def run_prediction((train_data, train_tags, test_data, test_tags, idx)):
logger.info('training sequential model...')
all_values = flatten(train_data)
# binarize
binarizers = fit_binarizers(all_values)
test_data = call_for_each_element(test_data,
binarize, [binarizers],
data_type='sequential')
train_data = call_for_each_element(train_data,
binarize, [binarizers],
data_type='sequential')
x_train = np.array([np.array(xi) for xi in train_data])
y_train = np.array([np.array(xi) for xi in train_tags])
x_test = np.array([np.array(xi) for xi in test_data])
y_test = np.array([np.array(xi) for xi in test_tags])
sequence_learner = PystructSequenceLearner()
sequence_learner.fit(x_train, y_train)
structured_hyp = sequence_learner.predict(x_test)
logger.info('scoring sequential model...')
flattened_hyp = flatten(structured_hyp)
flattened_ref = flatten(y_test)
test_tags = flattened_ref
logger.info('Structured prediction f1: ')
cur_res = f1_score(flattened_ref, flattened_hyp, average=None)
logger.info('[ {}, {} ], {}'.format(
cur_res[0], cur_res[1],
f1_score(flattened_ref, flattened_hyp, pos_label=None)))
return (cur_res, idx)
def main(config):
workers = config['workers']
# REPRESENTATION GENERATION
# main representations (source, target, tags)
# training
train_data_generator = build_object(config['datasets']['training'][0])
train_data = train_data_generator.generate()
# train_data = {}
# for gen in train_data_generators:
# data = gen.generate()
# for key in data:
# if key not in train_data:
# train_data[key] = []
# train_data[key].extend(data[key])
# test
test_data_generator = build_object(config['datasets']['test'][0])
test_data = test_data_generator.generate()
logger.info("Train data keys: {}".format(train_data.keys()))
logger.info("Train data sequences: {}".format(len(train_data['target'])))
logger.info("Sample sequence: {}".format(
[w.encode('utf-8') for w in train_data['target'][0]]))
# logger.info("Sample sequence: {}".format(train_data['similarity'][0]))
# sys.exit()
#logger.info("Alignment file: {}".format(train_data['alignments_file']))
#logger.info("Alignment file: {}".format(test_data['alignments_file']))
# additional representations
if 'representations' in config:
representation_generators = build_objects(config['representations'])
else:
representation_generators = []
for r in representation_generators:
train_data = r.generate(train_data)
test_data = r.generate(test_data)
borders = config['borders'] if 'borders' in config else False
if 'multiply_data_train' not in config:
pass
elif config['multiply_data_train'] == 'ngrams':
logger.info("Multiply data: {} and {}".format(
config['multiply_data_train'], config['multiply_data_test']))
train_data = multiply_data_ngrams(train_data, borders=borders)
logger.info("Sequences: {}, tag sequences: {}".format(
len(train_data['target']), len(train_data['tags'])))
elif config['multiply_data_train'] == '1ton':
logger.info("Multiply data: {} and {}".format(
config['multiply_data_train'], config['multiply_data_test']))
train_data = multiply_data(train_data, borders=borders)
elif config['multiply_data_train'] == 'duplicate':
train_data = multiply_data_base(train_data)
elif config['multiply_data_train'] == 'all':
train_data = multiply_data_all(train_data, borders=borders)
else:
print("Unknown 'multiply data train' value: {}".format(
config['multiply_data_train']))
logger.info("Train data example: {}".format(train_data['target'][:10]))
logger.info("Train tags example: {}".format(train_data['tags'][:10]))
logger.info("Extended train representations: {}".format(
len(train_data['target'])))
# print(train_data[:2])
logger.info("Simple test representations: {}".format(
len(test_data['target'])))
if 'multiply_data_test' not in config:
pass
elif config['multiply_data_test'] == 'ngrams':
test_data = multiply_data_ngrams(test_data, borders=borders)
elif config['multiply_data_test'] == '1ton':
test_data = multiply_data(test_data, borders=borders)
else:
print("Unknown 'multiply data test' value: {}".format(
config['multiply_data_test']))
logger.info("Extended test representations: {}".format(
len(test_data['target'])))
logger.info('here are the keys in your representations: {}'.format(
train_data.keys()))
# the data_type is the format corresponding to the model of the data that the user wishes to learn
# data_type = config['contexts'] if 'contexts' in config else 'plain'
data_type = config['data_type'] if 'data_type' in config else 'sequential'
test_contexts = create_contexts(test_data, data_type=data_type)
test_contexts_seq = create_contexts(test_data, data_type='sequential')
train_contexts = create_contexts(train_data, data_type=data_type)
logger.info('Vocabulary comparison -- coverage for each dataset: ')
logger.info(compare_vocabulary([train_data['target'],
test_data['target']]))
# END REPRESENTATION GENERATION
# FEATURE EXTRACTION
train_tags = call_for_each_element(train_contexts,
tags_from_contexts,
data_type=data_type)
test_tags = call_for_each_element(test_contexts,
tags_from_contexts,
data_type=data_type)
test_tags_seq = call_for_each_element(test_contexts_seq,
tags_from_contexts,
data_type='sequential')
logger.info('creating feature extractors...')
feature_extractors = build_objects(config['feature_extractors'])
logger.info('mapping the feature extractors over the contexts for test...')
test_features = call_for_each_element(test_contexts,
contexts_to_features,
[feature_extractors, workers],
data_type=data_type)
logger.info(
'mapping the feature extractors over the contexts for train...')
train_features = call_for_each_element(train_contexts,
contexts_to_features,
[feature_extractors, workers],
data_type=data_type)
logger.info('number of training instances: {}'.format(len(train_features)))
logger.info('number of testing instances: {}'.format(len(test_features)))
logger.info('train features sample: {}'.format(train_features[:5]))
logger.info('train tags sample: {}'.format(train_tags[:5]))
logger.info(
'All of your features now exist in their raw representation, but they may not be numbers yet'
)
# END FEATURE EXTRACTION
# BEGIN CONVERTING FEATURES TO NUMBERS
logger.info('binarization flag: {}'.format(config['features']['binarize']))
# flatten so that we can properly binarize the features
if config['features']['binarize'] is True:
logger.info('Binarizing your features...')
all_values = []
if data_type == 'sequential':
all_values = flatten(train_features)
elif data_type == 'plain':
all_values = train_features
elif data_type == 'token':
all_values = flatten(train_features.values())
feature_names = [
f for extractor in feature_extractors
for f in extractor.get_feature_names()
]
features_num = len(feature_names)
true_features_num = len(all_values[0])
logger.info('fitting binarizers...')
binarizers = fit_binarizers(all_values)
logger.info('binarizing test data...')
test_features = call_for_each_element(test_features,
binarize, [binarizers],
data_type=data_type)
logger.info('binarizing training data...')
# TODO: this line hangs with alignment+w2v
train_features = call_for_each_element(train_features,
binarize, [binarizers],
data_type=data_type)
logger.info('All of your features are now scalars in numpy arrays')
logger.info('training and test sets successfully generated')
# the way that we persist depends upon the structure of the data (plain/sequence/token_dict)
# TODO: remove this once we have a list containing all datasets
if config['features']['persist']:
if 'persist_format' in config['features']:
persist_format = config['features']['persist_format']
else:
persist_format = 'crf++'
experiment_datasets = [{
'name': 'test',
'features': test_features,
'tags': test_tags
}, {
'name': 'train',
'features': train_features,
'tags': train_tags
}]
feature_names = [
f for extractor in feature_extractors
for f in extractor.get_feature_names()
]
if config['persist_dir']:
persist_dir = config['persist_dir']
else:
persist_dir = os.path.getcwd()
logger.info('persisting your features to: {}'.format(persist_dir))
# for each dataset, write a file and persist the features
for dataset_obj in experiment_datasets:
persist_features(dataset_obj['name'],
dataset_obj['features'],
persist_dir,
feature_names=feature_names,
tags=dataset_obj['tags'],
file_format=persist_format)
sys.exit()
# BEGIN LEARNING
# TODO: different sequence learning modules need different representation, we should wrap them in a class
# TODO: create a consistent interface to sequence learners, will need to use *args and **kwargs because APIs are very different
from sklearn.metrics import f1_score, precision_score, recall_score
import numpy as np
tag_map = {u'OK': 1, u'BAD': 0, 0: 0, 1: 1}
if data_type == 'sequential':
logger.info('training sequential model...')
train_tags = [[tag_map[tag] for tag in seq] for seq in train_tags]
test_tags = [[tag_map[tag] for tag in seq] for seq in test_tags]
x_train = np.array([np.array(xi) for xi in train_features])
y_train = np.array([np.array(xi) for xi in train_tags])
x_test = np.array([numpy.array(xi) for xi in test_features])
y_test = np.array([numpy.array(xi) for xi in test_tags])
# pystruct
from marmot.learning.pystruct_sequence_learner import PystructSequenceLearner
sequence_learner = PystructSequenceLearner()
sequence_learner.fit(x_train, y_train)
structured_hyp = sequence_learner.predict(x_test)
# only the last word in every sequence should be counted
flattened_hyp = []
flattened_ref = []
if long_test:
for idx, seq in enumerate(structured_hyp):
flattened_hyp.append(seq[-1])
flattened_ref.append(y_test[idx][-1])
else:
flattened_hyp = flatten(structured_hyp)
flattened_ref = flatten(y_test)
logger.info('scoring sequential model...')
# TODO: the flattening is currently a hack to let us use the same evaluation code for structured and plain tasks
# flattened_hyp = flatten(structured_hyp)
# end pystruct
# for idx, seq in enumerate(test_tags_seq):
# cnt += len(seq)
# if cnt >= len(test_predictions):
# print("long predictions: {}, sequential: {}, sequence #{}".format(len(test_predictions), len(flatten(test_tags_seq)), idx))
# print("Sequence: ", test_contexts_seq[idx])
# if long_test:
# cnt = -1
# new_predictions = []
# new_true = []
# for seq in test_tags_seq:
# cnt += len(seq)
# new_predictions.append(tag_map[test_predictions[cnt]])
# new_true.append(tag_map[seq[-1]])
# test_predictions = new_predictions
# test_tags = new_true
#
# print(f1_score(test_predictions, test_tags, average=None))
print("Ref, hyp: ", len(flattened_ref), len(flattened_hyp))
logger.info('Structured prediction f1: ')
print(f1_score(flattened_ref, flattened_hyp, average=None))
print(
f1_score(flattened_ref,
flattened_hyp,
average='weighted',
pos_label=None))
logger.info("Sequence correlation: ")
print(
sequence_correlation_weighted(y_test, structured_hyp,
verbose=True)[1])
else:
train_tags = [tag_map[tag] for tag in train_tags]
test_tags = [tag_map[tag] for tag in test_tags]
# data_type is 'token' or 'plain'
logger.info('start training...')
classifier_type = import_class(
config['learning']['classifier']['module'])
# train the classifier(s)
classifier_map = map_classifiers(train_features,
train_tags,
classifier_type,
data_type=data_type)
logger.info('classifying the test instances')
test_predictions = predict_all(test_features,
classifier_map,
data_type=data_type)
# assert(len(test_predictions) == len(flatten(test_tags_seq))), "long predictions: {}, sequential: {}".format(len(test_predictions), len(flatten(test_tags_seq)))
cnt = 0
test_predictions_seq = []
test_tags_seq_num = []
tag_map = {'OK': 1, 'BAD': 0, 1: 1, 0: 0}
long_test = True if 'multiply_data_test' in config and (
config['multiply_data_test'] == 'ngrams'
or config['multiply_data_test'] == '1ton') else False
for idx, seq in enumerate(test_tags_seq):
test_predictions_seq.append([])
test_tags_seq_num.append([])
for w in seq:
test_predictions_seq[-1].append(tag_map[test_predictions[cnt]])
test_tags_seq_num[-1].append(tag_map[w])
cnt += 1
# cnt += len(seq)
# if cnt >= len(test_predictions):
# print("long predictions: {}, sequential: {}, sequence #{}".format(len(test_predictions), len(flatten(test_tags_seq)), idx))
# print("Sequence: ", test_contexts_seq[idx])
if long_test:
cnt = -1
new_predictions = []
new_true = []
for seq in test_tags_seq:
cnt += len(seq)
new_predictions.append(tag_map[test_predictions[cnt]])
new_true.append(tag_map[seq[-1]])
test_predictions = new_predictions
test_tags = new_true
print(f1_score(test_predictions, test_tags, average=None))
print(
f1_score(test_predictions,
test_tags,
average='weighted',
pos_label=None))
print("Precision: {}, recall: {}".format(
precision_score(test_predictions, test_tags, average=None),
recall_score(test_predictions, test_tags, average=None)))
logger.info("Sequence correlation: ")
print(
sequence_correlation_weighted(test_tags_seq_num,
test_predictions_seq,
verbose=True)[1])
