def test_lda_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]])
y = [2, 0, 1, 1, 0]
lda = LinearDiscriminantAnalysis()
with pytest.raises(ValueError, match="must be all numeric"):
lda.fit(X, y)
X = pd.DataFrame([[3, 0, 1, 6],
['a', 'b', 'a', 'b'],
[10, 2, 1, 6],
[10, 2, 2, 23],
[0, 2, 2, 5]])
lda = LinearDiscriminantAnalysis()
with pytest.raises(ValueError, match="must be all numeric"):
lda.fit_transform(X, y)
X_ok = pd.DataFrame([[3, 0, 1, 6],
[1, 2, 1, 6],
[10, 2, 1, 6],
[10, 2, 2, 5],
[6, 2, 2, 5]])
lda = LinearDiscriminantAnalysis()
lda.fit(X_ok, y)
with pytest.raises(ValueError, match="must be all numeric"):
lda.transform(X)
def test_lda_array():
X = np.array([[3, 0, 1, 6], [1, 2, 1, 6], [10, 2, 1, 6], [10, 2, 2, 5],
[6, 2, 2, 5]])
y = np.array([2, 2, 0, 1, 0])
lda = LinearDiscriminantAnalysis()
expected_X_t = pd.DataFrame([[-0.6412164311777084, 0.5197032695565076],
[0.9499648898073094, -0.6919658287324498],
[0.7364892645407753, 0.884637532109161],
[-0.570057889422197, -0.005831184057363141],
[-0.4751798337481819, -0.7065437888758568]],
columns=[f"component_{i}" for i in range(2)])
lda.fit(X, y)
X_t = lda.transform(X)
assert_frame_equal(expected_X_t, X_t.to_dataframe())
def test_invalid_n_components():
X = pd.DataFrame([[3, 0, 1, 6, 5, 10], [1, 3, 1, 3, 11, 4],
[10, 2, 3, 12, 5, 6], [10, 6, 4, 3, 0, 1],
[6, 8, 9, 3, 3, 5], [3, 2, 1, 2, 1, 3],
[12, 11, 1, 1, 3, 3]])
y = [0, 1, 2, 1, 2, 0, 2]
lda_invalid = LinearDiscriminantAnalysis(n_components=4)
with pytest.raises(ValueError, match="is too large"):
lda_invalid.fit(X, y)
X = pd.DataFrame([[3, 0, 1], [1, 3, 1], [10, 2, 3], [10, 6, 4], [6, 8, 9],
[3, 2, 1], [12, 11, 1]])
y = [0, 1, 2, 3, 4, 3, 4, 5]
lda_invalid = LinearDiscriminantAnalysis(n_components=4)
with pytest.raises(ValueError, match="is too large"):
lda_invalid.fit(X, y)
def test_lda_woodwork_custom_overrides_returned_by_components():
y = pd.Series([1, 2, 1])
X_df = pd.DataFrame([[3, 0, 1, 6],
[1, 2, 1, 6],
[10, 2, 1, 6],
[10, 2, 2, 5],
[6, 2, 2, 5]])
y = pd.Series([0, 1, 0, 1, 1])
override_types = [Integer, Double]
for logical_type in override_types:
X = ww.DataTable(X_df, logical_types={0: logical_type, 1: logical_type, 2: logical_type, 3: logical_type})
lda = LinearDiscriminantAnalysis(n_components=1)
lda.fit(X, y)
transformed = lda.transform(X, y)
assert isinstance(transformed, ww.DataTable)
assert transformed.logical_types == {'component_0': ww.logical_types.Double}