Add trending agents score

data-science/analytics/trending_agents.py

@@ -1,13 +1,17 @@
-"""Trending agents algorithm — placeholder.
+"""Trending agents score calculation.
 
-TODO: Implement trending score based on:
-  - Recent run count (weighted)
-  - Download velocity
-  - Rating trend (recent vs historical)
-  - Review volume
+This script reads mock usage and rating data, then calculates a simple
+trending score for each agent.
 
-Input: datasets/agent_usage.csv, datasets/agent_ratings.csv
-Output: Ranked list of agent IDs with trending scores
+Formula:
+trending_score = recent_runs * 0.6 + total_runs * 0.2 + avg_rating * 20
+
+Why:
+- recent_runs has the highest weight because trending agents should be active now.
+- total_runs rewards agents with proven usage.
+- avg_rating rewards agents with better user ratings.
+
+The script prints the top 5 trending agents.
 """
 
 import pandas as pd
@@ -17,33 +21,36 @@
 
 
 def load_usage_data() -> pd.DataFrame:
-    """Load mock usage data."""
     return pd.read_csv(DATASETS_DIR / "agent_usage.csv")
 
 
 def load_ratings_data() -> pd.DataFrame:
-    """Load mock ratings data."""
     return pd.read_csv(DATASETS_DIR / "agent_ratings.csv")
 
 
 def compute_trending_scores() -> pd.DataFrame:
-    """Compute trending scores for all agents.
-
-    TODO: Implement weighted scoring formula
-    TODO: Normalize scores to 0-100 range
-    """
     usage = load_usage_data()
     ratings = load_ratings_data()
 
-    # Placeholder: simple sort by recent_runs
     merged = usage.merge(ratings, on="agent_id", how="left")
-    merged["trending_score"] = merged["recent_runs"] * merged["avg_rating"]
-    merged = merged.sort_values("trending_score", ascending=False)
 
-    return merged[["agent_id", "agent_name", "trending_score"]]
+    if "agent_name" not in merged.columns:
+        merged["agent_name"] = merged["agent_id"]
+
+    merged["trending_score"] = (
+        merged["recent_runs"] * 0.6
+        + merged["total_runs"] * 0.2
+        + merged["avg_rating"] * 20
+    ).round(1)
+
+    return (
+        merged[["agent_id", "agent_name", "trending_score"]]
+        .sort_values("trending_score", ascending=False)
+        .head(5)
+    )
 
 
 if __name__ == "__main__":
     results = compute_trending_scores()
-    print("Trending Agents (placeholder algorithm):")
+    print("Top 5 Trending Agents:")
     print(results.to_string(index=False))