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 lale.docstrings
  import lale.operators
  
  try:
      import lightgbm.sklearn
  
      lightgbm_installed = True
  except ImportError:
      lightgbm_installed = False
  
  
  class _LGBMClassifierImpl:
      def __init__(
          self,
          boosting_type="gbdt",
          num_leaves=31,
          max_depth=-1,
          learning_rate=0.1,
          n_estimators=100,
          subsample_for_bin=200000,
          objective=None,
          class_weight=None,
          min_split_gain=0.0,
          min_child_weight=0.001,
          min_child_samples=20,
          subsample=1.0,
          subsample_freq=0,
          colsample_bytree=1.0,
          reg_alpha=0.0,
          reg_lambda=0.0,
          random_state=None,
          n_jobs=-1,
          silent=True,
          importance_type="split",
      ):
          assert lightgbm_installed, """Your Python environment does not have lightgbm installed. You can install it with
      pip install lightgbm
  or with
      pip install 'lale[full]'"""
          self._hyperparams = {
              "boosting_type": boosting_type,
              "num_leaves": num_leaves,
              "max_depth": max_depth,
              "learning_rate": learning_rate,
              "n_estimators": n_estimators,
              "subsample_for_bin": subsample_for_bin,
              "objective": objective,
              "class_weight": class_weight,
              "min_split_gain": min_split_gain,
              "min_child_weight": min_child_weight,
              "min_child_samples": min_child_samples,
              "subsample": subsample,
              "subsample_freq": subsample_freq,
              "colsample_bytree": colsample_bytree,
              "reg_alpha": reg_alpha,
              "reg_lambda": reg_lambda,
              "random_state": random_state,
              "n_jobs": n_jobs,
              "silent": silent,
              "importance_type": importance_type,
          }
          self._wrapped_model = lightgbm.sklearn.LGBMClassifier(**self._hyperparams)
  
      def fit(self, X, y=None, **fit_params):
          if X.shape[0] * self._wrapped_model.subsample < 1.0:
              self._wrapped_model.subsample = 1.001 / X.shape[0]
          try:
              self._wrapped_model.fit(X, y, **fit_params)
          except Exception as e:
              raise RuntimeError(str(self._hyperparams)) from e
          return self
  
      def predict(self, X):
          return self._wrapped_model.predict(X)
  
      def predict_proba(self, X):
          return self._wrapped_model.predict_proba(X)
  
      def score(self, X, y):
          from sklearn.metrics import accuracy_score
  
          y_pred = self.predict(X)
          return accuracy_score(y, y_pred)
  
