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docs: add snippet for evaluating a boosted tree model #1154

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tswast merged 8 commits into from
Nov 20, 2024
Merged

docs: add snippet for evaluating a boosted tree model #1154

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221374b
must verify evaluation_data and edit Output
rey-esp Nov 14, 2024
265bfd6
line edit
rey-esp Nov 14, 2024
17e7170
edit desc
rey-esp Nov 14, 2024
a634e86
Merge branch 'main' into b338872698-bigframes-v2
rey-esp Nov 14, 2024
d535acf
Merge branch 'main' into b338872698-bigframes-v2
rey-esp Nov 15, 2024
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merge create and evaluate
rey-esp Nov 19, 2024
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Merge branch 'b338872698-bigframes-v2' of github.com:googleapis/pytho…
rey-esp Nov 19, 2024
6a70279
correct model info
rey-esp Nov 20, 2024
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33 changes: 28 additions & 5 deletions samples/snippets/classification_boosted_tree_model_test.py
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Expand Up @@ -48,19 +48,42 @@ def test_boosted_tree_model(random_model_id: str) -> None:
y = training_data["income_bracket"]

# create and train the model
census_model = ensemble.XGBClassifier(
tree_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)
tree_model.fit(X, y)

census_model.to_gbq(
your_model_id, # For example: "your-project.census.census_model"
tree_model.to_gbq(
your_model_id, # For example: "your-project.bqml_tutorial.tree_model"
replace=True,
)
# [END bigquery_dataframes_bqml_boosted_tree_create]
# [START bigquery_dataframes_bqml_boosted_tree_explain]
# Select model you'll use for predictions. `read_gbq_model` loads model
# data from BigQuery, but you could also use the `tree_model` object
# from the previous step.
tree_model = bpd.read_gbq_model(
your_model_id, # For example: "your-project.bqml_tutorial.tree_model"
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)

# input_data is defined in an earlier step.
evaluation_data = input_data[input_data["dataframe"] == "evaluation"]
X = evaluation_data.drop(columns=["income_bracket", "dataframe"])
y = evaluation_data["income_bracket"]

# The score() method evaluates how the model performs compared to the
# actual data. Output DataFrame matches that of ML.EVALUATE().
score = tree_model.score(X, y)
score.peek()
# Output:
# precision recall accuracy f1_score log_loss roc_auc
# 0 0.671924 0.578804 0.839429 0.621897 0.344054 0.887335
# [END bigquery_dataframes_bqml_boosted_tree_explain]
assert tree_model is not None
assert evaluation_data is not None
assert score is not None
assert input_data is not None
assert census_model is not None