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train_FEVER_jointly_fine_fine_sentWiseEnsemble.py
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train_FEVER_jointly_fine_fine_sentWiseEnsemble.py
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import cPickle
import gzip
import os
import sys
sys.setrecursionlimit(6000)
import time
import math
import numpy as np
import theano
import theano.tensor as T
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
import random
from logistic_sgd import LogisticRegression
from mlp import HiddenLayer
from theano.tensor.signal import downsample
from random import shuffle
from theano.tensor.nnet.bn import batch_normalization
from load_data import load_fever_train, load_fever_dev, load_fever_train_NoEnoughInfo, load_fever_dev_NoEnoughInfo
from common_functions import store_model_to_file,Attentive_Conv_for_Pair_easy_version,load_word2vec,load_word2vec_to_init, store_model_to_file, elementwise_is_two,Conv_with_Mask_with_Gate, Conv_with_Mask, create_conv_para, L2norm_paraList, create_HiddenLayer_para, create_ensemble_para, cosine_matrix1_matrix2_rowwise, Diversify_Reg, Gradient_Cost_Para, GRU_Batch_Tensor_Input_with_Mask, create_LSTM_para
from preprocess import compute_f1_two_list_names,compute_f1_recall_two_list_names
from fever_scorer import fever_score
'''
lesson: new att conv para is better
'''
def evaluate_lenet5(learning_rate=0.02, n_epochs=100, emb_size=300, batch_size=50, filter_size=[3], sent_len=40, claim_len=40, cand_size=10,hidden_size=[300,300], max_pred_pick=5):
model_options = locals().copy()
print "model options", model_options
pred_id2label = {1:'SUPPORTS', 0:'REFUTES', 2:'NOT ENOUGH INFO'}
seed=1234
np.random.seed(seed)
rng = np.random.RandomState(seed) #random seed, control the model generates the same results
srng = T.shared_randomstreams.RandomStreams(rng.randint(seed))
"load raw data"
train_sents, train_sent_masks, train_sent_labels, train_claims, train_claim_mask, train_labels, word2id = load_fever_train(sent_len, claim_len, cand_size)
train_3th_sents, train_3th_sent_masks, train_3th_sent_labels, train_3th_claims, train_3th_claim_mask, train_3th_labels, word2id = load_fever_train_NoEnoughInfo(sent_len, claim_len, cand_size, word2id)
test_sents, test_sent_masks, test_sent_labels, test_claims, test_claim_mask, test_sent_names,test_ground_names,test_labels,word2id = load_fever_dev(sent_len, claim_len, cand_size, word2id)
test_3th_sents, test_3th_sent_masks, test_3th_sent_labels, test_3th_claims, test_3th_claim_mask, test_3th_labels, word2id = load_fever_dev_NoEnoughInfo(sent_len, claim_len, cand_size, word2id)
train_sents=np.asarray(train_sents, dtype='int32')
train_3th_sents=np.asarray(train_3th_sents, dtype='int32')
joint_train_sents = np.concatenate((train_sents,train_3th_sents))
test_sents=np.asarray(test_sents, dtype='int32')
test_3th_sents=np.asarray(test_3th_sents, dtype='int32')
joint_test_sents = np.concatenate((test_sents,test_3th_sents))
train_sent_masks=np.asarray(train_sent_masks, dtype=theano.config.floatX)
train_3th_sent_masks=np.asarray(train_3th_sent_masks, dtype=theano.config.floatX)
joint_train_sent_masks = np.concatenate((train_sent_masks,train_3th_sent_masks))
test_sent_masks=np.asarray(test_sent_masks, dtype=theano.config.floatX)
test_3th_sent_masks=np.asarray(test_3th_sent_masks, dtype=theano.config.floatX)
joint_test_sent_masks = np.concatenate((test_sent_masks,test_3th_sent_masks))
train_sent_labels=np.asarray(train_sent_labels, dtype='int32')
train_3th_sent_labels=np.asarray(train_3th_sent_labels, dtype='int32')
