在Keras下对结构化数据分类

Posted 2022-01-01 卓晴

 

§01 Keras分类

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这个分类问题来自于 Structured data classification from scratch ，从头开始介绍了一个在Keras下对于303位病人预测是否有心脏疾病。

一、数据集合

1、下载地址

• 数据集合下载链接： 数据集合下载地址 : http://storage.googleapis.com/download.tensorflow.org/data/heart.csv

2、基本内容

数据集合：

格式：CSV
数量：303行
用途：病人特征，预测是否有心脏疾病

【表1-1-2 数据内容介绍】

Column	Description	Feature Type
Age	Age in years	Numerical
Sex	(1 = male; 0 = female)	Categorical
CP	Chest pain type (0, 1, 2, 3, 4)	Categorical
Trestbpd	Resting blood pressure (in mm Hg on admission)	Numerical
Chol	Serum cholesterol in mg dl	Numerical
FBS	fasting blood sugar in 120 mg dl (1 = true; 0 = false)	Categorical
RestECG	Resting electrocardiogram results (0, 1, 2)	Categorical
Thalach	Maximum heart rate achieved	Numerical
Exang	Exercise induced angina (1 = yes; 0 = no)	Categorical
Oldpeak	ST depression induced by exercise relative to rest	Numerical
Slope	Slope of the peak exercise ST segment	Numerical
CA	Number of major vessels (0-3) colored by fluoroscopy	Both numerical & categorical
Thal	3 = normal; 6 = fixed defect; 7 = reversible defect	Categorical
Target	Diagnosis of heart disease (1 = true; 0 = false)	Target

3、获取数据

（1）获取代码

from headm import *

import tensorflow as tf
import numpy as np
import pandas as pd
from tensorflow import keras
from tensorflow.keras import layers

file_url = "http://storage.googleapis.com/download.tensorflow.org/data/heart.csv"
dataframe = pd.read_csv(file_url)

printf(dataframe)
printf(dataframe.shape)
printf(dataframe.head())


printf('\\a')

（2）运行结果

 Ⅰ.数据尺寸

[303 rows x 14 columns]
(303, 14)

 Ⅱ.前面几行数据

   age  sex  cp  trestbps  chol  ...  oldpeak  slope  ca        thal  target
0   63    1   1       145   233  ...      2.3      3   0       fixed       0
1   67    1   4       160   286  ...      1.5      2   3      normal       1
2   67    1   4       120   229  ...      2.6      2   2  reversible       0
3   37    1   3       130   250  ...      3.5      3   0      normal       0
4   41    0   2       130   204  ...      1.4      1   0      normal       0

[5 rows x 14 columns]

4、划分训练集合与测试集合

val_dataframe = dataframe.sample(frac=0.2, random_state=1337)
train_dataframe = dataframe.drop(val_dataframe.index)

printf(len(train_dataframe), len(val_dataframe))

运行结果：

训练：242
验证：61

242
61
   age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca    thal  target
0   63    1   1       145   233    1  ...      0      2.3      3   0   fixed       0
1   67    1   4       160   286    0  ...      1      1.5      2   3  normal       1
3   37    1   3       130   250    0  ...      0      3.5      3   0  normal       0
4   41    0   2       130   204    0  ...      0      1.4      1   0  normal       0
5   56    1   2       120   236    0  ...      0      0.8      1   0  normal       0

[5 rows x 14 columns]
     age  sex  cp  trestbps  chol  ...  oldpeak  slope  ca        thal  target
96    41    1   3       112   250  ...      0.0      1   0      normal       0
142   67    1   4       100   299  ...      0.9      2   2      normal       1
80    51    1   3        94   227  ...      0.0      1   1  reversible       0
67    41    1   2       135   203  ...      0.0      2   0       fixed       0
188   41    0   2       126   306  ...      0.0      1   0      normal       0

[5 rows x 14 columns]

5、设定批次数量

train_ds = train_ds.batch(32)
val_ds = val_ds.batch(32)

二、特征处理

from tensorflow.keras.layers import IntegerLookup
from tensorflow.keras.layers import Normalization
from tensorflow.keras.layers import StringLookup

def encode_numerical_feature(feature, name, dataset):
    # Create a Normalization layer for our feature
    normalizer = Normalization()

    # Prepare a Dataset that only yields our feature
    feature_ds = dataset.map(lambda x, y: x[name])
    feature_ds = feature_ds.map(lambda x: tf.expand_dims(x, -1))

    # Learn the statistics of the data
    normalizer.adapt(feature_ds)

    # Normalize the input feature
    encoded_feature = normalizer(feature)
    return encoded_feature

def encode_categorical_feature(feature, name, dataset, is_string):
    lookup_class = StringLookup if is_string else IntegerLookup
    # Create a lookup layer which will turn strings into integer indices
    lookup = lookup_class(output_mode="binary")

    # Prepare a Dataset that only yields our feature
    feature_ds = dataset.map(lambda x, y: x[name])
    feature_ds = feature_ds.map(lambda x: tf.expand_dims(x, -1))

    # Learn the set of possible string values and assign them a fixed integer index
    lookup.adapt(feature_ds)

    # Turn the string input into integer indices
    encoded_feature = lookup(feature)
    return encoded_feature