def FNC():
FNC_Xtrain, FNC_Xtest, FNC_ytrain, FNC_ytest = load_FNC_data(datapath)
FNC_vectorizer = TfidfVectorizer(ngram_range=(v, k), max_features=m)
FNC_Xtrain_vect = FNC_vectorizer.fit_transform(FNC_Xtrain)
FNC_Xtest_vect = FNC_vectorizer.transform(FNC_Xtest)
FNC_feats = [
'_'.join(s.split()) for s in FNC_vectorizer.get_feature_names()
]
clf_FNC = None
clf_FNC = LogisticRegression(random_state=16,
solver='saga',
C=np.inf,
max_iter=10000).fit(FNC_Xtrain_vect,
FNC_ytrain)
FNC_coefs = clf_FNC.coef_
allcoefs_FNC = pd.DataFrame.from_records(FNC_coefs, columns=FNC_feats)
allcoefs_FNC.to_csv("FakeNewsCorpus_coefs.csv", sep="\t", index=False)
preds_FNC_test = clf_FNC.predict(FNC_Xtest_vect)
preds_FNC_train = clf_FNC.predict(FNC_Xtrain_vect)
print_scores(FNC_ytest, preds_FNC_test, "FakeNewsCorpus Test Scores")
print_scores(FNC_ytrain, preds_FNC_train, "FakeNewsCorpus Train Scores")
print("TRAINING WITH", trainingdata)
NUM_LAYERS = 1
print("NUMBER OF LAYERS:", NUM_LAYERS)
# kør med forskellige random seeds og tag gennemsnit. eller cross-validate.
random.seed(42)
np.random.seed(42)
set_random_seed(42)
if trainingdata == "liar":
train, dev, test, train_lab, dev_lab, test_lab = load_liar_data(datapath)
elif trainingdata == "kaggle":
train, test, train_lab, test_lab = load_kaggle_data(datapath)
elif trainingdata == "FNC":
train, test, train_lab, test_lab = load_FNC_data(datapath)
elif trainingdata == "BS":
train, test, train_lab, test_lab = load_BS_data(datapath)
train = [nltk.word_tokenize(i.lower()) for i in train]
test = [nltk.word_tokenize(i.lower()) for i in test]
if trainingdata == "liar":
dev = [nltk.word_tokenize(i.lower()) for i in dev]
else:
dev = train[int(abs((len(train_lab) / 3) * 2)):]
dev_lab = train_lab[int(abs((len(train_lab) / 3) * 2)):]
train = train[:int(abs((len(train_lab) / 3) * 2))]
train_lab = train_lab[:int(abs((len(train_lab) / 3) * 2))]
print(len(train), len(dev))
def train_and_test(datapath="/home/ktj250/thesis/data/",
emb_model_path="/home/ktj250/thesis/",
TIMEDISTRIBUTED=False,
trainingdata="liar",
num_cells=32,
num_epochs=10,
dropout=0.4,
r_dropout=0.4,
num_batch=64,
learning_rate=0.0001):
K.clear_session()
#colab_directory_path = "/gdrive/My Drive/Thesis/"
#TIMEDISTRIBUTED = False
use_pretrained_embeddings = True
FAKE=1
#trainingdata = sys.argv[1] #"liar" # kaggle, FNC, BS
print("trainingdata=",trainingdata)
if trainingdata == "liar":
train, dev, test, train_lab, dev_lab, test_lab = load_liar_data(datapath)
elif trainingdata == "kaggle":
train, test, train_lab, test_lab = load_kaggle_data(datapath)
elif trainingdata == "FNC":
train, test, train_lab, test_lab = load_FNC_data(datapath)
elif trainingdata == "BS":
train, test, train_lab, test_lab = load_BS_data(datapath)
train = [nltk.word_tokenize(i.lower()) for i in train]
test = [nltk.word_tokenize(i.lower()) for i in test]
if trainingdata == "liar":
dev = [nltk.word_tokenize(i.lower()) for i in dev]
else:
dev = train[int(abs((len(train_lab)/3)*2)):]
dev_lab = train_lab[int(abs((len(train_lab)/3)*2)):]
train = train[:int(abs((len(train_lab)/3)*2))]
train_lab = train_lab[:int(abs((len(train_lab)/3)*2))]
print(len(train), len(dev))
all_train_tokens = []
for i in train:
for word in i:
all_train_tokens.append(word)
vocab = set(all_train_tokens)
word2id = {word: i+1 for i, word in enumerate(vocab)}# making the first id is 1, so that I can pad with zeroes.
