def test_cross_validate(toy_data):
# First test with a specified CV iterator.
current_dir = os.path.dirname(os.path.realpath(__file__))
folds_files = [(current_dir + '/custom_train',
current_dir + '/custom_test')]
reader = Reader(line_format='user item rating', sep=' ', skip_lines=3)
data = Dataset.load_from_folds(folds_files=folds_files, reader=reader,
rating_scale=(1, 5))
algo = NormalPredictor()
pkf = ms.PredefinedKFold()
ret = ms.cross_validate(algo, data, measures=['rmse', 'mae'], cv=pkf,
verbose=1)
# Basically just test that keys (dont) exist as they should
assert len(ret['test_rmse']) == 1
assert len(ret['test_mae']) == 1
assert len(ret['fit_time']) == 1
assert len(ret['test_time']) == 1
assert 'test_fcp' not in ret
assert 'train_rmse' not in ret
assert 'train_mae' not in ret
# Test that 5 fold CV is used when cv=None
# Also check that train_* key exist when return_train_measures is True.
ret = ms.cross_validate(algo, toy_data, measures=['rmse', 'mae'], cv=None,
return_train_measures=True, verbose=True)
assert len(ret['test_rmse']) == 5
assert len(ret['test_mae']) == 5
assert len(ret['fit_time']) == 5
assert len(ret['test_time']) == 5
assert len(ret['train_rmse']) == 5
assert len(ret['train_mae']) == 5
def test_user_based_field(u1_ml100k, pkf):
"""Ensure that the user_based field is taken into account (only) when
needed."""
algorithms = (KNNBasic, KNNWithMeans, KNNBaseline)
for klass in algorithms:
algo = klass(sim_options={'user_based': True})
rmses_user_based = cross_validate(algo, u1_ml100k, ['rmse'],
pkf)['test_rmse']
algo = klass(sim_options={'user_based': False})
rmses_item_based = cross_validate(algo, u1_ml100k, ['rmse'],
pkf)['test_rmse']
assert rmses_user_based != rmses_item_based
def test_SVDpp_parameters(u1_ml100k, pkf):
"""Ensure that all parameters are taken into account."""
# The baseline against which to compare.
algo = SVDpp(n_factors=1, n_epochs=1, random_state=1)
rmse_default = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
# n_factors
algo = SVDpp(n_factors=2, n_epochs=1, random_state=1)
rmse_factors = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_factors
# The rest is OK but just takes too long for now...
"""
def test_sgd_n_epoch_field(u1_ml100k, pkf):
"""Ensure the n_epoch field is taken into account."""
bsl_options = {'method': 'sgd',
'n_epochs': 1,
}
algo = BaselineOnly(bsl_options=bsl_options)
rmse_sgd_n_epoch_1 = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
bsl_options = {'method': 'sgd',
'n_epochs': 20,
}
algo = BaselineOnly(bsl_options=bsl_options)
rmse_sgd_n_epoch_5 = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_sgd_n_epoch_1 != rmse_sgd_n_epoch_5
def test_als_reg_i_field(u1_ml100k, pkf):
"""Ensure the reg_i field is taken into account."""
bsl_options = {'method': 'als',
'reg_i': 0,
}
algo = BaselineOnly(bsl_options=bsl_options)
rmse_als_regi_0 = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
bsl_options = {'method': 'als',
'reg_i': 10,
}
algo = BaselineOnly(bsl_options=bsl_options)
rmse_als_regi_10 = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_als_regi_0 != rmse_als_regi_10
def test_method_field(u1_ml100k, pkf):
"""Ensure the method field is taken into account."""
bsl_options = {'method': 'als'}
algo = BaselineOnly(bsl_options=bsl_options)
rmse_als = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
bsl_options = {'method': 'sgd'}
algo = BaselineOnly(bsl_options=bsl_options)
rmse_sgd = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_als != rmse_sgd
with pytest.raises(ValueError):
bsl_options = {'method': 'wrong_name'}
algo = BaselineOnly(bsl_options=bsl_options)
cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
def test_shrinkage_field(u1_ml100k, pkf):
"""Ensure the shrinkage field is taken into account."""
