Parallelization #9
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danyoungday
commented
Jan 28, 2025
| :return: The metrics of the population. | ||
| """ | ||
| if self.n_jobs != 1: | ||
| num_jobs = self.n_jobs if self.n_jobs != -1 else len(population) |
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We use 1 thread per candidate if we enter -1 for n_jobs
danyoungday
commented
Jan 28, 2025
| pop_results = self.evaluate_subset(pop_subset, verbose=verbose) | ||
| for candidate, metrics in zip(pop_subset, pop_results): | ||
| candidate.metrics = metrics | ||
| candidate.outcomes = self.outcomes |
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evaluate_population is now solely responsible for selecting the correct candidates to evaluate then setting their metrics. The meat of the work is done in the bottom-level functions which get delegated to by evaluate_subset
danyoungday
commented
Jan 28, 2025
| TODO: Make parallel_evaluation take in force argument and handle evaluating a subset of the population in | ||
| parallel. | ||
| """ | ||
| parallel_results = Parallel(n_jobs=n_jobs)(delayed(self.evaluate_candidate)(cand) for cand in population) |
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Uses the simple joblib convention for parallelization
danyoungday
commented
Jan 28, 2025
| val_metrics = self.validator.evaluate_candidate(candidate) | ||
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| # Get validation results manually without setting cand's metrics or outcomes | ||
| valid_results = self.validator.evaluate_subset(self.population, verbose=1) |
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Now that the code is refactored to have a function that takes in a population and returns the metrics without setting them, validation is much cleaner
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Added parallel functions into the evaluator as well as a unit test to help verify