written cf-examples

corerec/engines/contentFilterEngine/context_personalization/item_profiling.py

@@ -1,5 +1,8 @@
 # item_profiling implementation
 from typing import List, Dict, Any
+import logging
+
+logger = logging.getLogger(__name__)
 
 class ItemProfilingRecommender:
     def __init__(self):
@@ -24,17 +27,21 @@ def fit(self, data: Dict[int, List[int]], item_features: Dict[int, Dict[str, Any
                 for feature, value in item_features.get(item_id, {}).items():
                     self.item_profiles[item_id][feature] = self.item_profiles[item_id].get(feature, 0) + 1
 
-    def recommend(self, item_indices: List[int], top_n: int = 10) -> List[int]:
+    def recommend(self, query: str, top_n: int = 10) -> List[int]:
         """
-        Generate top-N item recommendations based on item profiles.
+        Recommend items based on the similarity of the query to the documents.
 
         Parameters:
-        - item_indices (List[int]): List of item indices to base recommendations on.
-        - top_n (int): The number of recommendations to generate.
+        - query (str): The query text for which to generate recommendations.
+        - top_n (int): Number of top recommendations to return.
 
         Returns:
         - List[int]: List of recommended item indices.
         """
-        # Placeholder implementation
-        # Implement similarity-based recommendations or other logic as needed
-        return []
+        logger.info("Generating recommendations using LSA.")
+        query_vec = self.transform([query])
+        doc_vecs = self.lsa_model.transform(self.vectorizer.transform(self.vectorizer.get_feature_names_out()))
+        similarity_scores = (doc_vecs @ query_vec.T).flatten()
+        top_indices = similarity_scores.argsort()[::-1][:top_n]
+        logger.info(f"Top {top_n} recommendations generated using LSA.")
+        return top_indices.tolist()

corerec/engines/contentFilterEngine/other_approaches/__init__.py

@@ -1,3 +1,3 @@
-from .rule_based import RULE_BASED as OTH_RULE_BASED
-from .ontology_based import ONTOLOGY_BASED as OTH_ONTOLOGY_BASED
-from .sentiment_analysis import SENTIMENT_ANALYSIS as OTH_SENTIMENT_ANALYSIS
+from .rule_based import RuleBasedFilter as OTH_RULE_BASED
+from .ontology_based import OntologyBasedFilter as OTH_ONTOLOGY_BASED
+from .sentiment_analysis import SentimentAnalysisFilter as OTH_SENTIMENT_ANALYSIS

corerec/engines/contentFilterEngine/other_approaches/ontology_based.py

@@ -1,2 +1,70 @@
-# ontology_based implementation
-pass
+from owlready2 import get_ontology
+
+class OntologyBasedFilter:
+    def __init__(self, ontology_path):
+        """
+        Initializes the OntologyBasedFilter with a specific ontology.
+
+        Parameters:
+        - ontology_path (str): The file path to the ontology (.owl) file.
+        """
+        try:
+            self.ontology = get_ontology(ontology_path).load()
+        except Exception as e:
+            raise ValueError(f"Failed to load ontology from {ontology_path}: {e}")
+
+    def get_concepts(self, content):
+        """
+        Extracts concepts from the content based on the ontology.
+
+        Parameters:
+        - content (str): The content to extract concepts from.
+
+        Returns:
+        - set: A set of concepts identified in the content.
+        """
+        concepts_found = set()
+        content_lower = content.lower()
+
+        for cls in self.ontology.classes():
+            if cls.name.lower() in content_lower:
+                concepts_found.add(cls.name)
+
+        return concepts_found
+
+    def filter_content(self, content):
+        """
+        Filters the content based on ontology-defined relationships.
+
+        Parameters:
+        - content (str): The content to be filtered.
+
+        Returns:
+        - dict: A dictionary with 'status' and 'related_concepts'.
+        """
+        concepts = self.get_concepts(content)
+        related_concepts = self.find_related_concepts(concepts)
+
+        if related_concepts:
+            return {'status': 'filtered', 'related_concepts': related_concepts}
+        else:
+            return {'status': 'allowed', 'related_concepts': related_concepts}
+
+    def find_related_concepts(self, concepts):
+        """
+        Finds related concepts within the ontology.
+
+        Parameters:
+        - concepts (set): A set of concepts to find relationships for.
+
+        Returns:
+        - dict: A dictionary mapping each concept to its related concepts.
+        """
+        related = {}
+        for concept in concepts:
+            try:
+                cls = self.ontology[concept]
+                related[concept] = [str(rel) for rel in cls.is_a]
+            except KeyError:
+                related[concept] = []
+        return related

