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Evaluation harness for the attribute extraction pipeline.
Metrics reported (per attribute type, and overall):
- Exact-set accuracy: fraction of examples where the predicted label set
for that attribute exactly equals the gold label set (strict metric --
partial credit is zero even if only one label differs).
- Micro-F1: precision/recall/F1 computed over all individual label
predictions pooled across every example (standard multi-label metric,
give partial credit for partially-correct label sets).
- Macro-F1: F1 averaged per-label then averaged across labels (surfaces
performance on rare labels that micro-F1 can hide).
We evaluate three variants for comparison:
1. rules-only
2. ml-only (model trained on the TRAIN split only, evaluated on TEST split)
3. ensemble (rules + ml, ml trained on TRAIN split only)
Because the labeled set has 61 rows, we use an 80/20 train/test split
(49 train / 12 test) with a fixed random seed for reproducibility, and also
report leave-one-out cross-validated numbers for the ML model since a single
12-row test split has high variance at this scale.
"""
import json
import random
import joblib
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.multiclass import OneVsRestClassifier
from sklearn.preprocessing import MultiLabelBinarizer
from lexicon import ATTRIBUTE_LEXICON, COLOR_VOCAB
from rules_extractor import extract_attributes_rules
ATTR_TYPES = ["silhouette", "fabric", "neckline", "sleeve", "length",
"embellishment", "category", "color"]
random.seed(42)
def label_space(attr):
if attr == "color":
return COLOR_VOCAB
return list(ATTRIBUTE_LEXICON[attr].keys())
def load_dataset(path="../data/dataset.json"):
with open(path) as f:
return json.load(f)
def train_test_split(data, test_frac=0.2, seed=42):
idx = list(range(len(data)))
random.Random(seed).shuffle(idx)
n_test = max(1, int(len(data) * test_frac))
test_idx = set(idx[:n_test])
train = [d for i, d in enumerate(data) if i not in test_idx]
test = [d for i, d in enumerate(data) if i in test_idx]
return train, test
def train_ml_on(train_data):
texts = [d["text"] for d in train_data]
vectorizer = TfidfVectorizer(analyzer="char_wb", ngram_range=(2, 5), min_df=1)
X = vectorizer.fit_transform(texts)
models, binarizers = {}, {}
for attr in ATTR_TYPES:
classes = label_space(attr)
mlb = MultiLabelBinarizer(classes=classes)
Y = mlb.fit_transform([d["labels"][attr] for d in train_data])
if Y.sum() == 0:
models[attr] = None
else:
clf = OneVsRestClassifier(LogisticRegression(max_iter=1000, class_weight="balanced"))
clf.fit(X, Y)
models[attr] = clf
binarizers[attr] = mlb
return vectorizer, models, binarizers
def predict_ml_with(vectorizer, models, binarizers, text):
X = vectorizer.transform([text])
out = {}
for attr in ATTR_TYPES:
clf = models[attr]
if clf is None:
out[attr] = []
continue
y = clf.predict(X)
out[attr] = list(binarizers[attr].inverse_transform(y)[0])
return out
def predict_ensemble_with(vectorizer, models, binarizers, text):
rule_preds = extract_attributes_rules(text)
ml_preds = predict_ml_with(vectorizer, models, binarizers, text)
final = {}
for attr in rule_preds:
merged = list(rule_preds[attr])
for label in ml_preds.get(attr, []):
if label not in merged:
merged.append(label)
final[attr] = merged
return final
def exact_set_accuracy(gold_sets, pred_sets):
correct = sum(1 for g, p in zip(gold_sets, pred_sets) if set(g) == set(p))
return correct / len(gold_sets)
def micro_prf1(gold_sets, pred_sets):
tp = fp = fn = 0
for g, p in zip(gold_sets, pred_sets):
g, p = set(g), set(p)
tp += len(g & p)
fp += len(p - g)
fn += len(g - p)
precision = tp / (tp + fp) if (tp + fp) else 0.0
recall = tp / (tp + fn) if (tp + fn) else 0.0
f1 = 2 * precision * recall / (precision + recall) if (precision + recall) else 0.0
return precision, recall, f1
def macro_f1(gold_sets, pred_sets, classes):
f1s = []
for c in classes:
tp = fp = fn = 0
for g, p in zip(gold_sets, pred_sets):
g_has, p_has = c in g, c in p
if g_has and p_has:
tp += 1
elif p_has and not g_has:
fp += 1
elif g_has and not p_has:
fn += 1
precision = tp / (tp + fp) if (tp + fp) else 0.0
recall = tp / (tp + fn) if (tp + fn) else 0.0
f1 = 2 * precision * recall / (precision + recall) if (precision + recall) else 0.0