sim_options = {'name': 'pearson_baseline',
'shrinkage': 0
}
bsl_options = {'n_epochs': 1}
algo = KNNBasic(sim_options=sim_options)
rmse_shrinkage_0 = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
sim_options = {'name': 'pearson_baseline',
'shrinkage': 100
}
bsl_options = {'n_epochs': 1}
algo = KNNBasic(sim_options=sim_options, bsl_options=bsl_options)
rmse_shrinkage_100 = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_shrinkage_0 != rmse_shrinkage_100
def test_sgd_reg_field(u1_ml100k, pkf):
"""Ensure the reg field is taken into account."""
bsl_options = {'method': 'sgd',
'n_epochs': 1,
'reg': 0.02,
}
algo = BaselineOnly(bsl_options=bsl_options)
rmse_sgd_reg_002 = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
bsl_options = {'method': 'sgd',
'n_epochs': 1,
'reg': 1,
}
algo = BaselineOnly(bsl_options=bsl_options)
rmse_sgd_reg_1 = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_sgd_reg_002 != rmse_sgd_reg_1
def test_sgd_learning_rate_field(u1_ml100k, pkf):
"""Ensure the learning_rate field is taken into account."""
bsl_options = {'method': 'sgd',
'n_epochs': 1,
'learning_rate': .005,
}
algo = BaselineOnly(bsl_options=bsl_options)
rmse_sgd_lr_005 = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
bsl_options = {'method': 'sgd',
'n_epochs': 1,
'learning_rate': .00005,
}
algo = BaselineOnly(bsl_options=bsl_options)
rmse_sgd_lr_00005 = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_sgd_lr_005 != rmse_sgd_lr_00005
def test_CoClustering_parameters(u1_ml100k, pkf):
"""Ensure that all parameters are taken into account."""
# The baseline against which to compare.
algo = CoClustering(n_epochs=1, random_state=1)
rmse_default = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
# n_cltr_u
algo = CoClustering(n_cltr_u=1, n_epochs=1, random_state=1)
rmse_n_cltr_u = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_n_cltr_u
# n_cltr_i
algo = CoClustering(n_cltr_i=1, n_epochs=1, random_state=1)
rmse_n_cltr_i = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_n_cltr_i
# n_epochs
algo = CoClustering(n_epochs=2, random_state=1)
rmse_n_epochs = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_n_epochs
def test_name_field(u1_ml100k, pkf):
"""Ensure the name field is taken into account."""
sim_options = {'name': 'cosine'}
algo = KNNBasic(sim_options=sim_options)
rmse_cosine = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
sim_options = {'name': 'msd'}
algo = KNNBasic(sim_options=sim_options)
rmse_msd = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
sim_options = {'name': 'pearson'}
algo = KNNBasic(sim_options=sim_options)
rmse_pearson = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
sim_options = {'name': 'pearson_baseline'}
bsl_options = {'n_epochs': 1}
algo = KNNBasic(sim_options=sim_options, bsl_options=bsl_options)
rmse_pearson_bsl = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
for rmse_a, rmse_b in combinations((rmse_cosine, rmse_msd, rmse_pearson,
rmse_pearson_bsl), 2):
assert (rmse_a != rmse_b)
with pytest.raises(NameError):
sim_options = {'name': 'wrong_name'}
algo = KNNBasic(sim_options=sim_options)
cross_validate(algo, u1_ml100k, ['rmse'], pkf)
def test_gridsearchcv_best_estimator(u1_ml100k):
"""Ensure that the best estimator is the one giving the best score (by
re-running it)"""
param_grid = {'n_epochs': [5], 'lr_all': [0.002, 0.005],
'reg_all': [0.4, 0.6], 'n_factors': [1], 'init_std_dev': [0]}
gs = GridSearchCV(SVD, param_grid, measures=['mae'],
cv=PredefinedKFold(), joblib_verbose=100)
gs.fit(u1_ml100k)
best_estimator = gs.best_estimator['mae']
# recompute MAE of best_estimator
mae = cross_validate(best_estimator, u1_ml100k, measures=['MAE'],
