Source code for evalml.pipelines.components.estimators.classifiers.lightgbm_classifier

"""LightGBM Classifier."""
import copy

import numpy as np
import pandas as pd
from pandas.api.types import is_integer_dtype
from sklearn.preprocessing import OrdinalEncoder
from import Integer, Real

from evalml.model_family import ModelFamily
from evalml.pipelines.components.estimators import Estimator
from evalml.pipelines.components.transformers import LabelEncoder
from evalml.problem_types import ProblemTypes
from evalml.utils import (

[docs]class LightGBMClassifier(Estimator): """LightGBM Classifier. Args: boosting_type (string): Type of boosting to use. Defaults to "gbdt". - 'gbdt' uses traditional Gradient Boosting Decision Tree - "dart", uses Dropouts meet Multiple Additive Regression Trees - "goss", uses Gradient-based One-Side Sampling - "rf", uses Random Forest learning_rate (float): Boosting learning rate. Defaults to 0.1. n_estimators (int): Number of boosted trees to fit. Defaults to 100. max_depth (int): Maximum tree depth for base learners, <=0 means no limit. Defaults to 0. num_leaves (int): Maximum tree leaves for base learners. Defaults to 31. min_child_samples (int): Minimum number of data needed in a child (leaf). Defaults to 20. bagging_fraction (float): LightGBM will randomly select a subset of features on each iteration (tree) without resampling if this is smaller than 1.0. For example, if set to 0.8, LightGBM will select 80% of features before training each tree. This can be used to speed up training and deal with overfitting. Defaults to 0.9. bagging_freq (int): Frequency for bagging. 0 means bagging is disabled. k means perform bagging at every k iteration. Every k-th iteration, LightGBM will randomly select bagging_fraction * 100 % of the data to use for the next k iterations. Defaults to 0. n_jobs (int or None): Number of threads to run in parallel. -1 uses all threads. Defaults to -1. random_seed (int): Seed for the random number generator. Defaults to 0. """ name = "LightGBM Classifier" 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), } """{ "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 """ModelFamily.LIGHTGBM""" supported_problem_types = [ ProblemTypes.BINARY, ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_BINARY, ProblemTypes.TIME_SERIES_MULTICLASS, ] """[ ProblemTypes.BINARY, ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_BINARY, ProblemTypes.TIME_SERIES_MULTICLASS, ]""" SEED_MIN = 0 SEED_MAX = SEED_BOUNDS.max_bound """SEED_BOUNDS.max_bound""" def __init__( self, boosting_type="gbdt", learning_rate=0.1, n_estimators=100, max_depth=0, num_leaves=31, min_child_samples=20, bagging_fraction=0.9, bagging_freq=0, n_jobs=-1, random_seed=0, **kwargs, ): 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, "verbose": -1, } 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 self._label_encoder = None lgbm_classifier = lgbm.sklearn.LGBMClassifier( random_state=random_seed, **lg_parameters ) super().__init__( parameters=parameters, component_obj=lgbm_classifier, random_seed=random_seed, ) def _encode_categories(self, X, fit=False): """Encodes each categorical feature using ordinal encoding.""" X = infer_feature_types(X) cat_cols = list("category", return_schema=True).columns) if fit: self.input_feature_names = list(X.columns) X_encoded = _rename_column_names_to_numeric(X) rename_cols_dict = dict(zip(X.columns, X_encoded.columns)) cat_cols = [rename_cols_dict[col] for col in cat_cols] 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 def _encode_labels(self, y): y_encoded = infer_feature_types(y) # change only if dtype isn't int if not is_integer_dtype(y_encoded): self._label_encoder = LabelEncoder() y_encoded = pd.Series( self._label_encoder.fit_transform(None, y_encoded)[1], dtype="int64", ) return y_encoded
[docs] def fit(self, X, y=None): """Fits LightGBM classifier component to data. Args: X (pd.DataFrame): The input training data of shape [n_samples, n_features]. y (pd.Series): The target training data of length [n_samples]. Returns: self """ X = infer_feature_types(X) if y is not None: y = infer_feature_types(y) X_encoded = self._encode_categories(X, fit=True) y_encoded = self._encode_labels(y), y_encoded) return self
[docs] def predict(self, X): """Make predictions using the fitted LightGBM classifier. Args: X (pd.DataFrame): Data of shape [n_samples, n_features]. Returns: pd.DataFrame: Predicted values. """ X_encoded = self._encode_categories(X) predictions = super().predict(X_encoded) if not self._label_encoder: return predictions predictions = self._label_encoder.inverse_transform( predictions.astype(np.int64), ) return predictions
[docs] def predict_proba(self, X): """Make prediction probabilities using the fitted LightGBM classifier. Args: X (pd.DataFrame): Data of shape [n_samples, n_features]. Returns: pd.DataFrame: Predicted probability values. """ X_encoded = self._encode_categories(X) return super().predict_proba(X_encoded)