276 lines
12 KiB
Diff
276 lines
12 KiB
Diff
From cd74e00d0e4f435d548444e1a5edc20155e371d7 Mon Sep 17 00:00:00 2001
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From: =?UTF-8?q?Jonas=20T=C3=B8rnes?= <jonas.tornes@gmail.com>
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Date: Wed, 15 Feb 2023 18:47:52 +0100
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Subject: [PATCH 1/5] Update RandomForesetRegressor criterion to be inline with
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scikit-learn change from mse to squared error this has the same funcitonality
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---
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requirements.txt | 6 +++---
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setup.py | 6 +++---
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skopt/learning/forest.py | 30 +++++++++++++++---------------
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3 files changed, 21 insertions(+), 21 deletions(-)
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diff --git a/requirements.txt b/requirements.txt
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index 1eaa3083a..23ab3d856 100644
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--- a/requirements.txt
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+++ b/requirements.txt
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@@ -1,6 +1,6 @@
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-numpy>=1.13.3
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-scipy>=0.19.1
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-scikit-learn>=0.20
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+numpy>=1.23.2
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+scipy>=1.10.0
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+scikit-learn>=1.2.1
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matplotlib>=2.0.0
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pytest
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pyaml>=16.9
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diff --git a/setup.py b/setup.py
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index 8879da880..e7f921765 100644
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--- a/setup.py
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+++ b/setup.py
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@@ -42,9 +42,9 @@
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classifiers=CLASSIFIERS,
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packages=['skopt', 'skopt.learning', 'skopt.optimizer', 'skopt.space',
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'skopt.learning.gaussian_process', 'skopt.sampler'],
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- install_requires=['joblib>=0.11', 'pyaml>=16.9', 'numpy>=1.13.3',
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- 'scipy>=0.19.1',
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- 'scikit-learn>=0.20.0'],
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+ install_requires=['joblib>=0.11', 'pyaml>=16.9', 'numpy>=1.23.2',
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+ 'scipy>=1.10.0',
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+ 'scikit-learn>=1.2.1'],
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extras_require={
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'plots': ["matplotlib>=2.0.0"]
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}
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diff --git a/skopt/learning/forest.py b/skopt/learning/forest.py
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index 096770c1d..ebde568f5 100644
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--- a/skopt/learning/forest.py
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+++ b/skopt/learning/forest.py
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@@ -27,7 +27,7 @@ def _return_std(X, trees, predictions, min_variance):
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-------
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std : array-like, shape=(n_samples,)
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Standard deviation of `y` at `X`. If criterion
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- is set to "mse", then `std[i] ~= std(y | X[i])`.
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+ is set to "squared_error", then `std[i] ~= std(y | X[i])`.
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"""
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# This derives std(y | x) as described in 4.3.2 of arXiv:1211.0906
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@@ -61,9 +61,9 @@ class RandomForestRegressor(_sk_RandomForestRegressor):
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n_estimators : integer, optional (default=10)
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The number of trees in the forest.
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- criterion : string, optional (default="mse")
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+ criterion : string, optional (default="squared_error")
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The function to measure the quality of a split. Supported criteria
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- are "mse" for the mean squared error, which is equal to variance
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+ are "squared_error" for the mean squared error, which is equal to variance
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reduction as feature selection criterion, and "mae" for the mean
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absolute error.
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@@ -194,7 +194,7 @@ class RandomForestRegressor(_sk_RandomForestRegressor):
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.. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
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"""
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- def __init__(self, n_estimators=10, criterion='mse', max_depth=None,
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+ def __init__(self, n_estimators=10, criterion='squared_error', max_depth=None,
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min_samples_split=2, min_samples_leaf=1,
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min_weight_fraction_leaf=0.0, max_features='auto',
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max_leaf_nodes=None, min_impurity_decrease=0.,
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@@ -228,20 +228,20 @@ def predict(self, X, return_std=False):
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Returns
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-------
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predictions : array-like of shape = (n_samples,)
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- Predicted values for X. If criterion is set to "mse",
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+ Predicted values for X. If criterion is set to "squared_error",
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then `predictions[i] ~= mean(y | X[i])`.
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std : array-like of shape=(n_samples,)
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Standard deviation of `y` at `X`. If criterion
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- is set to "mse", then `std[i] ~= std(y | X[i])`.
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+ is set to "squared_error", then `std[i] ~= std(y | X[i])`.
