How to use _finalize_result method in Lemoncheesecake

Best Python code snippet using lemoncheesecake

eval.py

Source:eval.py

...40        final_batch = self._data.get_final_batch(batch_size, return_indices=self._batch_indices)41        if final_batch is not None:42            result = self._aggregate_batch(result, self._run_batch(model, final_batch))43        model.train()44        return self._finalize_result(result)45    @abc.abstractmethod46    def _run_batch(self, model, batch):47        """ Evaluates the model on a given batch of data """48    @abc.abstractmethod49    def _aggregate_batch(self, agg, batch_result):50        """ Aggregates batch result into total """51    @abc.abstractmethod52    def _initialize_result(self):53        """ Initializes the aggregate result """54    @abc.abstractmethod55    def _finalize_result(self, result):56        """ Returns the final value given the aggregate result """57    def get_name(self):58        return self._name59    @staticmethod60    def run_all(evaluations, model):61        results = dict()62        for evaluation in evaluations:63            if evaluation.get_trials() == 1:64                results[evaluation.get_name()] = evaluation.run(model)65            else:66                mean_name = evaluation.get_name()67                std_name = evaluation.get_name() + " std (n=" + str(evaluation.get_trials()) + ")"68                mean, std = evaluation.run(model)69                results[mean_name] = mean70                results[std_name] = std71        return results72class Loss(Evaluation):73    def __init__(self, name, data, data_parameters, loss_criterion, norm_dim=False, trials=1):74        super(Loss, self).__init__(name, data, data_parameters, trials=trials)75        self._loss_criterion = loss_criterion76        self._norm_dim = norm_dim77    def _run_batch(self, model, batch):78        loss = model.loss(batch, self._data_parameters, self._loss_criterion)79        if self._norm_dim:80            return (loss[0].data[0], loss[1].data[0])81        else:82            return loss.data[0]83    def _aggregate_batch(self, agg, batch_result):84        if self._norm_dim:85            return (agg[0] + batch_result[0], agg[1] + batch_result[1])86        else:87            return agg + batch_result88    def _initialize_result(self):89        if self._norm_dim:90            return (0.0, 0.0)91        else:92            return 0.093    def _finalize_result(self, result):94        if self._norm_dim:95            return result[0] / result[1]96        else:97            return result / self._data.get_size()98class DistributionAccuracy(Evaluation):99    def __init__(self, name, data, data_parameters, model_fn=None, target_indexed = False, check_unique = False, trials=1):100        super(DistributionAccuracy, self).__init__(name, data, data_parameters, trials=trials)101        self._model_fn = model_fn102        self._target_indexed = target_indexed103        self._check_unique = check_unique104    def _run_batch(self, model, batch):105        dist = None106        if self._model_fn is None:107            dist = model.forward_batch(batch, self._data_parameters)108        else:109            dist = self._model_fn(batch, model, self._data_parameters)110        target = batch[self._data_parameters[DataParameter.TARGET]].squeeze()111        model_ps = dist.p().data.cpu()112        max_ps, max_index = torch.max(model_ps, 1, keepdim=True)113        # Indicators of whether maxima are unique114        if self._check_unique:115            max_unique = (torch.sum(max_ps.expand_as(model_ps) == model_ps, 1, keepdim=True) == 1).long()116        else:117            max_unique = torch.ones(target.size(0)).long()118        total_correct = None119        if self._target_indexed:120            total_correct = torch.sum(max_unique.squeeze()*((target == max_index.squeeze()).long()))121        else:122            target_index, has_missing, mask = dist.get_index(target)123            # Count of where model max is same as target124            total_correct = torch.sum(mask*max_unique.squeeze()*((target_index == max_index.squeeze()).long()))125        return float(total_correct)126    def _aggregate_batch(self, agg, batch_result):127        return agg + batch_result128    def _initialize_result(self):129        return 0.0130    def _finalize_result(self, result):131        return result / self._data.get_size()132class ModelStatistic(Evaluation):133    def __init__(self, name, data, data_parameters, stat_fn, trials=1):134        super(ModelStatistic, self).__init__(name, data, data_parameters, trials=trials)135        self._stat_fn = stat_fn136    def _run_once(self, model):137        model.eval()138        stat = self._stat_fn(model)139        model.train()140        return stat141    def _run_batch(self, model, batch):142        pass143    def _aggregate_batch(self, agg, batch_result):144        pass145    def _initialize_result(self):146        pass147    def _finalize_result(self, result):...

