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DBT + Hamilton Example #236
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skrawcz
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Nov 26, 2022
One task to train + infer, the other to load the data. Some caveats due to the earliness of the dbt API.
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skrawcz
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skrawcz
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examples/dbt/models/predict.py
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| titanic_dag = driver.Driver( | ||
| { | ||
| "random_state": 5, | ||
| "test_size": 0.2, | ||
| "model_to_use": "create_new", | ||
| }, | ||
| data_loader, | ||
| feature_transforms, | ||
| model_pipeline, | ||
| adapter=adapter, | ||
| ) | ||
| # gather resutls | ||
| results = titanic_dag.execute( | ||
| final_vars=["model_predict"], inputs={"raw_passengers_df": raw_passengers_df} | ||
| ) |
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Note: the inference set, is just all the data, i.e. the training set. Not a separate set or anything.
skrawcz
suggested changes
Nov 27, 2022
happy earlier -- a few corrections required given what is actually being returned/predicted over.
examples/dbt/models/predict.py
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| @@ -0,0 +1,44 @@ | |||
| def model(dbt, session): | |||
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yeah so predict probably isn't the right name of this file.
skrawcz
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Nov 27, 2022
examples/dbt/README.md
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| We've organized the code into two separate DBT models: | ||
| 1. [raw_passengers](models/raw_passengers.sql) This is a simple select and join using duckdb and DBT. Due to the simplicity of DBT -- its just as you would write if it were embedded within a python program, or you were executing SQL on your own! | ||
| It does, however, automatically get materialized. | ||
| 2. [predict](models/predict.py) |
skrawcz
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Nov 27, 2022
examples/dbt/README.md
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|
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| - feature engineering to extract a test/train set | ||
| - train a model using the train set | ||
| - run inference over an inference set |
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Suggested change
| - run inference over an inference set | |
| - run inference over the entire data set... |
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Shows how Hamilton + DBT work together
Changes
Adds a
dbt/in theexamplesdirectory.How I tested this
Ran locally.
Notes
Its simple, but a good start and will work for a write-up.
Checklist