def test_g2_p2a(self):
expected = {
'precision': 0.5,
'recall': 1.0,
'f1': 0.6666666666666666,
'tpr': 1.0,
'fpr': 1.0,
'accuracy': 0.5,
'ave_precision': 0.5,
'ave_recall': 1.0,
'ave_f1': 0.619047619,
'ave_tpr': 1.0,
'ave_fpr': 1.0,
'ave_accuracy': 0.5,
'l2h_ndcg10': 0.9971792416440344,
'h2l_ndcg10': 0.6830002811190978,
}
r = evaluation_script.evaluate("testfiles/ground_truth_set2.tsv",
"testfiles/predictions_set2a.tsv",
"supervised")
rdata = r['result'][0]['data']
rdata2 = evaluation_script.evaluate_submission(
"testfiles/ground_truth_set2.tsv",
"testfiles/predictions_set2a.tsv")
rdata3 = evaluation_script.evaluate_submission(
"testfiles/ground_truth_set2.tsv",
"testfiles/predictions_set2a.tsv", "testfiles/documents_set2.tsv")
for k, v in expected.items():
if k != 'l2h_ndcg10' and k != 'h2l_ndcg10':
self.assertAlmostEqual(v, rdata[k])
self.assertAlmostEqual(v, rdata2[k])
self.assertAlmostEqual(v, rdata3[k])
def test_g3_p3c(self):
expected = {
'precision': 0.5675675675675675,
'recall': 0.5833333333333334,
'f1': 0.5753424657534246,
'tpr': 0.5833333333333334,
'fpr': 0.45714285714285713,
'accuracy': 0.5633802816901409,
'ave_precision': 0.5708333333333333,
'ave_recall': 0.5851190476190475,
'ave_f1': 0.5680826118326119,
'ave_tpr': 0.5851190476190475,
'ave_fpr': 0.4125,
'ave_accuracy': 0.5734397546897546,
'l2h_ndcg10': 0.561254350448992,
'h2l_ndcg10': 0.5824553461136641,
}
r = evaluation_script.evaluate("testfiles/ground_truth_set3.tsv",
"testfiles/predictions_set3c.tsv",
"supervised")
rdata = r['result'][0]['data']
rdata2 = evaluation_script.evaluate_submission(
"testfiles/ground_truth_set3.tsv",
"testfiles/predictions_set3c.tsv")
rdata3 = evaluation_script.evaluate_submission(
"testfiles/ground_truth_set3.tsv",
"testfiles/predictions_set3c.tsv", "testfiles/documents_set3.tsv")
for k, v in expected.items():
if k != 'l2h_ndcg10' and k != 'h2l_ndcg10':
self.assertAlmostEqual(v, rdata[k])
self.assertAlmostEqual(v, rdata2[k])
self.assertAlmostEqual(v, rdata3[k])
def test_g3_p3b(self):
expected = {
'precision': 0.5333333333333333,
'recall': 0.4444444444444444,
'f1': 0.4848484848484848,
'tpr': 0.4444444444444444,
'fpr': 0.4,
'accuracy': 0.5211267605633803,
'ave_precision': 0.5708333333333334,
'ave_recall': 0.4857142857142857,
'ave_f1': 0.4978174603174603,
'ave_tpr': 0.4857142857142857,
'ave_fpr': 0.39166666666666666,
'ave_accuracy': 0.5315205627705628,
'l2h_ndcg10': 0.3287689269825538,
'h2l_ndcg10': 0.5639633827558276,
}
r = evaluation_script.evaluate("testfiles/ground_truth_set3.tsv",
"testfiles/predictions_set3b.tsv",
"supervised")
rdata = r['result'][0]['data']
rdata2 = evaluation_script.evaluate_submission(
"testfiles/ground_truth_set3.tsv",
"testfiles/predictions_set3b.tsv")
rdata3 = evaluation_script.evaluate_submission(
"testfiles/ground_truth_set3.tsv",
"testfiles/predictions_set3b.tsv", "testfiles/documents_set3.tsv")
for k, v in expected.items():
if k != 'l2h_ndcg10' and k != 'h2l_ndcg10':
