parser.add_argument('-v', '--verbose',
action='store_true',
dest='verbose',
default=False,
help='talk more')
return vars(parser.parse_args())
args = parse_args()
data_file = args['datafile'][0]
config_file = args['config'][0]
output_file = args['output'][0]
n_jobs = int(args['n_jobs'])
verbose = args['verbose']
with verb_print('loading configuration from {}'.format(config_file),
verbose=verbose):
config = load_config(config_file)
spec_config = config['features']['spectral']
del spec_config['normalize']
spec_config['noise_fr'] = spec_config['n_noise_fr']
del spec_config['n_noise_fr']
vad_config = config['features']['vad']
stacksize = config['features']['stacksize']
frate = int(1./spec_config['window_shift'])
if spec_config['fs'] != vad_config['fs']:
raise ValueError(
'samplerates in spectral and vad configuration'
'should be the same ({} and {})'.format(
help='file with predicted stimuli')
parser.add_argument('-v',
'--v',
action='store_true',
dest='verbose',
default=False,
help='talk more')
return vars(parser.parse_args())
args = parse_args()
gold_file = args['goldfile'][0]
predict_file = args['predictfile'][0]
verbose = args['verbose']
with verb_print('loading gold stimuli from {}'.format(gold_file), verbose):
df_gold = pd.read_csv(gold_file)
if verbose:
print 'loaded {} gold stimuli'.format(len(df_gold))
with verb_print('loading predicted stimuli from {}'.format(predict_file),
verbose):
df_pred = pd.read_csv(predict_file)
if verbose:
print 'loaded {} predicted stimuli'.format(len(df_pred))
if len(df_gold) != len(df_pred):
print 'error: different number of stimuli in gold and predicted sets '\
'({} vs {})'.format(len(df_gold), len(df_pred))
parser.add_argument('-v' '--verbose',
action='store_true',
dest='verbose',
default=False,
help='talk more')
return vars(parser.parse_args())
args = parse_args()
data_file = args['datafile'][0]
clf_file = args['clffile'][0]
output_file = args['output'][0]
verbose = args['verbose']
with verb_print('reading stimuli from {}'.format(data_file), verbose):
df = pd.read_csv(data_file)
with verb_print('loading classifier from {}'.format(clf_file),
verbose=verbose):
am, crf, label2ix, feat_params = joblib.load(clf_file)
spec_config = feat_params['spec_config']
vad_config = feat_params['vad_config']
stacksize = feat_params['stacksize']
frate = feat_params['frate']
smoothing = feat_params['smoothing']
with verb_print('extracting features', verbose=verbose):
X = extract_features(
df, label2ix, spec_config, vad_config, stacksize, frate,
return_y=False
'--verbose',
action='store_true',
dest='verbose',
default=False,
help='talk more')
return vars(parser.parse_args())
args = parse_args()
data_file = args['datafile'][0]
clf_file = args['clffile'][0]
output_file = args['output'][0]
verbose = args['verbose']
with verb_print('reading stimuli from {}'.format(data_file), verbose):
df = pd.read_csv(data_file)
with verb_print('loading classifier from {}'.format(clf_file),
verbose=verbose):
am, crf, label2ix, feat_params = joblib.load(clf_file)
spec_config = feat_params['spec_config']
vad_config = feat_params['vad_config']
stacksize = feat_params['stacksize']
frate = feat_params['frate']
smoothing = feat_params['smoothing']
with verb_print('extracting features', verbose=verbose):
X = extract_features(df,
label2ix,
spec_config,
'--verbose',
action='store_true',
dest='verbose',
default=False,
help='talk more')
return vars(parser.parse_args())
args = parse_args()
data_file = args['datafile'][0]
config_file = args['config'][0]
output_file = args['output'][0]
n_jobs = int(args['n_jobs'])
verbose = args['verbose']
with verb_print('reading stimuli from {}'.format(data_file),
verbose=verbose):
df = pd.read_csv(data_file)
X = df[['filename', 'start', 'end']].values
labels = df['label'].values
label2ix = {k: i for i, k in enumerate(np.unique(labels))}
y = np.array([label2ix[label] for label in labels])
with verb_print('loading configuration from {}'.format(config_file),
verbose=verbose):
config = load_config(config_file)
features_params = mcr.load_segmented.ensure_list(config['features'])
clf_params = mcr.load_segmented.ensure_list(config['svm'])
CLASS_WEIGHT = clf_params['class_weight'][0]
if not isinstance(CLASS_WEIGHT, bool):
'--verbose',
action='store_true',
dest='verbose',
default=False,
help='talk more')
