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codes/FM.py

@@ -1,5 +1,5 @@
 import pandas as pd
-import numpy as np 
+import numpy as np
 
 from tensorflow.keras import *
 from tensorflow.keras.layers import *
@@ -12,6 +12,7 @@ from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import LabelEncoder
 from tqdm import tqdm
 
+
 # dense特征取对数　　sparse特征进行类别编码
 def process_feat(data, dense_feats, sparse_feats):
     df = data.copy()
@@ -19,56 +20,64 @@ def process_feat(data, dense_feats, sparse_feats):
     df_dense = df[dense_feats].fillna(0.0)
     for f in tqdm(dense_feats):
         df_dense[f] = df_dense[f].apply(lambda x: np.log(1 + x) if x > -1 else -1)
-    
+
     # sparse
     df_sparse = df[sparse_feats].fillna('-1')
     for f in tqdm(sparse_feats):
         lbe = LabelEncoder()
         df_sparse[f] = lbe.fit_transform(df_sparse[f])
-    
-    df_new = pd.concat([df_dense, df_sparse], axis=1)
+
+    df_sparse_arr = []
+    for f in tqdm(sparse_feats):
+        data_new = pd.get_dummies(df_sparse.loc[:, f].values)
+        data_new.columns = [f + "_{}".format(i) for i in range(data_new.shape[1])]
+        df_sparse_arr.append(data_new)
+
+    df_new = pd.concat([df_dense] + df_sparse_arr, axis=1)
     return df_new
 
+
 # FM 特征组合层
 class crossLayer(layers.Layer):
-    def __init__(self,input_dim, output_dim=10, **kwargs):
+    def __init__(self, input_dim, output_dim=10, **kwargs):
         super(crossLayer, self).__init__(**kwargs)
 
         self.input_dim = input_dim
         self.output_dim = output_dim
         # 定义交叉特征的权重
-        self.kernel = self.add_weight(name='kernel', 
-                                     shape=(self.input_dim, self.output_dim),
-                                     initializer='glorot_uniform',
-                                     trainable=True)
-        
-    def call(self, x): # 对照上述公式中的二次项优化公式一起理解
+        self.kernel = self.add_weight(name='kernel',
+                                      shape=(self.input_dim, self.output_dim),
+                                      initializer='glorot_uniform',
+                                      trainable=True)
+
+    def call(self, x):  # 对照上述公式中的二次项优化公式一起理解
         a = K.pow(K.dot(x, self.kernel), 2)
         b = K.dot(K.pow(x, 2), K.pow(self.kernel, 2))
-        return 0.5 * K.mean(a-b, 1, keepdims=True)
+        return 0.5 * K.mean(a - b, 1, keepdims=True)
+
 
 # 定义FM模型
 def FM(feature_dim):
-    inputs = Input(shape=(feature_dim, ))
-    
+    inputs = Input(shape=(feature_dim,))
+
     # 一阶特征
-    linear = Dense(units=1, 
-                   kernel_regularizer=regularizers.l2(0.01), 
+    linear = Dense(units=1,
+                   kernel_regularizer=regularizers.l2(0.01),
                    bias_regularizer=regularizers.l2(0.01))(inputs)
-    
+
     # 二阶特征
     cross = crossLayer(feature_dim)(inputs)
     add = Add()([linear, cross])  # 将一阶特征与二阶特征相加构建FM模型
-    
-    pred = Activation('sigmoid')(add)
+
+    pred = Dense(units=1, activation="sigmoid")(add)
     model = Model(inputs=inputs, outputs=pred)
-    
-    model.summary()    
+
+    model.summary()
     model.compile(loss='binary_crossentropy',
                   optimizer=optimizers.Adam(),
                   metrics=['binary_accuracy'])
-    
-    return model    
+
+    return model
 
 
 # 读取数据