word2id["UNK"] = len(word2id)+1
id2word = {v: k for k, v in word2id.items()}
#trainTextsSeq: List of input sequence for each document (A matrix with size num_samples * max_doc_length)
trainTextsSeq = np.array([[word2id[w] for w in sent] for sent in train])
testTextsSeq = np.array([[word2id.get(w, word2id["UNK"]) for w in sent] for sent in test])
#if trainingdata == "liar":
devTextsSeq = np.array([[word2id.get(w, word2id["UNK"]) for w in sent] for sent in dev])
# PARAMETERS
# vocab_size: number of tokens in vocabulary
vocab_size = len(word2id)+1
# max_doc_length: length of documents after padding (in Keras, the length of documents are usually padded to be of the same size)
max_doc_length = 100 # LIAR 100 (like Wang), Kaggle 3391, FakeNewsCorpus 2669
# num_samples: number of training/testing data samples
num_samples = len(train_lab)
# num_time_steps: number of time steps in LSTM cells, usually equals to the size of input, i.e., max_doc_length
num_time_steps = max_doc_length
embedding_size = 300 # also just for now..
# padding with max doc lentgh
seq = sequence.pad_sequences(trainTextsSeq, maxlen=max_doc_length, dtype='int32', padding='post', truncating='post', value=0.0)
print("train seq shape",seq.shape)
test_seq = sequence.pad_sequences(testTextsSeq, maxlen=max_doc_length, dtype='int32', padding='post', truncating='post', value=0.0)
#if trainingdata == "liar":
dev_seq = sequence.pad_sequences(devTextsSeq, maxlen=max_doc_length, dtype='int32', padding='post', truncating='post', value=0.0)
if TIMEDISTRIBUTED:
train_lab = tile_reshape(train_lab, num_time_steps)
test_lab = tile_reshape(test_lab, num_time_steps)
print(train_lab.shape)
#if trainingdata == "liar":
dev_lab = tile_reshape(dev_lab, num_time_steps)
else:
train_lab = to_categorical(train_lab, 2)
test_lab = to_categorical(test_lab, 2)
print(train_lab.shape)
#if trainingdata == "liar":
dev_lab = to_categorical(dev_lab, 2)
print("Parameters:: num_cells: "+str(num_cells)+" num_samples: "+str(num_samples)+" embedding_size: "+str(embedding_size)+" epochs: "+str(num_epochs)+" batch_size: "+str(num_batch))
if use_pretrained_embeddings:
# https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html
# Load Google's pre-trained Word2Vec model.
model = gensim.models.KeyedVectors.load_word2vec_format(emb_model_path+'GoogleNews-vectors-negative300.bin', binary=True)
embedding_matrix = np.zeros((len(word2id) + 1, 300))
for word, i in word2id.items():
try:
embedding_vector = model.wv[word]
except:
embedding_vector = model.wv["UNK"]
if embedding_vector is not None:
embedding_matrix[i] = embedding_vector
myInput = Input(shape=(max_doc_length,), name='input')
print(myInput.shape)
if use_pretrained_embeddings:
x = Embedding(input_dim=vocab_size, output_dim=embedding_size, weights=[embedding_matrix],input_length=max_doc_length,trainable=True)(myInput)
else:
x = Embedding(input_dim=vocab_size, output_dim=embedding_size, input_length=max_doc_length)(myInput)
print(x.shape)
if TIMEDISTRIBUTED:
lstm_out = LSTM(num_cells, dropout=dropout, recurrent_dropout=r_dropout, return_sequences=True, kernel_constraint=NonNeg())(x)
predictions = TimeDistributed(Dense(1, activation='sigmoid', kernel_constraint=NonNeg()))(lstm_out)
else:
lstm_out = Bidirectional(LSTM(num_cells, dropout=dropout, recurrent_dropout=r_dropout))(x)
predictions = Dense(2, activation='softmax')(lstm_out)
model = Model(inputs=myInput, outputs=predictions)
opt = Adam(lr=learning_rate)
if TIMEDISTRIBUTED:
model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])
else:
model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
print("fitting model..")
#if trainingdata == "liar":
history = model.fit({'input': seq}, train_lab, epochs=num_epochs, verbose=2, batch_size=num_batch, validation_data=(dev_seq,dev_lab))
#else:
# history = model.fit({'input': seq}, train_lab, epochs=num_epochs, verbose=2, batch_size=num_batch)
print("Testing...")
