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map_reader.py
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map_reader.py
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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.
import sys
import json
import paddle.fluid.incubate.data_generator as dg
class MapDataset(dg.MultiSlotDataGenerator):
def setup(self, sparse_feature_dim):
self.profile_length = 65
self.dense_length = 3
#feature names
self.dense_feature_list = ["distance", "price", "eta"]
self.pid_list = ["pid"]
self.query_feature_list = ["weekday", "hour", "o1", "o2", "d1", "d2"]
self.plan_feature_list = ["transport_mode"]
self.rank_feature_list = ["plan_rank", "whole_rank", "price_rank", "eta_rank", "distance_rank"]
self.rank_whole_pic_list = ["mode_rank1", "mode_rank2", "mode_rank3", "mode_rank4",
"mode_rank5"]
self.weather_feature_list = ["max_temp", "min_temp", "wea", "wind"]
self.hash_dim = 1000001
self.train_idx_ = 2000000
#carefully set if you change the features
self.categorical_range_ = range(0, 22)
#process one instance
def _process_line(self, line):
instance = json.loads(line)
"""
profile = instance["profile"]
len_profile = len(profile)
if len_profile >= 10:
user_profile_feature = profile[0:10]
else:
profile.extend([0]*(10-len_profile))
user_profile_feature = profile
if len(profile) > 1 or (len(profile) == 1 and profile[0] != 0):
for p in profile:
if p >= 1 and p <= 65:
user_profile_feature[p - 1] = 1
"""
context_feature = []
context_feature_fm = []
dense_feature = [0] * self.dense_length
plan = instance["plan"]
for i, val in enumerate(self.dense_feature_list):
dense_feature[i] = plan[val]
if (instance["pid"] == ""):
instance["pid"] = 0
query = instance["query"]
weather_dic = instance["weather"]
for fea in self.pid_list:
context_feature.append([hash(fea + str(instance[fea])) % self.hash_dim])
context_feature_fm.append(hash(fea + str(instance[fea])) % self.hash_dim)
for fea in self.query_feature_list:
context_feature.append([hash(fea + str(query[fea])) % self.hash_dim])
context_feature_fm.append(hash(fea + str(query[fea])) % self.hash_dim)
for fea in self.plan_feature_list:
context_feature.append([hash(fea + str(plan[fea])) % self.hash_dim])
context_feature_fm.append(hash(fea + str(plan[fea])) % self.hash_dim)
for fea in self.rank_feature_list:
context_feature.append([hash(fea + str(instance[fea])) % self.hash_dim])
context_feature_fm.append(hash(fea + str(instance[fea])) % self.hash_dim)
for fea in self.rank_whole_pic_list:
context_feature.append([hash(fea + str(instance[fea])) % self.hash_dim])
context_feature_fm.append(hash(fea + str(instance[fea])) % self.hash_dim)
for fea in self.weather_feature_list:
context_feature.append([hash(fea + str(weather_dic[fea])) % self.hash_dim])
context_feature_fm.append(hash(fea + str(weather_dic[fea])) % self.hash_dim)
label = [int(instance["label"])]
return dense_feature, context_feature, context_feature_fm, label
def infer_reader(self, filelist, batch, buf_size):
print(filelist)
def local_iter():
for fname in filelist:
with open(fname.strip(), "r") as fin:
for line in fin:
dense_feature, sparse_feature, sparse_feature_fm, label = self._process_line(line)
yield [dense_feature] + sparse_feature + [sparse_feature_fm] + [label]
import paddle
batch_iter = paddle.batch(
paddle.reader.shuffle(
local_iter, buf_size=buf_size),
batch_size=batch)
return batch_iter
#generat inputs for testing
def test_reader(self, filelist, batch, buf_size):
print(filelist)
def local_iter():
for fname in filelist:
with open(fname.strip(), "r") as fin:
for line in fin:
dense_feature, sparse_feature, sparse_feature_fm, label = self._process_line(line)
yield [dense_feature] + sparse_feature + [sparse_feature_fm] + [label]
import paddle
batch_iter = paddle.batch(
paddle.reader.buffered(
local_iter, size=buf_size),
batch_size=batch)
return batch_iter
#generate inputs for trainig
def generate_sample(self, line):
def data_iter():
dense_feature, sparse_feature, sparse_feature_fm, label = self._process_line(line)
#feature_name = ["user_profile"]
feature_name = []
feature_name.append("dense_feature")
for idx in self.categorical_range_:
feature_name.append("context" + str(idx))
feature_name.append("context_fm")
feature_name.append("label")
yield zip(feature_name, [dense_feature] + sparse_feature + [sparse_feature_fm] + [label])
return data_iter
if __name__ == "__main__":
map_dataset = MapDataset()
map_dataset.setup(int(sys.argv[1]))
map_dataset.run_from_stdin()