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wide&deep data process

tags/v0.5.0-beta
wukesong 5 years ago
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      model_zoo/wide_and_deep/src/process_data.py

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# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""
Criteo data process
"""

import os
import pickle
import collections
import argparse

import numpy as np
import pandas as pd

TRAIN_LINE_COUNT = 45840617
TEST_LINE_COUNT = 6042135

class CriteoStatsDict():
"""create data dict"""
def __init__(self):
self.field_size = 39 # value_1-13; cat_1-26;
self.val_cols = ["val_{}".format(i+1) for i in range(13)]
self.cat_cols = ["cat_{}".format(i+1) for i in range(26)]
#
self.val_min_dict = {col: 0 for col in self.val_cols}
self.val_max_dict = {col: 0 for col in self.val_cols}
self.cat_count_dict = {col: collections.defaultdict(int) for col in self.cat_cols}
#
self.oov_prefix = "OOV_"

self.cat2id_dict = {}
self.cat2id_dict.update({col: i for i, col in enumerate(self.val_cols)})
self.cat2id_dict.update({self.oov_prefix + col: i + len(self.val_cols) for i, col in enumerate(self.cat_cols)})
#
def stats_vals(self, val_list):
"""vals status"""
assert len(val_list) == len(self.val_cols)
def map_max_min(i, val):
key = self.val_cols[i]
if val != "":
if float(val) > self.val_max_dict[key]:
self.val_max_dict[key] = float(val)
if float(val) < self.val_min_dict[key]:
self.val_min_dict[key] = float(val)
#
for i, val in enumerate(val_list):
map_max_min(i, val)
#
def stats_cats(self, cat_list):
assert len(cat_list) == len(self.cat_cols)
def map_cat_count(i, cat):
key = self.cat_cols[i]
self.cat_count_dict[key][cat] += 1
#
for i, cat in enumerate(cat_list):
map_cat_count(i, cat)
#
def save_dict(self, output_path, prefix=""):
with open(os.path.join(output_path, "{}val_max_dict.pkl".format(prefix)), "wb") as file_wrt:
pickle.dump(self.val_max_dict, file_wrt)
with open(os.path.join(output_path, "{}val_min_dict.pkl".format(prefix)), "wb") as file_wrt:
pickle.dump(self.val_min_dict, file_wrt)
with open(os.path.join(output_path, "{}cat_count_dict.pkl".format(prefix)), "wb") as file_wrt:
pickle.dump(self.cat_count_dict, file_wrt)
#
def load_dict(self, dict_path, prefix=""):
with open(os.path.join(dict_path, "{}val_max_dict.pkl".format(prefix)), "rb") as file_wrt:
self.val_max_dict = pickle.load(file_wrt)
with open(os.path.join(dict_path, "{}val_min_dict.pkl".format(prefix)), "rb") as file_wrt:
self.val_min_dict = pickle.load(file_wrt)
with open(os.path.join(dict_path, "{}cat_count_dict.pkl".format(prefix)), "rb") as file_wrt:
self.cat_count_dict = pickle.load(file_wrt)
print("val_max_dict.items()[:50]: {}".format(list(self.val_max_dict.items())))
print("val_min_dict.items()[:50]: {}".format(list(self.val_min_dict.items())))
#
#
def get_cat2id(self, threshold=100):
"""get cat to id"""
# before_all_count = 0
# after_all_count = 0
for key, cat_count_d in self.cat_count_dict.items():
new_cat_count_d = dict(filter(lambda x: x[1] > threshold, cat_count_d.items()))
for cat_str, _ in new_cat_count_d.items():
self.cat2id_dict[key + "_" + cat_str] = len(self.cat2id_dict)
# print("before_all_count: {}".format(before_all_count)) # before_all_count: 33762577
# print("after_all_count: {}".format(after_all_count)) # after_all_count: 184926
print("cat2id_dict.size: {}".format(len(self.cat2id_dict)))
print("cat2id_dict.items()[:50]: {}".format(self.cat2id_dict.items()[:50]))
#
def map_cat2id(self, values, cats):
"""map cat to id"""
def minmax_sclae_value(i, val):
# min_v = float(self.val_min_dict["val_{}".format(i+1)])
max_v = float(self.val_max_dict["val_{}".format(i + 1)])
# return (float(val) - min_v) * 1.0 / (max_v - min_v)
return float(val) * 1.0 / max_v

id_list = []
weight_list = []
for i, val in enumerate(values):
if val == "":
id_list.append(i)
weight_list.append(0)
else:
key = "val_{}".format(i + 1)
id_list.append(self.cat2id_dict[key])
weight_list.append(minmax_sclae_value(i, float(val)))
#
for i, cat_str in enumerate(cats):
key = "cat_{}".format(i + 1) + "_" + cat_str
if key in self.cat2id_dict:
id_list.append(self.cat2id_dict[key])
else:
id_list.append(self.cat2id_dict[self.oov_prefix + "cat_{}".format(i + 1)])
weight_list.append(1.0)
return id_list, weight_list
#



def mkdir_path(file_path):
if not os.path.exists(file_path):
os.makedirs(file_path)
#

