# Path: dlm/eval.py
# class Evaluator():
# 	def __init__(self, dataset, classifier):
# 	def classification_error(self):
# 	def mean_neg_log_likelihood(self):
# 	def mean_unnormalized_neg_log_likelihood(self):
# 	def perplexity(self):
# 	def unnormalized_perplexity(self):
# 	def get_sequence_log_prob(self, index):
# 	def get_unnormalized_sequence_log_prob(self, index):
# 	def get_ngram_log_prob(self, x, y):
# 	def get_denominator(self):
# 	def get_class(self, index, restricted_ids = []):
# 
# Path: dlm/algorithms/lr_tuner.py
# class LRTuner:
# 	def __init__(self, low, high, inc):
# 		self.low = low
# 		self.high = high
# 		self.inc = inc
# 		self.prev_ppl = numpy.inf
# 	
# 	def adapt_lr(self, curr_ppl, curr_lr):
# 		if curr_ppl >= self.prev_ppl:
# 			lr = max(curr_lr / 2, self.low)
# 		else:
# 			lr = min(curr_lr + self.inc, self.high)
# 		self.prev_ppl = curr_ppl
# 		return lr


import theano
import theano.tensor as T
import dlm.utils as U
import dlm.io.logging as L
import time
import numpy as np
import sys
import time
from dlm import eval
from dlm.algorithms.lr_tuner import LRTuner
		from dlm.algorithms.sgd import SGD as Trainer
		self.best_test_perplexity = np.inf
		self.t0 = time.time()
		self.total_num_iter = total_num_iter
		self.out_dir = out_dir

	def evaluate(self, curr_iter):
		denominator = self.dev_eval.get_denominator()
		dev_error = self.dev_eval.classification_error()
		dev_perplexity = self.dev_eval.perplexity()
		if self.test_eval:
			test_error = self.test_eval.classification_error()
			test_perplexity = self.test_eval.perplexity()

		if dev_perplexity < self.best_dev_perplexity:
			self.best_dev_perplexity = dev_perplexity
			self.best_iter = curr_iter
			if self.test_eval:
				self.best_test_perplexity = test_perplexity

		if curr_iter > 0:
			t1 = time.time()
			rem_time = int((self.total_num_iter - curr_iter) * (t1 - self.t0) / (curr_iter * 60))
			rem_time = str(rem_time) + "m"
		else:
			rem_time = ""

		L.info(('DEV  => Error=%.2f%%, PPL=' + U.b_yellow('%.2f @ %i') + ' (' + U.b_red('%.2f @ %i') + '), Denom=%.3f, %s')
			% (dev_error * 100., dev_perplexity, curr_iter, self.best_dev_perplexity, self.best_iter, denominator, rem_time))
		if self.test_eval:
			L.info(('TEST => Error=%.2f%%, PPL=' + U.b_yellow('%.2f @ %i') + ' (' + U.b_red('%.2f @ %i') + ')')

# Path: dlm/eval.py
# class Evaluator():
# 	def __init__(self, dataset, classifier):
# 	def classification_error(self):
# 	def mean_neg_log_likelihood(self):
# 	def mean_unnormalized_neg_log_likelihood(self):
# 	def perplexity(self):
# 	def unnormalized_perplexity(self):
# 	def get_sequence_log_prob(self, index):
# 	def get_unnormalized_sequence_log_prob(self, index):
# 	def get_ngram_log_prob(self, x, y):
# 	def get_denominator(self):
# 	def get_class(self, index, restricted_ids = []):
# 
# Path: dlm/algorithms/lr_tuner.py
# class LRTuner:
# 	def __init__(self, low, high, inc):
# 		self.low = low
# 		self.high = high
# 		self.inc = inc
# 		self.prev_ppl = numpy.inf
# 	
# 	def adapt_lr(self, curr_ppl, curr_lr):
# 		if curr_ppl >= self.prev_ppl:
# 			lr = max(curr_lr / 2, self.low)
# 		else:
# 			lr = min(curr_lr + self.inc, self.high)
# 		self.prev_ppl = curr_ppl
# 		return lr


# Path: dlm/trainer.py
import theano
import theano.tensor as T
import dlm.utils as U
import dlm.io.logging as L
import time
import numpy as np
import sys
import time
from dlm import eval
from dlm.algorithms.lr_tuner import LRTuner
		from dlm.algorithms.sgd import SGD as Trainer

		self.best_test_perplexity = np.inf
		self.t0 = time.time()
		self.total_num_iter = total_num_iter
		self.out_dir = out_dir

	def evaluate(self, curr_iter):
		denominator = self.dev_eval.get_denominator()
		dev_error = self.dev_eval.classification_error()
		dev_perplexity = self.dev_eval.perplexity()
		if self.test_eval:
			test_error = self.test_eval.classification_error()
			test_perplexity = self.test_eval.perplexity()

		if dev_perplexity < self.best_dev_perplexity:
			self.best_dev_perplexity = dev_perplexity
			self.best_iter = curr_iter
			if self.test_eval:
				self.best_test_perplexity = test_perplexity

		if curr_iter > 0:
			t1 = time.time()
			rem_time = int((self.total_num_iter - curr_iter) * (t1 - self.t0) / (curr_iter * 60))
			rem_time = str(rem_time) + "m"
		else:
			rem_time = ""

		L.info(('DEV  => Error=%.2f%%, PPL=' + U.b_yellow('%.2f @ %i') + ' (' + U.b_red('%.2f @ %i') + '), Denom=%.3f, %s')
			% (dev_error * 100., dev_perplexity, curr_iter, self.best_dev_perplexity, self.best_iter, denominator, rem_time))
		if self.test_eval:
			L.info(('TEST => Error=%.2f%%, PPL=' + U.b_yellow('%.2f @ %i') + ' (' + U.b_red('%.2f @ %i') + ')')

				% (test_error * 100., test_perplexity, curr_iter, self.best_test_perplexity, self.best_iter))