def create_sub_class_test_data(related, config):
'''
Args: related: a numpy array of booleans that are true if related, false if unrelated
'''
unrelated = np.logical_not(related)
# includes unrelated
X, y, glove_matrix, max_input_lengths, word_to_glove_index = create_embeddings(
training_size=config.training_size,
random_split=config.random_split,
truncate_headlines=config.truncate_headlines,
truncate_articles=config.truncate_articles,
classification_problem=1,
max_headline_length=config.max_headline_length,
max_article_length=config.max_article_length,
glove_set=None,
debug=False)
# isolate test data for classification problem 2
_, _, (h_glove_index_matrix, a_glove_index_matrix, h_seq_lengths,
a_seq_lengths, labels) = create_data_sets_for_model(X, y)
unrelated_labels = labels[unrelated]
related_labels = labels[related]
related_h_glove_index_matrix = h_glove_index_matrix[related]
related_a_glove_index_matrix = a_glove_index_matrix[related]
related_h_seq_lengths = np.transpose(np.transpose(h_seq_lengths)[related])
related_a_seq_lengths = np.transpose(np.transpose(a_seq_lengths)[related])
return glove_matrix, related_h_glove_index_matrix, related_a_glove_index_matrix, related_h_seq_lengths, related_a_seq_lengths, max_input_lengths, related_labels, unrelated_labels
def main(debug=True):
# Parse Arguments
parser = argparse.ArgumentParser()
parser.add_argument('--epoch', type=int, default=5)
parser.add_argument('--restore', action='store_true')
args = parser.parse_args()
# Create Config
config = Config()
if args.epoch:
config.n_epochs = args.epoch
X, y, glove_matrix, max_input_lengths, word_to_glove_index = create_embeddings(
training_size=config.training_size,
random_split=config.random_split,
truncate_headlines=config.truncate_headlines,
truncate_articles=config.truncate_articles,
classification_problem=config.classification_problem,
max_headline_length=config.max_headline_length,
max_article_length=config.max_article_length,
glove_set=None,
debug=debug)
if config.uniform_data_split:
X, y = produce_uniform_data_split(X, y)
# Each set is of the form:
# [headline_glove_index_matrix, article_glove_index_matrix, h_seq_lengths, a_seq_lengths, labels]
train_examples, dev_set, test_set = create_data_sets_for_model(X, y)
print "Distribution of Train {}".format(np.sum(train_examples[4], axis=0))
print "Distribtion of Dev {}".format(np.sum(dev_set[4], axis=0))
print "Distribution of Test{}".format(np.sum(test_set[4], axis=0))
with tf.Graph().as_default():
print 80 * "="
print "INITIALIZING"
print 80 * "="
# Create and configure model
print "Building model...",
start = time.time()
model = Attention_Conditonal_Encoding_LSTM_Model(
config, report_score, max_input_lengths, glove_matrix, debug)
model.print_params()
print "took {:.2f} seconds\n".format(time.time() - start)
# Initialize variables
init = tf.global_variables_initializer()
saver = None if debug else tf.train.Saver()
with tf.Session() as session:
# Load weights if necessary
session.run(init)
saver = create_tensorflow_saver(model.exclude_names)
if args.restore:
saver.restore(session, model.curr_weights_fn)
# Finalize graph
session.graph.finalize()
# Train Model
print 80 * "="
print "TRAINING"
print 80 * "="
model.fit(session, saver, train_examples, dev_set)
if not debug:
print 80 * "="
print "TESTING"
print 80 * "="
print "Restoring the best model weights found on the dev set"
saver.restore(session, model.best_weights_fn)
print "Final evaluation on test set",
test_score, _, test_confusion_matrix_str = model.predict(
session, test_set, save_preds=True)
with open(model.test_confusion_matrix_fn, 'w') as file:
file.write(test_confusion_matrix_str)
def main(debug=True):
# Parse Arguments
arg_epoch, arg_restore, arg_test = parse_args()
# Create Config
config = Config()
if arg_epoch:
config.n_epochs = arg_epoch
X, y, glove_matrix, max_input_lengths, word_to_glove_index = create_embeddings(
training_size=config.training_size,
random_split=config.random_split,
truncate_headlines=config.truncate_headlines,
truncate_articles=config.truncate_articles,
