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MTL2_CV_VeracityStance.py
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MTL2_CV_VeracityStance.py
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"""
Created on Sun Aug 27 14:57:40 2017
@author: Helen
"""
import os
from keras.models import Model
from keras.layers import Input,LSTM, Dense, Masking, Dropout, TimeDistributed
from keras import regularizers
import numpy as np
import pickle
from hyperopt import STATUS_OK
from keras.utils.np_utils import to_categorical
from branch2treelabels import branch2treelabels
from sklearn.metrics import f1_score, accuracy_score
from sklearn.metrics import precision_recall_fscore_support
from keras.preprocessing.sequence import pad_sequences
from copy import deepcopy
#%%
def build_model(params, num_features):
num_lstm_units = int(params['num_lstm_units'])
num_lstm_layers = int (params['num_lstm_layers'])
num_dense_layers = int(params['num_dense_layers'])
num_dense_units = int(params['num_dense_units'])
l2reg = params['l2reg']
inputs_ab = Input(shape=(None,num_features), name="input")
mask_ab = Masking(mask_value=0.)(inputs_ab)
lstm_ab = LSTM(num_lstm_units, return_sequences=True)(mask_ab)
for nl in range(num_lstm_layers-1):
lstm_ab2 = LSTM(num_lstm_units, return_sequences=True)(lstm_ab)
lstm_ab = lstm_ab2
# lstm_a = LSTM(num_lstm_units, return_sequences=True)(lstm_ab)
hidden1_a = TimeDistributed(Dense(num_dense_units))(lstm_ab)
for nl in range(num_dense_layers-1):
hidden2_a = TimeDistributed(Dense(num_dense_units))(hidden1_a)
hidden1_a = hidden2_a
dropout_a = TimeDistributed(Dropout(0.5))(hidden1_a)
softmax_a = TimeDistributed(
Dense(4,
activation='softmax',
activity_regularizer=regularizers.l2(l2reg)),
name='softmaxa')(dropout_a)
lstm_b = LSTM(num_lstm_units, return_sequences=False)(lstm_ab)
hidden1_b = Dense(num_dense_units)(lstm_b)
for nl in range(num_dense_layers-1):
hidden2_b = Dense(num_dense_units)(hidden1_b)
hidden1_b = hidden2_b
dropout_b = Dropout(0.5)(hidden1_b)
softmax_b = Dense(3,
activation='softmax',
activity_regularizer=regularizers.l2(l2reg),
name='softmaxb')(dropout_b)
model = Model(inputs=inputs_ab, outputs=[softmax_a, softmax_b])
model.compile(optimizer='adam',
loss={'softmaxa':'categorical_crossentropy',
'softmaxb':'categorical_crossentropy' },
loss_weights={'softmaxa': 0.5, 'softmaxb': 0.5},
metrics=['accuracy'],
sample_weight_mode={'softmaxa':'temporal',
'softmaxb':None})
return model
def training (params,x_train,y_trainA,y_trainB):
num_epochs = params['num_epochs']
batchsize = params['batchsize']
num_features = np.shape(x_train)[2]
model = build_model(params, num_features)
maskA = np.any(y_trainA,axis=2) + 0.00001
model.fit(x_train, {"softmaxa":y_trainA,"softmaxb": y_trainB},
sample_weight={"softmaxa":maskA, "softmaxb": None},
epochs=num_epochs, batch_size=batchsize,verbose=0)
return model
#%%
def objective_MTL2_stance_CV5(params):
path = 'saved_data/saved_data_MTL2_stance'
train = ['ferguson', 'ottawashooting','sydneysiege']
test = 'charliehebdo'
max_branch_len = 25
x_train = []
ya_train = []
yb_train = []
for t in train:
temp_x_train = np.load(os.path.join(path,t,
'train_array.npy'))
temp_ya_train = np.load(os.path.join(path,t,
'fold_stance_labels.npy'))
temp_yb_train = np.load(os.path.join(path,t, 'labels.npy'))
temp_x_train = pad_sequences(temp_x_train, maxlen=max_branch_len,
dtype='float32', padding='post',
truncating='post', value=0.)
temp_ya_train = pad_sequences(temp_ya_train, maxlen=max_branch_len,
dtype='int32', padding='post',
truncating='post', value=0.)