corerec/engines/contentFilterEngine/other_approaches/rule_based.py

@@ -1,2 +1,49 @@
 # rule_based implementation
-pass
+class RuleBasedFilter:
+    def __init__(self, rules=None):
+        """
+        Initializes the RuleBasedFilter with a set of rules.
+
+        Parameters:
+        - rules (list of dict): A list where each rule is a dictionary containing
+                                 'keyword' and 'action' keys.
+        """
+        if rules is None:
+            self.rules = []
+        else:
+            self.rules = rules
+
+    def add_rule(self, keyword, action):
+        """
+        Adds a new rule to the filter.
+
+        Parameters:
+        - keyword (str): The keyword to look for in the content.
+        - action (str): The action to take ('block', 'flag', etc.).
+        """
+        rule = {'keyword': keyword.lower(), 'action': action.lower()}
+        self.rules.append(rule)
+
+    def filter_content(self, content):
+        """
+        Filters the content based on the predefined rules.
+
+        Parameters:
+        - content (str): The content to be filtered.
+
+        Returns:
+        - dict: A dictionary with 'status' and 'actions' applied.
+        """
+        actions_applied = []
+        content_lower = content.lower()
+
+        for rule in self.rules:
+            if rule['keyword'] in content_lower:
+                actions_applied.append(rule['action'])
+
+        if 'block' in actions_applied:
+            return {'status': 'blocked', 'actions': actions_applied}
+        elif 'flag' in actions_applied:
+            return {'status': 'flagged', 'actions': actions_applied}
+        else:
+            return {'status': 'allowed', 'actions': actions_applied}

corerec/engines/contentFilterEngine/other_approaches/sentiment_analysis.py

@@ -1,2 +1,45 @@
-# sentiment_analysis implementation
-pass
+from textblob import TextBlob
+
+class SentimentAnalysisFilter:
+    def __init__(self, threshold=0.1):
+        """
+        Initializes the SentimentAnalysisFilter.
+
+        Parameters:
+        - threshold (float): The sentiment polarity threshold to trigger actions.
+                             Positive values can indicate positive sentiment,
+                             negative values indicate negative sentiment.
+        """
+        self.threshold = threshold
+
+    def analyze_sentiment(self, content):
+        """
+        Analyzes the sentiment of the given content.
+
+        Parameters:
+        - content (str): The content to analyze.
+
+        Returns:
+        - float: The sentiment polarity score ranging from -1.0 to 1.0.
+        """
+        blob = TextBlob(content)
+        return blob.sentiment.polarity
+
+    def filter_content(self, content):
+        """
+        Filters the content based on its sentiment.
+
+        Parameters:
+        - content (str): The content to be filtered.
+
+        Returns:
+        - dict: A dictionary with 'status' and 'sentiment_score'.
+        """
+        sentiment_score = self.analyze_sentiment(content)
+
+        if sentiment_score < -self.threshold:
+            return {'status': 'negative', 'sentiment_score': sentiment_score}
+        elif sentiment_score > self.threshold:
+            return {'status': 'positive', 'sentiment_score': sentiment_score}
+        else:
+            return {'status': 'neutral', 'sentiment_score': sentiment_score}

corerec/engines/contentFilterEngine/performance_scalability/__init__.py

@@ -1,3 +1,3 @@
-from .scalable_algorithms import SCALABLE_ALGORITHMS as PER_SCALABLE_ALGORITHMS
-from .feature_extraction import FEATURE_EXTRACTION as PER_FEATURE_EXTRACTION
-from .load_balancing import LOAD_BALANCING as PER_LOAD_BALANCING
+from .scalable_algorithms import ScalableAlgorithms as PER_SCALABLE_ALGORITHMS
+from .feature_extraction import FeatureExtraction as PER_FEATURE_EXTRACTION
+from .load_balancing import LoadBalancing as PER_LOAD_BALANCING

corerec/engines/contentFilterEngine/performance_scalability/feature_extraction.py

