def test_weighted_correlations():
"""Check weighted Pearson correlation coefficient matches scipy"""
random_state = check_random_state(415)
x1 = random_state.uniform(size=500)
x2 = random_state.uniform(size=500)
w1 = np.ones(500)
w2 = random_state.uniform(size=500)
# Pearson's correlation coefficient
scipy_pearson = pearsonr(x1, x2)[0]
# Check with constant weights (should be equal)
gplearn_pearson = weighted_pearson(x1, x2, w1)
assert_almost_equal(scipy_pearson, gplearn_pearson)
# Check with irregular weights (should be different)
gplearn_pearson = weighted_pearson(x1, x2, w2)
assert_true(abs(scipy_pearson - gplearn_pearson) > 0.01)
# Spearman's correlation coefficient
scipy_spearman = spearmanr(x1, x2)[0]
# Check with constant weights (should be equal)
gplearn_spearman = weighted_spearman(x1, x2, w1)
assert_almost_equal(scipy_spearman, gplearn_spearman)
# Check with irregular weights (should be different)
gplearn_spearman = weighted_pearson(x1, x2, w2)
assert_true(abs(scipy_spearman - gplearn_spearman) > 0.01)
def test_compute_class_weight():
# Test (and demo) compute_class_weight.
y = np.asarray([2, 2, 2, 3, 3, 4])
classes = np.unique(y)
cw = compute_class_weight("auto", classes, y)
assert_almost_equal(cw.sum(), classes.shape)
assert_true(cw[0] < cw[1] < cw[2])
def test_sample_weight():
"""Check sample_weight param works"""
# Check constant sample_weight has no effect
sample_weight = np.ones(boston.target.shape[0])
est1 = SymbolicRegressor(generations=2, random_state=0)
est1.fit(boston.data, boston.target)
est2 = SymbolicRegressor(generations=2, random_state=0)
est2.fit(boston.data, boston.target, sample_weight=sample_weight)
# And again with a scaled sample_weight
est3 = SymbolicRegressor(generations=2, random_state=0)
est3.fit(boston.data, boston.target, sample_weight=sample_weight * 1.1)
assert_almost_equal(est1._program.fitness_, est2._program.fitness_)
assert_almost_equal(est1._program.fitness_, est3._program.fitness_)
# And again for the transformer
sample_weight = np.ones(boston.target.shape[0])
est1 = SymbolicTransformer(generations=2, random_state=0)
est1 = est1.fit_transform(boston.data, boston.target)
est2 = SymbolicTransformer(generations=2, random_state=0)
est2 = est2.fit_transform(boston.data, boston.target,
sample_weight=sample_weight)
# And again with a scaled sample_weight
est3 = SymbolicTransformer(generations=2, random_state=0)
est3 = est3.fit_transform(boston.data, boston.target,
sample_weight=sample_weight * 1.1)
assert_array_almost_equal(est1, est2)
assert_array_almost_equal(est1, est3)
def test_sample_weight():
"""Check sample_weight param works"""
# Check constant sample_weight has no effect
sample_weight = np.ones(boston.target.shape[0])
est1 = SymbolicRegressor(generations=2, random_state=0)
est1.fit(boston.data, boston.target)
est2 = SymbolicRegressor(generations=2, random_state=0)
est2.fit(boston.data, boston.target, sample_weight=sample_weight)
# And again with a scaled sample_weight
est3 = SymbolicRegressor(generations=2, random_state=0)
est3.fit(boston.data, boston.target, sample_weight=sample_weight * 1.1)
assert_almost_equal(est1._program.fitness_, est2._program.fitness_)
assert_almost_equal(est1._program.fitness_, est3._program.fitness_)
# And again for the transformer
sample_weight = np.ones(boston.target.shape[0])
est1 = SymbolicTransformer(generations=2, random_state=0)
est1 = est1.fit_transform(boston.data, boston.target)
est2 = SymbolicTransformer(generations=2, random_state=0)
est2 = est2.fit_transform(boston.data,
boston.target,
sample_weight=sample_weight)
assert_array_almost_equal(est1, est2)
def test_weighted_correlations():
"""Check weighted Pearson correlation coefficient matches scipy"""
random_state = check_random_state(415)
x1 = random_state.uniform(size=500)
x2 = random_state.uniform(size=500)
w1 = np.ones(500)
w2 = random_state.uniform(size=500)
# Pearson's correlation coefficient
scipy_pearson = pearsonr(x1, x2)[0]
# Check with constant weights (should be equal)
gplearn_pearson = weighted_pearson(x1, x2, w1)
assert_almost_equal(scipy_pearson, gplearn_pearson)
# Check with irregular weights (should be different)
gplearn_pearson = weighted_pearson(x1, x2, w2)
