1 files changed, 138 insertions, 0 deletions
diff --git a/data/style_features.py b/data/style_features.py
new file mode 100644
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+++ b/data/style_features.py
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+"""Style feature extraction for SFD (Style Feature Distance) metric."""
+
+import nltk
+from nltk.tokenize import sent_tokenize, word_tokenize
+
+FEATURE_NAMES = [
+    'length', 'avg_sent_len', 'TTR', 'newline_rate', 'exclaim_rate',
+    'first_person_rate', 'adj_adv_rate', 'sentiment_score'
+]
+
+
+def extract_style_features(text: str) -> list:
+    """Extract style feature vector from text.
+
+    Returns:
+        [length, avg_sent_len, TTR, newline_rate, exclaim_rate,
+         first_person_rate, adj_adv_rate, sentiment_score]
+    """
+    if not text or not text.strip():
+        return [0.0] * 8
+
+    words = word_tokenize(text)
+    num_words = max(len(words), 1)
+
+    # 1. Length (word count)
+    length = float(num_words)
+
+    # 2. Average sentence length
+    sentences = sent_tokenize(text)
+    num_sents = max(len(sentences), 1)
+    avg_sent_len = num_words / num_sents
+
+    # 3. Type-Token Ratio
+    unique_words = set(w.lower() for w in words if w.isalpha())
+    alpha_words = [w for w in words if w.isalpha()]
+    ttr = len(unique_words) / max(len(alpha_words), 1)
+
+    # 4. Newline rate
+    newline_count = text.count('\n')
+    newline_rate = newline_count / num_sents
+
+    # 5. Exclamation rate
+    exclaim_count = text.count('!')
+    exclaim_rate = exclaim_count / num_sents
+
+    # 6. First-person rate
+    first_person = {'i', 'me', 'my', 'mine', 'myself', 'we', 'us', 'our', 'ours', 'ourselves'}
+    fp_count = sum(1 for w in words if w.lower() in first_person)
+    first_person_rate = fp_count / num_words
+
+    # 7. Adjective/Adverb rate
+    try:
+        tagged = nltk.pos_tag(words)
+        adj_adv_tags = {'JJ', 'JJR', 'JJS', 'RB', 'RBR', 'RBS'}
+        adj_adv_count = sum(1 for _, tag in tagged if tag in adj_adv_tags)
+        adj_adv_rate = adj_adv_count / num_words
+    except Exception:
+        adj_adv_rate = 0.0
+
+    # 8. Simple sentiment score (positive - negative word ratio)
+    positive_words = {
+        'good', 'great', 'excellent', 'amazing', 'wonderful', 'fantastic',
+        'love', 'loved', 'best', 'perfect', 'awesome', 'beautiful',
+        'enjoy', 'enjoyed', 'happy', 'glad', 'nice', 'brilliant',
+        'outstanding', 'superb', 'delightful', 'pleasant', 'favorite',
+        'recommend', 'recommended', 'impressive', 'incredible',
+    }
+    negative_words = {
+        'bad', 'terrible', 'awful', 'horrible', 'worst', 'poor',
+        'hate', 'hated', 'boring', 'disappointing', 'disappointed',
+        'ugly', 'annoying', 'waste', 'useless', 'mediocre', 'dull',
+        'pathetic', 'garbage', 'rubbish', 'disgusting', 'dreadful',
+    }
+    pos_count = sum(1 for w in words if w.lower() in positive_words)
+    neg_count = sum(1 for w in words if w.lower() in negative_words)
+    sentiment_score = (pos_count - neg_count) / num_words
+
+    return [length, avg_sent_len, ttr, newline_rate, exclaim_rate,
+            first_person_rate, adj_adv_rate, sentiment_score]
+
+
+def compute_sfd(generated_text: str, support_texts: list, exclude_length: bool = False) -> float:
+    """Compute Style Feature Distance.
+
+    Args:
+        generated_text: The model's generated output.
+        support_texts: List of the user's support set output texts.
+        exclude_length: If True, exclude length feature (index 0) from SFD.
+
+    Returns:
+        L1 distance between generated style and user style prototype.
+    """
+    gen_features = extract_style_features(generated_text)
+    support_features_list = [extract_style_features(t) for t in support_texts]
+    num_support = len(support_features_list)
+    if num_support == 0:
+        return 0.0
+
+    prototype = [0.0] * len(gen_features)
+    for sf in support_features_list:
+        for i in range(len(prototype)):
+            prototype[i] += sf[i]
+    prototype = [p / num_support for p in prototype]
+
+    start_idx = 1 if exclude_length else 0
+    sfd = 0.0
+    for i in range(start_idx, len(gen_features)):
+        g, p = gen_features[i], prototype[i]
+        scale = max(abs(p), 1.0)
+        sfd += abs(g - p) / scale
+    return sfd
+
+
+def compute_feature_deltas(generated_text: str, support_texts: list) -> dict:
+    """Compute per-feature deltas between generated text and user style prototype.
+
+    Returns dict mapping feature_name -> (gen_value, proto_value, delta).
+    """
+    gen_features = extract_style_features(generated_text)
+    support_features_list = [extract_style_features(t) for t in support_texts]
+    num_support = len(support_features_list)
+    if num_support == 0:
+        return {}
+
+    prototype = [0.0] * len(gen_features)
+    for sf in support_features_list:
+        for i in range(len(prototype)):
+            prototype[i] += sf[i]
+    prototype = [p / num_support for p in prototype]
+
+    deltas = {}
+    for i, name in enumerate(FEATURE_NAMES):
+        deltas[name] = {
+            'gen': gen_features[i],
+            'proto': prototype[i],
+            'delta': gen_features[i] - prototype[i],
+        }
+    return deltas