separate tools accuracy files

old_tools_accuracy.csv

@@ -0,0 +1,13 @@
+tool,tool_accuracy,total_requests,min,max
+claude-prediction-offline,58.76814365586698,54909,2025-03-28 08:16:20,2025-05-20 17:10:50
+claude-prediction-online,61.63179618559636,28104,2025-03-28 00:00:50,2025-05-21 18:47:35
+prediction-offline,55.357203757526605,263897,2025-03-28 00:00:10,2025-05-22 23:59:50
+prediction-offline-sme,55.578011317704124,2474,2025-03-28 07:16:30,2025-05-22 19:00:30
+prediction-online,47.6514007716826,11922,2025-03-28 07:07:05,2025-05-22 23:42:45
+prediction-online-sme,48.10738702684675,5997,2025-03-28 07:32:15,2025-05-22 23:59:05
+prediction-request-rag,48.03921568627451,204,2025-03-28 08:40:25,2025-05-20 15:37:10
+prediction-request-rag-claude,49.074074074074076,216,2025-03-28 08:48:25,2025-05-20 13:30:50
+prediction-request-reasoning,52.21228217314323,107310,2025-03-28 07:14:45,2025-05-22 23:58:30
+prediction-request-reasoning-claude,64.60674157303372,178,2025-03-28 15:23:25,2025-05-20 12:25:10
+prediction-url-cot-claude,57.27636849132176,749,2025-03-28 07:13:15,2025-05-20 15:21:20
+superforcaster,53.24888736733995,14605,2025-03-28 00:20:00,2025-05-22 23:42:50

scripts/update_tools_accuracy.py

@@ -11,7 +11,7 @@ IPFS_SERVER = "/dns/registry.autonolas.tech/tcp/443/https"
 GCP_IPFS_SERVER = "/dns/registry.gcp.autonolas.tech/tcp/443/https"
 
 
-def update_tools_accuracy(
+def update_tools_accuracy_same_model(
     tools_acc: pd.DataFrame, tools_df: pd.DataFrame, inc_tools: List[str]
 ) -> pd.DataFrame:
     """To compute/update the latest accuracy information for the different mech tools"""
@@ -94,6 +94,34 @@ def update_tools_accuracy(
     return tools_acc
 
 
+def update_tools_accuracy(
+    tools_acc: pd.DataFrame,
+    old_tools_acc: pd.DataFrame,
+    tools_df: pd.DataFrame,
+    inc_tools: List[str],
+) -> pd.DataFrame:
+    """To compute/update the latest accuracy information for the different mech tools but splitting by date 23rd of May 2025"""
+
+    # to compute the accuracy information up to the 23rd of May 2025
+    tools_df["request_time"] = pd.to_datetime(tools_df["request_time"])
+    tools_df["request_date"] = tools_df["request_time"].dt.date
+    tools_df["request_date"] = pd.to_datetime(tools_df["request_date"])
+    tools_df["request_date"] = tools_df["request_date"].dt.strftime("%Y-%m-%d")
+    # split the data into two parts: before and after 23rd of May 2025
+    split_date = pd.to_datetime("2025-05-23").tz_localize("UTC")
+    before_split = tools_df[tools_df["request_time"] < split_date]
+    after_split = tools_df[tools_df["request_time"] >= split_date]
+    print(f"Number of requests before {split_date}: {len(before_split)}")
+    print(f"Number of requests after {split_date}: {len(after_split)}")
+    # compute the accuracy information for the two parts
+    acc_info_before = update_tools_accuracy_same_model(
+        old_tools_acc, before_split, inc_tools
+    )
+    acc_info_after = update_tools_accuracy_same_model(tools_acc, after_split, inc_tools)
+    # return the two different dataframes
+    return acc_info_before, acc_info_after
+
+
 def compute_tools_accuracy():
     print("Computing accuracy of tools")
     print("Reading tools parquet file")
@@ -101,28 +129,32 @@ def compute_tools_accuracy():
     # Computing tools accuracy information
     print("Computing tool accuracy information")
     # Check if the file exists
-    acc_data = None
+    acc_data = old_acc_data = None
     if os.path.exists(ROOT_DIR / ACCURACY_FILENAME):
         acc_data = pd.read_csv(ROOT_DIR / ACCURACY_FILENAME)
-    acc_data = update_tools_accuracy(acc_data, tools, INC_TOOLS)
+    if os.path.exists(ROOT_DIR / "old_tools_accuracy.csv"):
+        old_acc_data = pd.read_csv(ROOT_DIR / "old_tools_accuracy.csv")
+    old_acc_data, new_acc_data = update_tools_accuracy(
+        acc_data, old_acc_data, tools, INC_TOOLS
+    )
 
