def predict_third_set(gram_train,
gram_test,
y_label,
scale=20000,
max_iter=1,
lambd=0.00001):
gram_train = gram_train[0] + gram_train[1] + gram_train[2]
gram_test = gram_test[0] + gram_test[1] + gram_test[2]
krl = KRL(gram_m=gram_train / scale, max_iter=max_iter, lambd=lambd)
krl.fit(np.array(y_label))
y_pred_krl = krl.predict(gram_test / scale)
clf = SVM(gram_m=gram_train)
clf.fit(np.array(y_label))
y_pred_svm = clf.predict(gram_test)
y_pred = np.sign(y_pred_svm + y_pred_krl)
return y_pred
def classify(messages_train,labels_train,messages_test,process_messages_train,process_messages_test,tokens_train,tokens_test,process_tokens_train,process_tokens_test,pos_tags_train,pos_tags_test,negationList,clusters,slangDictionary,lexicons,mpqa_lexicons):
# 0 - negative messages
# 1 - positives messages
labels_train = [0 if x=="negative" else 1 for x in labels_train]
#compute pos tag bigrams for all messages
pos_bigrams_train = getBigrams(pos_tags_train)
pos_bigrams_test = getBigrams(pos_tags_test)
#compute pos tag trigrams for all messages
pos_trigrams_train = getTrigrams(pos_tags_train)
pos_trigrams_test = getTrigrams(pos_tags_test)
#get the unique pos bigrams and trigrams from training set
unique_pos_tags = getPosTagsSet(pos_tags_train)
unique_bigrams = getBigramsSet(pos_bigrams_train)
unique_trigrams= getTrigramsSet(pos_trigrams_train)
#calculate pos bigrams score for all categories
#both dictionaries will be used for training and testing (cannot create new for testing because we don't know the labels of the new messages)
pos_tags_scores_negative = posTagsScore(unique_pos_tags,0,pos_tags_train,labels_train)
pos_tags_scores_positive = posTagsScore(unique_pos_tags,1,pos_tags_train,labels_train)
#calculate pos bigrams score for all categories
#both dictionaries will be used for training and testing (cannot create new for testing because we don't know the labels of the new messages)
pos_bigrams_scores_negative = posBigramsScore(unique_bigrams,0,pos_bigrams_train,labels_train)
pos_bigrams_scores_positive = posBigramsScore(unique_bigrams,1,pos_bigrams_train,labels_train)
#calculate pos bigrams score for all categories
#both dictionaries will be used for training and testing (cannot create new for testing because we don't know the labels of the new messages)
pos_trigrams_scores_negative = posTrigramsScore(unique_trigrams,0,pos_trigrams_train,labels_train)
pos_trigrams_scores_positive = posTrigramsScore(unique_trigrams,1,pos_trigrams_train,labels_train)
#assign a precision and F1 score to each word of to all mpqa lexicons
mpqaScores = getScores(mpqa_lexicons,process_messages_train,labels_train)
#get features from train messages
features_train = features.getFeatures(messages_train,process_messages_train,tokens_train,process_tokens_train,pos_tags_train,slangDictionary,lexicons,mpqa_lexicons,pos_bigrams_train,pos_trigrams_train,pos_bigrams_scores_negative,pos_bigrams_scores_positive,pos_trigrams_scores_negative,pos_trigrams_scores_positive,pos_tags_scores_negative,pos_tags_scores_positive,mpqaScores,negationList,clusters)
#regularize train features
features_train=regularization.regularize(features_train)
#get features from test messages
features_test = features.getFeatures(messages_test,process_messages_test,tokens_test,process_tokens_test,pos_tags_test,slangDictionary,lexicons,mpqa_lexicons,pos_bigrams_test,pos_trigrams_test,pos_bigrams_scores_negative,pos_bigrams_scores_positive,pos_trigrams_scores_negative,pos_trigrams_scores_positive,pos_tags_scores_negative,pos_tags_scores_positive,mpqaScores,negationList,clusters)
#regularize test features
features_test=regularization.regularize(features_test)
#feature selection
#features_train, features_test = selection.feature_selection(features_train,labels_train,features_test,1150)
#C parameter of SVM
C = 0.001953125
#C = 19.3392161013
#train classifier and return trained model
#model = LogisticRegression.train(features_train,labels_train)
model = SVM.train(features_train,labels_train,c=C,k="linear")
#predict labels
#prediction = LogisticRegression.predict(features_test,model)
prediction = SVM.predict(features_test,model)
return prediction
make_submission_data(y_preds, 'nb_1214.csv')
# run Perceptron -----------------------
perceptron = Perceptron(r=0.1, margin=0.01, n_epoch=20)
perceptron.fit(data_train)
print("Predict the TEST set")
perceptron.predict(data_test, perceptron.weights[-1])
print("Predict the EVAL set")
y_preds = perceptron.predict(data_eval, perceptron.weights[-1])['y_preds']
make_submission_data(y_preds, 'perceptron.csv')
# run SVM
svm = SVM(r=0.01, c=1, n_epoch=17)
svm.fit(data_train)
print("Predict the TEST set")
svm.predict(data_test, svm.weights[-1])
print("Predict the EVAL set")
y_preds = svm.predict(data_eval, svm.weights[-1])['y_preds']
make_submission_data(y_preds, 'svm.csv')
# run Logistic -----------------------------
logistic = Logistic(r=0.01, sigma=100, n_epoch=10)
logistic.fit(data_train)
print("Predict the TEST set")
logistic.predict(data_test, logistic.weights[-1])
print("Preidict the EVAL set")
y_preds = logistic.predict(data_eval, logistic.weights[-1])['y_preds']
make_submission_data(y_preds, 'logistic.csv')
## run Bagging ------------------------------
# Cross validation