joint_train_sent_labels = np.concatenate((train_sent_labels,train_3th_sent_labels))
test_sent_labels=np.asarray(test_sent_labels, dtype='int32')
test_3th_sent_labels=np.asarray(test_3th_sent_labels, dtype='int32')
joint_test_sent_labels = np.concatenate((test_sent_labels,test_3th_sent_labels))
train_claims=np.asarray(train_claims, dtype='int32')
train_3th_claims=np.asarray(train_3th_claims, dtype='int32')
joint_train_claims = np.concatenate((train_claims,train_3th_claims))
test_claims=np.asarray(test_claims, dtype='int32')
test_3th_claims=np.asarray(test_3th_claims, dtype='int32')
joint_test_claims = np.concatenate((test_claims,test_3th_claims))
train_claim_mask=np.asarray(train_claim_mask, dtype=theano.config.floatX)
train_3th_claim_mask=np.asarray(train_3th_claim_mask, dtype=theano.config.floatX)
joint_train_claim_mask = np.concatenate((train_claim_mask,train_3th_claim_mask))
test_claim_mask=np.asarray(test_claim_mask, dtype=theano.config.floatX)
test_3th_claim_mask=np.asarray(test_3th_claim_mask, dtype=theano.config.floatX)
joint_test_claim_mask = np.concatenate((test_claim_mask,test_3th_claim_mask))
train_labels=np.asarray(train_labels, dtype='int32')
train_3th_labels=np.asarray(train_3th_labels, dtype='int32')
joint_train_labels = np.concatenate((train_labels,train_3th_labels))
test_labels=np.asarray(test_labels, dtype='int32')
test_3th_labels=np.asarray(test_3th_labels, dtype='int32')
joint_test_labels = np.concatenate((test_labels,test_3th_labels))
joint_train_size=len(joint_train_claims)
joint_test_size=len(joint_test_claims)
train_size=len(train_claims)
test_size=len(test_claims)
test_3th_size = len(test_3th_claims)
vocab_size=len(word2id)+1
print 'joint_train size: ', joint_train_size, ' joint_test size: ', joint_test_size
print 'train size: ', train_size, ' test size: ', test_size
print 'vocab size: ', vocab_size
rand_values=rng.normal(0.0, 0.01, (vocab_size, emb_size)) #generate a matrix by Gaussian distribution
id2word = {y:x for x,y in word2id.iteritems()}
word2vec=load_word2vec()
rand_values=load_word2vec_to_init(rand_values, id2word, word2vec)
init_embeddings=theano.shared(value=np.array(rand_values,dtype=theano.config.floatX), borrow=True) #wrap up the python variable "rand_values" into theano variable
"now, start to build the input form of the model"
sents_ids=T.itensor3() #(batch, cand_size, sent_len)
sents_mask=T.ftensor3()
sents_labels=T.imatrix() #(batch, cand_size)
claim_ids = T.imatrix() #(batch, claim_len)
claim_mask = T.fmatrix()
joint_sents_ids=T.itensor3() #(batch, cand_size, sent_len)
joint_sents_mask=T.ftensor3()
joint_sents_labels=T.imatrix() #(batch, cand_size)
joint_claim_ids = T.imatrix() #(batch, claim_len)
joint_claim_mask = T.fmatrix()
joint_labels=T.ivector()
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
embed_input_sents=init_embeddings[sents_ids.flatten()].reshape((batch_size*cand_size, sent_len, emb_size)).dimshuffle(0,2,1)#embed_input(init_embeddings, sents_ids_l)#embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
embed_input_claim=init_embeddings[claim_ids.flatten()].reshape((batch_size,claim_len, emb_size)).dimshuffle(0,2,1)
conv_W, conv_b=create_conv_para(rng, filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]))
att_conv_W, att_conv_b=create_conv_para(rng, filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]))
conv_W_context, conv_b_context=create_conv_para(rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
