Add testing #102
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Add testing #102
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riley-harper
approved these changes
Oct 23, 2023
hlink/tests/training_test.py
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| tf = spark.table("training_feature_importances").toPandas() | ||
| for var in training_conf["training"]["independent_vars"]: | ||
| assert not tf.loc[tf["feature_name"].str.startswith(f"{var}", na=False)].empty | ||
| assert all([col in ['feature_name', 'coefficient_or_importance'] for col in tf.columns]) |
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Here we should be able to use a generator expression instead of a list comprehension, which means we can drop the outermost pair of [s. This is not a big deal, but a nice style thing.
| @@ -88,7 +88,7 @@ def _run(self): | |||
| features_df = self.task.spark.createDataFrame( | |||
| zip(column_names, feature_importances), | |||
| "feature_name: string, coefficient_or_importance: double", | |||
| ).sort("importance", ascending=False) | |||
| ).sort("coefficient_or_importance", ascending=False) | |||
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This small bug fix was made on the target branch too, looks good. 👍
riley-harper
merged commit 440cdff
into
ipums:fix_get_feature_importances
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This should lock in what currently is getting returned, with a little room for variance. Asserts everything exists and is reasonable. Can certainly be edited, this isn't exactly exhaustive testing.