def test_pca_inverse_transform(datatype):
gdf = pygdf.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 = pygdf.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_fit(datatype):
gdf = pygdf.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")
cutsvd = cuTSVD(n_components=1)
cutsvd.fit(gdf)
sktsvd = skTSVD(n_components=1)
sktsvd.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)