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SVM_Delete_data_column.py

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+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, 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\\35078\\OneDrive\\文档\\GitHub\\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)
+        print(f"Processing file: {file_path}")  # 调试信息
+
+        # 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}")
+
+# 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)
+
+# 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)
+
+# 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 = 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}')

SVM_Delete_data_row.py

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+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
+'''

SVM_input_and_change_data.py

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+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
+'''