Create definitions.py

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()
+© 2022 GitHub, Inc.
+Terms