Logging and cleanup
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@ -278,7 +278,7 @@ def run_workload_characterization(metric_data):
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# Fit factor analysis model
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# Fit factor analysis model
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fa_model = FactorAnalysis()
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fa_model = FactorAnalysis()
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# For now we use 5 latent variables
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# For now we use 5 latent variables
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fa_model.fit(unique_matrix, unique_columnlabels, n_components=5)
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fa_model.fit(shuffled_matrix, unique_columnlabels, n_components=5)
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# Components: metrics * factors
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# Components: metrics * factors
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components = fa_model.components_.T.copy()
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components = fa_model.components_.T.copy()
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@ -302,6 +302,7 @@ def run_workload_characterization(metric_data):
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# Return pruned metrics
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# Return pruned metrics
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save_execution_time(start_ts, "run_workload_characterization")
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save_execution_time(start_ts, "run_workload_characterization")
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LOG.info("Workload characterization finished in %.0f seconds.", time.time() - start_ts)
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return pruned_metrics
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return pruned_metrics
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@ -384,4 +385,5 @@ def run_knob_identification(knob_data, metric_data, dbms):
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consolidated_knobs = consolidate_columnlabels(encoded_knobs)
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consolidated_knobs = consolidate_columnlabels(encoded_knobs)
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save_execution_time(start_ts, "run_knob_identification")
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save_execution_time(start_ts, "run_knob_identification")
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LOG.info("Knob identification finished in %.0f seconds.", time.time() - start_ts)
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return consolidated_knobs
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return consolidated_knobs
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