task2_att_conv_W, task2_att_conv_b=create_conv_para(rng, filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]))
task2_conv_W_context, task2_conv_b_context=create_conv_para(rng, filter_shape=(hidden_size[0], 1, emb_size, 1))
NN_para=[conv_W, conv_b, att_conv_W, att_conv_b,conv_W_context, task2_att_conv_W, task2_att_conv_b, task2_conv_W_context]
conv_model_sents = Conv_with_Mask(rng, input_tensor3=embed_input_sents,
mask_matrix = sents_mask.reshape((sents_mask.shape[0]*sents_mask.shape[1],sents_mask.shape[2])),
image_shape=(batch_size*cand_size, 1, emb_size, sent_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]), W=conv_W, b=conv_b) #mutiple mask with the conv_out to set the features by UNK to zero
sent_embeddings=conv_model_sents.maxpool_vec #(batch_size*cand_size, hidden_size) # each sentence then have an embedding of length hidden_size
batch_sent_emb = sent_embeddings.reshape((batch_size, cand_size, hidden_size[0]))
conv_model_claims = Conv_with_Mask(rng, input_tensor3=embed_input_claim,
mask_matrix = claim_mask,
image_shape=(batch_size, 1, emb_size, claim_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]), W=conv_W, b=conv_b) #mutiple mask with the conv_out to set the features by UNK to zero
claim_embeddings=conv_model_claims.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
batch_claim_emb = T.repeat(claim_embeddings.dimshuffle(0,'x', 1), cand_size, axis=1)
'''
attentive conv
'''
attentive_conv_layer = Attentive_Conv_for_Pair_easy_version(rng,
input_tensor3=embed_input_sents, #batch_size*cand_size, emb_size, sent_len
input_tensor3_r = T.repeat(embed_input_claim, cand_size, axis=0),
mask_matrix = sents_mask.reshape((sents_mask.shape[0]*sents_mask.shape[1],sents_mask.shape[2])),
mask_matrix_r = T.repeat(claim_mask,cand_size, axis=0),
image_shape=(batch_size*cand_size, 1, emb_size, sent_len),
image_shape_r = (batch_size*cand_size, 1, emb_size, claim_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[0], 1,emb_size, 1),
W=att_conv_W, b=att_conv_b,
W_context=conv_W_context, b_context=conv_b_context)
attentive_sent_embeddings_l = attentive_conv_layer.attentive_maxpool_vec_l #(batch_size*cand_size, hidden_size)
attentive_sent_embeddings_r = attentive_conv_layer.attentive_maxpool_vec_r
# concate_claim_sent = T.concatenate([batch_claim_emb,batch_sent_emb ], axis=2)
# concate_2_matrix = concate_claim_sent.reshape((batch_size*cand_size, hidden_size[0]*2))
concate_claim_sent = T.concatenate([batch_claim_emb,batch_sent_emb, T.sum(batch_claim_emb*batch_sent_emb, axis=2).dimshuffle(0,1,'x')], axis=2)
concate_2_matrix = concate_claim_sent.reshape((batch_size*cand_size, hidden_size[0]*2+1))
LR_input = T.concatenate([concate_2_matrix, attentive_sent_embeddings_l,attentive_sent_embeddings_r], axis=1)
LR_input_size = hidden_size[0]*2+1 + hidden_size[0]*2
#classification layer, it is just mapping from a feature vector of size "hidden_size" to a vector of only two values: positive, negative
U_a = create_ensemble_para(rng, 1, LR_input_size) # the weight matrix hidden_size*2
# LR_b = theano.shared(value=np.zeros((8,),dtype=theano.config.floatX),name='LR_b', borrow=True) #bias for each target class
LR_para=[U_a]
# layer_LR=LogisticRegression(rng, input=LR_input, n_in=LR_input_size, n_out=8, W=U_a, b=LR_b) #basically it is a multiplication between weight matrix and input feature vector
score_matrix = T.nnet.sigmoid(LR_input.dot(U_a)) #batch * 12
inter_matrix = score_matrix.reshape((batch_size, cand_size))
# inter_sent_claim = T.batched_dot(batch_sent_emb, batch_claim_emb) #(batch_size, cand_size, 1)
# inter_matrix = T.nnet.sigmoid(inter_sent_claim.reshape((batch_size, cand_size)))
'''
