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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

...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:...

cem.py

Source:cem.py

...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        })...

check_learning_failures.py

Source:check_learning_failures.py

...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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