first

app.py

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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)

definitions.py

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+import pandas as pd
+import streamlit as st
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.datasets import load_breast_cancer
+from sklearn.metrics import roc_auc_score,roc_curve,auc,accuracy_score,classification_report,confusion_matrix,precision_recall_curve
+import lightgbm as lgb
+import matplotlib.pyplot as plt
+import warnings
+warnings.filterwarnings('ignore')
+
+def plot_roc(fpr, tpr, label=None):
+    roc_auc = auc(fpr, tpr)
+    plt.title('Receiver Operating Characteristic')
+    plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)
+    plt.legend(loc = 'lower right')
+    plt.plot([0, 1], [0, 1],'r--')
+    plt.xlim([0, 1])
+    plt.ylim([0, 1])
+    plt.ylabel('True Positive Rate')
+    plt.xlabel('False Positive Rate')
+    plt.show()
+    st.pyplot()
+

requirements.txt

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+pandas==1.3.1
+streamlit==1.8.1
+numpy==1.20.3
+lightgbm==3.3.2
+matplotlib==3.4.2
+scikit-learn==1.0.1