cv=PredefinedKFold())['test_mae']
assert mae == gs.best_score['mae']
def test_randomizedsearchcv_best_estimator(u1_ml100k):
"""Ensure that the best estimator is the one that gives the best score (by
re-running it)"""
param_distributions = {'n_epochs': [5], 'lr_all': uniform(0.002, 0.003),
'reg_all': uniform(0.04, 0.02), 'n_factors': [1],
'init_std_dev': [0]}
rs = RandomizedSearchCV(SVD, param_distributions, measures=['mae'],
cv=PredefinedKFold(), joblib_verbose=100)
rs.fit(u1_ml100k)
best_estimator = rs.best_estimator['mae']
# recompute MAE of best_estimator
mae = cross_validate(best_estimator, u1_ml100k, measures=['MAE'],
cv=PredefinedKFold())['test_mae']
assert mae == rs.best_score['mae']
def main():
class MyParser(argparse.ArgumentParser):
'''A parser which prints the help message when an error occurs. Taken from
http://stackoverflow.com/questions/4042452/display-help-message-with-python-argparse-when-script-is-called-without-any-argu.''' # noqa
def error(self, message):
sys.stderr.write('error: %s\n' % message)
self.print_help()
sys.exit(2)
parser = MyParser(
description='Evaluate the performance of a rating prediction ' +
'algorithm ' +
'on a given dataset using cross validation. You can use a built-in ' +
'or a custom dataset, and you can choose to automatically split the ' +
'dataset into folds, or manually specify train and test files. ' +
'Please refer to the documentation page ' +
'(http://amaze.readthedocs.io/) for more details.',
epilog="""Example:\n
amaze -algo SVD -params "{'n_epochs': 5, 'verbose': True}"
-load-builtin ml-100k -n-folds 3""")
algo_choices = {
'NormalPredictor': NormalPredictor,
'BaselineOnly': BaselineOnly,
'KNNBasic': KNNBasic,
'KNNBaseline': KNNBaseline,
'KNNWithMeans': KNNWithMeans,
'SVD': SVD,
'SVDpp': SVDpp,
'NMF': NMF,
'SlopeOne': SlopeOne,
'CoClustering': CoClustering,
}
parser.add_argument('-algo',
type=str,
choices=algo_choices,
help='The prediction algorithm to use. ' +
'Allowed values are ' +
', '.join(algo_choices.keys()) + '.',
metavar='<prediction algorithm>')
parser.add_argument('-params',
type=str,
metavar='<algorithm parameters>',
default='{}',
help='A kwargs dictionary that contains all the ' +
'algorithm parameters.' +
'Example: "{\'n_epochs\': 10}".')
parser.add_argument('-load-builtin',
type=str,
dest='load_builtin',
metavar='<dataset name>',
default='ml-100k',
help='The name of the built-in dataset to use.' +
'Allowed values are ' +
', '.join(dataset.BUILTIN_DATASETS.keys()) +
'. Default is ml-100k.')
parser.add_argument(
'-load-custom',
type=str,
dest='load_custom',
metavar='<file path>',
default=None,
help='A file path to custom dataset to use. ' + 'Ignored if ' +
'-loadbuiltin is set. The -reader parameter needs ' + 'to be set.')
parser.add_argument('-folds-files',
type=str,
dest='folds_files',
metavar='<train1 test1 train2 test2... >',
default=None,
help='A list of custom train and test files. ' +
'Ignored if -load-builtin or -load-custom is set. '
'The -reader parameter needs to be set.')
parser.add_argument('-reader',
type=str,
metavar='<reader>',
default=None,
help='A Reader to read the custom dataset. Example: ' +
'"Reader(line_format=\'user item rating timestamp\',' +
' sep=\'\\t\')"')
parser.add_argument('-n-folds',
type=int,
dest='n_folds',
metavar="<number of folds>",
default=5,
help='The number of folds for cross-validation. ' +
'Default is 5.')
parser.add_argument('-seed',
type=int,
metavar='<random seed>',
default=None,
help='The seed to use for RNG. ' +
'Default is the current system time.')
parser.add_argument('--with-dump',
dest='with_dump',
action='store_true',
help='Dump the algorithm ' +
'results in a file (one file per fold). ' +
'Default is False.')