  
  _hyperparams_schema = {
      "description": "LightGBM classifier. (https://lightgbm.readthedocs.io/en/latest/Python-API.html#scikit-learn-api)",
      "allOf": [
          {
              "type": "object",
              "required": [
                  "boosting_type",
                  "max_depth",
                  "learning_rate",
                  "n_estimators",
                  "min_child_samples",
                  "subsample",
                  "subsample_freq",
              ],
              "relevantToOptimizer": [
                  "boosting_type",
                  "num_leaves",
                  "learning_rate",
                  "n_estimators",
                  "min_child_weight",
                  "min_child_samples",
                  "subsample",
                  "subsample_freq",
                  "colsample_bytree",
                  "reg_alpha",
                  "reg_lambda",
              ],
              "additionalProperties": False,
              "properties": {
                  "boosting_type": {
                      "anyOf": [
                          {"enum": ["gbdt", "dart"]},
                          {"enum": ["goss", "rf"], "forOptimizer": False},
                      ],
                      "default": "gbdt",
                      "description": "‘gbdt’, traditional Gradient Boosting Decision Tree. ‘dart’, Dropouts meet Multiple Additive Regression Trees. ‘goss’, Gradient-based One-Side Sampling. ‘rf’, Random Forest.",
                  },
                  "num_leaves": {
                      "anyOf": [
                          {"type": "integer", "forOptimizer": False},
                          {"enum": [2, 4, 8, 32, 64, 128, 16]},
                      ],
                      "default": 31,
                      "description": "Maximum tree leaves for base learners",
                  },
                  "max_depth": {
                      "anyOf": [
                          {
                              "type": "integer",
                              "minimumForOptimizer": 3,
                              "maximumForOptimizer": 5,
                          }
                      ],
                      "default": -1,
                      "description": "Maximum tree depth for base learners, <=0 means no limit",
                  },
                  "learning_rate": {
                      "type": "number",
                      "minimumForOptimizer": 0.02,
                      "maximumForOptimizer": 1.0,
                      "distribution": "loguniform",
                      "default": 0.1,
                      "description": "Boosting learning rate.",
                  },
                  "n_estimators": {
                      "type": "integer",
                      "minimumForOptimizer": 50,
                      "maximumForOptimizer": 1000,
                      "distribution": "uniform",
                      "default": 200,
                      "description": "Number of boosted trees to fit.",
                  },
                  "subsample_for_bin": {
                      "type": "integer",
                      "default": 200000,
                      "description": "Number of samples for constructing bins.",
                  },
                  "objective": {
                      "anyOf": [
                          {"type": "object"},
                          {"enum": ["binary", "multiclass", None]},
                      ],
                      "default": None,
                      "description": "Specify the learning task and the corresponding learning objective or a custom objective function to be used",
                  },
                  "class_weight": {
                      "anyOf": [{"type": "object"}, {"enum": ["balanced", None]}],
                      "default": None,
                      "description": "Weights associated with classes",
                  },
                  "min_split_gain": {
                      "type": "number",
                      "default": 0.0,
                      "description": "Minimum loss reduction required to make a further partition on a leaf node of the tree.",
                  },
                  "min_child_weight": {
                      "type": "number",
                      "minimumForOptimizer": 0.0001,
                      "maximumForOptimizer": 0.01,
                      "default": 1e-3,
                      "description": "Minimum sum of instance weight (hessian) needed in a child (leaf).",
                  },
                  "min_child_samples": {
                      "type": "integer",
                      "minimumForOptimizer": 5,
                      "maximumForOptimizer": 30,
                      "distribution": "uniform",
                      "default": 20,
                      "description": "Minimum number of data needed in a child (leaf).",
                  },
                  "subsample": {
                      "type": "number",
                      "minimum": 0.0,
                      "maximum": 1.0,
                      "exclusiveMinimum": True,
                      "minimumForOptimizer": 0.01,
                      "maximumForOptimizer": 1.0,
                      "distribution": "uniform",
                      "default": 1.0,
                      "description": "Subsample ratio of the training instance.",
                  },
                  "subsample_freq": {
                      "type": "integer",
                      "minimumForOptimizer": 0,
                      "maximumForOptimizer": 5,
                      "distribution": "uniform",
                      "default": 0,
                      "description": "Frequence of subsample, <=0 means no enable.",
                  },
                  "colsample_bytree": {
                      "type": "number",
                      "default": 1.0,
                      "minimumForOptimizer": 0.01,
                      "maximumForOptimizer": 1.0,
                      "description": "Subsample ratio of columns when constructing each tree.",
                  },
                  "reg_alpha": {
                      "type": "number",
                      "minimumForOptimizer": 0.0,
                      "maximumForOptimizer": 1.0,
                      "default": 0.0,
                      "description": "L1 regularization term on weights.",
                  },
                  "reg_lambda": {
                      "type": "number",
                      "minimumForOptimizer": 0.0,
                      "maximumForOptimizer": 1.0,
                      "default": 0.0,
                      "description": "L2 regularization term on weights.",
                  },
                  "random_state": {
                      "anyOf": [
                          {"type": "integer"},
                          {"laleType": "numpy.random.RandomState"},
                          {"enum": [None]},
                      ],
                      "default": None,
                      "description": "Random number seed. If None, default seeds in C++ code will be used.",
                  },
                  "n_jobs": {
                      "type": "integer",
                      "default": -1,
                      "description": "Number of parallel threads.",
                  },
                  "silent": {
                      "type": "boolean",
                      "default": True,
                      "description": "Whether to print messages while running boosting.",
                  },
                  "importance_type": {
                      "enum": ["split", "gain"],
                      "default": "split",
                      "description": "The type of feature importance to be filled into feature_importances_.",
                  },
              },
          },
          {
              "description": "boosting_type `rf` needs bagging (which means subsample_freq > 0 and subsample < 1.0)",
              "anyOf": [
                  {
                      "type": "object",
                      "properties": {"boosting_type": {"not": {"enum": ["rf"]}}},
                  },
                  {
                      "allOf": [
                          {
                              "type": "object",
                              "properties": {"subsample_freq": {"not": {"enum": [0]}}},
                          },
                          {
                              "type": "object",
                              "properties": {"subsample": {"not": {"enum": [1.0]}}},
                          },
                      ]
                  },
              ],
          },
          {
              "description": "boosting_type `goss` can not use bagging (which means subsample_freq = 0 and subsample = 1.0)",
              "anyOf": [
                  {
                      "type": "object",
                      "properties": {"boosting_type": {"not": {"enum": ["goss"]}}},
                  },
                  {
                      "allOf": [
                          {
                              "type": "object",
                              "properties": {"subsample_freq": {"enum": [0]}},
                          },
                          {
                              "type": "object",
                              "properties": {"subsample": {"enum": [1.0]}},
                          },
                      ]
                  },
              ],
          },
      ],
  }
  
