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utils.py
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222 lines (195 loc) · 7.87 KB
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import os
import sys
import cPickle as cp
import numpy as np
import time
import pdb
def map_2_ix(word, word_2_ix, lower=False):
if lower:
word = word.lower()
if word in word_2_ix:
return word_2_ix[word] + 1
else:
return len(word_2_ix) + 1
def map_2_feats(features, feats_2_ix):
feature_vector = np.zeros((len(feats_2_ix),))
for feature in features:
if feature in feats_2_ix:
feature_vector[feats_2_ix[feature]] = 1.
return feature_vector
def data_generator(training_data, options):
if type(training_data) != list:
training_data = [training_data]
X = []
y = []
punctuation_set = set([".", "?", "!"])
for post in training_data:
post_y = []
sentence = []
feat_vectors = []
sentence_markers = []
post = post.split('\n')
for ix, features in enumerate(post):
features = features.split(' ')
if features[0] in punctuation_set:
sentence_markers.append(ix)
sentence.append(map_2_ix(features[0], options['VOCAB'], True))
feat_vectors.append(map_2_feats(features[:-1], options['FEATURE_VOCAB']))
if features[-1] not in options['CLASSES_2_IX']:
assert options['USE_PARTIAL'], "Label other than type, attr, location, O and not using partially labeled data"
assert features[-1] == '<UNK>', "The unknown tokens should be labeled <UNK>"
post_y.append(-1)
else:
post_y.append(options['CLASSES_2_IX'][features[-1]])
sentence_markers.append(len(post))
feat_vectors = np.array(feat_vectors) if len(feat_vectors) > 0 else None
sentence_markers = sentence_markers if options['SENTENCE_MARKERS'] else None
post_X = (sentence, feat_vectors, sentence_markers)
X.append(post_X)
y.append(post_y)
return X, y
def get_best_model_file(file_prefix, mode="max", model_suffix='.weights'):
'''
Finds the best model from a directory
'''
mode = mode.lower()
file_prefix = file_prefix.split('/')
assert mode in set(["min", "max"])
directory = '/'.join(file_prefix[:-1])
model_prefix = file_prefix[-1]
assert os.path.isdir(directory)
best_model_metric = None
best_model_file = None
comparison_function = min if mode == "min" else max
for file in os.listdir(directory):
if file.startswith(model_prefix) and file.endswith(model_suffix):
# metric = float('.'.join(file.split('_')[-1].split('.')[:-1]))
metric = float(file.rstrip(model_suffix).split('_')[-1])
if best_model_metric is None:
best_model_metric = metric
best_model_file = file
elif metric == comparison_function(best_model_metric, metric):
best_model_metric = metric
best_model_file = file
assert best_model_file is not None
print 'LOADING WEIGHTS FROM ...'
print best_model_file
sys.stdout.flush()
return directory + '/' + best_model_file
class Progbar(object):
"""Displays a progress bar.
# Arguments
target: Total number of steps expected, None if unknown.
interval: Minimum visual progress update interval (in seconds).
"""
def __init__(self, target, width=30, verbose=1, interval=0.05):
self.width = width
if target is None:
target = -1
self.target = target
self.sum_values = {}
self.unique_values = []
self.start = time.time()
self.last_update = 0
self.interval = interval
self.total_width = 0
self.seen_so_far = 0
self.verbose = verbose
def update(self, current, values=None, force=False):
"""Updates the progress bar.
# Arguments
current: Index of current step.
values: List of tuples (name, value_for_last_step).
The progress bar will display averages for these values.
force: Whether to force visual progress update.
"""
values = values or []
for k, v in values:
if k not in self.sum_values:
self.sum_values[k] = [v * (current - self.seen_so_far),
current - self.seen_so_far]
self.unique_values.append(k)
else:
self.sum_values[k][0] += v * (current - self.seen_so_far)
self.sum_values[k][1] += (current - self.seen_so_far)
self.seen_so_far = current
now = time.time()
if self.verbose == 1:
if not force and (now - self.last_update) < self.interval:
return
prev_total_width = self.total_width
sys.stdout.write('\b' * prev_total_width)
sys.stdout.write('\r')
if self.target is not -1:
numdigits = int(np.floor(np.log10(self.target))) + 1
barstr = '%%%dd/%%%dd [' % (numdigits, numdigits)
bar = barstr % (current, self.target)
prog = float(current) / self.target
prog_width = int(self.width * prog)
if prog_width > 0:
bar += ('=' * (prog_width - 1))
if current < self.target:
bar += '>'
else:
bar += '='
bar += ('.' * (self.width - prog_width))
bar += ']'
sys.stdout.write(bar)
self.total_width = len(bar)
if current:
time_per_unit = (now - self.start) / current
else:
time_per_unit = 0
eta = time_per_unit * (self.target - current)
info = ''
if current < self.target and self.target is not -1:
info += ' - ETA: %ds' % eta
else:
info += ' - %ds' % (now - self.start)
for k in self.unique_values:
info += ' - %s:' % k
if isinstance(self.sum_values[k], list):
avg = self.sum_values[k][0] / max(1, self.sum_values[k][1])
if abs(avg) > 1e-3:
info += ' %.4f' % avg
else:
info += ' %.4e' % avg
else:
info += ' %s' % self.sum_values[k]
self.total_width += len(info)
if prev_total_width > self.total_width:
info += ((prev_total_width - self.total_width) * ' ')
sys.stdout.write(info)
sys.stdout.flush()
if current >= self.target:
sys.stdout.write('\n')
if self.verbose == 2:
if current >= self.target:
info = '%ds' % (now - self.start)
for k in self.unique_values:
info += ' - %s:' % k
avg = self.sum_values[k][0] / max(1, self.sum_values[k][1])
if avg > 1e-3:
info += ' %.4f' % avg
else:
info += ' %.4e' % avg
sys.stdout.write(info + "\n")
self.last_update = now
def add(self, n, values=None):
self.update(self.seen_so_far + n, values)
def get_weights(idx):
BASE_DIR = '/scratch/cse/btech/cs1130773/BTP/SupervisedData/LSTM_MODELS/'
MODEL_DIR = BASE_DIR + 'MODEL_' + str(idx) + '/'
MAX_VAL_ACC = -1
best_model = ''
for filename in os.listdir(MODEL_DIR):
if not filename.startswith('weights'):
continue
val_acc = int(filename.split('.')[2])
if val_acc >= MAX_VAL_ACC:
MAX_VAL_ACC = val_acc
best_model = filename
assert best_model != '', "Could Not find the best model file for directory %s" % (str(idx))
print 'LOADING FOR IDX', idx, ' FROM FILE', MODEL_DIR + best_model
sys.stdout.flush()
return MODEL_DIR + best_model