def main():
print("Setting up dataset and evaluator...")
# Setup dataset and evaluator
dataset = Dataset(load_tags=True,
filter_tag=False,
weight_tag=False)
dataset.set_track_attr_weights_2(1.0, 1.0, 0.0, 0.0, 0.0,
1.0, 1.0, 0.0, 0.0)
ev = Evaluator(seed=False)
ev.cross_validation(5, dataset.train_final.copy())
for i in range(0, 5):
print("Training models for fold {}-th".format(i))
train_models(dataset, ev, n_clusters=20)
def evaluateMap():
from src.utils.evaluator import Evaluator
dataset = Dataset(load_tags=True, filter_tag=False, weight_tag=False)
dataset.set_track_attr_weights_2(2.0, 2.0, 0.0, 0.0, 0.0, 2.0, 2.0, 0.0,
0.0)
# seed = 0xcafebabe
# print("Evaluating with initial seed: {}".format(seed))
ev = Evaluator(seed=False)
ev.cross_validation(5, dataset.train_final.copy())
cbf = ContentBasedFiltering()
for i in range(0, 5):
urm, tg_tracks, tg_playlist = ev.get_fold(dataset)
cbf.fit(urm, list(tg_playlist), list(tg_tracks), dataset)
recs = cbf.predict()
ev.evaluate_fold(recs)
map_at_five = ev.get_mean_map()
print("MAP@5 ", map_at_five)
def evaluateMap():
from src.utils.evaluator import Evaluator
dataset = Dataset(load_tags=True, filter_tag=False, weight_tag=False)
dataset.set_track_attr_weights_2(1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0,
0.0)
dataset.set_playlist_attr_weights(1, 1, 1, 0, 0)
ev = Evaluator()
ev.cross_validation(5, dataset.train_final.copy())
cbf = ContentBasedFiltering(alfa=0.999)
for i in range(0, 5):
urm, tg_tracks, tg_playlist = ev.get_fold(dataset)
cbf.fit(urm, list(tg_playlist), list(tg_tracks), dataset)
recs = cbf.predict()
ev.evaluate_fold(recs)
map_at_five = ev.get_mean_map()
print("MAP@5 ", map_at_five)
def main(n_evaluations):
print("Setting up dataset...")
# Setup dataset and evaluator
dataset = Dataset(load_tags=True,
filter_tag=False,
weight_tag=False)
dataset.set_track_attr_weights_2(1.0, 1.0, 0.0, 0.0, 0.0,
1.0, 1.0, 0.0, 0.0)
# Run mAP evaluation a number of times to reduce the variance
# of the results
n_clusters = 20
best = np.zeros((4, n_clusters), dtype=int)
for i in range(n_evaluations):
print("Running cross validation no. {}".format(i))
print("Setting up the Evaluator...")
ev = Evaluator(seed=False)
ev.cross_validation(5, dataset.train_final.copy())
folding_best = train_models(dataset, ev,
n_folds=5, n_clusters=n_clusters)
best += folding_best
top = top_over_cluster(best)
_print_top(top)
return self.R_hat
def get_model(self):
"""
Returns the complete R_hat
"""
return self.R_hat.copy()
if __name__ == '__main__':
ds = Dataset(load_tags=True, filter_tag=True)
ds.set_track_attr_weights_2(1,
1,
1,
1,
1,
num_rating_weight=1,
inferred_album=1,
inferred_duration=1,
inferred_playcount=1)
ev = Evaluator()
ev.cross_validation(5, ds.train_final.copy())
for i in range(0, 5):
fm = FM()
urm, tg_tracks, tg_playlist = ev.get_fold(ds)
fm.fit(urm, list(tg_playlist), list(tg_tracks), ds)
recs = fm.predict()
ev.evaluate_fold(recs)
map_at_five = ev.get_mean_map()
print("MAP@5 Final", map_at_five)