Source code for lightgbm.sklearn

# coding: utf-8
# pylint: disable = invalid-name, W0105, C0111, C0301
"""Scikit-learn wrapper interface for LightGBM."""
from __future__ import absolute_import

import numpy as np
import warnings

from .basic import Dataset, LightGBMError
from .compat import (SKLEARN_INSTALLED, _LGBMClassifierBase,
                     LGBMNotFittedError, _LGBMLabelEncoder, _LGBMModelBase,
                     _LGBMRegressorBase, _LGBMCheckXY, _LGBMCheckArray, _LGBMCheckConsistentLength,
                     _LGBMAssertAllFinite, _LGBMCheckClassificationTargets, _LGBMComputeSampleWeight,
                     argc_, range_, string_type, DataFrame)
from .engine import train


def _objective_function_wrapper(func):
    """Decorate an objective function.

    Note
    ----
    For multi-class task, the y_pred is group by class_id first, then group by row_id.
    If you want to get i-th row y_pred in j-th class, the access way is y_pred[j * num_data + i]
    and you should group grad and hess in this way as well.

    Parameters
    ----------
    func : callable
        Expects a callable with signature ``func(y_true, y_pred)`` or ``func(y_true, y_pred, group):

            y_true : array-like of shape = [n_samples]
                The target values.
            y_pred : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
                The predicted values.
            group : array-like
                Group/query data, used for ranking task.

    Returns
    -------
    new_func : callable
        The new objective function as expected by ``lightgbm.engine.train``.
        The signature is ``new_func(preds, dataset)``:

            preds : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
                The predicted values.
            dataset : Dataset
                The training set from which the labels will be extracted using ``dataset.get_label()``.
    """
    def inner(preds, dataset):
        """Call passed function with appropriate arguments."""
        labels = dataset.get_label()
        argc = argc_(func)
        if argc == 2:
            grad, hess = func(labels, preds)
        elif argc == 3:
            grad, hess = func(labels, preds, dataset.get_group())
        else:
            raise TypeError("Self-defined objective function should have 2 or 3 arguments, got %d" % argc)
        """weighted for objective"""
        weight = dataset.get_weight()
        if weight is not None:
            """only one class"""
            if len(weight) == len(grad):
                grad = np.multiply(grad, weight)
                hess = np.multiply(hess, weight)
            else:
                num_data = len(weight)
                num_class = len(grad) // num_data
                if num_class * num_data != len(grad):
                    raise ValueError("Length of grad and hess should equal to num_class * num_data")
                for k in range_(num_class):
                    for i in range_(num_data):
                        idx = k * num_data + i
                        grad[idx] *= weight[i]
                        hess[idx] *= weight[i]
        return grad, hess
    return inner


def _eval_function_wrapper(func):
    """Decorate an eval function.

    Note
    ----
    For multi-class task, the y_pred is group by class_id first, then group by row_id.
    If you want to get i-th row y_pred in j-th class, the access way is y_pred[j * num_data + i].

    Parameters
    ----------
    func : callable
        Expects a callable with following signatures:
        ``func(y_true, y_pred)``,
        ``func(y_true, y_pred, weight)``
        or ``func(y_true, y_pred, weight, group)``
        and returns (eval_name->string, eval_result->float, is_bigger_better->bool):

            y_true : array-like of shape = [n_samples]
                The target values.
            y_pred : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
                The predicted values.
            weight : array-like of shape = [n_samples]
                The weight of samples.
            group : array-like
                Group/query data, used for ranking task.

    Returns
    -------
    new_func : callable
        The new eval function as expected by ``lightgbm.engine.train``.
        The signature is ``new_func(preds, dataset)``:

            preds : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)
                The predicted values.
            dataset : Dataset
                The training set from which the labels will be extracted using ``dataset.get_label()``.
    """
    def inner(preds, dataset):
        """Call passed function with appropriate arguments."""
        labels = dataset.get_label()
        argc = argc_(func)
        if argc == 2:
            return func(labels, preds)
        elif argc == 3:
            return func(labels, preds, dataset.get_weight())
        elif argc == 4:
            return func(labels, preds, dataset.get_weight(), dataset.get_group())
        else:
            raise TypeError("Self-defined eval function should have 2, 3 or 4 arguments, got %d" % argc)
    return inner


