Update app.py

app.py

@@ -57,4 +57,47 @@ data.model_names = ['const', 'mlr'] # add names of your models here
 preds = [const_pred_val, mlr_pred_val] # add your custom predictions here
 data.preds_val = dict(zip(data.model_names, preds))
 
-st.markdown('Streamlit c')
+
+data.reweight_target(data_split = 'val')
+data.reweight_preds(data_split = 'val')
+data.metrics_names = ['MAE', 'RMSE', 'R2', 'bias']
+data.create_metrics_df(data_split = 'val')
+%config InlineBackend.figure_format = 'retina'
+letters = string.ascii_lowercase
+
+# create custom dictionary for plotting
+dict_var = data.metrics_var_val
+plot_df_byvar = {}
+for metric in data.metrics_names:
+    plot_df_byvar[metric] = pd.DataFrame([dict_var[model][metric] for model in data.model_names],
+                                               index=data.model_names)
+    plot_df_byvar[metric] = plot_df_byvar[metric].rename(columns = data.var_short_names).transpose()
+
+# plot figure
+fig, axes = plt.subplots(nrows  = len(data.metrics_names), sharex = True)
+for i in range(len(data.metrics_names)):
+    plot_df_byvar[data.metrics_names[i]].plot.bar(
+        legend = False,
+        ax = axes[i])
+    if data.metrics_names[i] != 'R2':
+        axes[i].set_ylabel('$W/m^2$')
+    else:
+        axes[i].set_ylim(0,1)
+
+    axes[i].set_title(f'({letters[i]}) {data.metrics_names[i]}')
+axes[i].set_xlabel('Output variable')
+axes[i].set_xticklabels(plot_df_byvar[data.metrics_names[i]].index, \
+    rotation=0, ha='center')
+
+axes[0].legend(columnspacing = .9,
+               labelspacing = .3,
+               handleheight = .07,
+               handlelength = 1.5,
+               handletextpad = .2,
+               borderpad = .2,
+               ncol = 3,
+               loc = 'upper right')
+fig.set_size_inches(7,8)
+fig.tight_layout()
+
+st.markdown('Streamlit k')