e1b3e9a3d2
### What changes were proposed in this pull request? Implement common base ML classes (`Predictor`, `PredictionModel`, `Classifier`, `ClasssificationModel` `ProbabilisticClassifier`, `ProbabilisticClasssificationModel`, `Regressor`, `RegrssionModel`) for non-Java backends. Note - `Predictor` and `JavaClassifier` should be abstract as `_fit` method is not implemented. - `PredictionModel` should be abstract as `_transform` is not implemented. ### Why are the changes needed? To provide extensions points for non-JVM algorithms, as well as a public (as opposed to `Java*` variants, which are commonly described in docstrings as private) hierarchy which can be used to distinguish between different classes of predictors. For longer discussion see [SPARK-29212](https://issues.apache.org/jira/browse/SPARK-29212) and / or https://github.com/apache/spark/pull/25776. ### Does this PR introduce any user-facing change? It adds new base classes as listed above, but effective interfaces (method resolution order notwithstanding) stay the same. Additionally "private" `Java*` classes in`ml.regression` and `ml.classification` have been renamed to follow PEP-8 conventions (added leading underscore). It is for discussion if the same should be done to equivalent classes from `ml.wrapper`. If we take `JavaClassifier` as an example, type hierarchy will change from  to  Similarly the old model  will become  ### How was this patch tested? Existing unit tests. Closes #27245 from zero323/SPARK-29212. Authored-by: zero323 <mszymkiewicz@gmail.com> Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
34 lines
1.5 KiB
Python
34 lines
1.5 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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DataFrame-based machine learning APIs to let users quickly assemble and configure practical
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machine learning pipelines.
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"""
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from pyspark.ml.base import Estimator, Model, Predictor, PredictionModel, \
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Transformer, UnaryTransformer
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from pyspark.ml.pipeline import Pipeline, PipelineModel
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from pyspark.ml import classification, clustering, evaluation, feature, fpm, \
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image, pipeline, recommendation, regression, stat, tuning, util, linalg, param
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__all__ = [
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"Transformer", "UnaryTransformer", "Estimator", "Model",
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"Predictor", "PredictionModel", "Pipeline", "PipelineModel",
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"classification", "clustering", "evaluation", "feature", "fpm", "image",
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"recommendation", "regression", "stat", "tuning", "util", "linalg", "param",
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]
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