29d357b2dc4cf2b8eb8d98fe9d9ff89f9ea6012970a853c7", "bfab710d859c779f273cc48fb86af38d6e9210f38287df0069a63e40b45a2f5c", "c10d29019927301d524a22ced72706380de7cfc50f767217485a912b4c8bd82a", "dd6e2b598849b3d7aee2295ac765a578879830fb8966f70be8cd472e6069932e", "e408f1eacc0a68fed0c08da45f31d0ebb38079f043328dce69ff133b95c29dc1"] markupsafe = ["00bc623926325b26bb9605ae9eae8a215691f33cae5df11ca5424f06f2d1f473", "09027a7803a62ca78792ad89403b1b7a73a01c8cb65909cd876f7fcebd79b161", "09c4b7f37d6c648cb13f9230d847adf22f8171b1ccc4d5682398e77f40309235", "1027c282dad077d0bae18be6794e6b6b8c91d58ed8a8d89a89d59693b9131db5", "24982cc2533820871eba85ba648cd53d8623687ff11cbb805be4ff7b4c971aff", "29872e92839765e546828bb7754a68c418d927cd064fd4708fab9fe9c8bb116b", "43a55c2930bbc139570ac2452adf3d70cdbb3cfe5912c71cdce1c2c6bbd9c5d1", "46c99d2de99945ec5cb54f23c8cd5689f6d7177305ebff350a58ce5f8de1669e", "500d4957e52ddc3351cabf489e79c91c17f6e0899158447047588650b5e69183", "535f6fc4d397c1563d08b88e485c3496cf5784e927af890fb3c3aac7f933ec66", "62fe6c95e3ec8a7fad637b7f3d372c15ec1caa01ab47926cfdf7a75b40e0eac1", "6dd73240d2af64df90aa7c4e7481e23825ea70af4b4922f8ede5b9e35f78a3b1", "717ba8fe3ae9cc0006d7c451f0bb265ee07739daf76355d06366154ee68d221e", "79855e1c5b8da654cf486b830bd42c06e8780cea587384cf6545b7d9ac013a0b", "7c1699dfe0cf8ff607dbdcc1e9b9af1755371f92a68f706051cc8c37d447c905", "88e5fcfb52ee7b911e8bb6d6aa2fd21fbecc674eadd44118a9cc3863f938e735", "8defac2f2ccd6805ebf65f5eeb132adcf2ab57aa11fdf4c0dd5169a004710e7d", "98c7086708b163d425c67c7a91bad6e466bb99d797aa64f965e9d25c12111a5e", "9add70b36c5666a2ed02b43b335fe19002ee5235efd4b8a89bfcf9005bebac0d", "9bf40443012702a1d2070043cb6291650a0841ece432556f784f004937f0f32c", "ade5e387d2ad0d7ebf59146cc00c8044acbd863725f887353a10df825fc8ae21", "b00c1de48212e4cc9603895652c5c410df699856a2853135b3967591e4beebc2", "b1282f8c00509d99fef04d8ba936b156d419be841854fe901d8ae224c59f0be5", "b2051432115498d3562c084a49bba65d97cf251f5a331c64a12ee7e04dacc51b", "ba59edeaa2fc6114428f1637ffff42da1e311e29382d81b339c1817d37ec93c6", "c8716a48d94b06bb3b2524c2b77e055fb313aeb4ea620c8dd03a105574ba704f", "cd5df75523866410809ca100dc9681e301e3c27567cf498077e8551b6d20e42f", "e249096428b3ae81b08327a63a485ad0878de3fb939049038579ac0ef61e17e7"] +matplotlib = ["1febd22afe1489b13c6749ea059d392c03261b2950d1d45c17e3aed812080c93", "31a30d03f39528c79f3a592857be62a08595dec4ac034978ecd0f814fa0eec2d", "4442ce720907f67a79d45de9ada47be81ce17e6c2f448b3c64765af93f6829c9", "796edbd1182cbffa7e1e7a97f1e141f875a8501ba8dd834269ae3cd45a8c976f", "934e6243df7165aad097572abf5b6003c77c9b6c480c3c4de6f2ef1b5fdd4ec0", "bab9d848dbf1517bc58d1f486772e99919b19efef5dd8596d4b26f9f5ee08b6b", "c1fe1e6cdaa53f11f088b7470c2056c0df7d80ee4858dadf6cbe433fcba4323b", "e5b8aeca9276a3a988caebe9f08366ed519fff98f77c6df5b64d7603d0e42e36", "ec6bd0a6a58df3628ff269978f4a4b924a0d371ad8ce1f8e2b635b99e482877a"] mccabe = ["ab8a6258860da4b6677da4bd2fe5dc2c659cff31b3ee4f7f5d64e79735b80d42", "dd8d182285a0fe56bace7f45b5e7d1a6ebcbf524e8f3bd87eb0f125271b8831f"] mistune = ["59a3429db53c50b5c6bcc8a07f8848cb00d7dc8bdb431a4ab41920d201d4756e", "88a1051873018da288eee8538d476dffe1262495144b33ecb586c4ab266bb8d4"] mypy = ["2afe51527b1f6cdc4a5f34fc90473109b22bf7f21086ba3e9451857cf11489e6", "56a16df3e0abb145d8accd5dbb70eba6c4bd26e2f89042b491faa78c9635d1e2", "5764f10d27b2e93c84f70af5778941b8f4aa1379b2430f85c827e0f5464e8714", "5bbc86374f04a3aa817622f98e40375ccb28c4836f36b66706cf3c6ccce86eda", "6a9343089f6377e71e20ca734cd8e7ac25d36478a9df580efabfe9059819bf82", "6c9851bc4a23dc1d854d3f5dfd5f20a016f8da86bcdbb42687879bb5f86434b0", "b8e85956af3fcf043d6f87c91cbe8705073fc67029ba6e22d3468bfee42c4823", "b9a0af8fae490306bc112229000aa0c2ccc837b49d29a5c42e088c132a2334dd", "bbf643528e2a55df2c1587008d6e3bda5c0445f1240dfa85129af22ae16d7a9a", "c46ab3438bd21511db0f2c612d89d8344154c0c9494afc7fbc932de514cf8d15", "f7a83d6bd805855ef83ec605eb01ab4fa42bcef254b13631e451cbb44914a9b0"] diff --git a/pyproject.toml b/pyproject.toml index 9c2b2ac..dbe289e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,6 +20,7 @@ tqdm = "^4.31" jupyter = "^1.0" scikit-learn = "^0.21.2" black = {version = "^18.3-alpha.0",allows-prereleases = true} +matplotlib = "^3.1" [tool.poetry.dev-dependencies] flake8 = "^3.7" diff --git a/pyrvea/EAs/PPGA.py b/pyrvea/EAs/PPGA.py index ca828e0..17b9ace 100644 --- a/pyrvea/EAs/PPGA.py +++ b/pyrvea/EAs/PPGA.py @@ -42,10 +42,13 @@ def set_params( plotting: bool = True, logging: list = False, logfile=None, + opt: bool = False, crossover_type: str = None, mutation_type: str = None, + kill_interval: int = 7, prob_crossover: float = 0.8, prob_mutation: float = 0.3, + prob_prey_move: float = 0.3, mut_strength: float = 0.7, ): """Set up the parameters. @@ -84,7 +87,7 @@ def set_params( "target_pop_size": target_pop_size, "predator_pop_size": 50, "prey_max_moves": 10, - "prob_prey_move": 0.3, + "prob_prey_move": prob_prey_move, "offspring_place_attempts": 10, "generations": generations_per_iteration, "iterations": iterations, @@ -92,6 +95,7 @@ def set_params( "interact": interact, "plotting": plotting, "logging": logging, + "opt": opt, "logfile": logfile, "current_iteration_gen_count": 0, "current_total_gen_count": 0, @@ -99,7 +103,7 @@ def set_params( "prob_crossover": prob_crossover, "prob_mutation": prob_mutation, "mut_strength": mut_strength, - "kill_interval": 7, + "kill_interval": kill_interval, "max_rank": 20, } @@ -123,6 +127,7 @@ def _next_iteration(self, population: "Population"): population : "Population" Contains current population """ + self.params["current_iteration_gen_count"] = 1 while self.continue_iteration(): self._next_gen(population) @@ -164,14 +169,20 @@ def _next_gen(self, population: "Population"): / self.params["total_generations"] ) - for ind in range(len(population.individuals)): + # If optimizing instead of training, perform crossover over the entire pop at once + if self.params["opt"]: + offspring = population.mate(params=self.params) + else: + for ind in range(len(population.individuals)): - mate_idx = self.lattice.choose_mate(ind) - if not mate_idx: - continue - else: - mating_pop = [ind, mate_idx] - offspring += population.mate(mating_pop, self.params) + mate_idx = self.lattice.choose_mate(ind) + if not mate_idx: + continue + else: + mating_pop = [ind, mate_idx] + offsprings = population.mate(mating_pop, self.params) + for n in offsprings: + offspring.append(n) # Try to place the offspring to lattice, add to population if successful placed_indices = self.lattice.place_offspring(len(offspring)) @@ -193,59 +204,9 @@ def _next_gen(self, population: "Population"): self.lattice.update_lattice(selected) population.delete_or_keep(selected, "delete") - print( - str(self.params["current_total_gen_count"]) - + " " - + "population size: " - + str(len(population.individuals)) - + " Min Error: " - + str(np.amin(population.objectives[:, 0])) - + " Avg Error: " - + str(np.mean(population.objectives[:, 0])) - ) - # Move predators self.lattice.move_predator() - print( - "AFTER KILL " - + str(self.params["current_total_gen_count"]) - + " " - + "population size: " - + str(len(population.individuals)) - + " Min Error: " - + str(np.amin(population.objectives[:, 0])) - + " Avg Error: " - + str(np.mean(population.objectives[:, 0])) - ) - - if ( - self.params["current_iteration_count"] == self.params["iterations"] - 1 - and self.params["current_iteration_gen_count"] == self.params["generations"] - ): - print( - "last gen min error: " - + str(np.amin(population.objectives[:, 0])) - + " avg error: " - + str(np.mean(population.objectives[:, 0])), - file=self.params["logfile"], - ) - - # population.create_archive(population, population.objectives) - - # # Place new prey if kill interval condition satisfied - # if ( - # self.params["current_iteration_gen_count"] % self.params["kill_interval"] - # == 0 - # ): - # # Create new random individuals to get to preferred population size - # old_pop_size = population.individuals.shape[0] - # if self.params["target_pop_size"] - old_pop_size > 0: - # placed_indices = self.lattice.place_offspring( - # self.params["target_pop_size"] - old_pop_size - # ) - # population.create_new_individuals(pop_size=len(placed_indices), design="EvoNN") - def _run_interruption(self, population: "Population"): """Run the interruption phase of PPGA. @@ -258,8 +219,8 @@ def _run_interruption(self, population: "Population"): if self.params["interact"]: pass - if population.plotting: - population.plot_objectives() + #if population.plotting: + # population.plot_objectives() def select(self, population, max_rank=20) -> list: """Of the population, individuals lower than max_rank are selected. diff --git a/pyrvea/Population/Population.py b/pyrvea/Population/Population.py index 3b6de2f..e1c567f 100644 --- a/pyrvea/Population/Population.py +++ b/pyrvea/Population/Population.py @@ -40,6 +40,7 @@ def __init__( pop_size=None, crossover_type=None, mutation_type=None, + recombination_type=None, *args ): """Initialize the population. @@ -77,12 +78,14 @@ def __init__( } self.crossover_type = crossover_type self.mutation_type = mutation_type - self.crossover = self.recombination_funcs[crossover_type] - self.mutation = self.recombination_funcs[mutation_type] + self.recombination_type = recombination_type + if recombination_type