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# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer.PyDataProvider2 import *
import common_utils # parse
def hook(settings, meta, **kwargs):
"""
Init hook is invoked before process data. It will set obj.slots and store
data meta.
:param obj: global object. It will passed to process routine.
:type obj: object
:param meta: the meta file object, which passed from trainer_config. Meta
file record movie/user features.
:param kwargs: unused other arguments.
"""
del kwargs # unused kwargs
# Header define slots that used for paddle.
# first part is movie features.
# second part is user features.
# final part is rating score.
# header is a list of [USE_SEQ_OR_NOT?, SlotType]
headers = list(common_utils.meta_to_header(meta, 'movie'))
headers.extend(list(common_utils.meta_to_header(meta, 'user')))
headers.append(dense_vector(1)) # Score
# slot types.
settings.input_types = headers
settings.meta = meta
@provider(init_hook=hook, cache=CacheType.CACHE_PASS_IN_MEM)
def process(settings, filename):
with open(filename, 'r') as f:
for line in f:
# Get a rating from file.
user_id, movie_id, score = map(int, line.split('::')[:-1])
# Scale score to [-5, +5]
score = float(score) * 2 - 5.0
# Get movie/user features by movie_id, user_id
movie_meta = settings.meta['movie'][movie_id]
user_meta = settings.meta['user'][user_id]
outputs = [movie_id - 1]
# Then add movie features
for each_meta in movie_meta:
outputs.append(each_meta)
# Then add user id.
outputs.append(user_id - 1)
# Then add user features.
for each_meta in user_meta:
outputs.append(each_meta)
# Finally, add score
outputs.append([score])
# Return data to paddle
yield outputs