be65ce986a45bf2f35b5494db3fa6e993b905aeb,examples/run_classification_criteo_hash.py,,,#,9

Before Change

    sparse_feature_list = [SingleFeat(feat, 1000, hash_flag=True, dtype="string")  // since the input is string
                           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}, task="binary")

After Change

                              for feat in sparse_features] + [DenseFeat(feat, 1, )
                          for feat in dense_features]

    linear_feature_columns = fixlen_feature_columns
    dnn_feature_columns = fixlen_feature_columns
    fixlen_feature_names = get_fixlen_feature_names(linear_feature_columns + dnn_feature_columns, )

    // 3.generate input data for model

    train, test = train_test_split(data, test_size=0.2)
    train_model_input = [train[name] for name in fixlen_feature_names]

    test_model_input = [test[name] for name in fixlen_feature_names]


    // 4.Define Model,train,predict and evaluate
    model = DeepFM(linear_feature_columns,dnn_feature_columns, task="binary")

Instances