test_score = model.evaluate(test_seq, test_lab, batch_size=num_batch, verbose=0)
#if trainingdata == "liar":
dev_score = model.evaluate(dev_seq, dev_lab, batch_size=num_batch, verbose=0)
print("Test loss:", test_score[0])
print("Test accuracy:", test_score[1])
#if trainingdata == "liar":
print("Valid loss:", dev_score[0])
print("Valid accuracy:", dev_score[1])
if not TIMEDISTRIBUTED:
preds = model.predict(test_seq)
f1 = f1_score(np.argmax(test_lab,axis=1), np.argmax(preds, axis=1))
tn, fp, fn, tp = confusion_matrix(np.argmax(test_lab,axis=1), np.argmax(preds, axis=1)).ravel()
print("tn, fp, fn, tp")
print(tn, fp, fn, tp)
model.summary()
#if trainingdata=="liar":
# return dev_score[1], history
#else:
return test_score[1], dev_score[1], history, f1
def test_on_FNC():
print("Testing on FNC")
train, test, train_lab, test_lab = load_FNC_data(datapath)
model_loaded2 = load_model(model_path + '.h5')
commons_testing(model_loaded2, test, test_lab, trainingdata + "_FNC")
def pre_modelling_stuff(TIMEDISTRIBUTED=False,
trainingdata="liar",
datapath="/home/ktj250/thesis/data/",
emb_model_path="/home/ktj250/thesis/"):
#directory_path = "/gdrive/My Drive/Thesis/"
#TIMEDISTRIBUTED = False
use_pretrained_embeddings = True
FAKE=1
#trainingdata = sys.argv[1] #"liar" # kaggle, FNC, BS
print("trainingdata=",trainingdata)
if trainingdata == "liar":
train, dev, test, train_lab, dev_lab, test_lab = load_liar_data(datapath)
elif trainingdata == "kaggle":
train, test, train_lab, test_lab = load_kaggle_data(datapath)
elif trainingdata == "FNC":
train, test, train_lab, test_lab = load_FNC_data(datapath)
elif trainingdata == "BS":
train, test, train_lab, test_lab = load_BS_data(datapath)
train = [nltk.word_tokenize(i.lower()) for i in train]
test = [nltk.word_tokenize(i.lower()) for i in test]
if trainingdata == "liar":
dev = [nltk.word_tokenize(i.lower()) for i in dev]
else:
dev = train[int(abs((len(train_lab)/3)*2)):]
dev_lab = train_lab[int(abs((len(train_lab)/3)*2)):]
train = train[:int(abs((len(train_lab)/3)*2))]
train_lab = train_lab[:int(abs((len(train_lab)/3)*2))]
print(len(train), len(dev))
all_train_tokens = []
for i in train:
for word in i:
all_train_tokens.append(word)
vocab = set(all_train_tokens)
word2id = {word: i+1 for i, word in enumerate(vocab)}# making the first id is 1, so that I can pad with zeroes.
word2id["UNK"] = len(word2id)+1
id2word = {v: k for k, v in word2id.items()}
#trainTextsSeq: List of input sequence for each document (A matrix with size num_samples * max_doc_length)
trainTextsSeq = np.array([[word2id[w] for w in sent] for sent in train])
testTextsSeq = np.array([[word2id.get(w, word2id["UNK"]) for w in sent] for sent in test])
#if trainingdata == "liar":
devTextsSeq = np.array([[word2id.get(w, word2id["UNK"]) for w in sent] for sent in dev])
# PARAMETERS
# vocab_size: number of tokens in vocabulary
vocab_size = len(word2id)+1
# max_doc_length: length of documents after padding (in Keras, the length of documents are usually padded to be of the same size)
max_doc_length = 100 # LIAR 100 (like Wang), Kaggle 3391, FakeNewsCorpus 2669
# num_samples: number of training/testing data samples
num_samples = len(train_lab)
# num_time_steps: number of time steps in LSTM cells, usually equals to the size of input, i.e., max_doc_length
num_time_steps = max_doc_length
embedding_size = 300 # also just for now..
# padding with max doc lentgh
seq = sequence.pad_sequences(trainTextsSeq, maxlen=max_doc_length, dtype='int32', padding='post', truncating='post', value=0.0)
print("train seq shape",seq.shape)
test_seq = sequence.pad_sequences(testTextsSeq, maxlen=max_doc_length, dtype='int32', padding='post', truncating='post', value=0.0)
#if trainingdata == "liar":
dev_seq = sequence.pad_sequences(devTextsSeq, maxlen=max_doc_length, dtype='int32', padding='post', truncating='post', value=0.0)
if TIMEDISTRIBUTED:
train_lab = tile_reshape(train_lab, num_time_steps)
test_lab = tile_reshape(test_lab, num_time_steps)
print(train_lab.shape)
#if trainingdata == "liar":
dev_lab = tile_reshape(dev_lab, num_time_steps)
else:
train_lab = to_categorical(train_lab, 2)
test_lab = to_categorical(test_lab, 2)
print(train_lab.shape)
#if trainingdata == "liar":
dev_lab = to_categorical(dev_lab, 2)
if use_pretrained_embeddings:
# https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html
# Load Google's pre-trained Word2Vec model.
model = gensim.models.KeyedVectors.load_word2vec_format(emb_model_path+'GoogleNews-vectors-negative300.bin', binary=True)
embedding_matrix = np.zeros((len(word2id) + 1, 300))
for word, i in word2id.items():
try:
embedding_vector = model.wv[word]
except:
embedding_vector = model.wv["UNK"]
if embedding_vector is not None:
embedding_matrix[i] = embedding_vector
#if trainingdata=="liar":
return embedding_matrix, seq, test_seq, dev_seq, train_lab, test_lab, dev_lab, vocab_size