Fixes

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "iohinspector"
-version = "0.1.0"
+version = "0.1.1"
 authors = [
   { name="Diederick Vermetten", email="d.vermetten@gmail.com" },
   { name="Jacob de Nobel", email="jacobdenobel@gmail.com" },

src/iohinspector/align.py

@@ -13,6 +13,7 @@ def align_data(
     y_col: str = "raw_y",
     output: str = "long",
     maximization: bool = False,
+    silence_warning: bool = False
 ) -> pl.DataFrame:
     """Align data based on function evaluation counts
 
@@ -24,12 +25,14 @@ def align_data(
         y_col (str, optional): function value column. Defaults to 'raw_y'.
         output (str, optional): whether to return a long or wide dataframe as output. Defaults to 'long'.
         maximization (bool, optional): whether the data comes from maximization or minimization. Defaults to False (minimization).
+        silence_warning (bool, optional): whether to silence the deprication warning
 
     Returns:
         pl.DataFrame: Alligned DataFrame
     """
+    if not silence_warning:
+        warnings.warn( "turbo_align is favoured over this function", DeprecationWarning)
 
-    warnings.warn( "Turbo align is favoured over this function", DeprecationWarning)
     evals_df = pl.DataFrame({x_col: evals})
 
     def merge_asof_group(group):
@@ -60,7 +63,7 @@ def merge_asof_group(group):
     if output == "long":
         return result_df
 
-    pivot_df = result_df.pivot(index=x_col, columns=group_cols, values=y_col)
+    pivot_df = result_df.pivot(index=x_col, on=group_cols, values=y_col)
     return pivot_df
 
 
@@ -103,16 +106,16 @@ def turbo_align(
 
     if x_col != "evaluations" and maximization:
         result_df = x_vals.join_asof(
-            df, by="data_id", on=x_col, strategy="forward"
+            df, by="data_id", on=x_col, strategy="forward", check_sortedness=False,
         )
     else:
         result_df = x_vals.join_asof(
-            df, by="data_id", on=x_col, strategy="backward"
+            df, by="data_id", on=x_col, strategy="backward", check_sortedness=False,
         )
 
 
     if output == "long":
         return result_df
 
-    pivot_df = result_df.pivot(index=x_col, columns=("data_id",), values=y_col)
+    pivot_df = result_df.pivot(index=x_col, on=("data_id",), values=y_col)
     return pivot_df

src/iohinspector/metrics/eaf.py

@@ -1,5 +1,5 @@
 
-from iohinspector.align import align_data
+from iohinspector.align import align_data, turbo_align
 from iohinspector.metrics import transform_fval, get_sequence
 import numpy as np
 import pandas as pd
@@ -52,13 +52,13 @@ def get_discritized_eaf_single_objective(
             eval_min, eval_max, eval_targets, scale_log=scale_eval_log, cast_to_int=True
         )
 
-    dt_aligned = align_data(
+    dt_aligned = turbo_align(
        data,
        eval_values,
        x_col=eval_var,
        y_col=fval_var,
        output="long"
-       ) 
+    ) 
     dt_aligned = transform_fval(
         dt_aligned,
         lb=f_min,
@@ -105,8 +105,8 @@ def get_eaf_data(
     if eval_max is None:
         eval_max = data[eval_var].max()
 
-    evals = get_sequence(eval_min, eval_max, 50, scale_eval_log, True)
-    long = align_data(data, np.array(evals, "uint64"), ["data_id"], output="long")
+    evals = get_sequence(eval_min, eval_max, 50, scale_eval_log, True).astype("uint64")
+    long = turbo_align(data, evals, output='long')
 
     if return_as_pandas:
         return long.to_pandas()

src/iohinspector/metrics/ecdf.py

@@ -69,6 +69,7 @@ def get_data_ecdf(
             x_col=eval_var,
             y_col=fval_var,
             maximization=maximization,
+            silence_warning=True
         )
     dt_ecdf = (
         transform_fval(

src/iohinspector/metrics/fixed_budget.py

@@ -48,6 +48,7 @@ def aggregate_convergence(
         x_col=eval_var,
         y_col=fval_var,
         maximization=maximization,
+        silence_warning=True
     )
     aggregations = [
         pl.mean(fval_var).alias("mean"),

