From 5246afa12d3ee9b65792ce1788b124bb68bca285 Mon Sep 17 00:00:00 2001 From: faust-dante <2454575180@qq.com> Date: Mon, 2 Sep 2024 10:08:18 +0800 Subject: [PATCH 1/3] Add files via upload --- SVM_input_and_change_data.py | 117 +++++++++++++++++++++++++++++++++++ 1 file changed, 117 insertions(+) create mode 100644 SVM_input_and_change_data.py diff --git a/SVM_input_and_change_data.py b/SVM_input_and_change_data.py new file mode 100644 index 0000000..4d7fba7 --- /dev/null +++ b/SVM_input_and_change_data.py @@ -0,0 +1,117 @@ +import os +import json +import pandas as pd +import numpy as np +from rdkit import Chem +from rdkit.Chem import Descriptors +from rdkit.Chem.AllChem import GetMorganFingerprintAsBitVect +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from sklearn.svm import SVR +from sklearn.impute import SimpleImputer +from sklearn.metrics import mean_squared_error, r2_score + +# 指定包含 JSON 文件的本地文件夹路径 +folder_path = 'C:\\Users\\windows\\OneDrive\\Desktop\\ordjson' + +# 初始化一个空的列表来存储数据 +data_list = [] + +# 遍历文件夹中的所有文件 +for filename in os.listdir(folder_path): + if filename.endswith('.json'): + file_path = os.path.join(folder_path, filename) + + # 读取 JSON 文件 + with open(file_path, 'r') as file: + data = json.load(file) + + # 提取 SMILES、reagent、catalyst 和 yield 信息 + smiles_list = [] + reagents = [] + catalysts = [] + yield_value = None + + for input_type, input_data in data['inputs'].items(): + for component in input_data['components']: + for identifier in component['identifiers']: + if identifier['type'] == 'SMILES': + smiles_list.append(identifier['value']) + if input_type == 'Base': + reagents.append(component['amount']['moles']['value']) + elif input_type == 'metal and ligand': + catalysts.append(component['amount']['moles']['value']) + + for outcome in data.get('outcomes', []): + for product in outcome.get('products', []): + if product.get('is_desired_product', False): + for measurement in product.get('measurements', []): + if measurement['type'] == 'YIELD': + yield_value = measurement['percentage']['value'] + + # 计算分子指纹和描述符 + for smiles in smiles_list: + molecule = Chem.MolFromSmiles(smiles) + if molecule: + fingerprint = GetMorganFingerprintAsBitVect(molecule, radius=2, nBits=2048) + fingerprint_bits = list(fingerprint) + + # 计算分子描述符 + descriptors = {desc_name: desc_func(molecule) for desc_name, desc_func in Descriptors.descList} + + # 将数据添加到列表中 + data_list.append({ + 'fingerprint': fingerprint_bits, + 'reagent': sum(reagents), + 'catalyst': sum(catalysts), + 'yield': yield_value, + **descriptors + }) + else: + print(f"Invalid SMILES: {smiles}") + +# 将数据转换为 pandas 数据框 +df = pd.DataFrame(data_list) + +# 将分子指纹展开为单独的列 +fingerprint_df = pd.DataFrame(df['fingerprint'].tolist()) +df = df.drop(columns=['fingerprint']).join(fingerprint_df) + +# 将所有列名转换为字符串类型 +df.columns = df.columns.astype(str) + +# 分离特征和目标变量 +X = df.drop(columns=['yield']) +y = df['yield'] + +# 填补缺失值 +imputer = SimpleImputer(missing_values=np.nan, strategy='mean') +X_imputed = imputer.fit_transform(X) +X = pd.DataFrame(X_imputed, columns=X.columns.astype(str)) # 确保列名是字符串类型 + +# 划分训练集和测试集 +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + +# 标准化特征 +scaler = StandardScaler() +X_train = scaler.fit_transform(X_train) +X_test = scaler.transform(X_test) + +# 初始化并训练支持向量回归模型 +svr = SVR(kernel='linear') +svr.fit(X_train, y_train) + +# 预测测试集 +y_pred = svr.predict(X_test) + +# 评估模型性能 +mse = mean_squared_error(y_test, y_pred) +r2 = r2_score(y_test, y_pred) +print(f'Mean Squared Error: {mse}') +print(f'R² Score: {r2}') + +''' +Mean Squared Error: 877.5768873683029 +R² Score: -0.025876151665508385 +SVM输麻了，但推测应该不是SVM的问题，而是我填补数据太抽象了，用平均值填数据，直接寄了 +''' \ No newline at end of file From 643c765407f7b9e7014dbc2cfd3bd09edb96897c Mon Sep 17 00:00:00 2001 From: faust-dante <2454575180@qq.com> Date: Mon, 2 Sep 2024 10:42:37 +0800 Subject: [PATCH 2/3] Add files via upload --- SVM_Delete_data_column.py | 111 ++++++++++++++++++++++++++++++++++++++ SVM_Delete_data_row.py | 111 ++++++++++++++++++++++++++++++++++++++ 2 files changed, 222 insertions(+) create mode 100644 SVM_Delete_data_column.py create mode 100644 SVM_Delete_data_row.py diff --git a/SVM_Delete_data_column.py b/SVM_Delete_data_column.py new file mode 100644 index 0000000..b90d031 --- /dev/null +++ b/SVM_Delete_data_column.py @@ -0,0 +1,111 @@ +import os +import json +import pandas as pd +from rdkit import Chem +from rdkit.Chem import Descriptors +from rdkit.Chem.AllChem import GetMorganFingerprintAsBitVect +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from sklearn.svm import SVR +from sklearn.metrics import mean_squared_error, r2_score + +# 指定包含 JSON 文件的本地文件夹路径 +folder_path = 'C:\\Users\\windows\\OneDrive\\Desktop\\ordjson' + +# 初始化一个空的列表来存储数据 +data_list = [] + +# 遍历文件夹中的所有文件 +for filename in os.listdir(folder_path): + if filename.endswith('.json'): + file_path = os.path.join(folder_path, filename) + + # 读取 JSON 文件 + with open(file_path, 'r') as file: + data = json.load(file) + + # 提取 SMILES、reagent、catalyst 和 yield 信息 + smiles_list = [] + reagents = [] + catalysts = [] + yield_value = None + + for input_type, input_data in data['inputs'].items(): + for component in input_data['components']: + for identifier in component['identifiers']: + if identifier['type'] == 'SMILES': + smiles_list.append(identifier['value']) + if input_type == 'Base': + reagents.append(component['amount']['moles']['value']) + elif input_type == 'metal and ligand': + catalysts.append(component['amount']['moles']['value']) + + for outcome in data.get('outcomes', []): + for product in outcome.get('products', []): + if product.get('is_desired_product', False): + for measurement in product.get('measurements', []): + if measurement['type'] == 'YIELD': + yield_value = measurement['percentage']['value'] + + # 计算分子指纹和描述符 + for smiles in smiles_list: + molecule = Chem.MolFromSmiles(smiles) + if molecule: + fingerprint = GetMorganFingerprintAsBitVect(molecule, radius=2, nBits=2048) + fingerprint_bits = list(fingerprint) + + # 计算分子描述符 + descriptors = {desc_name: desc_func(molecule) for desc_name, desc_func in Descriptors.descList} + + # 将数据添加到列表中 + data_list.append({ + 'fingerprint': fingerprint_bits, + 'reagent': sum(reagents), + 'catalyst': sum(catalysts), + 'yield': yield_value, + **descriptors + }) + else: + print(f"Invalid SMILES: {smiles}") + +# 将数据转换为 pandas 数据框 +df = pd.DataFrame(data_list) + +# 将分子指纹展开为单独的列 +fingerprint_df = pd.DataFrame(df['fingerprint'].tolist()) +df = df.drop(columns=['fingerprint']).join(fingerprint_df) + +# 删除包含 NaN 值的列 +df.dropna(axis=1, inplace=True) + +# 将所有列名转换为字符串类型 +df.columns = df.columns.astype(str) + +# 分离特征和目标变量 +X = df.drop(columns=['yield']) +y = df['yield'] + +# 划分训练集和测试集 +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + +# 标准化特征 +scaler = StandardScaler() +X_train = scaler.fit_transform(X_train) +X_test = scaler.transform(X_test) + +# 初始化并训练支持向量回归模型 +svr_classifier = SVR(kernel='linear') +svr_classifier.fit(X_train, y_train) + +# 预测测试集 +y_pred = svr_classifier.predict(X_test) + +# 评估模型性能 +mse = mean_squared_error(y_test, y_pred) +r2 = r2_score(y_test, y_pred) +print(f'Mean Squared Error: {mse}') +print(f'R² Score: {r2}') +''' +Mean Squared Error: 878.8717817619925 +R² Score: -0.02738986664197629 +''' \ No newline at end of file diff --git a/SVM_Delete_data_row.py b/SVM_Delete_data_row.py new file mode 100644 index 0000000..27fcf34 --- /dev/null +++ b/SVM_Delete_data_row.py @@ -0,0 +1,111 @@ +import os +import json +import pandas as pd +from rdkit import Chem +from rdkit.Chem import Descriptors +from rdkit.Chem.AllChem import GetMorganFingerprintAsBitVect +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from sklearn.svm import SVR # 使用 SVR 而不是 SVC +from sklearn.metrics import mean_squared_error, r2_score + +# 指定包含 JSON 文件的本地文件夹路径 +folder_path = 'C:\\Users\\windows\\OneDrive\\Desktop\\ordjson' + +# 初始化一个空的列表来存储数据 +data_list = [] + +# 遍历文件夹中的所有文件 +for filename in os.listdir(folder_path): + if filename.endswith('.json'): + file_path = os.path.join(folder_path, filename) + + # 读取 JSON 文件 + with open(file_path, 'r') as file: + data = json.load(file) + + # 提取 SMILES、reagent、catalyst 和 yield 信息 + smiles_list = [] + reagents = [] + catalysts = [] + yield_value = None + + for input_type, input_data in data['inputs'].items(): + for component in input_data['components']: + for identifier in component['identifiers']: + if identifier['type'] == 'SMILES': + smiles_list.append(identifier['value']) + if input_type == 'Base': + reagents.append(component['amount']['moles']['value']) + elif input_type == 'metal and ligand': + catalysts.append(component['amount']['moles']['value']) + + for outcome in data.get('outcomes', []): + for product in outcome.get('products', []): + if product.get('is_desired_product', False): + for measurement in product.get('measurements', []): + if measurement['type'] == 'YIELD': + yield_value = measurement['percentage']['value'] + + # 计算分子指纹和描述符 + for smiles in smiles_list: + molecule = Chem.MolFromSmiles(smiles) + if molecule: + fingerprint = GetMorganFingerprintAsBitVect(molecule, radius=2, nBits=2048) + fingerprint_bits = list(fingerprint) + + # 计算分子描述符 + descriptors = {desc_name: desc_func(molecule) for desc_name, desc_func in Descriptors.descList} + + # 将数据添加到列表中 + data_list.append({ + 'fingerprint': fingerprint_bits, + 'reagent': sum(reagents), + 'catalyst': sum(catalysts), + 'yield': yield_value, + **descriptors + }) + else: + print(f"Invalid SMILES: {smiles}") + +# 将数据转换为 pandas 数据框 +df = pd.DataFrame(data_list) + +# 将分子指纹展开为单独的列 +fingerprint_df = pd.DataFrame(df['fingerprint'].tolist()) +df = df.drop(columns=['fingerprint']).join(fingerprint_df) + +# 删除包含 NaN 值的行 +df.dropna(inplace=True) + +# 