Merge branch 'master' into zz

tools/statvar_importer/ngram_matcher.py

@@ -0,0 +1,225 @@
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the 'License');
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#         https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an 'AS IS' BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Class to match sub-strings using ngrams.
+
+Example:
+  # Load the matcher with search-key: values
+  matcher = NgramMatcher({'ngram-size': 4})
+  matcher.add_key_value('California', 'dcid:geoId/06')
+  matcher.add_key_value('San Jose California', 'dcid:geoId/0668000')
+  matcher.add_key_value('San Jose Costa Rica', 'dcid:wikidataId/Q647808')
+
+  # Look for matching keys
+  results = matcher.lookup('SanJose')
+  # returns a ranked list of (key, value) tuples:
+  # [('San Jose California', 'dcid:geoId/0668000'), ('San Jose Costa Rica',
+  'dcid:wikidataId/Q647808')]
+
+  # To get top 10 results with match details:
+  results = matcher.lookup('SanJose', 10, True)
+  # Returns a list of tuples with (key, <details>):
+  # [(<key>, { 'value': <value>, 'info': {'score': 1.2, 'ngram_matches': 3} }),
+  # ...]
+"""
+
+import unicodedata
+
+from absl import logging
+
+# Default configuration settings for NgramMatcher
+_DEFAULT_CONFIG = {
+    'ngram_size': 4,
+    'ignore_non_alphanum': True,
+    'min_match_fraction': 0.8,
+}
+
+
+class NgramMatcher:
+
+    def __init__(self, config: dict = {}):
+        self._config = dict(_DEFAULT_CONFIG)
+        if config:
+            self._config.update(config)
+        self._ngram_size = self._config.get('ngram_size', 4)
+        # List of (key, value) tuples.
+        self._key_values = list()
+        # Dictionary of ngram to set of string ids that contain the ngram.
+        # { '<ngram>': { (id1, pos1), (id2, pos2), ...}, ...}
+        self._ngram_dict = {}
+
+    def get_tuples_count(self):
+        return len(self._key_values)
+
+    def get_key_values(self):
+        return dict(self._key_values)
+
+    def add_keys_values(self, kvs: dict[str, any]) -> None:
+        for key, value in kvs.items():
+            self.add_key_value(key, value)
+
+    def add_key_value(self, key: str, value):
+        """Add a key and value.
+
+    When the key matches a lookup string, the key and corresponding value is
+    returned.
+
+    Args:
+      key: string to be looked up
+      value: value to be returned on key match.
+    """
+        self._key_values.append((key, value))
+        key_index = len(self._key_values) - 1
+        self._add_key_index(key, key_index)
+
+    def get_ngrams_count(self) -> int:
+        """Returns the number of ngrams in the index."""
+        return len(self._ngram_dict)
+
+    def lookup(
+        self,
+        key: str,
+        num_results: int = None,
+        return_score: bool = False,
+        config: dict = None,
+    ) -> list:
+        """Lookup a key string.
+
+    Returns an ordered list of (key, value) tuples matching the key.
+    """
+        normalized_key = self._normalize_string(key)
+        ngrams = self._get_ngrams(normalized_key)
+        logging.level_debug() and logging.log(2,
+            f'looking up ngrams {ngrams} for {key}')
+        lookup_config = self._config
+        if config:
+            # Use the match config passed in.
+            lookup_config = dict(self._config)
+            lookup_config.update(config)
+        # Get the matching key indices for all ngrams.
+        matches = dict()
+        for ngram in ngrams:
+            ngram_matches = self._ngram_dict.get(ngram, {})
+            if ngram_matches:
+                # Use IDF score for each ngram
+                ngram_score = 1 / len(ngram_matches)
+                for key_index, ngram_pos in ngram_matches:
+                    # Collect matches and update score for each ngram
+                    if key_index not in matches:
+                        matches[key_index] = {
+                            'score': ngram_score,
+                            'ngram_matches': 1,
+                            'ngram_pos': ngram_pos,
+                        }
+                    else:
+                        key_match = matches[key_index]
+                        key_match['score'] = key_match['score'] + ngram_score
+                        key_match[
+                            'ngram_matches'] = key_match['ngram_matches'] + 1
+                        key_match['ngram_pos'] = min(key_match['ngram_pos'],
+                                                     ngram_pos)
+
+        logging.level_debug() and logging.log(2, f'Matches for {key}: {matches}')
+        # Collect all key indices that matches with counts.
+        match_indices = list()
+        min_matches = max(
+            1,
