def evaluate(self, clean_file, corrupt_file):
"""
clean_file = f"{DEFAULT_DATA_PATH}/traintest/clean.txt"
corrupt_file = f"{DEFAULT_DATA_PATH}/traintest/corrupt.txt"
"""
for x, y, z in zip([""], [clean_file], [corrupt_file]):
print(x, y, z)
test_data = load_data(x, y, z)
clean_data = [x[0] for x in test_data]
corrupt_data = [x[1] for x in test_data]
inference_st_time = time()
predictions_data = self.correct_strings(corrupt_data)
assert len(clean_data) == len(corrupt_data) == len(
predictions_data)
corr2corr, corr2incorr, incorr2corr, incorr2incorr = \
get_metrics(clean_data,corrupt_data,predictions_data)
print("total inference time for this data is: {:4f} secs".format(
time() - inference_st_time))
print("###############################################")
print("total token count: {}".format(corr2corr + corr2incorr +
incorr2corr + incorr2incorr))
print(
f"corr2corr:{corr2corr}, corr2incorr:{corr2incorr}, incorr2corr:{incorr2corr}, incorr2incorr:{incorr2incorr}"
)
print(
f"accuracy is {(corr2corr+incorr2corr)/(corr2corr+corr2incorr+incorr2corr+incorr2incorr)}"
)
print(
f"word correction rate is {(incorr2corr)/(incorr2corr+incorr2incorr)}"
)
print("###############################################")
return
def evaluate(self, clean_file, corrupt_file):
"""
clean_file = f"{DEFAULT_DATA_PATH}/traintest/clean.txt"
corrupt_file = f"{DEFAULT_DATA_PATH}/traintest/corrupt.txt"
"""
self.__model_status()
batch_size = 4 if self.device == "cpu" else 16
for x, y, z in zip([""], [clean_file], [corrupt_file]):
print(x, y, z)
test_data = load_data(x, y, z)
_ = model_inference(self.model,
test_data,
topk=1,
DEVICE=self.device,
BATCH_SIZE=batch_size,
vocab_=self.vocab)
return