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Removes normalize attributes (deprecated) #113

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Merged
merged 3 commits into from
Nov 29, 2022
Merged

Removes normalize attributes (deprecated) #113

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882ca5c
Removes normalize attributes (deprecated)
xadupre Nov 29, 2022
fd27a8b
Update appveyor.yml
xadupre Nov 29, 2022
7aabda6
Update appveyor.yml
xadupre Nov 29, 2022
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Removes normalize attributes (deprecated)
  • Loading branch information
@xadupre
xadupre committed Nov 29, 2022
commit 882ca5ce7c991baaa4e80b17f6e26ba2441d37b2
6 changes: 3 additions & 3 deletions _unittests/ut_mlmodel/test_quantile_regression.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,7 +61,7 @@ def test_quantile_regression_intercept(self):
self.assertNotEqual(clr.intercept_, 0)
self.assertNotEqual(clq.intercept_, 0)
self.assertEqualArray(clr.intercept_, clq.intercept_)
self.assertEqualArray(clr.coef_, clq.coef_)
self.assertEqualArray(clr.coef_, clq.coef_, atol=1e-10)

@unittest.skipIf(
compare_module_version(sklver, "0.24") == -1,
Expand All @@ -77,7 +77,7 @@ def test_quantile_regression_intercept_positive(self):
self.assertNotEqual(clr.intercept_, 0)
self.assertNotEqual(clq.intercept_, 0)
self.assertEqualArray(clr.intercept_, clq.intercept_)
self.assertEqualArray(clr.coef_, clq.coef_)
self.assertEqualArray(clr.coef_, clq.coef_, atol=1e-10)
self.assertGreater(clr.coef_.min(), 0)
self.assertGreater(clq.coef_.min(), 0)

Expand All @@ -92,7 +92,7 @@ def test_quantile_regression_intercept_weights(self):
self.assertNotEqual(clr.intercept_, 0)
self.assertNotEqual(clq.intercept_, 0)
self.assertEqualArray(clr.intercept_, clq.intercept_)
self.assertEqualArray(clr.coef_, clq.coef_)
self.assertEqualArray(clr.coef_, clq.coef_, atol=1e-10)

def test_quantile_regression_diff(self):
X = numpy.array([[0.1], [0.2], [0.3], [0.4], [0.5]])
Expand Down
17 changes: 5 additions & 12 deletions mlinsights/mlmodel/quantile_regression.py
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Expand Up @@ -29,21 +29,14 @@ class QuantileLinearRegression(LinearRegression):
value.
"""

def __init__(self, fit_intercept=True, normalize=False, copy_X=True,
def __init__(self, fit_intercept=True, copy_X=True,
n_jobs=1, delta=0.0001, max_iter=10, quantile=0.5,
positive=False, verbose=False):
"""
:param fit_intercept: boolean, optional, default True
whether to calculate the intercept for this model. If set
to False, no intercept will be used in calculations
(e.g. data is expected to be already centered).
:param normalize: boolean, optional, default False
This parameter is ignored when ``fit_intercept`` is set to False.
If True, the regressors X will be normalized before regression by
subtracting the mean and dividing by the l2-norm.
If you wish to standardize, please use
:class:`sklearn.preprocessing.StandardScaler` before calling ``fit`` on
an estimator with ``normalize=False``.
:param copy_X: boolean, optional, default True
If True, X will be copied; else, it may be overwritten.
:param n_jobs: int, optional, default 1
Expand All @@ -65,12 +58,12 @@ def __init__(self, fit_intercept=True, normalize=False, copy_X=True,
"""
try:
LinearRegression.__init__(
self, fit_intercept=fit_intercept, normalize=normalize,
self, fit_intercept=fit_intercept,
copy_X=copy_X, n_jobs=n_jobs, positive=positive)
except TypeError:
# scikit-learn<0.24
LinearRegression.__init__(
self, fit_intercept=fit_intercept, normalize=normalize,
self, fit_intercept=fit_intercept,
copy_X=copy_X, n_jobs=n_jobs)
self.max_iter = max_iter
self.verbose = verbose
Expand Down Expand Up @@ -140,12 +133,12 @@ def compute_z(Xm, beta, Y, W, delta=0.0001):

try:
clr = LinearRegression(fit_intercept=False, copy_X=self.copy_X,
n_jobs=self.n_jobs, normalize=self.normalize,
n_jobs=self.n_jobs,
positive=self.positive)
except AttributeError:
# scikit-learn<0.24
clr = LinearRegression(fit_intercept=False, copy_X=self.copy_X,
n_jobs=self.n_jobs, normalize=self.normalize)
n_jobs=self.n_jobs)

W = numpy.ones(X.shape[0]) if sample_weight is None else sample_weight
self.n_iter_ = 0
Expand Down
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