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| from definitions import * | |
| st.set_option('deprecation.showPyplotGlobalUse', False) | |
| st.sidebar.subheader("请选择模型参数:sunglasses:") | |
| num_leaves = st.sidebar.slider(label = 'num_leaves', min_value = 4, | |
| max_value = 200 , | |
| value = 31, | |
| step = 1) | |
| max_depth = st.sidebar.slider(label = 'max_depth', min_value = -1, | |
| max_value = 15, | |
| value = -1, | |
| step = 1) | |
| min_data_in_leaf = st.sidebar.slider(label = 'min_data_in_leaf', min_value = 8, | |
| max_value = 55, | |
| value = 20, | |
| step = 1) | |
| feature_fraction = st.sidebar.slider(label = 'feature_fraction', min_value = 0.0, | |
| max_value = 1.0 , | |
| value = 0.8, | |
| step = 0.1) | |
| min_data_per_group = st.sidebar.slider(label = 'min_data_per_group', min_value = 6, | |
| max_value = 289 , | |
| value = 100, | |
| step = 1) | |
| max_cat_threshold = st.sidebar.slider(label = 'max_cat_threshold', min_value = 6, | |
| max_value = 289 , | |
| value = 32, | |
| step = 1) | |
| learning_rate = st.sidebar.slider(label = 'learning_rate', min_value = 0.0, | |
| max_value = 1.00, | |
| value = 0.05, | |
| step = 0.01) | |
| num_leaves = st.sidebar.slider(label = 'num_leaves', min_value = 6, | |
| max_value = 289 , | |
| value = 31, | |
| step = 1) | |
| max_bin = st.sidebar.slider(label = 'max_bin', min_value = 6, | |
| max_value = 289 , | |
| value = 255, | |
| step = 1) | |
| num_iterations = st.sidebar.slider(label = 'num_iterations', min_value = 8, | |
| max_value = 289, | |
| value = 100, | |
| step = 1) | |
| st.header('LightGBM-parameter-tuning-with-streamlit') | |
| # 加载数据 | |
| breast_cancer = load_breast_cancer() | |
| data = breast_cancer.data | |
| target = breast_cancer.target | |
| # 划分训练数据和测试数据 | |
| X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2) | |
| # 转换为Dataset数据格式 | |
| lgb_train = lgb.Dataset(X_train, y_train) | |
| lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train) | |
| # 模型训练 | |
| params = {'num_leaves': num_leaves, 'max_depth': max_depth, | |
| 'min_data_in_leaf': min_data_in_leaf, | |
| 'feature_fraction': feature_fraction, | |
| 'min_data_per_group': min_data_per_group, | |
| 'max_cat_threshold': max_cat_threshold, | |
| 'learning_rate':learning_rate,'num_leaves':num_leaves, | |
| 'max_bin':max_bin,'num_iterations':num_iterations | |
| } | |
| gbm = lgb.train(params, lgb_train, num_boost_round=2000, valid_sets=lgb_eval, early_stopping_rounds=500) | |
| lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train) | |
| probs = gbm.predict(X_test, num_iteration=gbm.best_iteration) # 输出的是概率结果 | |
| fpr, tpr, thresholds = roc_curve(y_test, probs) | |
| st.write('------------------------------------') | |
| st.write('Confusion Matrix:') | |
| st.write(confusion_matrix(y_test, np.where(probs > 0.5, 1, 0))) | |
| st.write('------------------------------------') | |
| st.write('Classification Report:') | |
| report = classification_report(y_test, np.where(probs > 0.5, 1, 0), output_dict=True) | |
| report_matrix = pd.DataFrame(report).transpose() | |
| st.dataframe(report_matrix) | |
| st.write('------------------------------------') | |
| st.write('ROC:') | |
| plot_roc(fpr, tpr) |