def load_ml_100k(file_rating="ml-100k", content=False):
# Download data
Dataset.load_builtin("ml-100k")
# Load data
file_rating = builtin_datasets.get_dataset_dir() + 'ml-100k/ml-100k/' + file_rating
df_rating = pd.read_csv(file_rating, sep='\t', names=['user_id', 'item_id', 'rating', 'timestamp'],
dtype={'user_id': str, 'item_id': str, 'rating': np.float})
df_x = df_rating[['user_id', 'item_id']]
# Load content
if content:
file_movie = builtin_datasets.get_dataset_dir() + 'ml-100k/ml-100k/u.item'
file_user = builtin_datasets.get_dataset_dir() + 'ml-100k/ml-100k/u.user'
df_movie = pd.read_csv(file_movie, sep='|', encoding="ISO-8859-1",
names=['item_id', 'movie_title', 'release_date', 'video_release_date',
'IMDb_URL', 'unknown', 'Action', 'Adventure', 'Animation',
'Children\'s', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Fantasy',
'Film-Noir', 'Horror', 'Musical', 'Mystery', 'Romance', 'Sci-Fi',
'Thriller', 'War', 'Western'],
dtype={'item_id': str})
df_user = pd.read_csv(file_user, sep='|', names=['user_id', 'age', 'gender', 'occupation', 'zip_code'],
dtype={'user_id': str})
df_user['gender'] = (df_user['gender'] == 'M').astype(np.float)
df_x = pd.merge(df_x, df_user[['user_id', 'gender']], how='left')
df_x = pd.merge(df_x, df_movie[['item_id', 'unknown', 'Action', 'Adventure', 'Animation',
'Children\'s', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Fantasy',
'Film-Noir', 'Horror', 'Musical', 'Mystery', 'Romance', 'Sci-Fi',
'Thriller', 'War', 'Western']], how='left')
return df_x.to_dict(orient='records'), np.array(df_rating['rating'])
def test_get_dataset_dir():
'''Test the get_dataset_dir() function.'''
os.environ['SURPRISE_DATA_FOLDER'] = '/tmp/surprise_data'
assert get_dataset_dir() == '/tmp/surprise_data'
# Fall back to default
del os.environ['SURPRISE_DATA_FOLDER']
assert get_dataset_dir() == os.path.expanduser('~' + '/.surprise_data/')
def test_get_dataset_dir():
'''Test the get_dataset_dir() function.'''
os.environ['SURPRISE_DATA_FOLDER'] = '/tmp/surprise_data'
assert get_dataset_dir() == '/tmp/surprise_data'
# Fall back to default
del os.environ['SURPRISE_DATA_FOLDER']
assert get_dataset_dir() == os.path.expanduser('~' + '/.surprise_data/')
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://surprise.readthedocs.io/) for more details.',
epilog="""Example:\n
surprise -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)
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://surprise.readthedocs.io/) for more details.',
epilog="""Example:\n
surprise -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)
