probability=True,
gamma=0.646508 + .3,
random_state=23))
if MODE == 'cv':
scores, predictions = utils.make_blender_cv(clf,
train,
labels,
calibrate=True)
print 'CV:', scores, 'Mean log loss:', np.mean(scores)
utils.write_blender_data(consts.BLEND_PATH, MODEL_NAME + '.csv',
predictions)
elif MODE == 'submission':
calibrated_classifier = CalibratedClassifierCV(clf,
method='isotonic',
cv=utils.get_cv(labels))
fitted_classifier = calibrated_classifier.fit(train, labels)
predictions = fitted_classifier.predict_proba(test)
utils.save_submission(
consts.DATA_SAMPLE_SUBMISSION_PATH,
os.path.join(consts.ENSEMBLE_PATH, MODEL_NAME + '.csv'), predictions)
elif MODE == 'holdout':
score = utils.hold_out_evaluation(clf,
train,
labels,
calibrate=False,
test_size=0.9)
print 'Log loss:', score
elif MODE == 'tune':
train, labels, valid, valid_labels = utils.stratified_split(train,
labels,
test = feat_selector.transform(test)
print train.shape
# encode labels
lbl_enc = preprocessing.LabelEncoder()
labels = lbl_enc.fit_transform(labels)
# train classifier
clf = ensemble.ExtraTreesClassifier(n_jobs=3, n_estimators=600, max_features=20, min_samples_split=3,
bootstrap=False, verbose=3, random_state=23)
if MODE == 'cv':
scores, predictions = utils.make_blender_cv(clf, train, labels, calibrate=True)
print 'CV:', scores, 'Mean log loss:', np.mean(scores)
utils.write_blender_data(consts.BLEND_PATH, MODEL_NAME + '.csv', predictions)
elif MODE == 'submission':
calibrated_classifier = CalibratedClassifierCV(clf, method='isotonic', cv=utils.get_cv(labels))
fitted_classifier = calibrated_classifier.fit(train, labels)
predictions = fitted_classifier.predict_proba(test)
utils.save_submission(consts.DATA_SAMPLE_SUBMISSION_PATH,
os.path.join(consts.ENSEMBLE_PATH, MODEL_NAME + '.csv'),
predictions)
elif MODE == 'holdout':
score = utils.hold_out_evaluation(clf, train, labels, calibrate=True)
print 'Log loss:', score
else:
print 'Unknown mode'
# transform counts to TFIDF features
tfidf = feature_extraction.text.TfidfTransformer(smooth_idf=False)
train = tfidf.fit_transform(train).toarray()
test = tfidf.transform(test).toarray()
# encode labels
lbl_enc = preprocessing.LabelEncoder()
labels = lbl_enc.fit_transform(labels)
# train classifier
clf = ensemble.ExtraTreesClassifier(n_jobs=5, n_estimators=600, max_features=20, min_samples_split=3,
bootstrap=False, verbose=3, random_state=23)
if MODE == 'cv':
scores, predictions = utils.make_blender_cv(clf, train, labels, calibrate=True)
print( 'CV:', scores, 'Mean log loss:', np.mean(scores))
utils.write_blender_data(consts.BLEND_PATH, MODEL_NAME + '.csv', predictions)
elif MODE == 'submission':
calibrated_classifier = CalibratedClassifierCV(clf, method='isotonic', cv=utils.get_cv(labels))
fitted_classifier = calibrated_classifier.fit(train, labels)
predictions = fitted_classifier.predict_proba(test)
utils.save_submission(consts.DATA_SAMPLE_SUBMISSION_PATH,
os.path.join(consts.ENSEMBLE_PATH, MODEL_NAME + '.csv'),
predictions)
elif MODE == 'holdout':
score = utils.hold_out_evaluation(clf, train, labels, calibrate=True)
print( 'Log loss:', score)
else:
print( 'Unknown mode')
