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data_utils.py
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data_utils.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
功能:数据处理相关
时间:2018年04月19日16:41:03
"""
import os
import tensorflow as tf
import pandas as pd
import re
import json
def download_data():
"""下载鸢尾花数据集"""
train_url = "http://download.tensorflow.org/data/iris_training.csv"
test_url = "http://download.tensorflow.org/data/iris_test.csv"
save_path = "/Users/simon/Mycodes/Learn-TensorFlow/data2" # 指定保存路径
for url in [train_url, test_url]:
tf.keras.utils.get_file(fname=os.path.basename(url), origin=url, cache_subdir=save_path)
def load_iris_data():
csv_path = "data/iris_training.csv"
data = pd.read_csv(csv_path)
print(data.head())
return data
def parse_csv(line):
example_defaults = [[0.], [0.], [0.], [0.], [0]] # 设置字段类型
parsed_line = tf.decode_csv(line, example_defaults)
features = tf.reshape(parsed_line[:-1], shape=(4,)) # 抽取前四列特征
label = tf.reshape(parsed_line[-1], shape=()) # 抽取最后一列标签
return features, label
def data_transfer(data_path):
"""生成数据集"""
dataset = tf.data.TextLineDataset(data_path)
dataset = dataset.skip(1) # skip the first header row
dataset = dataset.map(parse_csv) # parse each row
dataset = dataset.shuffle(buffer_size=1000) # randomize
dataset = dataset.batch(32)
# features, label = tfe.Iterator(dataset).next() # 查看单个数据
return dataset
def aclImdb_data_transfer():
record = []
train_pos = "/Users/simon/Mycodes/Learn-TensorFlow/data/aclImdb/test/pos"
out = open("data/aclImdb_test_pos.json", "w+")
pat = re.compile("(.*?)_(.*?)\.txt")
for file_path in os.listdir(train_pos):
data = dict()
with tf.gfile.GFile(os.path.join(train_pos, file_path), "r") as f:
finds = re.findall(pat, file_path)
data["ID"] = int(finds[0][0])
data["sentence"] = f.read()
data["polarity"] = "pos"
data["sentiment"] = finds[0][1]
record.append(data)
record.sort(key=lambda x: x["ID"], reverse=False)
print(len(record))
json.dump(record, out, ensure_ascii=False, indent=4)
out.close()
aclImdb_data_transfer()
if __name__ == "__main__":
pass