pip install tensorflow

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# 导入所需的工具库
# tensorflow建模
from tensorflow.keras import models, layers, utils, backend as K
# 可视化
import matplotlib.pyplot as plt
# 特征重要度与模型归因分析
import shap

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model = models.Sequential(name="Perceptron", 
    layers=[    
        layers.Dense(             # 全连接层
          name="dense",
          input_dim=3,        # 输入维度为3
          units=1,            # 1个节点
          activation='linear' # 激活函数（这里是线性函数）
    )
    ])
model.summary()

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import tensorflow as tf
# 定义激活函数
def binary_step_activation(x):
    # 如果x>0返回1否则返回0 
    return K.switch(x>0, tf.math.divide(x,x), tf.math.multiply(x,0))

# 构建模型
model = models.Sequential(name="Perceptron", layers=[
      layers.Dense(             
          name="dense",
          input_dim=3,        
          units=1,            
          activation=binary_step_activation
      )
])

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n_features = 10

model = models.Sequential(name="DeepNN", layers=[
    # 第1个隐层
    layers.Dense(name="h1", input_dim=n_features,
                 units=int(round((n_features+1)/2)), 
                 activation='relu'),
    layers.Dropout(name="drop1", rate=0.2),
    
    # 第2个隐层
    layers.Dense(name="h2", units=int(round((n_features+1)/4)), 
                 activation='relu'),
    layers.Dropout(name="drop2", rate=0.2),
    
    # 第3层
    layers.Dense(name="output", units=1, activation='sigmoid')
])
model.summary()

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# 感知器
inputs = layers.Input(name="input", shape=(3,))
outputs = layers.Dense(name="output", units=1, 
                       activation='linear')(inputs)
model = models.Model(inputs=inputs, outputs=outputs, 
                     name="Perceptron")

# 深层神经网络
# 输入层
inputs = layers.Input(name="input", shape=(n_features,))

# 隐层1
h1 = layers.Dense(name="h1", units=int(round((n_features+1)/2)), activation='relu')(inputs)
h1 = layers.Dropout(name="drop1", rate=0.2)(h1)

# 隐层2
h2 = layers.Dense(name="h2", units=int(round((n_features+1)/4)), activation='relu')(h1)
h2 = layers.Dropout(name="drop2", rate=0.2)(h2)

# 输出层
outputs = layers.Dense(name="output", units=1, activation='sigmoid')(h2)

# 完整的模型
model = models.Model(inputs=inputs, outputs=outputs, name="DeepNN")

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'''
抽取tensorflow.keras模型中的每层信息
'''
def utils_nn_config(model):
    lst_layers = []
    if "Sequential" in str(model): #-> Sequential不显示输入层
        layer = model.layers[0]
        lst_layers.append({"name":"input", "in":int(layer.input.shape[-1]), "neurons":0, 
                           "out":int(layer.input.shape[-1]), "activation":None,
                           "params":0, "bias":0})
    for layer in model.layers:
        try:
            dic_layer = {"name":layer.name, "in":int(layer.input.shape[-1]), "neurons":layer.units, 
                         "out":int(layer.output.shape[-1]), "activation":layer.get_config()["activation"],
                         "params":layer.get_weights()[0], "bias":layer.get_weights()[1]}
        except:
            dic_layer = {"name":layer.name, "in":int(layer.input.shape[-1]), "neurons":0, 
                         "out":int(layer.output.shape[-1]), "activation":None,
                         "params":0, "bias":0}
        lst_layers.append(dic_layer)
    return lst_layers



'''
绘制神经网络的草图
'''
def visualize_nn(model, description=False, figsize=(10,8)):
    # 获取层次信息
    lst_layers = utils_nn_config(model)
    layer_sizes = [layer["out"] for layer in lst_layers]
    
    # 绘图设置
    fig = plt.figure(figsize=figsize)
    ax = fig.gca()
    ax.set(title=model.name)
    ax.axis('off')
    left, right, bottom, top = 0.1, 0.9, 0.1, 0.9
    x_space = (right-left) / float(len(layer_sizes)-1)
    y_space = (top-bottom) / float(max(layer_sizes))
    p = 0.025
    
    # 中间节点
    for i,n in enumerate(layer_sizes):
        top_on_layer = y_space*(n-1)/2.0 + (top+bottom)/2.0
        layer = lst_layers[i]
        color = "green" if i in [0, len(layer_sizes)-1] else "blue"
        color = "red" if (layer['neurons'] == 0) and (i > 0) else color
        
        ## 添加信息说明
        if (description is True):
            d = i if i == 0 else i-0.5
            if layer['activation'] is None:
                plt.text(x=left+d*x_space, y=top, fontsize=10, color=color, s=layer["name"].upper())
            else:
                plt.text(x=left+d*x_space, y=top, fontsize=10, color=color, s=layer["name"].upper())
                plt.text(x=left+d*x_space, y=top-p, fontsize=10, color=color, s=layer['activation']+" (")
                plt.text(x=left+d*x_space, y=top-2*p, fontsize=10, color=color, s="Σ"+str(layer['in'])+"[X*w]+b")
                out = " Y"  if i == len(layer_sizes)-1 else " out"
                plt.text(x=left+d*x_space, y=top-3*p, fontsize=10, color=color, s=") = "+str(layer['neurons'])+out)
        
        ## 遍历
        for m in range(n):
            color = "limegreen" if color == "green" else color
            circle = plt.Circle(xy=(left+i*x_space, top_on_layer-m*y_space-4*p), radius=y_space/4.0, color=color, ec='k', zorder=4)
            ax.add_artist(circle)
            