将所有列名转换为字符串类型 +df.columns = df.columns.astype(str) + +# 分离特征和目标变量 +X = df.drop(columns=['yield']) +y = df['yield'] + +# 划分训练集和测试集 +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + +# 标准化特征 +scaler = StandardScaler() +X_train = scaler.fit_transform(X_train) +X_test = scaler.transform(X_test) + +# 初始化并训练支持向量回归模型 +svr_classifier = SVR(kernel='linear') +svr_classifier.fit(X_train, y_train) + +# 预测测试集 +y_pred = svr_classifier.predict(X_test) + +# 评估模型性能 +mse = mean_squared_error(y_test, y_pred) +r2 = r2_score(y_test, y_pred) +print(f'Mean Squared Error: {mse}') +print(f'R² Score: {r2}') +''' +Mean Squared Error: 881.9533834528651 +R² Score: -0.022689262137703725 +''' \ No newline at end of file From 5d10ff0989051813635b9636e5f57ea0ee966b87 Mon Sep 17 00:00:00 2001 From: applefunaf <145281418+applefunaf@users.noreply.github.com> Date: Wed, 4 Sep 2024 00:14:05 +0800 Subject: [PATCH 3/3] Update SVM_Delete_data_column.py --- SVM_Delete_data_column.py | 39 ++++++++++++++++++++++++++++----------- 1 file changed, 28 insertions(+), 11 deletions(-) diff --git a/SVM_Delete_data_column.py b/SVM_Delete_data_column.py index b90d031..6373d39 100644 --- a/SVM_Delete_data_column.py +++ b/SVM_Delete_data_column.py @@ -4,13 +4,13 @@ from rdkit import Chem from rdkit.Chem import Descriptors from rdkit.Chem.AllChem import GetMorganFingerprintAsBitVect -from sklearn.model_selection import train_test_split +from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.preprocessing import StandardScaler from sklearn.svm import SVR from sklearn.metrics import mean_squared_error, r2_score # 指定包含 JSON 文件的本地文件夹路径 -folder_path = 'C:\\Users\\windows\\OneDrive\\Desktop\\ordjson' +folder_path = 'C:\\Users\\35078\\OneDrive\\文档\\GitHub\\ordjson' # 初始化一个空的列表来存储数据 data_list = [] @@ -19,6 +19,7 @@ for filename in os.listdir(folder_path): if filename.endswith('.json'): file_path = os.path.join(folder_path, filename) + print(f"Processing file: {file_path}") # 调试信息 # 读取 JSON 文件 with open(file_path, 'r') as file: @@ -68,6 +69,12 @@ else: print(f"Invalid SMILES: {smiles}") +# 检查数据列表是否为空 +if not data_list: + print("No data loaded. Please check the JSON files and folder path.") +else: + print(f"Loaded {len(data_list)} records.") + # 将数据转换为 pandas 数据框 df = pd.DataFrame(data_list) @@ -93,19 +100,29 @@ X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) -# 初始化并训练支持向量回归模型 -svr_classifier = SVR(kernel='linear') -svr_classifier.fit(X_train, y_train) +# 定义参数网格 +param_grid = { + 'kernel': ['linear', 'poly', 'rbf', 'sigmoid'], + 'C': [0.1, 1, 10, 100], + 'gamma': ['scale', 'auto'] +} + +# 使用网格搜索进行超参数调优 +grid_search = GridSearchCV(SVR(), param_grid, cv=5, scoring='r2') +grid_search.fit(X_train, y_train) + +# 输出最佳参数 +print(f"Best parameters: {grid_search.best_params_}") + +# 使用最佳参数训练模型 +best_svr = grid_search.best_estimator_ +best_svr.fit(X_train, y_train) # 预测测试集 -y_pred = svr_classifier.predict(X_test) +y_pred = best_svr.predict(X_test) # 评估模型性能 mse = mean_squared_error(y_test, y_pred) r2 = r2_score(y_test, y_pred) print(f'Mean Squared Error: {mse}') -print(f'R² Score: {r2}') -''' -Mean Squared Error: 878.8717817619925 -R² Score: -0.02738986664197629 -''' \ No newline at end of file +print(f'R² Score: {r2}') \ No newline at end of file