feat(SemanticSearch): Added a new semantic search agent that uses fuz…

…zy string mathcing and levenshtein distance.

atarashi/agents/semanticSearch.py

@@ -0,0 +1,208 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+Copyright 2024 Abdelrahman Jamal ([email protected])
+
+SPDX-License-Identifier: GPL-2.0
+
+This program is free software; you can redistribute it and/or
+modify it under the terms of the GNU General Public License
+version 2 as published by the Free Software Foundation.
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License along
+with this program; if not, write to the Free Software Foundation, Inc.,
+51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+"""
+import argparse
+import os
+import nirjas
+import numpy as np
+import pandas as pd
+from fuzzywuzzy import fuzz
+
+
+from atarashi.agents.atarashiAgent import AtarashiAgent, exactMatcher
+
+__author__ = "Abdelrahman Jamal"
+__email__ = "[email protected]"
+
+class SemanticSearchAgent(AtarashiAgent):
+    """
+    An agent that performs semantic search to identify potential licenses within files.
+    """
+
+    def __init__(self, licenseList):
+        super().__init__(licenseList)
+
+    def extract_comments(self, filePath: str):
+        """
+        Extracts comments from a file using the 'nirjas' library, falling back to reading the entire file if comment extraction fails.
+        """
+        if not os.path.exists(filePath):
+           raise Exception(f"File path '{filePath}' does not exist")
+        try: 
+            # * Manually extracting comments using nirjas and not the commentPreprocessor class
+            # * Because I prefer to do my own preprocessing and I also need accurate comment reading,
+            # * Normal nirjas works by appending comments together.
+            nirjas_comments = nirjas.extract(filePath)
+            if nirjas_comments.total_lines_of_comments == 0:
+                # Go to the except case to read all the file, even if it has no comments
+                # ! This is debatable, and I might remove it.
+                raise Exception()
+            all_comments = []
+            # Go through each comment type, and read the comment itself given it's starting and ending lines
+            # This is necessary because nirjas by default appends multi-line or continous single-line comments
+            # together, and for semantic search purposes, I want to read the comments exactly as they were in the file.
+            with open(filePath, "r") as f:
+                all_lines = f.readlines()
+            for single_line_comment in nirjas_comments['single_line_comment']:
+                all_comments.append(single_line_comment['comment'])
+            for cont_single_line_comment in nirjas_comments['cont_single_line_comment']:
+                start = cont_single_line_comment['start_line'] - 1
+                end = cont_single_line_comment['end_line']
+                for line_idx in range(start, end):
+                    comment = all_lines[line_idx]
+                    all_comments.append(comment)
+            for multi_line_comment in nirjas_comments['multi_line_comment']:
+                start = multi_line_comment['start_line'] - 1
+                end = multi_line_comment['end_line']
+                for line_idx in range(start, end):
+                    line = all_lines[line_idx]
+                    all_comments.append(line) 
+            comments = "".join(all_comments)
+        except:
+            with open(filePath, "r") as f:
+                comments = f.read()
+        return comments
+
+    def scan(self, filePath): 
+        '''
+        Scans a file for potential licenses using semantic search and fuzzy string matching.
+        '''
+
+        # Quick check if an exact match exists, if it does then return that.
+        temp = exactMatcher(super().loadFile(filePath), self.licenseList)
+        if temp != -1:
+            result = []
+            for shortname in temp:
+                result.append({
+                "shortname": str(shortname),
+                "sim_score": 1,
+                "sim_type": "SemanticSearch-LVD",
+                "description": "exact match"
+                })
+            return result
+
+        # Append The Fulll License Name, Short License Name, and SPDX-License-Identifier to the license text
+        # Some files only contain 'SPDX-License-Identifier: 0BSD' or 'License: 0BSD' and since this agent attempts
+        # To match based on license text matching, those will not be identified. Appending those lines
+        # To the license text helps this agent identify those cases more often
+        def convert(row):
+            row['text'] = f"License Name: {row['fullname']} \n License: {row['shortname']} \n SPDX-License-Identifier: {row['shortname']} \n{row['text']}"
+            return row
+
+        self.licenseList = self.licenseList.apply(convert, axis=1)
+
+        file_comments = self.extract_comments(filePath)
+
+        # Remove characters not found in license texts
