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tfRNN.py
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tfRNN.py
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import json
import os
import re
import time
import random
import numpy as np
import tensorflow as tf
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from collections import defaultdict
from RITutils import f1_score, recall, precision, w_categorical_crossentropy
import keras
import keras.backend as K
from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau, CSVLogger
from keras.layers import merge, recurrent, Dense, Input, Dropout, TimeDistributed
from keras.layers.embeddings import Embedding
from keras.layers.normalization import BatchNormalization
from keras.layers.core import Lambda
from keras.layers.wrappers import Bidirectional
from keras.models import Model, load_model
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.regularizers import l2
from keras.utils import np_utils
from keras.layers.recurrent import GRU,LSTM
from keras.layers import CuDNNGRU, CuDNNLSTM, Activation # CuDNN podržana implementacija LSTM i GRU-a
from keras.backend.tensorflow_backend import set_session
from keras.engine.topology import Layer
from keras.utils.vis_utils import plot_model # vizualizacija modela
from json_tricks import dump
import sys
def time_count(fn):
# Funtion wrapper used to memsure time consumption
def _wrapper(*args, **kwargs):
start = time.clock()
returns = fn(*args, **kwargs)
print("[time_count]: %s cost %fs" % (fn.__name__, time.clock() - start))
return returns
return _wrapper
class AttentionAlignmentModel:
# accepts options dict with hyperparameters
def __init__(self, options, annotation ='biGRU', dataset = 'snli'):
# 1, Set Basic Model Parameters
self.Layers = 1
self.EmbeddingSize = 300 # size of projected embeddings
self.BatchSize = options['BatchSize'] if 'BatchSize' in options else 128
self.Patience = 7 # original Chen et.al.
self.MaxEpoch = 25
self.SentMaxLen = 42 if dataset == 'snli' else 50
self.DropProb = 0.5 # original Chen et.al.
self.L2Strength = options['L2Strength'] if 'L2Strength' in options else 0.0
self.Activate = 'relu'
self.GradientClipping = options['GradientClipping'] if 'GradientClipping' in options else 10.0
# self.Optimizer = 'rmsprop' # originalna vrijednost
self.LearningRate = options['LearningRate'] if 'LearningRate' in options else 4e-4
if 'Optimizer' not in options or options['Optimizer'] == 'nadam':
self.Optimizer = keras.optimizers.Nadam(lr = self.LearningRate,
clipnorm = self.GradientClipping)
elif options['Optimizer'] == 'adam': # u radu naveden Adam, orig. rmsprop
self.Optimizer = keras.optimizers.Adam(lr = self.LearningRate,
clipnorm = self.GradientClipping)
elif options['Optimizer'] == 'rmsprop':
self.Optimizer = keras.optimizers.RMSprop(lr = self.LearningRate,
clipnorm = self.GradientClipping)
self.rnn_type = annotation
self.dataset = dataset
# whether to change tokens to lowercase before training
self.LowercaseTokens = options['LowercaseTokens'] if 'LowercaseTokens' in options else True
# changing this value requires setting RetrainEmbeddings to True
self.RetrainEmbeddings = options['RetrainEmbeddings'] if 'RetrainEmbeddings' in options else True
self.LoadExistingWeights = False # True: loading existing model weights
self.TrainableEmbeddings = True # True: update word embeddings during training
# True: last dropout layer has 1/2 of dropout factor
self.LastDropoutHalf = options['LastDropoutHalf'] if 'LastDropoutHalf' in options else False
self.OOVWordInit = options['OOVWordInit'] if 'OOVWordInit' in options else 'zeros'
# 2, Define Class Variable
self.Options = options
self.Options['Timestamp'] = time.strftime('%Y%m%d%H%M', time.localtime()) if 'ConfigTimestamp' not in options else options['ConfigTimestamp']
self.Timestamp = self.Options['Timestamp']
self.ResultFilepath = 'models/' + self.Timestamp + '_model/'