parser.add_argument('-dump-dir',
dest='dump_dir',
type=str,
metavar='<dir>',
default=None,
help='Where to dump the files. Ignored if ' +
'with-dump is not set. Default is ' +
os.path.join(get_dataset_dir(), 'dumps/'))
parser.add_argument('--clean',
dest='clean',
action='store_true',
help='Remove the ' + get_dataset_dir() +
' directory and exit.')
parser.add_argument('-v',
'--version',
action='version',
version=__version__)
args = parser.parse_args()
if args.clean:
folder = get_dataset_dir()
shutil.rmtree(folder)
print('Removed', folder)
exit()
# setup RNG
rd.seed(args.seed)
np.random.seed(args.seed)
# setup algorithm
params = eval(args.params)
if args.algo is None:
parser.error('No algorithm was specified.')
algo = algo_choices[args.algo](**params)
# setup dataset
if args.load_custom is not None: # load custom and split
if args.reader is None:
parser.error('-reader parameter is needed.')
reader = eval(args.reader)
data = Dataset.load_from_file(args.load_custom, reader=reader)
cv = KFold(n_splits=args.n_folds, random_state=args.seed)
elif args.folds_files is not None: # load from files
if args.reader is None:
parser.error('-reader parameter is needed.')
reader = eval(args.reader)
folds_files = args.folds_files.split()
folds_files = [(folds_files[i], folds_files[i + 1])
for i in range(0,
len(folds_files) - 1, 2)]
data = Dataset.load_from_folds(folds_files=folds_files, reader=reader)
cv = PredefinedKFold()
else: # load builtin dataset and split
data = Dataset.load_builtin(args.load_builtin)
cv = KFold(n_splits=args.n_folds, random_state=args.seed)
cross_validate(algo, data, cv=cv, verbose=True)
'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)
header = [LINK[dataset],
'RMSE',
'MAE',
'Time'
]
print(tabulate(table, header, tablefmt="pipe"))
"""
This module descibes how to load a custom dataset from a single file.
As a custom dataset we will actually use the movielens-100k dataset, but act as
if it were not built-in.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import os
from amaze import BaselineOnly
from amaze import Dataset
from amaze import Reader
from amaze.model_selection import cross_validate
# path to dataset file
file_path = os.path.expanduser('~/.amaze_data/ml-100k/ml-100k/u.data')
# As we're loading a custom dataset, we need to define a reader. In the
# movielens-100k dataset, each line has the following format:
# 'user item rating timestamp', separated by '\t' characters.
reader = Reader(line_format='user item rating timestamp', sep='\t')
data = Dataset.load_from_file(file_path, reader=reader, rating_scale=(1, 5))
# We can now use this dataset as we please, e.g. calling cross_validate
cross_validate(BaselineOnly(), data, verbose=True)
"""
This module descibes how to load a dataset from a pandas dataframe.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import pandas as pd
from amaze import NormalPredictor
from amaze import Dataset
from amaze.model_selection import cross_validate
# Creation of the dataframe. Column names are irrelevant.
ratings_dict = {
'itemID': [1, 1, 1, 2, 2],
'userID': [9, 32, 2, 45, 'user_foo'],
'rating': [3, 2, 4, 3, 1]
}
df = pd.DataFrame(ratings_dict)
# The columns must correspond to user id, item id and ratings (in that order).
data = Dataset.load_from_df(df[['userID', 'itemID', 'rating']],
rating_scale=(1, 5))
# We can now use this dataset as we please, e.g. calling cross_validate
cross_validate(NormalPredictor(), data, cv=2)
def test_SVD_parameters(u1_ml100k, pkf):
"""Ensure that all parameters are taken into account."""
