feat: collect top3 tools accuracy

scripts/update_tools_accuracy.py

@@ -501,9 +501,13 @@ def compute_global_weekly_accuracy(clean_tools_df):
     Compute accuracy following version 5.0 of spec"""
     # get the information in clean_tools_df from last two weeks only, timestamp column is request_time
 
-    three_weeks_ago = pd.Timestamp.now(tz="UTC") - pd.Timedelta(days=21)
+    three_weeks_ago = pd.Timestamp.now(tz="UTC") - pd.Timedelta(days=7)
     recent_df = clean_tools_df[clean_tools_df["request_time"] >= three_weeks_ago]
 
+    daily_requests = recent_df.groupby(clean_tools_df["request_time"].dt.date)["request_id"].count()
+    print("\nDaily request counts:")
+    print(daily_requests)
+
     # compute at the tool level (using "tool" column) the volume of requests per tool
     tool_volumes = (
         recent_df.groupby("tool")["request_id"].count().reset_index(name="volume")
@@ -525,13 +529,86 @@ def compute_global_weekly_accuracy(clean_tools_df):
     sampling_size = int(avg_volume / 2)
     print(f"Sampling size = {sampling_size}")
 
-    return compute_global_accuracy_same_population(
+    global_accuracies, new_tools = compute_global_accuracy_same_population(
         tools_df=recent_df,
         recent_samples_size=avg_volume,
         sample_size=sampling_size,
         n_subsets=50,
     )
 
+    def save_weekly_top_3_metric(recent_df):
+        """Save weekly top 3 tools percentage metric"""
+        
+        tool_volumes = (
+            recent_df.groupby("tool")["request_id"].count().reset_index(name="volume")
+        )
+        
+        global_accuracies, _ = compute_global_accuracy_same_population(
+            tools_df=recent_df,
+            recent_samples_size=len(recent_df) // len(recent_df['tool'].unique()) if len(recent_df) > 0 else 1000,
+            sample_size=sampling_size,
+            n_subsets=50
+        )
+        
+        tools_data = []
+        for _, row in tool_volumes.iterrows():
+            tool = row['tool']
+            volume = row['volume']
+            accuracy = global_accuracies.get(tool, {}).get('mean', 0)
+            tools_data.append({
+                'tool': tool,
+                'volume': volume,
+                'accuracy': accuracy
+            })
+        
+        tools_df = pd.DataFrame(tools_data)
+        
+        # sort by accuracy descending and get top 3
+        top_3_tools = tools_df.nlargest(3, 'accuracy')
+        
+        top_3_requests = top_3_tools['volume'].sum()
+        total_requests = tools_df['volume'].sum()
+        top_3_percentage = (top_3_requests / total_requests) * 100 if total_requests > 0 else 0
+        
+        # Create weekly report
+        weekly_report = {
+            'timestamp': pd.Timestamp.now(tz="UTC").strftime("%Y-%m-%d %H:%M:%S"),
+            'calculation_period': '3_weeks',
+            'top_3_percentage': round(top_3_percentage, 2),
+            'total_requests': int(total_requests),
+            'top_3_total_requests': int(top_3_requests),
+            'top_1_tool': top_3_tools.iloc[0]['tool'] if len(top_3_tools) > 0 else None,
+            'top_1_accuracy': round(top_3_tools.iloc[0]['accuracy'], 2) if len(top_3_tools) > 0 else None,
+            'top_1_volume': int(top_3_tools.iloc[0]['volume']) if len(top_3_tools) > 0 else None,
+            'top_2_tool': top_3_tools.iloc[1]['tool'] if len(top_3_tools) > 1 else None,
+            'top_2_accuracy': round(top_3_tools.iloc[1]['accuracy'], 2) if len(top_3_tools) > 1 else None,
+            'top_2_volume': int(top_3_tools.iloc[1]['volume']) if len(top_3_tools) > 1 else None,
+            'top_3_tool': top_3_tools.iloc[2]['tool'] if len(top_3_tools) > 2 else None,
+            'top_3_accuracy': round(top_3_tools.iloc[2]['accuracy'], 2) if len(top_3_tools) > 2 else None,
+            'top_3_volume': int(top_3_tools.iloc[2]['volume']) if len(top_3_tools) > 2 else None,
+        }
+        
+        # Save to CSV
+        report_df = pd.DataFrame([weekly_report])
+        filename = ROOT_DIR / "weekly_top_3_tools_report.csv"
+        
+        # Append to existing file or create new one
+        if os.path.exists(filename):
+            existing_df = pd.read_csv(filename)
+            combined_df = pd.concat([existing_df, report_df], ignore_index=True)
+            combined_df.to_csv(filename, index=False)
+        else:
+            report_df.to_csv(filename, index=False)
+        
+        print(f"Weekly top 3 tools report saved: {top_3_percentage:.2f}% requests served by top 3 most accurate tools")
+        print(f"Report saved to: {filename}")
+        
+        return weekly_report
+
+    save_weekly_top_3_metric(recent_df)
+
+    return global_accuracies, new_tools
+
 
 def get_accuracy_info(clean_tools_df: pd.DataFrame) -> [pd.DataFrame, bool, List]:
     """
@@ -552,7 +629,7 @@ def get_accuracy_info(clean_tools_df: pd.DataFrame) -> [pd.DataFrame, bool, List
                 "tool": tool,
                 "tool_accuracy": global_accuracies[tool]["mean"],
                 "std_accuracy": global_accuracies[tool]["std"],
-                "total_requests": SAMPLING_POPULATION_SIZE,
+                "nr_responses": SAMPLING_POPULATION_SIZE,
             }
             for tool in global_accuracies.keys()
         ]
@@ -614,7 +691,7 @@ def update_tools_accuracy_same_model(
             new_max_timeline = acc_info[acc_info["tool"] == tool]["max"].values[0]
 
         old_accuracy = tools_acc[tools_acc["tool"] == tool]["tool_accuracy"].values[0]
-        old_volume = tools_acc[tools_acc["tool"] == tool]["total_requests"].values[0]
+        old_volume = tools_acc[tools_acc["tool"] == tool]["nr_responses"].values[0]
         # update the accuracy information
         print(f"Updating tool {tool} with new accuracy {new_accuracy}")
         print(f"Old volume: {old_volume}, New volume: {new_volume}")
@@ -623,7 +700,7 @@ def update_tools_accuracy_same_model(
         new_row = {
             "tool": tool,
             "tool_accuracy": new_accuracy,
-            "total_requests": SAMPLING_POPULATION_SIZE,
+            "nr_responses": SAMPLING_POPULATION_SIZE,
             "min": new_min_timeline,
             "max": new_max_timeline,
         }
@@ -659,7 +736,7 @@ def update_tools_accuracy_same_model(
             new_row = {
                 "tool": tool,
                 "tool_accuracy": avg_accuracy,
-                "total_requests": SAMPLING_POPULATION_SIZE,
+                "nr_responses": SAMPLING_POPULATION_SIZE,
                 "min": new_min_timeline,
                 "max": new_max_timeline,
             }