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__main__.py
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__main__.py
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#!/usr/bin/env python
import argparse
import os
import random as rd
import shutil
import sys
import numpy as np
import surprise.dataset as dataset
from surprise import __version__
from surprise.builtin_datasets import get_dataset_dir
from surprise.dataset import Dataset
from surprise.model_selection import cross_validate, KFold, PredefinedKFold
from surprise.prediction_algorithms import (
BaselineOnly,
CoClustering,
KNNBaseline,
KNNBasic,
KNNWithMeans,
NMF,
NormalPredictor,
SlopeOne,
SVD,
SVDpp,
)
from surprise.reader import Reader # noqa
def main():
class MyParser(argparse.ArgumentParser):
"""A parser which prints the help message when an error occurs. Taken from
https://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 "
+ "(https://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)
if __name__ == "__main__":
main()