src/iohinspector/metrics/fixed_target.py

@@ -50,6 +50,7 @@ def aggregate_running_time(
         x_col=fval_var,
         y_col=eval_var,
         maximization=maximization,
+        silence_warning=True
     )
 
     if eval_max is None:

src/iohinspector/metrics/ranking.py

@@ -34,7 +34,7 @@ def get_tournament_ratings(
     fids = data[fid_vars].unique()
     aligned_comps = data.pivot(
         index=alg_vars,
-        columns=fid_vars,
+        on=fid_vars,
         values=fval_var,
         aggregate_function=pl.element(),
     )

src/iohinspector/metrics/trajectory.py

@@ -36,13 +36,15 @@ def get_trajectory(data: pl.DataFrame,
     else:
         max_fevals = traj_length + min_fevals
     x_values = np.arange(min_fevals, max_fevals + 1) 
+
     data_aligned = align_data(
         data.cast({evaluation_variable: pl.Int64}),
         x_values,
         group_cols=["data_id"] + free_variables,
         x_col=evaluation_variable,
         y_col=fval_variable,
         maximization=maximization,
+        silence_warning=True
     )
     if return_as_pandas:
         data_aligned = data_aligned.to_pandas()

src/iohinspector/plots/attractor_network.py

@@ -1,10 +1,13 @@
+import warnings
 from dataclasses import dataclass
+from typing import Iterable
+
 import numpy as np
 import pandas as pd
 import polars as pl
-from typing import Iterable, Tuple
 import matplotlib
 import matplotlib.pyplot as plt
+
 from iohinspector.metrics import get_attractor_network
 from iohinspector.plots.utils import BasePlotArgs, _create_plot_args, _save_fig
 
@@ -134,11 +137,16 @@ def plot_attractor_network(
         )
     network.remove_edges_from(nx.selfloop_edges(network))
 
-    decision_matrix = [network.nodes[node]["decision"] for node in network.nodes()]
-    mds = MDS(n_components=1, random_state=0)
-    x_positions = mds.fit_transform(
-        decision_matrix
-    ).flatten()  # Flatten to get 1D array for x-axis
+    D = [network.nodes[node]["decision"] for node in network.nodes()]
+    mds = MDS(n_components=1, random_state=0, n_init=4)
+    if len(D[0]) == len(D):
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", category=UserWarning)
+            x_positions = mds.fit_transform(D)
+    else:
+        x_positions = mds.fit_transform(D)
+
+    x_positions = x_positions.flatten()  # Flatten to get 1D array for x-axis
     y_positions = [network.nodes[node]["fitness"] for node in network.nodes()]
     pos = {
         node: (x, y) for node, x, y in zip(network.nodes(), x_positions, y_positions)
@@ -200,5 +208,4 @@ def plot_attractor_network(
     plot_args.apply(ax)
 
     _save_fig(fig, file_name, plot_args)
-
     return ax, nodes, edges

src/iohinspector/plots/ranking.py

@@ -1,14 +1,13 @@
 from typing import Iterable, Optional
-from iohinspector.metrics.ranking import get_robustrank_changes, get_robustrank_over_time
-from iohinspector.plots.utils import BasePlotArgs, _create_plot_args, _save_fig
+
 import polars as pl
-import numpy as np
-import pandas as pd
 import matplotlib
 import matplotlib.pyplot as plt
 import seaborn as sbs
+
+from iohinspector.metrics.ranking import get_robustrank_changes, get_robustrank_over_time
+from iohinspector.plots.utils import BasePlotArgs, _create_plot_args, _save_fig
 from iohinspector.metrics import get_tournament_ratings
-from iohinspector.indicators import add_indicator
 
 
 def plot_tournament_ranking(
@@ -215,7 +214,6 @@ def plot_robustrank_changes(
         fig, ax = plt.subplots(1, 1, figsize=plot_args.figsize)
     else:
         fig = None
-
     plot_line_ranks(comparisons, ax=ax)
 
     plot_args.apply(ax)

tests/test_align.py

@@ -1,8 +1,6 @@
 import unittest
-import numpy as np
 import polars as pl
-from iohinspector.align import align_data
-from iohinspector.align import turbo_align
+from iohinspector.align import align_data, turbo_align
 
 
 class TestAlignData(unittest.TestCase):
@@ -15,7 +13,7 @@ def test_align_data_minimization_long(self):
         })
 