[docs]class LGBMModel(_LGBMModelBase): """Implementation of the scikit-learn API for LightGBM.""" 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., min_child_weight=1e-3, min_child_samples=20, subsample=1., subsample_freq=0, colsample_bytree=1., reg_alpha=0., reg_lambda=0., random_state=None, n_jobs=-1, silent=True, importance_type='split', **kwargs): r"""Construct a gradient boosting model. Parameters ---------- boosting_type : string, optional (default='gbdt') 'gbdt', traditional Gradient Boosting Decision Tree. 'dart', Dropouts meet Multiple Additive Regression Trees. 'goss', Gradient-based One-Side Sampling. 'rf', Random Forest. num_leaves : int, optional (default=31) Maximum tree leaves for base learners. max_depth : int, optional (default=-1) Maximum tree depth for base learners, -1 means no limit. learning_rate : float, optional (default=0.1) Boosting learning rate. You can use ``callbacks`` parameter of ``fit`` method to shrink/adapt learning rate in training using ``reset_parameter`` callback. Note, that this will ignore the ``learning_rate`` argument in training. n_estimators : int, optional (default=100) Number of boosted trees to fit. subsample_for_bin : int, optional (default=200000) Number of samples for constructing bins. objective : string, callable or None, optional (default=None) Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below). Default: 'regression' for LGBMRegressor, 'binary' or 'multiclass' for LGBMClassifier, 'lambdarank' for LGBMRanker. class_weight : dict, 'balanced' or None, optional (default=None) Weights associated with classes in the form ``{class_label: weight}``. Use this parameter only for multi-class classification task; for binary classification task you may use ``is_unbalance`` or ``scale_pos_weight`` parameters. The 'balanced' mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))``. If None, all classes are supposed to have weight one. Note, that these weights will be multiplied with ``sample_weight`` (passed through the ``fit`` method) if ``sample_weight`` is specified. min_split_gain : float, optional (default=0.) Minimum loss reduction required to make a further partition on a leaf node of the tree. min_child_weight : float, optional (default=1e-3) Minimum sum of instance weight (hessian) needed in a child (leaf). min_child_samples : int, optional (default=20) Minimum number of data needed in a child (leaf). subsample : float, optional (default=1.) Subsample ratio of the training instance. subsample_freq : int, optional (default=0) Frequence of subsample, <=0 means no enable. colsample_bytree : float, optional (default=1.) Subsample ratio of columns when constructing each tree. reg_alpha : float, optional (default=0.) L1 regularization term on weights. reg_lambda : float, optional (default=0.) L2 regularization term on weights. random_state : int or None, optional (default=None) Random number seed. If None, default seeds in C++ code will be used. n_jobs : int, optional (default=-1) Number of parallel threads. silent : bool, optional (default=True) Whether to print messages while running boosting. importance_type : string, optional (default='split') The type of feature importance to be filled into ``feature_importances_``. If 'split', result contains numbers of times the feature is used in a model. If 'gain', result contains total gains of splits which use the feature. **kwargs Other parameters for the model. Check http://lightgbm.readthedocs.io/en/latest/Parameters.html for more parameters. Note ---- \*\*kwargs is not supported in sklearn, it may cause unexpected issues. Attributes ---------- n_features_ : int The number of features of fitted model. classes_ : array of shape = [n_classes] The class label array (only for classification problem). n_classes_ : int The number of classes (only for classification problem). best_score_ : dict or None The best score of fitted model. best_iteration_ : int or None The best iteration of fitted model if ``early_stopping_rounds`` has been specified. objective_ : string or callable The concrete objective used while fitting this model. booster_ : Booster The underlying Booster of this model. evals_result_ : dict or None The evaluation results if ``early_stopping_rounds`` has been specified. feature_importances_ : array of shape = [n_features] The feature importances (the higher, the more important the feature). Note ---- A custom objective function can be provided for the ``objective`` parameter. In this case, it should have the signature ``objective(y_true, y_pred) -> grad, hess`` or ``objective(y_true, y_pred, group) -> grad, hess``: y_true : array-like of shape = [n_samples] The target values. y_pred : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task) The predicted values. group : array-like Group/query data, used for ranking task. grad : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task) The value of the gradient for each sample point. hess : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task) The value of the second derivative for each sample point. For multi-class task, the y_pred is group by class_id first, then group by row_id. If you want to get i-th row y_pred in j-th class, the access way is y_pred[j * num_data + i] and you should group grad and hess in this way as well. """ if not SKLEARN_INSTALLED: raise LightGBMError('Scikit-learn is required for this module') self.boosting_type = boosting_type self.objective = objective self.num_leaves = num_leaves self.max_depth = max_depth self.learning_rate = learning_rate self.n_estimators = n_estimators self.subsample_for_bin = subsample_for_bin self.min_split_gain = min_split_gain self.min_child_weight = min_child_weight self.min_child_samples = min_child_samples self.subsample = subsample self.subsample_freq = subsample_freq self.colsample_bytree = colsample_bytree self.reg_alpha = reg_alpha self.reg_lambda = reg_lambda self.random_state = random_state self.n_jobs = n_jobs self.silent = silent self.importance_type = importance_type self._Booster = None self._evals_result = None self._best_score = None self._best_iteration = None self._other_params = {} self._objective = objective self.class_weight = class_weight self._n_features = None self._classes = None self._n_classes = None self.set_params(**kwargs)
[docs] def get_params(self, deep=True): """Get parameters for this estimator. Parameters ---------- deep : bool, optional (default=True) If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : dict Parameter names mapped to their values. """ params = super(LGBMModel, self).get_params(deep=deep) params.update(self._other_params) return params
# minor change to support `**kwargs`
[docs] def set_params(self, **params): """Set the parameters of this estimator. Parameters ---------- **params Parameter names with their new values. Returns ------- self : object Returns self. """ for key, value in params.items(): setattr(self, key, value) if hasattr(self, '_' + key): setattr(self, '_' + key, value) self._other_params[key] = value return self
[docs] def fit(self, X, y, sample_weight=None, init_score=None, group=None, eval_set=None, eval_names=None, eval_sample_weight=None, eval_class_weight=None, eval_init_score=None, eval_group=None, eval_metric=None, early_stopping_rounds=None, verbose=True, feature_name='auto', categorical_feature='auto', callbacks=None): """Build a gradient boosting model from the training set (X, y). Parameters ---------- X : array-like or sparse matrix of shape = [n_samples, n_features] Input feature matrix. y : array-like of shape = [n_samples] The target values (class labels in classification, real numbers in regression). sample_weight : array-like of shape = [n_samples] or None, optional (default=None) Weights of training data. init_score : array-like of shape = [n_samples] or None, optional (default=None) Init score of training data. group : array-like or None, optional (default=None) Group data of training data. eval_set : list or None, optional (default=None) A list of (X, y) tuple pairs to use as validation sets. eval_names : list of strings or None, optional (default=None) Names of eval_set. eval_sample_weight : list of arrays or None, optional (default=None) Weights of eval data. eval_class_weight : list or None, optional (default=None) Class weights of eval data. eval_init_score : list of arrays or None, optional (default=None) Init score of eval data. eval_group : list of arrays or None, optional (default=None) Group data of eval data. eval_metric : string, list of strings, callable or None, optional (default=None) If string, it should be a built-in evaluation metric to use. If callable, it should be a custom evaluation metric, see note below for more details. In either case, the ``metric`` from the model parameters will be evaluated and used as well. Default: 'l2' for LGBMRegressor, 'logloss' for LGBMClassifier, 'ndcg' for LGBMRanker. early_stopping_rounds : int or None, optional (default=None) Activates early stopping. The model will train until the validation score stops improving. Validation score needs to improve at least every ``early_stopping_rounds`` round(s) to continue training. Requires at least one validation data and one metric. If there's more than one, will check all of them. But the training data is ignored anyway. verbose : bool or int, optional (default=True) Requires at least one evaluation data. If True, the eval metric on the eval set is printed at each boosting stage. If int, the eval metric on the eval set is printed at every ``verbose`` boosting stage. The last boosting stage or the boosting stage found by using ``early_stopping_rounds`` is also printed. Example ------- With ``verbose`` = 4 and at least one item in ``eval_set``, an evaluation metric is printed every 4 (instead of 1) boosting stages. feature_name : list of strings or 'auto', optional (default='auto') Feature names. If 'auto' and data is pandas DataFrame, data columns names are used. categorical_feature : list of strings or int, or 'auto', optional (default='auto') Categorical features. If list of int, interpreted as indices. If list of strings, interpreted as feature names (need to specify ``feature_name`` as well). If 'auto' and data is pandas DataFrame, pandas categorical columns are used. All values in categorical features should be less than int32 max value (2147483647). Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values. callbacks : list of callback functions or None, optional (default=None) List of callback functions that are applied at each iteration. See Callbacks in Python API for more information. Returns ------- self : object Returns self. Note ---- Custom eval function expects a callable with following signatures: ``func(y_true, y_pred)``, ``func(y_true, y_pred, weight)`` or ``func(y_true, y_pred, weight, group)`` and returns (eval_name, eval_result, is_bigger_better) or list of (eval_name, eval_result, is_bigger_better): y_true : array-like of shape = [n_samples] The target values. y_pred : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task) The predicted values. weight : array-like of shape = [n_samples] The weight of samples. group : array-like Group/query data, used for ranking task. eval_name : string The name of evaluation. eval_result : float The eval result. is_bigger_better : bool Is eval result bigger better, e.g. AUC is bigger_better. For multi-class task, the y_pred is group by class_id first, then group by row_id. If you want to get i-th row y_pred in j-th class, the access way is y_pred[j * num_data + i]. """ if self._objective is None: if isinstance(self, LGBMRegressor): self._objective = "regression" elif isinstance(self, LGBMClassifier): self._objective = "binary" elif isinstance(self, LGBMRanker): self._objective = "lambdarank" else: raise ValueError("Unknown LGBMModel type.") if callable(self._objective): self._fobj = _objective_function_wrapper(self._objective) else: self._fobj = None evals_result = {} params = self.get_params() # user can set verbose with kwargs, it has higher priority if not any(verbose_alias in params for verbose_alias in ('verbose', 'verbosity')) and self.silent: params['verbose'] = -1 params.pop('silent', None) params.pop('importance_type', None) params.pop('n_estimators', None) params.pop('class_weight', None) if self._n_classes is not None and self._n_classes > 2: params['num_class'] = self._n_classes if hasattr(self, '_eval_at'): params['eval_at'] = self._eval_at params['objective'] = self._objective if self._fobj: params['objective'] = 'None' # objective = nullptr for unknown objective if callable(eval_metric): feval = _eval_function_wrapper(eval_metric) else: feval = None # register default metric for consistency with callable eval_metric case original_metric = self._objective if isinstance(self._objective, string_type) else None if original_metric is None: # try to deduce from class instance if isinstance(self, LGBMRegressor): original_metric = "l2" elif isinstance(self, LGBMClassifier): original_metric = "multi_logloss" if self._n_classes > 2 else "binary_logloss" elif isinstance(self, LGBMRanker): original_metric = "ndcg" # overwrite default metric by explicitly set metric for metric_alias in ['metric', 'metrics', 'metric_types']: if metric_alias in params: original_metric = params.pop(metric_alias) # concatenate metric from params (or default if not provided in params) and eval_metric original_metric = [original_metric] if isinstance(original_metric, (string_type, type(None))) else original_metric eval_metric = [eval_metric] if isinstance(eval_metric, (string_type, type(None))) else eval_metric params['metric'] = set(original_metric + eval_metric) if not isinstance(X, DataFrame): X, y = _LGBMCheckXY(X, y, accept_sparse=True, force_all_finite=False, ensure_min_samples=2) _LGBMCheckConsistentLength(X, y, sample_weight) if self.class_weight is not None: class_sample_weight = _LGBMComputeSampleWeight(self.class_weight, y) if sample_weight is None or len(sample_weight) == 0: sample_weight = class_sample_weight else: sample_weight = np.multiply(sample_weight, class_sample_weight) self._n_features = X.shape[1] def _construct_dataset(X, y, sample_weight, init_score, group, params): ret = Dataset(X, label=y, weight=sample_weight, group=group, params=params) return ret.set_init_score(init_score) train_set = _construct_dataset(X, y, sample_weight, init_score, group, params) valid_sets = [] if eval_set is not None: def _get_meta_data(collection, i): if collection is None: return None elif isinstance(collection, list): return collection[i] if len(collection) > i else None elif isinstance(collection, dict): return collection.get(i, None) else: raise TypeError('eval_sample_weight, eval_class_weight, eval_init_score, and eval_group ' 'should be dict or list') if isinstance(eval_set, tuple): eval_set = [eval_set] for i, valid_data in enumerate(eval_set): # reduce cost for prediction training data if valid_data[0] is X and valid_data[1] is y: valid_set = train_set else: valid_weight = _get_meta_data(eval_sample_weight, i) if _get_meta_data(eval_class_weight, i) is not None: valid_class_sample_weight = _LGBMComputeSampleWeight(_get_meta_data(eval_class_weight, i), valid_data[1]) if valid_weight is None or len(valid_weight) == 0: valid_weight = valid_class_sample_weight else: valid_weight = np.multiply(valid_weight, valid_class_sample_weight) valid_init_score = _get_meta_data(eval_init_score, i) valid_group = _get_meta_data(eval_group, i) valid_set = _construct_dataset(valid_data[0], valid_data[1], valid_weight, valid_init_score, valid_group, params) valid_sets.append(valid_set) self._Booster = train(params, train_set, self.n_estimators, valid_sets=valid_sets, valid_names=eval_names, early_stopping_rounds=early_stopping_rounds, evals_result=evals_result, fobj=self._fobj, feval=feval, verbose_eval=verbose, feature_name=feature_name, categorical_feature=categorical_feature, callbacks=callbacks) if evals_result: self._evals_result = evals_result if early_stopping_rounds is not None: self._best_iteration = self._Booster.best_iteration self._best_score = self._Booster.best_score # free dataset self.booster_.free_dataset() del train_set, valid_sets return self
[docs] def predict(self, X, raw_score=False, num_iteration=None, pred_leaf=False, pred_contrib=False, **kwargs): """Return the predicted value for each sample. Parameters ---------- X : array-like or sparse matrix of shape = [n_samples, n_features] Input features matrix. raw_score : bool, optional (default=False) Whether to predict raw scores. num_iteration : int or None, optional (default=None) Limit number of iterations in the prediction. If None, if the best iteration exists, it is used; otherwise, all trees are used. If <= 0, all trees are used (no limits). pred_leaf : bool, optional (default=False) Whether to predict leaf index. pred_contrib : bool, optional (default=False) Whether to predict feature contributions. Note ---- If you want to get more explanation for your model's predictions using SHAP values like SHAP interaction values, you can install shap package (https://github.com/slundberg/shap). **kwargs Other parameters for the prediction. Returns ------- predicted_result : array-like of shape = [n_samples] or shape = [n_samples, n_classes] The predicted values. X_leaves : array-like of shape = [n_samples, n_trees] or shape [n_samples, n_trees * n_classes] If ``pred_leaf=True``, the predicted leaf every tree for each sample. X_SHAP_values : array-like of shape = [n_samples, n_features + 1] or shape [n_samples, (n_features + 1) * n_classes] If ``pred_contrib=True``, the each feature contributions for each sample. """ if self._n_features is None: raise LGBMNotFittedError("Estimator not fitted, call `fit` before exploiting the model.") if not isinstance(X, DataFrame): X = _LGBMCheckArray(X, accept_sparse=True, force_all_finite=False) n_features = X.shape[1] if self._n_features != n_features: raise ValueError("Number of features of the model must " "match the input. Model n_features_ is %s and " "input n_features is %s " % (self._n_features, n_features)) return self.booster_.predict(X, raw_score=raw_score, num_iteration=num_iteration, pred_leaf=pred_leaf, pred_contrib=pred_contrib, **kwargs)
