【tensorflow2.0】处理结构化数据-titanic生存预测

时间:2020-04-05 西西嘛呦人气:0

1、准备数据

import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt
import tensorflow as tf 
from tensorflow.keras import models,layers
 
dftrain_raw = pd.read_csv('.https://img.qb5200.com/download-x/data/titanic/train.csv')
dftest_raw = pd.read_csv('.https://img.qb5200.com/download-x/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('Survived',fontsize = 15)
plt.show()

（2）年龄分布

年龄分布情况

%matplotlib inline
%config InlineBackend.figure_format = 'png'
ax = dftrain_raw['Age'].plot(kind = 'hist',bins = 20,color= 'purple',
                    figsize = (12,8),fontsize=15)
 
ax.set_ylabel('Frequency',fontsize = 15)
ax.set_xlabel('Age',fontsize = 15)
plt.show()

（3）年龄和标签之间的相关性

%matplotlib inline
%config InlineBackend.figure_format = 'png'
ax = dftrain_raw.query('Survived == 0')['Age'].plot(kind = 'density',
                      figsize = (12,8),fontsize=15)
dftrain_raw.query('Survived == 1')['Age'].plot(kind = 'density',
                      figsize = (12,8),fontsize=15)
ax.legend(['Survived==0','Survived==1'],fontsize = 12)
ax.set_ylabel('Density',fontsize = 15)
ax.set_xlabel('Age',fontsize = 15)
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],axis = 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['Age'] = dftrain_raw['Age'].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['Age']).astype('int32')
dfresult

（4）直接拼接SibSp、Parch、Fare

dfresult['SibSp'] = dftrain_raw['SibSp']
dfresult['Parch'] = dftrain_raw['Parch']
dfresult['Fare'] = dftrain_raw['Fare']
dfresult

（5）标记Cabin缺失的位置

#Carbin
dfresult['Cabin_null'] =  pd.isna(dftrain_raw['Cabin']).astype('int32')
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['Embarked'],dummy_na=True)
dfEmbarked.columns = ['Embarked_' + str(x) for x in dfEmbarked.columns]
dfresult = pd.concat([dfresult,dfEmbarked],axis = 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['Pclass'])
    dfPclass.columns = ['Pclass_' +str(x) for x in dfPclass.columns ]
    dfresult = pd.concat([dfresult,dfPclass],axis = 1)
 
    #Sex
    dfSex = pd.get_dummies(dfdata['Sex'])
    dfresult = pd.concat([dfresult,dfSex],axis = 1)
 
    #Age
    dfresult['Age'] = dfdata['Age'].fillna(0)
    dfresult['Age_null'] = pd.isna(dfdata['Age']).astype('int32')
 
    #SibSp,Parch,Fare
    dfresult['SibSp'] = dfdata['SibSp']
    dfresult['Parch'] = dfdata['Parch']
    dfresult['Fare'] = dfdata['Fare']
 
    #Carbin
    dfresult['Cabin_null'] =  pd.isna(dfdata['Cabin']).astype('int32')
 
    #Embarked
    dfEmbarked = pd.get_dummies(dfdata['Embarked'],dummy_na=True)
    dfEmbarked.columns = ['Embarked_' + str(x) for x in dfEmbarked.columns]
    dfresult = pd.concat([dfresult,dfEmbarked],axis = 1)
 
    return(dfresult)

然后进行数据预处理：

x_train = preprocessing(dftrain_raw)
y_train = dftrain_raw['Survived'].values
 
x_test = preprocessing(dftest_raw)
y_test = dftest_raw['Survived'].values
 
print("x_train.shape =", x_train.shape )
print("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 = 'relu',input_shape=(15,)))
model.add(layers.Dense(10,activation = 'relu' ))
model.add(layers.Dense(1,activation = '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='adam',
            loss='binary_crossentropy',
            metrics=['AUC'])
 
history = model.fit(x_train,y_train,
                    batch_size= 64,
                    epochs= 30,
                    validation_split=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/python3.6https://img.qb5200.com/download-x/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 in 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。

%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,"loss")
plot_metric(history,"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 ],
       [0.43986577],
       [0.7916608 ],
       [0.50268507],
       [0.536609  ],
       [0.29079646],
       [0.6085641 ],
       [0.34384924],
       [0.17756936]], dtype=float32)

（2）预测类别

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) is deprecated and will be removed after 2021-01-01.
Instructions for 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("int32")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).
array([[0],
       [0],
       [0],
       [1],
       [1],
       [1],
       [0],
       [1],
       [0],
       [0]], dtype=int32)

7、保存模型

可以使用Keras方式保存模型，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

# 保存模型结构及权重
model.save('.https://img.qb5200.com/download-x/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('.https://img.qb5200.com/download-x/data/keras_model.h5')
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: 0.7869
[0.5286471247673035, 0.786877453327179]

（2）保存模型结构和恢复模型结构

# 保存模型结构
json_str = model.to_json()
# 恢复模型结构
model_json = models.model_from_json(json_str)

（3）保存模型权重

# 保存模型权重
model.save_weights('.https://img.qb5200.com/download-x/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='adam',
        loss='binary_crossentropy',
        metrics=['AUC']
    )
 
# 加载权重
model_json.load_weights('.https://img.qb5200.com/download-x/data/keras_model_weight.h5')
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('.https://img.qb5200.com/download-x/data/tf_model_weights.ckpt',save_format = "tf")
# 保存模型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('.https://img.qb5200.com/download-x/data/tf_model_savedmodel', save_format="tf")
print('export saved model.')
 
model_loaded = tf.keras.models.load_model('.https://img.qb5200.com/download-x/data/tf_model_savedmodel')
model_loaded.evaluate(x_test,y_test)

INFO:tensorflow:Assets written to: .https://img.qb5200.com/download-x/data/tf_model_savedmodel/assets
export saved model.
6/6 [==============================] - 0s 2ms/step - loss: 0.5286 - auc_1: 0.7869
[0.5286471247673035, 0.786877453327179]

参考：

开源电子书地址：https://lyhue1991.github.io/eat_tensorflow2_in_30_days/

GitHub 项目地址：https://github.com/lyhue1991/eat_tensorflow2_in_30_days

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