diff --git a/_unittests/ut_mlmodel/test_quantile_regression.py b/_unittests/ut_mlmodel/test_quantile_regression.py
index 4b6d40b3..cbbe3d3a 100644
--- a/_unittests/ut_mlmodel/test_quantile_regression.py
+++ b/_unittests/ut_mlmodel/test_quantile_regression.py
@@ -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,
@@ -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)
@@ -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]])
diff --git a/appveyor.yml b/appveyor.yml
index 143ac0b5..20257516 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -12,7 +12,7 @@ init:
install:
- "%PYTHON%\\python -m pip install --upgrade pip"
# for many packages
- - pip install llvmlite numba
+ - "%PYTHON%\\Scripts\\pip install llvmlite numba"
- "%PYTHON%\\Scripts\\pip install -r requirements-win.txt"
# install precompiled versions not available on pypi
- "%PYTHON%\\Scripts\\pip install torch torchvision torchaudio"
diff --git a/mlinsights/mlmodel/quantile_regression.py b/mlinsights/mlmodel/quantile_regression.py
index 2afaac5a..903ed8e2 100644
--- a/mlinsights/mlmodel/quantile_regression.py
+++ b/mlinsights/mlmodel/quantile_regression.py
@@ -29,7 +29,7 @@ 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):
"""
@@ -37,13 +37,6 @@ def __init__(self, fit_intercept=True, normalize=False, copy_X=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
@@ -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
@@ -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
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