Hey.

I’m stuck on this for a while.

Please could you help in calculating the WMAE.

From Kaggle Walmart COmpetition Page:

Pseudo code:

```
def exp_wmae(y_pred, targ):
if holiday_week:
return (targ - y_pred)*5
else
return (targ - y_pred)
```

I want the WMAE error to be displayed each time I call the fit function.

I have a column holiday_week which checks for holiday, but wondering how do I access this column holiday_week while the model is training?

Just having trouble understanding this.

I looked at Rossmann code. For Rossman it was RMSE and it was passed via metrics.

```
def inv_y(a): return np.exp(a)
def exp_rmspe(y_pred, targ):
targ = inv_y(targ)
pct_var = (targ - inv_y(y_pred))/targ
return math.sqrt((pct_var**2).mean())
max_log_y = np.max(yl)
y_range = (0, max_log_y*1.2)
```

```
m.fit(lr, 3, metrics=[exp_rmspe], cycle_len=1)
```

There was no weighted mean in exp_rmspe. But here we need to check if each prediction is a holiday_week and accordingly add a weight to the formula.

My question is how do I check/access this holiday_week inside each prediction? Not sure if I am doing this correctly, as I am not able to understand how to do this at all. Also, not sure what y_range is doing in the Rossmann code.

Thanks for your help

Arjun