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89 lines
3.0 KiB
89 lines
3.0 KiB
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from paddle.trainer.PyDataProvider2 import *
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import common_utils # parse
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def __list_to_map__(lst):
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ret_val = dict()
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for each in lst:
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k, v = each
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ret_val[k] = v
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return ret_val
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def hook(settings, meta, **kwargs):
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"""
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Init hook is invoked before process data. It will set obj.slots and store
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data meta.
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:param obj: global object. It will passed to process routine.
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:type obj: object
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:param meta: the meta file object, which passed from trainer_config. Meta
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file record movie/user features.
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:param kwargs: unused other arguments.
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"""
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del kwargs # unused kwargs
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# Header define slots that used for paddle.
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# first part is movie features.
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# second part is user features.
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# final part is rating score.
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# header is a list of [USE_SEQ_OR_NOT?, SlotType]
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movie_headers = list(common_utils.meta_to_header(meta, 'movie'))
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settings.movie_names = [h[0] for h in movie_headers]
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headers = movie_headers
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user_headers = list(common_utils.meta_to_header(meta, 'user'))
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settings.user_names = [h[0] for h in user_headers]
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headers.extend(user_headers)
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headers.append(("rating", dense_vector(1))) # Score
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# slot types.
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settings.input_types = __list_to_map__(headers)
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settings.meta = meta
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@provider(init_hook=hook, cache=CacheType.CACHE_PASS_IN_MEM)
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def process(settings, filename):
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with open(filename, 'r') as f:
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for line in f:
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# Get a rating from file.
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user_id, movie_id, score = map(int, line.split('::')[:-1])
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# Scale score to [-5, +5]
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score = float(score) * 2 - 5.0
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# Get movie/user features by movie_id, user_id
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movie_meta = settings.meta['movie'][movie_id]
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user_meta = settings.meta['user'][user_id]
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outputs = [('movie_id', movie_id - 1)]
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# Then add movie features
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for i, each_meta in enumerate(movie_meta):
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outputs.append((settings.movie_names[i + 1], each_meta))
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# Then add user id.
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outputs.append(('user_id', user_id - 1))
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# Then add user features.
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for i, each_meta in enumerate(user_meta):
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outputs.append((settings.user_names[i + 1], each_meta))
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# Finally, add score
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outputs.append(('rating', [score]))
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# Return data to paddle
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yield __list_to_map__(outputs)
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