def coherence_metrics():
metric_fcts = load_metric_fcts('coherence')
if len(metric_fcts) == 0:
nbprint('No metrics active.')
return
topiclist_infos = data.get_all_topiclist_infos()
if len(topiclist_infos) == 0:
nbprint('No topics found.')
return
# Group them into batches based on topic_version and add num_tokens
topiclist_info_batches = defaultdict(list)
for info in topiclist_infos:
for num_tokens in config.metrics['num_tokens']:
extended_info = info.copy()
extended_info['num_tokens'] = num_tokens
if 'second_info' in info:
token_version = info['second_info']['token_version']
else:
token_version = info['token_version']
topiclist_info_batches[token_version].append(extended_info)
for token_version, batch in topiclist_info_batches.items():
nbprint('Batch {}'.format(token_version)).push()
for metric_id, fct in metric_fcts.items():
start = time.time()
nbprint('Metric: {}'.format(
config.metrics['coherence'][metric_id]['name'])).push()
coherence_metric_batch(token_version, batch, metric_id, fct)
end = time.time()
nbprint('Runtime: {} minutes'.format((end - start) / 60)).pop()
nbprint.pop()
def run_importer(info=None):
nbprint('Importer').push()
if info is None:
iterate(["data"], [import_data], depth=0)
else:
import_data(info)
nbprint.pop()
def run_tokenizer(info=None):
nbprint('Tokenizer').push()
if info is None:
iterate(['token:BC', 'data'], tokenize)
else:
tokenize(info)
nbprint.pop()
def run_vocab(info=None):
nbprint('Vocab').push()
if info is None:
iterate(["data", "token", "vocab"], [check_tokens, build_vocab])
else:
check_tokens(info)
build_vocab(info)
nbprint.pop()
def run_distiller():
global rejector
rejector = Rejector(0.99)
nbprint('Distiller').push()
iterate(['distiller', 'distillerinputs'],
add_second_info,
print_iterates=False)
nbprint.pop()
def add_data(self, filename):
nbprint("Loading '{}'".format(filename)).push()
folderpath = join(config.paths["rawdata"], "tweetsodp")
jsonfilename = join(folderpath, filename + ".json")
zipfilename = join(folderpath, filename + ".json.zip")
self.load_id_to_classname(folderpath, filename)
if isfile(jsonfilename):
with open(jsonfilename, "r") as jsonfile:
self.parse_files(jsonfile)
else:
with zipfile.ZipFile(zipfilename) as zip:
with zip.open(filename + ".json") as jsonfile:
self.parse_files(jsonfile)
nbprint.pop()
def run(self):
# Open Writer
with data.document_writer(self.info) as document_writer:
self.docinfo = DocumentInfo(document_writer)
# Iterate all archives
folder = join(config.paths["rawdata"], "tweetsla")
archives = self.get_archives(folder)
for idx, archive in enumerate(archives):
nbprint('{}/{}: {}'.format(idx + 1, len(archives),
archive)).push()
self.archivepath = join(folder, archive)
self.import_archive()
nbprint.pop()
# Print Meta Info
self.docinfo.save_meta(self.info)
def clustering_metrics():
metric_fcts = load_metric_fcts('clustering')
clustering_data = data.load_metric_data('clustering')
# First everything by taking the column wise maximum as cluster idx
nbprint('H Matrix').push()
h_mat_infos = data.get_all_h_mat_infos(labeled_only=True)
for info in ProgressIterator(h_mat_infos, print_every=1):
# Grab the corresponding entry from clustering data
metric_data_entry = grab_metric_data_entry(clustering_data, info)
# Iterate all metric functions and store result in entry
for metric_id, metric_fct in metric_fcts.items():
# Skip metric if it already exists:
if metric_id in metric_data_entry:
continue
# Compute the metric
labels_true = load_ground_truth_classes(info)
labels_pred = load_class_array_from_h_mat(info)
metric_data_entry[metric_id] = metric_fct(labels_true, labels_pred)
# Save everything in between
data.save_metric_data(clustering_data, 'clustering')
# Taking indices directly from c
nbprint.pop()('C Vector').push()
c_vec_infos = data.get_all_c_vec_infos(labeled_only=True)
for info in ProgressIterator(c_vec_infos, print_every=1):
# Grab the corresponding entry from clustering data
metric_data_entry = grab_metric_data_entry(clustering_data, info)
# Iterate all metric functions and store result in entry
for metric_id, metric_fct in metric_fcts.items():
# Skip metric if it already exists:
if metric_id in metric_data_entry:
continue
# Compute the metric
labels_true = load_ground_truth_classes(info)
labels_pred = data.load_c_vec(info)
metric_data_entry[metric_id] = metric_fct(labels_true, labels_pred)
# Save everything in between
data.save_metric_data(clustering_data, 'clustering')
nbprint.pop()
def run_models(info=None):
nbprint('Models').push()
if info is None:
iterate(['models', 'modelinputs', 'num_topics'],
[check_input_mat, run_model])
else:
info['model_info'] = config.models['list'][info['model_name']]
info['model'] = get_model(info)
if not info['model'].output_of(info):
nbprint('Model is not compatible to inputs.')
