daily data and final adjustments on global accuracy

all_trades_profitability.parquet.gz

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closed_market_metrics.parquet

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daily_info.parquet

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daily_mech_requests.parquet

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invalid_trades.parquet

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latest_result_DAA_Pearl.parquet

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latest_result_DAA_QS.parquet

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pearl_agents.parquet

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retention_activity.parquet.gz

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scripts/daily_data.py

@@ -38,7 +38,7 @@ def prepare_live_metrics(
     all_trades_df = analyse_all_traders(fpmmTrades, trader_mech_calls, daily_info=True)
 
     # staking label
-    all_trades_df = label_trades_by_staking(all_trades_df)
+    all_trades_df = label_trades_by_staking(all_trades_df, start=1)
 
     # create the unknown traders dataset
     unknown_traders_df, all_trades_df = create_unknown_traders_df(

scripts/pull_data.py

@@ -136,7 +136,7 @@ def only_new_weekly_analysis():
 
     save_historical_data()
     try:
-        clean_old_data_from_parquet_files("2025-05-20")
+        clean_old_data_from_parquet_files("2025-05-23")
         clean_old_data_from_json_files()
     except Exception as e:
         print("Error cleaning the oldest information from parquet files")

scripts/staking.py

@@ -138,7 +138,7 @@ def get_service_data(service_registry: Any, service_id: int) -> dict:
     return tmp_map
 
 
-def update_service_map(start: int = 1, end: int = 2500):
+def update_service_map(start: int = 1, end: int = 2800):
     if os.path.exists(ROOT_DIR / "service_map.pkl"):
         with open(ROOT_DIR / "service_map.pkl", "rb") as f:
             service_map = pickle.load(f)

scripts/update_tools_accuracy.py

@@ -306,51 +306,52 @@ def compute_global_accuracy_same_population(
     # first historical file download
     tool_names = list(more_sample_tools.keys())
     print(f"Tools with not enough samples: {tool_names}")
-    # Disbling the historical file download til we have enough old samples from "2025-06-03" (Latest model update)
-    # TODO reactivate the historical file download from 2025-08-16
-    # tools_historical_file = download_tools_historical_files(
-    #     client, exclude_filename=None
-    # )
-    # adding_historical_data(
-    #     tools_historical_file,
-    #     tools_df,
-    #     more_sample_tools,
-    #     sample_size,
-    #     valid_tools,
-    #     global_accuracies,
-    # )
-    # if len(more_sample_tools) > 0:
-    #     # second historical file download
-    #     tools_historical_file2 = download_tools_historical_files(
-    #         client,
-    #         exclude_filename=tools_historical_file,
-    #     )
-    #     adding_historical_data(
-    #         tools_historical_file2,
-    #         tools_df,
-    #         more_sample_tools,
-    #         sample_size,
-    #         valid_tools,
-    #         global_accuracies,
-    #     )
+
+    tools_historical_file = download_tools_historical_files(
+        client, exclude_filename=None
+    )
+    adding_historical_data(
+        tools_historical_file,
+        tools_df,
+        more_sample_tools,
+        sample_size,
+        valid_tools,
+        global_accuracies,
+    )
+    if len(more_sample_tools) > 0:
+        # second historical file download
+        tools_historical_file2 = download_tools_historical_files(
+            client,
+            exclude_filename=tools_historical_file,
+        )
+        adding_historical_data(
+            tools_historical_file2,
+            tools_df,
+            more_sample_tools,
+            sample_size,
+            valid_tools,
+            global_accuracies,
+        )
     # if not enough samples found in the historical data, upsample the tools that need more samples
     # Process tools that need upsampling
+    new_tools = []
     for tool in more_sample_tools.keys():
-        if more_sample_tools[tool] < SAMPLES_THRESHOLD:
-            # assign the default accuracy
-            print(f"Tool {tool} has not enough samples, assigning default accuracy")
-            global_accuracies[tool] = {
-                "mean": DEFAULT_ACCURACY,
-                "std": 0.0,  # No standard deviation for a single sample
-            }
-            continue
-        print(f"Upsampling tool: {tool}")
-        tool_samples = tools_df[tools_df["tool"] == tool]
-        upsampled_sets = upsample_tool_multiple(tool_samples, sample_size, n_subsets)
+        # assign the default accuracy
+        print(f"Tool {tool} has not enough samples, assigning default accuracy")
+        global_accuracies[tool] = {
+            "mean": DEFAULT_ACCURACY,
+            "std": 0.0,  # No standard deviation for a single sample
+        }
+        if more_sample_tools[tool] > SAMPLES_THRESHOLD:
+            # new tool but not reaching yet the population size
+            new_tools.append(tool)
+        # print(f"Upsampling tool: {tool}")
+        # tool_samples = tools_df[tools_df["tool"] == tool]
+        # upsampled_sets = upsample_tool_multiple(tool_samples, sample_size, n_subsets)
 