is None: + self.crossover = self.recombination_funcs[crossover_type] + self.mutation = self.recombination_funcs[mutation_type] + self.recombination = self.recombination_funcs[recombination_type] self.problem = problem self.filename = problem.name + "_" + str(problem.num_of_objectives) self.plotting = plotting - # These attributes contain the solutions. self.individuals = [] self.objectives = np.empty((0, self.problem.num_of_objectives), float) self.fitness = np.empty((0, self.problem.num_of_objectives), float) @@ -118,41 +121,43 @@ def add(self, new_pop: np.ndarray): Decision variable values for new population. """ for i in range(len(new_pop)): - self.append_individual(new_pop[i]) + self.append_individual(new_pop[i]) # print(self.ideal_fitness) self.update_ideal_and_nadir() - def keep(self, indices: list): - """Remove individuals from population which are not in "indices". + def append_individual(self, ind: np.ndarray): + """Evaluate and add individual to the population. Parameters ---------- - indices: list - Indices of individuals to keep + ind: np.ndarray """ - new_pop = self.individuals[indices, :] - new_obj = self.objectives[indices, :] - new_fitness = self.fitness[indices, :] - new_CV = self.constraint_violation[indices, :] - self.individuals = new_pop - self.objectives = new_obj - length_of_archive = len(self.archive) - if length_of_archive == 0: - gen_count = 0 - else: - gen_count = self.archive["generation"].iloc[-1] + 1 - new_entries = pd.DataFrame( - { - "generation": [gen_count] * len(new_obj), - "decision_variables": new_pop.tolist(), - "objective_values": new_obj.tolist(), - } - ) - self.archive = self.archive.append(new_entries, ignore_index=True) - self.fitness = new_fitness - self.constraint_violation = new_CV + self.individuals.append(ind) + obj, CV, fitness = self.evaluate_individual(ind) + self.objectives = np.vstack((self.objectives, obj)) + self.constraint_violation = np.vstack((self.constraint_violation, CV)) + self.fitness = np.vstack((self.fitness, fitness)) + + def evaluate_individual(self, ind: np.ndarray): + """Evaluate individual. + + Returns objective values, constraint violation, and fitness. + + Parameters + ---------- + ind: np.ndarray + """ + obj = self.problem.objectives(ind) + CV = np.empty((0, self.problem.num_of_constraints), float) + fitness = obj + + if self.problem.num_of_constraints: + CV = self.problem.constraints(ind, obj) + fitness = self.eval_fitness(ind, obj, self.problem) + + return (obj, CV, fitness) def delete_or_keep(self, indices, delete_or_keep="delete"): """Remove from population individuals which are in indices, or @@ -190,50 +195,17 @@ def delete_or_keep(self, indices, delete_or_keep="delete"): new_cv = self.constraint_violation if delete_or_keep == "delete": - self.individuals = new_pop.tolist() + self.individuals = list(new_pop) self.objectives = new_obj self.fitness = new_fitness self.constraint_violation = new_cv elif delete_or_keep == "keep": - self.individuals = deleted_pop.tolist() + self.individuals = list(deleted_pop) self.objectives = deleted_obj self.fitness = deleted_fitness self.constraint_violation = deleted_cv - def append_individual(self, ind: np.ndarray): - """Evaluate and add individual to the population. - - Parameters - ---------- - ind: np.ndarray - """ - - self.individuals.append(ind) - obj, CV, fitness = self.evaluate_individual(ind) - self.objectives = np.vstack((self.objectives, obj)) - self.constraint_violation = np.vstack((self.constraint_violation, CV)) - self.fitness = np.vstack((self.fitness, fitness)) - - def evaluate_individual(self, ind: np.ndarray): - """Evaluate individual. - - Returns objective values, constraint violation, and fitness. - - Parameters - ---------- - ind: np.ndarray - """ - obj = self.problem.objectives(ind) - CV = np.empty((0, self.problem.num_of_constraints), float) - fitness = obj - - if self.problem.num_of_constraints: - CV = self.problem.constraints(ind, obj) - fitness = self.eval_fitness(ind, obj, self.problem) - - return (obj, CV, fitness) - def evolve(self, EA: "BaseEA" = None, EA_parameters: dict = {}) -> "Population": """Evolve the population with interruptions. @@ -268,16 +240,16 @@ def evolve(self, EA: "BaseEA" = None, EA_parameters: dict = {}) -> "Population": if self.plotting: self.plot_objectives() + def mate(self, mating_pop=None, params=None): """Conduct crossover and mutation over the population. """ - offspring = self.crossover.mate(mating_pop, self.individuals, params) - if ( - not self.crossover_type == self.mutation_type - and self.mutation_type is not None - ): + if self.recombination_type is not None: + offspring = self.recombination.mate(mating_pop, self.individuals, params) + else: + offspring = self.crossover.mate(mating_pop, self.individuals, params) self.mutation.mutate(offspring, self.individuals, params, self.lower_limits, self.upper_limits) return offspring diff --git a/pyrvea/Population/create_individuals.py b/pyrvea/Population/create_individuals.py index 43b2e19..850892a 100644 --- a/pyrvea/Population/create_individuals.py +++ b/pyrvea/Population/create_individuals.py @@ -63,16 +63,30 @@ def create_new_individuals(design, problem, pop_size=None): One extra row is added for bias. """ + try: + w_low = problem.w_low + w_high = problem.w_high + in_nodes = problem.num_of_variables + num_nodes = problem.num_nodes + prob_omit = problem.prob_omit + + except AttributeError: + w_low = -5 + w_high = 5 + in_nodes = problem.num_of_variables + num_nodes = 20 + prob_omit = 0.2 + individuals = np.random.uniform( - problem.w_low, - problem.w_high, - size=(problem.params["pop_size"], problem.num_input_nodes, problem.num_nodes), + w_low, + w_high, + size=(pop_size, in_nodes, num_nodes), ) # Randomly set some weights to zero zeros = np.random.choice( np.arange(individuals.size), - ceil(individuals.size * problem.params["prob_omit"]), + ceil(individuals.size * prob_omit), ) individuals.ravel()[zeros] = 0 diff --git a/pyrvea/Problem/dataproblem.py b/pyrvea/Problem/dataproblem.py index 2b62280..4c72c0f 100644 --- a/pyrvea/Problem/dataproblem.py +++ b/pyrvea/Problem/dataproblem.py @@ -1,4 +1,8 @@ from sklearn.gaussian_process import GaussianProcessRegressor +from sklearn.neural_network import MLPRegressor +from pyrvea.Problem.evonn_problem import EvoNNModel as EvoNN +from pyrvea.Problem.evodn2_problem import EvoDN2Model as EvoDN2 + # from sklearn.gaussian_process.kernels import RBF, WhiteKernel, ConstantKernel as C from sklearn import preprocessing from sklearn.model_selection import train_test_split as tts @@ -18,23 +22,28 @@ def __init__( minimize: List[bool] = None, ideal: List[float] = None, nadir: List[float] = None, - lower_bound: List[float] = None, - upper_bound: List[float] = None, + num_of_constraints=0, + lower_limits: List[float] = None, + upper_limits: List[float] = None, + name=None, ): self.raw_data = data self.data = data self.x = x self.y = y - self.number_of_variables = len(x) - self.number_of_objectives = len(y) + self.num_of_variables = len(x) + self.num_of_objectives = len(y) + self.num_of_constraints = num_of_constraints self.number_of_samples = self.data.shape[0] + self.name = name + if name is None: + self.name = "dataproblem" if minimize is None: - self.minimize = [True] * self.number_of_objectives + self.minimize = [True] * self.num_of_objectives else: - assert len(minimize) == self.number_of_objectives + assert len(minimize) == self.num_of_objectives self.minimize = minimize self.preprocessing_transformations = [] - self.number_of_objectives = len(y) # These indices define how the training will occur. # all_indices is the list of indices that can be used. This does not include # outliers. @@ -44,28 +53,29 @@ def __init__( # The outer list, if it contains multiple elements, should lead to multiple # trained models. Can be used for k-fold validation. # Train and test indices super list should have the same length. - self.train_indices: List[List] = None - self.test_indices: List[List] = None + self.train_indices: List[List] = [] + self.test_indices: List[List] = [] self.validation_indices: List[List] = None - self.models = dict.fromkeys(self.y, []) + # self.models = dict.fromkeys(self.y, []) + self.models = {} self.metrics = [] # Defining bounds in the decision space - if lower_bound is None: - self.lower_bound = np.min(self.raw_data[x], axis=0) + if lower_limits is None: + self.lower_limits = np.min(self.raw_data[x], axis=0) else: - assert len(lower_bound) == self.num_of_variables - self.lower_bound = lower_bound - if upper_bound is None: - self.upper_bound = np.max(self.raw_data[x], axis=0) + assert len(lower_limits) == self.num_of_variables + self.lower_limits = lower_limits + if upper_limits is None: + self.upper_limits = np.max(self.raw_data[x], axis=0) else: - assert len(upper_bound) == self.num_of_variables - self.upper_bound = upper_bound + assert len(upper_limits) == self.num_of_variables + self.upper_limits = upper_limits def data_scaling(self, data_decision): # Scales the data from 0 to 1 # Check this range stuff min_max_scaler = preprocessing.MinMaxScaler( - feature_range=(self.lower_bound, self.upper_bound) + feature_range=(self.lower_limits, self.upper_limits) ) self.processed_data_decision = min_max_scaler.fit_transform(data_decision) self.preprocessing_transformations.append(min_max_scaler) @@ -76,28 +86,41 @@ def data_uniform_mapping(self): # Maps the data to uniform distribution def outlier_removal(self): # Removes the outliers pass - def train_test_split(self, train_percent: float = 0.7): # Split dataset - train_indices, test_indices = tts(self.all_indices, train_percent) + def train_test_split(self, train_size: float = 0.7): # Split dataset + + for x in range(1): + train_indices, test_indices = tts(self.all_indices, train_size=train_size) + self.train_indices.append(train_indices) + self.test_indices.append(test_indices) def train(self, model_type: str = None, objectives: str = None, **kwargs): if objectives is None: objectives = self.y if model_type is None: model_type = "GPR" - surrogate_model_options = {"GPR": GaussianProcessRegressor} + self.model_type_name = model_type + surrogate_model_options = { + "GPR": GaussianProcessRegressor, + "MLP": MLPRegressor, + "EvoNN": EvoNN, + "EvoDN2": EvoDN2, + } model_type = surrogate_model_options[model_type] # Build specific surrogate models print("Building Surrogate Models ...") # Fit to data using Maximum Likelihood Estimation of the parameters for obj in objectives: - print("Building model for " + obj) - for train_indices, train_run in enumerate(self.train_indices): - print("Training run number", train_run, 'of', len(self.train_indices)) - model = model_type(**kwargs) + print("Building model for " + str(obj)) + for train_run, train_indices in enumerate(self.train_indices): + print("Training run number", train_run, "of", len(self.train_indices)) + model = model_type(name=self.model_type_name + str(obj), **kwargs) model.fit( - self.data[self.x][train_indices], self.data[self.y][train_indices] + np.array(self.data[self.x])[train_indices], + np.array(self.data[obj])[train_indices], ) - self.models[obj] = self.models[obj].append(model) + self.models[obj] = [model] + + # Select model print("Surrogate models build completed.") def transform_new_data(self, decision_variables): @@ -113,9 +136,9 @@ def surrogates_predict(self, decision_variables): y_pred = None # transforms applied decision_variables_transformed = self.transform_new_data(decision_variables) - for obj, i in zip(self.obj_names, range(self.number_of_objectives)): - y = self.models[obj].predict( - decision_variables_transformed.reshape(1, self.number_of_variables), + for obj, i in zip(self.y, range(self.num_of_objectives)): + y = self.models[obj][0].predict( + decision_variables_transformed.reshape(1, self.num_of_variables), return_std=False, ) if y_pred is None: @@ -138,4 +161,23 @@ def testing_score(self): # Return R-squared of testing return y_pred def retrain_surrogate(self): - pass \ No newline at end of file + pass + + def objectives(self, decision_variables): + """ + + Parameters + ---------- + decision_variables + + Returns + ------- + + """ + objectives = [] + for obj in self.y: + objectives.append( + self.models[obj][0].predict(decision_variables.reshape(1, -1))[0] + ) + + return objectives diff --git a/pyrvea/Problem/evodn2_problem.py b/pyrvea/Problem/evodn2_problem.py index 34991c8..7f7c389 100644 --- a/pyrvea/Problem/evodn2_problem.py +++ b/pyrvea/Problem/evodn2_problem.py @@ -6,22 +6,30 @@ import plotly import plotly.graph_objs as go import random -from math import ceil class EvoDN2(baseProblem): - """Creates an Artificial Neural Network (ANN) for the EvoDN2 algorithm. + """Creates Deep Neural Networks (DNN) for the EvoDN2 algorithm. + + DNNs have a fixed number of subnets, each of which has random number of + layers and nodes in each layer, dependant on max layers and max nodes set by user. + Parameters ---------- name : str Name of the sample + X_train : ndarray + Training data input + y_train : ndarray + Training data target values num_of_objectives : int The number of objectives w_low : float The lower bound for randomly generated weights w_high : float The upper bound for randomly generated weights - params : + params : dict + Parameters for the models """ def __init__( @@ -75,13 +83,14 @@ def fit(self, training_data, target_values): return self.subsets def train(self, model): - + """Create a random population, evolve it and select a model based on criterion.""" pop = Population( self, assign_type="EvoDN2", pop_size=self.params["pop_size"], - crossover_type="DNN_gaussian_xover+mut", - mutation_type="DNN_gaussian_xover+mut", + recombination_type=self.params["recombination_type"], + crossover_type=self.params["crossover_type"], + mutation_type=self.params["mutation_type"], plotting=False, ) pop.evolve( @@ -102,8 +111,8 @@ def train(self, model): model.linear_layer, _ = self.minimize_error(model.non_linear_layer) def objectives(self, decision_variables) -> list: - """ Use this method to calculate objective functions. + Parameters ---------- decision_variables : ndarray @@ -168,21 +177,29 @@ def minimize_error(self, activated_layer): Output of the activation function Returns ------- - w_matrix[0] : ndarray - The weight matrix of the upper part of the network - rss : float - Sums of residuals + linear_solution[0] : ndarray + The linear layer of the network predicted_values : ndarray The prediction of the model """ if self.params["opt_func"] == "llsq": - w2 = np.linalg.lstsq(activated_layer, self.y_train, rcond=None) - predicted_values = np.dot(activated_layer, w2[0]) - return w2[0], predicted_values + linear_solution = np.linalg.lstsq(activated_layer, self.y_train, rcond=None) + predicted_values = np.dot(activated_layer, linear_solution[0]) + return linear_solution[0], predicted_values def loss_function(self, predicted_values): + """Calculate the final training error. + + Parameters + ---------- + predicted_values : ndarray + Output of the model. + Returns + ------- + Training error : float + """ if self.params["loss_func"] == "mse": return ((self.y_train - predicted_values) ** 2).mean() if self.params["loss_func"] == "rmse": @@ -308,6 +325,9 @@ def set_params( opt_func="llsq", loss_func="rmse", criterion="min_error", + crossover_type=None, + mutation_type=None, + recombination_type="DNN_gaussian_xover+mut", logging=False, plotting=False, ): @@ -332,6 +352,12 @@ def set_params( The loss function to use. criterion : str The criterion to use for selecting the model. + crossover_type : str + Crossover method. + mutation_type : str + Mutation method. + recombination_type : str + Combined crossover+mutation method. logging : bool True to create a logfile, False otherwise. plotting : bool @@ -347,6 +373,9 @@ def set_params( "opt_func": opt_func, "loss_func": loss_func, "criterion": criterion, + "crossover_type": crossover_type, + "mutation_type": mutation_type, + "recombination_type": recombination_type, "logging": logging, "plotting": plotting, } @@ -370,7 +399,7 @@ def fit(self, training_data, target_values): prob.train(self) - self.single_variable_response(ploton=False, log=self.log) + #self.single_variable_response(ploton=False, log=self.log) def plot(self, prediction, target): diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index c3c5b15..27beb14 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -92,8 +92,9 @@ def train(self, model): assign_type="EvoNN", pop_size=self.params["pop_size"], plotting=False, - crossover_type="EvoNN_xover", - mutation_type="2d_gaussian", + recombination_type=self.params["recombination_type"], + crossover_type=self.params["crossover_type"], + mutation_type=self.params["mutation_type"] ) pop.evolve( PPGA, @@ -101,7 +102,7 @@ def train(self, model): "logging": self.params["logging"], "logfile": model.log, "iterations": 10, - "generations_per_iteration": 10, + "generations_per_iteration": 5, }, ) @@ -334,7 +335,7 @@ class EvoNNModel(EvoNN): """ - def __init__(self, name, w_matrix=None, linear_layer=None): + def __init__(self, name="EvoNN", w_matrix=None, linear_layer=None): super().__init__(name) self.name = name self.w_matrix = w_matrix @@ -353,13 +354,15 @@ def fit(self, training_data, target_values): target_values : ndarray Target values """ + self.X_train = training_data + self.y_train = target_values prob = EvoNN(name=self.name, params=self.params) prob.fit(training_data, target_values) if prob.params["logging"]: self.log = prob.create_logfile() prob.train(self) - self.single_variable_response(ploton=False, log=self.log) + #self.single_variable_response(ploton=False, log=None) def predict(self, decision_variables): @@ -375,12 +378,15 @@ def set_params( self, name=None, pop_size=500, - num_nodes=15, + num_nodes=20, prob_omit=0.2, activation_func="sigmoid", opt_func="llsq", loss_func="rmse", criterion="akaike_corrected", + crossover_type="EvoNN_xover", + mutation_type="2d_gaussian", + recombination_type=None, logging=False, plotting=False, ): @@ -405,6 +411,12 @@ def set_params( The loss function to use. criterion : str The criterion to use for selecting the model. + crossover_type : str + Crossover method. + mutation_type : str + Mutation method. + recombination_type : str + Combined crossover+mutation method. logging : bool True to create a logfile, False otherwise. plotting : bool @@ -419,6 +431,9 @@ def set_params( "opt_func": opt_func, "loss_func": loss_func, "criterion": criterion, + "crossover_type": crossover_type, + "mutation_type": mutation_type, + "recombination_type": recombination_type, "logging": logging, "plotting": plotting, } diff --git a/pyrvea/Problem/evonn_test_functions.py b/pyrvea/Problem/evonn_test_functions.py index 5be166f..961535f 100644 --- a/pyrvea/Problem/evonn_test_functions.py +++ b/pyrvea/Problem/evonn_test_functions.py @@ -166,8 +166,8 @@ def objectives(self, decision_variables) -> list: self.obj_func = np.sin(x + y) + (x - y)**2 - 1.5 * x + 2.5 * y + 1 elif self.name == "min-ex": - x1 = decision_variables[1] - x2 = decision_variables[2] + x1 = decision_variables[0] + x2 = decision_variables[1] self.obj_func = [x1, (1 + x2) / x1] diff --git a/pyrvea/Recombination/evonn_mut_gaussian.py b/pyrvea/Recombination/evonn_mut_gaussian.py index 2e786a8..b8e692c 100644 --- a/pyrvea/Recombination/evonn_mut_gaussian.py +++ b/pyrvea/Recombination/evonn_mut_gaussian.py @@ -40,11 +40,15 @@ def mutate( offspring1 = np.copy(parent1) offspring2 = np.copy(parent2) - cur_gen = params["current_total_gen_count"] - total_gen = params["total_generations"] - prob_crossover = params["prob_crossover"] - prob_mutation = params["prob_mutation"] - std_dev = params["std_dev"] + try: + cur_gen = params["current_iteration_gen_count"] + total_gen = params["generations"] + prob_crossover = params["prob_crossover"] + prob_mutation = params["prob_mutation"] + std_dev = params["std_dev"] + except KeyError: + prob_mutation = 0.3 + std_dev = 1 connections = offspring1[1:, :].size From 4590738f8a4ecdb630474202f35b4b5ee29b8b9a Mon Sep 17 00:00:00 2001 From: delamorte Date: Thu, 4 Jul 2019 11:07:33 +0300 Subject: [PATCH 054/155] generalized parameter passing to the models and in dataproblem.py improved some parameter names in evodn2 model improved documentation removed sphinx autogen template files --- docs/.nojekyll | 0 docs/Makefile | 19 - docs/README.md | 1 - docs/_build/doctrees/README.doctree | Bin 5862 -> 0 bytes docs/_build/doctrees/environment.pickle | Bin 35003 -> 0 bytes docs/_build/doctrees/index.doctree | Bin 3504 -> 0 bytes docs/_build/doctrees/pyRVEA.EAs.doctree | Bin 42083 -> 0 bytes .../_build/doctrees/pyRVEA.OtherTools.doctree | Bin 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When it is not found, a full rebuild will be done. -config: d041dea352c4b59a784f5b68df7e5d13 -tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/_build/html/_sources/README.md.txt b/docs/_build/html/_sources/README.md.txt deleted file mode 100644 index f07083d..0000000 --- a/docs/_build/html/_sources/README.md.txt +++ /dev/null @@ -1,21 +0,0 @@ -# README -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/EchoBetaGlass/pyrvea/master) - -The python version reference vector guided evolutionary algorithm. - -Currently supported: Multi-objective minimization with visualization and interaction support. Preference is accepted as a reference point. - -To test the code, open the [binder link](https://mybinder.org/v2/gh/EchoBetaGlass/pyrvea/master) and read example.ipynb. - -See the details of RVEA in the following paper - -R. Cheng, Y. Jin, M. Olhofer and B. Sendhoff, -A Reference Vector Guided Evolutionary Algorithm for Many-objective -Optimization, IEEE Transactions on Evolutionary Computation, 2016 - -The source code of pyrvea is implemented by Bhupinder Saini - -If you have any questions about the code, please contact: - -Bhupinder Saini: bhupinder.s.saini@jyu.fi\ -Project researcher at University of Jyväskylä. \ No newline at end of file diff --git a/docs/_build/html/_sources/index.rst.txt b/docs/_build/html/_sources/index.rst.txt deleted file mode 100644 index 1dcdf36..0000000 --- a/docs/_build/html/_sources/index.rst.txt +++ /dev/null @@ -1,24 +0,0 @@ -.. pyrvea documentation master file, created by - sphinx-quickstart on Tue Apr 30 13:48:51 2019. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - - - -Welcome to pyrvea's documentation! -================================== - -.. README.md - -.. toctree:: - :hidden: - - Home - -.. toctree:: - :maxdepth: 4 - :caption: Contents: - - README.md - pyrvea - diff --git a/docs/_build/html/_sources/pyRVEA.EAs.rst.txt b/docs/_build/html/_sources/pyRVEA.EAs.rst.txt deleted file mode 100644 index 7163385..0000000 --- a/docs/_build/html/_sources/pyRVEA.EAs.rst.txt +++ /dev/null @@ -1,26 +0,0 @@ -pyrvea.EAs -================== - -pyrvea.EAs.NSGAIII module -------------------------- - -.. automodule:: pyrvea.EAs.NSGAIII - :members: - :undoc-members: - :show-inheritance: - -pyrvea.EAs.RVEA module ----------------------- - -.. automodule:: pyrvea.EAs.RVEA - :members: - :undoc-members: - :show-inheritance: - -pyrvea.EAs.baseEA module ------------------------- - -.. automodule:: pyrvea.EAs.baseEA - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_build/html/_sources/pyRVEA.OtherTools.rst.txt b/docs/_build/html/_sources/pyRVEA.OtherTools.rst.txt deleted file mode 100644 index c89ab07..0000000 --- a/docs/_build/html/_sources/pyRVEA.OtherTools.rst.txt +++ /dev/null @@ -1,44 +0,0 @@ -pyrvea.OtherTools -========================= - - -pyrvea.OtherTools.IsNotebook module ------------------------------------ - -.. automodule:: pyrvea.OtherTools.IsNotebook - :members: - :undoc-members: - :show-inheritance: - -pyrvea.OtherTools.ReferenceVectors module ------------------------------------------ - -.. automodule:: pyrvea.OtherTools.ReferenceVectors - :members: - :undoc-members: - :show-inheritance: - -pyrvea.OtherTools.newRV module ------------------------------- - -.. automodule:: pyrvea.OtherTools.newRV - :members: - :undoc-members: - :show-inheritance: - -pyrvea.OtherTools.plotlyanimate module --------------------------------------- - -.. automodule:: pyrvea.OtherTools.plotlyanimate - :members: - :undoc-members: - :show-inheritance: - -pyrvea.OtherTools.symmetric\_vectors module -------------------------------------------- - -.. automodule:: pyrvea.OtherTools.symmetric_vectors - :members: - :undoc-members: - :show-inheritance: - diff --git a/docs/_build/html/_sources/pyRVEA.Population.rst.txt b/docs/_build/html/_sources/pyRVEA.Population.rst.txt deleted file mode 100644 index f9e2618..0000000 --- a/docs/_build/html/_sources/pyRVEA.Population.rst.txt +++ /dev/null @@ -1,11 +0,0 @@ -pyrvea.Population -========================= - - -pyrvea.Population.Population module ------------------------------------ - -.. automodule:: pyrvea.Population.Population - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_build/html/_sources/pyRVEA.Problem.rst.txt b/docs/_build/html/_sources/pyRVEA.Problem.rst.txt deleted file mode 100644 index 7624fff..0000000 --- a/docs/_build/html/_sources/pyRVEA.Problem.rst.txt +++ /dev/null @@ -1,21 +0,0 @@ -pyrvea.Problem package -====================== - -Submodules ----------- - -pyrvea.Problem.baseProblem module ---------------------------------- - -.. automodule:: pyrvea.Problem.baseProblem - :members: - :undoc-members: - :show-inheritance: - -pyrvea.Problem.testProblem module ---------------------------------- - -.. automodule:: pyrvea.Problem.testProblem - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_build/html/_sources/pyRVEA.Recombination.rst.txt b/docs/_build/html/_sources/pyRVEA.Recombination.rst.txt deleted file mode 100644 index 87cec6a..0000000 --- a/docs/_build/html/_sources/pyRVEA.Recombination.rst.txt +++ /dev/null @@ -1,10 +0,0 @@ -pyrvea.Recombination package -============================ - -Module contents ---------------- - -.. automodule:: pyrvea.Recombination - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_build/html/_sources/pyRVEA.Selection.rst.txt b/docs/_build/html/_sources/pyRVEA.Selection.rst.txt deleted file mode 100644 index 5b5c45f..0000000 --- a/docs/_build/html/_sources/pyRVEA.Selection.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -pyrvea.Selection package -======================== - -Submodules ----------- - -pyrvea.Selection.APD\_select module ------------------------------------ - -.. automodule:: pyrvea.Selection.APD_select - :members: - :undoc-members: - :show-inheritance: - -pyrvea.Selection.NSGAIII\_select module ---------------------------------------- - -.. automodule:: pyrvea.Selection.NSGAIII_select - :members: - :undoc-members: - :show-inheritance: - diff --git a/docs/_build/html/_sources/pyRVEA.rst.txt b/docs/_build/html/_sources/pyRVEA.rst.txt deleted file mode 100644 index 8962c62..0000000 --- a/docs/_build/html/_sources/pyRVEA.rst.txt +++ /dev/null @@ -1,13 +0,0 @@ -pyrvea package -============== - - -.. toctree:: - - pyrvea.EAs - pyrvea.OtherTools - pyrvea.Population - pyrvea.Problem - pyrvea.Recombination - pyrvea.Selection - diff --git a/docs/_build/html/_static/basic.css b/docs/_build/html/_static/basic.css deleted file mode 100644 index 53acd09..0000000 --- a/docs/_build/html/_static/basic.css +++ /dev/null @@ -1,748 +0,0 @@ -/* - * basic.css - * ~~~~~~~~~ - * - * 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z8(cm%K|alp@wXnwJ`~j-9cJyjV!rHvNK{Y#k5akaZTo&(n{x2(MN!p0&Yll-G^t1} zrm{#X0`=Qp^`!E+efumW&p$U8|EIR^6frc~Q0)QZanCLDtKP!Bec0!%Os~1z!8@7_ zrWz?ZUus1JoFgI!QVaW13E01^mZ*_|x=y9Opb`?j2PSrEavR46Z(HtwPqz{%3P9ky z3Ss@tC;wI1&;J7F_U(_0hB{2m7485k@JQkiR=eTjXR=}7VuxA|U1X9?dOC6Y)t>L! zIO(}Y=y+FteW$WZl=`D^!ect7lQ3Uux^I?|QiT3S+r}ECZ-$J0bD#Ud8k`b^w4SFrkP0_v)YX0x(yTWyay- zO>EI#V=)yZa7L8mZ#}U4r^Nr8@Udx1m}XaXnD(@8nbpmZ+me5FU3h+)_f#byMY @@ -240,20 +241,26 @@

