92 lines
3.1 KiB
Python
92 lines
3.1 KiB
Python
"""
|
|
=============================================================
|
|
Receiver Operating Characteristic (ROC) with cross validation
|
|
=============================================================
|
|
|
|
Example of Receiver Operating Characteristic (ROC) metric to evaluate
|
|
classifier output quality using cross-validation.
|
|
|
|
ROC curves typically feature true positive rate on the Y axis, and false
|
|
positive rate on the X axis. This means that the top left corner of the plot is
|
|
the "ideal" point - a false positive rate of zero, and a true positive rate of
|
|
one. This is not very realistic, but it does mean that a larger area under the
|
|
curve (AUC) is usually better.
|
|
|
|
The "steepness" of ROC curves is also important, since it is ideal to maximize
|
|
the true positive rate while minimizing the false positive rate.
|
|
|
|
This example shows the ROC response of different datasets, created from K-fold
|
|
cross-validation. Taking all of these curves, it is possible to calculate the
|
|
mean area under curve, and see the variance of the curve when the
|
|
training set is split into different subsets. This roughly shows how the
|
|
classifier output is affected by changes in the training data, and how
|
|
different the splits generated by K-fold cross-validation are from one another.
|
|
|
|
.. note::
|
|
|
|
See also :func:`sklearn.metrics.auc_score`,
|
|
:func:`sklearn.cross_validation.cross_val_score`,
|
|
:ref:`example_plot_roc.py`,
|
|
|
|
"""
|
|
print(__doc__)
|
|
|
|
import numpy as np
|
|
from scipy import interp
|
|
import pylab as pl
|
|
|
|
from sklearn import svm, datasets
|
|
from sklearn.metrics import roc_curve, auc
|
|
from sklearn.cross_validation import StratifiedKFold
|
|
|
|
###############################################################################
|
|
# Data IO and generation
|
|
|
|
# import some data to play with
|
|
iris = datasets.load_iris()
|
|
X = iris.data
|
|
y = 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(y, n_folds=6)
|
|
classifier = svm.SVC(kernel='linear', probability=True,
|
|
random_state=random_state)
|
|
|
|
mean_tpr = 0.0
|
|
mean_fpr = np.linspace(0, 1, 100)
|
|
all_tpr = []
|
|
|
|
for i, (train, test) in enumerate(cv):
|
|
probas_ = classifier.fit(X[train], y[train]).predict_proba(X[test])
|
|
# Compute ROC curve and area the curve
|
|
fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])
|
|
mean_tpr += interp(mean_fpr, fpr, tpr)
|
|
mean_tpr[0] = 0.0
|
|
roc_auc = auc(fpr, tpr)
|
|
pl.plot(fpr, tpr, lw=1, label='ROC fold %d (area = %0.2f)' % (i, roc_auc))
|
|
|
|
pl.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Luck')
|
|
|
|
mean_tpr /= len(cv)
|
|
mean_tpr[-1] = 1.0
|
|
mean_auc = auc(mean_fpr, mean_tpr)
|
|
pl.plot(mean_fpr, mean_tpr, 'k--',
|
|
label='Mean ROC (area = %0.2f)' % mean_auc, lw=2)
|
|
|
|
pl.xlim([-0.05, 1.05])
|
|
pl.ylim([-0.05, 1.05])
|
|
pl.xlabel('False Positive Rate')
|
|
pl.ylabel('True Positive Rate')
|
|
pl.title('Receiver operating characteristic example')
|
|
pl.legend(loc="lower right")
|
|
pl.show()
|