bd5dc215e1c21bf12e6b5297d10f293c45e16c77,examples/run_classification_criteo.py,,,#,5

Before Change

    history = model.fit(model_input, data[target].values,

                        batch_size=256, epochs=10, verbose=2, validation_split=0.2, )
    print("demo done")

After Change

    train, test = train_test_split(data, test_size=0.2)
    train_model_input = [train[feat].values for feat in sparse_feature_dict] + \
        [train[feat].values for feat in dense_feature_list]
    test_model_input = [test[feat].values for feat in sparse_feature_dict] + \
        [test[feat].values for feat in dense_feature_list]

    // 4.Define Model,train,predict and evaluate
    model = DeepFM({"sparse": sparse_feature_dict,
                    "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))

Instances