     # save acc_data into a CSV file
-    print("Saving into a csv file")
-    acc_data.to_csv(ROOT_DIR / ACCURACY_FILENAME, index=False)
-    print(acc_data.head())
-
+    print("Saving into a csv files")
+    old_acc_data.to_csv(ROOT_DIR / "old_tools_accuracy.csv", index=False)
+    new_acc_data.to_csv(ROOT_DIR / ACCURACY_FILENAME, index=False)
     # save the data into IPFS
+    # push_csv_file_to_ipfs("old_tools_accuracy.csv")
     push_csv_file_to_ipfs()
 
 
-def push_csv_file_to_ipfs():
+def push_csv_file_to_ipfs(filename: str = ACCURACY_FILENAME) -> str:
     """Push the tools accuracy CSV file to IPFS."""
     client = ipfshttpclient.connect(IPFS_SERVER)
-    result = client.add(ROOT_DIR / ACCURACY_FILENAME)
+    result = client.add(ROOT_DIR / filename)
     print(f"HASH of the tools accuracy file: {result['Hash']}")
     return result["Hash"]
 
 
 if __name__ == "__main__":
-    # compute_tools_accuracy()
-    push_csv_file_to_ipfs()
+    compute_tools_accuracy()
+    # push_csv_file_to_ipfs()

tools_accuracy.csv

@@ -1,13 +1,13 @@
 tool,tool_accuracy,total_requests,min,max
-claude-prediction-offline,73.746312684365789,300,2025-05-25 10:30:10,2025-05-28 17:10:50
+claude-prediction-offline,0.0,1,2025-05-28 03:56:15,2025-05-28 03:56:15
 claude-prediction-online,74.41176470588236,300,2025-05-25 00:00:15,2025-05-28 18:47:35
-prediction-offline,74.6268656716418,300,2025-05-25 00:00:30,2025-05-28 23:44:15
-prediction-offline-sme,74.85207100591716,300,2025-05-25 00:13:15,2025-05-28 19:01:20
-prediction-online,75.07418397626113,300,2025-05-25 07:33:20,2025-05-28 23:43:40
-prediction-online-sme,74.30340557275542,300,2025-05-25 07:05:50,2025-05-28 23:26:15
-prediction-request-rag,79.09967845659164,300,2025-05-25 14:16:25,2025-05-28 15:37:10
+prediction-offline,54.8093137890777,30836,2025-05-23 00:00:00,2025-05-29 00:30:20
+prediction-offline-sme,57.14285714285714,70,2025-05-23 06:29:10,2025-05-28 01:49:45
+prediction-online,57.29783037475345,1014,2025-05-23 00:10:05,2025-05-28 22:06:20
+prediction-online-sme,50.60120240480962,998,2025-05-23 00:23:15,2025-05-28 23:17:15
+prediction-request-rag,79.09967845659165,300,2025-05-25 14:16:25,2025-05-28 15:37:10
 prediction-request-rag-claude,76.03550295857988,300,2025-05-25 13:21:55,2025-05-28 13:30:50
-prediction-request-reasoning,76.42585551330798,300,2025-05-25 07:14:15,2025-05-28 23:44:45
-prediction-request-reasoning-claude,78.93175074183977,300,2025-05-25 13:48:00,2025-05-28 12:25:10
-prediction-url-cot-claude,72.86135693215339,300,2025-05-25 00:12:40,2025-05-28 15:21:20
-superforcaster,76.38036809815951,300,2025-05-25 00:00:20,2025-05-28 19:47:45
+prediction-request-reasoning,55.003665689149564,5456,2025-05-23 00:01:15,2025-05-28 23:01:25
+prediction-request-reasoning-claude,78.93175074183976,300,2025-05-25 13:48:00,2025-05-28 12:25:10
+prediction-url-cot-claude,72.86135693215338,300,2025-05-25 00:12:40,2025-05-28 15:21:20
+superforcaster,56.797583081571,331,2025-05-23 00:21:15,2025-05-28 18:42:15