def statsdata(data_file_path, output_path, criteo_stats):
"""data status"""
with open(data_file_path, encoding="utf-8") as file_in:
errorline_list = []
count = 0
for line in file_in:
count += 1
line = line.strip("\n")
items = line.strip("\t")
if len(items) != 40:
errorline_list.append(count)
print("line: {}".format(line))
continue
if count % 1000000 == 0:
print("Have handle {}w lines.".format(count//10000))
# if count % 5000000 == 0:
# print("Have handle {}w lines.".format(count//10000))
# label = items[0]
values = items[1:14]
cats = items[14:]
assert len(values) == 13, "value.size: {}".format(len(values))
assert len(cats) == 26, "cat.size: {}".format(len(cats))
criteo_stats.stats_vals(values)
criteo_stats.stats_cats(cats)
criteo_stats.save_dict(output_path)
#


def add_write(file_path, wr_str):
with open(file_path, "a", encoding="utf-8") as file_out:
file_out.write(wr_str + "\n")
#


def random_split_trans2h5(in_file_path, output_path, criteo_stats, part_rows=2000000, test_size=0.1, seed=2020):
"""random split trans2h5"""
test_size = int(TRAIN_LINE_COUNT * test_size)
# train_size = TRAIN_LINE_COUNT - test_size
all_indices = [i for i in range(TRAIN_LINE_COUNT)]
np.random.seed(seed)
np.random.shuffle(all_indices)
print("all_indices.size: {}".format(len(all_indices)))
# lines_count_dict = collections.defaultdict(int)
test_indices_set = set(all_indices[:test_size])
print("test_indices_set.size: {}".format(len(test_indices_set)))
print("------" * 10 + "\n" * 2)

train_feature_file_name = os.path.join(output_path, "train_input_part_{}.h5")
train_label_file_name = os.path.join(output_path, "train_output_part_{}.h5")
test_feature_file_name = os.path.join(output_path, "test_input_part_{}.h5")
test_label_file_name = os.path.join(output_path, "test_input_part_{}.h5")
train_feature_list = []
train_label_list = []
test_feature_list = []
test_label_list = []
with open(in_file_path, encoding="utf-8") as file_in:
count = 0
train_part_number = 0
test_part_number = 0
for i, line in enumerate(file_in):
count += 1
if count % 1000000 == 0:
print("Have handle {}w lines.".format(count // 10000))
line = line.strip("\n")
items = line.split("\t")
if len(items) != 40:
continue
label = float(items[0])
values = items[1:14]
cats = items[14:]
assert len(values) == 13, "value.size: {}".format(len(values))
assert len(cats) == 26, "cat.size: {}".format(len(cats))
ids, wts = criteo_stats.map_cat2id(values, cats)
if i not in test_indices_set:
train_feature_list.append(ids + wts)
train_label_list.append(label)
else:
test_feature_list.append(ids + wts)
test_label_list.append(label)
if train_label_list and (len(train_label_list) % part_rows == 0):
pd.DataFrame(np.asarray(train_feature_list)).to_hdf(train_feature_file_name.format(train_part_number),
key="fixed")
pd.DataFrame(np.asarray(train_label_list)).to_hdf(train_label_file_name.format(train_part_number),
key="fixed")
train_feature_list = []
train_label_list = []
train_part_number += 1
if test_label_list and (len(test_label_list) % part_rows == 0):
pd.DataFrame(np.asarray(test_feature_list)).to_hdf(test_feature_file_name.format(test_part_number),
key="fixed")
pd.DataFrame(np.asarray(test_label_list)).to_hdf(test_label_file_name.format(test_part_number),
key="fixed")
test_feature_list = []
test_label_list = []
test_part_number += 1
#
if train_label_list:
pd.DataFrame(np.asarray(train_feature_list)).to_hdf(train_feature_file_name.format(train_part_number),
key="fixed")
pd.DataFrame(np.asarray(train_label_list)).to_hdf(train_label_file_name.format(train_part_number),
key="fixed")
if test_label_list:
pd.DataFrame(np.asarray(test_feature_list)).to_hdf(test_feature_file_name.format(test_part_number),
key="fixed")
pd.DataFrame(np.asarray(test_label_list)).to_hdf(test_label_file_name.format(test_part_number),
key="fixed")
#



if __name__ == "__main__":

parser = argparse.ArgumentParser(description="Get and Process datasets")
parser.add_argument("--raw_data_path", default="/opt/npu/data/origin_criteo_data/", help="The path to save dataset")
parser.add_argument("--output_path", default="/opt/npu/data/origin_criteo_data/h5_data/",
help="The path to save dataset")
args, _ = parser.parse_known_args()
base_path = args.raw_data_path
criteo_stat = CriteoStatsDict()
# step 1, stats the vocab and normalize value
datafile_path = base_path + "train_small.txt"
stats_out_path = base_path + "stats_dict/"
mkdir_path(stats_out_path)
statsdata(datafile_path, stats_out_path, criteo_stat)
print("------" * 10)
criteo_stat.load_dict(dict_path=stats_out_path, prefix="")
criteo_stat.get_cat2id(threshold=100)
# step 2, transform data trans2h5; version 2: np.random.shuffle
infile_path = base_path + "train_small.txt"
mkdir_path(args.output_path)
random_split_trans2h5(infile_path, args.output_path, criteo_stat, part_rows=2000000, test_size=0.1, seed=2020)

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