# print_iter_count += 1
except KeyboardInterrupt, SystemExit:
print ""
print "########################################################"
print "###### Pausing execution. Press ENTER to continue #####"
print "########################################################"
out = raw_input(
'Enter "pdb" to get prompt or ENTER to exit.> ')
if out == "pdb":
pdb.set_trace()
except Exception as e:
print e
print ">>>>> Is it intentional ?"
progbar.end()
if SAVE_MODEL_AFTER_EACH_EPOCH:
model.save("model_trainable_%s_epoc_%d.h5" %
(str(TRAINABLE_EMBEDDINGS), epoch + 1))
print ">> Epoch: %d/%d" % (epoch + 1, epochs)
print('accuracy training = {}'.format(np.mean(mean_tr_acc)))
print('recall training = {}'.format(np.mean(mean_tr_rec)))
print('loss training = {}'.format(np.mean(mean_tr_loss)))
testing_on_data("Wikipedia(DEVELOPMENT)",
X_test,
Y_test,
model,
batch_size,
summary_only=True)
def get_input(sample_type,
shuffle_documents,
pad,
trained_sent2vec_model=None):
# Returns X, Y
# X: Each row is a sample
# Y: A 1-D vector for ground truth
# Also pads the sample input as per the mentioned value of INPUT_VECTOR_LENGTH is needed
start = time.time()
data_handler = DataHandler()
print "==========================================="
if sample_type == 1:
# NOT SURE ABOUT THIS TYPE!
sample_type, samples = data_handler.get_samples(
) # Get samples, each sample is a set of INPUT_VECTOR_LENGTH consecutive sentences. No document information captured
elif sample_type == 2:
ld = load_data.LoadData()
sample_type, samples = ld.load_wikipedia_sequence()
elif sample_type in (2, 3):
# type2 : Get samples, each sample is a document (a set of sentences resulting in a sequence), or, (NUM_DOCUMENTS, NUM_SENTENCES, SENTENCE)
# type3 : Same as type2 just merge the samples to remove the sequence information and treat as simple sentence classification problem, i.e. (TOTAL_NUM_SENTENCES, SENTENCE)
# This processing will be done in the cnn_clssifier.py itself.
sample_type, samples = data_handler.get_sequence_samples(sample_type)
#sample_type, samples = data_handler.get_sequence_samples_PARALLEL() # Get samples, each sample is a document (a set of sentences resulting in a sequence)
elif sample_type == 4:
# type4: Clinical sequence of a multiple samples
# X.shape = (MULTIPLE_SAMPLES, TOTAL_SENTENCES)
# Y.shape = (MULTIPLE_SAMPLES, TOTAL_SENTENCES, 1)
ld = load_data.LoadData()
sample_type, samples = ld.load_clinical_sequence()
elif sample_type == 5:
# type5: Biography sequence of a single sample
# X.shape = (1, TOTAL_SENTENCES)
# Y.shape = (TOTAL_SENTENCES, 1)
ld = load_data.LoadData()
sample_type, samples = ld.load_biography_sequence()
elif sample_type == 6:
# type6: Fiction sequence of a multiple documents
# X.shape = (NO_OF_BOOKS, TOTAL_SENTENCES)
# Y.shape = (NO_OF_BOOKS, TOTAL_SENTENCES, 1)
ld = load_data.LoadData()
sample_type, samples = ld.load_fiction_sequence()
elif sample_type == 7:
# type7: Wiki sequence of a multiple sample
# Data format is just like the clinical sequence as each line is a sentence
# X.shape = (MULTIPLE_DOCUMENTS, TOTAL_SENTENCES)
# Y.shape = (MULTIPLE_DOCUMENTS, TOTAL_SENTENCES, 1)
ld = load_data.LoadData()
sample_type, samples = ld.load_wikipedia_sequence()
else:
print "NOTE: INVALID SAMPLE_TYPE!"
return None
del data_handler
print "Samples Loading took", time.time() - start, "seconds"
model = trained_sent2vec_model
if not trained_sent2vec_model:
#model = TFIDF(samples)
#model = MeanWord2vec()
#model = TFIDFweightedMeanWord2vec(samples)
model = CustomSent2vec()
X, Y = [], []
_total_samples, _start_time = len(samples), time.time()
print len(samples)
#pdb.set_trace()
for _idx, sample in enumerate(samples):
# Each sample is a document
# Each sample is a list of tuples with each tuple as (sentence, groundTruth)
sentences, groundTruths = zip(*sample) # Unpack a sample
## Create Wikipedia test set
CREATE_WIKI_TEST_SET = False
if CREATE_WIKI_TEST_SET:
wiki_prefix = "wiki_save/wiki_test"
if _idx >= 300:
break
with open(wiki_prefix + "_" + str(_idx + 1) + ".ref", "a") as f:
for (_s, _g) in sample:
if _g:
f.write("==========\r\n")
f.write(_s + "\r\n")
f.write("==========\r\n")
else:
# Traditional code
if not _idx % 50:
progbar.simple_update("Converting doc to martices",
_idx + 1,
_total_samples,
time_elapsed=(time.time() - _start_time))
if sample_type == 1:
# Correct groundtruth sync problem here
sentences, groundTruths = model.convert_sample_to_vec(
sentences, groundTruths)
elif sample_type in (2, 3, 4, 5, 6, 7):
sentences, groundTruths = model.convert_sequence_sample_to_vec(
sentences, groundTruths)
else:
print "Wrong Sample TYPE"
if sentences is None:
continue
X.append(sentences) # X[0].shape = matrix([[1,2,3,4.....]])
Y.append(np.asarray(
groundTruths)) # Y[0] = [1, 0, 0, ..... 0, 1, 0, 1....]
progbar.simple_update("Creating a standalone matrix for samples...", -1,
-1)
X, Y = np.asarray(X), np.asarray(Y)
progbar.end()
print "Total samples: %d" % (len(X))
if shuffle_documents: # Shuffle the X's and Y's if required
# Both of them have to be in unison
X, Y = unison_shuffled_copies(X, Y)
print "SHUFFLE: Shuffled input document order! (X:", X.shape, ", Y:", Y.shape, ")"
if sample_type == 2 and pad == False:
print "NOTE: Sample type2 requires PADDING!"
if pad:
#### THIS PAD is messy!!!!
### Check once before padding
if STATIC_PAD:
max_len = AVERAGE_WORDS
else:
max_len = None # Uses the max length of the sequences
doc_lengths = [len(doc) for doc in X]
print "Padding sequences. Doc-lengths: Mean=%d, Std=%d" % (
np.mean(doc_lengths), np.std(doc_lengths))
X = pad_sequences(X,
padding="post",
truncating="post",
value=0.0,
dtype=np.float32)
Y = pad_sequences(Y,
padding="post",
truncating="post",
value=0.0,
dtype=np.float32)
print "Size of new X(after padding):", X.shape
return sample_type, X, Y, model
def custom_fit(X_train, Y_train, X_test, Y_test, model, batch_size, epochs=10):
# Print Train stats
total_sentences, total_documents = 0, 0
total_documents = X_train.shape[0]
total_sentences = sum([doc.shape[0] for doc in X_train])
print "X-wiki TRAIN stats: Total %d sentences in %d documents" % (
total_sentences, total_documents)
class_weight = None
if SCALE_LOSS_FUN:
# Iterate as the no of sentences in each document is different
# so np.unique() messes up.
classes, counts = None, []
for _temp_Yi in Y_train:
classes, _temp_counts = np.unique(_temp_Yi, return_counts=True)
counts.append(_temp_counts)
counts = np.sum(counts, axis=0)
class_weight = dict(zip(classes.tolist(), counts / float(sum(counts))))
print class_weight
train_avg_seg_len = np.mean(
[helper.compute_avg_seg_len(Yi) for Yi in Y_train], axis=0)
print ">> Train AVG_SEGMENT_LENGTH:", train_avg_seg_len
print 'Train...'
start_epoch = 0
if LOAD_SAVED_MODEL_AND_CONTINUE_TRAIN: # If we have saved model, then continue from the last epoch where we stopped
start_epoch = saved_model_epoch_done # The epoch count is zero indexed in TRAIN, while the count in saved file is 1 indexed
for epoch in range(start_epoch, epochs):
mean_tr_acc, mean_tr_loss, mean_tr_rec = [], [], []
rLoss, rRecall, rAcc = 0, 0, 0 # Running parameters for printing while training
for batch_count, (
batch_X_left, batch_X_mid, batch_X_right,
batch_Y_mid) in enumerate(
batch_gen_consecutive_context_segments_from_big_seq(
X_train, Y_train, batch_size, ONE_SIDE_CONTEXT_SIZE)):
#batch_Y_vec = to_categorical_MULTI_DIM(batch_Y, nb_classes=2)
try:
start = time.time()
tr_loss, tr_acc, tr_rec = model.train_on_batch(
[batch_X_left, batch_X_mid, batch_X_right], batch_Y_mid)
speed = time.time() - start
mean_tr_acc.append(tr_acc)
mean_tr_loss.append(tr_loss)
mean_tr_rec.append(tr_rec)
#rLoss, rRecall, rAcc = (rLoss*batch_count + tr_loss)/(batch_count + 1), (rRecall*batch_count + tr_rec)/(batch_count + 1), (rAcc*batch_count + tr_acc)/(batch_count + 1)
#progbar.prog_bar(True, total_sentences, epochs, batch_size, epoch, batch_count, speed=speed, data={ 'rLoss': rLoss, 'rAcc': rAcc, 'rRec': rRecall })
progbar.prog_bar(True,
total_sentences,
epochs,
batch_size,
epoch,
batch_count,
speed=speed,
data={
'Loss': tr_loss,
'Acc': tr_acc,
'Rec': tr_rec
})
# Print test results after every 100 batch trains
if (not batch_count % 100) and batch_count != 0:
testing_on_data("Wikipedia",
X_test,
Y_test,
model,
batch_size,
summary_only=True)
testing_on_data("Clinical", X_cli, Y_cli, model,
batch_size)
testing_on_data("Biography", X_bio, Y_bio, model,
batch_size)
testing_on_data("Fiction",
X_fic,
Y_fic,
model,
batch_size,
summary_only=True)
except KeyboardInterrupt, SystemExit:
print ""
print "########################################################"
print "###### Pausing execution. Press ENTER to continue #####"
print "########################################################"
out = raw_input(
'Enter "pdb" to get prompt or ENTER to exit.> ')
if out == "pdb":
pdb.set_trace()
progbar.end()
if SAVE_MODEL_AFTER_EACH_EPOCH:
model.save("model_trainable_%s_epoc_%d.h5" %
(str(TRAINABLE_EMBEDDINGS), epoch + 1))
print ">> Epoch: %d/%d" % (epoch + 1, epochs)
print('accuracy training = {}'.format(np.mean(mean_tr_acc)))
print('recall training = {}'.format(np.mean(mean_tr_rec)))
print('loss training = {}'.format(np.mean(mean_tr_loss)))
testing_on_data("Wikipedia",
X_test,
Y_test,
model,
batch_size,
summary_only=True)
testing_on_data("Clinical", X_cli, Y_cli, model, batch_size)
testing_on_data("Biography", X_bio, Y_bio, model, batch_size)
testing_on_data("Fiction",
X_fic,
Y_fic,
model,
batch_size,
summary_only=True)
print('___________________________________')
def custom_fit(X_train, Y_train, X_test, Y_test, model, batch_size, epochs=10):
# Print Train stats
total_sentences, total_documents = 0, 0
total_documents = X_train.shape[0]
total_sentences = sum([doc.shape[0] for doc in X_train])
print "X-wiki TRAIN stats: Total %d sentences in %d documents" % (
total_sentences, total_documents)
class_weight = None
if SCALE_LOSS_FUN:
# Iterate as the no of sentences in each document is different
# so np.unique() messes up.
classes, counts = None, []
for _temp_Yi in Y_train:
classes, _temp_counts = np.unique(_temp_Yi, return_counts=True)
counts.append(_temp_counts)
counts = np.sum(counts, axis=0)
class_weight = dict(zip(classes.tolist(), counts / float(sum(counts))))
print class_weight
train_avg_seg_len = np.mean(
[helper.compute_avg_seg_len(Yi) for Yi in Y_train], axis=0)
print ">> Train AVG_SEGMENT_LENGTH:", train_avg_seg_len
print 'Train...'
start_epoch = 0
if LOAD_SAVED_MODEL_AND_CONTINUE_TRAIN: # If we have saved model, then continue from the last epoch where we stopped
start_epoch = saved_model_epoch_done # The epoch count is zero indexed in TRAIN, while the count in saved file is 1 indexed
print_iter_count = 0
for epoch in range(start_epoch, epochs):
mean_tr_acc, mean_tr_loss, mean_tr_rec = [], [], []
batch_count = 0
rLoss, rRecall, rAcc = 0, 0, 0 # Running parameters for printing while training
for i in range(total_documents):
X, Y = X_train[i], Y_train[i]
for (batch_X, batch_Y) in batch_gen_sentences_without_context(
X, Y, batch_size, fixed_size=False):
#pdb.set_trace()
batch_Y = to_categorical(
batch_Y, nb_classes=2) # Convert to output as 2 classes
start = time.time()
tr_loss, tr_acc, tr_rec = model.train_on_batch([batch_X],
batch_Y)
speed = time.time() - start
mean_tr_acc.append(tr_acc)
mean_tr_loss.append(tr_loss)
mean_tr_rec.append(tr_rec)
#rLoss, rRecall, rAcc = (rLoss*batch_count + tr_loss)/(batch_count + 1), (rRecall*batch_count + tr_rec)/(batch_count + 1), (rAcc*batch_count + tr_acc)/(batch_count + 1)
#progbar.prog_bar(True, total_sentences, epochs, batch_size, epoch, batch_count, speed=speed, data={ 'rLoss': rLoss, 'rAcc': rAcc, 'rRec': rRecall })
progbar.prog_bar(True,
total_sentences,
epochs,
batch_size,
epoch,
batch_count,
speed=speed,
data={
'Loss': tr_loss,
'Acc': tr_acc,
'Rec': tr_rec
})
batch_count += 1
progbar.end()
if SAVE_MODEL_AFTER_EACH_EPOCH:
model.save("model_trainable_%s_epoc_%d.h5" %
(str(TRAINABLE_EMBEDDINGS), epoch + 1))
print ">> Epoch: %d/%d" % (epoch + 1, epochs)
print('accuracy training = {}'.format(np.mean(mean_tr_acc)))
print('recall training = {}'.format(np.mean(mean_tr_rec)))
print('loss training = {}'.format(np.mean(mean_tr_loss)))
testing_on_data("Wikipedia(DEVELOPMENT)",
X_test,
Y_test,
model,
batch_size,
summary_only=True)
testing_on_data("Clinical",
X_cli,
Y_cli,
model,
batch_size,
summary_only=True)
#testing_on_data("Biography", X_bio, Y_bio, model, batch_size)
testing_on_data("Fiction",
X_fic,
Y_fic,
model,
batch_size,
summary_only=True)
testing_on_data("Wikipedia(BENCHMARK)",
X_wikitest,
Y_wikitest,
model,
batch_size,
summary_only=True)
print('___________________________________')
# Testing
print "####################################################################"
print ">> (TEST) >> Testing, X:", X_test.shape, "Y:", Y_test.shape
mean_te_acc, mean_te_loss, mean_te_rec = [], [], []
for i in range(X_test.shape[0]):
X, Y = X_test[i], Y_test[i]
for batch_X, batch_Y in batch_gen_sentences_without_context(
X, Y, batch_size, fixed_size=False):
te_loss, te_acc, te_rec = model.test_on_batch([batch_X], batch_Y)
mean_te_acc.append(te_acc)
mean_te_loss.append(te_loss)
mean_te_rec.append(te_rec)
print('accuracy testing = {}'.format(np.mean(mean_te_acc)))
print('recall testing = {}'.format(np.mean(mean_te_rec)))
print('loss testing = {}'.format(np.mean(mean_te_loss)))
