def test_warm_start_effectiveness():
# A 1-iteration second fit on same data should give almost same result
# with warm starting, and quite different result without warm starting.
nca_warm = NeighborhoodComponentsAnalysis(warm_start=True, random_state=0)
nca_warm.fit(iris_data, iris_target)
transformation_warm = nca_warm.components_
nca_warm.max_iter = 1
nca_warm.fit(iris_data, iris_target)
transformation_warm_plus_one = nca_warm.components_
nca_cold = NeighborhoodComponentsAnalysis(warm_start=False, random_state=0)
nca_cold.fit(iris_data, iris_target)
transformation_cold = nca_cold.components_
nca_cold.max_iter = 1
nca_cold.fit(iris_data, iris_target)
transformation_cold_plus_one = nca_cold.components_
diff_warm = np.sum(np.abs(transformation_warm_plus_one -
transformation_warm))
diff_cold = np.sum(np.abs(transformation_cold_plus_one -
transformation_cold))
assert diff_warm < 3.0, ("Transformer changed significantly after one "
"iteration even though it was warm-started.")
assert diff_cold > diff_warm, ("Cold-started transformer changed less "
"significantly than warm-started "
"transformer after one iteration.")
def test_warm_start_effectiveness():
# A 1-iteration second fit on same data should give almost same result
# with warm starting, and quite different result without warm starting.
nca_warm = NeighborhoodComponentsAnalysis(warm_start=True, random_state=0)
nca_warm.fit(iris_data, iris_target)
transformation_warm = nca_warm.components_
nca_warm.max_iter = 1
nca_warm.fit(iris_data, iris_target)
transformation_warm_plus_one = nca_warm.components_
nca_cold = NeighborhoodComponentsAnalysis(warm_start=False, random_state=0)
nca_cold.fit(iris_data, iris_target)
transformation_cold = nca_cold.components_
nca_cold.max_iter = 1
nca_cold.fit(iris_data, iris_target)
transformation_cold_plus_one = nca_cold.components_
diff_warm = np.sum(np.abs(transformation_warm_plus_one -
transformation_warm))
diff_cold = np.sum(np.abs(transformation_cold_plus_one -
transformation_cold))
assert diff_warm < 3.0, ("Transformer changed significantly after one "
"iteration even though it was warm-started.")
assert diff_cold > diff_warm, ("Cold-started transformer changed less "
"significantly than warm-started "
"transformer after one iteration.")
def test_warm_start_effectiveness():
# A 1-iteration second fit on same data should give almost same result
# with warm starting, and quite different result without warm starting.
X, y = make_classification(n_samples=30, n_features=5,
n_redundant=0, random_state=0)
n_iter = 10
nca_warm = NeighborhoodComponentsAnalysis(warm_start=True,
max_iter=n_iter, random_state=0)
nca_warm.fit(X, y)
transformation_warm = nca_warm.components_
nca_warm.max_iter = 1
nca_warm.fit(X, y)
transformation_warm_plus_one = nca_warm.components_
nca_cold = NeighborhoodComponentsAnalysis(warm_start=False,
max_iter=n_iter, random_state=0)
nca_cold.fit(X, y)
transformation_cold = nca_cold.components_
nca_cold.max_iter = 1
nca_cold.fit(X, y)
transformation_cold_plus_one = nca_cold.components_
diff_warm = np.sum(np.abs(transformation_warm_plus_one -
transformation_warm))
diff_cold = np.sum(np.abs(transformation_cold_plus_one -
transformation_cold))
assert_true(diff_warm < 2.0,
"Transformer changed significantly after one iteration even "
"though it was warm-started.")
assert_true(diff_cold > diff_warm,
"Cold-started transformer changed less significantly than "
"warm-started transformer after one iteration.")