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author | V3n3RiX <venerix@koprulu.sector> | 2024-04-11 18:33:04 +0100 |
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committer | V3n3RiX <venerix@koprulu.sector> | 2024-04-11 18:33:04 +0100 |
commit | 1f43daba2fbe6f53e67c63944941dc645657c5b3 (patch) | |
tree | 69847026d79bd01039e851e5d5b4933615e29f51 /sci-libs/scikit-optimize/files/scikit-optimize-0.9.0-scikit-learn-1.2.0.patch | |
parent | 95c20b170b50a028890f00e7e9c338427d92279f (diff) |
gentoo auto-resync : 11:04:2024 - 18:33:04
Diffstat (limited to 'sci-libs/scikit-optimize/files/scikit-optimize-0.9.0-scikit-learn-1.2.0.patch')
-rw-r--r-- | sci-libs/scikit-optimize/files/scikit-optimize-0.9.0-scikit-learn-1.2.0.patch | 104 |
1 files changed, 0 insertions, 104 deletions
diff --git a/sci-libs/scikit-optimize/files/scikit-optimize-0.9.0-scikit-learn-1.2.0.patch b/sci-libs/scikit-optimize/files/scikit-optimize-0.9.0-scikit-learn-1.2.0.patch deleted file mode 100644 index 8cf8cff9479f..000000000000 --- a/sci-libs/scikit-optimize/files/scikit-optimize-0.9.0-scikit-learn-1.2.0.patch +++ /dev/null @@ -1,104 +0,0 @@ -diff --git a/skopt/learning/forest.py b/skopt/learning/forest.py -index 096770c1d..ebde568f5 100644 ---- a/skopt/learning/forest.py -+++ b/skopt/learning/forest.py -@@ -27,7 +27,7 @@ def _return_std(X, trees, predictions, min_variance): - ------- - std : array-like, shape=(n_samples,) - Standard deviation of `y` at `X`. If criterion -- is set to "mse", then `std[i] ~= std(y | X[i])`. -+ is set to "squared_error", then `std[i] ~= std(y | X[i])`. - - """ - # This derives std(y | x) as described in 4.3.2 of arXiv:1211.0906 -@@ -61,9 +61,9 @@ class RandomForestRegressor(_sk_RandomForestRegressor): - n_estimators : integer, optional (default=10) - The number of trees in the forest. - -- criterion : string, optional (default="mse") -+ criterion : string, optional (default="squared_error") - The function to measure the quality of a split. Supported criteria -- are "mse" for the mean squared error, which is equal to variance -+ are "squared_error" for the mean squared error, which is equal to variance - reduction as feature selection criterion, and "mae" for the mean - absolute error. - -@@ -194,7 +194,7 @@ class RandomForestRegressor(_sk_RandomForestRegressor): - .. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001. - - """ -- def __init__(self, n_estimators=10, criterion='mse', max_depth=None, -+ def __init__(self, n_estimators=10, criterion='squared_error', max_depth=None, - min_samples_split=2, min_samples_leaf=1, - min_weight_fraction_leaf=0.0, max_features='auto', - max_leaf_nodes=None, min_impurity_decrease=0., -@@ -228,20 +228,20 @@ def predict(self, X, return_std=False): - Returns - ------- - predictions : array-like of shape = (n_samples,) -- Predicted values for X. If criterion is set to "mse", -+ Predicted values for X. If criterion is set to "squared_error", - then `predictions[i] ~= mean(y | X[i])`. - - std : array-like of shape=(n_samples,) - Standard deviation of `y` at `X`. If criterion -- is set to "mse", then `std[i] ~= std(y | X[i])`. -+ is set to "squared_error", then `std[i] ~= std(y | X[i])`. - - """ - mean = super(RandomForestRegressor, self).predict(X) - - if return_std: -- if self.criterion != "mse": -+ if self.criterion != "squared_error": - raise ValueError( -- "Expected impurity to be 'mse', got %s instead" -+ "Expected impurity to be 'squared_error', got %s instead" - % self.criterion) - std = _return_std(X, self.estimators_, mean, self.min_variance) - return mean, std -@@ -257,9 +257,9 @@ class ExtraTreesRegressor(_sk_ExtraTreesRegressor): - n_estimators : integer, optional (default=10) - The number of trees in the forest. - -- criterion : string, optional (default="mse") -+ criterion : string, optional (default="squared_error") - The function to measure the quality of a split. Supported criteria -- are "mse" for the mean squared error, which is equal to variance -+ are "squared_error" for the mean squared error, which is equal to variance - reduction as feature selection criterion, and "mae" for the mean - absolute error. - -@@ -390,7 +390,7 @@ class ExtraTreesRegressor(_sk_ExtraTreesRegressor): - .. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001. - - """ -- def __init__(self, n_estimators=10, criterion='mse', max_depth=None, -+ def __init__(self, n_estimators=10, criterion='squared_error', max_depth=None, - min_samples_split=2, min_samples_leaf=1, - min_weight_fraction_leaf=0.0, max_features='auto', - max_leaf_nodes=None, min_impurity_decrease=0., -@@ -425,19 +425,19 @@ def predict(self, X, return_std=False): - Returns - ------- - predictions : array-like of shape=(n_samples,) -- Predicted values for X. If criterion is set to "mse", -+ Predicted values for X. If criterion is set to "squared_error", - then `predictions[i] ~= mean(y | X[i])`. - - std : array-like of shape=(n_samples,) - Standard deviation of `y` at `X`. If criterion -- is set to "mse", then `std[i] ~= std(y | X[i])`. -+ is set to "squared_error", then `std[i] ~= std(y | X[i])`. - """ - mean = super(ExtraTreesRegressor, self).predict(X) - - if return_std: -- if self.criterion != "mse": -+ if self.criterion != "squared_error": - raise ValueError( -- "Expected impurity to be 'mse', got %s instead" -+ "Expected impurity to be 'squared_error', got %s instead" - % self.criterion) - std = _return_std(X, self.estimators_, mean, self.min_variance) - return mean, std |