def test_tsvd_fit_transform(datatype, name, use_handle):
if name == 'blobs':
X, y = make_blobs(n_samples=500000, n_features=1000, random_state=0)
elif name == 'random':
pytest.skip('fails when using random dataset '
'used by sklearn for testing')
shape = 5000, 100
rng = check_random_state(42)
X = rng.randint(-100, 20, np.product(shape)).reshape(shape)
else:
n, p = 500, 5
rng = np.random.RandomState(0)
X = rng.randn(n, p) * .1 + np.array([3, 4, 2, 3, 5])
if name != 'blobs':
skpca = skTSVD(n_components=1)
Xsktsvd = skpca.fit_transform(X)
handle, stream = get_handle(use_handle)
cutsvd = cuTSVD(n_components=1, handle=handle)
Xcutsvd = cutsvd.fit_transform(X)
cutsvd.handle.sync()
if name != 'blobs':
assert array_equal(Xcutsvd, Xsktsvd, 1e-3, with_sign=True)
def test_tsvd_inverse_transform(datatype, input_type,
name, use_handle):
if name == 'blobs':
pytest.skip('fails when using blobs dataset')
X, y = make_blobs(n_samples=500000,
n_features=1000, random_state=0)
elif name == 'random':
pytest.skip('fails when using random dataset '
'used by sklearn for testing')
shape = 5000, 100
rng = check_random_state(42)
X = rng.randint(-100, 20, np.product(shape)).reshape(shape)
else:
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]],
dtype=datatype)
X_pd = pd.DataFrame(
{'fea%d' % i: X[0:, i] for i in range(X.shape[1])})
X_cudf = cudf.DataFrame.from_pandas(X_pd)
cutsvd = cuTSVD(n_components=1)
if input_type == 'dataframe':
Xcutsvd = cutsvd.fit_transform(X_cudf)
else:
Xcutsvd = cutsvd.fit_transform(X)
input_gdf = cutsvd.inverse_transform(Xcutsvd)
cutsvd.handle.sync()
assert array_equal(input_gdf, X_cudf, 0.4, with_sign=True)
def test_tsvd_fit(datatype, input_type):
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]],
dtype=datatype)
sktsvd = skTSVD(n_components=1)
sktsvd.fit(X)
cutsvd = cuTSVD(n_components=1)
if input_type == 'dataframe':
gdf = cudf.DataFrame()
gdf['0'] = np.asarray([-1, -2, -3, 1, 2, 3], dtype=datatype)
gdf['1'] = np.asarray([-1, -1, -2, 1, 1, 2], dtype=datatype)
cutsvd.fit(gdf)
else:
cutsvd.fit(X)
for attr in [
'singular_values_', 'components_', 'explained_variance_ratio_'
]:
with_sign = False if attr in ['components_'] else True
assert array_equal(getattr(cutsvd, attr),
getattr(sktsvd, attr),
0.4,
with_sign=with_sign)
def test_tsvd_fit(datatype, name, use_handle):
if name == 'blobs':
X, y = make_blobs(n_samples=500000,
n_features=1000, random_state=0)
elif name == 'random':
pytest.skip('fails when using random dataset '
'used by sklearn for testing')
shape = 5000, 100
rng = check_random_state(42)
X = rng.randint(-100, 20, np.product(shape)).reshape(shape)
else:
n, p = 500, 5
rng = np.random.RandomState(0)
X = rng.randn(n, p) * .1 + np.array([3, 4, 2, 3, 5])
if name != 'blobs':
sktsvd = skTSVD(n_components=1)
sktsvd.fit(X)
handle, stream = get_handle(use_handle)
cutsvd = cuTSVD(n_components=1, handle=handle)
cutsvd.fit(X)
cutsvd.handle.sync()
if name != 'blobs':
for attr in ['singular_values_', 'components_',
'explained_variance_ratio_']:
with_sign = False if attr in ['components_'] else True
assert array_equal(getattr(cutsvd, attr), getattr(sktsvd, attr),
0.4, with_sign=with_sign)
def test_tsvd_inverse_transform(datatype, name, use_handle):
if name == 'blobs':
pytest.skip('fails when using blobs dataset')
X, y = make_blobs(n_samples=500000,
n_features=1000, random_state=0)
elif name == 'random':
pytest.skip('fails when using random dataset '
