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_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)
