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| from sklearn.model_selection import learning_curve | |
| from sklearn.naive_bayes import * | |
| import numpy as np | |
| from static.new_class import Container | |
| from static.process import grid_search, bayes_search | |
| from visualization.draw_line_graph import draw_line_graph | |
| from visualization.draw_scatter_line_graph import draw_scatter_line_graph | |
| from metrics.calculate_classification_metrics import calculate_classification_metrics | |
| from metrics.calculate_regression_metrics import calculate_regression_metrics | |
| class NaiveBayesClassifierParams: | |
| def get_params(cls, sort): | |
| if sort == "MultinomialNB": | |
| return { | |
| "alpha": [0.1, 0.5, 1.0, 2.0] | |
| } | |
| elif sort == "GaussianNB": | |
| return {} | |
| elif sort == "ComplementNB": | |
| return { | |
| "alpha": [0.1, 0.5, 1, 10], | |
| "fit_prior": [True, False], | |
| "norm": [True, False] | |
| } | |
| # 朴素贝叶斯分类 | |
| def naive_bayes_classification(container: Container, model=None): | |
| 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 = {} | |
| if model == "MultinomialNB": | |
| naive_bayes_model = MultinomialNB() | |
| params = NaiveBayesClassifierParams.get_params(model) | |
| elif model == "GaussianNB": | |
| naive_bayes_model = GaussianNB() | |
| params = NaiveBayesClassifierParams.get_params(model) | |
| elif model == "ComplementNB": | |
| naive_bayes_model = ComplementNB() | |
| params = NaiveBayesClassifierParams.get_params(model) | |
| else: | |
| naive_bayes_model = GaussianNB() | |
| params = NaiveBayesClassifierParams.get_params(model) | |
| if hyper_params_optimize == "grid_search": | |
| best_model = grid_search(params, naive_bayes_model, x_train, y_train) | |
| elif hyper_params_optimize == "bayes_search": | |
| best_model = bayes_search(params, naive_bayes_model, x_train, y_train) | |
| else: | |
| best_model = naive_bayes_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_classification_metrics(y_pred, y_test) | |
| container.set_info(info) | |
| container.set_status("trained") | |
| container.set_model(best_model) | |
| return container | |