plot_roc.py

"""
=======================================
Receiver Operating Characteristic (ROC)
=======================================

Multiclass settings
-------------------

ROC curves are typically used in binary classification to study the output of
a classifier. In order to extend ROC curve and ROC area to multi-class
or multi-label classification, it is necessary to binarize the output. One ROC
curve can be drawn per label, but one can also draw a ROC curve by considering
each element of the label indicator matrix as a binary prediction
(micro-averaging).

Another evaluation measure for multi-class classification is
macro-averaging, which gives equal weight to the classification of each
label.

.. note::

    See also :func:`sklearn.metrics.roc_auc_score`,
             :ref:`sphx_glr_auto_examples_model_selection_plot_roc_crossval.py`.

"""
#print(__doc__)

import numpy as np
import matplotlib.pyplot as plt
from itertools import cycle

from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.multiclass import OneVsRestClassifier
from scipy import interp

from keras.utils import np_utils
# Import some data to play with
iris = datasets.load_iris()
X = iris.data
y = iris.target

# Binarize the output
y = label_binarize(y, classes=[0, 1, 2])
#y = np_utils.to_categorical(y, num_classes=3)
n_classes = y.shape[1] # n_classes = 3

# Add noisy features to make the problem harder
random_state = np.random.RandomState(0)
n_samples, n_features = X.shape
X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]

# shuffle and split training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=0)

# Learn to predict each class against the other
classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True,
                                 random_state=random_state))
y_score = classifier.fit(X_train, y_train).decision_function(X_test)

# Compute ROC curve and ROC area for each class
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
    # print('y_test[:, {}] ='.format(i))
    # print(y_test[:, i])
    # print('y_score[:, {}] ='.format(i))
    # print(y_score[:, i])
    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])
    roc_auc[i] = auc(fpr[i], tpr[i])

# Compute micro-average ROC curve and ROC area
# print("y_test.ravel(): {}".format(y_test.ravel()))
# print("y_score.ravel(): {}".format(y_score.ravel()))
fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])

##############################################################################
# Plot of a ROC curve for a specific class
plt.figure()
lw = 2
plt.plot(fpr[2], tpr[2], color='darkorange',
         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[2])
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.show()

##############################################################################
# Plot ROC curves for the multiclass problem

# Compute macro-average ROC curve and ROC area

# First aggregate all false positive rates
# for i in range(n_classes):
#     print(len(fpr[i]))
#     print(fpr[i])
all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)])) # Returns the sorted unique elements of an array
#print("all_fpr: {}".format(all_fpr))

# Then interpolate all ROC curves at this points
mean_tpr = np.zeros_like(all_fpr) # Return an array of zeros with the same shape and type as a given array.
for i in range(n_classes):
    # numpy.interp(x, xp, fp, left=None, right=None, period=None)
    # Returns the one-dimensional piecewise linear interpolant to a function with given discrete data points (xp, fp), evaluated at x.
    mean_tpr += interp(all_fpr, fpr[i], tpr[i])

# Finally average it and compute AUC
mean_tpr /= n_classes

fpr["macro"] = all_fpr
tpr["macro"] = mean_tpr
roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])

# Plot all ROC curves
plt.figure()
plt.plot(fpr["micro"], tpr["micro"], abel='micro-average ROC curve (area = {0:0.2f})'.format(roc_auc["micro"]), color='deeppink', linestyle=':', linewidth=4)

plt.plot(fpr["macro"], tpr["macro"], label='macro-average ROC curve (area = {0:0.2f})'.format(roc_auc["macro"]), color='navy', linestyle=':', linewidth=4)

colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])
for i, color in zip(range(n_classes), colors):
    plt.plot(fpr[i], tpr[i], color=color, lw=lw, label='ROC curve of class {0} (area = {1:0.2f})'.format(i, roc_auc[i]))

plt.plot([0, 1], [0, 1], 'k--', lw=lw)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Some extension of Receiver operating characteristic to multi-class')
plt.legend(loc="lower right")
plt.show()