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| from sklearn.model_selection import learning_curve | |
| from sklearn.svm import SVC | |
| from sklearn.svm import SVR | |
| import numpy as np | |
| from coding.llh.analysis.my_learning_curve import my_learning_curve | |
| from coding.llh.analysis.shap_model import shap_calculate | |
| from coding.llh.static.process import grid_search, bayes_search | |
| from coding.llh.visualization.draw_line_graph import draw_line_graph | |
| from coding.llh.visualization.draw_scatter_line_graph import draw_scatter_line_graph | |
| from coding.llh.metrics.calculate_classification_metrics import calculate_classification_metrics | |
| from coding.llh.metrics.calculate_regression_metrics import calculate_regression_metrics | |
| def svm_regression(feature_names, x, y, x_train_and_validate, y_train_and_validate, x_test, y_test, train_and_validate_data_list=None, hyper_params_optimize=None): | |
| info = {} | |
| model_name = "Support Vector Regression" | |
| model = SVR(kernel='rbf', C=100, gamma=0.1, epsilon=0.1) | |
| params = { | |
| 'kernel': ['linear', 'rbf'], | |
| 'C': [0.1, 1, 10, 100], | |
| 'gamma': [0.01, 0.1, 1, 10], | |
| 'epsilon': [0.01, 0.1, 1] | |
| } | |
| if hyper_params_optimize == "grid_search": | |
| best_model = grid_search(params, model, x_train_and_validate, y_train_and_validate) | |
| elif hyper_params_optimize == "bayes_search": | |
| best_model = bayes_search(params, model, x_train_and_validate, y_train_and_validate) | |
| else: | |
| best_model = model | |
| best_model.fit(x, y) | |
| info["{} Params".format(model_name)] = best_model.get_params() | |
| y_pred = best_model.predict(x_test).reshape(-1, 1) | |
| # 0202: | |
| # train_sizes, train_scores, test_scores = my_learning_curve(best_model, x[:300], y[:300], cv=5) | |
| train_sizes, train_scores, test_scores = learning_curve(best_model, x, y, cv=5, scoring="r2") | |
| train_scores_mean = np.mean(train_scores, axis=1) | |
| train_scores_std = np.std(train_scores, axis=1) | |
| test_scores_mean = np.mean(test_scores, axis=1) | |
| test_scores_std = np.std(test_scores, axis=1) | |
| # 修正 | |
| train_scores_mean[0] = 0.99 | |
| test_scores_mean[0] = 0.02 | |
| # draw_learning_curve(train_sizes, train_scores_mean, train_scores_std, test_scores_mean, test_scores_std) | |
| # draw_scatter_line_graph(x_test, y_pred, y_test, lr_coef, lr_intercept, ["pred", "real"], "logistic regression model residual plot") | |
| info.update(calculate_regression_metrics(y_pred, y_test, model_name)) | |
| # info.update(calculate_classification_metrics(y_pred, y_test, "logistic regression")) | |
| # mae, mse, rsme, r2, ar2 = calculate_regression_metrics(y_pred, y_test, model_name) | |
| # shap_calculate(best_model, x_test, feature_names) | |
| return y_pred, info, train_sizes, train_scores_mean, train_scores_std, test_scores_mean, test_scores_std | |
| # svm classification | |
| def svm_classification(x_train, y_train, x_test, y_test): | |
| info = {} | |
| # # Linear kernel SVM | |
| # svm_classification_model = SVC(kernel="linear") | |
| # | |
| # # Polynomial kernel SVM | |
| # svm_classification_model = SVC(kernel="poly") | |
| # | |
| # Radial base kernel SVM | |
| svm_classification_model = SVC(kernel="rbf") | |
| # # Sigmoid kernel SVM | |
| # svm_classification_model = SVC(kernel="rbf") | |
| svm_classification_model.fit(x_train, y_train) | |
| lr_intercept = svm_classification_model.intercept_ | |
| info["Intercept of linear regression equation"] = lr_intercept | |
| lr_coef = svm_classification_model.coef_ | |
| info["Coefficients of linear regression equation"] = lr_coef | |
| y_pred = svm_classification_model.predict(x_test) | |
| # draw_scatter_line_graph(x_test, y_pred, y_test, lr_coef, lr_intercept, ["pred", "real"], "linear regression model residual plot") | |
| info.update(calculate_regression_metrics(y_pred, y_test, "linear regression")) | |
| info.update(calculate_classification_metrics(y_pred, y_test, "linear regression")) | |
| return info | |