Refactor feature extraction

mwmbl/tinysearchengine/ltr.py

@@ -4,7 +4,7 @@ Learning to rank predictor
 from pandas import DataFrame, Series
 from sklearn.base import BaseEstimator, RegressorMixin, TransformerMixin
 
-from mwmbl.tinysearchengine.rank import get_match_features, get_domain_score
+from mwmbl.tinysearchengine.rank import get_match_features, get_domain_score, score_match
 
 
 class ThresholdPredictor(BaseEstimator, RegressorMixin):
@@ -26,27 +26,19 @@ class ThresholdPredictor(BaseEstimator, RegressorMixin):
 
 def get_match_features_as_series(item: Series):
     terms = item['query'].lower().split()
-    last_match_char_title, match_length_title, total_possible_match_length_title = get_match_features(
-        terms, item['title'], True, False)
-    last_match_char_extract, match_length_extract, total_possible_match_length_extract = get_match_features(
-        terms, item['extract'], True, False)
-    last_match_char_url, match_length_url, total_possible_match_length_url = get_match_features(
-        terms, item['url'], True, False)
-    domain_score = get_domain_score(item['url'])
-    return Series({
-        'last_match_char_title': last_match_char_title,
-        'match_length_title': match_length_title,
-        'total_possible_match_length_title': total_possible_match_length_title,
-        'last_match_char_extract': last_match_char_extract,
-        'match_length_extract': match_length_extract,
-        'total_possible_match_length_extract': total_possible_match_length_extract,
-        'last_match_char_url': last_match_char_url,
-        'match_length_url': match_length_url,
-        'total_possible_match_length_url': total_possible_match_length_url,
-        'num_terms': len(terms),
-        'domain_score': domain_score,
-        'item_score': item['score'],
-    })
+    features = {}
+    for part in ['title', 'extract', 'url']:
+        last_match_char, match_length, total_possible_match_length = get_match_features(terms, item[part], True, False)
+        features[f'last_match_char_{part}'] = last_match_char
+        features[f'match_length_{part}'] = match_length
+        features[f'total_possible_match_length_{part}'] = total_possible_match_length
+        # features[f'score_{part}'] = score_match(last_match_char, match_length, total_possible_match_length)
+
+    features['num_terms'] = len(terms)
+    features['num_chars'] = len(' '.join(terms))
+    features['domain_score'] = get_domain_score(item['url'])
+    features['item_score'] = item['score']
+    return Series(features)
 
 
 class FeatureExtractor(BaseEstimator, TransformerMixin):

mwmbl/tinysearchengine/rank.py

@@ -39,13 +39,17 @@ def _score_result(terms, result: Document, is_complete: bool, max_score: float):
     last_match_char, match_length, total_possible_match_length = get_match_features(
         terms, result_string, is_complete, False)
 
-    match_score = (match_length + 1. / last_match_char) / (total_possible_match_length + 1)
+    match_score = score_match(last_match_char, match_length, total_possible_match_length)
     score = 0.01 * domain_score + 0.99 * match_score
     # score = (0.1 + 0.9*match_score) * (0.1 + 0.9*(result.score / max_score))
     # score = 0.01 * match_score + 0.99 * (result.score / max_score)
     return score
 
 
+def score_match(last_match_char, match_length, total_possible_match_length):
+    return (match_length + 1. / last_match_char) / (total_possible_match_length + 1)
+
+
 def get_domain_score(url):
     domain = urlparse(url).netloc
     domain_score = DOMAINS.get(domain, 0.0)
@@ -54,7 +58,6 @@ def get_domain_score(url):
 
 def get_match_features(terms, result_string, is_complete, is_url):
     query_regex = _get_query_regex(terms, is_complete, is_url)
-    print("Query regex", query_regex)
     matches = list(re.finditer(query_regex, result_string, flags=re.IGNORECASE))
     match_strings = {x.group(0).lower() for x in matches}
     match_length = sum(len(x) for x in match_strings)