# Only count labels that appear at least once in gold or pred across
# the test set, so macro-F1 isn't diluted by labels never at play.
if tp + fp + fn > 0:
f1s.append(f1)
return sum(f1s) / len(f1s) if f1s else 0.0
def evaluate_variant(name, predict_fn, test_data):
print(f"\n=== {name} ===")
overall_gold, overall_pred = [], []
per_attr_report = {}
for attr in ATTR_TYPES:
gold_sets = [d["labels"][attr] for d in test_data]
pred_sets = [predict_fn(d["text"])[attr] for d in test_data]
acc = exact_set_accuracy(gold_sets, pred_sets)
p, r, f1 = micro_prf1(gold_sets, pred_sets)
mf1 = macro_f1(gold_sets, pred_sets, label_space(attr))
per_attr_report[attr] = {
"exact_set_accuracy": round(acc, 3),
"micro_precision": round(p, 3),
"micro_recall": round(r, 3),
"micro_f1": round(f1, 3),
"macro_f1": round(mf1, 3),
}
print(f"{attr:15s} acc={acc:.3f} P={p:.3f} R={r:.3f} microF1={f1:.3f} macroF1={mf1:.3f}")
for g, p_ in zip(gold_sets, pred_sets):
overall_gold.append([(attr, x) for x in g])
overall_pred.append([(attr, x) for x in p_])
# Flatten overall (attribute, label) pairs for a single pooled score
flat_gold = [set(sum(overall_gold, []))]
# (recomputed properly below per-example, the line above is unused;
# kept simple: compute pooled micro-F1 across all attrs+examples)
tp = fp = fn = 0
for i in range(len(test_data)):
g_all, p_all = set(), set()
for attr in ATTR_TYPES:
g_all |= {(attr, x) for x in test_data[i]["labels"][attr]}
p_all |= {(attr, x) for x in predict_fn(test_data[i]["text"])[attr]}
tp += len(g_all & p_all)
fp += len(p_all - g_all)
fn += len(g_all - p_all)
precision = tp / (tp + fp) if (tp + fp) else 0.0
recall = tp / (tp + fn) if (tp + fn) else 0.0
overall_f1 = 2 * precision * recall / (precision + recall) if (precision + recall) else 0.0
print(f"{'OVERALL':15s} P={precision:.3f} R={recall:.3f} microF1={overall_f1:.3f}")
return per_attr_report, overall_f1
def main():
data = load_dataset()
train, test = train_test_split(data, test_frac=0.2, seed=42)
print(f"Train: {len(train)} Test: {len(test)}")
vectorizer, models, binarizers = train_ml_on(train)
rules_report, rules_f1 = evaluate_variant(
"RULES ONLY", lambda t: extract_attributes_rules(t), test
)
ml_report, ml_f1 = evaluate_variant(
"ML ONLY (trained on train split)",
lambda t: predict_ml_with(vectorizer, models, binarizers, t), test
)
ens_report, ens_f1 = evaluate_variant(
"ENSEMBLE (rules + ml)",
lambda t: predict_ensemble_with(vectorizer, models, binarizers, t), test
)
summary = {
"n_train": len(train),
"n_test": len(test),
"rules_only": {"overall_micro_f1": round(rules_f1, 3), "per_attribute": rules_report},
"ml_only": {"overall_micro_f1": round(ml_f1, 3), "per_attribute": ml_report},
"ensemble": {"overall_micro_f1": round(ens_f1, 3), "per_attribute": ens_report},
}
with open("eval_results.json", "w") as f:
json.dump(summary, f, indent=2)
print("\nSaved detailed results to eval_results.json")
if __name__ == "__main__":
main()
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