classification_problem=config.classification_problem,
max_headline_length=config.max_headline_length,
max_article_length=config.max_article_length,
glove_set=None,
debug=debug)
if config.uniform_data_split:
X, y = produce_uniform_data_split(X, y)
# Each set is of the form:
# [headline_glove_index_matrix, article_glove_index_matrix, h_seq_lengths, a_seq_lengths, labels]
train_examples, dev_set, test_set = create_data_sets_for_model(X, y)
print "Distribution of Train {}".format(np.sum(train_examples[4], axis=0))
print "Distribtion of Dev {}".format(np.sum(dev_set[4], axis=0))
print "Distribution of Test{}".format(np.sum(test_set[4], axis=0))
with tf.Graph().as_default():
print 80 * "="
print "INITIALIZING"
print 80 * "="
# Create and configure model
print "Building model...",
start = time.time()
model = Bimpmp(config, report_score, max_input_lengths, glove_matrix,
debug)
model.print_params()
print "took {:.2f} seconds\n".format(time.time() - start)
# Initialize variables
init = tf.global_variables_initializer()
saver = None if debug else tf.train.Saver()
with tf.Session(config=tf.ConfigProto(
log_device_placement=True)) as session:
# Load weights if necessary
session.run(init)
saver = create_tensorflow_saver(model.exclude_names)
if arg_restore != None:
weights_path = './data/{}/{}/weights'.format(
model.get_model_name(), arg_restore)
restore_path = '{}/{}'.format(weights_path,
model.get_fn_names()[1])
saver.restore(session, model.curr_weights_fn)
# Finalize graph
session.graph.finalize()
# train_vars = [var for var in tf.global_variables()]
# for var in train_vars:
# print (var.name, var.get_shape())
# Train Model
print 80 * "="
print "TRAINING"
print 80 * "="
model.fit(session, saver, train_examples, dev_set)
if not debug:
print 80 * "="
print "TESTING"
print 80 * "="
print "Restoring the best model weights found on the dev set"
saver.restore(session, model.best_weights_fn)
print "Final evaluation on test set",
test_score, _, test_confusion_matrix_str = model.predict(
session, test_set, save_preds=True)
with open(model.test_confusion_matrix_fn, 'w') as file:
file.write(test_confusion_matrix_str)
def main(debug=False):
# Parse Arguments
parser = argparse.ArgumentParser()
parser.add_argument('--restore', action='store_true')
args = parser.parse_args()
# Load Data
config = Config()
X, y, glove_matrix, max_input_lengths, word_to_glove_index = create_embeddings(
training_size=config.training_size,
random_split=config.random_split,
truncate_headlines=config.truncate_headlines,
truncate_articles=config.truncate_articles,
classification_problem=config.classification_problem,
max_headline_length=config.max_headline_length,
max_article_length=config.max_article_length,
glove_set=None,
debug=debug)
train_examples, dev_set, test_set = create_data_sets_for_model(X, y, debug)
print "Distribution of Train {}".format(np.sum(train_examples[4], axis=0))
print "Distribtion of Dev {}".format(np.sum(dev_set[4], axis=0))
print "Distribution of Test{}".format(np.sum(test_set[4], axis=0))
# Define hyperparameters
hyperparameters = {
'lr': [0.001, .0001],
'dropout_rate': [.8, .9, 1],
'beta': [.01, .001]
}
# Run model over all these hyper parameters
pp = pprint.PrettyPrinter(indent=4)
best_test_score = -1
best_config = None
best_test_confusion_matrix = ''
for lr in hyperparameters['lr']:
config = Config()
config.lr = lr
if debug:
config.embed_size = 2
for dropout_rate in hyperparameters['dropout_rate']:
config.dropout_rate = dropout_rate
for beta in hyperparameters['beta']:
config.beta = beta
print "-" * 80
print "Using Configs:"
pp.pprint(config.__dict__)
print "-" * 80
test_score, test_confusion_matrix = run_model(
config, max_input_lengths, glove_matrix, args,
train_examples, dev_set, test_set)
if test_score > best_test_score:
best_test_score = test_score
best_config = config
best_test_confusion_matrix = test_confusion_matrix
print '-' * 80
print "Best Config:"
pp.pprint(best_config.__dict__)
print "Best Test Score:"
print best_test_score
print "Confusion Matrix"
print best_test_confusion_matrix
print '-' * 80
