from sklearn.model_selection import learning_curve from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor from analysis.shap_model import * from metrics.calculate_classification_metrics import calculate_classification_metrics from metrics.calculate_regression_metrics import calculate_regression_metrics from static.new_class import * from static.process import grid_search, bayes_search class KNNClassifierParams: @classmethod def get_params(cls): return { "n_neighbors": [3, 5, 7, 9], "weights": ['uniform', 'distance'], "p": [1, 2] } # KNN分类 def knn_classifier(container: Container): x_train = container.x_train y_train = container.y_train x_test = container.x_test y_test = container.y_test hyper_params_optimize = container.hyper_params_optimize info = {} knn_classifier_model = KNeighborsClassifier() params = KNNClassifierParams.get_params() if hyper_params_optimize == "grid_search": best_model = grid_search(params, knn_classifier_model, x_train, y_train) elif hyper_params_optimize == "bayes_search": best_model = bayes_search(params, knn_classifier_model, x_train, y_train) else: best_model = knn_classifier_model best_model.fit(x_train, y_train) info["参数"] = best_model.get_params() y_pred = best_model.predict(x_test) container.set_y_pred(y_pred) train_sizes, train_scores, test_scores = learning_curve(best_model, x_train, y_train, cv=5) 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) container.set_learning_curve_values(train_sizes, train_scores_mean, train_scores_std, test_scores_mean, test_scores_std) info["指标"] = calculate_classification_metrics(y_pred, y_test) container.set_info(info) container.set_status("trained") container.set_model(best_model) return container class KNNRegressionParams: @classmethod def get_params(cls): return { "n_neighbors": [3, 5, 7, 9], "weights": ['uniform', 'distance'], "p": [1, 2] } # KNN回归 def knn_regression(container: Container): x_train = container.x_train y_train = container.y_train x_test = container.x_test y_test = container.y_test hyper_params_optimize = container.hyper_params_optimize info = {} knn_regression_model = KNeighborsRegressor() params = KNNRegressionParams.get_params() if hyper_params_optimize == "grid_search": best_model = grid_search(params, knn_regression_model, x_train, y_train) elif hyper_params_optimize == "bayes_search": best_model = bayes_search(params, knn_regression_model, x_train, y_train) else: best_model = knn_regression_model best_model.fit(x_train, y_train) info["参数"] = best_model.get_params() y_pred = best_model.predict(x_test) # y_pred = best_model.predict(x_test).reshape(-1, 1) container.set_y_pred(y_pred) train_sizes, train_scores, test_scores = learning_curve(best_model, x_train, y_train, cv=5) 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) container.set_learning_curve_values(train_sizes, train_scores_mean, train_scores_std, test_scores_mean, test_scores_std) info["指标"] = calculate_regression_metrics(y_pred, y_test) container.set_info(info) container.set_status("trained") container.set_model(best_model) return container