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assert GPU CPU intercept_ equal when fit_intercept is false in cuml.LogisticRegression #5567

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assert GPU CPU intercept_ equal when fit_intercept is false in cuml.LogisticRegression #5567

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62ee5f9
assert GPU CPU intercept_ equal when fit_intercept is false
lijinf2 Aug 30, 2023
f284d4f
revise self.qnparams to self.solver_model.qnparams
lijinf2 Sep 20, 2023
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4 changes: 2 additions & 2 deletions python/cuml/linear_model/logistic_regression_mg.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -103,7 +103,7 @@ class LogisticRegressionMG(MGFitMixin, LogisticRegression):
self.solver_model.coef_ = value

def prepare_for_fit(self, n_classes):
self.qnparams = QNParams(
self.solver_model.qnparams = QNParams(
loss=self.loss,
penalty_l1=self.l1_strength,
penalty_l2=self.l2_strength,
Expand Down Expand Up @@ -179,7 +179,7 @@ class LogisticRegressionMG(MGFitMixin, LogisticRegression):
self.prepare_for_fit(self._num_classes)
cdef uintptr_t mat_coef_ptr = self.coef_.ptr

cdef qn_params qnpams = self.qnparams.params
cdef qn_params qnpams = self.solver_model.qnparams.params

if self.dtype == np.float32:
qnFit(
Expand Down
9 changes: 8 additions & 1 deletion python/cuml/solvers/qn.pyx
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Expand Up @@ -930,8 +930,15 @@ class QN(Base,

if self.fit_intercept:
self.intercept_ = self._coef_[-1]
else:
return

_num_classes_dim, _ = self.coef_.shape
_num_classes = self.get_num_classes(_num_classes_dim)

if _num_classes == 2:
self.intercept_ = CumlArray.zeros(shape=1)
else:
self.intercept_ = CumlArray.zeros(shape=_num_classes)

def get_param_names(self):
return super().get_param_names() + \
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3 changes: 3 additions & 0 deletions python/cuml/tests/test_linear_model.py
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Expand Up @@ -561,6 +561,9 @@ def test_logistic_regression(
)
assert len(np.unique(cu_preds)) == len(np.unique(y_test))

if fit_intercept is False:
assert np.array_equal(culog.intercept_, sklog.intercept_)
Comment on lines +564 to +565
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I thought the point of this change was to ensure that intercept_ is equal in both cases. Why are we only testing for equality when fit_intercept is False ?

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@lijinf2 lijinf2 Sep 19, 2023 •
edited
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When fit_intercept is True, intercept_ can be a list of non-zero. It is possible that culog.intercept_ and sklog.intercept_ are different, given their implementations are not exactly the same.

When fit_intercept is False, intercept_ is a list of zero. The test case is able to assert equal the culog.intercept_ and sklog.intercept_.

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But they would still be similar, no? You can use the cuml.testing.utils.array_equal function like here.

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Had tried assert array_equal(culog.intercept_, sklog.intercept_, 1e-3, with_sign=True).
Some tests failed:
FAILED tests/test_linear_model.py::test_logistic_regression[column_info0-1000-2-float32-none-1.0-True-1.0-0.001] - AssertionError: assert <array_equal: [0.60477465] [0.6086404] unit_tol=0.001 total_tol=0.0001 with_sign=True>

FAILED tests/test_linear_model.py::test_logistic_regression[column_info0-1000-10-float32-l1-1.0-True-1.0-0.001] - AssertionError: assert <array_equal: [-0.15404558 0.10340142 0.12519604 ... -0.13840534 0.13014889 -0.01927175] [-0.14051172 0.11931933 0.14109698 ... -0.12780005 0.14333196 -0.0074189 ] unit_tol=0.001 t...

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Mean accuracies of culog and sklog are the same though. It seems the models converged to different optima.

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Ok, that's a valid point.



@given(
dtype=floating_dtypes(sizes=(32, 64)),
Expand Down