I built sentiment analyzer using SVM classifier. I trained model with probability=True and it can give me probability. But when I pickled my model and load it again later, the probability doesn't work anymore.
The model:
from sklearn.svm import SVC, LinearSVC
pipeline_svm = Pipeline([
('bow', CountVectorizer()),
('tfidf', TfidfTransformer()),
('classifier', SVC(probability=True)),])
# pipeline parameters to automatically explore and tune
param_svm = [
{'classifier__C': [1, 10, 100, 1000], 'classifier__kernel': ['linear']},
{'classifier__C': [1, 10, 100, 1000], 'classifier__gamma': [0.001, 0.0001], 'classifier__kernel': ['rbf']},
]
grid_svm = GridSearchCV(
pipeline_svm,
param_grid=param_svm,
refit=True,
n_jobs=-1,
scoring='accuracy',
cv=StratifiedKFold(label_train, n_folds=5),)
svm_detector_reloaded = cPickle.load(open('svm_sentiment_analyzer.pkl', 'rb'))
print(svm_detector_reloaded.predict([""""Today is awesome day"""])[0])
Gives me:
AttributeError: predict_proba is not available when probability=False
Use: SVM(probability=True)
or
grid_svm = GridSearchCV(
probability=True
pipeline_svm,
param_grid=param_svm,
refit=True,
n_jobs=-1,
scoring='accuracy',
cv=StratifiedKFold(label_train, n_folds=5),)
Adding (probability=True) while initializing the classifier as someone above suggested, resolved my error:
clf = SVC(kernel='rbf', C=1e9, gamma=1e-07, probability=True).fit(xtrain,ytrain)
You can use CallibratedClassifierCV for probability score output.
from sklearn.calibration import CalibratedClassifierCV
model_svc = LinearSVC()
model = CalibratedClassifierCV(model_svc)
model.fit(X_train, y_train)
Save model using pickle.
import pickle
filename = 'linearSVC.sav'
pickle.dump(model, open(filename, 'wb'))
Load model using pickle.load.
model = pickle.load(open(filename, 'rb'))
Now start prediction.
pred_class = model.predict(pred)
probability = model.predict_proba(pred)