  _input_fit_schema = {
      "description": "Build a lightgbm model from the training set (X, y).",
      "type": "object",
      "required": ["X", "y"],
      "properties": {
          "X": {
              "type": "array",
              "items": {
                  "type": "array",
                  "items": {"type": "number"},
              },
              "description": "The input samples. Internally, it will be converted to",
          },
          "y": {
              "anyOf": [
                  {"type": "array", "items": {"type": "number"}},
                  {"type": "array", "items": {"type": "string"}},
                  {"type": "array", "items": {"type": "boolean"}},
              ],
              "description": "Labels",
          },
          "sample_weight": {
              "anyOf": [
                  {
                      "type": "array",
                      "items": {"type": "number"},
                  },
                  {"enum": [None]},
              ],
              "default": None,
              "description": "Weights of training data.",
          },
          "init_score": {
              "anyOf": [
                  {
                      "type": "array",
                      "items": {"type": "number"},
                  },
                  {"enum": [None]},
              ],
              "default": None,
              "description": "Init score of training data.",
          },
          "group": {"default": None, "description": "Group data of training data."},
          "eval_set": {
              "default": None,
              "description": "A list of (X, y) tuple pairs to use as validation sets.",
          },
          "eval_names": {"default": None, "description": "Names of eval_set."},
          "eval_sample_weight": {"default": None, "description": "Weights of eval data."},
          "eval_class_weight": {
              "anyOf": [{"type": "array", "items": {"type": "number"}}, {"enum": [None]}],
              "default": None,
              "description": "Class weights of eval data.",
          },
          "eval_init_score": {"default": None, "description": "Init score of eval data."},
          "eval_group": {"default": None, "description": "Group data of eval data."},
          "eval_metric": {
              "anyOf": [
                  {"type": "array", "items": {"type": "string"}},
                  {"enum": ["logloss", None]},
                  {"laleType": "callable"},
              ],
              "default": None,
              "description": "string, list of strings, callable or None, optional (default=None).",
          },
          "early_stopping_rounds": {
              "anyOf": [{"type": "integer"}, {"enum": [None]}],
              "default": None,
              "description": "Activates early stopping. The model will train until the validation score stops improving.",
          },
          "verbose": {
              "anyOf": [{"type": "boolean"}, {"type": "integer"}],
              "default": True,
              "description": "Requires at least one evaluation data.",
          },
          "feature_name": {
              "anyOf": [
                  {"type": "array", "items": {"type": "string"}},
                  {"enum": ["auto"]},
              ],
              "default": "auto",
              "description": "Feature names. If ‘auto’ and data is pandas DataFrame, data columns names are used.",
          },
          "categorical_feature": {
              "anyOf": [
                  {
                      "type": "array",
                      "items": {"anyOf": [{"type": "string"}, {"type": "integer"}]},
                  },
                  {"enum": ["auto"]},
              ],
              "default": "auto",
              "description": "Categorical features. If list of int, interpreted as indices. If list of strings, interpreted as feature names.",
          },
          "callbacks": {
              "anyOf": [{"type": "array", "items": {"type": "object"}}, {"enum": [None]}],
              "default": None,
              "description": "List of callback functions that are applied at each iteration. ",
          },
      },
  }
  _input_predict_schema = {
      "description": "Return the predicted value for each sample.",
      "type": "object",
      "properties": {
          "X": {
              "type": "array",
              "items": {
                  "type": "array",
                  "items": {"type": "number"},
              },
              "description": " Input features matrix.",
          },
          "raw_score": {
              "type": "boolean",
              "default": False,
              "description": "Whether to predict raw scores.",
          },
          "num_iteration": {
              "anyOf": [{"type": "integer"}, {"enum": [None]}],
              "default": None,
              "description": "Limit number of iterations in the prediction.",
          },
          "pred_leaf": {
              "type": "boolean",
              "default": False,
              "description": "Whether to predict leaf index.",
          },
          "pred_contrib": {
              "type": "boolean",
              "default": False,
              "description": "Whether to predict feature contributions.",
          },
      },
  }
  