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"""
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mean = super(RandomForestRegressor, self).predict(X)
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if return_std:
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- if self.criterion != "mse":
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+ if self.criterion != "squared_error":
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raise ValueError(
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- "Expected impurity to be 'mse', got %s instead"
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+ "Expected impurity to be 'squared_error', got %s instead"
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% self.criterion)
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std = _return_std(X, self.estimators_, mean, self.min_variance)
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return mean, std
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@@ -257,9 +257,9 @@ class ExtraTreesRegressor(_sk_ExtraTreesRegressor):
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n_estimators : integer, optional (default=10)
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The number of trees in the forest.
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- criterion : string, optional (default="mse")
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+ criterion : string, optional (default="squared_error")
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The function to measure the quality of a split. Supported criteria
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- are "mse" for the mean squared error, which is equal to variance
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+ are "squared_error" for the mean squared error, which is equal to variance
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reduction as feature selection criterion, and "mae" for the mean
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absolute error.
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@@ -390,7 +390,7 @@ class ExtraTreesRegressor(_sk_ExtraTreesRegressor):
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.. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
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"""
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- def __init__(self, n_estimators=10, criterion='mse', max_depth=None,
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+ def __init__(self, n_estimators=10, criterion='squared_error', max_depth=None,
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min_samples_split=2, min_samples_leaf=1,
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min_weight_fraction_leaf=0.0, max_features='auto',
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max_leaf_nodes=None, min_impurity_decrease=0.,
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@@ -425,19 +425,19 @@ def predict(self, X, return_std=False):
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Returns
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-------
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predictions : array-like of shape=(n_samples,)
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- Predicted values for X. If criterion is set to "mse",
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+ Predicted values for X. If criterion is set to "squared_error",
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then `predictions[i] ~= mean(y | X[i])`.
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std : array-like of shape=(n_samples,)
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Standard deviation of `y` at `X`. If criterion
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- is set to "mse", then `std[i] ~= std(y | X[i])`.
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+ is set to "squared_error", then `std[i] ~= std(y | X[i])`.
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"""
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mean = super(ExtraTreesRegressor, self).predict(X)
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if return_std:
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- if self.criterion != "mse":
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+ if self.criterion != "squared_error":
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raise ValueError(
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- "Expected impurity to be 'mse', got %s instead"
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+ "Expected impurity to be 'squared_error', got %s instead"
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% self.criterion)
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std = _return_std(X, self.estimators_, mean, self.min_variance)
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return mean, std
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From 6eb2d4ddaa299ae47d9a69ffb31ebc4ed366d1c1 Mon Sep 17 00:00:00 2001
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From: =?UTF-8?q?Jonas=20T=C3=B8rnes?= <jonas.tornes@gmail.com>
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Date: Thu, 16 Feb 2023 11:34:58 +0100
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Subject: [PATCH 2/5] Change test to be consistent with code changes.
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---
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skopt/learning/tests/test_forest.py | 4 ++--
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1 file changed, 2 insertions(+), 2 deletions(-)
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diff --git a/skopt/learning/tests/test_forest.py b/skopt/learning/tests/test_forest.py
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index 0711cde9d..c6ed610f3 100644
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--- a/skopt/learning/tests/test_forest.py
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+++ b/skopt/learning/tests/test_forest.py
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@@ -35,7 +35,7 @@ def test_random_forest():
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assert_array_equal(clf.predict(T), true_result)
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assert 10 == len(clf)
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- clf = RandomForestRegressor(n_estimators=10, criterion="mse",
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+ clf = RandomForestRegressor(n_estimators=10, criterion="squared_error",
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max_depth=None, min_samples_split=2,
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min_samples_leaf=1,
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min_weight_fraction_leaf=0.,
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@@ -80,7 +80,7 @@ def test_extra_forest():
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assert_array_equal(clf.predict(T), true_result)
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assert 10 == len(clf)
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- clf = ExtraTreesRegressor(n_estimators=10, criterion="mse",
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+ clf = ExtraTreesRegressor(n_estimators=10, criterion="squared_error",
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max_depth=None, min_samples_split=2,
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min_samples_leaf=1, min_weight_fraction_leaf=0.,
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max_features="auto", max_leaf_nodes=None,
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From 52c620add07d845debbaff2ce2b1c5faf3eae79b Mon Sep 17 00:00:00 2001
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From: =?UTF-8?q?Jonas=20T=C3=B8rnes?= <jonas.tornes@gmail.com>
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Date: Wed, 22 Feb 2023 16:59:03 +0100
