From d0f83b1538d5714d0c28b29c650994185daab0cb Mon Sep 17 00:00:00 2001 From: Reid Johnson Date: Mon, 15 Apr 2024 06:00:25 -0700 Subject: [PATCH] Update sklearn version docstrings --- quantile_forest/_quantile_forest.py | 44 ----------------------------- 1 file changed, 44 deletions(-) diff --git a/quantile_forest/_quantile_forest.py b/quantile_forest/_quantile_forest.py index 7936ebf..0b09606 100755 --- a/quantile_forest/_quantile_forest.py +++ b/quantile_forest/_quantile_forest.py @@ -875,10 +875,6 @@ class RandomForestQuantileRegressor(BaseForestQuantileRegressor): n_estimators : int, default=100 The number of trees in the forest. - .. sklearn-versionchanged:: 0.22 - The default value of `n_estimators` changed from 10 to 100 - in 0.22. - default_quantiles : float, list, or "mean", default=0.5 The default quantile or list of quantiles that the model tries to predict. Each quantile must be strictly between 0 and 1. If "mean", @@ -899,12 +895,6 @@ class RandomForestQuantileRegressor(BaseForestQuantileRegressor): Training using "absolute_error" is significantly slower than when using "squared_error". - .. sklearn-versionadded:: 0.18 - Mean Absolute Error (MAE) criterion. - - .. sklearn-versionadded:: 1.0 - Poisson criterion. - max_depth : int, default=None The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than @@ -918,9 +908,6 @@ class RandomForestQuantileRegressor(BaseForestQuantileRegressor): `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. - .. sklearn-versionchanged:: 0.18 - Added float values for fractions. - min_samples_leaf : int or float, default=1 The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at @@ -933,9 +920,6 @@ class RandomForestQuantileRegressor(BaseForestQuantileRegressor): `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. - .. sklearn-versionchanged:: 0.18 - Added float values for fractions. - max_samples_leaf : int, float or None, default=1 The maximum number of samples permitted to be at a leaf node. @@ -994,8 +978,6 @@ class RandomForestQuantileRegressor(BaseForestQuantileRegressor): ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed. - .. sklearn-versionadded:: 0.19 - bootstrap : bool, default=True Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree. @@ -1031,8 +1013,6 @@ class RandomForestQuantileRegressor(BaseForestQuantileRegressor): subtree with the largest cost complexity that is smaller than ``ccp_alpha`` will be chosen. By default, no pruning is performed. - .. sklearn-versionadded:: 0.22 - max_samples : int or float, default=None If bootstrap is True, the number of samples to draw from X to train each base estimator. @@ -1042,8 +1022,6 @@ class RandomForestQuantileRegressor(BaseForestQuantileRegressor): - If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus, `max_samples` should be in the interval `(0.0, 1.0]`. - .. sklearn-versionadded:: 0.22 - monotonic_cst : array-like of int of shape (n_features), default=None Indicates the monotonicity constraint to enforce on each feature. - 1: monotonically increasing @@ -1083,14 +1061,10 @@ class RandomForestQuantileRegressor(BaseForestQuantileRegressor): n_features_in_ : int Number of features seen during :term:`fit`. - .. sklearn-versionadded:: 0.24 - feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. - .. sklearn-versionadded:: 1.0 - n_outputs_ : int The number of outputs when ``fit`` is performed. @@ -1244,10 +1218,6 @@ class ExtraTreesQuantileRegressor(BaseForestQuantileRegressor): n_estimators : int, default=100 The number of trees in the forest. - .. sklearn-versionchanged:: 0.22 - The default value of `n_estimators` changed from 10 to 100 - in 0.22. - default_quantiles : float, list, or "mean", default=0.5 The default quantile or list of quantiles that the model tries to predict. Each quantile must be strictly between 0 and 1. If "mean", @@ -1281,9 +1251,6 @@ class ExtraTreesQuantileRegressor(BaseForestQuantileRegressor): `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. - .. sklearn-versionchanged:: 0.18 - Added float values for fractions. - min_samples_leaf : int or float, default=1 The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at @@ -1296,9 +1263,6 @@ class ExtraTreesQuantileRegressor(BaseForestQuantileRegressor): `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. - .. sklearn-versionchanged:: 0.18 - Added float values for fractions. - max_samples_leaf : int, float or None, default=1 The maximum number of samples permitted to be at a leaf node. @@ -1356,8 +1320,6 @@ class ExtraTreesQuantileRegressor(BaseForestQuantileRegressor): ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed. - .. sklearn-versionadded:: 0.19 - bootstrap : bool, default=False Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree. @@ -1396,8 +1358,6 @@ class ExtraTreesQuantileRegressor(BaseForestQuantileRegressor): subtree with the largest cost complexity that is smaller than ``ccp_alpha`` will be chosen. By default, no pruning is performed. - .. sklearn-versionadded:: 0.22 - max_samples : int or float, default=None If bootstrap is True, the number of samples to draw from X to train each base estimator. @@ -1446,14 +1406,10 @@ class ExtraTreesQuantileRegressor(BaseForestQuantileRegressor): n_features_in_ : int Number of features seen during :term:`fit`. - .. sklearn-versionadded:: 0.24 - feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. - .. sklearn-versionadded:: 1.0 - n_outputs_ : int The number of outputs.