'used by sklearn for testing')
shape = 5000, 100
rng = check_random_state(42)
X = rng.randint(-100, 20, np.product(shape)).reshape(shape)
else:
n, p = 500, 5
rng = np.random.RandomState(0)
X = rng.randn(n, p) * .1 + np.array([3, 4, 2, 3, 5])
cutsvd = cuTSVD(n_components=1)
Xcutsvd = cutsvd.fit_transform(X)
input_gdf = cutsvd.inverse_transform(Xcutsvd)
cutsvd.handle.sync()
assert array_equal(input_gdf, X, 0.4, with_sign=True)
def test_pca_inverse_transform(datatype):
gdf = cudf.DataFrame()
gdf['0'] = np.asarray([-1, -2, -3, 1, 2, 3], dtype=datatype)
gdf['1'] = np.asarray([-1, -1, -2, 1, 1, 2], dtype=datatype)
cutsvd = cuTSVD(n_components=1)
Xcutsvd = cutsvd.fit_transform(gdf)
print("Calling inverse_transform")
input_gdf = cutsvd.inverse_transform(Xcutsvd)
print(input_gdf)
assert array_equal(input_gdf, gdf, 0.4, with_sign=True)
def test_pca_fit_transform(datatype):
gdf = cudf.DataFrame()
gdf['0'] = np.asarray([-1, -2, -3, 1, 2, 3], dtype=datatype)
gdf['1'] = np.asarray([-1, -1, -2, 1, 1, 2], dtype=datatype)
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]],
dtype=datatype)
print("Calling fit_transform")
cutsvd = cuTSVD(n_components=1)
Xcutsvd = cutsvd.fit_transform(gdf)
sktsvd = skTSVD(n_components=1)
Xsktsvd = sktsvd.fit_transform(X)
assert array_equal(Xcutsvd, Xsktsvd, 1e-3, with_sign=False)
def test_tsvd_inverse_transform(datatype, input_type):
gdf = cudf.DataFrame()
gdf['0'] = np.asarray([-1, -2, -3, 1, 2, 3], dtype=datatype)
gdf['1'] = np.asarray([-1, -1, -2, 1, 1, 2], dtype=datatype)
cutsvd = cuTSVD(n_components=1)
if input_type == 'dataframe':
Xcutsvd = cutsvd.fit_transform(gdf)
else:
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]],
dtype=datatype)
Xcutsvd = cutsvd.fit_transform(X)
input_gdf = cutsvd.inverse_transform(Xcutsvd)
assert array_equal(input_gdf, gdf, 0.4, with_sign=True)
def test_tsvd_fit_transform(datatype, input_type):
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]],
dtype=datatype)
skpca = skTSVD(n_components=1)
Xsktsvd = skpca.fit_transform(X)
cutsvd = cuTSVD(n_components=1)
if input_type == 'dataframe':
gdf = cudf.DataFrame()
gdf['0'] = np.asarray([-1, -2, -3, 1, 2, 3], dtype=datatype)
gdf['1'] = np.asarray([-1, -1, -2, 1, 1, 2], dtype=datatype)
Xcutsvd = cutsvd.fit_transform(gdf)
else:
Xcutsvd = cutsvd.fit_transform(X)
assert array_equal(Xcutsvd, Xsktsvd, 1e-3, with_sign=True)
def test_tsvd_fit(datatype, input_type,
name, use_handle):
if name == 'blobs':
X, y = make_blobs(n_samples=500000,
n_features=1000, random_state=0)
elif name == 'random':
pytest.skip('fails when using random dataset '
'used by sklearn for testing')
shape = 5000, 100
rng = check_random_state(42)
X = rng.randint(-100, 20, np.product(shape)).reshape(shape)
else:
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]],
dtype=datatype)
if name != 'blobs':
sktsvd = skTSVD(n_components=1)
sktsvd.fit(X)
handle, stream = get_handle(use_handle)
cutsvd = cuTSVD(n_components=1, handle=handle)
if input_type == 'dataframe':
X = pd.DataFrame(
{'fea%d' % i: X[0:, i] for i in range(X.shape[1])})
X_cudf = cudf.DataFrame.from_pandas(X)
cutsvd.fit(X_cudf)
else:
cutsvd.fit(X)
cutsvd.handle.sync()
if name != 'blobs':
for attr in ['singular_values_', 'components_',
'explained_variance_ratio_']:
with_sign = False if attr in ['components_'] else True
assert array_equal(getattr(cutsvd, attr), getattr(sktsvd, attr),
0.4, with_sign=with_sign)
