File size: 996 Bytes
fe8d248 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 |
import os.path
import numpy as np
import pandas as pd
import argparse
from sklearn.metrics import roc_auc_score
parser = argparse.ArgumentParser()
parser.add_argument('--path', type=str, required=True)
parser.add_argument('--name', type=str, required=True)
parser.add_argument('--answer_file', type=str, required=True)
parser.add_argument('--predict_file', type=str, required=True)
parser.add_argument('--value', type=str, default="HasDetections")
args = parser.parse_args()
# Compute MAE
def mean_absolute_error(y_true, y_pred):
return np.mean(np.abs(y_pred - y_true))
answers = pd.read_csv(args.answer_file)
predictions = pd.read_csv(args.predict_file)
answers.sort_values(by=["MachineIdentifier"])
predictions.sort_values(by=['MachineIdentifier'])
y_true = answers[args.value].values
y_pred = predictions[args.value].values
performance = roc_auc_score(y_true, y_pred)
with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
f.write(str(performance))
|