def test_token_classifiers(self):
interesting_tokens = set(['the','it', 'a'])
context_creators = import_utils.build_objects(self.config['context_creators'])
token_contexts = experiment_utils.map_contexts(interesting_tokens, context_creators)
feature_extractors = import_utils.build_objects(self.config['feature_extractors'])
token_context_features = experiment_utils.token_contexts_to_features(token_contexts, feature_extractors)
binarizers = experiment_utils.fit_binarizers(experiment_utils.flatten(token_context_features.values()))
token_context_features = {k: [experiment_utils.binarize(v, binarizers) for v in val] for k, val in token_context_features.items()}
token_context_tags = experiment_utils.tags_from_contexts(token_contexts)
# train the classifier for each token
classifier_type = experiment_utils.import_class(self.config['learning']['classifier']['module'])
classifier_map = learning_utils.token_classifiers(token_context_features, token_context_tags, classifier_type)
self.assertEqual(set(token_context_tags.keys()), set(classifier_map.keys()))
for tok, classifier in classifier_map.items():
self.assertTrue(hasattr(classifier, 'predict'))
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):
# 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)