fix: model.fit metric not collected issue. #1085
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bigframes/ml/core.py
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| # fit the model, synchronously | ||
| _, job = session._start_query_ml_ddl(sql) | ||
| if session._metrics is not None: | ||
| session._metrics.count_job_stats(job) |
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Should we not do this inside _start_query_ml_ddl? That way all ML DDLs will be accounted in the session metrics
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Updated, and added an assertion in test for register.
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| start_execution_count = df._block._expr.session._metrics.execution_count | ||
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| model.fit(X_train, y_train) |
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I think we have the opportunity to make this work work all ML APIs - fit, score, predict, transform, .... We should write such tests for all APIs by writing the metrics collection logic in more central place in the code.
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score, predict works, seems internally is calling other functions.
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Good to know, thanks for checking! We could just pick one or two of the existing tests that does fit, score, predict, register and transform and assert everywhere that we are counting stats for those operations.
bigframes/session/__init__.py
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| job_config.destination_encryption_configuration = None | ||
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| return bf_io_bigquery.start_query_with_client(self.bqclient, sql, job_config) | ||
| results_iterator, query_job = bf_io_bigquery.start_query_with_client( |
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I see start_query_with_client already has the stats update
python-bigquery-dataframes/bigframes/session/_io/bigquery/__init__.py
Lines 245 to 246 in fd06d31
so feels like we could be adding double counting
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It's not double counting, but yes here we can send the metric into the start_query_with_client, thanks for point that out.
| model.fit(X_train, y_train) | ||
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| end_execution_count = df._block._expr.session._metrics.execution_count | ||
| assert end_execution_count - start_execution_count == 2 |
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Worth adding a comment why fit does 2 queries
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Fixes #<issue_number_goes_here> 🦕