COMP7404 Machine Learing——ROC

ROC

import pandas as pd
import numpy as np
df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data', header=None)

from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
X = df.loc[:, 2:].values
y = df.loc[:, 1].values
le = 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, auc
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import StratifiedKFold
import matplotlib.pyplot as plt
pipe_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 = 0.0
mean_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] = 1.0
mean_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()