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# Copyright 2017 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import pickle as pkl
import numpy as np
import tensorflow as tf
import utils
tf.app.flags.DEFINE_string(
'data_dir', 'reproduce',
'Path to directory containing data and model checkpoints.')
FLAGS = tf.app.flags.FLAGS
class EnsembleLM(object):
"""Ensemble of language models."""
def __init__(self, test_data_name='wsc273'):
vocab_file = os.path.join(FLAGS.data_dir, 'vocab.txt')
self.vocab = utils.CharsVocabulary(vocab_file, 50)
assert test_data_name in ['pdp60', 'wsc273'], (
'Test data must be pdp60 or wsc273, got {}'.format(test_data_name))
self.test_data_name = test_data_name
test_data = utils.parse_commonsense_reasoning_test(test_data_name)
self.question_ids, self.sentences, self.labels = test_data
self.all_probs = [] # aggregate single-model prediction here.
def add_single_model(self, model_name='lm1'):
"""Add a single model into the current ensemble."""
# Create single LM
single_lm = SingleRecurrentLanguageModel(self.vocab, model_name)
# Add the single LM prediction.
probs = single_lm.assign_probs(self.sentences, self.test_data_name)
self.all_probs.append(probs)
print('Done adding {}'.format(model_name))
def evaluate(self):
"""Evaluate the current ensemble."""
# Attach word probabilities and correctness label to each substitution
ensembled_probs = sum(self.all_probs) / len(self.all_probs)
scorings = []
for i, sentence in enumerate(self.sentences):
correctness = self.labels[i]
word_probs = ensembled_probs[i, :len(sentence)]
joint_prob = np.prod(word_probs, dtype=np.float64)
scorings.append(dict(
correctness=correctness,
sentence=sentence,
joint_prob=joint_prob,
word_probs=word_probs))
scoring_mode = 'full' if self.test_data_name == 'pdp60' else 'partial'
return utils.compare_substitutions(
self.question_ids, scorings, scoring_mode)
class SingleRecurrentLanguageModel(object):
"""Single Recurrent Language Model."""
def __init__(self, vocab, model_name='lm01'):
self.vocab = vocab
self.log_dir = os.path.join(FLAGS.data_dir, model_name)
def reset(self):
self.sess.run(self.tensors['states_init'])
def _score(self, word_patch):
"""Score a matrix of shape (batch_size, num_timesteps+1) str tokens."""
word_ids = np.array(
[[self.vocab.word_to_id(word) for word in row]
for row in word_patch])
char_ids = np.array(
[[self.vocab.word_to_char_ids(word) for word in row]
for row in word_patch])
print('Probs for \n{}\n='.format(np.array(word_patch)[:, 1:]))
input_ids, target_ids = word_ids[:, :-1], word_ids[:, 1:]
input_char_ids = char_ids[:, :-1, :]
softmax = self.sess.run(self.tensors['softmax_out'], feed_dict={
self.tensors['inputs_in']: input_ids,
self.tensors['char_inputs_in']: input_char_ids
})
batch_size, num_timesteps = self.shape
softmax = softmax.reshape((num_timesteps, batch_size, -1))
softmax = np.transpose(softmax, [1, 0, 2])
probs = np.array([[softmax[row, col, target_ids[row, col]]
for col in range(num_timesteps)]
for row in range(batch_size)])
print(probs)
return probs
def _score_patches(self, word_patches):
"""Score a 2D matrix of word_patches and stitch results together."""
batch_size, num_timesteps = self.shape
nrow, ncol = len(word_patches), len(word_patches[0])
max_len = num_timesteps * ncol
probs = np.zeros([0, max_len]) # accumulate results into this.
# Loop through the 2D matrix of word_patches and score each.
for i, row in enumerate(word_patches):
print('Reset RNN states.')
self.reset() # reset states before processing each row.
row_probs = np.zeros([batch_size, 0])
for j, word_patch in enumerate(row):
print('Processing patch '
'({}, {}) / ({}, {})'.format(i+1, j+1, nrow, ncol))
patch_probs = (self._score(word_patch) if word_patch else
np.zeros([batch_size, num_timesteps]))
row_probs = np.concatenate([row_probs, patch_probs], 1)
probs = np.concatenate([probs, row_probs], 0)
return probs
def assign_probs(self, sentences, test_data_name='wsc273'):
"""Return prediction accuracy using this LM for a test."""
probs_cache = os.path.join(self.log_dir, '{}.probs'.format(test_data_name))
if os.path.exists(probs_cache):
print('Reading cached result from {}'.format(probs_cache))
with tf.gfile.Open(probs_cache, 'r') as f:
probs = pkl.load(f)
else:
tf.reset_default_graph()
self.sess = tf.Session()
# Build the graph.
saver = tf.train.import_meta_graph(
os.path.join(self.log_dir, 'ckpt-best.meta'))
saver.restore(self.sess, os.path.join(self.log_dir, 'ckpt-best'))
print('Restored from {}'.format(self.log_dir))
graph = tf.get_default_graph()
self.tensors = dict(
inputs_in=graph.get_tensor_by_name('test_inputs_in:0'),
char_inputs_in=graph.get_tensor_by_name('test_char_inputs_in:0'),
softmax_out=graph.get_tensor_by_name('SotaRNN_1/softmax_out:0'),
states_init=graph.get_operation_by_name('SotaRNN_1/states_init'))
self.shape = self.tensors['inputs_in'].shape.as_list()
# Cut sentences into patches of shape processable by the LM.
batch_size, num_timesteps = self.shape
word_patches = utils.cut_to_patches(sentences, batch_size, num_timesteps)
probs = self._score_patches(word_patches)
# Cache the probs since they are expensive to evaluate
with tf.gfile.Open(probs_cache, 'w') as f:
pkl.dump(probs, f)
return probs
def evaluate_ensemble(test_data_name, number_of_lms):
ensemble = EnsembleLM(test_data_name)
model_list = ['lm{:02d}'.format(i+1) for i in range(number_of_lms)]
for model_name in model_list:
ensemble.add_single_model(model_name)
accuracy = ensemble.evaluate()
print('Accuracy of {} LM(s) on {} = {}'.format(
number_of_lms, test_data_name, accuracy))
def main(_):
evaluate_ensemble('pdp60', 1) # 60%
evaluate_ensemble('pdp60', 5) # 70%
evaluate_ensemble('wsc273', 10) # 61.5%
evaluate_ensemble('wsc273', 14) # 63.7%
if __name__ == '__main__':
tf.app.run(main)
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