self.History = None
# self.Verbose = 0
self.Vocab = 0
self.model = None
self.GloVe = defaultdict(np.array)
self.glove_path = self.ResultFilepath + self.Timestamp + '_GloVe_' + self.dataset + '.npy'
self.indexer,self.Embed = None, None
self.train, self.validation, self.test = [],[],[]
self.Labels = {'contradiction': 0, 'neutral': 1, 'entailment': 2}
self.rLabels = {0:'contradiction', 1:'neutral', 2:'entailment'}
# writes a report containing hyperparameters and learning details
def format_report(self):
outFile = self.ResultFilepath + self.Timestamp + '_ESIM_' + self.dataset.upper() + '_report.json'
with open(outFile, 'w', encoding='utf-8') as outFile:
dump(self.Options, outFile, indent=2)
# helper function for data preprocessing - pads sentences to same length
def padd(self, x):
def padding(x, MaxLen):
return pad_sequences(sequences=self.indexer.texts_to_sequences(x), maxlen=MaxLen)
def pad_data(x):
return padding(x[0], self.SentMaxLen), padding(x[1], self.SentMaxLen), x[2]
return pad_data(x)
# loads data depending on declared dataset
def load_data(self):
self.model_mkdir()
if self.dataset == 'snli':
trn = json.loads(open('snli_train.json', 'r').read())
vld = json.loads(open('snli_validation.json', 'r').read())
tst = json.loads(open('snli_test.json', 'r').read())
elif self.dataset == 'rte':
trn = json.loads(open('RTE_train.json', 'r').read())
vld = json.loads(open('RTE_valid.json', 'r').read())
tst = json.loads(open('RTE_test.json', 'r').read())
elif self.dataset == 'mnli': # validating with matched validation set
trn = json.loads(open('mnli_train.json', 'r').read())
vld = json.loads(open('mnli_validation_matched.json', 'r').read())
tst = json.loads(open('mnli_validation_mismatched.json', 'r').read())
elif self.dataset == 'mnlisnli':
trn = json.loads(open('mnli_train.json', 'r').read())
trn2 = json.loads(open('snli_train.json', 'r').read())
vld = json.loads(open('mnli_validation_matched.json', 'r').read())
vld2 = json.loads(open('snli_validation.json', 'r').read())
# sljedeću liniju NIKAKO ne brisati, o tome ovisi evaluate_on_set()
tst = json.loads(open('mnli_validation_mismatched.json', 'r').read())
"""
Since joint training randomly picks 15% of SNLI examples, to evaluate such
trained model, you need to provide timestamp of JSON file containing
which random examples were picked for training so that same embeddings
could be loaded into the model for evaluation. Why? Because embeddings are
trainable which makes them model parameters, which requires same
embeddings to be loaded when evaluating model as when that same model was
trained. Provide timestamp of trained model in options['ConfigTimestamp'].
If not provided, random examples are picked and saved to JSON for later
reproducibility.
"""
# validation set
subset = [[],[],[]]
if 'ConfigTimestamp' in self.Options: # if timestamp exists, load examples
indices = json.load(open(self.ResultFilepath + self.Options['ConfigTimestamp'] + '_validconfig.json', 'r'))
else: # else pick randomly
indices = random.sample(range(len(vld2[0])), 2000)
json.dump(indices, open(self.ResultFilepath + self.Timestamp + '_validconfig.json', 'w'))
# merging with MNLI
for index in indices:
for i in range(3):
subset[i].append(vld2[i][index])
for i in range(3):
vld[i].extend(subset[i])
# train set
subset = [[],[],[]]
if 'ConfigTimestamp' in self.Options:
indices = json.load(open(self.ResultFilepath + self.Options['ConfigTimestamp'] + '_trainconfig.json', 'r'))
else:
indices = random.sample(range(len(trn2[0])), int(0.15 * len(trn2[0]) ) )
json.dump(indices, open(self.ResultFilepath + self.Timestamp + '_trainconfig.json', 'w'))
for index in indices:
for i in range(3):
subset[i].append(trn2[i][index])
for i in range(3):
trn[i].extend(subset[i])
else:
raise ValueError('Unknown Dataset')
trn[2] = np_utils.to_categorical(trn[2], 3)
vld[2] = np_utils.to_categorical(vld[2], 3)
