add calculation for one-run pearson correlation between predictions a…

…nd responces

scripts/ci_plots_script.py

@@ -83,8 +83,11 @@
 # ----------------------------------------------------------------------------
 # variables to save things while the loop runs
 #
+
+
 # one run
 one_run_correlations_p_values = {}
+one_run_correlations_prediction_vs_responce = {}
 # mult runs mean, std
 mult_runs_corr_rmse_percentile_pm_stds = {}
 mult_runs_rmse_pm_std = {}
@@ -108,6 +111,7 @@
     #
     # one run
     one_run_correlations_p_values[f'{dataset}_{cf}'] = {}
+    one_run_correlations_prediction_vs_responce[f'{dataset}_{cf}'] = {}
     # mult runs mean, std
     mult_runs_corr_rmse_percentile_pm_stds[f'{dataset}_{cf}'] = {}
     mult_runs_rmse_pm_std[f'{dataset}_{cf}'] = {}
@@ -139,6 +143,10 @@
         y_test_pred = df_pred.iloc[:, 0]
         y_test_std = df_std.iloc[:, 0]
 
+        # model performance assessment
+        corr, p_val = pearsonr(y_test, y_test_pred)
+        one_run_correlations_prediction_vs_responce[f'{dataset}_{cf}'][model] = round(corr, 2), round(p_val, 5)
+
         # calculate everything
         upper = y_test_pred + 1.96 * y_test_std
         lower = y_test_pred - 1.96 * y_test_std
@@ -354,6 +362,14 @@
 column_order  = ['Random Forests', 'Gaussian Processes']
 
 
+print("\nOne run predictions vs responce correlations, p-values:")
+df = pd.DataFrame(one_run_correlations_prediction_vs_responce).T
+df = df.rename(mapper=row_mapper, axis='index')
+df = df.rename(mapper=column_mapper, axis='columns')
+df = df.loc[row_order, column_order]
+print(df)
+
+
 print("\nOne run correlations and p-values:")
 df = pd.DataFrame(one_run_correlations_p_values).T
 df = df.rename(mapper=row_mapper, axis='index')