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evaluate_STS_CNN.py
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evaluate_STS_CNN.py
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import os
import subprocess
from utils import has_header
import sys
import argparse
import numpy as np
import sklearn.metrics
import random,copy,string
from nltk.tokenize import word_tokenize
from scipy.stats import pearsonr
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Input, Convolution1D, MaxPooling1D, Flatten
from tensorflow.python.keras.layers import Lambda, multiply, concatenate, Dense
from tensorflow.python.keras.regularizers import l2
from tensorflow.python.keras.callbacks import Callback
from tensorflow.python.keras.preprocessing.sequence import pad_sequences
from embedding import load_embedding
from utils import perf_measure
ADD_PREFIX = False
TEXT_EVAL = '[Train, Valid, Test]='
class Embedder(object):
def __init__(self, dictname, wordvectdim):
print('Loading ' + dictname + '...(This might take one or two minutes.)')
self.wordtoindex = dict()
self.indextovector = []
self.indextovector.append(np.zeros(wordvectdim))
emb = load_embedding(dictname, length_normalize=False, to_unicode=False, lower=False, delete_duplicates=True)
for i_word, word in enumerate(emb.words):
self.wordtoindex.update([(word, i_word+1)])
self.indextovector.append(emb.word_to_vector(word))
del emb
#print(self.indextovector.shape)
self.indextovector = np.array(self.indextovector, dtype='float32')
def _worker(self,args):
wordtoindex = dict()
indextovector = []
for line in args[0]:
elements = line.split(' ')
wordtoindex[elements[0]] = len(indextovector)+args[1]+1
indextovector.append(np.array(elements[1:]).astype(float))
return (wordtoindex,indextovector)
def matrixize(self, sentencelist, sentencepad):
indexlist = []
for sentence in sentencelist:
indexes = []
for word in sentence:
#word = word.lower()
if word not in self.wordtoindex: indexes.append(1); print(word)
else: indexes.append(self.wordtoindex[word])
indexlist.append(indexes)
return self.indextovector[(pad_sequences(indexlist, maxlen=sentencepad, truncating='post', padding='post'))]
class STSTask():
def __init__(self, c):
self.c = c
def load_resc(self,dictname):
self.embed = Embedder(dictname, self.c['wordvectdim'])
def load_data(self, trainfile, validfile, testfile):
self.traindata= self._load_data(trainfile)
self.validdata= self._load_data(validfile)
self.testdata = self._load_data(testfile)
def _load_data(self, filename):
global ADD_PREFIX
print(filename)
s0,s1,labels = [],[],[]
lines=open(filename,'r', encoding='utf8').read().splitlines()
for line in lines:
try:
s0x, s1x, label = line.rstrip().split('\t')
labels.append(float(label))
if ADD_PREFIX:
prefix = ''
if filename == 'Dataset/train_set.csv' or filename=='Dataset/dev_set.csv':
prefix = 'en/'
else:
prefix = 'es/'
s0.append([prefix + word for word in word_tokenize(s0x) if word not in string.punctuation])
s1.append([prefix + word for word in word_tokenize(s1x) if word not in string.punctuation])
#s0.append([prefix+word.lower() for word in word_tokenize(s0x) if word not in string.punctuation])
#s1.append([prefix+word.lower() for word in word_tokenize(s1x) if word not in string.punctuation])
else:
s0.append([word for word in word_tokenize(s0x) if word not in string.punctuation])
s1.append([word for word in word_tokenize(s1x) if word not in string.punctuation])
#s0.append([word.lower() for word in word_tokenize(s0x) if word not in string.punctuation])
#s1.append([word.lower() for word in word_tokenize(s1x) if word not in string.punctuation])
except:
print('Error in line: ' + str(line))