# The baseline against which to compare.
algo = SVD(n_factors=1, n_epochs=1, random_state=1)
rmse_default = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
# n_factors
algo = SVD(n_factors=2, n_epochs=1, random_state=1)
rmse_factors = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_factors
# n_epochs
algo = SVD(n_factors=1, n_epochs=2, random_state=1)
rmse_n_epochs = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_n_epochs
# biased
algo = SVD(n_factors=1, n_epochs=1, biased=False, random_state=1)
rmse_biased = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_biased
# lr_all
algo = SVD(n_factors=1, n_epochs=1, lr_all=5, random_state=1)
rmse_lr_all = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_lr_all
# reg_all
algo = SVD(n_factors=1, n_epochs=1, reg_all=5, random_state=1)
rmse_reg_all = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_reg_all
# lr_bu
algo = SVD(n_factors=1, n_epochs=1, lr_bu=5, random_state=1)
rmse_lr_bu = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_lr_bu
# lr_bi
algo = SVD(n_factors=1, n_epochs=1, lr_bi=5, random_state=1)
rmse_lr_bi = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_lr_bi
# lr_pu
algo = SVD(n_factors=1, n_epochs=1, lr_pu=5, random_state=1)
rmse_lr_pu = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_lr_pu
# lr_qi
algo = SVD(n_factors=1, n_epochs=1, lr_qi=5, random_state=1)
rmse_lr_qi = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_lr_qi
# reg_bu
algo = SVD(n_factors=1, n_epochs=1, reg_bu=5, random_state=1)
rmse_reg_bu = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_reg_bu
# reg_bi
algo = SVD(n_factors=1, n_epochs=1, reg_bi=5, random_state=1)
rmse_reg_bi = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_reg_bi
# reg_pu
algo = SVD(n_factors=1, n_epochs=1, reg_pu=5, random_state=1)
rmse_reg_pu = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_reg_pu
# reg_qi
algo = SVD(n_factors=1, n_epochs=1, reg_qi=5, random_state=1)
rmse_reg_qi = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_reg_qi
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)
cross_validate(algo, data, verbose=True)
"""
This module describes the most basic usage of Amaze: you define a prediction
algorithm, (down)load a dataset and run a cross-validation procedure.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
from amaze import SVD
from amaze import Dataset
from amaze.model_selection import cross_validate
# Load the movielens-100k dataset (download it if needed),
data = Dataset.load_builtin('ml-100k')
# We'll use the famous SVD algorithm.
algo = SVD()
# Run 5-fold cross-validation and print results
cross_validate(algo, data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
def test_NMF_parameters(u1_ml100k, pkf):
"""Ensure that all parameters are taken into account."""
# The baseline against which to compare.
algo = NMF(n_factors=1, n_epochs=1, random_state=1)
rmse_default = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
# n_factors
algo = NMF(n_factors=2, n_epochs=1, random_state=1)
rmse_factors = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_factors
# n_epochs
algo = NMF(n_factors=1, n_epochs=2, random_state=1)
rmse_n_epochs = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_n_epochs
# biased
algo = NMF(n_factors=1, n_epochs=1, biased=True, random_state=1)
rmse_biased = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_biased
# reg_pu
algo = NMF(n_factors=1, n_epochs=1, reg_pu=1, random_state=1)
rmse_reg_pu = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_reg_pu
# reg_qi
algo = NMF(n_factors=1, n_epochs=1, reg_qi=1, random_state=1)
rmse_reg_qi = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_reg_qi
# reg_bu
algo = NMF(n_factors=1, n_epochs=1, reg_bu=1, biased=True, random_state=1)
rmse_reg_bu = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_reg_bu
# reg_bi
algo = NMF(n_factors=1, n_epochs=1, reg_bi=1, biased=True, random_state=1)
rmse_reg_bi = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_reg_bi
# lr_bu
algo = NMF(n_factors=1, n_epochs=1, lr_bu=1, biased=True, random_state=1)
rmse_lr_bu = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_lr_bu
# lr_bi
algo = NMF(n_factors=1, n_epochs=1, lr_bi=1, biased=True, random_state=1)
rmse_lr_bi = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_lr_bi
# init_low
algo = NMF(n_factors=1, n_epochs=1, init_low=.5, random_state=1)
rmse_init_low = cross_validate(algo, u1_ml100k, ['rmse'], pkf)['test_rmse']
assert rmse_default != rmse_init_low
# init_low
with pytest.raises(ValueError):
algo = NMF(n_factors=1, n_epochs=1, init_low=-1, random_state=1)
# init_high
algo = NMF(n_factors=1, n_epochs=1, init_high=.5, random_state=1)
rmse_init_high = cross_validate(algo, u1_ml100k, ['rmse'],
pkf)['test_rmse']
assert rmse_default != rmse_init_high