maybe 1.0-inter_matrix can be rewritten into 1/e^(inter_matrix)
'''
# prob_pos = T.where( sents_labels < 1, 1.0-inter_matrix, inter_matrix)
# loss = -T.mean(T.log(prob_pos))
#f1 as loss
batch_overlap = T.sum(sents_labels*inter_matrix, axis=1)
batch_recall = batch_overlap / T.sum(sents_labels, axis=1)
batch_precision = batch_overlap / T.sum(inter_matrix, axis=1)
batch_f1 = 2.0*batch_recall*batch_precision/(batch_recall+batch_precision)
loss = -T.mean(T.log(batch_f1))
# loss = T.nnet.nnet.binary_crossentropy(inter_matrix, sents_labels).mean()
'''
training task2, predict 3 labels
'''
joint_embed_input_sents=init_embeddings[joint_sents_ids.flatten()].reshape((batch_size*cand_size, sent_len, emb_size)).dimshuffle(0,2,1)#embed_input(init_embeddings, sents_ids_l)#embeddings[sents_ids_l.flatten()].reshape((batch_size,maxSentLen, emb_size)).dimshuffle(0,2,1) #the input format can be adapted into CNN or GRU or LSTM
joint_embed_input_claim=init_embeddings[joint_claim_ids.flatten()].reshape((batch_size,claim_len, emb_size)).dimshuffle(0,2,1)
joint_conv_model_sents = Conv_with_Mask(rng, input_tensor3=joint_embed_input_sents,
mask_matrix = joint_sents_mask.reshape((joint_sents_mask.shape[0]*joint_sents_mask.shape[1],joint_sents_mask.shape[2])),
image_shape=(batch_size*cand_size, 1, emb_size, sent_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]), W=conv_W, b=conv_b) #mutiple mask with the conv_out to set the features by UNK to zero
joint_sent_embeddings=joint_conv_model_sents.maxpool_vec #(batch_size*cand_size, hidden_size) # each sentence then have an embedding of length hidden_size
joint_batch_sent_emb = joint_sent_embeddings.reshape((batch_size, cand_size, hidden_size[0]))
joint_premise_emb = T.sum(joint_batch_sent_emb*joint_sents_labels.dimshuffle(0,1,'x'), axis=1) #(batch, hidden_size)
joint_conv_model_claims = Conv_with_Mask(rng, input_tensor3=joint_embed_input_claim,
mask_matrix = joint_claim_mask,
image_shape=(batch_size, 1, emb_size, claim_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]), W=conv_W, b=conv_b) #mutiple mask with the conv_out to set the features by UNK to zero
joint_claim_embeddings=joint_conv_model_claims.maxpool_vec #(batch_size, hidden_size) # each sentence then have an embedding of length hidden_size
joint_premise_hypo_emb = T.concatenate([joint_premise_emb,joint_claim_embeddings], axis=1) #(batch, 2*hidden_size)
'''
attentive conv in task2
'''
task2_attentive_conv_layer = Attentive_Conv_for_Pair_easy_version(rng,
input_tensor3=joint_embed_input_sents, #batch_size*cand_size, emb_size, sent_len
input_tensor3_r = T.repeat(joint_embed_input_claim, cand_size, axis=0),
mask_matrix = joint_sents_mask.reshape((joint_sents_mask.shape[0]*joint_sents_mask.shape[1],joint_sents_mask.shape[2])),
mask_matrix_r = T.repeat(joint_claim_mask,cand_size, axis=0),
image_shape=(batch_size*cand_size, 1, emb_size, sent_len),
image_shape_r = (batch_size*cand_size, 1, emb_size, claim_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[0], 1,emb_size, 1),
W=task2_att_conv_W, b=task2_att_conv_b,
W_context=task2_conv_W_context, b_context=task2_conv_b_context)
task2_attentive_sent_embeddings_l = task2_attentive_conv_layer.attentive_maxpool_vec_l#.reshape((batch_size, cand_size, hidden_size[0])) #(batch_size*cand_size, hidden_size)
task2_attentive_sent_embeddings_r = task2_attentive_conv_layer.attentive_maxpool_vec_r#.reshape((batch_size, cand_size, hidden_size[0]))
