| ################################################################################ |
| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| ################################################################################ |
| import typing |
| from typing import Tuple |
| |
| from pyflink.ml.core.param import Param, FloatArrayArrayParam |
| from pyflink.ml.core.wrapper import JavaWithParams |
| from pyflink.ml.lib.feature.common import JavaFeatureTransformer |
| from pyflink.ml.lib.param import HasInputCols, HasOutputCols, HasHandleInvalid |
| |
| |
| class _BucketizerParams( |
| JavaWithParams, |
| HasInputCols, |
| HasOutputCols, |
| HasHandleInvalid |
| ): |
| """ |
| Params for :class:`Bucketizer`. |
| """ |
| |
| SPLITS_ARRAY: Param[Tuple[float, ...]] = FloatArrayArrayParam( |
| "splits_array", |
| "Array of split points for mapping continuous features into buckets.", |
| None) |
| |
| def __init__(self, java_params): |
| super(_BucketizerParams, self).__init__(java_params) |
| |
| def set_splits_array(self, value: Tuple[Tuple[float, ...]]): |
| return typing.cast(_BucketizerParams, self.set(self.SPLITS_ARRAY, value)) |
| |
| def get_split_array(self) -> Tuple[Tuple[float, ...]]: |
| return self.get(self.SPLITS_ARRAY) |
| |
| @property |
| def split_array(self): |
| return self.get_split_array() |
| |
| |
| class Bucketizer(JavaFeatureTransformer, _BucketizerParams): |
| """ |
| A Transformer that maps multiple columns of continuous features to multiple |
| columns of discrete features, i.e., buckets indices. The indices are in |
| [0, numSplitsInThisColumn - 1]. |
| |
| The `keep` option of HasHandleInvalid means that we put the invalid data in the last |
| bucket of the splits, whose index is the number of the buckets. |
| """ |
| |
| def __init__(self, java_model=None): |
| super(Bucketizer, self).__init__(java_model) |
| |
| @classmethod |
| def _java_transformer_package_name(cls) -> str: |
| return "bucketizer" |
| |
| @classmethod |
| def _java_transformer_class_name(cls) -> str: |
| return "Bucketizer" |