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| import time | |
| import numpy as np | |
| import comet.src.train.batch as batch_utils | |
| import comet.utils.utils as utils | |
| import comet.src.evaluate.evaluate as base_evaluate | |
| def make_evaluator(opt, *args, **kwargs): | |
| return ConceptNetGenerationEvaluator(opt, *args, **kwargs) | |
| class ConceptNetGenerationEvaluator(base_evaluate.Evaluator): | |
| def __init__(self, opt, model, data_loader, track=False): | |
| super(ConceptNetGenerationEvaluator, self).__init__( | |
| opt, model, data_loader) | |
| if track: | |
| self.tracker = {"positive": [], "negative": []} | |
| else: | |
| self.tracker = None | |
| def batch(self, opt, nums, average_loss, batch_variables, eval_mode): | |
| batch_variables["category"] = self.current_category | |
| outputs = batch_utils.batch_conceptnet_generate( | |
| opt, nums, average_loss, batch_variables, eval_mode, | |
| tracking_mode=self.tracker is not None) | |
| if outputs.get("tracking", None) is not None: | |
| self.tracker[self.current_category] += outputs["tracking"] | |
| if outputs["reset"] and batch_variables["category"] == "positive": | |
| outputs["reset"] = False | |
| self.current_category = "negative" | |
| return outputs | |
| def initialize_losses(self): | |
| average_loss = {"total_micro": 0, "total_macro": 0, | |
| "negative_micro": 0, "negative_macro": 0} | |
| nums = {"total_micro": 0, "total_macro": 0, | |
| "negative_micro": 0, "negative_macro": 0} | |
| self.current_category = "positive" | |
| if self.tracker is not None: | |
| self.tracker = {"positive": [], "negative": []} | |
| return average_loss, nums | |
| def compute_final_scores(self, average_loss, nums): | |
| average_loss["total_macro"] /= nums["total_macro"] | |
| average_loss["total_micro"] /= nums["total_micro"] | |
| if nums["negative_micro"]: | |
| average_loss["negative_macro"] /= nums["negative_macro"] | |
| average_loss["negative_micro"] /= nums["negative_micro"] | |
| else: | |
| average_loss["negative_macro"] = 0 | |
| average_loss["negative_micro"] = 0 | |
| average_loss["macro_diff"] = (average_loss["negative_macro"] - | |
| average_loss["total_macro"]) | |
| average_loss["micro_diff"] = (average_loss["negative_micro"] - | |
| average_loss["total_micro"]) | |
| average_loss["ppl_macro"] = np.exp(average_loss["total_macro"]) | |
| average_loss["ppl_micro"] = np.exp(average_loss["total_micro"]) | |
| return average_loss | |
| def counter(self, nums): | |
| return nums["total_macro"] | |
| def print_result(self, split, epoch_losses): | |
| print("{} Loss: \t {}".format( | |
| split, epoch_losses["total_micro"])) | |
| print("{} Diff: \t {}".format( | |
| split, epoch_losses["micro_diff"])) | |
| print("{} Perplexity: \t {}".format( | |
| split, epoch_losses["ppl_micro"])) | |