DeepCTR：易用可扩展的深度学习点击率预测算法库

import pandas as pdfrom sklearn.preprocessing import LabelEncoder, MinMaxScalerfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import log_loss, roc_auc_scorefrom deepctr.models import DeepFMfrom deepctr.utils import SingleFeatif __name__ == "__main__":
    data = pd.read_csv('./criteo_sample.txt')
    sparse_features = ['C' + str(i) for i in range(1, 27)]    dense_features = ['I'+str(i) for i in range(1, 14)]
    data[sparse_features] = data[sparse_features].fillna('-1', )    data[dense_features] = data[dense_features].fillna(0,)    target = ['label']    # 1.Label Encoding for sparse features,and do simple Transformation for dense features    for feat in sparse_features:        lbe = LabelEncoder()        data[feat] = lbe.fit_transform(data[feat])    mms = MinMaxScaler(feature_range=(0, 1))    data[dense_features] = mms.fit_transform(data[dense_features])    # 2.count #unique features for each sparse field,and record dense feature field name    sparse_feature_list = [SingleFeat(feat, data[feat].nunique())                           for feat in sparse_features]    dense_feature_list = [SingleFeat(feat, 0)                          for feat in dense_features]    # 3.generate input data for model    train, test = train_test_split(data, test_size=0.2)    train_model_input = [train[feat.name].values for feat in sparse_feature_list] + \        [train[feat.name].values for feat in dense_feature_list]    test_model_input = [test[feat.name].values for feat in sparse_feature_list] + \        [test[feat.name].values for feat in dense_feature_list]    # 4.Define Model,train,predict and evaluate    model = DeepFM({"sparse": sparse_feature_list,                    "dense": dense_feature_list}, final_activation='sigmoid')    model.compile("adam", "binary_crossentropy",                  metrics=['binary_crossentropy'], )    history = model.fit(train_model_input, train[target].values,                        batch_size=256, epochs=10, verbose=2, validation_split=0.2, )    pred_ans = model.predict(test_model_input, batch_size=256)
    print("test LogLoss", round(log_loss(test[target].values, pred_ans), 4))    print("test AUC", round(roc_auc_score(test[target].values, pred_ans), 4)))

fm_input = Concatenate(axis=1)(embed_list)#将输入拼接成FM层需要的shapedeep_input = Flatten()(fm_input)#将输入拼接成Deep网络需要的shapefm_out = FM()(fm_input)#调用FM组件deep_out = MLP(hidden_size, activation, l2_reg_deep, keep_prob,use_bn, seed)(deep_input)#调用Deep网络组件deep_logit = Dense(1, use_bias=False, activation=None)(deep_out)

pip install deepctr