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