【tensorflow2.0】处理结构化数据-titanic生存预测

1、准备数据

import numpy as np 
 pandas as pd 
 matplotlib.pyplot as plt
 tensorflow as tf 
from tensorflow.keras  models,layers
 
dftrain_raw = pd.read_csv('./data/titanic/train.csv')
dftest_raw = pd.read_csv(./data/titanic/test.csv)
dftrain_raw.head(10)

部分数据：

相关字段说明：

Survived:0代表死亡，1代表存活【y标签】
Pclass:乘客所持票类，有三种值(1,2,3) 【转换成onehot编码】
Name:乘客姓名【舍去】
Sex:乘客性别【转换成bool特征】
Age:乘客年龄(有缺失) 【数值特征，添加“年龄是否缺失”作为辅助特征】
SibSp:乘客兄弟姐妹/配偶的个数(整数值) 【数值特征】
Parch:乘客父母/孩子的个数(整数值)【数值特征】
Ticket:票号(字符串)【舍去】
Fare:乘客所持票的价格(浮点数，0-500不等) 【数值特征】
Cabin:乘客所在船舱(有缺失) 【添加“所在船舱是否缺失”作为辅助特征】
Embarked:乘客登船港口:S、C、Q(有缺失)【转换成onehot编码，四维度 S,C,Q,nan】

2、探索数据

（1）标签分布

%matplotlib inline
%config InlineBackend.figure_format = png
ax = dftrain_raw[Survived'].value_counts().plot(kind = bar,figsize = (12,8),fontsize=15,rot = 0)
ax.set_ylabel(Counts',fontsize = 15)
ax.set_xlabel()
plt.show()

（2）年龄分布

年龄分布情况

%Age'].plot(kind = histpurple)
 
ax.set_ylabel(Frequency)
plt.show()

（3）年龄和标签之间的相关性

%
ax = dftrain_raw.query(Survived == 0')[density)
dftrain_raw.query(Survived == 1)
ax.legend([Survived==0Survived==1'],fontsize = 12)
ax.set_ylabel(Density)
plt.show()

3、数据预处理

（1）将Pclass转换为one-hot编码

dfresult=pd.DataFrame()
#将船票类型转换为one-hot编码
dfPclass=pd.get_dummies(dftrain_raw["Pclass"])
设置列名
dfPclass.columns =[Pclass_'+str(x) for x in dfPclass.columns]
dfresult = pd.concat([dfresult,dfPclass],axis = 1)
dfresult

（2）将Sex转换为One-hot编码

Sex
dfSex = pd.get_dummies(dftrain_raw[Sex])
dfresult = pd.concat([dfresult,dfSex],1)">)
dfresult

（3）用0填充Age列缺失值，并重新定义一列Age_null用来标记缺失值的位置

将缺失值用0填充
dfresult['] = dftrain_raw[].fillna(0)
增加一列数据为Age_null，同时将不为0的数据用0，将为0的数据用1表示，也就是标记出现0的位置
dfresult[Age_null'] = pd.isna(dftrain_raw[']).astype(int32)
dfresult

（4）直接拼接SibSp、Parch、Fare

dfresult[SibSp]
dfresult[ParchFare]
dfresult

（5）标记Cabin缺失的位置

Carbin
dfresult[Cabin_null'] =  pd.isna(dftrain_raw[Cabin)
dfresult

（6）将Embarked转换成one-hot编码

Embarked
#需要注意的参数是dummy_na=True，将缺失值另外标记出来
dfEmbarked = pd.get_dummies(dftrain_raw[EmbarkedTrue)
dfEmbarked.columns = [Embarked_' + str(x)  dfEmbarked.columns]
dfresult = pd.concat([dfresult,dfEmbarked],1)">)
dfresult

最后，我们将上述操作封装成一个函数：

def preprocessing(dfdata):
 
    dfresult= pd.DataFrame()
 
    Pclass
    dfPclass = pd.get_dummies(dfdata[])
    dfPclass.columns = [' +str(x)  dfPclass.columns ]
    dfresult = pd.concat([dfresult,1)">)
 
    Sex
    dfSex = pd.get_dummies(dfdata[])
    dfresult = pd.concat([dfresult,1)">Age
    dfresult['] = dfdata[].fillna(0)
    dfresult['] = pd.isna(dfdata[SibSp,Parch,Fare
    dfresult[]
    dfresult[]
 
    Carbin
    dfresult['] =  pd.isna(dfdata[Embarked
    dfEmbarked = pd.get_dummies(dfdata[True)
    dfEmbarked.columns = [ dfEmbarked.columns]
    dfresult = pd.concat([dfresult,1)">return(dfresult)

然后进行数据预处理：

x_train = preprocessing(dftrain_raw)
y_train = dftrain_raw[].values
 
x_test = preprocessing(dftest_raw)
y_test = dftest_raw[].values
 
print(x_train.shape =x_test.shape =",x_test.shape )

x_train.shape = (712,15)

x_test.shape = (179,15)

3、使用tensorflow定义模型

使用Keras接口有以下3种方式构建模型：使用Sequential按层顺序构建模型，使用函数式API构建任意结构模型，继承Model基类构建自定义模型。此处选择使用最简单的Sequential，按层顺序模型。

tf.keras.backend.clear_session()
 
model = models.Sequential()
model.add(layers.Dense(20,activation = relu10,1)"> ))
model.add(layers.Dense(1,1)">sigmoid ))
 
model.summary()

4、训练模型

训练模型通常有3种方法，内置fit方法，内置train_on_batch方法，以及自定义训练循环。此处我们选择最常用也最简单的内置fit方法

 二分类问题选择二元交叉熵损失函数
model.compile(optimizer=adambinary_crossentropyAUC])
 
history = model.fit(x_train,y_train,batch_size= 64=0.2 分割一部分训练数据用于验证
                   )

结果：

Epoch 1/30
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed  a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
9/9 [==============================] - 0s 30ms/step - loss: 4.3524 - auc: 0.4888 - val_loss: 3.0274 - val_auc: 0.5492
Epoch 2/30
9/9 [==============================] - 0s 6ms/step - loss: 2.7962 - auc: 0.4710 - val_loss: 1.8653 - val_auc: 0.4599
Epoch 3/30
9/9 [==============================] - 0s 6ms/step - loss: 1.6765 - auc: 0.4040 - val_loss: 1.2673 - val_auc: 0.4067
Epoch 4/30
9/9 [==============================] - 0s 7ms/step - loss: 1.1195 - auc: 0.3799 - val_loss: 0.9501 - val_auc: 0.4006
Epoch 5/30
9/9 [==============================] - 0s 6ms/step - loss: 0.8156 - auc: 0.4874 - val_loss: 0.7090 - val_auc: 0.5514
Epoch 6/30
9/9 [==============================] - 0s 5ms/step - loss: 0.6355 - auc: 0.6611 - val_loss: 0.6550 - val_auc: 0.6502
Epoch 7/30
9/9 [==============================] - 0s 6ms/step - loss: 0.6308 - auc: 0.7169 - val_loss: 0.6502 - val_auc: 0.6546
Epoch 8/30
9/9 [==============================] - 0s 6ms/step - loss: 0.6088 - auc: 0.7156 - val_loss: 0.6463 - val_auc: 0.6610
Epoch 9/30
9/9 [==============================] - 0s 6ms/step - loss: 0.6066 - auc: 0.7163 - val_loss: 0.6372 - val_auc: 0.6644
Epoch 10/30
9/9 [==============================] - 0s 6ms/step - loss: 0.5964 - auc: 0.7253 - val_loss: 0.6283 - val_auc: 0.6646
Epoch 11/30
9/9 [==============================] - 0s 7ms/step - loss: 0.5876 - auc: 0.7326 - val_loss: 0.6253 - val_auc: 0.6717
Epoch 12/30
9/9 [==============================] - 0s 6ms/step - loss: 0.5827 - auc: 0.7409 - val_loss: 0.6195 - val_auc: 0.6708
Epoch 13/30
9/9 [==============================] - 0s 6ms/step - loss: 0.5769 - auc: 0.7489 - val_loss: 0.6170 - val_auc: 0.6762
Epoch 14/30
9/9 [==============================] - 0s 6ms/step - loss: 0.5719 - auc: 0.7555 - val_loss: 0.6156 - val_auc: 0.6803
Epoch 15/30
9/9 [==============================] - 0s 6ms/step - loss: 0.5662 - auc: 0.7629 - val_loss: 0.6119 - val_auc: 0.6826
Epoch 16/30
9/9 [==============================] - 0s 6ms/step - loss: 0.5627 - auc: 0.7694 - val_loss: 0.6107 - val_auc: 0.6892
Epoch 17/30
9/9 [==============================] - 0s 6ms/step - loss: 0.5586 - auc: 0.7753 - val_loss: 0.6084 - val_auc: 0.6927
Epoch 18/30
9/9 [==============================] - 0s 6ms/step - loss: 0.5539 - auc: 0.7837 - val_loss: 0.6051 - val_auc: 0.6983
Epoch 19/30
9/9 [==============================] - 0s 7ms/step - loss: 0.5479 - auc: 0.7930 - val_loss: 0.6011 - val_auc: 0.7056
Epoch 20/30
9/9 [==============================] - 0s 9ms/step - loss: 0.5451 - auc: 0.7986 - val_loss: 0.5996 - val_auc: 0.7128
Epoch 21/30
9/9 [==============================] - 0s 7ms/step - loss: 0.5406 - auc: 0.8047 - val_loss: 0.5962 - val_auc: 0.7192
Epoch 22/30
9/9 [==============================] - 0s 6ms/step - loss: 0.5357 - auc: 0.8123 - val_loss: 0.5948 - val_auc: 0.7212
Epoch 23/30
9/9 [==============================] - 0s 6ms/step - loss: 0.5295 - auc: 0.8181 - val_loss: 0.5928 - val_auc: 0.7267
Epoch 24/30
9/9 [==============================] - 0s 6ms/step - loss: 0.5275 - auc: 0.8223 - val_loss: 0.5910 - val_auc: 0.7296
Epoch 25/30
9/9 [==============================] - 0s 6ms/step - loss: 0.5263 - auc: 0.8227 - val_loss: 0.5884 - val_auc: 0.7325
Epoch 26/30
9/9 [==============================] - 0s 7ms/step - loss: 0.5199 - auc: 0.8313 - val_loss: 0.5860 - val_auc: 0.7356
Epoch 27/30
9/9 [==============================] - 0s 6ms/step - loss: 0.5145 - auc: 0.8356 - val_loss: 0.5835 - val_auc: 0.7386
Epoch 28/30
9/9 [==============================] - 0s 6ms/step - loss: 0.5138 - auc: 0.8383 - val_loss: 0.5829 - val_auc: 0.7402
Epoch 29/30
9/9 [==============================] - 0s 7ms/step - loss: 0.5092 - auc: 0.8405 - val_loss: 0.5806 - val_auc: 0.7416
Epoch 30/30
9/9 [==============================] - 0s 6ms/step - loss: 0.5082 - auc: 0.8394 - val_loss: 0.5792 - val_auc: 0.7424

