TensorFlow2利用泰坦尼克号获救CSV数据集完成数据预处理

本案例使用TensorFlow2加载CSV数据到tf.data.Dataset中，使用经典的数据集：泰坦尼克乘客数据。

1. 导入所需的库

import tensorflow as tf
import numpy as np
import pandas as pd

import functools

for i in [tf,np,pd]:
    print(i.__name__,": ",i.__version__,sep="")

输出：

tensorflow: 2.2.0
numpy: 1.17.4
pandas: 0.25.3

2. 下载并导入数据

2.1 下载数据到本地

trainDataUrl = "https://storage.googleapis.com/tf-datasets/titanic/train.csv"
testDataUrl = "https://storage.googleapis.com/tf-datasets/titanic/eval.csv"

trainFilePath = tf.keras.utils.get_file("trainTitanic.csv",trainDataUrl)
testFilePath = tf.keras.utils.get_file("testTitanic.csv",testDataUrl)

输出：

Downloading data from https://storage.googleapis.com/tf-datasets/titanic/train.csv
32768/30874 [===============================] - 1s 29us/step
Downloading data from https://storage.googleapis.com/tf-datasets/titanic/eval.csv
16384/13049 [=====================================] - 0s 28us/step

Windows系统中下载的文件保存在：系统盘：\users\用户名.keras\datasets目录下

2.2 加载数据

labelColumn = "survived"  # 指定数据标签的列名 
labels = [0,1]

def getDataset(filePath, **kwargs):
    dataset = tf.data.experimental.make_csv_dataset(filePath,
                                                   batch_size=5,
                                                   label_name=labelColumn,
                                                   na_value="?",
                                                   num_epochs=1,
                                                   ignore_errors=True,
                                                   **kwargs)
    return dataset

rawTrainData = getDataset(trainFilePath)
rawTestData = getDataset(testFilePath)

def showBatch(dataset):
    for batch, label in dataset.take(1):
        for key, value in batch.items():
            print("{:20s}:{}".format(key,value.numpy()))
        print("{:20s}:{}".format("label",label.numpy()))
            
showBatch(rawTrainData)

输出：

sex                 :[b'male' b'female' b'male' b'male' b'male']
age                 :[50. 30. 28. 31. 27.]
n_siblings_spouses  :[0 0 0 1 0]
parch               :[0 0 0 0 0]
fare                :[ 13.     106.425    8.4583  52.       8.6625]
class               :[b'Second' b'First' b'Third' b'First' b'Third']
deck                :[b'unknown' b'unknown' b'unknown' b'B' b'unknown']
embark_town         :[b'Southampton' b'Cherbourg' b'Queenstown' b'Southampton' b'Southampton']
alone               :[b'y' b'y' b'y' b'n' b'y']
label               :[0 1 0 0 1]

3. 数据预处理

通过CSV文件导入的数据每列的数据类型可能不一样，这就需要将数据喂给模型前进行数据预处理。可以使用sklearn等工具进行前处理，再将数据传给Tens