特征列通常用于对结构化数据实施特征工程时候使用，图像或者文本数据一般不会用到特征列。
tf.feature_column 主要针对离散特征和连续特征, 概括而言, 可以实现四种场景：
(1) 连续特征离散化, 分箱, hash, onehot, mutihot
(2) 高维离散特征映射成低维稠密向量
(3) 连续特征归一化
(4) 离散特征交叉组合
1 特征列用法概述
使用特征列可以将类别特征转换为 one-hot 编码特征，将连续特征构建分桶特征，以及对多个特征生成交叉特征等等。
要创建特征列，请调用 tf.feature_column 模块的函数。该模块中常用的九个函数如下图所示，所有九个函数都会返回一个 Categorical-Column 或一个 Dense-Column 对象，但却不会返回 bucketized_column，后者继承自这两个类。
注意：所有的 Catogorical Column 类型最终都要通过 indicator_column 转换成 Dense Column 类型才能传入模型！
-
numeric_column数值列，最常用。 -
bucketized_column分桶列，由数值列生成，可以由一个数值列出多个特征，one-hot 编码。 -
categorical_column_with_identity分类标识列，one-hot 编码，相当于分桶列每个桶为1个整数的情况。 -
categorical_column_with_vocabulary_list分类词汇列，one-hot 编码，由 list 指定词典。 -
categorical_column_with_vocabulary_file分类词汇列，由文件 file 指定词典。 -
categorical_column_with_hash_bucket哈希列，整数或词典较大时采用。 -
indicator_column指标列，由 Categorical Column 生成，one-hot 编码 -
embedding_column嵌入列，由 Categorical Column 生成，嵌入矢量分布参数需要学习。嵌入矢量维数建议取类别数量的 4 次方根。 -
crossed_column交叉列，可以由除categorical_column_with_hash_bucket的任意分类列构成。
2 特征列使用范例
以下是一个使用特征列解决 Titanic 生存问题的完整范例。
import datetime
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import tensorflow as tf
from tensorflow.keras import layers,models
#打印日志
def printlog(info):
nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
print("\n"+"=========="*8 + "%s"%nowtime)
print(info+'...\n\n')
#================================================================================
# 一，构建数据管道
#================================================================================
printlog("step1: prepare dataset...")
dftrain_raw = pd.read_csv("./data/titanic/train.csv")
dftest_raw = pd.read_csv("./data/titanic/test.csv")
dfraw = pd.concat([dftrain_raw,dftest_raw])
def prepare_dfdata(dfraw):
dfdata = dfraw.copy()
dfdata.columns = [x.lower() for x in dfdata.columns]
dfdata = dfdata.rename(columns={'survived':'label'})
dfdata = dfdata.drop(['passengerid','name'],axis = 1)
for col,dtype in dict(dfdata.dtypes).items():
# 判断是否包含缺失值
if dfdata[col].hasnans:
# 添加标识是否缺失列
dfdata[col + '_nan'] = pd.isna(dfdata[col]).astype('int32')
# 填充
if dtype not in [np.object,np.str,np.unicode]:
dfdata[col].fillna(dfdata[col].mean(),inplace = True)
else:
dfdata[col].fillna('',inplace = True)
return(dfdata)
dfdata = prepare_dfdata(dfraw)
dftrain = dfdata.iloc[0:len(dftrain_raw),:]
dftest = dfdata.iloc[len(dftrain_raw):,:]
# 从 dataframe 导入数据
def df_to_dataset(df, shuffle=True, batch_size=32):
dfdata = df.copy()
if 'label' not in dfdata.columns:
ds = tf.data.Dataset.from_tensor_slices(dfdata.to_dict(orient = 'list'))
else:
labels = dfdata.pop('label')
ds = tf.data.Dataset.from_tensor_slices((dfdata.to_dict(orient = 'list'), labels))
if shuffle:
ds = ds.shuffle(buffer_size=len(dfdata))
ds = ds.batch(batch_size)
return ds
ds_train = df_to_dataset(dftrain)
ds_test = df_to_dataset(dftest)
#================================================================================
# 二，定义特征列
#================================================================================
printlog("step2: make feature columns...")
feature_columns = []
# 数值列
for col in ['age','fare','parch','sibsp'] + [
c for c in dfdata.columns if c.endswith('_nan')]:
feature_columns.append(tf.feature_column.numeric_column(col))
# 分桶列
age = tf.feature_column.numeric_column('age')
age_buckets = tf.feature_column.bucketized_column(age,
boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
feature_columns.append(age_buckets)
# 类别列
# 注意：所有的Catogorical Column类型最终都要通过indicator_column转换成Dense Column类型才能传入模型！！
sex = tf.feature_column.indicator_column(
tf.feature_column.categorical_column_with_vocabulary_list(
key='sex',vocabulary_list=["male", "female"]))
feature_columns.append(sex)
pclass = tf.feature_column.indicator_column(
tf.feature_column.categorical_column_with_vocabulary_list(
key='pclass',vocabulary_list=[1,2,3]))
feature_columns.append(pclass)
ticket = tf.feature_column.indicator_column(
tf.feature_column.categorical_column_with_hash_bucket('ticket',3))
feature_columns.append(ticket)
embarked = tf.feature_column.indicator_column(
tf.feature_column.categorical_column_with_vocabulary_list(
key='embarked',vocabulary_list=['S','C','B']))
feature_columns.append(embarked)
# 嵌入列
cabin = tf.feature_column.embedding_column(
tf.feature_column.categorical_column_with_hash_bucket('cabin',32),2)
feature_columns.append(cabin)
# 交叉列
pclass_cate = tf.feature_column.categorical_column_with_vocabulary_list(
key='pclass',vocabulary_list=[1,2,3])
crossed_feature = tf.feature_column.indicator_column(
tf.feature_column.crossed_column([age_buckets, pclass_cate],hash_bucket_size=15))
feature_columns.append(crossed_feature)
#================================================================================
# 三，定义模型
#================================================================================
printlog("step3: define model...")
