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data.py
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data.py
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import itertools
import os
import zipfile
import numpy as np
import requests
import scipy.sparse as sp
def _get_movielens_path():
"""
Get path to the movielens dataset file.
"""
return os.path.join(os.path.dirname(os.path.abspath(__file__)),
'movielens.zip')
def _download_movielens(dest_path):
"""
Download the dataset.
"""
url = 'http://files.grouplens.org/datasets/movielens/ml-100k.zip'
req = requests.get(url, stream=True)
with open(dest_path, 'wb') as fd:
for chunk in req.iter_content():
fd.write(chunk)
def _get_raw_movielens_data():
"""
Return the raw lines of the train and test files.
"""
path = _get_movielens_path()
if not os.path.isfile(path):
_download_movielens(path)
with zipfile.ZipFile(path) as datafile:
return (datafile.read('ml-100k/ua.base').decode().split('\n'),
datafile.read('ml-100k/ua.test').decode().split('\n'))
def _parse(data):
"""
Parse movielens dataset lines.
"""
for line in data:
if not line:
continue
uid, iid, rating, timestamp = [int(x) for x in line.split('\t')]
yield uid, iid, rating, timestamp
def _build_interaction_matrix(rows, cols, data):
"""
Build the training matrix (no_users, no_items),
with ratings >= 4.0 being marked as positive and
the rest as negative.
"""
mat = sp.lil_matrix((rows, cols), dtype=np.int32)
for uid, iid, rating, timestamp in data:
if rating >= 4.0:
mat[uid, iid] = 1.0
else:
mat[uid, iid] = -1.0
return mat.tocoo()
def _get_movie_raw_metadata():
"""
Get raw lines of the genre file.
"""
path = _get_movielens_path()
if not os.path.isfile(path):
_download_movielens(path)
with zipfile.ZipFile(path) as datafile:
return datafile.read('ml-100k/u.item').decode(errors='ignore').split('\n')
def get_movielens_item_metadata(use_item_ids):
"""
Build a matrix of genre features (no_items, no_features).
If use_item_ids is True, per-item features will also be used.
"""
features = {}
genre_set = set()
for line in _get_movie_raw_metadata():
if not line:
continue
splt = line.split('|')
item_id = int(splt[0])
genres = [idx for idx, val in
zip(range(len(splt[5:])), splt[5:])
if int(val) > 0]
if use_item_ids:
# Add item-specific features too
genres.append(item_id)
for genre_id in genres:
genre_set.add(genre_id)
features[item_id] = genres
mat = sp.lil_matrix((len(features) + 1,
len(genre_set)),
dtype=np.int32)
for item_id, genre_ids in features.items():
for genre_id in genre_ids:
mat[item_id, genre_id] = 1
return mat
def get_movielens_data():
"""
Return (train_interactions, test_interactions).
"""
train_data, test_data = _get_raw_movielens_data()
uids = set()
iids = set()
for uid, iid, rating, timestamp in itertools.chain(_parse(train_data),
_parse(test_data)):
uids.add(uid)
iids.add(iid)
rows = max(uids) + 1
cols = max(iids) + 1
return (_build_interaction_matrix(rows, cols, _parse(train_data)),
_build_interaction_matrix(rows, cols, _parse(test_data)))