def __init__(self, clf, genres, data, display_name):
self.logger = get_logger('benchmark')
self.display_name = display_name
self.clf = clf
self.genres = genres
self.m_genres = {genre: i for i, genre in enumerate(genres)}
self.scaler = StandardScaler()
self.X, self.y = data
def __init__(self, clf_obj=None, **metadata):
# Process mandatory fields
for field in self.MANDATORY_CLASSIFIER_METADATA_FIELDS:
if field not in metadata:
raise LookupError(f'Field "{field}" not found in metadata"')
setattr(self, field, metadata[field])
self.logger = get_logger(f'classifier ({self.name})')
# Process optional fields
for field, default in self.OPTIONAL_CLASSIFIER_METADATA_FIELDS:
value = metadata[field] if field in metadata else default
setattr(self, field, value)
self._init_classifier(clf_obj)
def __init__(self, genres, feature_extractor):
self.genres = genres
self.ft_extractor = feature_extractor
self.logger = get_logger('dataset')
import json
import os.path
from sklearn.externals import joblib
from genrec.classifier import Classifier
from genrec.logger import get_logger
from web.config import *
from web.utils import scandir_filtered
logger = get_logger('classifiers')
def _get_valid_dirs():
dirpath = os.path.dirname(__file__)
filters = [
(lambda entry: entry.is_dir(), None),
(lambda entry: entry.name in AVAILABLE_DATASETS,
lambda entry: logger.debug(f'Ignoring directory "{entry.path}"'))
]
yield from map(lambda entry: entry.path,
scandir_filtered(dirpath, filters))
def _get_classifiers(dir):
logger.info(f'Scanning directory "{dir}"...')
filters = [
def __init__(self, sr=22050, fft_len=512):
self.target_sr = sr
self.fft_len = fft_len
self.logger = get_logger('ft-extractor')