103 lines
3.3 KiB
Python
103 lines
3.3 KiB
Python
import pandas as pd
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import numpy as np
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from tensorflow.keras import *
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from tensorflow.keras.layers import *
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from tensorflow.keras.models import *
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from tensorflow.keras.callbacks import *
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import tensorflow.keras.backend as K
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from sklearn.model_selection import train_test_split
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import LabelEncoder
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from tqdm import tqdm
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# dense特征取对数 sparse特征进行类别编码
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def process_feat(data, dense_feats, sparse_feats):
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df = data.copy()
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# dense
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df_dense = df[dense_feats].fillna(0.0)
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for f in tqdm(dense_feats):
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df_dense[f] = df_dense[f].apply(lambda x: np.log(1 + x) if x > -1 else -1)
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# sparse
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df_sparse = df[sparse_feats].fillna('-1')
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for f in tqdm(sparse_feats):
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lbe = LabelEncoder()
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df_sparse[f] = lbe.fit_transform(df_sparse[f])
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df_sparse_arr = []
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for f in tqdm(sparse_feats):
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data_new = pd.get_dummies(df_sparse.loc[:, f].values)
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data_new.columns = [f + "_{}".format(i) for i in range(data_new.shape[1])]
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df_sparse_arr.append(data_new)
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df_new = pd.concat([df_dense] + df_sparse_arr, axis=1)
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return df_new
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# FM 特征组合层
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class crossLayer(layers.Layer):
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def __init__(self, input_dim, output_dim=10, **kwargs):
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super(crossLayer, self).__init__(**kwargs)
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self.input_dim = input_dim
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self.output_dim = output_dim
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# 定义交叉特征的权重
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self.kernel = self.add_weight(name='kernel',
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shape=(self.input_dim, self.output_dim),
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initializer='glorot_uniform',
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trainable=True)
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def call(self, x): # 对照上述公式中的二次项优化公式一起理解
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a = K.pow(K.dot(x, self.kernel), 2)
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b = K.dot(K.pow(x, 2), K.pow(self.kernel, 2))
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return 0.5 * K.mean(a - b, 1, keepdims=True)
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# 定义FM模型
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def FM(feature_dim):
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inputs = Input(shape=(feature_dim,))
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# 一阶特征
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linear = Dense(units=1,
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kernel_regularizer=regularizers.l2(0.01),
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bias_regularizer=regularizers.l2(0.01))(inputs)
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# 二阶特征
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cross = crossLayer(feature_dim)(inputs)
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add = Add()([linear, cross]) # 将一阶特征与二阶特征相加构建FM模型
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pred = Dense(units=1, activation="sigmoid")(add)
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model = Model(inputs=inputs, outputs=pred)
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model.summary()
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model.compile(loss='binary_crossentropy',
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optimizer=optimizers.Adam(),
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metrics=['binary_accuracy'])
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return model
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# 读取数据
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print('loading data...')
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data = pd.read_csv('./data/kaggle_train.csv')
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# dense 特征开头是I，sparse特征开头是C，Label是标签
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cols = data.columns.values
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dense_feats = [f for f in cols if f[0] == 'I']
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sparse_feats = [f for f in cols if f[0] == 'C']
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# 对dense数据和sparse数据分别处理
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print('processing features')
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feats = process_feat(data, dense_feats, sparse_feats)
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# 划分训练和验证数据
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x_trn, x_tst, y_trn, y_tst = train_test_split(feats, data['Label'], test_size=0.2, random_state=2020)
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# 定义模型
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model = FM(feats.shape[1])
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# 训练模型
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model.fit(x_trn, y_trn, epochs=10, batch_size=128, validation_data=(x_tst, y_tst)) |