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Add TargetEncoder wrapper (as of scikit-learn 1.3)
More sklearn schema updates Signed-off-by: Avi Shinnar <shinnar@us.ibm.com>
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| Original file line number | Diff line number | Diff line change |
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| # Copyright 2019 IBM Corporation | ||
| # | ||
| # Licensed 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. | ||
|
|
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| import sklearn.preprocessing | ||
| from packaging import version | ||
|
|
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| import lale.docstrings | ||
| import lale.operators | ||
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| class _TargetEncoderNotFoundImpl: | ||
| def __init__(self, **hyperparams): | ||
| raise NotImplementedError( | ||
| "TargetEncoder is only available with scikit-learn versions >= 1.3" | ||
| ) | ||
|
|
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| def transform(self, X): | ||
| raise NotImplementedError( | ||
| "TargetEncoder is only available with scikit-learn versions >= 1.3" | ||
| ) | ||
|
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|
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| _hyperparams_schema = { | ||
| "description": "Hyperparameter schema for the TargetEncoder model from scikit-learn.", | ||
| "allOf": [ | ||
| { | ||
| "type": "object", | ||
| "additionalProperties": False, | ||
| "required": ["categories", "target_type"], | ||
| "relevantToOptimizer": [], | ||
| "properties": { | ||
| "categories": { | ||
| "anyOf": [ | ||
| { | ||
| "description": "Determine categories automatically from training data.", | ||
| "enum": ["auto"], | ||
| }, | ||
| { | ||
| "description": "The ith list element holds the categories expected in the ith column.", | ||
| "type": "array", | ||
| "items": { | ||
| "anyOf": [ | ||
| { | ||
| "type": "array", | ||
| "items": {"type": "string"}, | ||
| }, | ||
| { | ||
| "type": "array", | ||
| "items": {"type": "number"}, | ||
| "description": "Should be sorted.", | ||
| }, | ||
| ] | ||
| }, | ||
| }, | ||
| ], | ||
| "default": "auto", | ||
| "description": "Categories (unique values) per feature.", | ||
| }, | ||
| "target_type": { | ||
| "anyOf": [ | ||
| { | ||
| "enum": ["auto"], | ||
| "description": "Type of target is inferred with type_of_target.", | ||
| }, | ||
| {"enum": ["continuous"], "description": "Continuous target"}, | ||
| {"enum": ["binary"], "description": "Binary target"}, | ||
| ], | ||
| "description": "Type of target.", | ||
| "default": "auto", | ||
| }, | ||
| "smooth": { | ||
| "anyOf": [ | ||
| { | ||
| "enum": ["auto"], | ||
| "description": "Set to an empirical Bayes estimate.", | ||
| }, | ||
| { | ||
| "type": "number", | ||
| "minimum": 0.0, | ||
| "maximum": 1.0, | ||
| "description": "A larger smooth value will put more weight on the global target mean", | ||
| }, | ||
| ], | ||
| "description": "The amount of mixing of the target mean conditioned on the value of the category with the global target mean.", | ||
| "default": "auto", | ||
| }, | ||
| "cv": { | ||
| "type": "integer", | ||
| "minimum": 1, | ||
| "description": "Determines the number of folds in the cross fitting strategy used in fit_transform. For classification targets, StratifiedKFold is used and for continuous targets, KFold is used.", | ||
| "default": 5, | ||
| }, | ||
| "shuffle": { | ||
| "type": "boolean", | ||
| "description": "Whether to shuffle the data in fit_transform before splitting into folds. Note that the samples within each split will not be shuffled.", | ||
| "default": True, | ||
| }, | ||
| "random_state": { | ||
| "description": "When shuffle is True, random_state affects the ordering of the indices, which controls the randomness of each fold. Otherwise, this parameter has no effect. Pass an int for reproducible output across multiple function calls.", | ||
