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ROC

import pandas as pdimport numpy as npdf = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data', header=None)from sklearn.preprocessing import LabelEncoderfrom sklearn.model_selection import train_test_splitX = df.loc[:, 2:].valuesy = df.loc[:, 1].valuesle = LabelEncoder()y = le.fit_transform(y)X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, stratify=y, random_state=1)from sklearn.metrics import roc_curve, aucfrom sklearn.preprocessing import StandardScalerfrom sklearn.decomposition import PCAfrom sklearn.linear_model import LogisticRegressionfrom sklearn.pipeline import make_pipelinefrom sklearn.model_selection import StratifiedKFoldimport matplotlib.pyplot as pltpipe_lr = make_pipeline(StandardScaler(), PCA(n_components=2),                         LogisticRegression(penalty='l2', random_state=1, C=100.0, solver='liblinear'))X_train2 = X_train[:, [4, 14]] cv = list(StratifiedKFold(n_splits=3, random_state=1, shuffle=True).split(X_train, y_train))fig = plt.figure(figsize=(7, 5))mean_tpr = 0.0mean_fpr = np.linspace(0, 1, 100)all_tpr = []for i, (train, test) in enumerate(cv):    probas = pipe_lr.fit(X_train2[train], y_train[train]).predict_proba(X_train2[test])    fpr, tpr, thresholds = roc_curve(y_train[test], probas[:, 1], pos_label=1)    mean_tpr += np.interp(mean_fpr, fpr, tpr)    mean_tpr[0] = 0.0    roc_auc = auc(fpr, tpr)    plt.plot(fpr, tpr, label='ROC fold %d (area = %0.2f)' % (i+1, roc_auc))plt.plot([0, 1], [0, 1], linestyle='--', color=(0.6, 0.6, 0.6), label='random guessing')mean_tpr /= len(cv)mean_tpr[-1] = 1.0mean_auc = auc(mean_fpr, mean_tpr)plt.plot(mean_fpr, mean_tpr, 'k--', label='mean ROC (area = %0.2f)' % mean_auc, lw=2)plt.plot([0, 0, 1], [0, 1, 1], linestyle=':', color='black', label='perfect performance')plt.xlim([-0.05, 1.05])plt.ylim([-0.05, 1.05])plt.xlabel('false positive rate')plt.ylabel('true positive rate')plt.legend(loc="lower right")plt.tight_layout()plt.show()

 

 

 

 

 

 

 

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