Getting perfect ROC-AUC score for Linear SVC - scikit-learn

Question

I am evaluating different classifiers for my sentiment analysis model. I am looking at all available metrics, and whilst most achieve a similar precision, recall, F1-scores and ROC-AUC scores, Linear SVM appears to get a perfect ROC-AUC score. Look at the chart below:
Abbreviations: MNB=Multinomial Naive Bayes, SGD=Stochastic Gradient Descent, LR=Logistic Regression, LSVC=Linear Support Vector Classification
Here are the rest of the performance metrics for LSVC, which are very similar to the rest of the classifiers:
precision recall f1-score support
neg 0.83 0.90 0.87 24979
pos 0.90 0.82 0.86 25021
avg / total 0.87 0.86 0.86 50000
As you can see the dataset is balanced for pos and neg comments.
Here is the relevant code:
def evaluate(classifier):
predicted = classifier.predict(testing_text)
if isinstance(classifier.steps[2][1], LinearSVC):
probabilities = np.array(classifier.decision_function(testing_text))
scores = probabilities
else:
probabilities = np.array(classifier.predict_proba(testing_text))
scores = np.max(probabilities, axis=1)
pos_idx = np.where(predicted == 'pos')
predicted_true_binary = np.zeros(predicted.shape)
predicted_true_binary[pos_idx] = 1
fpr, tpr, thresholds = metrics.roc_curve(predicted_true_binary, scores)
auc = metrics.roc_auc_score(predicted_true_binary, scores)
mean_acc = np.mean(predicted == testing_category)
report = metrics.classification_report(testing_category, predicted)
confusion_matrix = metrics.confusion_matrix(testing_category, predicted)
return fpr, tpr, auc, mean_acc, report, confusion_matrix
I am using predict_proba for all classifiers apart from LSVC which uses decision_function instead (since it does not have a predict_proba method`)
What's going on?
EDIT: changes according to #Vivek Kumar's comments:
def evaluate(classifier):
predicted = classifier.predict(testing_text)
if isinstance(classifier.steps[2][1], LinearSVC):
probabilities = np.array(classifier.decision_function(testing_text))
scores = probabilities
else:
probabilities = np.array(classifier.predict_proba(testing_text))
scores = probabilities[:, 1] # NEW
testing_category_array = np.array(testing_category) # NEW
pos_idx = np.where(testing_category_array == 'pos')
predicted_true_binary = np.zeros(testing_category_array.shape)
predicted_true_binary[pos_idx] = 1
fpr, tpr, thresholds = metrics.roc_curve(predicted_true_binary, scores)
auc = metrics.roc_auc_score(predicted_true_binary, scores)
mean_acc = np.mean(predicted == testing_category)
report = metrics.classification_report(testing_category, predicted)
confusion_matrix = metrics.confusion_matrix(testing_category, predicted)
return fpr, tpr, auc, mean_acc, report, confusion_matrix
This now yields this graph:

Answer 1

I don't think it is valid to compare the methods predict_proba and decision_function like for like. The first sentence in the docs for LSVC decision function "Predict confidence scores for samples." must not be read as "predicting probabilties". The second sentences clarifies it, it is similar to the decision function for the general SVC.
You can use predict_proba for a linear SVC with sklearn; then you need to specific under the general SVC the kernel as 'linear'. However, then you are changing the implementation under the hood (away from "LIBLINEAR").