Comments (3)
@dfsnow - Is there a particular output that you want for the value / treatment tests? I ran the ingest script twice, once with the unload = TRUE and once without the unload = TRUE. I then sorted the columns (they were ordered differently which is something to note), and used dataCompareR
to test the different outputs. The only differences were related to small decimal disparities.
from model-res-avm.
@dfsnow - Is there a particular output that you want for the value / treatment tests? I ran the ingest script twice, once with the unload = TRUE and once without the unload = TRUE. I then sorted the columns (they were ordered differently which is something to note), and used
dataCompareR
to test the different outputs. The only differences were related to small decimal disparities.
@Damonamajor As mentioned in the issue, I would specifically focus on the categorical columns that are stored in Athena as arrays. That's mostly anything with a loc_tax_
prefix. I'm worried that the string processing we set up for the previous method may interfere with the new one and give us different results.
from model-res-avm.
@dfsnow - Is there a particular output that you want for the value / treatment tests? I ran the ingest script twice, once with the unload = TRUE and once without the unload = TRUE. I then sorted the columns (they were ordered differently which is something to note), and used
dataCompareR
to test the different outputs. The only differences were related to small decimal disparities.
@Damonamajor As mentioned in the issue, I would specifically focus on the categorical columns that are stored in Athena as arrays. That's mostly anything with a
loc_tax_
prefix. I'm worried that the string processing we set up for the previous method may interfere with the new one and give us different results.
Those columns didn't ping anything, but I'll take a deeper dive into them.
from model-res-avm.
Related Issues (20)
- Add workflow/process for tagging models with `run_type` HOT 1
- Add SHAP maps for location and proximity features
- Diagnostics: "Challenge groups" HOT 1
- Create aggregate maps using comps output HOT 2
- Refactor comps to calculate tree weights on a per-card basis HOT 1
- Add sales val run_id to res model outputs
- Add sale ratio column to desk review sheets HOT 1
- Flesh out characteristics in desk review workbooks
- Refactor `export` stage to use a config dict representing the workbook structure
- Comps check HOT 1
- Improve modeling multi-cards
- Spike upgrading comps algorithm with taichi HOT 1
- Spike model rebuild in Python/polars
- Create explainer/diagrams for multi-PIN and multi-card aggregations
- Investigate merging the residential and condo model codebases
- Adjust `model_fetch_run()` to optionally fetch input data
- Update comps algorithm to save `instruno` in addition to `parid` HOT 1
- Generate historical model API workbooks
- Edit `dvc.yaml` to include files invoked as deps
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