import copy import pandas as pd from sklearn.preprocessing import OrdinalEncoder from skopt.space import Integer, Real from evalml.model_family import ModelFamily from evalml.pipelines.components.estimators import Estimator from evalml.problem_types import ProblemTypes from evalml.utils import SEED_BOUNDS, get_random_seed, import_or_raise from evalml.utils.gen_utils import ( _convert_to_woodwork_structure, _convert_woodwork_types_wrapper, _rename_column_names_to_numeric ) [docs]class LightGBMRegressor(Estimator): """LightGBM Regressor""" name = "LightGBM Regressor" hyperparameter_ranges = { "learning_rate": Real(0.000001, 1), "boosting_type": ["gbdt", "dart", "goss", "rf"], "n_estimators": Integer(10, 100), "max_depth": Integer(0, 10), "num_leaves": Integer(2, 100), "min_child_samples": Integer(1, 100), "bagging_fraction": Real(0.000001, 1), "bagging_freq": Integer(0, 1) } model_family = ModelFamily.LIGHTGBM supported_problem_types = [ProblemTypes.REGRESSION] SEED_MIN = 0 SEED_MAX = SEED_BOUNDS.max_bound [docs] def __init__(self, boosting_type="gbdt", learning_rate=0.1, n_estimators=20, max_depth=0, num_leaves=31, min_child_samples=20, n_jobs=-1, random_state=0, bagging_fraction=0.9, bagging_freq=0, **kwargs): # lightGBM's current release doesn't currently support numpy.random.RandomState as the random_state value so we convert to int instead random_seed = get_random_seed(random_state, self.SEED_MIN, self.SEED_MAX) parameters = {"boosting_type": boosting_type, "learning_rate": learning_rate, "n_estimators": n_estimators, "max_depth": max_depth, "num_leaves": num_leaves, "min_child_samples": min_child_samples, "n_jobs": n_jobs, "bagging_freq": bagging_freq, "bagging_fraction": bagging_fraction} parameters.update(kwargs) lg_parameters = copy.copy(parameters) # when boosting type is random forest (rf), LightGBM requires bagging_freq == 1 and 0 < bagging_fraction < 1.0 if boosting_type == "rf": lg_parameters['bagging_freq'] = 1 # when boosting type is goss, LightGBM requires bagging_fraction == 1 elif boosting_type == "goss": lg_parameters['bagging_fraction'] = 1 # avoid lightgbm warnings having to do with parameter aliases if lg_parameters['bagging_freq'] is not None or lg_parameters['bagging_fraction'] is not None: lg_parameters.update({'subsample': None, 'subsample_freq': None}) lgbm_error_msg = "LightGBM is not installed. Please install using `pip install lightgbm`." lgbm = import_or_raise("lightgbm", error_msg=lgbm_error_msg) self._ordinal_encoder = None lgbm_regressor = lgbm.sklearn.LGBMRegressor(random_state=random_seed, **lg_parameters) super().__init__(parameters=parameters, component_obj=lgbm_regressor, random_state=random_seed) def _encode_categories(self, X, fit=False): """Encodes each categorical feature using ordinal encoding.""" X_encoded = _convert_to_woodwork_structure(X) X_encoded = _rename_column_names_to_numeric(X_encoded) cat_cols = list(X_encoded.select('category').columns) X_encoded = _convert_woodwork_types_wrapper(X_encoded.to_dataframe()) if len(cat_cols) == 0: return X_encoded if fit: self._ordinal_encoder = OrdinalEncoder() encoder_output = self._ordinal_encoder.fit_transform(X_encoded[cat_cols]) else: encoder_output = self._ordinal_encoder.transform(X_encoded[cat_cols]) X_encoded[cat_cols] = pd.DataFrame(encoder_output) X_encoded[cat_cols] = X_encoded[cat_cols].astype('category') return X_encoded [docs] def fit(self, X, y=None): X_encoded = self._encode_categories(X, fit=True) return super().fit(X_encoded, y) [docs] def predict(self, X): X_encoded = self._encode_categories(X) predictions = super().predict(X_encoded) return predictions