| "anyOf": [ | ||
| { | ||
| "enum": [None], | ||
| }, | ||
| { | ||
| "description": "Use the provided random state, only affecting other users of that same random state instance.", | ||
| "laleType": "numpy.random.RandomState", | ||
| }, | ||
| {"description": "Explicit seed.", "type": "integer"}, | ||
| ], | ||
| "default": None, | ||
| }, | ||
| }, | ||
| } | ||
| ], | ||
| } | ||
|
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| _input_fit_schema = { | ||
| "type": "object", | ||
| "required": ["X"], | ||
| "additionalProperties": False, | ||
| "properties": { | ||
| "X": { | ||
| "description": "Features; the outer array is over samples.", | ||
| "type": "array", | ||
| "items": { | ||
| "anyOf": [ | ||
| {"type": "array", "items": {"type": "number"}}, | ||
| {"type": "array", "items": {"type": "string"}}, | ||
| ] | ||
| }, | ||
| }, | ||
| "y": { | ||
| "description": "The target data used to encode the categories.", | ||
| "type": "array", | ||
| }, | ||
| }, | ||
| } | ||
|
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| _input_transform_schema = { | ||
| "type": "object", | ||
| "required": ["X"], | ||
| "additionalProperties": False, | ||
| "properties": { | ||
| "X": { | ||
| "description": "Features; the outer array is over samples.", | ||
| "type": "array", | ||
| "items": { | ||
| "anyOf": [ | ||
| {"type": "array", "items": {"type": "number"}}, | ||
| {"type": "array", "items": {"type": "string"}}, | ||
| ] | ||
| }, | ||
| } | ||
| }, | ||
| } | ||
|
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| _output_transform_schema = { | ||
| "description": "Transformed input; the outer array is over samples.", | ||
| "type": "array", | ||
| "items": { | ||
| "anyOf": [ | ||
| {"type": "array", "items": {"type": "number"}}, | ||
| {"type": "array", "items": {"type": "string"}}, | ||
| ] | ||
| }, | ||
| } | ||
|
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| _combined_schemas = { | ||
| "$schema": "http://json-schema.org/draft-04/schema#", | ||
| "description": """`Target encoder`_ for regression and classification targets.. | ||
| .. _`Target encoder`: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.TargetEncoder.html | ||
| """, | ||
| "documentation_url": "https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.target_encoder.html", | ||
| "import_from": "sklearn.preprocessing", | ||
| "type": "object", | ||
| "tags": {"pre": ["categoricals"], "op": ["transformer"], "post": []}, | ||
| "properties": { | ||
| "hyperparams": _hyperparams_schema, | ||
| "input_fit": _input_fit_schema, | ||
| "input_transform": _input_transform_schema, | ||
| "output_transform": _output_transform_schema, | ||
| }, | ||
| } | ||
|
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| if lale.operators.sklearn_version >= version.Version("1.3"): | ||
| TargetEncoder = lale.operators.make_operator( | ||
| sklearn.preprocessing.TargetEncoder, _combined_schemas | ||
| ) | ||
| else: | ||
| TargetEncoder = lale.operators.make_operator( | ||
| _TargetEncoderNotFoundImpl, _combined_schemas | ||
| ) | ||
|
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|
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| if lale.operators.sklearn_version >= version.Version("1.4"): | ||
| TargetEncoder = TargetEncoder.customize_schema( | ||
| target_type={ | ||
| "anyOf": [ | ||
| { | ||
| "enum": ["auto"], | ||
| "description": "Type of target is inferred with type_of_target.", | ||
| }, | ||
| {"enum": ["continuous"], "description": "Continuous target"}, | ||
| {"enum": ["binary"], "description": "Binary target"}, | ||
| {"enum": ["multiclass"], "description": "Multiclass target"}, | ||
| ], | ||
| "description": "Type of target.", | ||
| "default": "auto", | ||
| }, | ||
| set_as_available=True, | ||
| ) | ||
|
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| lale.docstrings.set_docstrings(TargetEncoder) |
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