147 lines
5.3 KiB
Python
147 lines
5.3 KiB
Python
"""This module contains utility routines."""
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from ...base import is_classifier
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from .binning import _BinMapper
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def get_equivalent_estimator(estimator, lib="lightgbm", n_classes=None):
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"""Return an unfitted estimator from another lib with matching hyperparams.
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This utility function takes care of renaming the sklearn parameters into
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their LightGBM, XGBoost or CatBoost equivalent parameters.
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# unmapped XGB parameters:
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# - min_samples_leaf
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# - min_data_in_bin
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# - min_split_gain (there is min_split_loss though?)
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# unmapped Catboost parameters:
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# max_leaves
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# min_*
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"""
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if lib not in ("lightgbm", "xgboost", "catboost"):
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raise ValueError(
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"accepted libs are lightgbm, xgboost, and catboost. got {}".format(lib)
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)
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sklearn_params = estimator.get_params()
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if sklearn_params["loss"] == "auto":
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raise ValueError(
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"auto loss is not accepted. We need to know if "
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"the problem is binary or multiclass classification."
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)
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if sklearn_params["early_stopping"]:
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raise NotImplementedError("Early stopping should be deactivated.")
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lightgbm_loss_mapping = {
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"squared_error": "regression_l2",
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"absolute_error": "regression_l1",
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"log_loss": "binary" if n_classes == 2 else "multiclass",
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"gamma": "gamma",
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"poisson": "poisson",
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}
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lightgbm_params = {
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"objective": lightgbm_loss_mapping[sklearn_params["loss"]],
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"learning_rate": sklearn_params["learning_rate"],
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"n_estimators": sklearn_params["max_iter"],
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"num_leaves": sklearn_params["max_leaf_nodes"],
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"max_depth": sklearn_params["max_depth"],
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"min_data_in_leaf": sklearn_params["min_samples_leaf"],
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"reg_lambda": sklearn_params["l2_regularization"],
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"max_bin": sklearn_params["max_bins"],
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"min_data_in_bin": 1,
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"min_sum_hessian_in_leaf": 1e-3,
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"min_split_gain": 0,
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"verbosity": 10 if sklearn_params["verbose"] else -10,
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"boost_from_average": True,
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"enable_bundle": False, # also makes feature order consistent
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"subsample_for_bin": _BinMapper().subsample,
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"poisson_max_delta_step": 1e-12,
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"feature_fraction_bynode": sklearn_params["max_features"],
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}
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if sklearn_params["loss"] == "log_loss" and n_classes > 2:
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# LightGBM multiplies hessians by 2 in multiclass loss.
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lightgbm_params["min_sum_hessian_in_leaf"] *= 2
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# LightGBM 3.0 introduced a different scaling of the hessian for the multiclass
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# case.
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# It is equivalent of scaling the learning rate.
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# See https://github.com/microsoft/LightGBM/pull/3256.
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if n_classes is not None:
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lightgbm_params["learning_rate"] *= n_classes / (n_classes - 1)
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# XGB
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xgboost_loss_mapping = {
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"squared_error": "reg:linear",
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"absolute_error": "LEAST_ABSOLUTE_DEV_NOT_SUPPORTED",
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"log_loss": "reg:logistic" if n_classes == 2 else "multi:softmax",
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"gamma": "reg:gamma",
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"poisson": "count:poisson",
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}
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xgboost_params = {
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"tree_method": "hist",
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"grow_policy": "lossguide", # so that we can set max_leaves
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"objective": xgboost_loss_mapping[sklearn_params["loss"]],
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"learning_rate": sklearn_params["learning_rate"],
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"n_estimators": sklearn_params["max_iter"],
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"max_leaves": sklearn_params["max_leaf_nodes"],
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"max_depth": sklearn_params["max_depth"] or 0,
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"lambda": sklearn_params["l2_regularization"],
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"max_bin": sklearn_params["max_bins"],
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"min_child_weight": 1e-3,
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"verbosity": 2 if sklearn_params["verbose"] else 0,
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"silent": sklearn_params["verbose"] == 0,
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"n_jobs": -1,
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"colsample_bynode": sklearn_params["max_features"],
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}
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# Catboost
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catboost_loss_mapping = {
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"squared_error": "RMSE",
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# catboost does not support MAE when leaf_estimation_method is Newton
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"absolute_error": "LEAST_ASBOLUTE_DEV_NOT_SUPPORTED",
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"log_loss": "Logloss" if n_classes == 2 else "MultiClass",
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"gamma": None,
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"poisson": "Poisson",
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}
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catboost_params = {
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"loss_function": catboost_loss_mapping[sklearn_params["loss"]],
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"learning_rate": sklearn_params["learning_rate"],
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"iterations": sklearn_params["max_iter"],
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"depth": sklearn_params["max_depth"],
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"reg_lambda": sklearn_params["l2_regularization"],
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"max_bin": sklearn_params["max_bins"],
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"feature_border_type": "Median",
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"leaf_estimation_method": "Newton",
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"verbose": bool(sklearn_params["verbose"]),
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}
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if lib == "lightgbm":
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from lightgbm import LGBMClassifier, LGBMRegressor
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if is_classifier(estimator):
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return LGBMClassifier(**lightgbm_params)
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else:
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return LGBMRegressor(**lightgbm_params)
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elif lib == "xgboost":
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from xgboost import XGBClassifier, XGBRegressor
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if is_classifier(estimator):
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return XGBClassifier(**xgboost_params)
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else:
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return XGBRegressor(**xgboost_params)
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else:
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from catboost import CatBoostClassifier, CatBoostRegressor
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if is_classifier(estimator):
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return CatBoostClassifier(**catboost_params)
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else:
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return CatBoostRegressor(**catboost_params)
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