tst[2] = np_utils.to_categorical(tst[2], 3)
return trn, vld, tst
def model_mkdir(self):
if not os.path.exists('models/'):
os.mkdir('models/')
if not os.path.exists(self.ResultFilepath):
os.mkdir(self.ResultFilepath)
@time_count
def prep_data(self):
# 1, Read raw Training,Validation and Test data
self.train,self.validation,self.test = self.load_data()
# 2, Prep Word Indexer: assign each word a number
self.indexer = Tokenizer(lower = self.LowercaseTokens, filters = '') # nova linija
# indexer fitamo nad training podacima
self.indexer.fit_on_texts(self.train[0] + self.train[1])
# self.Vocab je veličina vokabulara
self.Vocab = len(self.indexer.word_counts) + 1
print('Vocabulary size:', self.Vocab)
# 3, Convert each word in set to num and zero pad
self.train = self.padd(self.train)
self.validation = self.padd(self.validation)
self.test = self.padd(self.test)
def load_GloVe(self):
# Create an embedding matrix for word2vec (use GloVe)
# embedding matrix contains word embeddings for each word
embed_index = {}
for line in open('glove.840B.300d.txt','r'):
value = line.split(' ') # Warning: Can't use split()! I don't know why...
word = value[0]
embed_index[word] = np.asarray(value[1:],dtype='float32')
# embed matrix is of size 300*(no. of vocabulary words)
# hence it CANNOT be reloaded when changing dataset
if self.dataset == 'mnlisnli' and os.path.exists(self.glove_path):
embed_matrix = np.load(self.glove_path)
elif self.OOVWordInit == 'random':
embed_matrix = np.random.randn(self.Vocab,self.EmbeddingSize)
elif self.OOVWordInit == 'zeros':
embed_matrix = np.zeros((self.Vocab,self.EmbeddingSize))
unregistered = []
for word,i in self.indexer.word_index.items():
vec = embed_index.get(word)
# if word with index 'vec' not in word_index, add it to out-of-voc list
if vec is None: unregistered.append(word)
# else save it in embedding matrix on its position
else: embed_matrix[i] = vec
np.save(self.glove_path, embed_matrix)
open('unregisterd_word.txt','w').write(str(unregistered))
def load_GloVe_dict(self):
for line in open('glove.840B.300d.txt','r'):
value = line.split(' ') # Warning: Can't use split()! I don't know why...
word = value[0]
self.GloVe[word] = np.asarray(value[1:],dtype='float32')
@time_count
def prep_embd(self):
# Add Embed Layer to convert word index to vector
if self.dataset != 'mnlisnli':
self.glove_path = self.ResultFilepath + 'GloVe_' + self.dataset + '.npy'
if 'ConfigTimestamp' in self.Options:
self.glove_path = self.ResultFilepath + self.Options['ConfigTimestamp'] + '_GloVe_' + self.dataset + '.npy'
# with joint training, we always delete previous embedding matrix
# if self.dataset == 'mnlisnli' and os.path.exists(glove_path) and 'ConfigTimestamp' not in self.Options:
# os.remove(glove_path)
if not os.path.exists(self.glove_path) or self.RetrainEmbeddings:
self.load_GloVe()
# loading freshly made embedding matrix
embed_matrix = np.load(self.glove_path)
self.Embed = Embedding(input_dim = self.Vocab,
output_dim = self.EmbeddingSize,
input_length = self.SentMaxLen,
trainable = self.TrainableEmbeddings,
weights = [embed_matrix],
name = 'embed_' + self.dataset.upper())
# Enhanced LSTM Attention model by Qian Chen et al. 2016
def create_enhanced_attention_model(self):
# 0, (Optional) Set the upper limit of GPU memory
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
set_session(tf.Session(config=config))
# 1, Embedding the input and project the embeddings
premise = Input(shape=(self.SentMaxLen,), dtype='int32')
hypothesis = Input(shape=(self.SentMaxLen,), dtype='int32')
embed_p = self.Embed(premise) # [batchsize, Psize, Embedsize]
embed_h = self.Embed(hypothesis) # [batchsize, Hsize, Embedsize]
# 2, Encoder words with its surrounding context
# initialization of LSTM input matrix with random Gauss distr
Encoder = Bidirectional(CuDNNLSTM(units=300, return_sequences=True, kernel_initializer='RandomNormal')) # nova linija - CuDNNLSTM
