cat_imputer.py

# Copyright 2020 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.

import autoai_libs.transformers.exportable
import lale.docstrings
import lale.operators
import numpy as np

class CatImputerImpl():
    def __init__(self, strategy, missing_values, sklearn_version_family, activate_flag):
        self._hyperparams = {
            'strategy': strategy,
            'missing_values': missing_values,
            'sklearn_version_family': sklearn_version_family,
            'activate_flag': activate_flag}
        self._wrapped_model = autoai_libs.transformers.exportable.CatImputer(**self._hyperparams)

    def fit(self, X, y=None):
        self._wrapped_model.fit(X, y)
        return self

    def transform(self, X):
        return self._wrapped_model.transform(X)

_hyperparams_schema = {
    'allOf': [{
        'description': 'This first object lists all constructor arguments with their types, but omits constraints for conditional hyperparameters.',
        'type': 'object',
        'additionalProperties': False,
        'required': ['strategy', 'missing_values', 'sklearn_version_family', 'activate_flag'],
        'relevantToOptimizer': ['strategy'],
        'properties': {
            'strategy': {
                'description': 'The imputation strategy.',
                'anyOf': [
                {   'enum': ['mean'],
                    'description': 'Replace using the mean along each column. Can only be used with numeric data.'},
                {   'enum': ['median'],
                    'description': 'Replace using the median along each column. Can only be used with numeric data.'},
                {   'enum': ['most_frequent'],
                    'description': 'Replace using most frequent value each column. Used with strings or numeric data.'},
                {   'enum': ['constant'],
                    'description': 'Replace with fill_value. Can be used with strings or numeric data.'}],
                'default': 'mean'},
            'missing_values': {
                'description': 'The placeholder for the missing values. All occurrences of missing_values will be imputed.',
                'anyOf': [
                {   'type': 'number'},
                {   'type': 'string'},
                {   'enum': [np.nan]},
                {   'enum': [None]}],
                'default': np.nan},
            'sklearn_version_family': {
                'description': 'The sklearn version for backward compatibiity with versions 019 and 020dev. Currently unused.',
                'enum': ['20', None],
                'default': None},
            'activate_flag': {
                'description': 'If False, transform(X) outputs the input numpy array X unmodified.',
                'type': 'boolean',
                'default': True}}}]}

_input_fit_schema = {
    'type': 'object',
    'required': ['X'],
    'additionalProperties': False,
    'properties': {
        'X': {#Handles 1-D arrays as well
            'anyOf': [
                {'type': 'array', 'items': {'laleType': 'Any'}},
                {'type': 'array', 'items': {'type': 'array', 'items': {'laleType': 'Any'}}}]},
        'y': {
            'laleType': 'Any'}}}

_input_transform_schema = {
    'type': 'object',
    'required': ['X'],
    'additionalProperties': False,
    'properties': {
        'X': {#Handles 1-D arrays as well
            'anyOf': [
                {'type': 'array', 'items': {'laleType': 'Any'}},
                {'type': 'array', 'items': {'type': 'array', 'items': {'laleType': 'Any'}}}]}}}

_output_transform_schema = {
    'description': 'Features; the outer array is over samples.',
    'anyOf': [
        {'type': 'array', 'items': {'laleType': 'Any'}},
        {'type': 'array', 'items': {'type': 'array', 'items': {'laleType': 'Any'}}}]}

_combined_schemas = {
    '$schema': 'http://json-schema.org/draft-04/schema#',
    'description': """Operator from `autoai_libs`_. Missing value imputation for categorical features, currently internally uses the sklearn SimpleImputer_.

.. _`autoai_libs`: https://pypi.org/project/autoai-libs
.. _SimpleImputer: https://scikit-learn.org/0.20/modules/generated/sklearn.impute.SimpleImputer.html#sklearn-impute-simpleimputer""",
    'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.autoai_libs.cat_imputer.html',
    'type': 'object',
    'tags': {
        'pre': [],
        'op': ['transformer'],
        'post': []},
    'properties': {
        'hyperparams': _hyperparams_schema,
        'input_fit': _input_fit_schema,
        'input_transform': _input_transform_schema,
        'output_transform': _output_transform_schema}}

lale.docstrings.set_docstrings(CatImputerImpl, _combined_schemas)

CatImputer = lale.operators.make_operator(CatImputerImpl, _combined_schemas)