Update PGym test run script

scripts/ProteinGym_runs/run_performance_tests_proteingym_data.py

@@ -7,7 +7,7 @@
 from scipy.stats import spearmanr
 import matplotlib.pyplot as plt
 # Add local PyPEF path if not using pip-installed PyPEF version
-#sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')))
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')))
 from pypef.dca.gremlin_inference import GREMLIN
 from pypef.dca.hybrid_model import get_delta_e_statistical_model, remove_gap_pos
 from pypef.utils.variant_data import get_seqs_from_var_name
@@ -21,14 +21,15 @@ def plot_performance(mut_data, plot_name, mut_sep=':'):
     tested_dsets = []
     dset_perfs = []
     for i, (dset_key, dset_paths) in enumerate(mut_data.items()):
-            #if i < 3:
+        if i < 8:
             #try:
             print('\n', i, '\n===============================================================')
             csv_path = dset_paths['CSV_path']
             msa_path = dset_paths['MSA_path']
             wt_seq = dset_paths['WT_sequence']
             print(msa_path)
             variant_fitness_data = pd.read_csv(csv_path, sep=',')
+            print(np.shape(variant_fitness_data))
             variants = variant_fitness_data['mutant']
             fitnesses = variant_fitness_data['DMS_score']
             variants_split = []
@@ -68,15 +69,16 @@ def plot_performance(mut_data, plot_name, mut_sep=':'):
             assert len(x_dca) == len(fitnesses) == len(variants) == len(sequences)
             #except SystemError:  # Check MSAs
             #   continue
-            tested_dsets.append(f'{dset_key} ({100.0 - (ratio_input_vars_at_gaps * 100):.2f}%, ' 
-                                + r'$N_\mathrm{AASubs:}$' + f'{max_muts})')
+            tested_dsets.append(f'{dset_key} ({100.0 - (ratio_input_vars_at_gaps * 100):.2f}%, {max_muts})')
             dset_perfs.append(abs(spearmanr(fitnesses, y_pred_new)[0]))
-    plt.figure(figsize=(20, 12))
-    plt.plot(range(len(tested_dsets)), dset_perfs, 'o--', markersize=12)
+    plt.figure(figsize=(26, 12))
+    plt.plot(range(len(tested_dsets)), dset_perfs, 'o--', markersize=8)
     plt.plot(range(len(tested_dsets)), np.full(np.shape(tested_dsets), np.mean(dset_perfs)), 'k--')
     plt.text(len(tested_dsets) - 1, np.mean(dset_perfs), f'{np.mean(dset_perfs):.2f}')
     plt.xticks(range(len(tested_dsets)), tested_dsets, rotation=45, ha='right')
     plt.tight_layout()
+    plt.ylim(0.0, 1.0)
+    plt.ylabel(r'|Spearmanr $\rho$|')
     plt.savefig(os.path.join(os.path.dirname(__file__), f'{plot_name}.png'), dpi=300)
     print('Saved file as ' + os.path.join(os.path.dirname(__file__),  f'{plot_name}.png') + '.')
 
@@ -86,4 +88,4 @@ def plot_performance(mut_data, plot_name, mut_sep=':'):
 with open(higher_mut_data, 'r') as fh:
     h_mut_data = json.loads(fh.read())
 plot_performance(mut_data=s_mut_data, plot_name='single_point_mut_performance')
-plot_performance(mut_data=h_mut_data, plot_name='multi_point_mut_performance')
+#plot_performance(mut_data=h_mut_data, plot_name='multi_point_mut_performance')