task2_sent_claim_concate = T.concatenate([task2_attentive_sent_embeddings_l,task2_attentive_sent_embeddings_r],axis=1) #(batch_size*cand_size, 2*hidden_size[0]
"Logistic Regression layer"
joint_LR_input = joint_premise_hypo_emb#T.concatenate([attentive_sent_embeddings_l,attentive_sent_embeddings_r,attentive_sent_embeddings_l+attentive_sent_embeddings_r,attentive_sent_embeddings_l*attentive_sent_embeddings_r],axis=1)
joint_LR_input_size=2*hidden_size[0]
joint_U_a = create_ensemble_para(rng, 3, joint_LR_input_size) # (input_size, 3)
joint_LR_b = theano.shared(value=np.zeros((3,),dtype=theano.config.floatX),name='LR_b', borrow=True) #bias for each target class
joint_LR_para=[joint_U_a, joint_LR_b]
joint_layer_LR=LogisticRegression(rng, input=joint_LR_input, n_in=joint_LR_input_size, n_out=3, W=joint_U_a, b=joint_LR_b) #basically it is a multiplication between weight matrix and input feature vector
joint_layer_LR_sent_claim=LogisticRegression(rng, input=task2_sent_claim_concate, n_in=joint_LR_input_size, n_out=3, W=joint_U_a, b=joint_LR_b) #basically it is a multiplication between weight matrix and input feature vector
joint_layer_LR_prob = joint_layer_LR.p_y_given_x #(batch, 3)
joint_layer_LR_sent_claim_raw_prob = joint_layer_LR_sent_claim.p_y_given_x #(batch_size*cand_size, 3)
joint_layer_LR_sent_claim_prob = T.sum(joint_layer_LR_sent_claim_raw_prob.reshape((batch_size, cand_size,3))*joint_sents_labels.dimshuffle(0,1,'x'), axis=1) #(batch_size, 3)
joint_comb_prob = T.nnet.softmax(joint_layer_LR_prob+joint_layer_LR_sent_claim_prob)
joint_loss = -T.mean(T.log(joint_comb_prob)[T.arange(joint_labels.shape[0]), joint_labels])
# joint_loss=joint_layer_LR.negative_log_likelihood(joint_labels) #for classification task, we usually used negative log likelihood as loss, the lower the better.
'''
testing
'''
# binarize_prob = T.where( inter_matrix > 0.5, 1, 0) #(batch_size, cand_size
masked_inter_matrix = inter_matrix * sents_labels #(batch, cand_size)
test_premise_emb = T.sum(batch_sent_emb*masked_inter_matrix.dimshuffle(0,1,'x'), axis=1)
test_premise_hypo_emb = T.concatenate([test_premise_emb,claim_embeddings], axis=1)
test_layer_LR=LogisticRegression(rng, input=test_premise_hypo_emb, n_in=joint_LR_input_size, n_out=3, W=joint_U_a, b=joint_LR_b) #basically it is a multiplication between weight matrix and input feature vector
test_attentive_conv_layer = Attentive_Conv_for_Pair_easy_version(rng,
input_tensor3=embed_input_sents, #batch_size*cand_size, emb_size, sent_len
input_tensor3_r = T.repeat(embed_input_claim, cand_size, axis=0),
mask_matrix = sents_mask.reshape((sents_mask.shape[0]*sents_mask.shape[1],sents_mask.shape[2])),
mask_matrix_r = T.repeat(claim_mask,cand_size, axis=0),
image_shape=(batch_size*cand_size, 1, emb_size, sent_len),
image_shape_r = (batch_size*cand_size, 1, emb_size, claim_len),
filter_shape=(hidden_size[0], 1, emb_size, filter_size[0]),
filter_shape_context=(hidden_size[0], 1,emb_size, 1),
W=task2_att_conv_W, b=task2_att_conv_b,
W_context=task2_conv_W_context, b_context=task2_conv_b_context)
test_attentive_sent_embeddings_l = test_attentive_conv_layer.attentive_maxpool_vec_l #(batch_size*cand_size, hidden_size)
test_attentive_sent_embeddings_r = test_attentive_conv_layer.attentive_maxpool_vec_r
test_sent_claim_concate = T.concatenate([test_attentive_sent_embeddings_l,test_attentive_sent_embeddings_r], axis=1) #(batch_size*cand_size, 2*hidden_size[0]
test_layer_LR_sent_claim_concate=LogisticRegression(rng, input=test_sent_claim_concate, n_in=joint_LR_input_size, n_out=3, W=joint_U_a, b=joint_LR_b) #basically it is a multiplication between weight matrix and input feature vector