m0 = self.embed.matrixize(s0, self.c['sentencepad'])
m1 = self.embed.matrixize(s1, self.c['sentencepad'])
classes = np.zeros((len(labels), self.c['num_classes']))
for i, label in enumerate(labels):
if np.floor(label) + 1 < self.c['num_classes']:
classes[i, int(np.floor(label)) + 1] = label - np.floor(label)
classes[i, int(np.floor(label))] = np.floor(label) - label + 1
#print(classes)
return {'labels': labels, 's0': s0, 's1': s1, 'classes': classes, 'm0': m0, 'm1': m1}
def create_model(self):
K.clear_session()
input0 = Input(shape=(self.c['sentencepad'], self.c['wordvectdim']))
input1 = Input(shape=(self.c['sentencepad'], self.c['wordvectdim']))
Convolt_Layer=[]
MaxPool_Layer=[]
Flatten_Layer=[]
for kernel_size, filters in self.c['cnnfilters'].items():
Convolt_Layer.append(Convolution1D(filters=filters,
kernel_size=kernel_size,
padding='valid',
activation=self.c['cnnactivate'],
kernel_initializer=self.c['cnninitial']))
MaxPool_Layer.append(MaxPooling1D(pool_size=int(self.c['sentencepad']-kernel_size+1)))
Flatten_Layer.append(Flatten())
Convolted_tensor0=[]
Convolted_tensor1=[]
for channel in range(len(self.c['cnnfilters'])):
Convolted_tensor0.append(Convolt_Layer[channel](input0))
Convolted_tensor1.append(Convolt_Layer[channel](input1))
MaxPooled_tensor0=[]
MaxPooled_tensor1=[]
for channel in range(len(self.c['cnnfilters'])):
MaxPooled_tensor0.append(MaxPool_Layer[channel](Convolted_tensor0[channel]))
MaxPooled_tensor1.append(MaxPool_Layer[channel](Convolted_tensor1[channel]))
Flattened_tensor0=[]
Flattened_tensor1=[]
for channel in range(len(self.c['cnnfilters'])):
Flattened_tensor0.append(Flatten_Layer[channel](MaxPooled_tensor0[channel]))
Flattened_tensor1.append(Flatten_Layer[channel](MaxPooled_tensor1[channel]))
if len(self.c['cnnfilters']) > 1:
Flattened_tensor0=concatenate(Flattened_tensor0)
Flattened_tensor1=concatenate(Flattened_tensor1)
else:
Flattened_tensor0=Flattened_tensor0[0]
Flattened_tensor1=Flattened_tensor1[0]
absDifference = Lambda(lambda X:K.abs(X[0] - X[1]))([Flattened_tensor0,Flattened_tensor1])
mulDifference = multiply([Flattened_tensor0,Flattened_tensor1])
allDifference = concatenate([absDifference,mulDifference])
for ilayer, densedimension in enumerate(self.c['densedimension']):
allDifference = Dense(units=int(densedimension),
activation=self.c['denseactivate'],
kernel_initializer=self.c['denseinitial'])(allDifference)
output = Dense(name='output',
units=self.c['num_classes'],
activation='softmax',
kernel_initializer=self.c['denseinitial'])(allDifference)
self.model = Model(inputs=[input0,input1], outputs=output)
self.model.compile(loss='mean_squared_error',optimizer=self.c['optimizer'])
#self.model.compile(loss={'output': self._lossfunction}, optimizer=self.c['optimizer'])
def _lossfunction(self,y_true,y_pred):
ny_true = y_true[:,1] #+ 2*y_true[:,2] + 3*y_true[:,3] + 4*y_true[:,4] + 5*y_true[:,5]
ny_pred = y_pred[:,1] #+ 2*y_pred[:,2] + 3*y_pred[:,3] + 4*y_pred[:,4] + 5*y_pred[:,5]
my_true = K.mean(ny_true)
my_pred = K.mean(ny_pred)
var_true = (ny_true - my_true)**2
var_pred = (ny_pred - my_pred)**2
return -K.sum((ny_true-my_true)*(ny_pred-my_pred),axis=-1) / (K.sqrt(K.sum(var_true,axis=-1)*K.sum(var_pred,axis=-1)))
def eval_model(self):
global TEXT_EVAL
results = []
for data in [self.traindata, self.validdata, self.testdata]:
predictionclasses = []
for dataslice,_ in self._sample_pairs(data, len(data['classes']), shuffle=False, once=True):
predictionclasses += list(self.model.predict(dataslice))
#print(predictionclasses)
scores = []
for p in predictionclasses:
if p[0]>p[1]:
scores.append(0)
else:
scores.append(1)
