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diff --git a/pygam/pygam.py b/pygam/pygam.py
index bebcef1..a5c9229 100644
--- a/pygam/pygam.py
+++ b/pygam/pygam.py
@@ -703,7 +703,7 @@ class GAM(Core, MetaTermMixin):
# solve the linear problem
return np.linalg.solve(
- load_diagonal(modelmat.T.dot(modelmat).A), modelmat.T.dot(y_)
+ load_diagonal(modelmat.T.dot(modelmat).toarray()), modelmat.T.dot(y_)
)
# not sure if this is faster...
@@ -780,7 +780,7 @@ class GAM(Core, MetaTermMixin):
self._on_loop_start(vars())
WB = W.dot(modelmat[mask, :]) # common matrix product
- Q, R = np.linalg.qr(WB.A)
+ Q, R = np.linalg.qr(WB.toarray())
if not np.isfinite(Q).all() or not np.isfinite(R).all():
raise ValueError(
@@ -1401,7 +1401,7 @@ class GAM(Core, MetaTermMixin):
idxs = self.terms.get_coef_indices(term)
cov = self.statistics_['cov'][idxs][:, idxs]
- var = (modelmat.dot(cov) * modelmat.A).sum(axis=1)
+ var = (modelmat.dot(cov) * modelmat.toarray()).sum(axis=1)
if prediction:
var += self.distribution.scale
diff --git a/pygam/terms.py b/pygam/terms.py
index c033e72..83fb22a 100644
--- a/pygam/terms.py
+++ b/pygam/terms.py
@@ -1500,7 +1500,7 @@ class TensorTerm(SplineTerm, MetaTermMixin):
)
# now enter it into the composite
- composite_C[tuple(np.meshgrid(slice_, slice_))] = slice_C.A
+ composite_C[tuple(np.meshgrid(slice_, slice_))] = slice_C.toarray()
return sp.sparse.csc_matrix(composite_C)
diff --git a/pygam/tests/test_penalties.py b/pygam/tests/test_penalties.py
index bf6dd68..ac32ff0 100644
--- a/pygam/tests/test_penalties.py
+++ b/pygam/tests/test_penalties.py
@@ -23,13 +23,13 @@ def test_single_spline_penalty():
monotonic_ and convexity_ should be 0.
"""
coef = np.array(1.0)
- assert np.alltrue(derivative(1, coef).A == 0.0)
- assert np.alltrue(l2(1, coef).A == 1.0)
- assert np.alltrue(monotonic_inc(1, coef).A == 0.0)
- assert np.alltrue(monotonic_dec(1, coef).A == 0.0)
- assert np.alltrue(convex(1, coef).A == 0.0)
- assert np.alltrue(concave(1, coef).A == 0.0)
- assert np.alltrue(none(1, coef).A == 0.0)
+ assert np.all(derivative(1, coef).toarray() == 0.0)
+ assert np.all(l2(1, coef).toarray() == 1.0)
+ assert np.all(monotonic_inc(1, coef).toarray() == 0.0)
+ assert np.all(monotonic_dec(1, coef).toarray() == 0.0)
+ assert np.all(convex(1, coef).toarray() == 0.0)
+ assert np.all(concave(1, coef).toarray() == 0.0)
+ assert np.all(none(1, coef).toarray() == 0.0)
def test_wrap_penalty():
@@ -43,12 +44,12 @@ def test_wrap_penalty():
fit_linear = True
p = wrap_penalty(none, fit_linear, linear_penalty=linear_penalty)
- P = p(n, coef).A
+ P = p(n, coef).toarray()
assert P.sum() == linear_penalty
fit_linear = False
p = wrap_penalty(none, fit_linear, linear_penalty=linear_penalty)
- P = p(n, coef).A
+ P = p(n, coef).toarray()
assert P.sum() == 0.0
diff --git a/pygam/tests/test_terms.py b/pygam/tests/test_terms.py
index 72c9ce0..a773ac9 100644
--- a/pygam/tests/test_terms.py
+++ b/pygam/tests/test_terms.py
@@ -315,10 +315,10 @@ def test_tensor_composite_constraints_equal_penalties():
# check all the dimensions
for i in range(3):
- P = term._build_marginal_penalties(i).A
+ P = term._build_marginal_penalties(i).toarray()
C = term._build_marginal_constraints(
i, -np.arange(term.n_coefs), constraint_lam=1, constraint_l2=0
- ).A
+ ).toarray()
assert (P == C).all()
@@ -362,11 +362,11 @@ class TestRegressions(object):
term = SplineTerm(feature=0, penalties=['auto', 'none'])
# penalties should be equivalent
- assert (term.build_penalties() == base_term.build_penalties()).A.all()
+ assert (term.build_penalties() == base_term.build_penalties()).toarray().all()
# multitple penalties should be additive, not multiplicative,
# so 'none' penalty should have no effect
- assert np.abs(term.build_penalties().A).sum() > 0
+ assert np.abs(term.build_penalties().toarray()).sum() > 0
def test_compose_constraints(self, hepatitis_X_y):
"""we should be able to compose penalties
diff --git a/pygam/utils.py b/pygam/utils.py
index bc65ad5..279989b 100644
--- a/pygam/utils.py
+++ b/pygam/utils.py
@@ -64,7 +64,7 @@ def cholesky(A, sparse=True, verbose=True): # noqa: F811
if sparse:
return L.T # upper triangular factorization
- return L.T.A # upper triangular factorization
+ return L.T.toarray() # upper triangular factorization
else:
msg = (
@@ -78,7 +78,7 @@ def cholesky(A, sparse=True, verbose=True): # noqa: F811
warnings.warn(msg)
if sp.sparse.issparse(A):
- A = A.A
+ A = A.toarray()
try:
L = sp.linalg.cholesky(A, lower=False)
@@ -951,10 +951,10 @@ def tensor_product(a, b, reshape=True):
raise ValueError('both arguments must have the same number of samples')
if sp.sparse.issparse(a):
- a = a.A
+ a = a.toarray()
if sp.sparse.issparse(b):
- b = b.A
+ b = b.toarray()
tensor = a[..., :, None] * b[..., None, :]
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