def testLR():
print "Loading the data"
t0 = time()
vect = CountVectorizer(min_df=5, max_df=1.0, binary=True, ngram_range=(1, 1))
X_train, y_train, X_test, y_test, train_corpus, test_corpus = load_imdb("C:\\Users\\Mustafa\\Desktop\\aclImdb", shuffle=True, vectorizer=vect)
feature_names = vect.get_feature_names()
duration = time() - t0
print
print "Loading took %0.2fs." % duration
print
print "Fitting the classifier"
t0 = time()
clf = TransparentLogisticRegression(penalty='l1', C=0.1)
clf.fit(X_train, y_train)
duration = time() - t0
print
print "Fitting took %0.2fs." % duration
print
print "Predicting the evidences"
t0 = time()
neg_evi, pos_evi = clf.predict_evidences(X_test)
duration = time() - t0
print
print "Predicting evidences took %0.2fs." % duration
print
print "Predicting the probs"
t0 = time()
probs = clf.predict_proba(X_test)
duration = time() - t0
print
print "Predicting probs took %0.2fs." % duration
print
ti = TopInstances(neg_evi, pos_evi, clf.get_bias())
total_evi = neg_evi + pos_evi
print
print "Most negative"
print
i = ti.most_negatives()[0]
print total_evi[i], neg_evi[i], pos_evi[i], probs[i]
print test_corpus[i]
print
print "Most positive"
print
i = ti.most_positives()[0]
print total_evi[i], neg_evi[i], pos_evi[i], probs[i]
print test_corpus[i]
print "Predicting the evidences"
t0 = time()
neg_evi, pos_evi = clf.predict_evidences(X_test)
duration = time() - t0
print
print "Predicting evidences took %0.2fs." % duration
print
print "Predicting the probs"
t0 = time()
probs = clf.predict_proba(X_test)
duration = time() - t0
print
print "Predicting probs took %0.2fs." % duration
print
total_evi = neg_evi + pos_evi
evi_sorted = np.argsort(total_evi)
coef_diags = diags(clf.coef_[0], 0)
# Most negative
t0 = time()
vect = Vectorizer(min_df=5, max_df=1.0, binary=False, ngram_range=(1, 1))
X_train, y_train, X_val, y_val, X_test, y_test, train_corpus, val_corpus, test_corpus = load_imdb("./aclImdb", shuffle=True, vectorizer=vect)
y_test_na = y_test[:, np.newaxis]
y_test_na = np.append(y_test_na, 1-y_test_na, axis=1)
y_val_na = y_val[:, np.newaxis]
y_val_na = np.append(y_val_na, 1-y_val_na, axis=1)
clf = Classifier()
clf.fit(X_train, y_train)
ctrl_clf = clf
ctrl_error = ce_squared(y_test_na, clf.predict_proba(X_test))
ctrl_acc = clf.score(X_test, y_test)
duration = time() - t0
print("Loading the dataset took {:0.2f}s.".format(duration), '\n')
with open('clf12.arch', 'rb') as f:
clf_arch = pickle.load(f)
clf_arch.stats()
ctrl_clf = clf_arch.ctrl_clf
best_clf = clf_arch.classifiers[-1]
first_clf = clf_arch.classifiers[0]
train_indices = clf_arch.train_indices[-1]
y_modified = np.copy(clf_arch.modified_labels[-1])