         evals = [1, 2, 3, 4, 5]
-        result = align_data(df, evals, group_cols=("data_id",), x_col="evaluations", y_col="raw_y", output="long", maximization=False)
+        result = align_data(df, evals, group_cols=("data_id",), x_col="evaluations", y_col="raw_y", output="long", maximization=False, silence_warning=True)
         expected = pl.DataFrame({
             "evaluations": [1, 2, 3, 4, 5, 1, 2, 3, 4, 5],
             "raw_y": [10, 8, 8, 8, 6, 20, 20, 20, 18, 16],
@@ -32,7 +30,7 @@ def test_align_data_maximization_long(self):
             "raw_y": [5, 7, 6]
         })
         evals = [1, 2, 3]
-        result = align_data(df, evals, group_cols=("data_id",), x_col="evaluations", y_col="raw_y", output="long", maximization=True)
+        result = align_data(df, evals, group_cols=("data_id",), x_col="evaluations", y_col="raw_y", output="long", maximization=True, silence_warning=True)
         expected = pl.DataFrame({
             "evaluations": [1, 2, 3],
             "raw_y": [5, 7, 7],
@@ -50,7 +48,7 @@ def test_align_data_wide_output(self):
         })
         evals = [1, 2, 3, 4, 5]
 
-        result = align_data(df, evals, group_cols=("data_id",), x_col="evaluations", y_col="raw_y", output="wide", maximization=False)
+        result = align_data(df, evals, group_cols=("data_id",), x_col="evaluations", y_col="raw_y", output="wide", maximization=False, silence_warning=True)
         # Should pivot to wide format
         self.assertIn("1", result.columns)
         self.assertIn("2", result.columns)
@@ -65,7 +63,7 @@ def test_align_data_custom_group_col(self):
             "raw_y": [5, 3, 7, 6]
         })
         evals = [1, 2]
-        result = align_data(df, evals, group_cols=("exp_id",), x_col="evaluations", y_col="raw_y", output="long", maximization=False)
+        result = align_data(df, evals, group_cols=("exp_id",), x_col="evaluations", y_col="raw_y", output="long", maximization=False, silence_warning=True)
         self.assertTrue(set(result["exp_id"].to_list()) == {1, 2})
 
     def test_align_data_non_default_x_col(self):
@@ -75,7 +73,7 @@ def test_align_data_non_default_x_col(self):
             "score": [100, 90, 80]
         })
         evals = [10, 20, 30]
-        result = align_data(df, evals, group_cols=("data_id",), x_col="steps", y_col="score", output="long", maximization=False)
+        result = align_data(df, evals, group_cols=("data_id",), x_col="steps", y_col="score", output="long", maximization=False, silence_warning=True)
         self.assertTrue(result["steps"].to_list() == [10, 20, 30])
 
 class TestTurboAlignData(unittest.TestCase):  
@@ -134,7 +132,7 @@ def test_turbo_align_non_default_x_col(self):
             "score": [100, 90, 80]
         })
         evals = [10, 20, 30]
-        result = align_data(df, evals, group_cols=("data_id",), x_col="steps", y_col="score", output="long", maximization=False)
+        result = turbo_align(df, evals, x_col="steps", y_col="score", output="long", maximization=False)
         self.assertTrue(result["steps"].to_list() == [10, 20, 30])
 
 if __name__ == "__main__":

tests/test_metrics/test_eaf.py

@@ -33,7 +33,7 @@ def test_basic_single_data_id(self):
         self.assertTrue(len(result.columns) == 10) # default x_targets
         self.assertEqual(result.shape[0], 101)  # default y_targets
         # Assert all values are 1 or 0
-        self.assertTrue(result[self.data["evaluations"].to_list()].applymap(lambda x: x in [1, 0]).all().all())
+        self.assertTrue(result[self.data["evaluations"].to_list()].map(lambda x: x in [1, 0]).all().all())
         self.assertEqual(result[1].tolist()[-1], 0)
         self.assertEqual(result[1000].tolist()[0], 1)
 