embed_p = Dropout(self.DropProb)(embed_p) # firstly dropout
embed_h = Dropout(self.DropProb)(embed_h) # firstly dropout
embed_p = Encoder(embed_p) # then BiLSTM encoding
embed_h = Encoder(embed_h) # then BiLSTM encoding
# 2, Score each words and calc score matrix Eph.
F_p, F_h = embed_p, embed_h
Eph = keras.layers.Dot(axes=(2, 2))([F_h, F_p]) # [batch_size, Hsize, Psize]
Eh = Lambda(lambda x: keras.activations.softmax(x))(Eph) # [batch_size, Hsize, Psize]
Ep = keras.layers.Permute((2, 1))(Eph) # [batch_size, Psize, Hsize)
Ep = Lambda(lambda x: keras.activations.softmax(x))(Ep) # [batch_size, Psize, Hsize]
# 4, Normalize score matrix, encoder premesis and get alignment
PremAlign = keras.layers.Dot((2, 1))([Ep, embed_h]) # [-1, Psize, dim]
HypoAlign = keras.layers.Dot((2, 1))([Eh, embed_p]) # [-1, Hsize, dim]
mm_1 = keras.layers.Multiply()([embed_p, PremAlign])
mm_2 = keras.layers.Multiply()([embed_h, HypoAlign])
sb_1 = keras.layers.Subtract()([embed_p, PremAlign])
sb_2 = keras.layers.Subtract()([embed_h, HypoAlign])
# concat [a_, a~, a_ * a~, a_ - a~], isto za b_, b~
PremAlign = keras.layers.Concatenate()([embed_p, PremAlign, sb_1, mm_1,]) # [batch_size, Psize, 2*unit]
HypoAlign = keras.layers.Concatenate()([embed_h, HypoAlign, sb_2, mm_2]) # [batch_size, Hsize, 2*unit]
# ff layer w/RELU activation
Compresser = TimeDistributed(Dense(300,
kernel_regularizer=l2(self.L2Strength),
bias_regularizer=l2(self.L2Strength),
activation='relu'),
name='Compresser')
PremAlign = Compresser(PremAlign)
HypoAlign = Compresser(HypoAlign)
# 5, Final biLST < Encoder + Softmax Classifier
Decoder = Bidirectional(CuDNNLSTM(units=300, return_sequences=True, kernel_initializer='RandomNormal'),
name='finaldecoder') # [-1,2*units]
PremAlign = Dropout(self.DropProb)(PremAlign)
HypoAlign = Dropout(self.DropProb)(HypoAlign)
final_p = Decoder(PremAlign)
final_h = Decoder(HypoAlign)
AveragePooling = Lambda(lambda x: K.mean(x, axis=1)) # outs [-1, dim]
MaxPooling = Lambda(lambda x: K.max(x, axis=1)) # outs [-1, dim]
avg_p = AveragePooling(final_p)
avg_h = AveragePooling(final_h)
max_p = MaxPooling(final_p)
max_h = MaxPooling(final_h)
# concat of avg and max pooling for hypothesis and premise
Final = keras.layers.Concatenate()([avg_p, max_p, avg_h, max_h])
# dropout layer
Final = Dropout(self.DropProb)(Final)
# ff layer w/tanh activation
Final = Dense(300,
kernel_regularizer=l2(self.L2Strength),
bias_regularizer=l2(self.L2Strength),
name='dense300_' + self.dataset,
activation='tanh')(Final)
# last dropout factor
factor = 1
if self.LastDropoutHalf:
factor = 2
Final = Dropout(self.DropProb / factor)(Final)
# softmax classifier
Final = Dense(2 if self.dataset == 'rte' else 3,
activation='softmax',
name='judge300_' + self.dataset)(Final)
self.model = Model(inputs=[premise, hypothesis], outputs=Final)
@time_count
def compile_model(self):
""" Load Possible Existing Weights and Compile the Model """
self.model.compile(optimizer=self.Optimizer,
loss=w_categorical_crossentropy if self.dataset == 'rte'
else 'categorical_crossentropy',
metrics=['accuracy' , precision, recall, f1_score]
if self.dataset == 'rte' else ['accuracy'])
self.model.summary()
fn = self.rnn_type + '_' + self.dataset + '.check'
if os.path.exists(fn) and self.LoadExistingWeights:
self.model.load_weights(fn, by_name=True)
