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P4.py
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P4.py
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from P1 import TrainProbabilities, START_TOK, STOP_TOK
from P2 import CRF
import numpy as np
from collections import defaultdict
from scipy.optimize import fmin_l_bfgs_b
class CRF(CRF):
def __init__(self, training_path="data/ES/train", l2_param=0.1):
self.training_path = training_path
self.l2_param = l2_param
super(CRF, self).__init__()
def _forward(self, w, sentence):
possible_y = self.train_probabilities.y_count.keys()
# base case
alpha_list = [{START_TOK: 0}]
# START to all y
last_y = START_TOK
alpha_score = {}
for next_y in possible_y:
transition_key = "transition:%s+%s"%(last_y,next_y)
# force constraint to never transition to start token
# if next_y == START_TOK:
# w[transition_key] = -10000.
# try:
alpha_score[next_y] = w[transition_key]
# except KeyError:
# alpha_score[next_y] = 0
alpha_list.append(alpha_score)
# all y to all y
for index, x in enumerate(sentence[:-1]):
alpha_score = {}
for next_y in possible_y:
alpha_score[next_y] = 0
scores = []
for last_y in possible_y:
transition_key = "transition:%s+%s"%(last_y,next_y)
emission_key = "emission:%s+%s"%(last_y, x)
# if next_y == START_TOK or last_y == STOP_TOK:
# w[transition_key] = -10000.
# try:
score = w[transition_key] + alpha_list[index+1][last_y] + w[emission_key]
scores.append(score)
# except KeyError:
# scores.append(-10000.)
alpha_score[next_y] = log_sum_exp(np.array(scores))
alpha_list.append(alpha_score)
# all y to STOP
next_y = STOP_TOK
x = sentence[-1]
alpha_score = {}
alpha_score[next_y] = 0
scores = []
for last_y in possible_y:
transition_key = "transition:%s+%s"%(last_y,next_y)
emission_key = "emission:%s+%s"%(last_y, x)
# if last_y == STOP_TOK:
# W[transition_key] = -10000.
# try:
score = w[transition_key] + alpha_list[-1][last_y] + w[emission_key]
scores.append(score)
# except KeyError:
# scores.append(-10000.)
alpha_score[next_y] = log_sum_exp(np.array(scores))
alpha_list.append(alpha_score)
return alpha_list
def calculate_loss(self, w, path):
loss = 0
running_x = []
running_y = []
with open(path,mode='r',encoding="utf-8") as file:
for line in file:
# End of a sequence
if line=='\n':
forward_score = self._forward(w, running_x)
sentence = " ".join(running_x)
tag = " ".join(running_y)
# log the score stored at the last element of forward score
loss += forward_score[-1][STOP_TOK] - self._score(sentence, tag, w=w)
running_x = []
running_y = []
else:
x,y = line.split()
running_x.append(x)
running_y.append(y)
loss += self.l2_param * np.sum(np.power(list(w.values()), 2))
return loss
def _backward(self, w, sentence):
possible_y = self.train_probabilities.y_count.keys()
# base case
beta_list = [{STOP_TOK: 0}]
# all y to STOP
next_y = STOP_TOK
beta_score = {}
x = sentence[-1]
for last_y in possible_y:
emission_key = "emission:%s+%s"%(last_y, x)
transition_key = "transition:%s+%s"%(last_y,next_y)
# if last_y == STOP_TOK:
# w[transition_key] = -10000.
# try:
beta_score[last_y] = w[transition_key] + w[emission_key]
# except KeyError:
# beta_score[last_y] = 0
beta_list.append(beta_score)
# all y to all y
for index, x in enumerate(sentence[:-1][::-1]):
beta_score = {}
for last_y in possible_y:
beta_score[last_y] = 0
scores = []
for next_y in possible_y:
transition_key = "transition:%s+%s"%(last_y,next_y)
emission_key = "emission:%s+%s"%(last_y, x)
# if last_y == STOP_TOK or next_y == START_TOK:
# w[transition_key] = -10000.
# try:
score = w[transition_key] + beta_list[index+1][next_y] + w[emission_key]
scores.append(score)
# except KeyError:
# scores.append(-10000)
beta_score[last_y] = log_sum_exp(np.array(scores))
beta_list.append(beta_score)
# START to all y
last_y = START_TOK
beta_score = {}
sum_prob = []
for next_y in possible_y:
transition_key = "transition:%s+%s"%(last_y,next_y)
