How to use get_iteration_results method in autotest

Best Python code snippet using autotest_python

check_top_results_and_by_model.py

Source: check_top_results_and_by_model.py Github

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...30 result = dict(sorted(result.items(), key=lambda item: item[1], reverse=True))31 return result32def analyze(input_dirs):33 results: List[ResultsSummary] = load_results(input_dirs)34 iteration_results: List[IterationResults] = get_iteration_results(results)35 iteration_results = sorted(iteration_results, key=lambda it_results: get_f1_sum_dev(it_results), reverse=True)36 best_results = iteration_results[:30]37 # IterationResults.to_df_multiple(best_results[:30], "output/​websites/​top30.csv")38 # experiments_distribution = count_experiments_distribution(best_results, by=lambda ir: ir.experiment_name)39 # models_distribution = count_experiments_distribution(best_results, by=lambda ir: get_model_name(ir.experiment_name))40 detailed_analysis(best_results)41 z = 142def detailed_analysis(iteration_results: List[IterationResults]):43 top_10_by_model = count_experiments_distribution(iteration_results[:10],44 by=lambda ir: get_model_name(ir.experiment_name))45 top_10_by_lr = count_experiments_distribution(iteration_results[:10],46 by=lambda ir: get_lr(ir.experiment_name))47 top_10_by_wd = count_experiments_distribution(iteration_results[:10],48 by=lambda ir: get_wd(ir.experiment_name))49 top_20_by_model = count_experiments_distribution(iteration_results[:20],50 by=lambda ir: get_model_name(ir.experiment_name))51 top_20_by_lr = count_experiments_distribution(iteration_results[:20],52 by=lambda ir: get_lr(ir.experiment_name))53 top_20_by_wd = count_experiments_distribution(iteration_results[:20],54 by=lambda ir: get_wd(ir.experiment_name))55 top_30_by_model = count_experiments_distribution(iteration_results[:30],56 by=lambda ir: get_model_name(ir.experiment_name))57 top_30_by_lr = count_experiments_distribution(iteration_results[:30],58 by=lambda ir: get_lr(ir.experiment_name))59 top_30_by_wd = count_experiments_distribution(iteration_results[:30],60 by=lambda ir: get_wd(ir.experiment_name))61 z = 162def get_f1_sum_dev(iteration_results: IterationResults):63 return iteration_results.dev_results.f11 + iteration_results.dev_results.f1064def r5(value):65 return round(value, 5)66def get_experiment_pretty_name(name):67 file_name = re.search(".*(test.*)", name).group(1)68 for k, v in NAMES_MAP.items():69 if k in file_name:70 file_name = file_name.replace(k, v)71 break72 return file_name.replace("_", "\\_")73def print_top(dirs_with_experiments, set_attribute):74 results: List[ResultsSummary] = load_results_summaries(dirs_with_experiments)75 iteration_results: List[IterationResults] = get_iteration_results(results)76 iteration_results = sorted(iteration_results,77 key=lambda it_results: getattr(it_results, set_attribute).f10 + getattr(it_results,78 set_attribute).f11,79 reverse=True)80 best_10_subject_results_with_positions = get_top_10_subject_results(iteration_results)81 best_10_subject_results = [result[1] for result in best_10_subject_results_with_positions]82 best_subject_position = best_10_subject_results_with_positions[0][0]83 best_10_video_results = iteration_results[:10]84 worst_10_video_results_that_are_still_better_than_top1_subject = iteration_results[85 best_subject_position - 10:best_subject_position]86 for top_10_results in [best_10_video_results,87 worst_10_video_results_that_are_still_better_than_top1_subject,88 best_10_subject_results]:89 for ir in top_10_results:...

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cem.py

Source: cem.py Github

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...76 # generate new pop77 self.generate_pop()78 self.sync_pop_weights()79 @override(NeuroEvolution)80 def get_iteration_results(self):81 data = super().get_iteration_results()82 data.update({83 "fitness": self.fitnesses,84 "var_noise": self.noise,85 "pop_var_mean": np.mean(self.variance),86 "pop_var_max": np.max(self.variance),87 "pop_var_min": np.min(self.variance),88 "target_pop_l2_distance": np.linalg.norm(self.target_weights_flat-self.mean, ord=2)89 })...

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check_learning_failures.py

Source: check_learning_failures.py Github

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...14 result = dict(sorted(result.items(), key=lambda item: item[1], reverse=True))15 return result16def analyze(input_dirs):17 results: List[Results] = load_results(input_dirs)18 iteration_results: List[IterationResults] = get_iteration_results(results)19 iteration_results = sorted(iteration_results, key=lambda it_results: get_f1_sum_dev(it_results), reverse=True)20 failures = [ir for ir in iteration_results if is_failure(ir)]21 failures_by_experiment_name = count_experiments_distribution(failures,22 lambda ir: ir.experiment_name)23 failures_by_model_name = count_experiments_distribution(failures,24 lambda ir: get_model_name(ir.experiment_name))25 failures_by_wd = count_experiments_distribution(failures,26 lambda ir: get_wd(ir.experiment_name))27 failures_by_lr = count_experiments_distribution(failures,28 lambda ir: get_lr(ir.experiment_name))29 failures_by_lrwd = count_experiments_distribution(failures,30 lambda ir: get_lr(ir.experiment_name) + get_wd(31 ir.experiment_name))32 z = 1...

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