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Subject: [PATCH 3/5] Update skopt/learning/forest.py
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MIME-Version: 1.0
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Content-Type: text/plain; charset=UTF-8
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Content-Transfer-Encoding: 8bit
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Fix max line width
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Co-authored-by: Roland Laurès <roland@laures-valdivia.net>
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---
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skopt/learning/forest.py | 4 ++--
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1 file changed, 2 insertions(+), 2 deletions(-)
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diff --git a/skopt/learning/forest.py b/skopt/learning/forest.py
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index ebde568f5..07dc42664 100644
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--- a/skopt/learning/forest.py
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+++ b/skopt/learning/forest.py
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@@ -194,8 +194,8 @@ class RandomForestRegressor(_sk_RandomForestRegressor):
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.. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
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"""
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- def __init__(self, n_estimators=10, criterion='squared_error', max_depth=None,
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- min_samples_split=2, min_samples_leaf=1,
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+ def __init__(self, n_estimators=10, criterion='squared_error',
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+ max_depth=None, min_samples_split=2, min_samples_leaf=1,
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min_weight_fraction_leaf=0.0, max_features='auto',
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max_leaf_nodes=None, min_impurity_decrease=0.,
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bootstrap=True, oob_score=False,
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From 52a7db95cb567186fb4e9003139fea4592bdbf05 Mon Sep 17 00:00:00 2001
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From: =?UTF-8?q?Jonas=20T=C3=B8rnes?= <jonas.tornes@gmail.com>
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Date: Wed, 22 Feb 2023 17:03:25 +0100
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Subject: [PATCH 4/5] Fix line widht issues
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---
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skopt/learning/forest.py | 4 ++--
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1 file changed, 2 insertions(+), 2 deletions(-)
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diff --git a/skopt/learning/forest.py b/skopt/learning/forest.py
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index 07dc42664..d4c24456b 100644
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--- a/skopt/learning/forest.py
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+++ b/skopt/learning/forest.py
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@@ -390,8 +390,8 @@ class ExtraTreesRegressor(_sk_ExtraTreesRegressor):
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.. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
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"""
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- def __init__(self, n_estimators=10, criterion='squared_error', max_depth=None,
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- min_samples_split=2, min_samples_leaf=1,
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+ def __init__(self, n_estimators=10, criterion='squared_error',
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+ max_depth=None, min_samples_split=2, min_samples_leaf=1,
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min_weight_fraction_leaf=0.0, max_features='auto',
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max_leaf_nodes=None, min_impurity_decrease=0.,
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bootstrap=False, oob_score=False,
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From 6b185e489fb4a56625e8505292a20c80434f0633 Mon Sep 17 00:00:00 2001
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From: =?UTF-8?q?Jonas=20T=C3=B8rnes?= <jonas.tornes@gmail.com>
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Date: Wed, 22 Feb 2023 18:37:11 +0100
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Subject: [PATCH 5/5] Fix lin width issues for comments.
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---
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skopt/learning/forest.py | 12 ++++++------
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1 file changed, 6 insertions(+), 6 deletions(-)
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diff --git a/skopt/learning/forest.py b/skopt/learning/forest.py
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index d4c24456b..eb3bd6648 100644
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--- a/skopt/learning/forest.py
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+++ b/skopt/learning/forest.py
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@@ -63,9 +63,9 @@ class RandomForestRegressor(_sk_RandomForestRegressor):
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criterion : string, optional (default="squared_error")
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The function to measure the quality of a split. Supported criteria
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- are "squared_error" for the mean squared error, which is equal to variance
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- reduction as feature selection criterion, and "mae" for the mean
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- absolute error.
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+ are "squared_error" for the mean squared error, which is equal to
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+ variance reduction as feature selection criterion, and "mae" for the
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+ mean absolute error.
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max_features : int, float, string or None, optional (default="auto")
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The number of features to consider when looking for the best split:
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@@ -259,9 +259,9 @@ class ExtraTreesRegressor(_sk_ExtraTreesRegressor):
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criterion : string, optional (default="squared_error")
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The function to measure the quality of a split. Supported criteria
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- are "squared_error" for the mean squared error, which is equal to variance
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- reduction as feature selection criterion, and "mae" for the mean
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- absolute error.
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+ are "squared_error" for the mean squared error, which is equal to
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+ variance reduction as feature selection criterion, and "mae" for the
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+ mean absolute error.
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max_features : int, float, string or None, optional (default="auto")
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The number of features to consider when looking for the best split:
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