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.
  
  import sklearn
  import sklearn.linear_model
  
  import lale.docstrings
  import lale.operators
  
  _hyperparams_schema = {
      "description": "inherited docstring for SGDRegressor    Linear model fitted by minimizing a regularized empirical loss with SGD",
      "allOf": [
          {
              "type": "object",
              "required": [
                  "loss",
                  "penalty",
                  "alpha",
                  "l1_ratio",
                  "fit_intercept",
                  "max_iter",
                  "tol",
                  "shuffle",
                  "verbose",
                  "epsilon",
                  "random_state",
                  "learning_rate",
                  "eta0",
                  "power_t",
                  "early_stopping",
                  "validation_fraction",
                  "n_iter_no_change",
                  "warm_start",
                  "average",
              ],
              "relevantToOptimizer": [
                  "loss",
                  "penalty",
                  "alpha",
                  "l1_ratio",
                  "fit_intercept",
                  "max_iter",
                  "tol",
                  "shuffle",
                  "epsilon",
                  "learning_rate",
                  "eta0",
                  "power_t",
              ],
              "additionalProperties": False,
              "properties": {
                  "loss": {
                      "enum": [
                          "epsilon_insensitive",
                          "huber",
                          "squared_epsilon_insensitive",
                          "squared_loss",
                      ],
                      "default": "squared_loss",
                      "description": "The loss function to be used. The possible values are 'squared_loss',",
                  },
                  "penalty": {
                      "description": "The penalty (aka regularization term) to be used. Defaults to 'l2'",
                      "enum": ["elasticnet", "l1", "l2"],
                      "default": "l2",
                  },
                  "alpha": {
                      "type": "number",
                      "minimumForOptimizer": 1e-10,
                      "maximumForOptimizer": 1.0,
                      "distribution": "loguniform",
                      "default": 0.0001,
                      "description": "Constant that multiplies the regularization term. Defaults to 0.0001",
                  },
                  "l1_ratio": {
                      "type": "number",
                      "minimumForOptimizer": 1e-9,
                      "maximumForOptimizer": 1.0,
                      "distribution": "loguniform",
                      "default": 0.15,
                      "description": "The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1.",
                  },
                  "fit_intercept": {
                      "type": "boolean",
                      "default": True,
                      "description": "Whether the intercept should be estimated or not. If False, the",
                  },
                  "max_iter": {
                      "anyOf": [
                          {
                              "type": "integer",
                              "minimumForOptimizer": 10,
                              "maximumForOptimizer": 1000,
                              "distribution": "uniform",
                          },
                          {"enum": [None]},
                      ],
                      "default": None,
                      "description": "The maximum number of passes over the training data (aka epochs).",
                  },
                  "tol": {
                      "anyOf": [
                          {
                              "type": "number",
                              "minimumForOptimizer": 1e-08,
                              "maximumForOptimizer": 0.01,
                              "distribution": "loguniform",
                          },
                          {"enum": [None]},
                      ],
                      "default": None,
                      "description": "The stopping criterion. If it is not None, the iterations will stop",
                  },
                  "shuffle": {
                      "type": "boolean",
                      "default": True,
                      "description": "Whether or not the training data should be shuffled after each epoch.",
                  },
                  "verbose": {
                      "type": "integer",
                      "default": 0,
                      "description": "The verbosity level.",
                  },
                  "epsilon": {
                      "type": "number",
                      "minimumForOptimizer": 1e-08,
                      "maximumForOptimizer": 1.35,
                      "distribution": "loguniform",
                      "default": 0.1,
                      "description": "Epsilon in the epsilon-insensitive loss functions; only if `loss` is",
                  },
                  "random_state": {
                      "anyOf": [
                          {"type": "integer"},
                          {"laleType": "numpy.random.RandomState"},
                          {"enum": [None]},
                      ],
                      "default": None,
                      "description": "The seed of the pseudo random number generator to use when shuffling",
                  },
                  "learning_rate": {
                      "enum": ["optimal", "constant", "invscaling", "adaptive"],
                      "default": "invscaling",
                      "description": "The learning rate schedule:",
                  },
                  "eta0": {
                      "type": "number",
                      "minimumForOptimizer": 0.01,
                      "maximumForOptimizer": 1.0,
                      "distribution": "loguniform",
                      "default": 0.01,
                      "description": "The initial learning rate for the 'constant', 'invscaling' or",
                  },
                  "power_t": {
                      "type": "number",
                      "minimumForOptimizer": 0.00001,
                      "maximumForOptimizer": 1.0,
                      "distribution": "uniform",
                      "default": 0.25,
                      "description": "The exponent for inverse scaling learning rate [default 0.5].",
                  },
                  "early_stopping": {
                      "type": "boolean",
                      "default": False,
                      "description": "Whether to use early stopping to terminate training when validation",
                  },
                  "validation_fraction": {
                      "type": "number",
                      "minimum": 0.0,
                      "maximum": 1.0,
                      "default": 0.1,
                      "description": "The proportion of training data to set aside as validation set for",
                  },
                  "n_iter_no_change": {
                      "type": "integer",
                      "minimumForOptimizer": 5,
                      "maximumForOptimizer": 10,
                      "default": 5,
                      "description": "Number of iterations with no improvement to wait before early stopping.",
                  },
                  "warm_start": {
                      "type": "boolean",
                      "default": False,
                      "description": "When set to True, reuse the solution of the previous call to fit as",
                  },
                  "average": {
                      "anyOf": [
                          {"type": "boolean"},
                          {"type": "integer", "forOptimizer": False},
                      ],
                      "default": False,
                      "description": "When set to True, computes the averaged SGD weights and stores the result in the ``coef_`` attribute.",
                  },
              },
          },
          {
              "description": "eta0 must be greater than 0 if the learning_rate is not ‘optimal’.",
              "anyOf": [
                  {
                      "type": "object",
                      "properties": {
                          "learning_rate": {"enum": ["optimal"]},
                      },
                  },
                  {
                      "type": "object",
                      "properties": {
                          "eta0": {
                              "type": "number",
                              "minimum": 0.0,
                              "exclusiveMinimum": True,
                          },
                      },
                  },
              ],
          },
      ],
  }
  
