Sure! I was mostly wanting it for my own custom implementation of plot_top_losses() for tabular data, and here’s what I came up with:
def plot_top_losses(self, k, largest = True, return_table:bool=False):
"Shows the respective rows in top_losses along with their prediction, actual, loss, and probability"
tl_val, tl_idx = self.top_losses(k)
classes = self.data.classes
cat_names = self.data.x.cat_names
cont_names = self.data.x.cont_names
df = pd.DataFrame(columns = [['Prediction', 'Actual', 'Loss','Prob'] + cat_names + cont_names])
for i, idx in enumerate(tl_idx):
da, cl = self.data.dl().dataset[idx]
cl = int(cl)
t1 = str(da) <-
t1 = t1.split(';') <-
arr = []
arr.append(classes[self.pred_class[idx]])
arr.append(classes[cl])
arr.append(f'{self.losses[idx]:.2f}')
arr.append(f'{self.probs[idx][cl]:.2f}')
for x in range(len(t1)-1): <-
_, value = t1[x].rsplit(' ',1) <-
arr.append(value) <-
df.loc[i] = arr <-
display(df)
if return_table: return df
Focus specifically on when tl and cl are being used. Let me know if you have questions! 