def test_cmi_gauss_data(): """Test CMI estimation on two sets of Gaussian random data. The first test is on correlated variables, the second on uncorrelated variables. Note that the calculation is based on a random variable (because the generated data is a set of random variables) - the result will be of the order of what we expect, but not exactly equal to it; in fact, there will be a large variance around it. """ expected_mi, source1, source2, target = _get_gauss_data() # Test Kraskov mi_estimator = JidtKraskovCMI(settings={}) mi_cor = mi_estimator.estimate(source1, target) mi_uncor = mi_estimator.estimate(source2, target) _assert_result(mi_cor, expected_mi, 'JidtKraskovCMI', 'CMI (corr.)') _assert_result(mi_uncor, 0, 'JidtKraskovCMI', 'CMI (uncorr.)') # Test Gaussian mi_estimator = JidtGaussianCMI(settings={}) mi_cor = mi_estimator.estimate(source1, target) mi_uncor = mi_estimator.estimate(source2, target) _assert_result(mi_cor, expected_mi, 'JidtGaussianCMI', 'CMI (corr.)') _assert_result(mi_uncor, 0, 'JidtGaussianCMI', 'CMI (uncorr.)') # Test Discrete settings = {'discretise_method': 'equal', 'num_discrete_bins': 5} mi_estimator = JidtDiscreteCMI(settings=settings) mi_cor = mi_estimator.estimate(source1, target) mi_uncor = mi_estimator.estimate(source2, target) _assert_result(mi_cor, expected_mi, 'JidtDiscreteCMI', 'CMI (corr.)') _assert_result(mi_uncor, 0, 'JidtDiscreteCMI', 'CMI (uncorr.)')
def test_cmi_gauss_data_no_cond(): """Test estimators on correlated Gauss data without a conditional. The estimators should return the MI if no conditional variable is provided. Note that the calculation is based on a random variable (because the generated data is a set of random variables) - the result will be of the order of what we expect, but not exactly equal to it; in fact, there will be a large variance around it. """ expected_mi, source1, source2, target = _get_gauss_data() # Test Kraskov mi_estimator = JidtKraskovCMI(settings={}) mi_cor = mi_estimator.estimate(source1, target) mi_uncor = mi_estimator.estimate(source2, target) _assert_result(mi_cor, expected_mi, 'JidtKraskovCMI', 'CMI (no cond.)') _assert_result(mi_uncor, 0, 'JidtKraskovCMI', 'CMI (uncorr., no cond.)') # Test Gaussian mi_estimator = JidtGaussianCMI(settings={}) mi_cor = mi_estimator.estimate(source1, target) mi_uncor = mi_estimator.estimate(source2, target) _assert_result(mi_cor, expected_mi, 'JidtGaussianCMI', 'CMI (no cond.)') _assert_result(mi_uncor, 0, 'JidtGaussianCMI', 'CMI (uncorr., no cond.)') # Test Discrete settings = {'discretise_method': 'equal', 'num_discrete_bins': 5} mi_estimator = JidtDiscreteCMI(settings=settings) mi_cor = mi_estimator.estimate(source1, target) mi_uncor = mi_estimator.estimate(source2, target) _assert_result(mi_cor, expected_mi, 'JidtDiscreteCMI', 'CMI (no cond.)') _assert_result(mi_uncor, 0, 'JidtDiscreteCMI', 'CMI (uncorr., no cond.)')
def test_one_two_dim_input_gaussian(): """Test one- and two-dimensional input for Gaussian estimators.""" expected_mi, src_one, s, target_one = _get_gauss_data(expand=False) src_two = np.expand_dims(src_one, axis=1) target_two = np.expand_dims(target_one, axis=1) ar_src_one, s = _get_ar_data(expand=False) ar_src_two = np.expand_dims(ar_src_one, axis=1) # MI mi_estimator = JidtGaussianMI(settings={}) mi_cor_one = mi_estimator.estimate(src_one, target_one) _assert_result(mi_cor_one, expected_mi, 'JidtGaussianMI', 'MI') mi_cor_two = mi_estimator.estimate(src_two, target_two) _assert_result(mi_cor_two, expected_mi, 'JidtGaussianMI', 'MI') _compare_result(mi_cor_one, mi_cor_two, 'JidtGaussianMI one dim', 'JidtGaussianMI two dim', 'MI') # CMI cmi_estimator = JidtGaussianCMI(settings={}) mi_cor_one = cmi_estimator.estimate(src_one, target_one) _assert_result(mi_cor_one, expected_mi, 'JidtGaussianCMI', 'CMI') mi_cor_two = cmi_estimator.estimate(src_two, target_two) _assert_result(mi_cor_two, expected_mi, 'JidtGaussianCMI', 'CMI') _compare_result(mi_cor_one, mi_cor_two, 'JidtGaussianMI one dim', 'JidtGaussianMI two dim', 'CMI') # TE te_estimator = JidtGaussianTE(settings={'history_target': 1}) mi_cor_one = te_estimator.estimate(src_one[1:], target_one[:-1]) _assert_result(mi_cor_one, expected_mi, 'JidtGaussianTE', 'TE') mi_cor_two = te_estimator.estimate(src_one[1:], target_one[:-1]) _assert_result(mi_cor_two, expected_mi, 'JidtGaussianTE', 'TE') _compare_result(mi_cor_one, mi_cor_two, 'JidtGaussianMI one dim', 'JidtGaussianMI two dim', 'TE') # AIS ais_estimator = JidtGaussianAIS(settings={'history': 2}) mi_cor_one = ais_estimator.estimate(ar_src_one) mi_cor_two = ais_estimator.estimate(ar_src_two) _compare_result(mi_cor_one, mi_cor_two, 'JidtGaussianAIS one dim', 'JidtGaussianAIS two dim', 'AIS (AR process)')
def test_one_two_dim_input_gaussian(): """Test one- and two-dimensional input for Gaussian estimators.""" expected_mi, src_one, s, target_one = _get_gauss_data(expand=False, seed=SEED) src_two = np.expand_dims(src_one, axis=1) target_two = np.expand_dims(target_one, axis=1) ar_src_one, s = _get_ar_data(expand=False, seed=SEED) ar_src_two = np.expand_dims(ar_src_one, axis=1) # MI mi_estimator = JidtGaussianMI(settings={}) mi_cor_one = mi_estimator.estimate(src_one, target_one) _assert_result(mi_cor_one, expected_mi, 'JidtGaussianMI', 'MI') mi_cor_two = mi_estimator.estimate(src_two, target_two) _assert_result(mi_cor_two, expected_mi, 'JidtGaussianMI', 'MI') _compare_result(mi_cor_one, mi_cor_two, 'JidtGaussianMI one dim', 'JidtGaussianMI two dim', 'MI') # CMI cmi_estimator = JidtGaussianCMI(settings={}) mi_cor_one = cmi_estimator.estimate(src_one, target_one) _assert_result(mi_cor_one, expected_mi, 'JidtGaussianCMI', 'CMI') mi_cor_two = cmi_estimator.estimate(src_two, target_two) _assert_result(mi_cor_two, expected_mi, 'JidtGaussianCMI', 'CMI') _compare_result(mi_cor_one, mi_cor_two, 'JidtGaussianMI one dim', 'JidtGaussianMI two dim', 'CMI') # TE te_estimator = JidtGaussianTE(settings={'history_target': 1}) mi_cor_one = te_estimator.estimate(src_one[1:], target_one[:-1]) _assert_result(mi_cor_one, expected_mi, 'JidtGaussianTE', 'TE') mi_cor_two = te_estimator.estimate(src_one[1:], target_one[:-1]) _assert_result(mi_cor_two, expected_mi, 'JidtGaussianTE', 'TE') _compare_result(mi_cor_one, mi_cor_two, 'JidtGaussianMI one dim', 'JidtGaussianMI two dim', 'TE') # AIS ais_estimator = JidtGaussianAIS(settings={'history': 2}) mi_cor_one = ais_estimator.estimate(ar_src_one) mi_cor_two = ais_estimator.estimate(ar_src_two) _compare_result(mi_cor_one, mi_cor_two, 'JidtGaussianAIS one dim', 'JidtGaussianAIS two dim', 'AIS (AR process)')
def test_invalid_settings_input(): """Test handling of wrong inputs for settings dictionary.""" # Wrong input type for settings dict. with pytest.raises(TypeError): JidtDiscreteMI(settings=1) with pytest.raises(TypeError): JidtDiscreteCMI(settings=1) with pytest.raises(TypeError): JidtDiscreteAIS(settings=1) with pytest.raises(TypeError): JidtDiscreteTE(settings=1) with pytest.raises(TypeError): JidtGaussianMI(settings=1) with pytest.raises(TypeError): JidtGaussianCMI(settings=1) with pytest.raises(TypeError): JidtGaussianAIS(settings=1) with pytest.raises(TypeError): JidtGaussianTE(settings=1) with pytest.raises(TypeError): JidtKraskovMI(settings=1) with pytest.raises(TypeError): JidtKraskovCMI(settings=1) with pytest.raises(TypeError): JidtKraskovAIS(settings=1) with pytest.raises(TypeError): JidtKraskovTE(settings=1) # Test if settings dict is initialised correctly. e = JidtDiscreteMI() assert type( e.settings) is dict, 'Did not initialise settings as dictionary.' e = JidtDiscreteCMI() assert type( e.settings) is dict, 'Did not initialise settings as dictionary.' e = JidtGaussianMI() assert type( e.settings) is dict, 'Did not initialise settings as dictionary.' e = JidtGaussianCMI() assert type( e.settings) is dict, 'Did not initialise settings as dictionary.' e = JidtKraskovMI() assert type( e.settings) is dict, 'Did not initialise settings as dictionary.' e = JidtKraskovCMI() assert type( e.settings) is dict, 'Did not initialise settings as dictionary.' # History parameter missing for AIS and TE estimation. with pytest.raises(RuntimeError): JidtDiscreteAIS(settings={}) with pytest.raises(RuntimeError): JidtDiscreteTE(settings={}) with pytest.raises(RuntimeError): JidtGaussianAIS(settings={}) with pytest.raises(RuntimeError): JidtGaussianTE(settings={}) with pytest.raises(RuntimeError): JidtKraskovAIS(settings={}) with pytest.raises(RuntimeError): JidtKraskovTE(settings={})
def test_local_values(): """Test estimation of local values and their return type.""" expected_mi, source, s, target = _get_gauss_data(expand=False) ar_proc, s = _get_ar_data(expand=False) settings = { 'discretise_method': 'equal', 'n_discrete_bins': 4, 'history_target': 1, 'history': 2, 'local_values': True } # MI - Discrete mi_estimator = JidtDiscreteMI(settings=settings) mi = mi_estimator.estimate(source, target) _assert_result(np.mean(mi), expected_mi, 'JidtDiscreteMI', 'MI', 0.08) # More variability here assert type(mi) is np.ndarray, 'Local values are not a numpy array.' # MI - Gaussian mi_estimator = JidtGaussianMI(settings=settings) mi = mi_estimator.estimate(source, target) _assert_result(np.mean(mi), expected_mi, 'JidtGaussianMI', 'MI') assert type(mi) is np.ndarray, 'Local values are not a numpy array.' # MI - Kraskov mi_estimator = JidtKraskovMI(settings=settings) mi = mi_estimator.estimate(source, target) _assert_result(np.mean(mi), expected_mi, 'JidtKraskovMI', 'MI') assert type(mi) is np.ndarray, 'Local values are not a numpy array.' # CMI - Discrete cmi_estimator = JidtDiscreteCMI(settings=settings) mi = cmi_estimator.estimate(source, target) _assert_result(np.mean(mi), expected_mi, 'JidtDiscreteCMI', 