-        tool_mean_accuracy, tool_std = compute_tool_estimated_accuracy(upsampled_sets)
-        global_accuracies[tool] = {"mean": float(tool_mean_accuracy), "std": tool_std}
-    return global_accuracies
+        # tool_mean_accuracy, tool_std = compute_tool_estimated_accuracy(upsampled_sets)
+        # global_accuracies[tool] = {"mean": float(tool_mean_accuracy), "std": tool_std}
+    return global_accuracies, new_tools
 
 
 def get_accuracy_info(tools_df: pd.DataFrame) -> [pd.DataFrame, bool, Dict]:
@@ -359,14 +360,15 @@ def get_accuracy_info(tools_df: pd.DataFrame) -> [pd.DataFrame, bool, Dict]:
     """
     clean_tools_df = clean_tools_dataset(tools_df)
     # compute global accuracy information for the tools
-    global_accuracies = compute_global_accuracy_same_population(tools_df=clean_tools_df)
+    global_accuracies, new_tools = compute_global_accuracy_same_population(
+        tools_df=clean_tools_df
+    )
     # transform the dictionary global_accuracies into a DataFrame
     wins = pd.DataFrame(
         [
             {
                 "tool": tool,
                 "tool_accuracy": global_accuracies[tool]["mean"],
-                "std_accuracy": global_accuracies[tool]["std"],
                 "total_requests": clean_tools_df[clean_tools_df["tool"] == tool].shape[
                     0
                 ],
@@ -385,7 +387,7 @@ def get_accuracy_info(tools_df: pd.DataFrame) -> [pd.DataFrame, bool, Dict]:
         print("NO REQUEST TIME INFORMATION AVAILABLE")
         no_timeline_info = True
         acc_info = wins
-    return acc_info, no_timeline_info
+    return acc_info, no_timeline_info, new_tools
 
 
 def update_tools_accuracy_same_model(
@@ -395,7 +397,7 @@ def update_tools_accuracy_same_model(
 
     # computation of the accuracy information
     tools_inc = tools_df[tools_df["tool"].isin(inc_tools)]
-    acc_info, no_timeline_info = get_accuracy_info(tools_inc)
+    acc_info, no_timeline_info, new_tools = get_accuracy_info(tools_inc)
 
     if tools_acc is None:
         print("Creating accuracy file for the first time")
@@ -409,9 +411,10 @@ def update_tools_accuracy_same_model(
     # dt.strftime("%Y-%m-%d %H:%M:%S")
     acc_info["min"] = acc_info["min"].dt.strftime("%Y-%m-%d %H:%M:%S")
     acc_info["max"] = acc_info["max"].dt.strftime("%Y-%m-%d %H:%M:%S")
-    new_tools = []
     all_accuracies = []
     for tool in tools_to_update:
+        if tool in new_tools:
+            continue
         new_accuracy = round(
             acc_info[acc_info["tool"] == tool]["tool_accuracy"].values[0], 2
         )

service_map.pkl

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tools_accuracy.csv

@@ -1,13 +1,13 @@
 tool,tool_accuracy,total_requests,min,max
-claude-prediction-offline,61.25,800,2025-06-06 00:13:05,2025-07-20 23:30:50
-claude-prediction-online,44.25,800,2025-06-11 07:23:05,2025-07-20 23:10:30
-prediction-offline,66.25,800,2025-06-03 00:00:05,2025-07-20 23:42:15
-prediction-offline-sme,56.44,800,2025-06-03 11:55:10,2025-07-17 12:28:50
-prediction-online,48.25,800,2025-06-03 00:00:05,2025-07-20 23:41:50
-prediction-online-sme,61.12,800,2025-06-03 00:04:30,2025-07-20 23:38:00
-prediction-request-rag,43.25,800,2025-06-03 18:59:40,2025-07-20 23:38:30
-prediction-request-rag-claude,44.38,800,2025-06-03 17:51:10,2025-07-20 19:25:25
-prediction-request-reasoning,54.25,800,2025-06-03 00:00:30,2025-07-20 23:43:45
-prediction-request-reasoning-claude,53.5,800,2025-06-16 11:02:15,2025-07-20 17:57:35
-prediction-url-cot-claude,51.0,800,2025-06-12 20:36:25,2025-07-01 07:40:40
-superforcaster,62.75,800,2025-06-03 01:15:10,2025-07-20 23:42:00
+claude-prediction-offline,61.25,800,2025-06-06 00:13:05,2025-07-23 22:52:15
+claude-prediction-online,51.12,800,2025-06-11 07:23:05,2025-07-23 23:44:45
+prediction-offline,67.62,800,2025-06-03 00:00:05,2025-07-23 23:44:45
+prediction-offline-sme,56.09,800,2025-06-03 11:55:10,2025-07-21 10:13:15
+prediction-online,69.62,800,2025-06-03 00:00:05,2025-07-23 23:44:45
+prediction-online-sme,51.88,800,2025-06-03 00:04:30,2025-07-23 23:30:15
+prediction-request-rag,65.12,800,2025-06-03 18:59:40,2025-07-23 23:13:00
+prediction-request-rag-claude,44.88,800,2025-06-03 17:51:10,2025-07-23 22:01:40
+prediction-request-reasoning,71.5,800,2025-06-03 00:00:30,2025-07-23 23:30:15
+prediction-request-reasoning-claude,55.0,800,2025-06-16 11:02:15,2025-07-23 14:13:35
+prediction-url-cot-claude,42.86,800,2025-06-12 20:36:25,2025-07-01 07:40:40
+superforcaster,64.88,800,2025-06-03 01:15:10,2025-07-23 23:24:05

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