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))