return vars(parser.parse_args())
args = parse_args()
data_file = args['datafile'][0]
config_file = args['config'][0]
output_file = args['output'][0]
n_jobs = int(args['n_jobs'])
verbose = args['verbose']
with verb_print('loading configuration from {}'.format(config_file),
verbose=verbose):
config = load_config(config_file)
spec_config = config['features']['spectral']
del spec_config['normalize']
spec_config['noise_fr'] = spec_config['n_noise_fr']
del spec_config['n_noise_fr']
vad_config = config['features']['vad']
stacksize = config['features']['stacksize']
frate = int(1. / spec_config['window_shift'])
if spec_config['fs'] != vad_config['fs']:
raise ValueError('samplerates in spectral and vad configuration'
'should be the same ({} and {})'.format(
spec_config['fs'], vad_config['fs']))
default=False,
help='talk more'
)
return vars(parser.parse_args())
args = parse_args()
gold_file = args['goldfile'][0]
pred_file = args['predfile'][0]
tolerance = float(args['tolerance'])
winshift = float(args['winshift'])
frame_scores = args['frame_scores']
verbose = args['verbose']
with verb_print('reading gold stimuli from {}'.format(gold_file), verbose):
gold_df = pd.read_csv(gold_file)
quantize_calls(gold_df, winshift)
with verb_print('reading predicted stimuli from {}'
.format(pred_file), verbose):
pred_df = pd.read_csv(pred_file)
quantize_calls(pred_df, winshift)
with verb_print('calculating utterance score', verbose):
oc = utterance_score(gold_df, pred_df)
with verb_print('calculating call score (tolerance={:.3f})'
.format(tolerance), verbose):
prec, rec = call_score(gold_df, pred_df, tolerance)
with verb_print('calculating onset call score (tolerance={:.3f})'
parser.add_argument('-v' '--verbose',
action='store_true',
dest='verbose',
default=False,
help='talk more')
return vars(parser.parse_args())
args = parse_args()
data_file = args['datafile'][0]
clf_file = args['clffile'][0]
output_file = args['output'][0]
verbose = args['verbose']
with verb_print('reading stimuli from {}'.format(data_file), verbose):
df = pd.read_csv(data_file)
X = df[['filename', 'start', 'end']].values
if verbose:
print 'loaded {} stimuli'.format(X.shape[0])
with verb_print('loading classifier from {}'.format(clf_file), verbose):
clf, _, label2ix = joblib.load(clf_file)
ix2label = {ix:label for label, ix in label2ix.iteritems()}
if verbose:
print pformat(clf.get_params(deep=False))
with verb_print('predicting labels', verbose):
y_pred = clf.predict(X)
with verb_print('writing output to {}'.format(output_file), verbose):
parser.add_argument('-v', '--verbose',
action='store_true',
dest='verbose',
default=False,
help='talk more')
return vars(parser.parse_args())
args = parse_args()
data_file = args['datafile'][0]
config_file = args['config'][0]
output_file = args['output'][0]
n_jobs = int(args['n_jobs'])
verbose = args['verbose']
with verb_print('reading stimuli from {}'.format(data_file),
verbose=verbose):
df = pd.read_csv(data_file)
X = df[['filename', 'start', 'end']].values
labels = df['label'].values
label2ix = {k: i for i, k in enumerate(np.unique(labels))}
y = np.array([label2ix[label] for label in labels])
with verb_print('loading configuration from {}'.format(config_file),
verbose=verbose):
config = load_config(config_file)
features_params = mcr.load_segmented.ensure_list(config['features'])
clf_params = mcr.load_segmented.ensure_list(config['svm'])
CLASS_WEIGHT = clf_params['class_weight'][0]
if not isinstance(CLASS_WEIGHT, bool):
nargs=1,
help='file with predicted stimuli')
parser.add_argument('-v', '--v',
action='store_true',
dest='verbose',
default=False,
help='talk more')
return vars(parser.parse_args())
args = parse_args()
gold_file = args['goldfile'][0]
predict_file = args['predictfile'][0]
verbose = args['verbose']
with verb_print('loading gold stimuli from {}'.format(gold_file), verbose):
df_gold = pd.read_csv(gold_file)
if verbose:
print 'loaded {} gold stimuli'.format(len(df_gold))
with verb_print('loading predicted stimuli from {}'.format(predict_file),
verbose):
df_pred = pd.read_csv(predict_file)
if verbose:
print 'loaded {} predicted stimuli'.format(len(df_pred))
if len(df_gold) != len(df_pred):
print 'error: different number of stimuli in gold and predicted sets '\
'({} vs {})'.format(len(df_gold), len(df_pred))