File size: 4,940 Bytes
22272f6 | 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 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 | #!/usr/bin/env python3
import argparse
from decimal import Decimal
import json
from pathlib import Path
import random
import statistics
from typing import Any, Dict, Tuple, List, Set, Optional
import tabulate
def main() -> None:
""" Perform evaluation for all prediction files, comparing
results with ground truth.
Python3.8+ is required.
"""
parser = argparse.ArgumentParser()
parser.add_argument('--n-bootstrap', type=int, default=1000)
parser.add_argument('--output', type=Path, help='output results as json')
parser.add_argument('--show-errors', action='store_true')
args = parser.parse_args()
root = Path('dataset')
ground_truth = load_json(root / 'ground-truth.json')
attributes = {
'price': decimal_matching,
'sku': hard_matching,
'availability': hard_matching,
'InStock': hard_matching,
'OutOfStock': hard_matching,
}
metrics_by_name: Dict[str, Dict[str, Dict]] = {}
predictions = {
path: load_json(path)
for path in sorted((root / 'output').glob('*.json'))}
header = ['Attribute', 'System', 'F1', 'Precision', 'Recall', 'Support']
table = []
for attribute, metric in attributes.items():
for path, prediction in predictions.items():
m = evaluate(
ground_truth, prediction, attribute, metric,
n_bootstrap=args.n_bootstrap,
show_errors=args.show_errors)
table.append([
attribute,
path.stem,
f'{m["f1"]:.3f} ± {m["f1_std"]:.3f}',
f'{m["precision"]:.3f} ± {m["precision_std"]:.3f}',
f'{m["recall"]:.3f} ± {m["recall_std"]:.3f}',
m['support'],
])
metrics_by_name.setdefault(path.stem, {})[attribute] = m
print(tabulate.tabulate(table, header, tablefmt='grid'))
if args.output:
args.output.write_text(
json.dumps(metrics_by_name, indent=4, sort_keys=True))
def evaluate(
ground_truth: Dict[str, Dict[str, Set[str]]], # multiple gt values
prediction: Dict[str, Dict[str, str]], # single predicted value
attribute: str,
metric,
*,
n_bootstrap: int,
show_errors: bool,
) -> Dict[str, float]:
""" Evaluate one attribute.
"""
if ground_truth.keys() != prediction.keys():
raise ValueError('prediction keys do not match ground truth')
tp_fp_fns = []
support = 0
for key in ground_truth.keys():
true = set(ground_truth[key].get(attribute, []))
support += bool(true)
pred = prediction[key].get(attribute)
tp_fp_fn = metric(true, pred)
if show_errors and any(tp_fp_fn[1:]):
print(attribute, true, repr(pred), tp_fp_fn, key, sep='\t')
tp_fp_fns.append(tp_fp_fn)
metrics = metrics_from_tp_fp_fns(tp_fp_fns)
metrics['support'] = support
# add bootstrap estimates of condifence intervals
random.seed(42)
b_values: Dict[str, List[float]] = {}
for _ in range(n_bootstrap):
n = len(tp_fp_fns)
indices = [random.randint(0, n - 1) for _ in range(n)]
b_metrics = metrics_from_tp_fp_fns([tp_fp_fns[i] for i in indices])
for key in b_metrics:
b_values.setdefault(key, []).append(b_metrics[key])
for key, values in sorted(b_values.items()):
metrics[f'{key}_std'] = statistics.stdev(values)
return metrics
TP_FP_FN = Tuple[int, int, int]
def metrics_from_tp_fp_fns(tp_fp_fns: List[TP_FP_FN]) -> Dict[str, float]:
tp_fp_fn: TP_FP_FN = tuple(map(sum, zip(*tp_fp_fns))) # type: ignore
precision = precision_score(*tp_fp_fn)
recall = recall_score(*tp_fp_fn)
if precision + recall > 0:
f1 = 2 * precision * recall / (precision + recall)
else:
f1 = 0
return {
'f1': f1,
'precision': precision,
'recall': recall,
}
def precision_score(tp: int, fp: int, fn: int) -> float:
if fp == fn == 0:
return 1.
if tp == fp == 0:
return 0.
return tp / (tp + fp)
def recall_score(tp: int, fp: int, fn: int) -> float:
if fp == fn == 0:
return 1.
if tp == fn == 0:
return 0.
return tp / (tp + fn)
def decimal_matching(true: Set[str], pred: Optional[str]) -> TP_FP_FN:
return hard_matching(
{Decimal(x) for x in true},
Decimal(pred) if pred is not None else None,
)
def hard_matching(true: Set[Any], pred: Optional[Any]) -> TP_FP_FN:
tp = fp = fn = 0
if pred is None:
if true:
fn += 1
else:
if pred in true:
tp += 1
else:
fp += 1
if true:
fn += 1
return tp, fp, fn
def load_json(path: Path):
with path.open('rt', encoding='utf8') as f:
return json.load(f)
if __name__ == '__main__':
main()
|