results: stratified in_pocket metrics #94

playground.py

@@ -1,8 +1,46 @@
 # %%
-from src.analysis.figures import tbl_dpkd_metrics_overlap, tbl_dpkd_metrics_n_mut
+from src.analysis.figures import tbl_dpkd_metrics_n_mut, tbl_stratified_dpkd_metrics
 MODEL = lambda i: f"results/model_media/test_set_pred/GVPLM_PDBbind{i}D_nomsaF_aflowE_128B_0.00022659LR_0.02414D_2000E_gvpLF_binaryLE_PLATINUM.csv"
 TRAIN_DATA_P = lambda set: f'/cluster/home/t122995uhn/projects/data/PDBbindDataset/nomsa_aflow_gvp_binary/{set}0/cleaned_XY.csv'
 NORMALIZE = True
 
-print('NUM MUTATIONS:')
-mkdnm = tbl_dpkd_metrics_n_mut(MODEL, NORMALIZE, conditions=[1,2], plot=True)
+
+#%%
+# add in_binding info to df
+def get_in_binding(df, dfr):
+    """
+    df is the predicted csv with index as <raw_idx>_wt (or *_mt) where raw_idx 
+    corresponds to an index in dfr which contains the raw data for platinum including 
+    ('mut.in_binding_site')
+        - 0: wildtype rows
+        - 1: in pocket
+        - 2: outside of pocket
+    """
+    in_pocket = dfr[dfr['mut.in_binding_site'] == 'YES'].index   
+    pclass = []
+    for code in df.index:
+        if '_wt' in code:
+            pclass.append(0)
+        elif int(code.split('_')[0]) in in_pocket:
+            pclass.append(1)
+        else:
+            pclass.append(2)
+
+    df['in_pocket'] = pclass
+    return df
+
+# get df_binding info from /cluster/home/t122995uhn/projects/data/PlatinumDataset/raw/platinum_flat_file.csv
+conditions = ['(in_pocket == 0) | (in_pocket == 1)', '(in_pocket == 0) | (in_pocket == 2)']
+names = ['mutation in pocket', 'mutation NOT in pocket']
+#%%
+import pandas as pd
+dfr = pd.read_csv('/cluster/home/t122995uhn/projects/data/PlatinumDataset/raw/platinum_flat_file.csv', index_col=0)
+dfp = pd.read_csv(MODEL(0), index_col=0)
+
+df = get_in_binding(dfp, dfr)
+print(df.in_pocket.value_counts())
+
+#%%
+tbl_stratified_dpkd_metrics(MODEL, NORMALIZE, n_models=5, df_transform=get_in_binding,
+                                       conditions=conditions, names=names, verbose=True, plot=True, dfr=dfr)
+# %%

src/analysis/utils.py

@@ -67,7 +67,7 @@ def get_mut_count(df):
     df['n_mut'] = n_mut
     return df
 
-def generate_markdown(results, names=None, verbose=False):
+def generate_markdown(results, names=None, verbose=False, thresh_sig=False):
     """
     generates a markdown given a list or single df containing metrics from get_metrics
     
@@ -97,7 +97,10 @@ def generate_markdown(results, names=None, verbose=False):
     if n_groups == 2: # no support for sig  if groups are more than 2
         # T-tests for significance
         ttests = {col: ttest_ind(results_df[0][col], results_df[1][col]) for col in results_df[0].columns}
-        sig = pd.Series({col: '*' if ttests[col].pvalue < 0.05 else '' for col in results_df[0].columns})
+        if thresh_sig:
+            sig = pd.Series({col: '*' if ttests[col].pvalue < 0.05 else '' for col in results_df[0].columns})
+        else:
+            sig =pd.Series({col: f"{ttests[col].pvalue:.4f}" for col in results_df[0].columns})
 
         md_table = pd.concat([md_table, sig], axis=1)
         md_table.columns = [*names, 'Sig']