tf.keras.backend.clear_session()
model = tf.keras.Sequential([
layers.DenseFeatures(feature_columns), #将特征列放入到tf.keras.layers.DenseFeatures中!!!
layers.Dense(64, activation='relu'),
layers.Dense(64, activation='relu'),
layers.Dense(1, activation='sigmoid')
])
#================================================================================
# 四，训练模型
#================================================================================
printlog("step4: train model...")
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
history = model.fit(ds_train,
validation_data=ds_test,
epochs=10)
#================================================================================
# 五，评估模型
#================================================================================
printlog("step5: eval model...")
model.summary()
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
import matplotlib.pyplot as plt
def plot_metric(history, metric):
train_metrics = history.history[metric]
val_metrics = history.history['val_'+metric]
epochs = range(1, len(train_metrics) + 1)
plt.plot(epochs, train_metrics, 'bo--')
plt.plot(epochs, val_metrics, 'ro-')
plt.title('Training and validation '+ metric)
plt.xlabel("Epochs")
plt.ylabel(metric)
plt.legend(["train_"+metric, 'val_'+metric])
plt.show()
plot_metric(history,"accuracy")
输出：
================================================================================2020-09-10 12:15:54
step1: prepare dataset......
================================================================================2020-09-10 12:15:54
step2: make feature columns......
================================================================================2020-09-10 12:15:54
step3: define model......
================================================================================2020-09-10 12:15:54
step4: train model......
WARNING:tensorflow:From C:\ProgramData\Anaconda3\envs\AutoAI\lib\site-packages\tensorflow_core\python\feature_column\feature_column_v2.py:4267: IndicatorColumn._variable_shape (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.
WARNING:tensorflow:From C:\ProgramData\Anaconda3\envs\AutoAI\lib\site-packages\tensorflow_core\python\feature_column\feature_column_v2.py:4322: CrossedColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.
WARNING:tensorflow:From C:\ProgramData\Anaconda3\envs\AutoAI\lib\site-packages\tensorflow_core\python\feature_column\feature_column_v2.py:4322: VocabularyListCategoricalColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.
WARNING:tensorflow:From C:\ProgramData\Anaconda3\envs\AutoAI\lib\site-packages\tensorflow_core\python\feature_column\feature_column_v2.py:4322: HashedCategoricalColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.
Train for 23 steps, validate for 6 steps
Epoch 1/10
23/23 [==============================] - 1s 52ms/step - loss: 1.0642 - accuracy: 0.6011 - val_loss: 0.6113 - val_accuracy: 0.6480
Epoch 2/10
23/23 [==============================] - 0s 3ms/step - loss: 0.6201 - accuracy: 0.6685 - val_loss: 0.5636 - val_accuracy: 0.6816
Epoch 3/10
23/23 [==============================] - 0s 3ms/step - loss: 0.5707 - accuracy: 0.7065 - val_loss: 0.5530 - val_accuracy: 0.7039
Epoch 4/10
23/23 [==============================] - 0s 3ms/step - loss: 0.5825 - accuracy: 0.7163 - val_loss: 0.5559 - val_accuracy: 0.6983
Epoch 5/10
23/23 [==============================] - 0s 3ms/step - loss: 0.5741 - accuracy: 0.7163 - val_loss: 0.5304 - val_accuracy: 0.7039
Epoch 6/10
23/23 [==============================] - 0s 3ms/step - loss: 0.5348 - accuracy: 0.7317 - val_loss: 0.5088 - val_accuracy: 0.7542
Epoch 7/10
23/23 [==============================] - 0s 4ms/step - loss: 0.5313 - accuracy: 0.7430 - val_loss: 0.5014 - val_accuracy: 0.7542
Epoch 8/10
23/23 [==============================] - 0s 3ms/step - loss: 0.5148 - accuracy: 0.7612 - val_loss: 0.4917 - val_accuracy: 0.7318
Epoch 9/10
23/23 [==============================] - 0s 3ms/step - loss: 0.5016 - accuracy: 0.7683 - val_loss: 0.5009 - val_accuracy: 0.7318
Epoch 10/10
23/23 [==============================] - 0s 3ms/step - loss: 0.5036 - accuracy: 0.7753 - val_loss: 0.4617 - val_accuracy: 0.7709
================================================================================2020-09-10 12:15:56
step5: eval model......
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_features (DenseFeature multiple 64
_________________________________________________________________
dense (Dense) multiple 3008
_________________________________________________________________
dense_1 (Dense) multiple 4160
_________________________________________________________________
dense_2 (Dense) multiple 65
=================================================================
Total params: 7,297
Trainable params: 7,297
Non-trainable params: 0
_________________________________________________________________