print('--------Load Weights Successful!--------')
# returns history of train/val loss/acc values
def start_train(self):
""" Starts to Train the entire Model Based on set Parameters """
# 1, Prep
callback = [EarlyStopping(patience=self.Patience, verbose=2),
ReduceLROnPlateau(patience=5, verbose=1),
CSVLogger(filename=self.rnn_type+'log.csv'),
ModelCheckpoint(filepath = self.ResultFilepath + self.Timestamp
+ '_' + self.dataset
+ 'weights.{epoch:02d}-{val_loss:.2f}.check',
save_best_only=False,
save_weights_only=True)]
# 2, Train
self.History = self.model.fit(x = [self.train[0],self.train[1]],
y = self.train[2],
batch_size = self.BatchSize,
epochs = self.MaxEpoch,
validation_data=([self.validation[0], self.validation[1]], self.validation[2]),
callbacks = callback)
self.Options['History'] = self.History.history
self.format_report()
return self.History
# eval_set: forward exact filename upon which to test (without file extension)
def evaluate_on_set(self, eval_set = 'snli_test', weights_file = None):
# checks validity of eval_set name
assert eval_set in ['snli_validation', 'snli_test',
'mnli_validation_matched',
'mnli_validation_mismatched']
dataset = None
if weights_file is not None: # loads weights from given file
self.model.load_weights(self.ResultFilepath + weights_file)
# or tries to load default named weights
elif os.path.exists(self.rnn_type + '_' + self.dataset + '.check'):
self.model.load_weights(self.rnn_type + '_' + self.dataset + '.check') # revert to the best model
else: # or initializes weights to random
print('No weights found for model!')
print('Using random initialized weights...')
# if testing on same set, load it from member variables...
if (self.dataset == 'snli' and 'snli' in eval_set) or (self.dataset == 'mnli' and 'mnli' in eval_set):
if eval_set == 'snli_validation' or eval_set == 'mnli_validation_matched':
dataset = self.validation
elif eval_set == 'snli_test' or eval_set == 'mnli_validation_mismatched':
dataset = self.test
# if cross-testing, load dataset from file...
else:
print('Loading ' + eval_set + ' data...')
dataset = json.loads(open(eval_set + '.json', 'r').read())
dataset[2] = np_utils.to_categorical(dataset[2], 3)
dataset = self.padd(dataset)
# evaluation
loss, acc = self.model.evaluate([dataset[0], dataset[1]],
dataset[2], batch_size=self.BatchSize)
print('Trained on: ' + self.dataset.upper())
print('Evaluated on: ' + eval_set.title() + ": loss = {:.5f}, acc = {:.4f}%".format(loss, acc))
return (loss, acc)
def evaluate_on_test_sets(self, weights_file = None):
results = {}
score = {}
# evaluates on all test sets
for set in ['snli_test', 'mnli_validation_matched','mnli_validation_mismatched']:
results[set] = {}
results[set]['loss'], results[set]['acc'] = self.evaluate_on_set(eval_set = set, weights_file = weights_file)
# dumping results to a file
dump(results, open(self.ResultFilepath + self.Timestamp + '_test_results.json', 'w'), indent = 2)
# evaluates model on ALL MNLI categories: matched & mismatched
def evaluate_on_all_mnli_categories(self, weights_file = None):
if weights_file is not None: # loads weights from given file
self.model.load_weights(self.ResultFilepath + weights_file)
sets = ['mnli_validation_matched',
'mnli_validation_mismatched']
results = {}
for eval_set in sets:
print('Loading ' + eval_set + ' data...')