assert_true(abs(scipy_pearson - gplearn_pearson) > 0.01)
# Spearman's correlation coefficient
scipy_spearman = spearmanr(x1, x2)[0]
# Check with constant weights (should be equal)
gplearn_spearman = weighted_spearman(x1, x2, w1)
assert_almost_equal(scipy_spearman, gplearn_spearman)
# Check with irregular weights (should be different)
gplearn_spearman = weighted_pearson(x1, x2, w2)
assert_true(abs(scipy_spearman - gplearn_spearman) > 0.01)
def test_compute_class_weight_auto_unordered():
# Test compute_class_weight when classes are unordered
classes = np.array([1, 0, 3])
y = np.asarray([1, 0, 0, 3, 3, 3])
cw = compute_class_weight("auto", classes, y)
assert_almost_equal(cw.sum(), classes.shape)
assert_equal(len(cw), len(classes))
assert_array_almost_equal(cw, np.array([1.636, 0.818, 0.545]), decimal=3)
def test_pinvh_nonpositive():
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float64)
a = np.dot(a, a.T)
u, s, vt = np.linalg.svd(a)
s[0] *= -1
a = np.dot(u * s, vt) # a is now symmetric non-positive and singular
a_pinv = pinv2(a)
a_pinvh = pinvh(a)
assert_almost_equal(a_pinv, a_pinvh)
def test_pinvh_nonpositive():
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float64)
a = np.dot(a, a.T)
u, s, vt = np.linalg.svd(a)
s[0] *= -1
a = np.dot(u * s, vt) # a is now symmetric non-positive and singular
a_pinv = pinv2(a)
a_pinvh = pinvh(a)
assert_almost_equal(a_pinv, a_pinvh)
def test_compute_class_weight_auto_negative():
# Test compute_class_weight when labels are negative
# Test with balanced class labels.
classes = np.array([-2, -1, 0])
y = np.asarray([-1, -1, 0, 0, -2, -2])
cw = compute_class_weight("auto", classes, y)
assert_almost_equal(cw.sum(), classes.shape)
assert_equal(len(cw), len(classes))
assert_array_almost_equal(cw, np.array([1., 1., 1.]))
# Test with unbalanced class labels.
y = np.asarray([-1, 0, 0, -2, -2, -2])
cw = compute_class_weight("auto", classes, y)
assert_almost_equal(cw.sum(), classes.shape)
assert_equal(len(cw), len(classes))
assert_array_almost_equal(cw, np.array([0.545, 1.636, 0.818]), decimal=3)
def test_pipeline():
"""Check that SymbolicRegressor/Transformer can work in a pipeline"""
# Check the regressor
est = make_pipeline(StandardScaler(),
SymbolicRegressor(population_size=50,
generations=5,
tournament_size=5,
random_state=0))
est.fit(boston.data, boston.target)
assert_almost_equal(est.score(boston.data, boston.target), -4.84921978246)
# Check the transformer
est = make_pipeline(SymbolicTransformer(population_size=50,
hall_of_fame=20,
generations=5,
tournament_size=5,
random_state=0),
DecisionTreeRegressor())
est.fit(boston.data, boston.target)
assert_almost_equal(est.score(boston.data, boston.target), 1.0)
def test_pipeline():
"""Check that SymbolicRegressor/Transformer can work in a pipeline"""
# Check the regressor
est = make_pipeline(
StandardScaler(),
SymbolicRegressor(population_size=50,
generations=5,
tournament_size=5,
random_state=0))
est.fit(boston.data, boston.target)
assert_almost_equal(est.score(boston.data, boston.target), -4.00270923)
# Check the transformer
est = make_pipeline(
SymbolicTransformer(population_size=50,
hall_of_fame=20,
generations=5,
tournament_size=5,
random_state=0), DecisionTreeRegressor())
est.fit(boston.data, boston.target)
assert_almost_equal(est.score(boston.data, boston.target), 1.0)
def test_pinvh_simple_complex():
a = (np.array([[1, 2, 3], [4, 5, 6], [7, 8, 10]]) +
1j * np.array([[10, 8, 7], [6, 5, 4], [3, 2, 1]]))
a = np.dot(a, a.conj().T)
a_pinv = pinvh(a)
assert_almost_equal(np.dot(a, a_pinv), np.eye(3))
def test_pinvh_simple_real():
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 10]], dtype=np.float64)
a = np.dot(a, a.T)
a_pinv = pinvh(a)
assert_almost_equal(np.dot(a, a_pinv), np.eye(3))
def test_pinvh_simple_complex():
a = (np.array([[1, 2, 3], [4, 5, 6], [7, 8, 10]])
+ 1j * np.array([[10, 8, 7], [6, 5, 4], [3, 2, 1]]))
a = np.dot(a, a.conj().T)
a_pinv = pinvh(a)
assert_almost_equal(np.dot(a, a_pinv), np.eye(3))
def test_pinvh_simple_real():
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 10]], dtype=np.float64)
a = np.dot(a, a.T)
a_pinv = pinvh(a)
assert_almost_equal(np.dot(a, a_pinv), np.eye(3))