x_train.extend(temp_x_train)
ya_train.extend(temp_ya_train)
yb_train.extend(temp_yb_train)
x_train = np.asarray(x_train)
ya_train = np.asarray(ya_train)
yb_train = np.asarray(yb_train)
yb_train = to_categorical(yb_train, num_classes=3)
x_test = np.load(os.path.join(path,test, 'train_array.npy'))
ya_test = np.load(os.path.join(path,test, 'fold_stance_labels.npy'))
yb_test = np.load(os.path.join(path,test, 'labels.npy'))
ids_testA = np.load(os.path.join(path, test, 'tweet_ids.npy'))
ids_testB = np.load(os.path.join(path, test, 'ids.npy'))
model = training( params, x_train, ya_train, yb_train)
pred_probabilities_a, pred_probabilities_b = model.predict(x_test,
verbose=0)
Y_pred_a = np.argmax(pred_probabilities_a, axis=2)
Y_pred_b = np.argmax(pred_probabilities_b, axis=1)
trees, tree_prediction, tree_label = branch2treelabels(
ids_testB,
yb_test,
Y_pred_b)
mactest_F_b = f1_score(tree_label, tree_prediction,
average='macro', labels=[0,1,2])
mask = np.any(np.any(ya_test,axis=2),axis=1)
if np.any(mask):
Y_true_a = ya_test[mask]
Y_true_a = np.argmax(Y_true_a, axis=2)
Y_pred_a = Y_pred_a[mask]
ids_testA = ids_testA[mask]
fids_test = ids_testA.flatten()
fy_pred = Y_pred_a.flatten()
fy_test = Y_true_a.flatten()
uniqtwid, uindices2 = np.unique(fids_test, return_index=True)
uniqtwid = uniqtwid.tolist()
uindices2 = uindices2.tolist()
del uindices2[uniqtwid.index(b'a')]
del uniqtwid[uniqtwid.index(b'a')]
uniq_dev_prediction = [fy_pred[i] for i in uindices2]
uniq_dev_label = [fy_test[i] for i in uindices2]
mactest_F_a = f1_score(uniq_dev_prediction, uniq_dev_label,
average='macro', labels=[0,1,2,3])
else:
mactest_F_a = 0
uniq_dev_prediction = []
uniq_dev_label = []
output = {'loss': (1-mactest_F_a)+(1-mactest_F_b),
'Params': params,
'status': STATUS_OK
}
return output
#%%
def objective_MTL2_stance_CV9(params):
path = 'saved_data/saved_data_MTL2_stance'
train = ['ferguson', 'ottawashooting','sydneysiege', 'putinmissing',
'prince-toronto', 'gurlitt', 'ebola-essien']
test = 'charliehebdo'
max_branch_len = 25
x_train = []
ya_train = []
yb_train = []
for t in train:
temp_x_train = np.load(os.path.join(path,t,
'train_array.npy'))
temp_ya_train = np.load(os.path.join(path,t,
'fold_stance_labels.npy'))
temp_yb_train = np.load(os.path.join(path,t, 'labels.npy'))
temp_x_train = pad_sequences(temp_x_train, maxlen=max_branch_len,
dtype='float32', padding='post',
truncating='post', value=0.)
temp_ya_train = pad_sequences(temp_ya_train, maxlen=max_branch_len,
dtype='int32', padding='post',
truncating='post', value=0.)