dataset = json.loads(open(eval_set + '.json', 'r').read())
all_categories = list(set( dataset[3] ))
for category in all_categories:
subdataset = [[],[],[]]
print('Category: ' + category)
for i in range(len(dataset[0])):
if dataset[3][i] == category:
for j in range(3):
subdataset[j].append(dataset[j][i])
subdataset[2] = np_utils.to_categorical(subdataset[2], 3)
subdataset = self.padd(subdataset)
result = {}
result['loss'], result['acc'] = self.model.evaluate([subdataset[0], subdataset[1]],
subdataset[2],
batch_size=self.BatchSize)
results[category] = result
dump(results, open(self.ResultFilepath + self.Timestamp + '_categoric_results.json', 'w'), indent = 2)
@staticmethod
def plotHeatMap(df, psize=(8,8), filename='Heatmap'):
ax = sns.heatmap(df, vmax=.85, square=True, cbar=False, annot=True)
plt.xticks(rotation=40), plt.yticks(rotation=360)
fig = ax.get_figure()
fig.set_size_inches(psize)
fig.savefig(filename)
plt.clf()
def interactive_predict(self, test_mode = False):
"""[ONLY WORK FOR SNLI] The model must be compiled before execuation """
prep_alfa = lambda X: pad_sequences(sequences=self.indexer.texts_to_sequences(X),
maxlen=self.SentMaxLen)
while True:
prem = input("Please input the premise:\n")
hypo = input("Please input another sent:\n")
unknown = set([word for word in list(filter(lambda x: x and x != ' ',
re.split(r'(\W)',prem) + re.split(r'(\W)',hypo)))
if word not in self.indexer.word_counts.keys()])
if unknown:
print('[WARNING] {}s Unregistered Words:{}'.format(len(unknown),unknown))
prem_pad, hypo_pad = prep_alfa([prem]), prep_alfa([hypo])
if test_mode:
ans = self.model.predict(x=[prem_pad, hypo_pad], batch_size=1)
Ep, Eh = np.array(ans[0]).reshape(36,36), np.array(ans[1]).reshape(36,36) # [P,H] [H,P]
Ep = Ep[-len(prem.split(' ')):,-len(hypo.split(' ')):] # [P,H]
Eh = Eh[-len(hypo.split(' ')):,-len(prem.split(' ')):] # [H,P]
self.plotHeatMap(pd.DataFrame(Ep,columns=hypo.split(' '),index=prem.split(' ')),
psize=(7, 10), filename='Ep')
self.plotHeatMap(pd.DataFrame(Eh,columns=prem.split(' '),index=hypo.split(' ')),
psize=(10,7), filename='Eh')
ans = np.reshape(ans[2], -1)
else:
ans = np.reshape(self.model.predict(x=[prem_pad, hypo_pad],batch_size=1),-1) # PREDICTION
print('\n Contradiction {:.1f}%\n'.format(float(ans[0]) * 100),
'Neutral {:.1f}%\n'.format(float(ans[1]) * 100),
'Entailment {:.1f}%\n'.format(float(ans[2]) * 100))
def label_test_file(self):
outfile = open("pred_vld.txt","w")
prep_alfa = lambda X: pad_sequences(sequences=self.indexer.texts_to_sequences(X),
maxlen=self.SentMaxLen)
vld = json.loads(open('validation.json', 'r').read())
for prem, hypo, label in zip(vld[0], vld[1], vld[2]):
prem_pad, hypo_pad = prep_alfa([prem]), prep_alfa([hypo])
ans = np.reshape(self.model.predict(x=[prem_pad, hypo_pad], batch_size = 1), -1) # PREDICTION
if np.argmax(ans) != label:
outfile.write(prem + "\n" + hypo + "\n")
outfile.write("Truth: " + self.rLabels[label] + "\n")
outfile.write('Contradiction {:.1f}%\n'.format(float(ans[0]) * 100) +
'Neutral {:.1f}%\n'.format(float(ans[1]) * 100) +
'Entailment {:.1f}%\n'.format(float(ans[2]) * 100))
outfile.write("-"*15 + "\n")
outfile.close()