self.assertAlmostEqual(v, rdata[k])
self.assertAlmostEqual(v, rdata2[k])
self.assertAlmostEqual(v, rdata3[k])
def test_g2_p2e(self):
expected = {
'precision': 0.375,
'recall': 0.375,
'f1': 0.375,
'tpr': 0.375,
'fpr': 0.625,
'accuracy': 0.375,
'ave_precision': 0.342857143,
'ave_recall': 0.428571429,
'ave_f1': 0.285714286,
'ave_tpr': 0.428571429,
'ave_fpr': 0.714285714,
'ave_accuracy': 0.34523809523809523,
'l2h_ndcg10': 0.36866848828077303,
'h2l_ndcg10': 0.4462728422747134,
}
r = evaluation_script.evaluate("testfiles/ground_truth_set2.tsv",
"testfiles/predictions_set2e.tsv",
"supervised")
rdata = r['result'][0]['data']
rdata2 = evaluation_script.evaluate_submission(
"testfiles/ground_truth_set2.tsv",
"testfiles/predictions_set2e.tsv")
rdata3 = evaluation_script.evaluate_submission(
"testfiles/ground_truth_set2.tsv",
"testfiles/predictions_set2e.tsv", "testfiles/documents_set2.tsv")
for k, v in expected.items():
if k != 'l2h_ndcg10' and k != 'h2l_ndcg10':
self.assertAlmostEqual(v, rdata[k])
self.assertAlmostEqual(v, rdata2[k])
self.assertAlmostEqual(v, rdata3[k])
def test_g2_p2d(self):
expected = {
'precision': 0.625,
'recall': 0.625,
'f1': 0.625,
'tpr': 0.625,
'fpr': 0.375,
'accuracy': 0.625,
'ave_precision': 0.6666666666666666,
'ave_recall': 0.714285714,
'ave_f1': 0.585714286,
'ave_tpr': 0.714285714,
'ave_fpr': 0.428571429,
'ave_accuracy': .6547619047619048,
'l2h_ndcg10': 0.8070645353018062,
'h2l_ndcg10': 0.6555242102166945,
}
r = evaluation_script.evaluate("testfiles/ground_truth_set2.tsv",
"testfiles/predictions_set2d.tsv",
"supervised")
rdata = r['result'][0]['data']
rdata2 = evaluation_script.evaluate_submission(
"testfiles/ground_truth_set2.tsv",
"testfiles/predictions_set2d.tsv")
rdata3 = evaluation_script.evaluate_submission(
"testfiles/ground_truth_set2.tsv",
"testfiles/predictions_set2d.tsv", "testfiles/documents_set2.tsv")
for k, v in expected.items():
if k != 'l2h_ndcg10' and k != 'h2l_ndcg10':
self.assertAlmostEqual(v, rdata[k])
self.assertAlmostEqual(v, rdata2[k])
self.assertAlmostEqual(v, rdata3[k])
def test_g3_p3a(self):
expected = {
'precision': 0.6923076923076923,
'recall': 0.75,
'f1': 0.7199999999999999,
'tpr': 0.75,
'fpr': 0.34285714285714286,
'accuracy': 0.704225352112676,
'ave_precision': 0.7517857142857143,
'ave_recall': 0.7547619047619047,
'ave_f1': 0.7340659340659341,
'ave_tpr': 0.7547619047619047,
'ave_fpr': 0.3625,
'ave_accuracy': 0.7050369769119769,
'l2h_ndcg10': 0.5995043313788928,
'h2l_ndcg10': 0.8314918192086054,
}
r = evaluation_script.evaluate("testfiles/ground_truth_set3.tsv",
"testfiles/predictions_set3a.tsv",
"supervised")
rdata = r['result'][0]['data']
rdata2 = evaluation_script.evaluate_submission(
"testfiles/ground_truth_set3.tsv",
"testfiles/predictions_set3a.tsv")
rdata3 = evaluation_script.evaluate_submission(
"testfiles/ground_truth_set3.tsv",
"testfiles/predictions_set3a.tsv", "testfiles/documents_set3.tsv")