5、评估模型

我们首先评估一下模型在训练集和验证集上的效果。

%svg'
 
 matplotlib.pyplot as plt
 
 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--ro-)
    plt.title(Training and validation  metric)
    plt.xlabel(Epochs)
    plt.ylabel(metric)
    plt.legend([train_"+metric,1)">metric])
    plt.show()
plot_metric(history,1)">loss)
plot_metric(history,1)">auc")

然后看在在测试集上的效果：

model.evaluate(x = x_test,y = y_test)

结果：

6/6 [==============================] - 0s 2ms/step - loss: 0.5286 - auc: 0.7869
[0.5286471247673035,0.786877453327179]

6、使用模型

（1）预测概率

model.predict(x_test[0:10])

结果：

array([[0.34822357],[0.4793241 ],[0.439865770.50268507  ],[0.290796460.34384924
model.predict_classes(x_test[0:10])

结果：

WARNING:tensorflow:From <ipython-input-36-a161a0a6b51e>:1: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) and will be removed after 2021-01-01.
Instructions  updating:
Please use instead:* `np.argmax(model.predict(x),axis=-1)`,if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(")`,1)">if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
array([[0],[0],[17、@R_399_301@
可以使用Keras方式@R_399_301@，也可以使用TensorFlow原生方式保存。前者仅仅适合使用Python环境恢复模型，后者则可以跨平台进行模型部署。推荐使用后一种方式进行保存
1）使用keras方式保存

 @R_399_301@结构及权重
model.save(./data/keras_model.h5)  
del model  删除现有模型

（1）加载模型

 identical to the prevIoUs one
model = models.load_model()
model.evaluate(x_test,y_test)


WARNING:tensorflow:Error in loading the saved optimizer state. As a result,your model is starting with a freshly initialized optimizer.
6/6 [==============================] - 0s 2ms/step - loss: 0.5286 - auc_1: 0.7869301@结构和恢复模型结构

 @R_399_301@结构
json_str = model.to_json()
 恢复模型结构
model_json = models.model_from_json(json_str)

（3）@R_399_301@权重

 @R_399_301@权重
model.save_weights(./data/keras_model_weight.h5')

（4）恢复模型结构并加载权重

 恢复模型结构
model_json = models.model_from_json(json_str)
model_json.compile(
        optimizer=]
    )
 
 加载权重
model_json.load_weights()
model_json.evaluate(x_test,y_test)


6/6 [==============================] - 0s 3ms/step - loss: 0.5217 - auc: 0.8123
[0.521678626537323,0.8122605681419373]

2）tensorflow原生方式

 保存权重，该方式仅仅保存权重张量
model.save_weights(./data/tf_model_weights.ckpttf)
 @R_399_301@结构与模型参数到文件,该方式保存的模型具有跨平台性便于部署
 
model.save(./data/tf_model_savedmodelexport saved model.)
 
model_loaded = tf.keras.models.load_model()
model_loaded.evaluate(x_test,y_test)


INFO:tensorflow:Assets written to: ./data/tf_model_savedmodel/assets
export saved model.
6/6 [==============================] - 0s 2ms/step - loss: 0.5286 - auc_1: 0.7869 原文链接：/tensorflow/991503.html
      
      
                 
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