@@ -1,2 +1,86 @@
 # feature_extraction implementation
-pass
+import logging
+from sklearn.feature_extraction.text import TfidfVectorizer
+from nltk.stem import WordNetLemmatizer
+import nltk
+from typing import List, Any
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Download NLTK resources if not already present
+nltk.download('punkt', quiet=True)
+nltk.download('wordnet', quiet=True)
+
+class FeatureExtraction:
+    def __init__(self, max_features=5000):
+        """
+        Initializes the FeatureExtraction with a TF-IDF vectorizer.
+
+        Parameters:
+        - max_features (int): The maximum number of features (vocabulary size).
+        """
+        self.max_features = max_features
+        self.lemmatizer = WordNetLemmatizer()
+        self.vectorizer = TfidfVectorizer(
+            max_features=self.max_features,
+            stop_words='english',  # Use built-in stop words
+            tokenizer=self.tokenize
+        )
+        logger.info(f"FeatureExtraction initialized with max_features={self.max_features}.")
+
+    def tokenize(self, text: str) -> List[str]:
+        """
+        Tokenizes and lemmatizes the input text.
+
+        Parameters:
+        - text (str): The text to tokenize.
+
+        Returns:
+        - list: A list of processed tokens.
+        """
+        tokens = nltk.word_tokenize(text.lower())
+        lemmatized = [
+            self.lemmatizer.lemmatize(token)
+            for token in tokens
+            if token.isalpha()
+        ]
+        logger.debug(f"Tokenized text: {lemmatized}")
+        return lemmatized
+
+    def fit_transform(self, documents: List[str]):
+        """
+        Fits the TF-IDF vectorizer on the documents and transforms them into feature vectors.
+
+        Parameters:
+        - documents (list of str): The list of documents to process.
+
+        Returns:
+        - sparse matrix: The TF-IDF feature matrix.
+        """
+        logger.info("Fitting and transforming documents into TF-IDF features.")
+        return self.vectorizer.fit_transform(documents)
+
+    def transform(self, documents: List[str]) -> Any:
+        """
+        Transforms the documents into TF-IDF feature vectors using the already fitted vectorizer.
+
+        Parameters:
+        - documents (list of str): The list of documents to transform.
+
+        Returns:
+        - sparse matrix: The TF-IDF feature matrix.
+        """
+        logger.info("Transforming documents into LSA latent space.")
+        tfidf_matrix = self.vectorizer.transform(documents)  # Use transform, not fit
+        return self.lsa_model.transform(tfidf_matrix)
+
+    def get_feature_names(self) -> List[str]:
+        """
+        Retrieves the feature names (vocabulary) from the vectorizer.
+
+        Returns:
+        - list: A list of feature names.
+        """
+        return self.vectorizer.get_feature_names_out()

corerec/engines/contentFilterEngine/performance_scalability/load_balancing.py

@@ -1,2 +1,86 @@
 # load_balancing implementation
-pass
+import logging
+from queue import Queue
+from threading import Thread
+import time
+import threading
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class LoadBalancing:
+    def __init__(self, num_workers=4):
+        """
+        Initializes the LoadBalancing with a specified number of worker threads.
+
+        Parameters:
+        - num_workers (int): The number of worker threads to spawn.
+        """
+        self.num_workers = num_workers
+        self.task_queue = Queue()
+        self.results = []
+        self.threads = []
+        self._init_workers()
+        logger.info(f"LoadBalancing initialized with {self.num_workers} workers.")
+
+    def _init_workers(self):
+        """
+        Initializes worker threads that continuously process tasks from the queue.
+        """
+        for i in range(self.num_workers):
+            thread = Thread(target=self._worker, name=f"Worker-{i+1}", daemon=True)
+            thread.start()
+            self.threads.append(thread)
+            logger.debug(f"Started {thread.name}.")
+
+    def _worker(self):
+        """
+        Worker thread that processes tasks from the queue.
+        """
+        while True:
+            func, args, kwargs = self.task_queue.get()
+            if func is None:
+                # Sentinel found, terminate the thread
+                logger.debug(f"{threading.current_thread().name} received sentinel. Exiting.")
+                break
+            try:
+                result = func(*args, **kwargs)
+                self.results.append(result)
+                logger.debug(f"{threading.current_thread().name} processed a task with result: {result}")
+            except Exception as e:
+                logger.error(f"Error processing task: {e}")
+            finally:
+                self.task_queue.task_done()
+
+    def add_task(self, func, *args, **kwargs):
+        """
+        Adds a new task to the queue.
+
+        Parameters:
+        - func (callable): The function to execute.
+        - *args: Positional arguments for the function.
+        - **kwargs: Keyword arguments for the function.
+        """
+        self.task_queue.put((func, args, kwargs))
+        logger.debug(f"Added task {func.__name__} to the queue.")
+
+    def get_results(self):
+        """
+        Waits for all tasks to be processed and returns the results.
+
+        Returns:
+        - list: A list of results from all tasks.
+        """
+        self.task_queue.join()
+        return self.results
+
+    def shutdown(self):
+        """
+        Shuts down all worker threads gracefully by sending sentinel tasks.
+        """
+        for _ in self.threads:
+            self.task_queue.put((None, (), {}))  # Sentinel
+        for thread in self.threads:
+            thread.join()
+            logger.debug(f"{thread.name} has terminated.")
+        logger.info("LoadBalancing has been shutdown.")