def evaluate(sample_file, **kwargs):
use_duration = True if kwargs['D'] else False
window = int(kwargs['w'])
pv('window', 'use_duration', stdout=True)
samples = None
models = None
if kwargs['M'] or kwargs['B']:
samples = {classification.normalize_name(t): [t] for t in sample_file}
models = extract_models(ZIPPED_MODELS)
if samples and models:
if kwargs['M']:
classification.evaluate(samples, models,
use_duration=use_duration,
window=window)
if kwargs['B']:
binary_samples = classification.binarize(samples)
binary_models = classification.binarize(models)
if binary_samples and binary_models:
classification.evaluate(binary_samples, binary_models,
use_duration=use_duration,
window=window)
def extract_models(zipped_models):
zf = zipfile.ZipFile(zipped_models, 'r')
models = {}
for model_filename in zf.namelist():
models[classification.normalize_name(model_filename)] = \
extract_model(zipped_models, model_filename)
return models
