roc_vis.py

print(__doc__)

import numpy as np
import matplotlib.pyplot as plt

from sklearn import svm, datasets
from sklearn.metrics import auc
from sklearn.metrics import plot_roc_curve
from sklearn.model_selection import StratifiedKFold

# #############################################################################
# Data IO and generation

# Import some data to play with
def roc_vis(X, y, classifier, n_splits=3):
    # X = X_train#iris.data
    # y = y_train#iris.target
    #X, y = X[y != 2], y[y != 2]
    n_samples, n_features = X.shape

    # Add noisy features
    random_state = np.random.RandomState(0)
    X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]

    # #############################################################################
    # Classification and ROC analysis

    # Run classifier with cross-validation and plot ROC curves
    cv = StratifiedKFold(n_splits=n_splits)
    # classifier = svm.SVC(kernel='linear', probability=True,
    #                     random_state=7)

    tprs = []
    aucs = []
    mean_fpr = np.linspace(0, 1, 100)

    fig, ax = plt.subplots()
    for i, (train, test) in enumerate(cv.split(X, y)):
        classifier.fit(X[train], y[train])
        viz = plot_roc_curve(classifier, X[test], y[test],
                            name='ROC fold {}'.format(i),
                            alpha=0.3, lw=1, ax=ax)
        interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)
        interp_tpr[0] = 0.0
        tprs.append(interp_tpr)
        aucs.append(viz.roc_auc)
        print('finished group {}'.format(i))

    ax.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',
            label='Chance', alpha=.8)

    mean_tpr = np.mean(tprs, axis=0)
    mean_tpr[-1] = 1.0
    mean_auc = auc(mean_fpr, mean_tpr)
    std_auc = np.std(aucs)
    ax.plot(mean_fpr, mean_tpr, color='b',
            label=r'Mean ROC (AUC = %0.2f $\pm$ %0.2f)' % (mean_auc, std_auc),
            lw=2, alpha=.8)

    std_tpr = np.std(tprs, axis=0)
    tprs_upper = np.minimum(mean_tpr + std_tpr, 1)
    tprs_lower = np.maximum(mean_tpr - std_tpr, 0)
    ax.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,
                    label=r'$\pm$ 1 std. dev.')

    ax.set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05],
        title="Receiver operating characteristic example")
    ax.legend(loc="lower right")
    plt.show()