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evaluate.py
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evaluate.py
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import json
import pickle
import argparse
import numpy as np
def load_file(path):
with open(path, "r") as f:
return [line.strip("\n").split(" ") for line in f.readlines()]
def get_distinct(data, k):
# get distinct-k stats
if k==1:
tokens = [w for l in data for w in l]
return len(set(tokens))/len(tokens)
elif k==2:
bigrams = []
for line in data:
bigrams.extend(zip(line[:-1], line[1:]))
return len(set(bigrams))/len(bigrams)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--config_id', type=int, required=True)
parser.add_argument('--translate_step', type=int, required=True)
parser.add_argument('--target_file', type=str, required=True)
parser.add_argument('--output_file', type=str, required=True)
parser.add_argument('--dataset', type=str, required=True)
args = parser.parse_args()
config_id = args.config_id
translate_step = args.translate_step
target = load_file(args.target_file)
output = load_file(args.output_file)
dataset = args.dataset
assert(len(target) == len(output))
# init
distinct_1 = 0
distinct_2 = 0
# diversity
print("calculating diversity...")
distinct_1 = get_distinct(output, 1)
distinct_2 = get_distinct(output, 2)
print("calculating averaged sentence length...")
avg_sent_length = len([w for l in output for w in l])/len(output)
result = {
"config_id": config_id,
"translate_step": translate_step,
"distinct-1": distinct_1,
"distinct-2": distinct_2,
"avg_sent_length": avg_sent_length
}
with open("./log/evaluation.json", "a") as f:
f.write('\n')
json.dump(result, f)