10 fold plot ROC with many classifers python3.6 I want to apply ROC curve for 10

Question

10 fold plot ROC with many classifers python3.6

I want to apply ROC curve for 10 fold cross validation with two classifier in python.

I follow some code but I still have trouble to present mean of 10 fold that present two classifier one for decision tree and other for regression

Here is my code: as you know 10 fold will test 10 times I want to present average of 10 times as ROC curve for decision tree and regression

iris= datasets.load_iris()

X,y = iris.data[50:,[1,2]], iris.target[50:]

X_train, X_test,y_train,y_test=

train_test_split(X,y,test_size=0.5,random_state=1)

#Run classifier with cross-validation and plot ROC curves

cv = StratifiedKFold(n_splits=10)

#classifier = svm.SVC(kernel='linear', probability=True,

classifier1 = DecisionTreeClassifier(max_depth=1,criterion='entropy',random_state=0)                 #random_state=random_state)

classifier2 = LogisticRegression()

classifier3=[classifier1,classifier2]

mean_tpr = 0.0

mean_fpr = np.linspace(0, 1, 100)

colors = cycle(['cyan', 'indigo'])

lw = 2

i = 0

for (train, test), color,classifier in zip(cv.split(X, y), colors,classifier3):

    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)

    plt.plot(fpr, tpr, lw=lw, color=color,

             label='ROC fold %d (area = %0.2f)' % (i, roc_auc))

    i += 1

plt.plot([0, 1], [0, 1], linestyle='--', lw=lw, color='k',

         label='Luck')

mean_tpr /= cv.get_n_splits(X, y)

mean_tpr[-1] = 1.0

mean_auc = auc(mean_fpr, mean_tpr)

plt.plot(mean_fpr, mean_tpr, color='g', linestyle='--',

         label='Mean ROC (area = %0.2f)' % mean_auc, lw=lw)

plt.xlim([-0.05, 1.05])

plt.ylim([-0.05, 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()

10 r 0.8 0.6 0.4 0.2 0.0 0.0 Receiver operating characteristic example ROC fold 0 (area 0.900 ROC fold 1 (area 1.00) Luck Mean ROC (area 0.190 10 0.2 0.8 0.4 0.6 False Positive Rate

Explanation / Answer

The below code will make use of iris dataset to plot ROC.

print __doc__
from scipy import interp
import pylab as pl
import numpy as np

from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.cross_validation import StratifiedKFold

# Data IO and generation

# load simple weight iris dataset
iris = datasets.load_iris()
X = iris.data
y = iris.target
X, y = X[y != 2], y[y != 2]
n_samples, n_features = X.shape

# Introduce noisy features
X = np.c_[X, np.random.randn(n_samples, 200 * n_features)]

# Run classifier with crossvalidation and plot 10 ROC curves
cv = StratifiedKFold(y, k=10)
classifier = svm.SVC(kernel='linear', probability=True)

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()

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