feat: Added scoring system for professors

score/main.py

@@ -0,0 +1,139 @@
+import ratemyprofessor
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+from typing import List, Dict, Optional
+
+class ProfessorAnalyzer:
+    def __init__(self):
+        self.sentiment_analyzer = SentimentIntensityAnalyzer()
+
+    def get_professor(self, name: str, school_name: str) -> Optional[ratemyprofessor.Professor]:
+        school = ratemyprofessor.get_school_by_name(school_name)
+        professors = ratemyprofessor.get_professors_by_school_and_name(school, name)
+
+        for prof in professors:
+            if prof.name == name:
+                return prof
+        return None
+
+    def analyze_comments(self, professor: ratemyprofessor.Professor) -> Dict[str, float]:
+        ratings = professor.get_ratings()
+        weighted_compound = 0
+        total_weight = 0
+
+        for rating in ratings:
+            # Base weight of 1, increased by positive feedback
+            weight = 1
+            if rating.thumbs_up > 0 or rating.thumbs_down > 0:
+                weight += (rating.thumbs_up - rating.thumbs_down) / max(
+                    sum(r.thumbs_up + r.thumbs_down for r in ratings), 1)
+
+            sentiment = self.sentiment_analyzer.polarity_scores(rating.comment)
+            weighted_compound += sentiment['compound'] * weight
+            total_weight += weight
+
+        avg_sentiment = weighted_compound / total_weight if total_weight > 0 else 0
+
+        return {
+            'compound': avg_sentiment,
+            'pos': max(0, avg_sentiment),
+            'neg': max(0, -avg_sentiment),
+            'neu': 1 - abs(avg_sentiment)
+        }
+
+    def calculate_overall_score(self, professor: ratemyprofessor.Professor, sentiment: Dict[str, float]) -> float:
+        weights = {
+            'rating': 0.25,
+            'sentiment': 0.25,
+            'difficulty_inverse': 0.2,
+            'would_take_again': 0.3
+        }
+
+        normalized_scores = {
+            'rating': professor.rating / 5.0,
+            'sentiment': (sentiment['compound'] + 1) / 2,
+            'difficulty_inverse': (5.0 - professor.difficulty) / 5.0,
+            'would_take_again': professor.would_take_again / 100 if professor.would_take_again else 0.5
+        }
+
+        overall_score = sum(weights[key] * normalized_scores[key] for key in weights)
+        return overall_score * 100
+
+    def analyze_professor(self, name: str, school_name: str) -> Optional[Dict]:
+        professor = self.get_professor(name, school_name)
+
+        if professor is None:
+            print(f"Professor {name} not found.")
+            return None
+
+        sentiment = self.analyze_comments(professor)
+        overall_score = self.calculate_overall_score(professor, sentiment)
+
+        return {
+            'professor': professor,
+            'sentiment': sentiment,
+            'overall_score': overall_score
+        }
+
+def print_professor_details(analysis: Dict) -> None:
+    professor = analysis['professor']
+    sentiment = analysis['sentiment']
+    overall_score = analysis['overall_score']
+
+    print(f"\nProfessor: {professor.name}")
+    print(f"Department: {professor.department}")
+    print(f"School: {professor.school.name}")
+    print(f"Rating: {professor.rating}/5.0")
+    print(f"Difficulty: {professor.difficulty}/5.0")
+    print(f"Total Ratings: {professor.num_ratings}")
+    if professor.would_take_again:
+        print(f"Would Take Again: {round(professor.would_take_again, 1)}%")
+    else:
+        print("Would Take Again: N/A")
+
+    print("\nWeighted Comment Sentiment Analysis:")
+    print(f"Positive: {sentiment['pos']:.2f}")
+    print(f"Neutral: {sentiment['neu']:.2f}")
+    print(f"Negative: {sentiment['neg']:.2f}")
+    print(f"Compound: {sentiment['compound']:.2f}")
+
+    print(f"\nOverall Professor Score: {overall_score:.1f}%")
+
+def main():
+    analyzer = ProfessorAnalyzer()
+
+    # Get input for school and professors
+    school = input("Enter school name: ")
+    print("Enter professor names (one per line, press Enter twice when done):")
+
+    professor_names = []
+    while True:
+        name = input()
+        if not name:
+            break
+        professor_names.append(name)
+
+    # Analyze all professors
+    professor_analyses = []
+    for name in professor_names:
+        analysis = analyzer.analyze_professor(name, school)
+        if analysis:
+            professor_analyses.append(analysis)
+
+    # Sort professors by overall score
+    professor_analyses.sort(key=lambda x: x['overall_score'], reverse=True)
+
+    # Print individual details
+    print("\n=== Detailed Professor Analyses ===")
+    for analysis in professor_analyses:
+        print_professor_details(analysis)
+        print("\n" + "="*40)
+
+    # Print rankings
+    print("\n=== Top 3 Professors ===")
+    for i, analysis in enumerate(professor_analyses[:3], 1):
+        prof = analysis['professor']
+        score = analysis['overall_score']
+        print(f"{i}. {prof.name} ({prof.department}) - Score: {score:.1f}%")
+
+if __name__ == "__main__":
+    main()

score/requirements.txt

@@ -0,0 +1,2 @@
+RateMyProfessorAPI==1.3.6
+vaderSentiment==3.3.2