Closed thanasibakis closed 3 weeks ago
While we're working on configurability, we also want to be able to configure an analysis to filter not just to data relevant to the forecast date (date <= forecast_date
) but also data available before the forecast date (date_submitted <= forecast_date
).
We should also find a way to keep the forecast date around as a date, so that we can choose to plot things on a non-arbitrary-time-axis (that is, against something other than $-30 \leq t \leq 14$).
We should also find a way to keep the forecast date around as a date, so that we can choose to plot things on a non-arbitrary-time-axis (that is, against something other than − 30 ≤ t ≤ 14 ).
Done :)
While we're working on configurability, we also want to be able to configure an analysis to filter not just to data relevant to the forecast date (
date <= forecast_date
) but also data available before the forecast date (date_submitted <= forecast_date
).
Done. And now that we're on this topic, I've updated the preprocessing script to give us two datasets for a given horizon [forecast_date - L, forecast_date + H]
:
[forecast_date - L, forecast_date]
This resolves #26.
Until now, we've been missing the ability to easily configure how data is preprocessed.
The
linmod.data
script now accepts a single commandline argument for the path to a YAML file configuring its behavior. This is optional; without it, the default behavior (seen in the dictionarylinmod.data.DEFAULT_CONFIG
) will be used. The YAML file only needs to define the keys it wants to modify from default; missing keys will be populated with the default values.An example is given in
present-day-forecasting/config.yaml
. As described in the README, this is run aspython3 linmod.data config.yaml
.