#prediction = np.dot(np.array(predictionclasses),np.arange(self.c['num_classes']))
gold_scores = data['labels']
result = sklearn.metrics.log_loss(gold_scores, scores)
TP, FP, TN, FN = perf_measure(gold_scores, scores)
acc = np.sum(np.array(gold_scores) == np.array(scores)) / len(gold_scores)
print('Log Loss: ' + str(result))
print('Acc: ' + str(acc))
print('TP: ' + str(TP) + '\tFP: ' + str(FP) + '\tTN: ' + str(TN) + '\tFN: ' + str(FN))
#print(scores)
#print(gold_scores)
results.append([result, acc, TP, FP, TN, FN])
#result=pearsonr(prediction, goldlabels)[0]
#results.append(round(result,4))
print('TEST:' + str(results[0]))
print('DEV:' + str(results[1]))
print('TEST:' + str(results[2]))
#print(results)
return results
def fit_model(self, wfname):
kwargs = dict()
kwargs['generator'] = self._sample_pairs(self.traindata, self.c['batch_size'])
kwargs['steps_per_epoch'] = self.c['num_batchs']
kwargs['epochs'] = self.c['num_epochs']
class Evaluate(Callback):
def __init__(self, task, wfname):
self.task = task
self.bestresult = 0.0
self.wfname = wfname
def on_epoch_end(self, epoch, logs={}):
results = self.task.eval_model()
if results[1][0] > self.bestresult:
self.bestresult = results[1][0]
self.task.model.save(self.wfname)
#_,validresult,_,_,_,_,_ = self.task.eval_model()
#if validresult > self.bestresult:
# self.bestresult = validresult
# self.task.model.save(self.wfname)
kwargs['callbacks'] = [Evaluate(self, wfname)]
return self.model.fit_generator(verbose=2,**kwargs)
def _sample_pairs(self, data, batch_size, shuffle=True, once=False):
num = len(data['classes'])
idN = int((num+batch_size-1) / batch_size)
ids = list(range(num))
while True:
if shuffle: random.shuffle(ids)
datacopy= copy.deepcopy(data)
for name, value in datacopy.items():
valuer=copy.copy(value)
for i in range(num):
valuer[i]=value[ids[i]]
datacopy[name] = valuer
for i in range(idN):
sl = slice(i*batch_size, (i+1)*batch_size)
dataslice= dict()
for name, value in datacopy.items():
dataslice[name] = value[sl]
x = [dataslice['m0'],dataslice['m1']]
y = dataslice['classes']
yield (x,y)
if once: break
c = dict()
c['num_runs'] = 3
c['num_epochs'] = 64
c['num_batchs'] = 2
c['batch_size'] = 256
c['wordvectdim'] = 300
c['sentencepad'] = 60
c['num_classes'] = 2
c['cnnfilters'] = {1: 1800}
c['cnninitial'] = 'he_uniform'
c['cnnactivate'] = 'relu'
c['densedimension'] = list([1800])
c['denseinitial'] = 'he_uniform'
c['denseactivate'] = 'tanh'
c['optimizer'] = 'adam'
def main():
global ADD_PREFIX
global TEXT_EVAL
tsk = STSTask(c)
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', type=str, required=True)
parser.add_argument('-p', '--add_lang_prefix', action='store_true')
args = parser.parse_args()
ADD_PREFIX = args.add_lang_prefix
tsk.load_resc(args.input)
tsk.load_data('Dataset/train_set.csv', 'Dataset/dev_set.csv', 'Dataset/test_set.csv')
bestresult = 0.0
bestwfname = None
for i_run in range(tsk.c['num_runs']):
print('RunID: %s' %i_run)
tsk.create_model()
print('Training')
wfname = './weightfile'+str(i_run)
tsk.fit_model(wfname)
print('Prediction(best valid epoch)')
tsk.model.load_weights(wfname)
results = tsk.eval_model()
if results[1][0]>bestresult:
bestresult = results[1][0]
bestwfname = wfname
print('Prediction(best run)')
tsk.model.load_weights(bestwfname)
traindata, validdata, testdata = tsk.eval_model()
if not os.path.exists('Results'):
os.makedirs('Results')
output_name = 'Results/Results_CNN.csv'
with open(output_name, 'a+') as file:
print(args.input + ',' + str(traindata) + ',' + str(validdata) + ',' + str(testdata), file=file)
print('Results exported to ' + str(output_name))
if __name__ == "__main__":
main()