    C

  1. calc_perpendicular_distance() (in module pyrvea.Selection.NSGAIII_select)
    2. +
  2. constraints() (pyrvea.Problem.baseproblem.BaseProblem method) + +
  3. continue_evolution() (pyrvea.EAs.baseEA.BaseDecompositionEA method)
    4. + + - -
  4. mate() (in module pyrvea.Recombination.evodn2_gaussian) +
  5. mate() (in module pyrvea.Recombination.biogp_xover) @@ -459,10 +493,14 @@

    N

  6. niching() (in module pyrvea.Selection.NSGAIII_select)
    7. -
  7. non_dominated() (pyrvea.Population.Population.Population method) +
  8. Node (class in pyrvea.Problem.biogp_problem) +
    9. +
  9. node_label() (pyrvea.Problem.biogp_problem.Node method)
    10. + - @@ -631,6 +671,8 @@

    P

    R

    + - - +
    -
  10. single_variable_response() (pyrvea.Problem.evodn2_problem.EvoDN2Model method) +
  11. single_variable_response() (pyrvea.Problem.biogp_problem.BioGPModel method)
    12. @@ -714,6 +766,10 @@

    S

  12. slow_interactive_adapt() (pyrvea.OtherTools.ReferenceVectors.ReferenceVectors method)
  13. slowRVEA (class in pyrvea.EAs.slowRVEA) +
    14. +
  14. sqrt() (pyrvea.Problem.biogp_problem.BioGP static method) +
    15. +
  15. sub() (pyrvea.Problem.biogp_problem.BioGP static method)
  16. surrogates_predict() (pyrvea.Problem.dataproblem.DataProblem method)
    17. @@ -733,16 +789,22 @@

    T

  17. TestProblem (class in pyrvea.Problem.testproblem)
    18. -
  18. train() (pyrvea.Problem.dataproblem.DataProblem method) +
  19. tour_select() (in module pyrvea.Selection.tournament_select) +
    20. +
  20. TournamentEA (class in pyrvea.EAs.TournamentEA) +
    21. +

    Contents:

     @@ -132,7 +133,7 @@
  21. Docs »
    22. -
  22. Creating surrogate models in Python with EvoNN and EvoDN2
    23. +
  23. Creating surrogate models in Python with EvoNN, EvoDN2 and BioGP
  24. @@ -151,70 +152,98 @@
    -
    -

    Creating surrogate models in Python with EvoNN and EvoDN2

    -

    This example will show how to create surrogate models for multi-objective optimization problems using Evolutionary (Deep) Neural Networks. The code is currently capable of training the models with two algorithms, EvoNN and EvoDN2. EvoNN uses artificial, single layer neural networks, whereas EvoDN2 uses deep neural networks with multiple hidden layers and subnets.

