def main():
loader = MsdBbLoader(
hits_file_path=
'./storage/msd_bb_matches.csv',
non_hits_file_path=
'./storage/msd_bb_non_matches.csv',
features_path='./tools',
non_hits_per_hit=1,
features=['hl'],
label='weeks',
)
print(loader.load()[0][0].shape, loader.load()[1].shape)
print(loader.configuration())
print(loader.load()[0][0].columns)
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
import common
from dataloaders import MsdBbLoader
import evaluations
dataloader = MsdBbLoader(
hits_file_path='/storage/nas3/datasets/music/billboard/msd_bb_matches.csv',
non_hits_file_path=
'/storage/nas3/datasets/music/billboard/msd_bb_non_matches.csv',
features_path='/storage/nas3/datasets/music/billboard',
non_hits_per_hit=1,
features=[
*common.hl_list(),
],
label='peak',
nan_value=150,
random_state=42,
)
pipeline = Pipeline([
('scale', MinMaxScaler()),
('linreg', Lasso(alpha=1.0, normalize=False)),
])
evaluator = GridEvaluator(
parameters={},
grid_parameters=evaluations.grid_parameters(),
