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)