@@ -46,7 +46,7 @@ def test_basic_multi_data_id(self):
         self.assertTrue(len(result.columns) == 10) # default x_targets
         self.assertEqual(result.shape[0], 101)  # default y_targets
         # Assert all values are 1, 0.5 or 0
-        self.assertTrue(result[self.multi_data["evaluations"].to_list()].applymap(lambda x: x in [1, 0.5, 0]).all().all())
+        self.assertTrue(result[self.multi_data["evaluations"].to_list()].map(lambda x: x in [1, 0.5, 0]).all().all())
         self.assertEqual(result[1].tolist()[-1], 0)
         self.assertEqual(result[1000].tolist()[0], 1)
 
@@ -60,7 +60,7 @@ def test_custom_eval_min_max(self):
         self.assertTrue(all(x in result.columns for x in [2, 4]))
 
     def test_custom_f_min_max_targets(self):
-        result = get_discritized_eaf_single_objective(self.data, f_min=0.0, f_max=1.0, f_targets=5)
+        result = get_discritized_eaf_single_objective(self.data, f_min=1e-12, f_max=1.0, f_targets=5)
         self.assertEqual(result.shape[0], 5)
         self.assertAlmostEqual(result.index.min(), 0.0)
         self.assertAlmostEqual(result.index.max(), 1.0)

tests/test_plots/test_attractor_network.py

@@ -12,7 +12,8 @@ class TestPlotAttractorNetwork(unittest.TestCase):
     def setUp(self):
         self.data = pl.DataFrame({
             "x1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
-            "x2": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],   
+            "x2": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 3, 2], 
+            "x3": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13],   
             "raw_y": [35, 33, 31, 29, 27, 23, 18, 16, 14, 12, 10, 9, 6],
             "evaluations": [1,42, 81,121,161,201,241,281,321,361,401,442,481],
             "data_id": [1]*13
@@ -21,10 +22,10 @@ def setUp(self):
     def test_basic_call_returns_axes_and_data(self):
         ax, nodes, edges = plot_attractor_network(
             self.data, 
-            coord_vars=["x1", "x2"], 
+            coord_vars=["x1", "x2", "x3"], 
             fval_var="raw_y", 
             eval_var="evaluations",
-            )
+        )
 
         self.assertIsNotNone(ax)
         self.assertIsNotNone(nodes)

tests/test_plots/test_ranking.py

@@ -1,12 +1,13 @@
 import unittest
+import warnings
+
 import polars as pl
 import matplotlib
-from iohinspector.plots import plot_robustrank_over_time,plot_tournament_ranking, plot_robustrank_changes
-from iohinspector.indicators import HyperVolume
 
 matplotlib.use("Agg")  # Use non-interactive backend for tests
-import matplotlib.pyplot as plt
 
+from iohinspector.plots import plot_robustrank_over_time,plot_tournament_ranking, plot_robustrank_changes
+from iohinspector.indicators import HyperVolume
 
 class TestPlotTournamentRanking(unittest.TestCase):
     def setUp(self):
@@ -72,12 +73,14 @@ def setUp(self):
 
     def test_basic_call_returns_axes_and_data(self):
         evals = [1, 10, 100]
-        axs, comparison, benchmark = plot_robustrank_over_time(
-            self.data,
-            obj_vars=["f1", "f2", "f3"],
-            evals=evals,
-            indicator=HyperVolume(reference_point=[5.0, 5.0, 5.0]),
-        )
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", category=UserWarning)
+            axs, comparison, benchmark = plot_robustrank_over_time(
+                self.data,
+                obj_vars=["f1", "f2", "f3"],
+                evals=evals,
+                indicator=HyperVolume(reference_point=[5.0, 5.0, 5.0]),
+            )
         self.assertIsNotNone(axs)
         self.assertIsNotNone(comparison)
         self.assertIsNotNone(benchmark)
@@ -135,12 +138,14 @@ def setUp(self):
 
     def test_basic_call_returns_axes_and_data(self):
         evals = [1, 10, 100]
-        ax, dt = plot_robustrank_changes(
-            self.data, 
-            obj_vars=["f1","f2", "f3"], 
-            evals=evals, 
-            indicator=HyperVolume(reference_point=[5.0, 5.0, 5.0]),
-        )
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", category=UserWarning)
+            ax, dt = plot_robustrank_changes(
+                self.data, 
+                obj_vars=["f1","f2", "f3"], 
+                evals=evals, 
+                indicator=HyperVolume(reference_point=[5.0, 5.0, 5.0]),
+            )
         self.assertIsNotNone(ax)
         self.assertIsNotNone(dt)