text: # 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.
  
  from numpy import nan
  from sklearn.impute import MissingIndicator as SKLModel
  
  import lale.docstrings
  import lale.operators
  
  _hyperparams_schema = {
      "description": "inherited docstring for MissingIndicator    Binary indicators for missing values.",
      "allOf": [
          {
              "type": "object",
              "required": ["missing_values", "features", "sparse", "error_on_new"],
              "relevantToOptimizer": [],
              "additionalProperties": False,
              "properties": {
                  "missing_values": {
                      "anyOf": [
                          {"type": "number"},
                          {"type": "string"},
                          {"enum": [nan]},
                          {"enum": [None]},
                      ],
                      "description": "The placeholder for the missing values.",
                      "default": nan,
                  },
                  "features": {
                      "enum": ["missing-only", "all"],
                      "default": "missing-only",
                      "description": "Whether the imputer mask should represent all or a subset of features.",
                  },
                  "sparse": {
                      "anyOf": [{"type": "boolean"}, {"enum": ["auto"]}],
                      "description": "Whether the imputer mask format should be sparse or dense.",
                      "default": "auto",
                  },
                  "error_on_new": {
                      "type": "boolean",
                      "default": True,
                      "description": "If True (default), transform will raise an error when there are",
                  },
              },
          },
          {
              "description": 'error_on_new, only when features="missing-only"',
              "anyOf": [
                  {"type": "object", "properties": {"error_on_new": {"enum": [True]},}},
                  {
                      "type": "object",
                      "properties": {"features": {"enum": ["missing-only"]},},
                  },
              ],
          },
      ],
  }
  _input_fit_schema = {
      "description": "Fit the transformer on X.",
      "type": "object",
      "required": ["X"],
      "properties": {
          "X": {
              "type": "array",
              "items": {"type": "array", "items": {"type": "number"},},
              "description": "Input data, where ``n_samples`` is the number of samples and",
          },
      },
  }
  _input_transform_schema = {
      "description": "Generate missing values indicator for X.",
      "type": "object",
      "required": ["X"],
      "properties": {
          "X": {
              "type": "array",
              "items": {"type": "array", "items": {"type": "number"},},
              "description": "The input data to complete.",
          },
      },
  }
  _output_transform_schema = {
      "description": "The missing indicator for input data.",
      "type": "array",
      "items": {"type": "array", "items": {"type": "boolean"}},
  }
  _combined_schemas = {
      "$schema": "http://json-schema.org/draft-04/schema#",
      "description": """`Missing values indicator`_ transformer from scikit-learn.
  
  .. _`Missing values indicator`: https://scikit-learn.org/stable/modules/generated/sklearn.impute.MissingIndicator.html
  """,
      "documentation_url": "https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.missing_indicator.html",
      "import_from": "sklearn.impute",
      "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,
      },
  }
  
  
  MissingIndicator = lale.operators.make_operator(SKLModel, _combined_schemas)
  
  lale.docstrings.set_docstrings(MissingIndicator)
  