# Data sets 4 and 5 test the evaluate function reading from the default document
# file when present in the directory of the evaluation_script package.
shutil.copy2('testfiles2/documents.tsv', 'evaluation_script')
r4 = evaluation_script.evaluate("testfiles2/ground_truth_set3.tsv",
"testfiles2/predictions_set3a.tsv",
"supervised")
rdata4 = r4['result'][0]['data']
os.remove('evaluation_script/documents.tsv')
shutil.copy2('testfiles3/documents.tsv.gz', 'evaluation_script')
r5 = evaluation_script.evaluate("testfiles3/ground_truth_set3.tsv.gz",
"testfiles3/predictions_set3a.tsv.gz",
"supervised")
rdata5 = r5['result'][0]['data']
os.remove('evaluation_script/documents.tsv.gz')
for k, v in expected.items():
if k != 'l2h_ndcg10' and k != 'h2l_ndcg10':
self.assertAlmostEqual(v, rdata[k])
self.assertAlmostEqual(v, rdata2[k])
self.assertAlmostEqual(v, rdata3[k])
self.assertAlmostEqual(v, rdata4[k])
self.assertAlmostEqual(v, rdata5[k])
def test_g1_p1(self):
expected = {
'precision': 0.6,
'recall': 0.75,
'f1': 0.6666666666666665,
'tpr': 0.75,
'fpr': 0.6666666666666666,
'accuracy': 0.5714285714285714,
'ave_precision': 0.7777777777777777,
'ave_recall': 0.8333333333333334,
'ave_f1': 0.7222222222222222,
'ave_tpr': 0.8333333333333334,
'ave_fpr': 0.6666666666666666,
'ave_accuracy': 0.611111111111111,
'l2h_ndcg10': 0.7169361380260636,
'h2l_ndcg10': 0.7959842760619721,
}
r = evaluation_script.evaluate("testfiles/ground_truth_set1.tsv",
"testfiles/predictions_set1a.tsv",
"supervised")
rdata = r['result'][0]['data']
rdata2 = evaluation_script.evaluate_submission(
"testfiles/ground_truth_set1.tsv",
"testfiles/predictions_set1a.tsv")
rdata3 = evaluation_script.evaluate_submission(
"testfiles/ground_truth_set1.tsv",
"testfiles/predictions_set1a.tsv", "testfiles/documents_set1.tsv")
for k, v in expected.items():
if k != 'l2h_ndcg10' and k != 'h2l_ndcg10':
self.assertAlmostEqual(v, rdata[k])
self.assertAlmostEqual(v, rdata2[k])
self.assertAlmostEqual(v, rdata3[k])
r = evaluation_script.evaluate("testfiles/ground_truth_set1.tsv",
"testfiles/predictions_set1b.tsv",
"supervised")
rdata = r['result'][0]['data']
rdata2 = evaluation_script.evaluate_submission(
"testfiles/ground_truth_set1.tsv",
"testfiles/predictions_set1b.tsv")
rdata3 = evaluation_script.evaluate_submission(
"testfiles/ground_truth_set1.tsv",
"testfiles/predictions_set1b.tsv", "testfiles/documents_set1.tsv")
for k, v in expected.items():
if k != 'l2h_ndcg10' and k != 'h2l_ndcg10':
self.assertAlmostEqual(v, rdata[k])
self.assertAlmostEqual(v, rdata2[k])
self.assertAlmostEqual(v, rdata3[k])
r = evaluation_script.evaluate(
"testfiles/ground_truth_set1_compressed.tsv.gz",
"testfiles/predictions_set1a_compressed.tsv.gz", "supervised")
rdata = r['result'][0]['data']
rdata2 = evaluation_script.evaluate_submission(
"testfiles/ground_truth_set1_compressed.tsv.gz",
"testfiles/predictions_set1a_compressed.tsv.gz")
rdata3 = evaluation_script.evaluate_submission(
"testfiles/ground_truth_set1_compressed.tsv.gz",
"testfiles/predictions_set1a_compressed.tsv.gz",
"testfiles/documents_set1_compressed.tsv.gz")
for k, v in expected.items():
if k != 'l2h_ndcg10' and k != 'h2l_ndcg10':
self.assertAlmostEqual(v, rdata[k])
self.assertAlmostEqual(v, rdata2[k])
self.assertAlmostEqual(v, rdata3[k])
import evaluation_script
import argparse
parser = argparse.ArgumentParser(
description=
'Evaluation script used in the eBay SIGIR 2019 eCommerce Search Challenge.'
)
parser.add_argument('-g',
'--ground-truth-file',
required=True,
help="Ground truth file")
parser.add_argument('-p',
'--prediction-file',
required=True,
help="Prediction file")
parser.add_argument('-d',
'--document-file',
required=False,
default=None,
help="Document file")
args = parser.parse_args()
r = evaluation_script.evaluate_submission(args.ground_truth_file,
args.prediction_file,
args.document_file)
print()
print(r)