+        chars_to_remove = ['—', '…', '•', '§', '«', '»', '„', '・', '−', '*', '>', '<']
+        for char_to_remove in chars_to_remove:
+            file_comments = file_comments.replace(char_to_remove, '')
+
+        # Separate the comments into single line comments
+        file_comments = file_comments.split('\n')
+
+        # Separate each license text line by line and append them to one big list (used for matching)
+        # The license_index_map maps each line to the correct license index in the licenseList dataframe
+        license_index_map = {}
+        all_license_texts = []
+        for license_index, license_text in enumerate(self.licenseList['text']):
+            for line in license_text.split('\n'):
+                all_license_texts.append(line)
+                license_index_map[len(all_license_texts) - 1] = license_index
+
+        # Perform first level fuzzy matching for all lines in the comments
+        results = []
+        fuzzy_similarity_matrix = np.zeros((len(file_comments), len(all_license_texts)))
+        for index, comment in enumerate(file_comments):
+            for i in range(len(all_license_texts)):
+                fuzzy_similarity_matrix[index][i] = fuzz.ratio(comment, all_license_texts[i]) 
+            max_score_index = np.argmax(fuzzy_similarity_matrix[index]) 
+            results.append(
+                        (
+                            fuzzy_similarity_matrix[index][max_score_index], 
+                            comment
+                        )
+                    ) 
+
+        # Try to append lines that match with a similarity score of more than 40% with one of the lines in 
+        # Any of the licenses. The goal is to get a bigger and bigger text chunk that matches to a bigger 
+        # text chunk in one of the license texts - The bigger the chunk, the more likely that this match is 
+        # correct.
+        appended_comments = []
+        appended_comment = []
+        for result in results:
+            if result[0] >= 40:
+                if appended_comment == [] and result[1] == '':
+                    continue
+                appended_comment.append(result[1])
+            else:
+                appended_comments.append(appended_comment)
+                appended_comment = []
+
+        if len(appended_comment) > 0 and appended_comment not in appended_comments:
+            appended_comments.append(appended_comment)
+
+        # Attempt the final - second level license match with all the bigger text chunks
+        # In some licenses, the license header is used instead of the license text
+        # In case that a license has a license header, we match all chunks with both and take 
+        # the match with the highest similarity score
+        results = []
+        for appended_comment in appended_comments:
+            appended_comment = "\n".join(appended_comment)
+            fuzzy_similarity_matrix_2 = np.zeros(len(self.licenseList))
+            for i in range(len(self.licenseList)):
+                fuzzy_similarity_matrix_2[i] = fuzz.ratio(appended_comment, self.licenseList.loc[i, 'text'])
+                if pd.notna(licenseList.loc[i, 'license_header']):
+                    license_header_sim_score = fuzz.ratio(appended_comment, self.licenseList.loc[i, 'license_header'].replace('\n\n', '\n'))
+                    fuzzy_similarity_matrix_2[i] = license_header_sim_score if license_header_sim_score > fuzzy_similarity_matrix_2[i] else fuzzy_similarity_matrix_2[i]
+                if self.verbose > 0:
+                    print('Comment: ' + appended_comment + " - License: " + self.licenseList.iloc[i]['shortname'] + " Similarity Score: " + str(fuzzy_similarity_matrix_2[i]))
+            top_5_license_text_indices = np.argsort(fuzzy_similarity_matrix_2)[-5:][::-1]
+
+            if fuzzy_similarity_matrix_2[top_5_license_text_indices[0]] >= 50:
+                results.append({
+                "shortname": self.licenseList.loc[top_5_license_text_indices[0], 'shortname'],
+                "sim_score": fuzzy_similarity_matrix_2[top_5_license_text_indices[0]],
+                "sim_type": "SemanticSearch-LVD",
+                "description": ""
+                })
+        return results
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("processedLicenseList", help="Specify the processed license list file")
+    parser.add_argument("inputFile", help="Specify the input file which needs to be scanned")
+    parser.add_argument("-v", "--verbose", help="increase output verbosity",
+                        action='count', default=0)
+    args = parser.parse_args()
+
+    inputFile = args.inputFile
+    licenseList = args.processedLicenseList
+    verbose = args.verbose
+
+    scanner = SemanticSearchAgent(licenseList, verbose=verbose)
+
+    results = scanner.scan(inputFile)
+    if len(results) == 0:
+        print("Result is nothing")
+    for result in results:
+        print("License Detected using Semantic Search: " + result[0]['shortname'])
+
+