'CMI', 0.08) # More variability here assert type(mi) is np.ndarray, 'Local values are not a numpy array.' # MI - Gaussian mi_estimator = JidtGaussianCMI(settings=settings) mi = mi_estimator.estimate(source, target) _assert_result(np.mean(mi), expected_mi, 'JidtGaussianCMI', 'MI') assert type(mi) is np.ndarray, 'Local values are not a numpy array.' # MI - Kraskov mi_estimator = JidtKraskovCMI(settings=settings) mi = mi_estimator.estimate(source, target) _assert_result(np.mean(mi), expected_mi, 'JidtKraskovCMI', 'MI') assert type(mi) is np.ndarray, 'Local values are not a numpy array.' # TE - Discrete te_estimator = JidtDiscreteTE(settings=settings) mi = te_estimator.estimate(source[1:], target[:-1]) _assert_result(np.mean(mi), expected_mi, 'JidtDiscreteTE', 'TE', 0.08) # More variability here assert type(mi) is np.ndarray, 'Local values are not a numpy array.' # TE - Gaussian mi_estimator = JidtGaussianTE(settings=settings) mi = mi_estimator.estimate(source[1:], target[:-1]) _assert_result(np.mean(mi), expected_mi, 'JidtGaussianTE', 'MI') assert type(mi) is np.ndarray, 'Local values are not a numpy array.' # TE - Kraskov mi_estimator = JidtKraskovTE(settings=settings) mi = mi_estimator.estimate(source[1:], target[:-1]) _assert_result(np.mean(mi), expected_mi, 'JidtKraskovTE', 'MI') assert type(mi) is np.ndarray, 'Local values are not a numpy array.' # AIS - Kraskov ais_estimator = JidtKraskovAIS(settings=settings) mi_k = ais_estimator.estimate(ar_proc) assert type(mi_k) is np.ndarray, 'Local values are not a numpy array.' # AIS - Discrete ais_estimator = JidtDiscreteAIS(settings=settings) mi_d = ais_estimator.estimate(ar_proc) assert type(mi_d) is np.ndarray, 'Local values are not a numpy array.' # TODO should we compare these? # _compare_result(np.mean(mi_k), np.mean(mi_d), # 'JidtKraskovAIS', 'JidtDiscreteAIS', 'AIS (AR process)') # AIS - Gaussian ais_estimator = JidtGaussianAIS(settings=settings) mi_g = ais_estimator.estimate(ar_proc) assert type(mi_g) is np.ndarray, 'Local values are not a numpy array.' _compare_result(np.mean(mi_k), np.mean(mi_g), 'JidtKraskovAIS', 'JidtGaussianAIS', 'AIS (AR process)')
print('Estimated CMI: {0:.5f}, expected CMI: {1:.5f}'.format(cmi, expected_mi)) est = JidtKraskovMI(settings) mi = est.estimate(source_cor, target) print('Estimated MI: {0:.5f}, expected MI: {1:.5f}'.format(mi, expected_mi)) settings['history_target'] = 1 est = JidtKraskovTE(settings) te = est.estimate(source_cor[1:n], target[0:n - 1]) print('Estimated TE: {0:.5f}, expected TE: {1:.5f}'.format(te, expected_mi)) settings['history'] = 1 est = JidtKraskovAIS(settings) ais = est.estimate(target) print('Estimated AIS: {0:.5f}, expected AIS: ~0'.format(ais)) # JIDT Gaussian estimators settings = {} est = JidtGaussianCMI(settings) cmi = est.estimate(source_cor, target, source_uncor) print('Estimated CMI: {0:.5f}, expected CMI: {1:.5f}'.format(cmi, expected_mi)) est = JidtGaussianMI(settings) mi = est.estimate(source_cor, target) print('Estimated MI: {0:.5f}, expected MI: {1:.5f}'.format(mi, expected_mi)) settings['history_target'] = 1 est = JidtGaussianTE(settings) te = est.estimate(source_cor[1:n], target[0:n - 1]) print('Estimated TE: {0:.5f}, expected TE: {1:.5f}'.format(te, expected_mi)) settings['history'] = 1 est = JidtGaussianAIS(settings) ais = est.estimate(target) print('Estimated AIS: {0:.5f}, expected AIS: ~0'.format(ais)) # OpenCL Kraskov estimators