# if next_y == START_TOK:
# w[transition_key] = -10000.
# try:
sum_prob.append(w[transition_key] + beta_list[-1][next_y])
# except KeyError:
# sum_prob.append(-10000)
beta_score[last_y] = log_sum_exp(np.array(sum_prob))
beta_list.append(beta_score)
return beta_list[::-1]
def calculate_gradient(self, w, path):
w_score = defaultdict(float)
y_x_count = defaultdict(int)
y0_y1_count = defaultdict(int)
last_y = START_TOK
running_x = []
running_y = []
with open(path,mode='r',encoding="utf-8") as file:
for line in file:
# End of a sequence
if line=='\n':
# calculate forward backward scores and denom
y0_y1_count[(last_y,STOP_TOK)] += 1
forward_score = self._forward(w, running_x)
backward_score = self._backward(w, running_x)
# print(forward_score)
# print(backward_score)
# denom = np.exp(forward_score[-1][STOP_TOK])
denom = forward_score[-1][STOP_TOK]
# iterate through the y,x sequences in the sentence
for (y,x), counts in y_x_count.items():
emission_key = "emission:%s+%s"%(y, x)
'''
expected_counts = 0
# omit y as START and STOP
for index in range(1,len(forward_score)-1):
# include all possible transitions
for next_y in forward_score[index+1].keys():
try:
transition_key = "transition:%s+%s"%(y,next_y)
expected_counts += forward_score[index][y] * backward_score[index+1][next_y] * np.exp(w[emission_key]) * np.exp(w[transition_key])
except:
pass
w_score[emission_key] += expected_counts/denom - counts
'''
expected_counts = 0
# omit y as START and STOP
for index in range(len(running_x)):
# include all possible transitions
if x == running_x[index]:
try:
# expected_counts += np.exp(forward_score[index+1][y] + backward_score[index+1][y])
expected_counts += np.exp(forward_score[index+1][y] + backward_score[index+1][y] - denom)
except KeyError as e:
pass
w_score[emission_key] += expected_counts/denom - counts
# iterate through the y0,y1 sequences in the sentence
for (y0,y1), counts in y0_y1_count.items():
transition_key = "transition:%s+%s"%(y0,y1)
expected_counts = 0
# omit y_n as STOP
for index in range(0,len(forward_score)-1):
# START doesnt have emission
if index == 0:
try:
# expected_counts += np.exp(forward_score[index][y0] + backward_score[index+1][y1] + w[transition_key])
expected_counts += np.exp(forward_score[index][y0] + backward_score[index+1][y1] + w[transition_key] - denom)
except KeyError:
pass
# include y0 emission
else:
x = running_x[index-1]
emission_key = "emission:%s+%s"%(y0, x)
try:
# expected_counts += np.exp(forward_score[index][y0] + backward_score[index+1][y1] + w[emission_key] + w[transition_key])
expected_counts += np.exp(forward_score[index][y0] + backward_score[index+1][y1] + w[emission_key] + w[transition_key] - denom)
except KeyError as e:
pass
# w_score[transition_key] += expected_counts/denom - counts
w_score[transition_key] += expected_counts/denom - counts
# reset
y_x_count = defaultdict(int)
y0_y1_count = defaultdict(int)
last_y = START_TOK
running_x = []
running_y = []
else:
x,y = line.split()
y_x_count[(y,x)] += 1
y0_y1_count[(last_y,y)] += 1
last_y = y
running_x.append(x)
running_y.append(y)
w_score_temp = {k:v+2*self.l2_param*w[k] for k,v in w_score.items()}
w_score.update(w_score_temp)
return w_score
def test_gradient(self, w, path, key, value =0.01):
gradient = crf.calculate_gradient(w, path)[key]
w_test = copy.deepcopy(w)
w_test[key] += value
diff = (crf.calculate_loss(w_test, path) - crf.calculate_loss(w, path))
diff /= value
return gradient, diff
def callbackF(self, w):
'''
This function will be called by "fmin_l_bfgs_b"
Arg:
w: weights, numpy array
'''
loss = self.get_loss_grad(w)[0]
print('Loss:{0:.4f}'.format(loss))
def get_loss_grad(self, w):
'''
This function will be called by "fmin_l_bfgs_b"
Arg:
w: weights, numpy array
Returns:
loss: loss, float
grads: gradients, numpy array
'''
w_dict = defaultdict(lambda:0)
w_dict.update({k:v for k, v in zip(self.w_keys, w)})
loss = self.calculate_loss(w_dict, self.training_path)
# print(loss)
grads = self.calculate_gradient(w_dict, self.training_path)
grads = np.array([grads[k] for k in self.w_keys])
# print(grads)
return loss, grads
def train(self):
# init_w = np.zeros(len(self.train_probabilities.f.keys()))
# init_w = np.array(list(self.train_probabilities.f.values()))
self.w_keys = list(self.train_probabilities.f.keys())
init_w = np.full(len(self.w_keys), 0)
trained_weights = init_w
final_loss = None
for ep in range(5):
result = fmin_l_bfgs_b(self.get_loss_grad, trained_weights, pgtol=0.01, callback=self.callbackF)
trained_weights = result[0]
final_loss = result[1]
print(final_loss)
w_dict = defaultdict(lambda:-10000.)
w_dict.update({k:v for k, v in zip(self.train_probabilities.f.keys(), trained_weights)})
return w_dict
def decode(self, w, test_path, out_path):
self.train_probabilities.f = w
self.apply_viterbi(test_path, out_path)
def log_sum_exp(vec):
max_score = np.max(vec)
return max_score + np.log(np.sum(np.exp(vec - max_score)))
if __name__ == "__main__":
import sys
if len(sys.argv) < 4:
print ('Please make sure you have installed Python 3.4 or above!')
print ("Usage on Windows: python P4.py <train file> <dev in file> <dev out file>")
print ("Usage on Linux/Mac: python3 P4.py <train file> <dev in file> <dev out file>")
sys.exit()
# Command: python P4.py <train file> <dev in file> <dev out file>
crf = CRF(sys.argv[1])
trained_weights = crf.train()
# np.save("P4_ES_weights.npy", trained_weights)
crf.decode(trained_weights, sys.argv[2], sys.argv[3])