@property def n_features_(self): """Get the number of features of fitted model.""" if self._n_features is None: raise LGBMNotFittedError('No n_features found. Need to call fit beforehand.') return self._n_features @property def best_score_(self): """Get the best score of fitted model.""" if self._n_features is None: raise LGBMNotFittedError('No best_score found. Need to call fit beforehand.') return self._best_score @property def best_iteration_(self): """Get the best iteration of fitted model.""" if self._n_features is None: raise LGBMNotFittedError('No best_iteration found. Need to call fit with early_stopping_rounds beforehand.') return self._best_iteration @property def objective_(self): """Get the concrete objective used while fitting this model.""" if self._n_features is None: raise LGBMNotFittedError('No objective found. Need to call fit beforehand.') return self._objective @property def booster_(self): """Get the underlying lightgbm Booster of this model.""" if self._Booster is None: raise LGBMNotFittedError('No booster found. Need to call fit beforehand.') return self._Booster @property def evals_result_(self): """Get the evaluation results.""" if self._n_features is None: raise LGBMNotFittedError('No results found. Need to call fit with eval_set beforehand.') return self._evals_result @property def feature_importances_(self): """Get feature importances. Note ---- Feature importance in sklearn interface used to normalize to 1, it's deprecated after 2.0.4 and is the same as Booster.feature_importance() now. ``importance_type`` attribute is passed to the function to configure the type of importance values to be extracted. """ if self._n_features is None: raise LGBMNotFittedError('No feature_importances found. Need to call fit beforehand.') return self.booster_.feature_importance(importance_type=self.importance_type)
[docs]class LGBMRegressor(LGBMModel, _LGBMRegressorBase): """LightGBM regressor."""
[docs] def fit(self, X, y, sample_weight=None, init_score=None, eval_set=None, eval_names=None, eval_sample_weight=None, eval_init_score=None, eval_metric=None, early_stopping_rounds=None, verbose=True, feature_name='auto', categorical_feature='auto', callbacks=None): """Docstring is inherited from the LGBMModel.""" super(LGBMRegressor, self).fit(X, y, sample_weight=sample_weight, init_score=init_score, eval_set=eval_set, eval_names=eval_names, eval_sample_weight=eval_sample_weight, eval_init_score=eval_init_score, eval_metric=eval_metric, early_stopping_rounds=early_stopping_rounds, verbose=verbose, feature_name=feature_name, categorical_feature=categorical_feature, callbacks=callbacks) return self
_base_doc = LGBMModel.fit.__doc__ fit.__doc__ = (_base_doc[:_base_doc.find('eval_class_weight :')] + _base_doc[_base_doc.find('eval_init_score :'):])
[docs]class LGBMClassifier(LGBMModel, _LGBMClassifierBase): """LightGBM classifier."""
[docs] def fit(self, X, y, sample_weight=None, init_score=None, eval_set=None, eval_names=None, eval_sample_weight=None, eval_class_weight=None, eval_init_score=None, eval_metric=None, early_stopping_rounds=None, verbose=True, feature_name='auto', categorical_feature='auto', callbacks=None): """Docstring is inherited from the LGBMModel.""" _LGBMAssertAllFinite(y) _LGBMCheckClassificationTargets(y) self._le = _LGBMLabelEncoder().fit(y) _y = self._le.transform(y) self._classes = self._le.classes_ self._n_classes = len(self._classes) if self._n_classes > 2: # Switch to using a multiclass objective in the underlying LGBM instance ova_aliases = ("multiclassova", "multiclass_ova", "ova", "ovr") if self._objective not in ova_aliases and not callable(self._objective): self._objective = "multiclass" if eval_metric in ('logloss', 'binary_logloss'): eval_metric = "multi_logloss" elif eval_metric in ('error', 'binary_error'): eval_metric = "multi_error" else: if eval_metric in ('logloss', 'multi_logloss'): eval_metric = 'binary_logloss' elif eval_metric in ('error', 'multi_error'): eval_metric = 'binary_error' if eval_set is not None: if isinstance(eval_set, tuple): eval_set = [eval_set] for i, (valid_x, valid_y) in enumerate(eval_set): if valid_x is X and valid_y is y: eval_set[i] = (valid_x, _y) else: eval_set[i] = (valid_x, self._le.transform(valid_y)) super(LGBMClassifier, self).fit(X, _y, sample_weight=sample_weight, init_score=init_score, eval_set=eval_set, eval_names=eval_names, eval_sample_weight=eval_sample_weight, eval_class_weight=eval_class_weight, eval_init_score=eval_init_score, eval_metric=eval_metric, early_stopping_rounds=early_stopping_rounds, verbose=verbose, feature_name=feature_name, categorical_feature=categorical_feature, callbacks=callbacks) return self