writer.py

Source:writer.py

...33        result.rank = test.rank34        result.start_time = start_time35        return result36    @staticmethod37    def _finalize_result(result, end_time):38        result.end_time = end_time39        result.status = "passed" if result.is_successful() else "failed"40    def _lookup_step(self, event):41        try:42            return self.active_steps[event.thread_id]43        except KeyError:44            return None45    def on_test_session_start(self, event):46        self.report.start_time = event.time47    def on_test_session_end(self, event):48        self.report.end_time = event.time49    def on_test_session_setup_start(self, event):50        self.report.test_session_setup = self._initialize_result(event.time)51    def on_test_session_setup_end(self, event):52        self._finalize_result(self.report.test_session_setup, event.time)53    def on_test_session_teardown_start(self, event):54        self.report.test_session_teardown = self._initialize_result(event.time)55    def on_test_session_teardown_end(self, event):56        self._finalize_result(self.report.test_session_teardown, event.time)57    def on_suite_start(self, event):58        suite = event.suite59        suite_result = SuiteResult(suite.name, suite.description)60        suite_result.start_time = event.time61        suite_result.tags.extend(suite.tags)62        suite_result.properties.update(suite.properties)63        suite_result.links.extend(suite.links)64        suite_result.rank = suite.rank65        if suite.parent_suite:66            parent_suite_result = self._get_suite_result(suite.parent_suite)67            parent_suite_result.add_suite(suite_result)68        else:69            self.report.add_suite(suite_result)70    def on_suite_end(self, event):71        suite_result = self._get_suite_result(event.suite)72        suite_result.end_time = event.time73    def on_suite_setup_start(self, event):74        suite_result = self._get_suite_result(event.suite)75        suite_result.suite_setup = self._initialize_result(event.time)76    def on_suite_setup_end(self, event):77        suite_result = self._get_suite_result(event.suite)78        self._finalize_result(suite_result.suite_setup, event.time)79    def on_suite_teardown_start(self, event):80        suite_result = self._get_suite_result(event.suite)81        suite_result.suite_teardown = self._initialize_result(event.time)82    def on_suite_teardown_end(self, event):83        suite_result = self._get_suite_result(event.suite)84        self._finalize_result(suite_result.suite_teardown, event.time)85    def on_test_start(self, event):86        test_result = self._initialize_test_result(event.test, event.time)87        suite_result = self._get_suite_result(event.test.parent_suite)88        suite_result.add_test(test_result)89    def on_test_end(self, event):90        test_result = self._get_test_result(event.test)91        self._finalize_result(test_result, event.time)92    def _bypass_test(self, test, status, status_details, time):93        test_result = self._initialize_test_result(test, time)94        test_result.end_time = time95        test_result.status = status96        test_result.status_details = status_details97        suite_result = self._get_suite_result(test.parent_suite)98        suite_result.add_test(test_result)99    def on_test_skipped(self, event):100        self._bypass_test(event.test, "skipped", event.skipped_reason, event.time)101    def on_test_disabled(self, event):102        self._bypass_test(event.test, "disabled", event.disabled_reason, event.time)103    def on_step_start(self, event):104        result = self.report.get(event.location)105        step = Step(event.step_description)...

main.py

Source:main.py

...73            self.arena.yellow_knight.move(direction=direction)74            self.arena.position_knight_to_arena(self.arena.yellow_knight)75        76    @staticmethod77    def _finalize_result(knight:Knight) -> list:78        item = knight.item79        if item:80            item = str(item)81        return [knight.position, knight.status, item, knight.attack_score, knight.defense_score]82    def finalize_knight_monitor(self):83        results = {}84        results['red'] = self._finalize_result(self.arena.red_knight)85        results['blue'] = self._finalize_result(self.arena.blue_knight)86        results['green'] = self._finalize_result(self.arena.green_knight)87        results['yellow'] = self._finalize_result(self.arena.yellow_knight)88        return results89    def finalize_item_monitor(self) -> None:90        for item_str, item_obj in self.items.items():91            self.item_monitor[item_str] = [item_obj.position, item_obj.equipped]92        return self.item_monitor93if __name__ == '__main__':94    instruction_file = sys.argv[1]95    battle_knights = Main(instruction_file=instruction_file)96    for element, element_details in battle_knights.leaderboard.items():...

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