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(),
)
result_handlers = [
result_handlers.print_gridsearch_results,
]
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from nlp4musa2020.dataloaders import vectorizer
from nlp4musa2020.dataloaders.alf200k import ALF200KLoader
from nlp4musa2020.dataloaders.alf200k import genre_target_labels
import nlp4musa2020.evaluators as evaluators
dataloader = ALF200KLoader(
path='data/processed/dataset-lfm-genres.pickle',
load_feature_groups=[],
text_vectorizers=vectorizer.lda(),
target=genre_target_labels(),
)
pipeline = Pipeline([
('scaler', StandardScaler()),
('model', RandomForestClassifier(n_jobs=-1)),
])
evaluator = GridEvaluator(
parameters={
'model__n_estimators': [10, 100, 300],
},
grid_parameters=evaluators.grid_parameters_genres(),
)
result_handlers = [
result_handlers.print_gridsearch_results,
]
from nlp4musa2020.dataloaders.vectorizer import tfidf
import nlp4musa2020.evaluators as evaluators
from nlp4musa2020.models.simplenn_genre import SimpleGenreNN
dataloader = ALF200KLoader('data/processed/dataset-lfm-genres.pickle',
load_feature_groups=[],
text_vectorizers=tfidf(),
target=genre_target_labels())
pipeline = Pipeline([
('scaler', StandardScaler()),
('model', SimpleGenreNN(epochs=50)),
])
evaluator = GridEvaluator(
parameters={
'model__dense_sizes': [
(32, 32),
(64, 64),
],
'model__dropout_rate': [
0.1,
],
},
grid_parameters=evaluators.grid_parameters_genres(),
)
result_handlers = [
result_handlers.print_gridsearch_results,
]
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.all_list(),
],
label='peak',
nan_value=150,
random_state=42,
)
pipeline = Pipeline([
('scale', MinMaxScaler()),
('wide_and_deep', WideAndDeep(features=dataloader.feature_indices)),
])
evaluator = GridEvaluator(
parameters={
'wide_and_deep__epochs': [10, 50, 100, 200],
'wide_and_deep__batch_normalization': [False, True],
'wide_and_deep__dropout_rate': [None, 0.25, 0.5],
},
grid_parameters=evaluations.grid_parameters(),
)
result_handlers = [
result_handlers.print_gridsearch_results,
]
dataloader = ALF200KLoader(path='data/processed/dataset-lfm-genres.pickle',
load_feature_groups=[
'explicitness',
],
text_vectorizers=None,
target=[
'alternative', 'blues', 'country', 'dance',
'electronic', 'funk', 'hip hop', 'indie',
'jazz', 'metal', 'pop', 'punk', 'rap', 'rnb',
'rock', 'soul'
])
pipeline = Pipeline([
('scaler', StandardScaler()),
('model', KNeighborsClassifier(n_jobs=-1, algorithm='ball_tree')),
])
evaluator = GridEvaluator(
parameters={
'model__n_neighbors': [3, 4, 5, 10],
'model__weights': ['distance'],
'model__p': [1, 2],
},
grid_parameters=evaluators.grid_parameters_genres(),
)
result_handlers = [
result_handlers.print_gridsearch_results,
]
dataloader = ALF200KLoader('data/processed/dataset-lfm-genres.pickle',
load_feature_groups=[
'rhymes',
],
text_vectorizers=None,
target=genre_target_labels())
pipeline = Pipeline([
('scaler', StandardScaler()),
('model', MultiOutputClassifier(LinearSVC())),
])
evaluator = GridEvaluator(
parameters={
'model__estimator__C': [
0.1,
0.5,
1.0,
2.0,
5.0,
],
'model__estimator__loss': ['epsilon_insensitive'],
},
grid_parameters=evaluators.grid_parameters_genres(),
)
result_handlers = [
result_handlers.print_gridsearch_results,
]
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(),
*common.ll_list(),
],
label='peak',
nan_value=150,
random_state=42,
)
pipeline = Pipeline([
('scale', MinMaxScaler()),
('logreg', LogisticRegression(multi_class='auto', solver='lbfgs')),
])
evaluator = GridEvaluator(
parameters={
'logreg__C': [1.0],
},
grid_parameters=evaluations.grid_parameters(),
)
result_handlers = [
result_handlers.print_gridsearch_results,
]
from nlp4musa2020.dataloaders.alf200k import ALF200KLoader, genre_target_labels
import nlp4musa2020.evaluators as evaluators
from nlp4musa2020.models.simplenn_genre import SimpleGenreNN
dataloader = ALF200KLoader('data/processed/dataset-lfm-genres.pickle',
load_feature_groups=[
'audio',
],
text_vectorizers=None,
target=genre_target_labels())
pipeline = Pipeline([
('scaler', StandardScaler()),
('model',
DummyClassifier(strategy="constant",
constant=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0])), #rock
])
evaluator = GridEvaluator(
parameters={
"model__random_state": [42],
},
grid_parameters=evaluators.grid_parameters_genres(),
)
result_handlers = [
result_handlers.print_gridsearch_results,
]