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Victor Phan
2026-07-16 19:32:43 +07:00
parent 25969cb0f5
commit a258db54cd
69 changed files with 3377 additions and 645 deletions
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new_train.py
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@@ -234,7 +234,15 @@ X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, r
# In[21]:
get_ipython().run_cell_magic('time', '', 'from sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import accuracy_score\n\n# Define the models\nrf_model = RandomForestClassifier(random_state=42, n_jobs=-1)\nknn_model = KNeighborsClassifier()\nnb_model = GaussianNB()\nsvm_model = SVC()\n\n# Create a pipeline\npipeline = Pipeline([\n (\'scaler\', StandardScaler()), # Apply scaling\n (\'classifier\', rf_model) # Placeholder, will be set by param_grid\n])\n\n# Define the parameter grid for each classifier\nparam_grid = [\n # RandomForest\n {\n \'classifier\': [rf_model],\n \'classifier__n_estimators\': [100, 300, 500, 700],\n \'classifier__max_depth\': [6, 8, 10, 15],\n \'classifier__criterion\': [\'gini\', \'entropy\'],\n },\n # KNeighborsClassifier\n {\n \'classifier\': [knn_model],\n \'classifier__n_neighbors\': [3, 5, 7, 9],\n \'classifier__weights\': [\'uniform\', \'distance\'],\n \'classifier__metric\': [\'euclidean\', \'manhattan\']\n },\n # Naive Bayes (GaussianNB doesn\'t have hyperparameters to tune here)\n {\n \'classifier\': [nb_model],\n },\n # SVM\n {\n \'classifier\': [svm_model],\n \'classifier__C\': [0.1, 1, 10, 100],\n \'classifier__kernel\': [\'linear\', \'rbf\'],\n \'classifier__gamma\': [\'scale\', \'auto\']\n }\n]\n\n# Use GridSearchCV to find the best classifier and hyperparameters\ngrid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring=\'accuracy\', n_jobs=-1)\ngrid_search.fit(X_train, y_train)\n\n# Print out the best parameters and classifier\nbest_params = grid_search.best_params_\nprint("Best Parameters:", best_params)\n\n# Make predictions on the validation set\ny_pred = grid_search.predict(X_val)\n\n# Evaluate the results\naccuracy = accuracy_score(y_val, y_pred)\nprint(f"Accuracy: {round(accuracy, 2)*100} %")\n')
get_ipython().run_cell_magic('time', '', 'from sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import GridSearchCV\nfrom xgboost import XGBClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import accuracy_score\n\n# Define the models\nrf_model = XGBClassifier(
n_estimators=200,
max_depth=30,
tree_method="hist",
device="cuda",
random_state=42,
n_jobs=-1,
verbosity=1
)\nknn_model = KNeighborsClassifier()\nnb_model = GaussianNB()\nsvm_model = SVC()\n\n# Create a pipeline\npipeline = Pipeline([\n (\'scaler\', StandardScaler()), # Apply scaling\n (\'classifier\', rf_model) # Placeholder, will be set by param_grid\n])\n\n# Define the parameter grid for each classifier\nparam_grid = [\n # RandomForest\n {\n \'classifier\': [rf_model],\n \'classifier__n_estimators\': [100, 300, 500, 700],\n \'classifier__max_depth\': [6, 8, 10, 15],\n \'classifier__criterion\': [\'gini\', \'entropy\'],\n },\n # KNeighborsClassifier\n {\n \'classifier\': [knn_model],\n \'classifier__n_neighbors\': [3, 5, 7, 9],\n \'classifier__weights\': [\'uniform\', \'distance\'],\n \'classifier__metric\': [\'euclidean\', \'manhattan\']\n },\n # Naive Bayes (GaussianNB doesn\'t have hyperparameters to tune here)\n {\n \'classifier\': [nb_model],\n },\n # SVM\n {\n \'classifier\': [svm_model],\n \'classifier__C\': [0.1, 1, 10, 100],\n \'classifier__kernel\': [\'linear\', \'rbf\'],\n \'classifier__gamma\': [\'scale\', \'auto\']\n }\n]\n\n# Use GridSearchCV to find the best classifier and hyperparameters\ngrid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring=\'accuracy\', n_jobs=-1)\ngrid_search.fit(X_train, y_train)\n\n# Print out the best parameters and classifier\nbest_params = grid_search.best_params_\nprint("Best Parameters:", best_params)\n\n# Make predictions on the validation set\ny_pred = grid_search.predict(X_val)\n\n# Evaluate the results\naccuracy = accuracy_score(y_val, y_pred)\nprint(f"Accuracy: {round(accuracy, 2)*100} %")\n')
# In[22]: