def main(config):
# load ContextCreators from config file, run their input functions, and pass the result into the initialization function
# init() all context creators specified by the user with their arguments
# import them according to their fully-specified class names in the config file
# it's up to the user to specify context creators which extract both negative and positive examples (if that's what they want)
# Chris - working - we want to hit every token
interesting_tokens = experiment_utils.import_and_call_function(
config['interesting_tokens'])
print "INTERESTING TOKENS: ", interesting_tokens
logger.info('The number of interesting tokens is: ' +
str(len(interesting_tokens)))
workers = config['workers']
# Note: context creators currently create their own interesting tokens internally (interesting tokens controls the index of the context creator)
logger.info('building the context creators...')
train_context_creators = experiment_utils.build_objects(
config['context_creators'])
# get the contexts for all of our interesting words (may be +,- or, multi-class)
logger.info(
'mapping the training contexts over the interesting tokens in train...'
)
train_contexts = experiment_utils.map_contexts(interesting_tokens,
train_context_creators,
workers=workers)
# load and parse the test data
logger.info(
'mapping the training contexts over the interesting tokens in test...')
test_context_creator = experiment_utils.build_objects(config['testing'])
test_contexts = experiment_utils.map_contexts(interesting_tokens,
[test_context_creator])
min_total = config['filters']['min_total']
# filter token contexts based on the user-specified filter criteria
logger.info(
'filtering the contexts by the total number of available instances...')
train_contexts = experiment_utils.filter_contexts(train_contexts,
min_total=min_total)
test_contexts = experiment_utils.filter_contexts(test_contexts,
min_total=min_total)
# make sure the test_context and train_context keys are in sync
experiment_utils.sync_keys(train_contexts, test_contexts)
# test_contexts = filter_contexts(test_contexts, min_total=min_total)
assert set(test_contexts.keys()) == set(train_contexts.keys())
# extract the 'tag' attribute into the y-value for classification
# tags may need to be converted to be consistent with the training data
wmt_binary_classes = {u'BAD': 0, u'OK': 1}
train_context_tags = experiment_utils.tags_from_contexts(train_contexts)
train_context_tags = {
k: np.array([wmt_binary_classes[v] for v in val])
for k, val in train_context_tags.items()
}
test_contexts = experiment_utils.convert_tagset(wmt_binary_classes,
test_contexts)
test_tags_actual = experiment_utils.tags_from_contexts(test_contexts)
# all of the feature extraction should be parallelizable
# note that a feature extractor MUST be able to parse the context exchange format, or it should throw an error:
# { 'token': <token>, index: <idx>, 'source': [<source toks>]', 'target': [<target toks>], 'tag': <tag>}
feature_extractors = experiment_utils.build_feature_extractors(
config['feature_extractors'])
logger.info('mapping the feature extractors over the contexts for test...')
test_context_features = experiment_utils.token_contexts_to_features_categorical(
test_contexts, feature_extractors, workers=workers)
logger.info(
'mapping the feature extractors over the contexts for train...')
train_context_features = experiment_utils.token_contexts_to_features_categorical(
train_contexts, feature_extractors, workers=workers)
# flatten so that we can properly binarize the features
all_values = experiment_utils.flatten(test_context_features.values())
all_values.extend(experiment_utils.flatten(
train_context_features.values()))
binarizers = experiment_utils.fit_binarizers(all_values)
test_context_features = {
k: [experiment_utils.binarize(v, binarizers) for v in val]
for k, val in test_context_features.items()
}
train_context_features = {
k: [experiment_utils.binarize(v, binarizers) for v in val]
for k, val in train_context_features.items()
}
# BEGIN LEARNING
classifier_type = experiment_utils.import_class(
config['learning']['classifier']['module'])
# train the classifier for each token
classifier_map = learning_utils.token_classifiers(train_context_features,
train_context_tags,
classifier_type)
# classify the test instances
# TODO: output a file in WMT format
# WORKING - dump the output in WMT format
logger.info('classifying the test instances')
test_predictions = {}
for key, features in test_context_features.iteritems():
try:
classifier = classifier_map[key]
predictions = classifier.predict(features)
test_predictions[key] = predictions
except KeyError as e:
print(key + " - is NOT in the classifier map")
raise
#### put the rest of the code into a separate 'evaluate' function that reads the WMT files
# create the performance report for each word in the test data that we had a classifier for
# TODO: Working - evaluate based on the format
f1_map = {}
for token, predicted in test_predictions.iteritems():
logger.info("Evaluating results for token = " + token)
actual = test_tags_actual[token]
print 'Actual: ', actual
print 'Predicted: ', predicted
logger.info("\ttotal instances: " + str(len(predicted)))
f1_map[token] = weighted_fmeasure(actual, predicted)
logger.info('Printing the map of f1 scores by token: ')
print(f1_map)
def main(config):
workers = config['workers']
# REPRESENTATION GENERATION
# main representations (source, target, tags)
# training
train_data_generators = build_objects(config['datasets']['training'])
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()
# 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)
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'
test_contexts = create_contexts(test_data, data_type=data_type)
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
# make sure the test_context and train_context keys are in sync
# TODO: this is important when we are learning token-level classifiers
# experiment_utils.sync_keys(train_contexts, test_contexts)
# TODO: this is important when we are learning token-level classifiers
# test_contexts = filter_contexts(test_contexts, min_total=min_total)
# assert set(test_contexts.keys()) == set(train_contexts.keys())
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)
# all of the feature extraction should be parallelizable
# note that a feature extractor MUST be able to parse the context exchange format, or it should throw an error:
# { 'token': <token>, index: <idx>, 'source': [<source toks>]', 'target': [<target toks>], 'tag': <tag>}
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('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())
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['features']['persist_dir']:
persist_dir = config['features']['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)
# TODO: we should only learn and evaluate the model if this is what the user wants
# TODO: we should be able to dump the features for each of the user's datasets to a file specified by the user
# 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
import numpy as np
if data_type == 'sequential':
logger.info('training sequential model...')
# TODO: move the tag and array conversion code to the utils of this module
# TODO: check if X and y are in the format we expect
# TODO: don't hardcode the dictionary
tag_map = {u'OK': 1, u'BAD': 0}
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]
# make sure that everything is numpy
# cast the dataset to numpy array (ndarrays)
# note that these are _NOT_ matrices, because the inner sequences have different lengths
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])
# SEQLEARN
# from seqlearn.perceptron import StructuredPerceptron
#
# # seqlearn needs a flat list of instances
# x_train = np.array([i for seq in x_train for i in seq])
# y_train = np.array([i for seq in y_train for i in seq])
# x_test = np.array([i for seq in x_test for i in seq])
# y_test = np.array([i for seq in y_test for i in seq])
#
# # seqlearn requires the lengths of each sequence
# lengths_train = [len(seq) for seq in train_features]
# lengths_test = [len(seq) for seq in test_features]
#
# clf = StructuredPerceptron(verbose=True, max_iter=400)
# clf.fit(x_train, y_train, lengths_train)
#
# structured_predictions = clf.predict(x_test, lengths_test)
# logger.info('f1 from seqlearn: {}'.format(f1_score(y_test, structured_predictions, average=None)))
# END SEQLEARN
# 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)
logger.info('scoring sequential model...')
# print('score: ' + str(structured_predictor.score(x_test, y_test)))
# TODO: implement this in the config
# classifier_type = import_class(config['learning']['classifier']['module'])
# 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
test_predictions = flattened_hyp
flattened_ref = flatten(y_test)
test_tags = flattened_ref
logger.info('Structured prediction f1: ')
print(f1_score(flattened_ref, flattened_hyp, average=None))
else:
# 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)
# TODO: this section only works for 'plain'
print(f1_score(test_predictions, test_tags, average=None))
# EVALUATION
logger.info('evaluating your results')
# TODO: remove the hard coding of the tags here
bad_count = sum(1 for t in test_tags if t == u'BAD' or t == 0)
good_count = sum(1 for t in test_tags if t == u'OK' or t == 1)
total = len(test_tags)
assert (total == bad_count+good_count), 'tag counts should be correct'
percent_good = good_count / total
logger.info('percent good in test set: {}'.format(percent_good))
logger.info('percent bad in test set: {}'.format(1 - percent_good))
import numpy as np
random_class_results = []
random_weighted_results = []
for i in range(20):
random_tags = list(np.random.choice([1, 0], total, [percent_good, 1-percent_good]))
# random_tags = [u'GOOD' for i in range(total)]
random_class_f1 = f1_score(test_tags, random_tags, average=None)
random_class_results.append(random_class_f1)
logger.info('two class f1 random score ({}): {}'.format(i, random_class_f1))
# random_average_f1 = f1_score(random_tags, test_tags, average='weighted')
random_average_f1 = weighted_fmeasure(test_tags, random_tags)
random_weighted_results.append(random_average_f1)
# logger.info('average f1 random score ({}): {}'.format(i, random_average_f1))
avg_random_class = np.average(random_class_results, axis=0)
avg_weighted = np.average(random_weighted_results)
logger.info('two class f1 random average score: {}'.format(avg_random_class))
logger.info('weighted f1 random average score: {}'.format(avg_weighted))
actual_class_f1 = f1_score(test_tags, test_predictions, average=None)
actual_average_f1 = weighted_fmeasure(test_tags, test_predictions)
logger.info('two class f1 ACTUAL SCORE: {}'.format(actual_class_f1))
logger.info('weighted f1 ACTUAL SCORE: {}'.format(actual_average_f1))
if data_type == 'token':
f1_map = {}
for token, predicted in test_predictions.iteritems():
logger.info("Evaluating results for token = " + token)
actual = test_tags_actual[token]
# print('Actual: ', actual)
# print('Predicted: ', predicted)
# logger.info("\ttotal instances: " + str(len(predicted)))
f1_map[token] = weighted_fmeasure(actual, predicted)
logger.info('Printing the map of f1 scores by token: ')
print(f1_map)
elif data_type == 'plain':
f1 = weighted_fmeasure(test_tags, test_predictions)
def main(config):
# load ContextCreators from config file, run their input functions, and pass the result into the initialization function
# init() all context creators specified by the user with their arguments
# import them according to their fully-specified class names in the config file
# it's up to the user to specify context creators which extract both negative and positive examples (if that's what they want)
# Chris - working - we want to hit every token
interesting_tokens = experiment_utils.import_and_call_function(config['interesting_tokens'])
print "INTERESTING TOKENS: ", interesting_tokens
logger.info('The number of interesting tokens is: ' + str(len(interesting_tokens)))
workers = config['workers']
# Note: context creators currently create their own interesting tokens internally (interesting tokens controls the index of the context creator)
logger.info('building the context creators...')
train_context_creators = experiment_utils.build_objects(config['context_creators'])
# get the contexts for all of our interesting words (may be +,- or, multi-class)
logger.info('mapping the training contexts over the interesting tokens in train...')
train_contexts = experiment_utils.map_contexts(interesting_tokens, train_context_creators, workers=workers)
# load and parse the test data
logger.info('mapping the training contexts over the interesting tokens in test...')
test_context_creator = experiment_utils.build_objects(config['testing'])
test_contexts = experiment_utils.map_contexts(interesting_tokens, [test_context_creator])
min_total = config['filters']['min_total']
# filter token contexts based on the user-specified filter criteria
logger.info('filtering the contexts by the total number of available instances...')
train_contexts = experiment_utils.filter_contexts(train_contexts, min_total=min_total)
test_contexts = experiment_utils.filter_contexts(test_contexts, min_total=min_total)
# make sure the test_context and train_context keys are in sync
experiment_utils.sync_keys(train_contexts, test_contexts)
# test_contexts = filter_contexts(test_contexts, min_total=min_total)
assert set(test_contexts.keys()) == set(train_contexts.keys())
# extract the 'tag' attribute into the y-value for classification
# tags may need to be converted to be consistent with the training data
wmt_binary_classes = {u'BAD': 0, u'OK': 1}
train_context_tags = experiment_utils.tags_from_contexts(train_contexts)
train_context_tags = {k: np.array([wmt_binary_classes[v] for v in val]) for k, val in train_context_tags.items()}
test_contexts = experiment_utils.convert_tagset(wmt_binary_classes, test_contexts)
test_tags_actual = experiment_utils.tags_from_contexts(test_contexts)
# all of the feature extraction should be parallelizable
# note that a feature extractor MUST be able to parse the context exchange format, or it should throw an error:
# { 'token': <token>, index: <idx>, 'source': [<source toks>]', 'target': [<target toks>], 'tag': <tag>}
feature_extractors = experiment_utils.build_feature_extractors(config['feature_extractors'])
logger.info('mapping the feature extractors over the contexts for test...')
test_context_features = experiment_utils.token_contexts_to_features_categorical(test_contexts, feature_extractors, workers=workers)
logger.info('mapping the feature extractors over the contexts for train...')
train_context_features = experiment_utils.token_contexts_to_features_categorical(train_contexts, feature_extractors, workers=workers)
# flatten so that we can properly binarize the features
all_values = experiment_utils.flatten(test_context_features.values())
all_values.extend(experiment_utils.flatten(train_context_features.values()))
binarizers = experiment_utils.fit_binarizers(all_values)
test_context_features = {k: [experiment_utils.binarize(v, binarizers) for v in val] for k, val in test_context_features.items()}
train_context_features = {k: [experiment_utils.binarize(v, binarizers) for v in val] for k, val in train_context_features.items()}
# BEGIN LEARNING
classifier_type = experiment_utils.import_class(config['learning']['classifier']['module'])
# train the classifier for each token
classifier_map = learning_utils.token_classifiers(train_context_features, train_context_tags, classifier_type)
# classify the test instances
# TODO: output a file in WMT format
# WORKING - dump the output in WMT format
logger.info('classifying the test instances')
test_predictions = {}
for key, features in test_context_features.iteritems():
try:
classifier = classifier_map[key]
predictions = classifier.predict(features)
test_predictions[key] = predictions
except KeyError as e:
print(key + " - is NOT in the classifier map")
raise
#### put the rest of the code into a separate 'evaluate' function that reads the WMT files
# create the performance report for each word in the test data that we had a classifier for
# TODO: Working - evaluate based on the format
f1_map = {}
for token, predicted in test_predictions.iteritems():
logger.info("Evaluating results for token = " + token)
actual = test_tags_actual[token]
print 'Actual: ', actual
print 'Predicted: ', predicted
logger.info("\ttotal instances: " + str(len(predicted)))
f1_map[token] = weighted_fmeasure(actual, predicted)
logger.info('Printing the map of f1 scores by token: ')
print(f1_map)