    +
    +

    Creating surrogate models in Python with EvoNN, EvoDN2 and BioGP

    +

    This example will show how to use the code and structure in pyRVEA to create models using EvoNN, EvoDN2 and BioGP (see descriptions of the algorithms here). The code is currently capable of training and optimizing the models with all of the genetic algorithms implemented in pyRVEA/EAs.

    The basic workflow is as follows:

      -

    1. Create a test problem with training data

      2. -

    2. Create a dataproblem class which handles the training of the models

      3. +

    3. Create or import training data

      4. +

    4. Create a problem class which handles the training of the models

    5. After training, create a new population and evolve it using the models to optimize the problem

    -

    First, create the test problem:

    +

    For training data, example functions can be found in pyRVEA/Problem/testproblem.py and test_functions.py packages, or a custom data set can be imported.

    +

    Using Fonseca-Fleming two objective function with 2 variables as an example:

    import numpy as np
+import pandas as pd
 from pyrvea.Problem.testproblem import TestProblem
 from pyrvea.Problem.dataproblem import DataProblem
 
-# TestProblem class contains a number of testing functions
-# Schaffer function N.1, -100 <= x <= 100
-# Minimize:
-# f1 = x ** 2
-# f2 = (x - 2) ** 2
-
-test_prob = TestProblem(name="SchafferN1")
+test_prob = TestProblem(name="Fonseca-Fleming", num_of_variables=2)
 
 # Random data for training
 # x = list of variable names, y = list of objectives
-dataset, x, y = test_prob.create_training_data(samples=500, method="lhs")
+dataset, x, y = test_prob.create_training_data(samples=500)
+
    +
    +

    Or you can import a data set, here an example data set for ZDT1 problem with 30 variables and 1000 samples is used. The DataProblem class requires the names of the variables and objectives as lists. If your data set does not contain them in the header, you will have to create them yourself.

    +
    dataset = pd.read_excel("ZDT1_1000.xls", header=0)
+x = dataset.columns[0:30].tolist()
+# x = ['x1', 'x2', 'x3', ...]
+y = dataset.columns[30:].tolist()
+# y = ['f1', 'f2']

    After you have the data, create the DataProblem class and pass the data, variables and objectives.

    problem = DataProblem(data=dataset, x=x, y=y)
    -

    Split data into training and testing set:

    +

    You can split the data into a training and testing set:

    problem.train_test_split(train_size=0.7)
    -

    Select the Evolutionary Algorithm you want to use and set the parameters (or use defaults). Check documentation for details.

    -

    Train the models. Model specific parameters can be passed as kwargs. If no parameters are passed, defaults are used. See docs for EvoNN parameters and EvoDN2 parameters.

    -

    Both EA parameters and the model parameters can greatly affect the performance of the model. The best options depend on the problem, so experimenting with different values is strongly encouraged.

    +

    Parameters can be passed for the model and for the genetic algorithm as dictionaries. If no parameters are passed, the defaults are used. +Select the model and the genetic algorithm you want to use and set the parameters (or use defaults). Check EA documentation for details for the genetic algorithms and see docs for EvoNN parameters, EvoDN2 parameters and BioGP parameters.

    +

    Both EA parameters and the model parameters can greatly affect the performance of the model. The best options depend on the problem, so experimenting with different values is encouraged.

    from pyrvea.EAs.PPGA import PPGA
 
-ea_params = {
-    "target_pop_size": 100,
+ea_parameters = {
     "generations_per_iteration": 10,
     "iterations": 10,
 }
 
-problem.train(model_type="EvoNN", algorithm=PPGA, num_nodes=25, ea_parameters=ea_params)
+model_parameters = {
+    "training_algorithm": PPGA
+}
+
+problem.train(
+    model_type="EvoDN2",
+    model_parameters=model_parameters,
+    ea_parameters=ea_parameters)
+
    +
    +

    Note that for BioGP, function set and terminal should be adjusted according to the problem. By default, function set includes addition, substraction, multiplication and division, and terminal set includes the variables from the data. +For Fonseca-Fleming function, square root and negative should be added to the function set and constants 1 and 2 to the terminal set:

    +
    f_set = ("add", "sub", "mul", "div", "sqrt", "neg")
+t_set = [1, 2]
+
+model_parameters = {
+    function_set = f_set
+    terminal_set = t_set
+}
+
+problem.train(
+    model_type="BioGP",
+    model_parameters=model_parameters
+)
    -

    EvoNN and EvoDN2 models can currently be trained with available algorithms in pyRVEA/EAs. For explanations of the different EAs, see their respective documentation.

    +

    The models can currently be trained with available algorithms in pyRVEA/EAs. For explanations of the different EAs, see their respective documentation.

    The model’s prediction vs. target values can be plotted as follows:

    # Prediction for f1
-f1_y_pred = problem.models["f1"][0].predict(np.asarray(problem.data[problem.x]))
+f1_y_pred = problem.models["f1"][0].predict(problem.data[problem.x])
+
 problem.models["f1"][0].plot(
-    f1_y_pred, np.asarray(problem.data["f1"]), name=test_prob.name + "f1"
+    f1_y_pred, np.asarray(problem.data["f1"]), name="filename" + "f1"
 )
 # Prediction for f2
-f2_y_pred = problem.models["f2"][0].predict(np.asarray(problem.data[problem.x]))
+f2_y_pred = problem.models["f2"][0].predict(problem.data[problem.x])
+
 problem.models["f2"][0].plot(
-    f2_y_pred, np.asarray(problem.data["f2"]), name=test_prob.name + "f2"
+    f2_y_pred, np.asarray(problem.data["f2"]), name="filename" + "f2"
 )
    -

    After the models have been trained, the test problem can be optimized by creating a new population, passing the data problem class (containing the trained models) and calling evolve. EA parameters can be modified for optimization phase if wanted.

    +

    After the models have been trained, the objectives can be optimized by creating a new population, passing the data problem class (containing the trained models) and calling evolve. EA parameters can be modified for optimization phase if wanted.

    from pyrvea.EAs.RVEA import RVEA
 
 pop = Population(problem)
@@ -223,7 +252,7 @@ 
    Creating surrogate models in Python with EvoNN and EvoDN2
    pop.plot_pareto(filename="my-test-function")
+
    pop.plot_pareto(name="my-test-function")
 
    
 
    
 
    
@@ -239,7 +268,7 @@ 
    Creating surrogate models in Python with EvoNN and EvoDN2
       
       
-        
+
    diff --git a/docs/_build/html/genindex.html b/docs/_build/html/genindex.html index ced22ad..a7bd10c 100644 --- a/docs/_build/html/genindex.html +++ b/docs/_build/html/genindex.html @@ -84,7 +84,8 @@

    Contents:

    @@ -269,8 +270,6 @@

    C

  25. (pyrvea.EAs.PPGA.PPGA method)
    26. -
    @@ -429,14 +438,26 @@

    L

  26. lattice_wrap_idx() (pyrvea.EAs.PPGA.Lattice static method)
    27. +
  27. linear_layer (pyrvea.Problem.evodn2_problem.EvoDN2Model attribute) + +
    28. @@ -465,6 +486,8 @@

    M

    Contents:

      -
    • Creating surrogate models in Python with EvoNN and EvoDN2
      • +
    • Evolutionary algorithms in pyRVEA
      • +
    • Creating surrogate models in Python with EvoNN, EvoDN2 and BioGP
    • pyrvea package
      • Subpackages
        • pyrvea.EAs package
          • diff --git a/docs/_build/html/pyrvea.Population.html b/docs/_build/html/pyrvea.Population.html index d07662e..3ef7281 100644 --- a/docs/_build/html/pyrvea.Population.html +++ b/docs/_build/html/pyrvea.Population.html @@ -83,7 +83,8 @@

        Contents:

          -
        • Creating surrogate models in Python with EvoNN and EvoDN2
          • +
        • Evolutionary algorithms in pyRVEA
          • +
        • Creating surrogate models in Python with EvoNN, EvoDN2 and BioGP
        • pyrvea package
          • Subpackages
            • pyrvea.EAs package
              • @@ -178,7 +179,7 @@

              Submodules

              pyrvea.Population.Population module

              -class pyrvea.Population.Population.Population(problem: BaseProblem, assign_type: str = 'RandomDesign', plotting: bool = True, pop_size=None, recombination_type=None, crossover_type='simulated_binary_crossover', mutation_type='bounded_polynomial_mutation', *args)
              +class pyrvea.Population.Population.Population(problem: BaseProblem, assign_type: str = 'RandomDesign', plotting: bool = False, pop_size=None, recombination_type=None, crossover_type='simulated_binary_crossover', mutation_type='bounded_polynomial_mutation', *args)

              Bases: object

              Define the population.

              Methods

              diff --git a/docs/_build/html/pyrvea.Problem.html b/docs/_build/html/pyrvea.Problem.html index 60bbbfe..416e4c0 100644 --- a/docs/_build/html/pyrvea.Problem.html +++ b/docs/_build/html/pyrvea.Problem.html @@ -83,7 +83,8 @@

            Contents:

              -
            • Creating surrogate models in Python with EvoNN and EvoDN2
              • +
            • Evolutionary algorithms in pyRVEA
              • +
            • Creating surrogate models in Python with EvoNN, EvoDN2 and BioGP
            • pyrvea package
              • Subpackages
                • pyrvea.EAs package
                  • @@ -242,8 +243,38 @@

                  Submodules

                  pyrvea.Problem.biogp_problem module

                  -class pyrvea.Problem.biogp_problem.BioGP(name=None, X_train=None, y_train=None, num_of_objectives=2, params=None, num_samples=None, terminal_set=None, function_set=None, fitness=None)
                  +class pyrvea.Problem.biogp_problem.BioGP(name=None, X_train=None, y_train=None, num_of_objectives=2, params=None, num_samples=None, function_set=None, terminal_set=None)

                  Bases: pyrvea.Problem.baseproblem.BaseProblem

                  +

                  Creates syntax tree models to use for genetic programming through bi-objective genetic algorithms.

                  +

                  The BioGP technique initially minimizes training error through a single objective optimization procedure and then a +trade-off between complexity and accuracy is worked out through a genetic algorithm based bi-objective +optimization strategy.

                  +

                  The benefit of the BioGP approach is that an expert user or a decision maker (DM) can +flexibly select the mathematical operations involved to construct a meta-model of desired complexity or +accuracy. It is also designed to combat bloat – a perennial problem in genetic programming along with +over fitting and under fitting problems.

                  +

                  Notes

                  +

                  The algorithm has been created earlier in MATLAB, and this Python implementation has been using +that code as a basis.

                  +

                  Python code has been written by Niko Rissanen under the supervision of professor Nirupam Chakraborti.

                  +
                  +
                  Parameters
                  +
                    +

                  • name (str) – Name of the problem.

                    • +

                  • X_train (np.ndarray) – Training data input.

                    • +

                  • y_train (np.ndarray) – Training data target values.

                    • +

                  • num_of_objectives (int) – The number of objectives.

                    • +

                  • params (dict) – Parameters for model training.

                    • +

                  • num_samples (int) – The number of data points, or samples.

                    • +

                  • function_set (array_like) – The function set to use when creating the trees.

                    • +

                  • terminal_set (array_like) – The terminals (variables and constants) to use when creating the trees.

                    • +
                  +
                  +
                  +

                  References

                  +

                  [1] Giri, B. K., Hakanen, J., Miettinen, K., & Chakraborti, N. (2013). Genetic programming through bi-objective +genetic algorithms with a study of a simulated moving bed process involving multiple objectives. +Applied Soft Computing, 13(5), 2613-2623.

                  Methods

                  @@ -274,29 +305,41 @@

                  Submodules

                  - + + + + - + - + - + - + - + + + + + + + + + +

                  add

                  create_individuals

                  cos

                  create_individuals

                  div

                  div

                  log

                  log

                  mul

                  mul

                  sqrt

                  neg

                  sub

                  sin

                  sqrt

                  sub

                  tan
                  -create_individuals(self, method='ramped_half_and_half')
                  +create_individuals(self)
                  @@ -362,12 +405,107 @@

                  Submodulesstatic log(x)

                  +
                  +
                  +static sin(x)
                  +
                  + +
                  +
                  +static cos(x)
                  +
                  + +
                  +
                  +static tan(x)
                  +
                  + +
                  +
                  +static neg(x)
                  +
                  +
                  -class pyrvea.Problem.biogp_problem.BioGPModel(**kwargs)
                  +class pyrvea.Problem.biogp_problem.BioGPModel(model_parameters=None, ea_parameters=None)

                  Bases: pyrvea.Problem.biogp_problem.BioGP

                  +

                  The class for the BioGP surrogate model.

                  +
                  +
                  Parameters
                  +
                    +

                  • model_parameters (dict) – Parameters passed for the model.

                    • +

                  • ea_parameters (dict) – Parameters passed for the genetic algorithm.

                    • +
                  +
                  +
                  +
                  +
                  +name
                  +

                  Name of the model.

                  +
                  +
                  Type
                  +

                  str

                  +
                  +
                  +
                  + +
                  +
                  +linear_node
                  +

                  The parent node of the tree containing the linear values.

                  +
                  +
                  Type
                  +

                  LinearNode

                  +
                  +
                  +
                  + +
                  +
                  +fitness
                  +

                  Fitness of the trained model.

                  +
                  +
                  Type
                  +

                  list

                  +
                  +
                  +
                  + +
                  +
                  +minimize
                  +

                  List of which objectives to minimize.

                  +
                  +
                  Type
                  +

                  list (bool)

                  +
                  +
                  +
                  + +
                  +
                  +svr
                  +

                  Single variable response of the model.

                  +
                  +
                  Type
                  +

                  np.ndarray

                  +
                  +
                  +
                  + +
                  +
                  +log
                  +

                  If logging set to True in params, external log file is stored here.

                  +
                  +
                  Type
                  +

                  file

                  +
                  +
                  +
                  +

                  Methods

                  @@ -396,7 +534,7 @@

                  Submodules

                  - + @@ -419,35 +557,47 @@

                  Submodules

                  - + + + + - + - + - + - + - + + + + + + + + + +

                  select(self, pop, non_dom_front[, selection])

                  Select target model from the population.

                  set_params(self[, name, algorithm, …])

                  set_params(self[, name, training_algorithm, …])

                  Set parameters for the BioGP model.

                  single_variable_response(self[, ploton])

                  add

                  create_individuals

                  cos

                  create_individuals

                  div

                  div

                  log

                  log

                  mul

                  mul

                  sqrt

                  neg

                  sub

                  sin

                  sqrt

                  sub

                  tan
                  -set_params(self, name='BioGP_Model', algorithm=<class 'pyrvea.EAs.PPGA.PPGA'>, pop_size=500, max_depth=5, max_subtrees=4, prob_terminal=0.5, complexity_scalar=0.5, error_lim=0.001, selection='min_error', loss_func='root_mean_square', recombination_type=None, crossover_type='biogp_xover', mutation_type='biogp_mut', single_obj_generations=5, logging=False, plotting=False, ea_parameters=None, function_set=('add', 'sub', 'mul', 'div'), terminal_set=None)
                  +set_params(self, name='BioGP_Model', training_algorithm=<class 'pyrvea.EAs.PPGA.PPGA'>, pop_size=500, max_depth=5, max_subtrees=4, prob_terminal=0.5, complexity_scalar=0.5, error_lim=0.001, init_method='ramped_half_and_half', selection='min_error', loss_func='root_mean_square', recombination_type=None, crossover_type='biogp_xover', mutation_type='biogp_mut', single_obj_generations=10, logging=False, plotting=False, function_set=('add', 'sub', 'mul', 'div'), terminal_set=None)

                  Set parameters for the BioGP model.