            ## 添加文本说明
            if i == 0:
                plt.text(x=left-4*p, y=top_on_layer-m*y_space-4*p, fontsize=10, s=r'$X_{'+str(m+1)+'}$')
            elif i == len(layer_sizes)-1:
                plt.text(x=right+4*p, y=top_on_layer-m*y_space-4*p, fontsize=10, s=r'$y_{'+str(m+1)+'}$')
            else:
                plt.text(x=left+i*x_space+p, y=top_on_layer-m*y_space+(y_space/8.+0.01*y_space)-4*p, fontsize=10, s=r'$H_{'+str(m+1)+'}$')
    
    # 添加链接箭头等
    for i, (n_a, n_b) in enumerate(zip(layer_sizes[:-1], layer_sizes[1:])):
        layer = lst_layers[i+1]
        color = "green" if i == len(layer_sizes)-2 else "blue"
        color = "red" if layer['neurons'] == 0 else color
        layer_top_a = y_space*(n_a-1)/2. + (top+bottom)/2. -4*p
        layer_top_b = y_space*(n_b-1)/2. + (top+bottom)/2. -4*p
        for m in range(n_a):
            for o in range(n_b):
                line = plt.Line2D([i*x_space+left, (i+1)*x_space+left], 
                                  [layer_top_a-m*y_space, layer_top_b-o*y_space], 
                                  c=color, alpha=0.5)
                if layer['activation'] is None:
                    if o == m:
                        ax.add_artist(line)
                else:
                    ax.add_artist(line)
    plt.show()

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visualize_nn(model, description=True, figsize=(10,8))

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utils.plot_model(model, to_file='model.png', show_shapes=True, show_layer_names=True)

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# 定义评估准则
def Recall(y_true, y_pred):
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
    recall = true_positives / (possible_positives + K.epsilon())
    return recall

def Precision(y_true, y_pred):
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
    precision = true_positives / (predicted_positives + K.epsilon())
    return precision

def F1(y_true, y_pred):
    precision = Precision(y_true, y_pred)
    recall = Recall(y_true, y_pred)
    return 2*((precision*recall)/(precision+recall+K.epsilon()))
    
# 编译神经网络
model.compile(optimizer='adam', loss='binary_crossentropy', 
              metrics=['accuracy',F1]) 

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# 定义R方评估准则
def R2(y, y_hat):
    ss_res =  K.sum(K.square(y - y_hat)) 
    ss_tot = K.sum(K.square(y - K.mean(y))) 
    return ( 1 - ss_res/(ss_tot + K.epsilon()) )
    
# 编译神经网络
model.compile(optimizer='adam', loss='mean_absolute_error', 
              metrics=[R2])

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import numpy as np
X = np.random.rand(1000,10)
y = np.random.choice([1,0], size=1000)

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# 训练和评估
training = model.fit(x=X, y=y, batch_size=32, epochs=100, shuffle=True, verbose=0, validation_split=0.2)

# 绘制评估指标
metrics = [k for k in training.history.keys() if ("loss" not in k) and ("val" not in k)]    
fig, ax = plt.subplots(nrows=1, ncols=2, sharey=True, figsize=(15,3))
       
# 训练阶段 
ax[0].set(title="Training")    
ax11 = ax[0].twinx()    
ax[0].plot(training.history['loss'], color='black')    ax[0].set_xlabel('Epochs')    
ax[0].set_ylabel('Loss', color='black')    
for metric in metrics:        
    ax11.plot(training.history[metric], label=metric)    ax11.set_ylabel("Score", color='steelblue')    
ax11.legend()
        
# 验证集评估阶段
ax[1].set(title="Validation")    
ax22 = ax[1].twinx()    
ax[1].plot(training.history['val_loss'], color='black')    ax[1].set_xlabel('Epochs')    
ax[1].set_ylabel('Loss', color='black')    
for metric in metrics:          
    ax22.plot(training.history['val_'+metric], label=metric)    ax22.set_ylabel("Score", color="steelblue")    
plt.show()

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'''
使用shap构建解释器
:parameter
    :param model: model instance (after fitting)
    :param X_names: list
    :param X_instance: array of size n x 1 (n,)
    :param X_train: array - if None the model is simple machine learning, if not None then it's a deep learning model
    :param task: string - "classification", "regression"
    :param top: num - top features to display
:return
    dtf with explanations
'''
def explainer_shap(model, X_names, X_instance, X_train=None, task="classification", top=10):
    # 构建解释器
    # 机器学习（树模型）
    if X_train is None:
        explainer = shap.TreeExplainer(model)
        shap_values = explainer.shap_values(X_instance)
    # 深度学习（神经网络）
    else:
        explainer = shap.DeepExplainer(model, data=X_train[:100])
        shap_values = explainer.shap_values(X_instance.reshape(1,-1))[0].reshape(-1)

    # 绘图
    # 分类场景
    if task == "classification":
        shap.decision_plot(explainer.expected_value, shap_values, link='logit', feature_order='importance',
                           features=X_instance, feature_names=X_names, feature_display_range=slice(-1,-top-1,-1))
    # 回归场景
    else:
        shap.waterfall_plot(explainer.expected_value[0], shap_values, 
                            features=X_instance, feature_names=X_names, max_display=top)

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i = 1explainer_shap(model, 
               X_names=list_feature_names, 
               X_instance=X[i], 
               X_train=X, 
               task="classification", # 分类任务
               top=10)

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