| ################################################################################ |
| # 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 pyflink.ml.core.param import IntParam, ParamValidators |
| from pyflink.ml.core.wrapper import JavaWithParams |
| from pyflink.ml.lib.feature.common import JavaFeatureModel, JavaFeatureEstimator |
| from pyflink.ml.lib.param import HasInputCol, HasOutputCol, HasHandleInvalid |
| |
| |
| class _VectorIndexerModelParams( |
| JavaWithParams, |
| HasInputCol, |
| HasOutputCol, |
| HasHandleInvalid |
| ): |
| """ |
| Params for :class:`VectorIndexerModel`. |
| """ |
| |
| def __init__(self, java_params): |
| super(_VectorIndexerModelParams, self).__init__(java_params) |
| |
| |
| class _VectorIndexerParams(_VectorIndexerModelParams): |
| """ |
| Params for :class:`VectorIndexer`. |
| """ |
| |
| MAX_CATEGORIES: IntParam = IntParam( |
| "max_categories", |
| "Threshold for the number of values a categorical feature can take (>= 2). " |
| + "If a feature is found to have > maxCategories values, then it is declared continuous.", |
| 20, |
| ParamValidators.gt_eq(2) |
| ) |
| |
| def __init__(self, java_params): |
| super(_VectorIndexerParams, self).__init__(java_params) |
| |
| def set_max_categories(self, value: int): |
| return typing.cast(_VectorIndexerParams, self.set(self.MAX_CATEGORIES, value)) |
| |
| def get_max_categories(self) -> int: |
| return self.get(self.MAX_CATEGORIES) |
| |
| @property |
| def max_categories(self): |
| return self.get_max_categories() |
| |
| |
| class VectorIndexerModel(JavaFeatureModel, _VectorIndexerModelParams): |
| """ |
| A Model which encodes input vector to an output vector using the model data computed by |
| :class::VectorIndexer. |
| |
| The `keep` option of {@link HasHandleInvalid} means that we put the invalid entries in a |
| special bucket, whose index is the number of distinct values in this column. |
| """ |
| |
| def __init__(self, java_model=None): |
| super(VectorIndexerModel, self).__init__(java_model) |
| |
| @classmethod |
| def _java_model_package_name(cls) -> str: |
| return "vectorindexer" |
| |
| @classmethod |
| def _java_model_class_name(cls) -> str: |
| return "VectorIndexerModel" |
| |
| |
| class VectorIndexer(JavaFeatureEstimator, _VectorIndexerParams): |
| """ |
| An Estimator which implements the vector indexing algorithm. |
| |
| A vector indexer maps each column of the input vector into a continuous/categorical |
| feature. Whether one feature is transformed into a continuous or categorical feature |
| depends on the number of distinct values in this column. If the number of distinct |
| values in one column is greater than a specified parameter (i.e., maxCategories), |
| the corresponding output column is unchanged. Otherwise, it is transformed into |
| a categorical value. For categorical outputs, the indices are |
| in [0, numDistinctValuesInThisColumn]. |
| |
| The output model is organized in ascending order except that 0.0 is always mapped |
| to 0 (for sparsity). We list two examples here: |
| |
| <ul> |
| <li>If one column contains {-1.0, 1.0}, then -1.0 should be encoded as 0 |
| and 1.0 will be encoded as 1. |
| <li>If one column contains {-1.0, 0.0, 1.0}, then -1.0 should be encoded as 1, |
| 0.0 should be encoded as 0 and 1.0 should be encoded as 2. |
| </ul> |
| |
| The `keep` option of {@link HasHandleInvalid} means that we put the invalid entries |
| in a special bucket, whose index is the number of distinct values in this column. |
| """ |
| |
| def __init__(self): |
| super(VectorIndexer, self).__init__() |
| |
| @classmethod |
| def _create_model(cls, java_model) -> VectorIndexerModel: |
| return VectorIndexerModel(java_model) |
| |
| @classmethod |
| def _java_estimator_package_name(cls) -> str: |
| return "vectorindexer" |
| |
| @classmethod |
| def _java_estimator_class_name(cls) -> str: |
| return "VectorIndexer" |