test_layer_LR_prob = test_layer_LR.p_y_given_x #(batch, 3)
test_layer_LR_sent_claim_concate_raw_prob = test_layer_LR_sent_claim_concate.p_y_given_x #(batch_size*cand_size, 3)
test_layer_LR_sent_claim_concate_prob = T.sum(test_layer_LR_sent_claim_concate_raw_prob.reshape((batch_size, cand_size,3))*masked_inter_matrix.dimshuffle(0,1,'x'), axis=1) #(batch_size, 3)
test_comb_prob = T.nnet.softmax(test_layer_LR_prob+test_layer_LR_sent_claim_concate_prob)
test_pred = T.argmax(test_comb_prob, axis=1)
test_error = T.mean(T.neq(test_pred, joint_labels))
params = [init_embeddings]+NN_para+LR_para + joint_LR_para
cost=loss+joint_loss
"Use AdaGrad to update parameters"
updates = Gradient_Cost_Para(cost,params, learning_rate)
train_model = theano.function([sents_ids,sents_mask,sents_labels,claim_ids,claim_mask,joint_sents_ids,joint_sents_mask,joint_sents_labels, joint_claim_ids, joint_claim_mask, joint_labels], cost, updates=updates, allow_input_downcast=True, on_unused_input='ignore')
# dev_model = theano.function([sents_ids_l, sents_mask_l, sents_ids_r, sents_mask_r, labels], layer_LR.errors(labels), allow_input_downcast=True, on_unused_input='ignore')
test_model = theano.function([sents_ids,sents_mask,sents_labels, claim_ids,claim_mask, joint_labels], [inter_matrix,test_error, test_pred], allow_input_downcast=True, on_unused_input='ignore')
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 50000000000 # look as this many examples regardless
start_time = time.time()
mid_time = start_time
past_time= mid_time
epoch = 0
done_looping = False
joint_n_train_batches=joint_train_size/batch_size
joint_train_batch_start=list(np.arange(joint_n_train_batches)*batch_size)+[joint_train_size-batch_size]
n_train_batches=train_size/batch_size
train_batch_start=list(np.arange(n_train_batches)*batch_size)+[train_size-batch_size]
n_test_batches=test_size/batch_size
test_batch_start=list(np.arange(n_test_batches)*batch_size)+[test_size-batch_size]
n_test_3th_batches=test_3th_size/batch_size
test_3th_batch_start=list(np.arange(n_test_3th_batches)*batch_size)+[test_3th_size-batch_size]
max_acc=0.0
max_test_f1=0.0
max_acc_full_evi = 0.0
cost_i=0.0
joint_train_indices = range(joint_train_size)
train_indices = range(train_size)
while epoch < n_epochs:
epoch = epoch + 1
random.Random(100).shuffle(joint_train_indices) #shuffle training set for each new epoch, is supposed to promote performance, but not garrenteed
random.Random(100).shuffle(train_indices)
iter_accu=0
for joint_batch_id in joint_train_batch_start: #for each batch
# iter means how many batches have been run, taking into loop
iter = (epoch - 1) * joint_n_train_batches + iter_accu +1
iter_accu+=1
joint_train_id_batch = joint_train_indices[joint_batch_id:joint_batch_id+batch_size]
for i in range(3):
batch_id = random.choice(train_batch_start)
train_id_batch = train_indices[batch_id:batch_id+batch_size]
cost_i+= train_model(
train_sents[train_id_batch],
train_sent_masks[train_id_batch],
train_sent_labels[train_id_batch],
train_claims[train_id_batch],
train_claim_mask[train_id_batch],
#joint_sents_ids,joint_sents_mask,joint_sents_labels, joint_claim_ids, joint_claim_mask, joint_labels
joint_train_sents[joint_train_id_batch],
joint_train_sent_masks[joint_train_id_batch],
joint_train_sent_labels[joint_train_id_batch],
joint_train_claims[joint_train_id_batch],
joint_train_claim_mask[joint_train_id_batch],
joint_train_labels[joint_train_id_batch]
)