atarashi/atarashii.py

@@ -28,6 +28,7 @@
 from atarashi.agents.dameruLevenDist import DameruLevenDist
 from atarashi.agents.tfidf import TFIDF
 from atarashi.agents.wordFrequencySimilarity import WordFrequencySimilarity
+from atarashi.agents.semanticSearch import SemanticSearchAgent
 
 __author__ = "Aman Jain"
 __email__ = "[email protected]"
@@ -98,6 +99,8 @@ def build_scanner_obj(processedLicense, agent_name, similarity="CosineSim",
     else:
       print("Please choose similarity from {CosineSim,DiceSim,BigramCosineSim}")
       return -1
+  elif agent_name == 'SemanticSearch':
+    scanner = SemanticSearchAgent(processedLicense)
 
   scanner.setVerbose(verbose)
   return scanner
@@ -128,7 +131,7 @@ def main():
   parser.add_argument("-l", "--processedLicenseList", required=False,
                       help="Specify the location of processed license list file")
   parser.add_argument("-a", "--agent_name", required=True,
-                      choices=['wordFrequencySimilarity', 'DLD', 'tfidf', 'Ngram'],
+                      choices=['wordFrequencySimilarity', 'DLD', 'tfidf', 'Ngram', 'SemanticSearch'],
                       help="Name of the agent that needs to be run")
   parser.add_argument("-s", "--similarity", required=False, default="CosineSim",
                       choices=["ScoreSim", "CosineSim", "DiceSim", "BigramCosineSim"],

atarashi/evaluator/evaluator.py

@@ -118,7 +118,7 @@ def evaluate(scanner):
   defaultJSON = resource_filename("atarashi", "data/Ngram_keywords.json")
   parser = argparse.ArgumentParser()
   parser.add_argument("-a", "--agent_name", required=True,
-                      choices=['wordFrequencySimilarity', 'DLD', 'tfidf', 'Ngram'],
+                      choices=['wordFrequencySimilarity', 'DLD', 'tfidf', 'Ngram', 'SemanticSearch'],
                       help="Name of the agent that needs to be run")
   parser.add_argument("-s", "--similarity", required=False, default="CosineSim",
                       choices=["ScoreSim", "CosineSim", "DiceSim", "BigramCosineSim"],

requirements.txt

@@ -8,3 +8,4 @@ textdistance>=3.0.3
 setuptools>=39.2.0
 nirjas>=0.0.5
 urllib3>=1.24.1
+fuzzywuzzy[speedup]>=0.18.0

setup.py

@@ -68,7 +68,8 @@ def read(fname):
   'textdistance>=3.0.3',
   'pyxDamerauLevenshtein>=1.5',
   'urllib3>=1.24.1',
-  'nirjas>=0.0.5'
+  'nirjas>=0.0.5',
+  'fuzzywuzzy[speedup]>=0.18.0'
 ]
 
 class BuildAtarashiDependencies(distutils.cmd.Command):