docs: add snippet for creating boosted tree model #1142
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@@ -14,7 +14,7 @@ | |
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| def test_boosted_tree_model(random_model_id: str) -> None: | ||
| your_model_id = random_model_id | ||
| # [START bigquery_dataframes_bqml_boosted_tree_prepare] | ||
| import bigframes.pandas as bpd | ||
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@@ -39,4 +39,28 @@ def test_boosted_tree_model(random_model_id: str) -> None: | |
| ) | ||
| del input_data["functional_weight"] | ||
| # [END bigquery_dataframes_bqml_boosted_tree_prepare] | ||
| # [START bigquery_dataframes_bqml_boosted_tree_create] | ||
| from bigframes.ml import ensemble | ||
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| # input_data is defined in an earlier step. | ||
| training_data = input_data[input_data["dataframe"] == "training"] | ||
| X = training_data.drop(columns=["income_bracket", "dataframe"]) | ||
| y = training_data["income_bracket"] | ||
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Collaborator
There was a problem hiding this comment. Presumably you ran this code sample and it worked OK? I remember we had some bugs where
Contributor
Author
There was a problem hiding this comment. The code sample seems to run! Not sure if I did it right so here's the colab: https://colab.sandbox.google.com/drive/10jA6zSRiptXWrTkCcmyCT_sYBjLqGJx0?resourcekey=0-0TrIkmDzAJw_F6ONFikwaA#scrollTo=wU367u1SAj3Y |
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| # create and train the model | ||
| census_model = ensemble.XGBClassifier( | ||
| n_estimators=1, | ||
| booster="gbtree", | ||
| tree_method="hist", | ||
| max_iterations=1, # For a more accurate model, try 50 iterations. | ||
| subsample=0.85, | ||
| ) | ||
| census_model.fit(X, y) | ||
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| census_model.to_gbq( | ||
| your_model_id, # For example: "your-project.census.census_model" | ||
| replace=True, | ||
| ) | ||
| # [END bigquery_dataframes_bqml_boosted_tree_create] | ||
| assert input_data is not None | ||
| assert census_model is not None |
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No action needed, but something to consider for future: it would be nice to update the
preparesection above to work without referencing an index (e.g. when ordering mode = "partial").We have a few options, but the easiest will be to start with a string column and add
(True, "training")as the last in the list of cases.Aside: we have an issue open (349926559) to allow selecting any column in the dataframe (such as
functional_weight, which would be a natural choice in this example) even if its a different type, so long as aTrue(default) case is provided.