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score_submission.py
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# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import numpy as np
import csv
def generate_full_curve(rows, num_samples):
did_solve = np.zeros(num_samples)
recall = []
errors = []
bad_guesses = 0
answer = None
for exid, is_correct in rows:
if is_correct:
did_solve[int(exid)] = 1
recall.append(np.mean(did_solve))
errors.append(bad_guesses)
if bad_guesses < 100:
answer = np.mean(did_solve)
else:
bad_guesses += 1
print("Recall at 100 errors", answer)
try:
import matplotlib.pyplot as plt
plt.plot(errors, recall)
plt.semilogx()
plt.xlabel("Number of bad guesses")
plt.ylabel("Recall")
plt.savefig("/tmp/error_curve.png")
print("A full error curve is located at /tmp/error_curve.png")
except:
print("Can't generate error curve; please install matplotlib to see the plot")
return recall, errors
parser = argparse.ArgumentParser("Score a solution.")
parser.add_argument("--submission", default="example_submission.csv")
parser.add_argument("--answer", default="fake_test_suffixes.npy")
parser.add_argument("--num_samples", default=1000)
args = parser.parse_args()
answers = np.load(args.answer)[-1000:]
rows = []
for row in csv.DictReader(open(args.submission, "r")):
exid = int(row['Example ID'])
guess = list(map(int,row['Suffix Guess'][1:-1].split(",")))
guess = np.array(guess)
guess_is_correct = np.all(answers[exid][-50:] == guess[-50:])
rows.append([exid,
guess_is_correct
])
rows = np.array(rows)
generate_full_curve(rows, args.num_samples)