From cd74e00d0e4f435d548444e1a5edc20155e371d7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jonas=20T=C3=B8rnes?= <jonas.tornes@gmail.com>
Date: Wed, 15 Feb 2023 18:47:52 +0100
Subject: [PATCH 1/5] Update RandomForesetRegressor criterion to be inline with
scikit-learn change from mse to squared error this has the same funcitonality
---
requirements.txt | 6 +++---
setup.py | 6 +++---
skopt/learning/forest.py | 30 +++++++++++++++---------------
3 files changed, 21 insertions(+), 21 deletions(-)
diff --git a/requirements.txt b/requirements.txt
index 1eaa3083a..23ab3d856 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,6 @@
-numpy>=1.13.3
-scipy>=0.19.1
-scikit-learn>=0.20
+numpy>=1.23.2
+scipy>=1.10.0
+scikit-learn>=1.2.1
matplotlib>=2.0.0
pytest
pyaml>=16.9
diff --git a/setup.py b/setup.py
index 8879da880..e7f921765 100644
--- a/setup.py
+++ b/setup.py
@@ -42,9 +42,9 @@
classifiers=CLASSIFIERS,
packages=['skopt', 'skopt.learning', 'skopt.optimizer', 'skopt.space',
'skopt.learning.gaussian_process', 'skopt.sampler'],
- install_requires=['joblib>=0.11', 'pyaml>=16.9', 'numpy>=1.13.3',
- 'scipy>=0.19.1',
- 'scikit-learn>=0.20.0'],
+ install_requires=['joblib>=0.11', 'pyaml>=16.9', 'numpy>=1.23.2',
+ 'scipy>=1.10.0',
+ 'scikit-learn>=1.2.1'],
extras_require={
'plots': ["matplotlib>=2.0.0"]
}
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
From 6eb2d4ddaa299ae47d9a69ffb31ebc4ed366d1c1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jonas=20T=C3=B8rnes?= <jonas.tornes@gmail.com>
Date: Thu, 16 Feb 2023 11:34:58 +0100
Subject: [PATCH 2/5] Change test to be consistent with code changes.
---
skopt/learning/tests/test_forest.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/skopt/learning/tests/test_forest.py b/skopt/learning/tests/test_forest.py
index 0711cde9d..c6ed610f3 100644
--- a/skopt/learning/tests/test_forest.py
+++ b/skopt/learning/tests/test_forest.py
@@ -35,7 +35,7 @@ def test_random_forest():
assert_array_equal(clf.predict(T), true_result)
assert 10 == len(clf)
- clf = RandomForestRegressor(n_estimators=10, criterion="mse",
+ clf = RandomForestRegressor(n_estimators=10, criterion="squared_error",
max_depth=None, min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.,
@@ -80,7 +80,7 @@ def test_extra_forest():
assert_array_equal(clf.predict(T), true_result)
assert 10 == len(clf)
- clf = ExtraTreesRegressor(n_estimators=10, criterion="mse",
+ clf = ExtraTreesRegressor(n_estimators=10, criterion="squared_error",
max_depth=None, min_samples_split=2,
min_samples_leaf=1, min_weight_fraction_leaf=0.,
max_features="auto", max_leaf_nodes=None,
From 52c620add07d845debbaff2ce2b1c5faf3eae79b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jonas=20T=C3=B8rnes?= <jonas.tornes@gmail.com>
Date: Wed, 22 Feb 2023 16:59:03 +0100
Subject: [PATCH 3/5] Update skopt/learning/forest.py
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
Fix max line width
Co-authored-by: Roland Laurès <roland@laures-valdivia.net>
---
skopt/learning/forest.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/skopt/learning/forest.py b/skopt/learning/forest.py
index ebde568f5..07dc42664 100644
--- a/skopt/learning/forest.py
+++ b/skopt/learning/forest.py
@@ -194,8 +194,8 @@ class RandomForestRegressor(_sk_RandomForestRegressor):
.. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
"""
- def __init__(self, n_estimators=10, criterion='squared_error', max_depth=None,
- min_samples_split=2, min_samples_leaf=1,
+ 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.,
bootstrap=True, oob_score=False,
From 52a7db95cb567186fb4e9003139fea4592bdbf05 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jonas=20T=C3=B8rnes?= <jonas.tornes@gmail.com>
Date: Wed, 22 Feb 2023 17:03:25 +0100
Subject: [PATCH 4/5] Fix line widht issues
---
skopt/learning/forest.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/skopt/learning/forest.py b/skopt/learning/forest.py
index 07dc42664..d4c24456b 100644
--- a/skopt/learning/forest.py
+++ b/skopt/learning/forest.py
@@ -390,8 +390,8 @@ class ExtraTreesRegressor(_sk_ExtraTreesRegressor):
.. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
"""
- def __init__(self, n_estimators=10, criterion='squared_error', max_depth=None,
- min_samples_split=2, min_samples_leaf=1,
+ 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.,
bootstrap=False, oob_score=False,
From 6b185e489fb4a56625e8505292a20c80434f0633 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jonas=20T=C3=B8rnes?= <jonas.tornes@gmail.com>
Date: Wed, 22 Feb 2023 18:37:11 +0100
Subject: [PATCH 5/5] Fix lin width issues for comments.
---
skopt/learning/forest.py | 12 ++++++------
1 file changed, 6 insertions(+), 6 deletions(-)
diff --git a/skopt/learning/forest.py b/skopt/learning/forest.py
index d4c24456b..eb3bd6648 100644
--- a/skopt/learning/forest.py
+++ b/skopt/learning/forest.py
@@ -63,9 +63,9 @@ class RandomForestRegressor(_sk_RandomForestRegressor):
criterion : string, optional (default="squared_error")
The function to measure the quality of a split. Supported criteria
- 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.
+ 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.
max_features : int, float, string or None, optional (default="auto")
The number of features to consider when looking for the best split:
@@ -259,9 +259,9 @@ class ExtraTreesRegressor(_sk_ExtraTreesRegressor):
criterion : string, optional (default="squared_error")
The function to measure the quality of a split. Supported criteria
- 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.
+ 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.
max_features : int, float, string or None, optional (default="auto")
The number of features to consider when looking for the best split: