adjusted tests

pyrasa/utils/fit_funcs.py

@@ -76,11 +76,14 @@ def _get_gof(psd: np.ndarray, psd_pred: np.ndarray, k: int, fit_type: str) -> pd
     n = len(psd)
 
     # https://robjhyndman.com/hyndsight/lm_aic.html
-    c = n + n * np.log(2 * np.pi)
-    aic = 2 * k + n * np.log(mse) + c
+    # c is in practice sometimes dropped. Only relevant when comparing models with different n
+    # c = n + n * np.log(2 * np.pi)
+    # aic = 2 * k + n * np.log(mse) + c #real
+    aic = 2 * k + np.log(n) * np.log(mse)
     # according to Sclove 1987 only difference between BIC and AIC
-    # is that BIC uses log(n) * k instead of 2
-    bic = np.log(n) * k + n * np.log(mse) + c
+    # is that BIC uses log(n) * k instead of 2 * k
+    # bic = np.log(n) * k + n * np.log(mse) + c #real
+    bic = np.log(n) * k + np.log(n) * np.log(mse)
     # Sclove 1987 also hints at sample size adjusted bic
     an = np.log((n + 2) / 24)  # defined in Rissanen 1978 based on minimum-bit representation of a signal
     # abic -> https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7299313/
@@ -89,7 +92,7 @@ def _get_gof(psd: np.ndarray, psd_pred: np.ndarray, k: int, fit_type: str) -> pd
     r2 = 1 - (ss_res / ss_tot)
     r2_adj = 1 - (((1 - r2) * (n - 1)) / (n - k - 1))
 
-    gof = pd.DataFrame({'mse': mse, 'R2': r2, 'R2_adj.': r2_adj, 'BIC': bic, 'ABIC': abic, 'AIC': aic}, index=[0])
+    gof = pd.DataFrame({'mse': mse, 'R2': r2, 'R2_adj.': r2_adj, 'BIC': bic, 'BIC_adj.': abic, 'AIC': aic}, index=[0])
     gof['fit_type'] = fit_type
     return gof
 

tests/test_compute_slope.py

@@ -37,7 +37,7 @@ def test_slope_fitting_fixed(fixed_aperiodic_signal, fs, exponent):
     # test the effect of scaling
     aperiodic_fit_fs = compute_aperiodic_model(psd, freqs, fit_func='fixed', scale=True)
     assert np.isclose(aperiodic_fit_fs.aperiodic_params['Exponent'], aperiodic_fit_f.aperiodic_params['Exponent'])
-    assert np.isclose(aperiodic_fit_fs.gof['r_squared'], aperiodic_fit_f.gof['r_squared'])
+    assert np.isclose(aperiodic_fit_fs.gof['R2'], aperiodic_fit_f.gof['R2'])
 
 
 @pytest.mark.parametrize('exponent, fs', [(-1, 500)], scope='session')

tests/test_irasa_knee.py

@@ -43,7 +43,7 @@ def test_irasa_knee_peakless(load_knee_aperiodic_signal, fs, exponent, knee):
     assert bool(np.isclose(knee_hat, knee_real, atol=KNEE_TOLERANCE))
     # test bic/aic -> should be better for knee
     assert slope_fit_k.gof['AIC'][0] < slope_fit_f.gof['AIC'][0]
-    assert slope_fit_k.gof['BIC'][0] < slope_fit_f.gof['BIC'][0]
+    assert slope_fit_k.gof['BIC_adj.'][0] < slope_fit_f.gof['BIC_adj.'][0]
 
 
 # knee model
@@ -80,7 +80,7 @@ def test_irasa_knee_cmb(load_knee_cmb_signal, fs, exponent, knee, osc_freq):
     assert bool(np.isclose(knee_hat, knee_real, atol=KNEE_TOLERANCE))
     # test bic/aic -> should be better for knee
     assert slope_fit_k.gof['AIC'][0] < slope_fit_f.gof['AIC'][0]
-    assert slope_fit_k.gof['BIC'][0] < slope_fit_f.gof['BIC'][0]
+    assert slope_fit_k.gof['BIC_adj.'][0] < slope_fit_f.gof['BIC_adj.'][0]
     # test whether we can reconstruct the peak frequency correctly
     pe_params = irasa_out.get_peaks()
     assert bool(np.isclose(np.round(pe_params['cf'], 0), osc_freq))

tests/test_irasa_sprint.py

@@ -28,7 +28,7 @@ def test_irasa_sprint(ts4sprint, fs, exponent_1, exponent_2):
     #       irasa_tf.aperiodic[np.newaxis, :, :], freqs=irasa_tf.freqs, times=irasa_tf.time,
     #  )
 
-    assert slope_fit.gof['r_squared'].mean() > MIN_R2_SPRINT
+    assert slope_fit.gof['R2'].mean() > MIN_R2_SPRINT
     assert np.isclose(
         np.mean(slope_fit.aperiodic_params.query('time < 7')['Exponent']), np.abs(exponent_1), atol=SPRINT_TOLERANCE
     )