You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
83 lines
3.0 KiB
83 lines
3.0 KiB
# Copyright (c) 2016 PaddlePaddle Authors, Inc. 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 sys
|
|
import numpy as np
|
|
TERM_NUM = 24
|
|
FORECASTING_NUM = 24
|
|
LABEL_VALUE_NUM = 4
|
|
|
|
|
|
def initHook(settings, file_list, **kwargs):
|
|
"""
|
|
Init hook is invoked before process data. It will set obj.slots and store data meta.
|
|
|
|
:param settings: global object. It will passed to process routine.
|
|
:type obj: object
|
|
:param file_list: the meta file object, which passed from trainer_config.py,but unused in this function.
|
|
:param kwargs: unused other arguments.
|
|
"""
|
|
del kwargs #unused
|
|
|
|
settings.pool_size = sys.maxint
|
|
#Use a time seires of the past as feature.
|
|
#Dense_vector's expression form is [float,float,...,float]
|
|
settings.input_types = [dense_vector(TERM_NUM)]
|
|
#There are next FORECASTING_NUM fragments you need predict.
|
|
#Every predicted condition at time point has four states.
|
|
for i in range(FORECASTING_NUM):
|
|
settings.input_types.append(integer_value(LABEL_VALUE_NUM))
|
|
|
|
|
|
@provider(
|
|
init_hook=initHook, cache=CacheType.CACHE_PASS_IN_MEM, should_shuffle=True)
|
|
def process(settings, file_name):
|
|
with open(file_name) as f:
|
|
#abandon fields name
|
|
f.next()
|
|
for row_num, line in enumerate(f):
|
|
speeds = map(int, line.rstrip('\r\n').split(",")[1:])
|
|
# Get the max index.
|
|
end_time = len(speeds)
|
|
# Scanning and generating samples
|
|
for i in range(TERM_NUM, end_time - FORECASTING_NUM):
|
|
# For dense slot
|
|
pre_spd = map(float, speeds[i - TERM_NUM:i])
|
|
|
|
# Integer value need predicting, values start from 0, so every one minus 1.
|
|
fol_spd = [j - 1 for j in speeds[i:i + FORECASTING_NUM]]
|
|
|
|
# Predicting label is missing, abandon the sample.
|
|
if -1 in fol_spd:
|
|
continue
|
|
yield [pre_spd] + fol_spd
|
|
|
|
|
|
def predict_initHook(settings, file_list, **kwargs):
|
|
settings.pool_size = sys.maxint
|
|
settings.input_types = [dense_vector(TERM_NUM)]
|
|
|
|
|
|
@provider(init_hook=predict_initHook, should_shuffle=False)
|
|
def process_predict(settings, file_name):
|
|
with open(file_name) as f:
|
|
#abandon fields name
|
|
f.next()
|
|
for row_num, line in enumerate(f):
|
|
speeds = map(int, line.rstrip('\r\n').split(","))
|
|
end_time = len(speeds)
|
|
pre_spd = map(float, speeds[end_time - TERM_NUM:end_time])
|
|
yield pre_spd
|