plot ROC in skit learn library with python 3.6 i\'m trying to plot ROC with 5 fo

Question

plot ROC in skit learn library with python 3.6

i'm trying to plot ROC with 5 fold cross validation for regression classifer but I don't know how to do it.

this cod draw ROC for split data

# read the data into a Pandas DataFrame
import pandas as pd
url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data'
col_names = ['pregnant', 'glucose', 'bp', 'skin', 'insulin', 'bmi', 'pedigree', 'age', 'label']
pima = pd.read_csv(url, header=None, names=col_names)

# define X and y
feature_cols = ['pregnant', 'insulin', 'bmi', 'age']
X = pima[feature_cols]
y = pima.label

# split X and y into training and testing sets
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

# train a logistic regression model on the training set
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression()
logreg.fit(X_train, y_train)

y_pred_prob=logreg.predict_proba(X_test)[:,1]

# IMPORTANT: first argument is true values, second argument is predicted probabilities

fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred_prob)
plt.plot(fpr, tpr)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.0])
plt.title('ROC curve for diabetes classifier')
plt.xlabel('False Positive Rate (1 - Specificity)')
plt.ylabel('True Positive Rate (Sensitivity)')
plt.grid(True)

I tried this code to plot ROC for 5 fold cross valdation but that only plot ROC curve for only one point

predictedRG = model_selection.cross_val_predict(logreg, X, y , cv=5)

fpr, tpr, thresholds = metrics.roc_curve(y, predictedRG)
plt.plot(fpr, tpr)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.0])
plt.title('ROC curve for diabetes classifier')
plt.xlabel('False Positive Rate (1 - Specificity)')
plt.ylabel('True Positive Rate (Sensitivity)')
plt.grid(True)

so my question how can I plot ROC for 5 fold cross valdiation with regression classifer in right way. This figure is not right beacuse it is only show one point.

Explanation / Answer

# Run classifier with cross-validation and plot ROC curves cv = StratifiedKFold(n_splits=6) classifier = svm.SVC(kernel='linear', probability=True, random_state=random_state) mean_tpr = 0.0 mean_fpr = np.linspace(0, 1, 100) colors = cycle(['cyan', 'indigo', 'seagreen', 'yellow', 'blue', 'darkorange']) lw = 2 i = 0 for (train, test), color in zip(cv.split(X, y), colors): 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)

Related Questions

Navigate

• Browse (All)
• Browse P
• Subjects

Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.