  _output_predict_schema = {
      "description": "Return the predicted value for each sample.",
      "anyOf": [
          {"type": "array", "items": {"type": "number"}},
          {"type": "array", "items": {"type": "string"}},
          {"type": "array", "items": {"type": "boolean"}},
      ],
  }
  
  _input_predict_proba_schema = {
      "description": "Return the predicted probability for each class for each sample.",
      "type": "object",
      "properties": {
          "X": {
              "type": "array",
              "items": {
                  "type": "array",
                  "items": {"type": "number"},
              },
              "description": " Input features matrix.",
          },
          "raw_score": {
              "type": "boolean",
              "default": False,
              "description": "Whether to predict raw scores.",
          },
          "num_iteration": {
              "anyOf": [{"type": "integer"}, {"enum": [None]}],
              "default": None,
              "description": "Limit number of iterations in the prediction.",
          },
          "pred_leaf": {
              "type": "boolean",
              "default": False,
              "description": "Whether to predict leaf index.",
          },
          "pred_contrib": {
              "type": "boolean",
              "default": False,
              "description": "Whether to predict feature contributions.",
          },
      },
  }
  _output_predict_proba_schema = {
      "description": "Return the predicted probability for each class for each sample.",
      "type": "array",
      "items": {
          "type": "array",
          "items": {"type": "number"},
      },
  }
  _combined_schemas = {
      "$schema": "http://json-schema.org/draft-04/schema#",
      "description": "Combined schema for expected data and hyperparameters.",
      "documentation_url": "https://lale.readthedocs.io/en/latest/modules/lale.lib.lightgbm.LGBMClassifier.html",
      "import_from": "lightgbm.sklearn",
      "type": "object",
      "tags": {"pre": [], "op": ["estimator", "classifier"], "post": []},
      "properties": {
          "hyperparams": _hyperparams_schema,
          "input_fit": _input_fit_schema,
          "input_predict": _input_predict_schema,
          "output_predict": _output_predict_schema,
          "input_predict_proba": _input_predict_proba_schema,
          "output_predict_proba": _output_predict_proba_schema,
      },
  }
  
  
  LGBMClassifier = lale.operators.make_operator(_LGBMClassifierImpl, _combined_schemas)
  
  lale.docstrings.set_docstrings(LGBMClassifier)
  