#after each 1000 batches, we test the performance of the model on all test data
# if (epoch==1 and iter%1000==0) or (epoch>=2 and iter%5==0):
if iter%100==0:
print 'Epoch ', epoch, 'iter '+str(iter)+' average cost: '+str(cost_i/iter), 'uses ', (time.time()-past_time)/60.0, 'min'
past_time = time.time()
f1_sum=0.0
error_sum = 0.0
full_evi = 0
predictions = []
for test_batch_id in test_batch_start: # for each test batch
batch_prob, error_i, pred_i=test_model(
test_sents[test_batch_id:test_batch_id+batch_size],
test_sent_masks[test_batch_id:test_batch_id+batch_size],
test_sent_labels[test_batch_id:test_batch_id+batch_size],
test_claims[test_batch_id:test_batch_id+batch_size],
test_claim_mask[test_batch_id:test_batch_id+batch_size],
test_labels[test_batch_id:test_batch_id+batch_size]
)
error_sum+=error_i
batch_sent_labels = test_sent_labels[test_batch_id:test_batch_id+batch_size]
batch_sent_names = test_sent_names[test_batch_id:test_batch_id+batch_size]
batch_ground_names = test_ground_names[test_batch_id:test_batch_id+batch_size]
batch_ground_labels = test_labels[test_batch_id:test_batch_id+batch_size]
for i in range(batch_size):
instance_i = {}
instance_i['label'] = pred_id2label.get(batch_ground_labels[i])
instance_i['predicted_label'] = pred_id2label.get(pred_i[i])
pred_sent_names = []
gold_sent_names = batch_ground_names[i]
zipped=[(batch_prob[i,k],batch_sent_labels[i][k],batch_sent_names[i][k]) for k in range(cand_size)]
sorted_zip = sorted(zipped, key=lambda x: x[0], reverse=True)
for j in range(cand_size):
triple = sorted_zip[j]
if triple[1] == 1.0:
'''
we should consider a rank, instead of binary
if triple[0] >0.5: can control the recall, influence the strict_acc
'''
if triple[0] >0.5:
# pred_sent_names.append(batch_sent_names[i][j])
pred_sent_names.append(triple[2])
# if len(pred_sent_names) == max_pred_pick:
# break
instance_i['predicted_evidence'] = pred_sent_names
# print 'pred_sent_names:',pred_sent_names
# print 'gold_sent_names:',gold_sent_names
new_gold_names = []
for gold_name in gold_sent_names:
new_gold_names.append([None, None]+gold_name)
instance_i['evidence'] = [new_gold_names]
predictions.append(instance_i)
strict_score, label_accuracy, precision, recall, f1 = fever_score(predictions)
print 'strict_score, label_accuracy, precision, recall, f1: ', strict_score, label_accuracy, precision, recall, f1
# test_f1=f1_sum/(len(test_batch_start)*batch_size)
for test_batch_id in test_3th_batch_start: # for each test batch
_, error_i, pred_i=test_model(
test_3th_sents[test_batch_id:test_batch_id+batch_size],
test_3th_sent_masks[test_batch_id:test_batch_id+batch_size],
test_3th_sent_labels[test_batch_id:test_batch_id+batch_size],
test_3th_claims[test_batch_id:test_batch_id+batch_size],
test_3th_claim_mask[test_batch_id:test_batch_id+batch_size],
test_3th_labels[test_batch_id:test_batch_id+batch_size]
)
for i in range(batch_size):
instance_i = {}
instance_i['label'] = pred_id2label.get(2)
instance_i['predicted_label'] = pred_id2label.get(pred_i[i])
instance_i['predicted_evidence'] = []
instance_i['evidence'] = []
predictions.append(instance_i)
strict_score, label_accuracy, precision, recall, f1 = fever_score(predictions)
print 'strict_score, label_accuracy, precision, recall, f1: ', strict_score, label_accuracy, precision, recall, f1
print 'Epoch ', epoch, 'uses ', (time.time()-mid_time)/60.0, 'min'
mid_time = time.time()
#print 'Batch_size: ', update_freq
end_time = time.time()
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
return max_acc_test
if __name__ == '__main__':
evaluate_lenet5()