QOL improvements for table

app.py

@@ -13,6 +13,11 @@ from sklearn import linear_model
 import joblib
 import os
 from sklearn.metrics import roc_auc_score, precision_score, recall_score
+import datetime
+from pandas.tseries.offsets import BDay
+from datasets import load_dataset
+
+dataset = load_dataset("boomsss/SPX_full_30min")
 
 def walk_forward_validation(df, target_column, num_training_rows, num_periods):
     
@@ -363,6 +368,9 @@ if st.button('🤖 Run it'):
 
         new_pred = pd.DataFrame(new_pred).T
         # new_pred_show = pd.DataFrame(index=[new_pred.columns], columns=[new_pred.index], data=[[v] for v in new_pred.values])
+        # last_date = datetime.datetime.strptime(data.loc[final_row], '%Y-%m-%d')
+        curr_date = final_row + BDay(1)
+        curr_date = curr_date.strftime('%Y-%m-%d')
 
         new_pred['BigNewsDay'] = new_pred['BigNewsDay'].astype(float)
         new_pred['Quarter'] = new_pred['Quarter'].astype(int)
@@ -440,13 +448,15 @@ if st.button('🤖 Run it'):
     score_fmt = f'{score:.1%}'
 
     results = pd.DataFrame(index=[
+        'PrevClose',
         'Confidence Score',
         'Success Rate',
-        f'NumObs {operator} {"" if do_not_play else score_fmt}'
+        f'NumObs {operator} {"" if do_not_play else score_fmt}',
     ], data = [
+        f"{data.loc[final_row,'Close']:.2f}",
         f'{text_cond} {score:.1%}',
         f'{historical_proba:.1%}', 
-        num_obs
+        num_obs,
         ])
 
     results.columns = ['Outputs']
@@ -502,8 +512,9 @@ if st.button('🤖 Run it'):
 
     perf_daily = res1.copy()
     perf_daily['Accuracy'] = [get_acc(t, p) for t, p in zip(perf_daily['True'], perf_daily['Predicted'])]
+    
 
-    tab1.subheader('Preds and Probabilities')
+    tab1.subheader(f'Pred for {curr_date}')
     tab1.write(results)
     tab1.write(df_probas)