else:
try:
check_input_mat(info)
run_model(info)
except BreakIteration:
pass
nbprint.pop()
def call_next(what, callbacks, print_string, new_data, info, depth,
print_iterates):
if print_string and print_iterates == True:
nbprint(print_string)
if print_iterates == True:
nbprint.push()
new_info = {**info, **new_data}
if len(callbacks) < len(what):
iterate(what[1:], callbacks, new_info, depth + 1)
else:
try:
if callbacks[0]:
callbacks[0](new_info)
if len(what) > 1:
iterate(what[1:], callbacks[1:], new_info, depth + 1,
print_iterates)
except BreakIteration:
if print_iterates == True:
nbprint('skipping')
pass
if print_iterates == True:
nbprint.pop()
def run_distiller_on(first_info, second_info):
global rejector
must_execute = False
info = first_info.copy()
if second_info is None:
must_execute = True
else:
must_execute = (first_info.get('token_version',
None) == second_info['token_version']
and first_info.get('vocab_version', None)
== second_info['vocab_version']
and first_info.get('vector_version', None)
== second_info['vector_version'])
info['second_info'] = second_info
if must_execute or rejector.allow():
nbprint('({}), ({})'.format(info_summary_str(first_info),
info_summary_str(second_info))).push()
if config.skip_existing and data.topiclist_exists(info):
nbprint('Skipping Distiller (file(s) exists)')
else:
info['distiller'].run_distiller(info)
info['distiller'].save()
nbprint('Distiller: success')
nbprint.pop()
def run_topic_metrics():
nbprint('Topic Metrics').push()
nbprint('Coherence').push()
coherence_metrics()
nbprint.pop()
nbprint('similarity').push()
similarity_metrics()
nbprint.pop()
nbprint.pop()
def run_model_metrics():
nbprint('Model Metrics').push()
nbprint('Clustering').push()
clustering_metrics()
nbprint.pop()
nbprint('Classification').push()
classification_metrics()
nbprint.pop()
nbprint.pop()
def run_vectorizer(info=None):
nbprint('Vectorizer').push()
global runvars
if info is None:
if config.vectorizer['run_B']:
nbprint('BoW').push()
runvars = {}
iterate(['data', 'token:BC', 'vocab', 'vector:B'],
[count_mat, bow])
nbprint.pop()
if config.vectorizer['run_C']:
nbprint('cBoW').push()
runvars = {}
iterate(['data', 'token:C', 'vocab', 'vector:C'],
[count_mat, cbow])
nbprint.pop()
if config.vectorizer['run_P']:
nbprint('Phrase').push()
runvars = {}
iterate(['data', 'vector:P'], [phrase])
nbprint.pop()
else:
runvars = {}
vector_bcp, vector_id = config.split(info['vector_version'])
if vector_bcp == 'B' or vector_bcp == 'C':
count_mat(info)
if vector_bcp == 'B':
bow(info)
else:
cbow(info)
else:
phrase(info)
runvars = None
nbprint.pop()