  _input_fit_schema = {
      "description": "Fit linear model with Stochastic Gradient Descent.",
      "required": ["X", "y"],
      "type": "object",
      "properties": {
          "X": {
              "type": "array",
              "items": {
                  "type": "array",
                  "items": {"type": "number"},
              },
              "description": "Training data",
          },
          "y": {
              "type": "array",
              "items": {"type": "number"},
              "description": "Target values",
          },
          "coef_init": {
              "type": "array",
              "items": {"type": "number"},
              "description": "The initial coefficients to warm-start the optimization.",
          },
          "intercept_init": {
              "type": "array",
              "items": {"type": "number"},
              "description": "The initial intercept to warm-start the optimization.",
          },
          "sample_weight": {
              "anyOf": [
                  {
                      "type": "array",
                      "items": {"type": "number"},
                  },
                  {"enum": [None]},
              ],
              "default": None,
              "description": "Weights applied to individual samples (1. for unweighted).",
          },
      },
  }
  _input_predict_schema = {
      "description": "Predict using the linear model",
      "type": "object",
      "properties": {
          "X": {
              "type": "array",
              "items": {
                  "type": "array",
                  "items": {"type": "number"},
              },
          },
      },
  }
  _output_predict_schema = {
      "description": "Predicted target values per element in X.",
      "type": "array",
      "items": {"type": "number"},
  }
  _combined_schemas = {
      "$schema": "http://json-schema.org/draft-04/schema#",
      "description": """`SGD regressor`_ from scikit-learn uses linear regressors (SVM, logistic regression, a.o.) with stochastic gradient descent training.
  
  .. _`SGD regressor`: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html
  """,
      "documentation_url": "https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.sgd_regressor.html",
      "import_from": "sklearn.linear_model",
      "type": "object",
      "tags": {"pre": [], "op": ["estimator", "regressor"], "post": []},
      "properties": {
          "hyperparams": _hyperparams_schema,
          "input_fit": _input_fit_schema,
          "input_predict": _input_predict_schema,
          "output_predict": _output_predict_schema,
      },
  }
  
  SGDRegressor = lale.operators.make_operator(
      sklearn.linear_model.SGDRegressor, _combined_schemas
  )
  
  if sklearn.__version__ >= "0.21":
      # old: https://scikit-learn.org/0.20/modules/generated/sklearn.linear_model.SGDRegressor.html
      # new: https://scikit-learn.org/0.23/modules/generated/sklearn.linear_model.SGDRegressor.html
      import typing
  
      from lale.schemas import Int
  
      SGDRegressor = typing.cast(
          lale.operators.PlannedIndividualOp,
          SGDRegressor.customize_schema(
              max_iter=Int(
                  minimumForOptimizer=5,
                  maximumForOptimizer=1000,
                  distribution="uniform",
                  desc="The maximum number of passes over the training data (aka epochs).",
                  default=1000,
              ),
              set_as_available=True,
          ),
      )
  
  
  lale.docstrings.set_docstrings(SGDRegressor)
  