def test_normalize_sparse():
X_sparse = tosparse(X)
for norm in ["l1", "l2", "max"]:
assert np.allclose(
normalize_pure(X_sparse, norm=norm, axis=1).todense(),
normalize(X, norm=norm, axis=1),
)
def test_max_abs_scaler_sparse():
X_sparse = tosparse(X)
tform = MaxAbsScaler()
tform.fit(X)
tform_ = convert_estimator(tform)
X_t = tform.transform(X)
X_t_ = tform_.transform(X_sparse)
np.allclose(X_t, X_t_.todense())
def test_standard_scaler_sparse():
X_sparse = tosparse(X)
for with_std in [True, False]:
tform = StandardScaler(with_mean=False, with_std=with_std)
tform.fit(X)
tform_ = convert_estimator(tform)
X_t = tform.transform(X)
X_t_ = tform_.transform(X_sparse)
np.allclose(X_t, X_t_.todense())
def test_normalizer_sparse():
X_sparse = tosparse(X)
for norm in ["l1", "l2", "max"]:
tform = Normalizer(norm=norm)
tform.fit(X)
tform_ = convert_estimator(tform)
X_t = tform.transform(X)
X_t_ = tform_.transform(X_sparse)
np.allclose(X_t, X_t_.todense())
def test_feature_union_sparse():
X, y = load_iris(return_X_y=True)
X_ = tosparse(X.tolist())
union = FeatureUnion([("ss", StandardScaler(with_mean=False)),
("mms", MaxAbsScaler())])
union.fit(X, y)
union_ = convert_estimator(union)
assert np.allclose(union.transform(X), union_.transform(X_).todense())
def test_decision_tree_clf():
X, y = load_iris(return_X_y=True)
X_ = X.tolist()
X_sparse = tosparse(X_)
for y_ in [y, (y == 0).astype(int), (y == 2).astype(int)]:
for max_depth in [5, 10, None]:
clf = DecisionTreeClassifier(max_depth=max_depth, random_state=5)
clf.fit(X, y_)
clf_ = convert_estimator(clf)
for method in METHODS:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
scores = getattr(clf, method)(X)
scores_ = getattr(clf_, method)(X_)
scores_sparse = getattr(clf_, method)(X_sparse)
assert np.allclose(scores, scores_, equal_nan=True)
assert np.allclose(scores, scores_sparse, equal_nan=True)
def test_logistic():
X, y = load_iris(return_X_y=True)
X_ = X.tolist()
X_sparse = tosparse(X_)
for y_ in [y, (y == 0).astype(int), (y == 2).astype(int)]:
for multi_class in ["ovr", "multinomial"]:
for fit_intercept in [True, False]:
clf = LogisticRegression(
solver=SOLVER,
multi_class=multi_class,
fit_intercept=fit_intercept,
max_iter=MAX_ITER,
)
clf.fit(X, y_)
clf_ = convert_estimator(clf)
for method in METHODS:
scores = getattr(clf, method)(X)
scores_ = getattr(clf_, method)(X_)
scores_sparse = getattr(clf_, method)(X_sparse)
assert np.allclose(scores, scores_)
assert np.allclose(scores, scores_sparse, equal_nan=True)
def test_sfmax_sparse():
A_sparse = tosparse([A])[0]
lst = sfmax(A)
assert sfmax(A_sparse) == {0: lst[0], 1: lst[1]}
def test_matmult_sparse():
sparse_B = tosparse(B)
X = matmult_same_dim(sparse_B, sparse_B)
assert np.allclose(X.todense(), [[1, 4], [9, 16]])
def test_dot_2d_sparse():
A_sparse = tosparse([A])
assert dot_2d(A_sparse, B) == [[5, 11]]
def test_dot_sparse():
A_sparse = tosparse([A])[0]
assert dot(A_sparse, B) == [5, 11]
def test_slice_array_sparse():
assert slice_column(tosparse(B), [0, 1]).todense() == B
def test_slice_sparse():
assert slice_column(tosparse(B), 0) == [1, 3]
def test_apply_axis_2d_sparse():
B_sparse = tosparse(B)
assert apply_axis_2d(B_sparse, sum) == [3, 7]
def test_apply_2d_sparse():
B_sparse = tosparse(B)
B_applied = apply_2d_sparse(B_sparse, lambda x: 2 * x).todense()
assert B_applied == [[2, 4], [6, 8]]