x_train.extend(temp_x_train)
ya_train.extend(temp_ya_train)
yb_train.extend(temp_yb_train)
x_train = np.asarray(x_train)
ya_train = np.asarray(ya_train)
yb_train = np.asarray(yb_train)
yb_train = to_categorical(yb_train, num_classes=3)
x_test = np.load(os.path.join(path,test, 'train_array.npy'))
ya_test = np.load(os.path.join(path,test, 'fold_stance_labels.npy'))
yb_test = np.load(os.path.join(path,test, 'labels.npy'))
ids_testA = np.load(os.path.join(path, test, 'tweet_ids.npy'))
ids_testB = np.load(os.path.join(path, test, 'ids.npy'))
model = training( params, x_train, ya_train, yb_train)
pred_probabilities_a, pred_probabilities_b = model.predict(x_test,
verbose=0)
Y_pred_a = np.argmax(pred_probabilities_a, axis=2)
Y_pred_b = np.argmax(pred_probabilities_b, axis=1)
trees, tree_prediction, tree_label = branch2treelabels(
ids_testB,
yb_test,
Y_pred_b)
mactest_F_b = f1_score(tree_label, tree_prediction,
average='macro', labels=[0,1,2])
mask = np.any(np.any(ya_test,axis=2),axis=1)
if np.any(mask):
Y_true_a = ya_test[mask]
Y_true_a = np.argmax(Y_true_a, axis=2)
Y_pred_a = Y_pred_a[mask]
ids_testA = ids_testA[mask]
fids_test = ids_testA.flatten()
fy_pred = Y_pred_a.flatten()
fy_test = Y_true_a.flatten()
uniqtwid, uindices2 = np.unique(fids_test, return_index=True)
uniqtwid = uniqtwid.tolist()
uindices2 = uindices2.tolist()
del uindices2[uniqtwid.index(b'a')]
del uniqtwid[uniqtwid.index(b'a')]
uniq_dev_prediction = [fy_pred[i] for i in uindices2]
uniq_dev_label = [fy_test[i] for i in uindices2]
mactest_F_a = f1_score(uniq_dev_prediction, uniq_dev_label,
average='macro', labels=[0,1,2,3])
else:
mactest_F_a = 0
uniq_dev_prediction = []
uniq_dev_label = []
output = {'loss': (1-mactest_F_a)+(1-mactest_F_b),
'Params': params,
'status': STATUS_OK
}
return output
#%%
def eval_MTL2_stance_CV(params, data, fname):
path = 'saved_data/saved_data_MTL2_stance'
if data=='PHEME5':
folds = ['charliehebdo', 'germanwings-crash', 'ferguson',
'ottawashooting', 'sydneysiege']
else:
folds = ['charliehebdo', 'germanwings-crash', 'ferguson',
'ottawashooting', 'sydneysiege', 'putinmissing',
'prince-toronto', 'gurlitt', 'ebola-essien']
allfolds = []
cv_ids_b = []
cv_prediction_b = []
cv_label_b = []
cv_ids_a = []
cv_prediction_a = []
cv_label_a = []
# for each of the folds
for number in range(len(folds)):
#print (number)
test = folds[number]
train = deepcopy(folds)
del train[number]
#print (train)
print (test)
max_branch_len = 25
x_train = []
ya_train = []
yb_train = []
for t in train:
temp_x_train = np.load(os.path.join(path,t, 'train_array.npy'))
temp_ya_train = np.load(os.path.join(path,t,
'fold_stance_labels.npy'))
temp_yb_train = np.load(os.path.join(path,t, 'labels.npy'))
temp_x_train = pad_sequences(temp_x_train, maxlen=max_branch_len,
dtype='float32', padding='post',
truncating='post', value=0.)
temp_ya_train = pad_sequences(temp_ya_train,
maxlen=max_branch_len,
dtype='int32', padding='post',
truncating='post', value=0.)
x_train.extend(temp_x_train)
ya_train.extend(temp_ya_train)
yb_train.extend(temp_yb_train)
x_train = np.asarray(x_train)
ya_train = np.asarray(ya_train)
yb_train = np.asarray(yb_train)
yb_train = to_categorical(yb_train, num_classes=3)
x_test = np.load(os.path.join(path,test, 'train_array.npy'))
ya_test = np.load(os.path.join(path,test, 'fold_stance_labels.npy'))
yb_test = np.load(os.path.join(path,test, 'labels.npy'))
ids_testA = np.load(os.path.join(path, test, 'tweet_ids.npy'))
ids_testB = np.load(os.path.join(path, test, 'ids.npy'))
model = training(params, x_train, ya_train, yb_train)
pred_probabilities_a, pred_probabilities_b = model.predict(x_test,
verbose=0)
Y_pred_a = np.argmax(pred_probabilities_a, axis=2)
Y_pred_b = np.argmax(pred_probabilities_b, axis=1)
trees, tree_prediction, tree_label = branch2treelabels(ids_testB,
yb_test,
Y_pred_b)
mask = np.any(np.any(ya_test,axis=2),axis=1)
if np.any(mask):
Y_true_a = ya_test[mask]
Y_true_a = np.argmax(Y_true_a, axis=2)
Y_pred_a = Y_pred_a[mask]
ids_testA = ids_testA[mask]
fids_test = ids_testA.flatten()
fy_pred = Y_pred_a.flatten()
fy_test = Y_true_a.flatten()
uniqtwid, uindices2 = np.unique(fids_test, return_index=True)
uniqtwid = uniqtwid.tolist()
uindices2 = uindices2.tolist()
del uniqtwid[0]
del uindices2[0]
uniq_dev_prediction = [ fy_pred[i] for i in uindices2]
uniq_dev_label = [ fy_test[i] for i in uindices2]
else:
uniq_dev_prediction = []
uniq_dev_label = []
uniqtwid = []
perfold_result = {
'Task A':{'ID': uniqtwid,
'Label':uniq_dev_label,
'Prediction': uniq_dev_prediction},
'Task B':{'ID': trees,
'Label':tree_label,
'Prediction': tree_prediction},
'Branch': {'ID': ids_testB,
'Label':yb_test,
'Prediction': Y_pred_b}
}
cv_ids_b.extend(trees)
cv_prediction_b.extend(tree_prediction)
cv_label_b.extend(tree_label)
cv_ids_a.extend(uniqtwid)
cv_prediction_a.extend(uniq_dev_prediction)
cv_label_a.extend(uniq_dev_label)
allfolds.append(perfold_result)
Bmactest_P, Bmactest_R, Bmactest_F, _ = precision_recall_fscore_support(
cv_label_b,
cv_prediction_b,
labels=[0,1,2],
average='macro')
Bmictest_P, Bmictest_R, Bmictest_F, _ = precision_recall_fscore_support(
cv_label_b,
cv_prediction_b,
labels=[0,1,2],
average='micro')
Btest_P, Btest_R, Btest_F, _ = precision_recall_fscore_support(
cv_label_b,
cv_prediction_b,
labels=[0,1,2])
Bacc = accuracy_score(cv_label_b, cv_prediction_b)
Amactest_P, Amactest_R, Amactest_F, _ = precision_recall_fscore_support(
cv_label_a,
cv_prediction_a,
labels=[0,1,2,3],
average='macro')
Amictest_P, Amictest_R, Amictest_F, _ = precision_recall_fscore_support(
cv_label_a,
cv_prediction_a,
labels=[0,1,2,3],
average='micro')
Atest_P, Atest_R, Atest_F, _ = precision_recall_fscore_support(
cv_label_a,
cv_prediction_a,
labels=[0,1,2,3])
Aacc = accuracy_score(cv_label_a, cv_prediction_a)
output = {
'Params': params,
'TaskA':{
'accuracy': Aacc,
'Macro': {'Macro_Precision': Amactest_P,
'Macro_Recall': Amactest_R,
'macro_F_score': Amactest_F },
'Micro': {'Micro_Precision': Amictest_P,
'Micro_Recall': Amictest_R,
'Micro_F_score': Amictest_F },
'Per_class': {'Pclass_Precision': Atest_P,
'Pclass_Recall': Atest_R,
'Pclass_F_score': Atest_F}
},
'TaskB':{
'accuracy': Bacc,
'Macro': {'Macro_Precision': Bmactest_P,
'Macro_Recall': Bmactest_R,
'macro_F_score': Bmactest_F},
'Micro': {'Micro_Precision': Bmictest_P,
'Micro_Recall': Bmictest_R,
'Micro_F_score': Bmictest_F},
'Per_class': {'Pclass_Precision': Btest_P,
'Pclass_Recall': Btest_R,
'Pclass_F_score': Btest_F}
},
'attachments': {'Task A':{'ID': cv_ids_a,'Label':cv_label_a,
'Prediction': cv_prediction_a},
'Task B':{'ID': cv_ids_b,'Label':cv_label_b,
'Prediction': cv_prediction_b},
'allfolds': allfolds}
}
directory = "output"
if not os.path.exists(directory):
os.mkdir(directory)
with open('output/output'+fname+'.pkl', 'wb') as outfile:
pickle.dump(output, outfile)
return output