def train_LLDA(n_topics=2):
### Load data
dataloader = csv_dataloader()
dataloader.load('output/data_cache.pk')
print "Read in finished"
train_id = dataloader.id
train_data = dataloader.data_retrieve(train_id)
labels = []
corpus = []
for id in train_id:
corpus.append(train_data['ldata'][id])
labels.append(score2label(train_data['score'][id]))
### Train LLDA
topics = get_topics2(method='LLDA', max_iter=2)
model_file = 'output/LLDA_8.pk'
if not os.path.exists(model_file):
print 'Training LLDA...'
topics.fit(corpus, labels, verbose=True)
topics.save(model_file)
else:
topics.load(model_file)
topics.summary()
def main(n_fold=10):
### Load trainning data
dataloader = csv_dataloader(extrafile="data/fixed_train_gender_class.csv", extra=True)
if not os.path.exists("output/data_cache.pk"):
dataloader.read_csv(applyfun=preprocess)
dataloader.save("output/data_cache.pk")
else:
dataloader.load("output/data_cache.pk")
dataloader.summary()
print "Read in finished"
### Calculate LIWC hist
LIWC = get_LIWC()
# print LIWC.calculate_hist(tokens, normalize=False)
### Train and Load LLDA
topics = get_topics2(method="LLDA", max_iter=20, n_topics=8)
model_file = "output/LLDA_8_20.pk"
if not os.path.exists(model_file):
train_id = dataloader.id
train_data = dataloader.data_retrieve(train_id)
labels = []
corpus = []
for id in train_id:
corpus.append(train_data["data"][id])
labels.append(score2label(train_data["score"][id]))
print "Training LLDA..."
topics.fit(corpus, labels, verbose=True)
topics.save(model_file)
print "Saved"
else:
topics.load(model_file)
topics.summary()
### ============================================================
### n fold
### ============================================================
nfolds = dataloader.nfold(n_fold)
fscores = []
models = []
for fold_ind in range(n_fold):
print "======================== FOLD " + str(fold_ind + 1) + "========================"
test_id = nfolds[fold_ind]
train_id = []
for i in range(n_fold):
if i != fold_ind:
train_id += nfolds[i]
### ============================================================
### Train Part
### ============================================================
print "Training>>>>>>>>>>>>>>>>>>>>>>>>>"
train_data = dataloader.data_retrieve(train_id)
### Balance Train Data
_, train_pos_id, train_neg_id = dataloader.balance(train_id, K=2)
encode_pos = encode_feature(train_data, train_pos_id, [topics, LIWC])
encode_pos = SMOTE(encode_pos, 200, len(train_pos_id) / 4)
label_pos = np.ones(len(encode_pos))
encode_neg = encode_feature(train_data, train_pos_id, [topics, LIWC])
label_neg = np.zeros(len(encode_neg))
encode = np.concatenate((encode_pos, encode_neg), axis=0)
label = np.concatenate((label_pos, label_neg), axis=0)
print encode.shape
print label.shape
### Train
encode = preprocessing.scale(encode)
classifier = svm.LinearSVC(verbose=True)
classifier.fit(encode, label)
print "F1 score: " + str(f1_score(label, classifier.predict(encode)))
### ============================================================
### Test Part
### ============================================================
print "Testing>>>>>>>>>>>>>>>>>>>>>>>>>"
test_data = dataloader.data_retrieve(test_id)
### Generate Test Data Encodings
encode = encode_feature(test_data, test_id, [topics, LIWC])
label = dataloader.label_retrieve(test_id)
### Test
encode = preprocessing.scale(encode)
print "F1 score: " + str(f1_score(label, classifier.predict(encode)))
fscores.append(f1_score(label, classifier.predict(encode)))
models.append(classifier)
print "MEAN F1 score: " + str(np.mean(fscores))
print "BEST F1 score: " + str(np.max(fscores)) + " by Model " + str(np.argmax(fscores) + 1)
print "VAR F1 score: " + str(np.var(fscores))
save(models[np.argmax(fscores)], "output/model_LLDA_8_" + str(fscores[np.argmax(fscores)]) + ".pk")
