def test_pca_invalid():
X = pd.DataFrame([[3, 0, 1, 6],
[1, None, 1, 6],
[10, 2, 1, 6],
[10, 2, 2, np.nan],
[None, 2, 2, 5]])
pca = PCA()
with pytest.raises(ValueError, match="must be all numeric"):
pca.fit(X)
X = pd.DataFrame([[3, 0, 1, 6],
['a', 'b', 'a', 'b'],
[10, 2, 1, 6],
[10, 2, 2, 23],
[0, 2, 2, 5]])
pca = PCA()
with pytest.raises(ValueError, match="must be all numeric"):
pca.fit_transform(X)
X_ok = pd.DataFrame([[3, 0, 1, 6],
[1, 2, 1, 6],
[10, 2, 1, 6],
[10, 2, 2, 5],
[6, 2, 2, 5]])
pca = PCA()
pca.fit(X_ok)
with pytest.raises(ValueError, match="must be all numeric"):
pca.transform(X)
def test_n_components():
X = pd.DataFrame([[3, 0, 1, 6, 5, 10], [1, 2, 1, 3, 11, 4],
[10, 2, 1, 12, 5, 6], [10, 6, 4, 4, 0, 1],
[6, 8, 9, 3, 1, 5]])
pca = PCA(n_components=5)
X_t = pca.fit_transform(X)
assert X_t.shape[1] == 5
pca = PCA(n_components=3)
X_t = pca.fit_transform(X)
assert X_t.shape[1] == 3
pca = PCA(n_components=1)
X_t = pca.fit_transform(X)
assert X_t.shape[1] == 1
def test_pca_numeric(data_type, make_data_type):
X = pd.DataFrame([[3, 0, 1, 6], [1, 2, 1, 6], [10, 2, 1, 6], [10, 2, 2, 5],
[6, 2, 2, 5]])
X = make_data_type(data_type, X)
pca = PCA()
expected_X_t = pd.DataFrame(
[[3.176246, 1.282616], [4.969987, -0.702976], [-3.954182, 0.429071],
[-4.079174, -0.252790], [-0.112877, -0.755922]],
columns=[f"component_{i}" for i in range(2)])
X_t = pca.fit_transform(X)
assert_frame_equal(expected_X_t, X_t.to_dataframe())
def test_variance():
X = pd.DataFrame([[3, 0, 1, 6, 5, 10], [1, 2, 1, 3, 11, 4],
[10, 2, 1, 12, 5, 6], [10, 6, 4, 4, 0, 1],
[6, 8, 9, 3, 1, 5]])
pca = PCA(variance=0.97)
expected_X_t = pd.DataFrame([[-5.581732, 0.469307, 3.985657, 1.760273],
[-6.961064, -5.026062, -3.170519, -0.624576],
[-1.352624, 7.778657, -0.778879, -1.554429],
[7.067179, 0.645894, -2.633617, 2.159135],
[6.828241, -3.867796, 2.597358, -1.740404]],
columns=[f"component_{i}" for i in range(4)])
X_t_90 = pca.fit_transform(X)
assert_frame_equal(expected_X_t, X_t_90.to_dataframe())
pca = PCA(variance=0.75)
X_t_75 = pca.fit_transform(X)
assert X_t_75.shape[1] < X_t_90.shape[1]
pca = PCA(variance=0.50)
X_t_50 = pca.fit_transform(X)
assert X_t_50.shape[1] < X_t_75.shape[1]