'NormalPredictor': '[{}]({})'.format('Random',
stable +
'basic_algorithms.html#amaze.prediction_algorithms.random_pred.NormalPredictor'),
'ml-100k': '[{}]({})'.format('Movielens 100k',
'http://grouplens.org/datasets/movielens/100k'),
'ml-1m': '[{}]({})'.format('Movielens 1M',
'http://grouplens.org/datasets/movielens/1m'),
}
# set RNG
np.random.seed(0)
random.seed(0)
dataset = 'ml-1m'
data = Dataset.load_builtin(dataset)
kf = KFold(random_state=0) # folds will be the same for all algorithms.
table = []
for klass in classes:
start = time.time()
out = cross_validate(klass(), data, ['rmse', 'mae'], kf)
cv_time = str(datetime.timedelta(seconds=int(time.time() - start)))
link = LINK[klass.__name__]
mean_rmse = '{:.3f}'.format(np.mean(out['test_rmse']))
mean_mae = '{:.3f}'.format(np.mean(out['test_mae']))
new_line = [link, mean_rmse, mean_mae, cv_time]
print(tabulate([new_line], tablefmt="pipe")) # print current algo perf
table.append(new_line)
"""
This module gives an example of how to configure similarity measures
computation.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
from amaze import KNNBasic
from amaze import Dataset
from amaze.model_selection import cross_validate
# Load the movielens-100k dataset.
data = Dataset.load_builtin('ml-100k')
# Example using cosine similarity
sim_options = {
'name': 'cosine',
'user_based': False # compute similarities between items
}
algo = KNNBasic(sim_options=sim_options)
cross_validate(algo, data, verbose=True)
# Example using pearson_baseline similarity
sim_options = {
'name': 'pearson_baseline',
'shrinkage': 0 # no shrinkage
}
algo = KNNBasic(sim_options=sim_options)