+            len(ngrams) * lookup_config.get('min_match_fraction', 0.8))
+        for key_index, result in matches.items():
+            if result['ngram_matches'] >= min_matches:
+                match_indices.append((key_index, result))
+
+        # Order key_index by decreasing number of matches.
+        key_len = len(normalized_key)
+        match_indices.sort(
+            key=lambda x: self._get_ngram_match_score(x[1], key_len),
+            reverse=True)
+        logging.level_debug() and logging.log(2,
+            f'Sorted matches for {key}: {match_indices}')
+
+        # Collect results in sorted order
+        results = list()
+        for match in match_indices:
+            result_key, result_value = self._key_values[match[0]]
+            if return_score:
+                results.append((result_key, {
+                    'value': result_value,
+                    'info': match[1]
+                }))
+            else:
+                results.append((result_key, result_value))
+            if num_results and len(results) >= num_results:
+                # There are enough results. Return these.
+                break
+        return results
+
+    def _get_ngrams(self, key: str) -> list:
+        """Returns a list of ngrams for the key."""
+        normalized_key = self._normalize_string(key)
+        ngrams = normalized_key.split(' ')
+        max_index = max(len(normalized_key) - self._ngram_size, 0) + 1
+        for pos in range(max_index):
+            ngram = normalized_key[pos:pos + self._ngram_size]
+            if ngram not in ngrams:
+                ngrams.append(ngram)
+        return ngrams
+
+    def _add_key_index(self, key: str, key_index: int):
+        """Adds the key into the ngrams index."""
+        # Remove extra characters and convert to lower case.
+        normalized_key = self._normalize_string(key)
+        # index by all unique ngrams in the key
+        ngrams = self._get_ngrams(normalized_key)
+        for ngram in ngrams:
+            if ngram not in self._ngram_dict:
+                self._ngram_dict[ngram] = set()
+            ngram_pos = normalized_key.find(ngram)
+            self._ngram_dict[ngram].add((key_index, ngram_pos))
+            logging.level_debug() and logging.log(
+                3, f'Added ngram "{ngram}" for {key}:{key_index}')
+
+    def _normalize_string(self, key: str) -> str:
+        """Returns a normalized string removing special characters"""
+        return normalized_string(key,
+                                 self._config.get('ignore_non_alphanum', True))
+
+    def _get_ngram_match_score(self, match: dict, key_len: int) -> float:
+        """Returns a score for the ngram match components."""
+        # IDF score
+        score = match['score']
+        # Boost for match at the beginning of the key.
+        score += (key_len - match['ngram_pos']) * 10000
+        # DF score
+        score += match['ngram_matches'] * 100
+        return score
+
+
+def normalized_string(key: str, ignore_non_alnum: bool = True) -> str:
+    """Returns a normalized string for match.
+
+    Args:
+      key: string to be normalized.
+      ignore_non_alnum: if True, non alpha numeric characters are removed.
+
+    Returns:
+      normalized string
+    """
+    normalized_key = unicodedata.normalize('NFKD', key)
+    normalized_key = normalized_key.lower()
+    # Remove extra spaces
+    normalized_key = ' '.join([w for w in normalized_key.split(' ') if w])
+    # Remove extra punctuation.
+    if ignore_non_alnum:
+        normalized_key = ''.join(
+            [c for c in normalized_key if c.isalnum() or c == ' '])
+    return normalized_key

tools/statvar_importer/ngram_matcher_test.py

@@ -0,0 +1,42 @@
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the 'License');
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#         https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an 'AS IS' BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Unit tests for NgramMatcher."""
+
+import unittest
+
+from absl import app
+from absl import logging
+import ngram_matcher
+
+
+class NgramMatcherTest(unittest.TestCase):
+
+    def setUp(self):
+        # logging.set_verbosity(2)
+        return
+
+    def test_lookup_string(self):
+        matcher = ngram_matcher.NgramMatcher(config={'ngram_size': 4})
+        matcher.add_key_value('Test Key 1', 1)
+        matcher.add_key_value('TESTKey Two', 'two')
+        matches = matcher.lookup('Test')
+        self.assertEqual([('TESTKey Two', 'two'), ('Test Key 1', 1)], matches)
+        self.assertTrue(
+            matcher.lookup('Tester', config={'min_match_fraction': 0.1}))
+        self.assertFalse(matcher.lookup('ABCDEF'))
+
+
+if __name__ == '__main__':
+    app.run()
+    unittest.main()