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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"]))
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