fit.__doc__ = LGBMModel.fit.__doc__
[docs] def predict(self, X, raw_score=False, num_iteration=None, pred_leaf=False, pred_contrib=False, **kwargs): """Docstring is inherited from the LGBMModel.""" result = self.predict_proba(X, raw_score, num_iteration, pred_leaf, pred_contrib, **kwargs) if raw_score or pred_leaf or pred_contrib: return result else: class_index = np.argmax(result, axis=1) return self._le.inverse_transform(class_index)
predict.__doc__ = LGBMModel.predict.__doc__
[docs] def predict_proba(self, X, raw_score=False, num_iteration=None, pred_leaf=False, pred_contrib=False, **kwargs): """Return the predicted probability for each class for each sample. Parameters ---------- X : array-like or sparse matrix of shape = [n_samples, n_features] Input features matrix. raw_score : bool, optional (default=False) Whether to predict raw scores. num_iteration : int or None, optional (default=None) Limit number of iterations in the prediction. If None, if the best iteration exists, it is used; otherwise, all trees are used. If <= 0, all trees are used (no limits). pred_leaf : bool, optional (default=False) Whether to predict leaf index. pred_contrib : bool, optional (default=False) Whether to predict feature contributions. Note ---- If you want to get more explanation for your model's predictions using SHAP values like SHAP interaction values, you can install shap package (https://github.com/slundberg/shap). **kwargs Other parameters for the prediction. Returns ------- predicted_probability : array-like of shape = [n_samples, n_classes] The predicted probability for each class for each sample. X_leaves : array-like of shape = [n_samples, n_trees * n_classes] If ``pred_leaf=True``, the predicted leaf every tree for each sample. X_SHAP_values : array-like of shape = [n_samples, (n_features + 1) * n_classes] If ``pred_contrib=True``, the each feature contributions for each sample. """ result = super(LGBMClassifier, self).predict(X, raw_score, num_iteration, pred_leaf, pred_contrib, **kwargs) if self._n_classes > 2 or raw_score or pred_leaf or pred_contrib: return result else: return np.vstack((1. - result, result)).transpose()
@property def classes_(self): """Get the class label array.""" if self._classes is None: raise LGBMNotFittedError('No classes found. Need to call fit beforehand.') return self._classes @property def n_classes_(self): """Get the number of classes.""" if self._n_classes is None: raise LGBMNotFittedError('No classes found. Need to call fit beforehand.') return self._n_classes
[docs]class LGBMRanker(LGBMModel): """LightGBM ranker."""
[docs] def fit(self, X, y, sample_weight=None, init_score=None, group=None, eval_set=None, eval_names=None, eval_sample_weight=None, eval_init_score=None, eval_group=None, eval_metric=None, eval_at=[1], early_stopping_rounds=None, verbose=True, feature_name='auto', categorical_feature='auto', callbacks=None): """Docstring is inherited from the LGBMModel.""" # check group data if group is None: raise ValueError("Should set group for ranking task") if eval_set is not None: if eval_group is None: raise ValueError("Eval_group cannot be None when eval_set is not None") elif len(eval_group) != len(eval_set): raise ValueError("Length of eval_group should be equal to eval_set") elif (isinstance(eval_group, dict) and any(i not in eval_group or eval_group[i] is None for i in range_(len(eval_group))) or isinstance(eval_group, list) and any(group is None for group in eval_group)): raise ValueError("Should set group for all eval datasets for ranking task; " "if you use dict, the index should start from 0") self._eval_at = eval_at super(LGBMRanker, self).fit(X, y, sample_weight=sample_weight, init_score=init_score, group=group, eval_set=eval_set, eval_names=eval_names, eval_sample_weight=eval_sample_weight, eval_init_score=eval_init_score, eval_group=eval_group, eval_metric=eval_metric, early_stopping_rounds=early_stopping_rounds, verbose=verbose, feature_name=feature_name, categorical_feature=categorical_feature, callbacks=callbacks) return self
_base_doc = LGBMModel.fit.__doc__ fit.__doc__ = (_base_doc[:_base_doc.find('eval_class_weight :')] + _base_doc[_base_doc.find('eval_init_score :'):]) _base_doc = fit.__doc__ _before_early_stop, _early_stop, _after_early_stop = _base_doc.partition('early_stopping_rounds :') fit.__doc__ = (_before_early_stop + 'eval_at : list of int, optional (default=[1])\n' + ' ' * 12 + 'The evaluation positions of the specified metric.\n' + ' ' * 8 + _early_stop + _after_early_stop)