def check_transform(Estimator, n_components, shape):
X = DATA[shape]
Y1 = Estimator(n_components, **KWDS[Estimator]).fit(X).transform(X)
Y2 = Estimator(n_components, **KWDS[Estimator]).fit_transform(X)
assert_columns_allclose_upto_sign(Y1, Y2)
def check_transform(Estimator, n_components, shape):
X = DATA[shape]
Y1 = Estimator(n_components, **KWDS[Estimator]).fit(X).transform(X)
Y2 = Estimator(n_components, **KWDS[Estimator]).fit_transform(X)
assert_columns_allclose_upto_sign(Y1, Y2)
def check_components(Estimator, n_components, shape):
X = DATA[shape]
pca = Estimator(n_components, **KWDS[Estimator]).fit(X)
skpca = SKPCA(n_components).fit(X)
assert_columns_allclose_upto_sign(pca.components_.T,
skpca.components_.T)
def check_components(Estimator, n_components, shape):
X = DATA[shape]
pca = Estimator(n_components, **KWDS[Estimator]).fit(X)
skpca = SKPCA(n_components).fit(X)
assert_columns_allclose_upto_sign(pca.components_.T,
skpca.components_.T)
def check_transform(Estimator, n_components, shape):
X = DATA[shape]
pca = Estimator(n_components, **KWDS[Estimator])
skpca = SKPCA(n_components)
Y = pca.fit_transform(X)
Ysk = skpca.fit_transform(X)
assert_columns_allclose_upto_sign(Y, Ysk)
def check_transform(Estimator, n_components, shape):
X = DATA[shape]
pca = Estimator(n_components, **KWDS[Estimator])
skpca = SKPCA(n_components)
Y = pca.fit_transform(X)
Ysk = skpca.fit_transform(X)
assert_columns_allclose_upto_sign(Y, Ysk)
def check_results(Estimator, copy_data):
if Estimator is EMPCA:
# copy_data not yet implemented
pca = Estimator(2, **KWDS[Estimator])
else:
pca = Estimator(2, copy_data=copy_data, **KWDS[Estimator])
Y1 = pca.fit_transform(X.copy(), weights=W.copy())
Y2 = pca.transform(X.copy(), weights=W.copy())
assert_columns_allclose_upto_sign(Y1, Y2)
def check_results(Estimator, copy_data):
if Estimator is EMPCA:
# copy_data not yet implemented
pca = Estimator(2, **KWDS[Estimator])
else:
pca = Estimator(2, copy_data=copy_data, **KWDS[Estimator])
Y1 = pca.fit_transform(X.copy(), weights=W.copy())
Y2 = pca.transform(X.copy(), weights=W.copy())
assert_columns_allclose_upto_sign(Y1, Y2)
def check_results(Estimator, n_outliers, noise_level, rtol):
i = rand.randint(0, 100, size=n_outliers)
j = rand.randint(0, 2, size=n_outliers)
X2 = X.copy()
X2[i, j] += noise_level * rand.randn(n_outliers)
W2 = np.ones_like(X2)
W2[i, j] = 1. / noise_level
pca2 = Estimator(2, **KWDS[Estimator]).fit(X2, weights=W2)
assert_columns_allclose_upto_sign(pca.components_.T,
pca2.components_.T,
rtol=rtol)
def check_results(Estimator, n_outliers, noise_level, rtol):
i = rand.randint(0, 100, size=n_outliers)
j = rand.randint(0, 2, size=n_outliers)
X2 = X.copy()
X2[i, j] += noise_level * rand.randn(n_outliers)
W2 = np.ones_like(X2)
W2[i, j] = 1. / noise_level
pca2 = Estimator(2, **KWDS[Estimator]).fit(X2, weights=W2)
assert_columns_allclose_upto_sign(pca.components_.T,
pca2.components_.T,
rtol=rtol)
def check_results(Estimator):
pca1 = Estimator(2, **KWDS[Estimator]).fit(X, weights=W)
pca2 = Estimator(2, **KWDS[Estimator]).fit(X2, weights=W)
assert_columns_allclose_upto_sign(pca1.components_.T,
pca2.components_.T)
assert_allclose(pca1.explained_variance_, pca2.explained_variance_)
assert_allclose(pca1.explained_variance_ratio_,
pca2.explained_variance_ratio_)
Y1 = pca1.transform(X, W)
Y2 = pca2.transform(X2, W)
assert_columns_allclose_upto_sign(Y1, Y2)
Z1 = pca1.reconstruct(X, W)
Z2 = pca2.reconstruct(X2, W)
assert_allclose(Z1, Z2)
def check_results(Estimator):
pca1 = Estimator(2, **KWDS[Estimator]).fit(X, weights=W)
pca2 = Estimator(2, **KWDS[Estimator]).fit(X2, weights=W)
assert_columns_allclose_upto_sign(pca1.components_.T,
pca2.components_.T)
assert_allclose(pca1.explained_variance_,
pca2.explained_variance_)
assert_allclose(pca1.explained_variance_ratio_,
pca2.explained_variance_ratio_)
Y1 = pca1.transform(X, W)
Y2 = pca2.transform(X2, W)
assert_columns_allclose_upto_sign(Y1, Y2)
Z1 = pca1.reconstruct(X, W)
Z2 = pca2.reconstruct(X2, W)
assert_allclose(Z1, Z2)
def check_results(Estimator):
pca1 = Estimator(2, **KWDS[Estimator]).fit(X)
pca2 = Estimator(2, **KWDS[Estimator]).fit(X, weights=W)
assert_columns_allclose_upto_sign(pca1.components_.T,
pca2.components_.T)
assert_allclose(pca1.explained_variance_,
pca2.explained_variance_)
assert_allclose(pca1.explained_variance_ratio_,
pca2.explained_variance_ratio_)
Y1 = pca1.transform(X)
Y2 = pca2.transform(X, weights=W)
assert_columns_allclose_upto_sign(Y1, Y2)
X1 = pca1.inverse_transform(Y1)
X2 = pca2.inverse_transform(Y2)
assert_allclose(X1, X2)
assert_columns_allclose_upto_sign(pca1.fit_transform(X),
pca2.fit_transform(X, weights=W))
assert_allclose(pca1.reconstruct(X),
pca2.reconstruct(X, weights=W))
assert_allclose(pca1.fit_reconstruct(X),
pca2.fit_reconstruct(X, weights=W))
def check_results(Estimator):
pca1 = Estimator(2, **KWDS[Estimator]).fit(X)
pca2 = Estimator(2, **KWDS[Estimator]).fit(X, weights=W)
assert_columns_allclose_upto_sign(pca1.components_.T,
pca2.components_.T)
assert_allclose(pca1.explained_variance_, pca2.explained_variance_)
assert_allclose(pca1.explained_variance_ratio_,
pca2.explained_variance_ratio_)
Y1 = pca1.transform(X)
Y2 = pca2.transform(X, weights=W)
assert_columns_allclose_upto_sign(Y1, Y2)
X1 = pca1.inverse_transform(Y1)
X2 = pca2.inverse_transform(Y2)
assert_allclose(X1, X2)
assert_columns_allclose_upto_sign(pca1.fit_transform(X),
pca2.fit_transform(X, weights=W))
assert_allclose(pca1.reconstruct(X), pca2.reconstruct(X, weights=W))
assert_allclose(pca1.fit_reconstruct(X),
pca2.fit_reconstruct(X, weights=W))