                  Parameters

                  Contents:

                    -
                  • Creating surrogate models in Python with EvoNN and EvoDN2
                    • +
                  • Evolutionary algorithms in pyRVEA
                    • +
                  • Creating surrogate models in Python with EvoNN, EvoDN2 and BioGP
                  • pyrvea package
                    • Subpackages
                      • pyrvea.EAs package
                        • diff --git a/docs/_build/html/pyrvea.Selection.html b/docs/_build/html/pyrvea.Selection.html index e1d0999..e8d5791 100644 --- a/docs/_build/html/pyrvea.Selection.html +++ b/docs/_build/html/pyrvea.Selection.html @@ -84,7 +84,8 @@

                      Contents:

                        -
                      • Creating surrogate models in Python with EvoNN and EvoDN2
                        • +
                      • Evolutionary algorithms in pyRVEA
                        • +
                      • Creating surrogate models in Python with EvoNN, EvoDN2 and BioGP
                      • pyrvea package
                        • Subpackages
                          • pyrvea.EAs package
                            • diff --git a/docs/_build/html/pyrvea.html b/docs/_build/html/pyrvea.html index 8c8a748..524bddd 100644 --- a/docs/_build/html/pyrvea.html +++ b/docs/_build/html/pyrvea.html @@ -36,7 +36,7 @@ - + @@ -85,7 +85,8 @@

                          Contents:

                            -
                          • Creating surrogate models in Python with EvoNN and EvoDN2
                            • +
                          • Evolutionary algorithms in pyRVEA
                            • +
                          • Creating surrogate models in Python with EvoNN, EvoDN2 and BioGP
                          • pyrvea package
                            • Subpackages
                              • pyrvea.EAs package
                                • @@ -279,7 +280,7 @@

                                Subpackages - + diff --git a/docs/_build/html/search.html b/docs/_build/html/search.html index 1ad0bbf..ddfe6d5 100644 --- a/docs/_build/html/search.html +++ b/docs/_build/html/search.html @@ -84,7 +84,8 @@

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package"],titleterms:{EAs:5,algorithm:0,apd_select:10,baseea:5,baseproblem:8,biogp:[0,1],biogp_mut:9,biogp_problem:8,biogp_xov:9,bounded_polynomial_mut:9,contact:0,content:[4,5,6,7,8,9,10],creat:1,create_individu:7,dataproblem:8,document:2,evodn2:[0,1],evodn2_problem:8,evodn2_xover_mut:9,evolutionari:0,evonn:[0,1],evonn_problem:8,evonn_xover_mut:9,indic:[],introduct:0,isnotebook:6,model:1,modul:[4,5,6,7,8,9,10],newrv:6,note:0,nsgaiii:5,nsgaiii_select:10,othertool:6,packag:[4,5,6,7,8,9,10],plotlyanim:6,popul:7,ppga:[0,5],problem:8,pyrvea:[0,2,3,4,5,6,7,8,9,10],python:1,recombin:9,refer:0,referencevector:6,rvea:5,select:10,simulated_binary_crossov:9,slowrvea:5,submodul:[5,6,7,8,9,10],subpackag:4,surrog:1,symmetric_vector:6,tabl:[],test_funct:8,testproblem:8,tournament_select:10,tournamentea:5,welcom:2,what:0}}) \ No newline at end of file diff --git a/evodn2_example.py b/evodn2_example.py index a897606..066f421 100644 --- a/evodn2_example.py +++ b/evodn2_example.py @@ -60,7 +60,7 @@ } model_parameters = { - "training_algorithm": RVEA, + "training_algorithm": PPGA, "num_nodes": 25 } @@ -71,11 +71,11 @@ ) -y = problem.models["f1"][0].predict(problem.data[problem.x]) -problem.models["f1"][0].plot(y, problem.data["f1"], name="ZDT1_100" + "f1") +y_pred = problem.surrogates_predict(problem.data[problem.x]) -y2 = problem.models["f2"][0].predict(problem.data[problem.x]) -problem.models["f2"][0].plot(y2, problem.data["f2"], name="ZDT1_100" + "f2") +problem.models["f1"][0].plot(y_pred[:, 0], problem.data["f1"], name="ZDT1_1000" + "f1") + +problem.models["f2"][0].plot(y_pred[:, 1], problem.data["f2"], name="ZDT1_1000" + "f2") # Optimize # PPGA @@ -92,7 +92,7 @@ "neighbourhood_radius": 5, } -pop_ppga.evolve(PPGA, ea_parameters=ppga_parameters) +pop_ppga.evolve(EA=PPGA, ea_parameters=ppga_parameters) pop_ppga.plot_pareto( name="Tests/" + problem.models["f1"][0].__class__.__name__ + "_ppga_" + "ZDT1_100" @@ -109,7 +109,7 @@ rvea_parameters = {"iterations": 10, "generations_per_iteration": 25} -pop_rvea.evolve(RVEA, ea_parameters=rvea_parameters) +pop_rvea.evolve(EA=RVEA, ea_parameters=rvea_parameters) pop_rvea.plot_pareto( name="Tests/" + problem.models["f1"][0].__class__.__name__ + "_rvea_" + "ZDT1_100" diff --git a/pyrvea/Problem/evonn_problem.py b/pyrvea/Problem/evonn_problem.py index 1299678..84dc356 100644 --- a/pyrvea/Problem/evonn_problem.py +++ b/pyrvea/Problem/evonn_problem.py @@ -488,8 +488,8 @@ def plot(self, prediction, target, name=None): if name is None: name = self.name - trace0 = go.Scatter(x=prediction, y=target, mode="markers") - trace1 = go.Scatter(x=target, y=target) + trace0 = go.Scatter(x=prediction, y=target, name="prediction", mode="markers") + trace1 = go.Scatter(x=target, y=target, name="target") data = [trace0, trace1] plotly.offline.plot( data, From dbc4ce36ef7289ee07484eb1b2b7f72766953241 Mon Sep 17 00:00:00 2001 From: delamorte Date: Wed, 14 Aug 2019 13:59:03 +0300 Subject: [PATCH 155/155] tested zdt1 problem with biogp and evodn2 --- evodn2_example.py | 64 +++++++++++++++++++++-------------------------- 1 file changed, 29 insertions(+), 35 deletions(-) diff --git a/evodn2_example.py b/evodn2_example.py index 066f421..f98cfc2 100644 --- a/evodn2_example.py +++ b/evodn2_example.py @@ -21,55 +21,49 @@ problem = DataProblem(data=dataset, x=x, y=y) problem.train_test_split(train_size=0.7) -# f_set = ("add", "sub", "mul", "div", "sqrt", "neg") -# t_set = [1, 2] -#f_set = ("add", "sub", "mul", "div", "sqrt") -#t_set = [1, 9, 30] - +# f_set = ("add", "sub", "mul", "div", "sqrt") +# t_set = [1, 2, 9, 29] +# # ea_parameters = { -# "generations_per_iteration": 10, +# "generations_per_iteration": 20, # "iterations": 10, -# "predator_pop_size": 60, -# "target_pop_size": 300, -# "prob_prey_move": 0.5, -# "prob_crossover": 0.9, -# "prob_mutation": 0.3, -# "kill_interval": 5, -# "max_rank": 20, +# # } # model_params = { # "training_algorithm": RVEA, # "terminal_set": t_set, -# "function_set": f_set +# "function_set": f_set, +# "max_depth": 8 # } # # problem.train( # model_type="BioGP", # model_parameters=model_params, +# ea_parameters=ea_parameters # # ) -ea_parameters = { - "generations_per_iteration": 15, - "iterations": 10, - "prob_mutation": 0.7, - "neighbourhood_radius": 5, - "target_pop_size": 100, - "mut_strength": 0.7 - -} - -model_parameters = { - "training_algorithm": PPGA, - "num_nodes": 25 -} - -problem.train( - model_type="EvoNN", - model_parameters=model_parameters, - ea_parameters=ea_parameters - -) +# ea_parameters = { +# "generations_per_iteration": 15, +# "iterations": 10, +# "prob_mutation": 0.7, +# "neighbourhood_radius": 5, +# "target_pop_size": 100, +# "mut_strength": 0.7 +# +# } +# +# model_parameters = { +# "training_algorithm": RVEA, +# +# } +# +# problem.train( +# model_type="EvoDN2", +# model_parameters=model_parameters, +# ea_parameters=ea_parameters +# +# ) y_pred = problem.surrogates_predict(problem.data[problem.x])