updating scripts and weekly data

active_traders.parquet

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+size 117739

all_trades_profitability.parquet.gz

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+size 7700097

closed_market_metrics.parquet

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+size 124695

closed_markets_div.parquet

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+size 77529

daily_info.parquet

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+size 3486823

daily_mech_requests.parquet

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+oid sha256:3a8f50980b501de7ddccc126a89d10bfabc324f306d02ce8866b4e2d57f856cb
+size 10542

error_by_markets.parquet

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+size 11426

errors_by_mech.parquet

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+size 5361

invalid_trades.parquet

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-size 116581
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+size 117613

retention_activity.parquet.gz

@@ -1,3 +1,3 @@
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-size 5897848
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+size 6576832

scripts/active_traders.py

@@ -2,6 +2,8 @@ import pandas as pd
 import pickle
 from web3_utils import ROOT_DIR, TMP_DIR
 from staking import check_list_addresses
+from tools_metrics import prepare_tools
+import pandas as pd
 
 
 def get_trader_type(address: str, service_map: dict) -> str:
@@ -23,6 +25,7 @@ def compute_active_traders_dataset():
         service_map = pickle.load(f)
     # read tools info
     tools_df = pd.read_parquet(TMP_DIR / "tools.parquet")
+    tools_df = prepare_tools(tools_df)
     # rename the request_month_year_week
     tools_df.rename(
         columns={"request_month_year_week": "month_year_week"}, inplace=True

scripts/pull_data.py

@@ -120,7 +120,7 @@ def only_new_weekly_analysis():
 
     add_current_answer("new_tools.parquet")
 
-    # # Run profitability analysis
+    # Run profitability analysis
     logging.info("Running profitability analysis")
     run_profitability_analysis(
         tools_filename="new_tools.parquet",
@@ -135,14 +135,14 @@ def only_new_weekly_analysis():
 
     save_historical_data()
     try:
-        clean_old_data_from_parquet_files("2025-02-01")
+        clean_old_data_from_parquet_files("2025-02-18")
         clean_old_data_from_json_files()
     except Exception as e:
         print("Error cleaning the oldest information from parquet files")
         print(f"reason = {e}")
     compute_tools_accuracy()
     compute_tools_based_datasets()
-    # # move to tmp folder the new generated files
+    # move to tmp folder the new generated files
     move_files()
     logging.info("Weekly analysis files generated and saved")
 
@@ -170,11 +170,11 @@ def restoring_trades_data(from_date: str, to_date: str):
 
 
 if __name__ == "__main__":
-    # only_new_weekly_analysis()
+    only_new_weekly_analysis()
     # save_historical_data()
     # try:
     #     clean_old_data_from_parquet_files("2025-01-23")
     # except Exception as e:
     #     print("Error cleaning the oldest information from parquet files")
     #     print(f"reason = {e}")
-    add_current_answer("tools.parquet")
+    # add_current_answer("tools.parquet")

scripts/tools_metrics.py

@@ -114,6 +114,7 @@ def get_errors_by_mech_address(
 
 
 def compute_tools_based_datasets():
+    print("Computing tools based datasets")
     try:
         tools_df = pd.read_parquet(TMP_DIR / "tools.parquet")
         tools_df = prepare_tools(tools_df)

scripts/update_tools_accuracy.py

@@ -5,6 +5,7 @@ from utils import INC_TOOLS
 from typing import List
 from utils import TMP_DIR, ROOT_DIR
 
+
 ACCURACY_FILENAME = "tools_accuracy.csv"
 IPFS_SERVER = "/dns/registry.autonolas.tech/tcp/443/https"
 GCP_IPFS_SERVER = "/dns/registry.gcp.autonolas.tech/tcp/443/https"

scripts/web3_utils.py

@@ -161,7 +161,7 @@ def updating_timestamps(rpc: str, tools_filename: str):
     t_map.update(new_timestamps)
 
     # filtering old timestamps
-    cutoff_date = datetime(2024, 9, 9)
+    cutoff_date = datetime(2025, 2, 18)
     filtered_map = {
         k: v
         for k, v in t_map.items()

service_map.pkl

@@ -1,3 +1,3 @@
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-size 201040
+oid sha256:e1dbde1804d1a2ed976df038d551a62259e1a5f8fd864de0a502a7ac1eacae5e
+size 203458

tools_accuracy.csv

@@ -1,13 +1,13 @@
 tool,tool_accuracy,total_requests,min,max
-claude-prediction-offline,62.43682707097341,54612,2025-02-01 10:54:20,2025-03-31 23:28:15
-claude-prediction-online,63.81170925328703,32248,2025-02-01 00:00:35,2025-04-01 02:23:45
-prediction-offline,54.89672711443071,174586,2025-02-01 00:00:05,2025-04-01 23:42:30
-prediction-offline-sme,46.28849270664506,6170,2025-02-01 02:48:10,2025-03-31 23:38:20
-prediction-online,44.92406075139888,5004,2025-02-01 09:17:30,2025-04-01 07:14:40
-prediction-online-sme,43.25597040026909,2973,2025-02-01 08:10:05,2025-03-31 19:30:00
-prediction-request-rag,47.11246200607903,329,2025-02-01 10:59:45,2025-03-30 15:16:40
-prediction-request-rag-claude,52.18978102189781,274,2025-02-01 12:16:20,2025-03-31 17:22:45
-prediction-request-reasoning,49.67020722129365,55641,2025-02-01 08:10:20,2025-04-01 14:44:05
-prediction-request-reasoning-claude,67.1280276816609,289,2025-02-01 16:41:35,2025-03-30 11:22:00
-prediction-url-cot-claude,55.223880597014926,804,2025-02-01 03:39:30,2025-03-31 19:26:25
-superforcaster,54.956591964465986,9906,2025-02-01 00:02:25,2025-04-01 22:06:25
+claude-prediction-offline,60.82916174894524,73713,2025-02-18 00:15:25,2025-04-17 01:31:35
+claude-prediction-online,62.72578365158077,44567,2025-02-18 00:00:10,2025-04-17 06:41:30
+prediction-offline,55.68178522049369,310281,2025-02-18 00:00:10,2025-04-17 23:46:35
+prediction-offline-sme,44.511070110701105,4336,2025-02-18 00:07:45,2025-04-16 20:38:00
+prediction-online,45.12924844423169,8356,2025-02-18 08:27:50,2025-04-17 17:17:15
+prediction-online-sme,46.00216684723727,4615,2025-02-18 09:58:50,2025-04-17 23:42:20
+prediction-request-rag,41.60756501182033,423,2025-02-18 12:21:30,2025-04-16 17:09:10
+prediction-request-rag-claude,55.06329113924051,316,2025-02-18 13:44:25,2025-04-16 14:10:25
+prediction-request-reasoning,51.86754608988294,81417,2025-02-18 08:03:55,2025-04-17 23:42:30
+prediction-request-reasoning-claude,61.627906976744185,344,2025-02-18 10:40:05,2025-04-16 14:57:40
+prediction-url-cot-claude,60.763888888888886,1152,2025-02-18 01:08:25,2025-04-15 18:24:40
+superforcaster,52.090072157253054,16076,2025-02-18 00:00:35,2025-04-17 18:00:00

unknown_traders.parquet

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weekly_mech_calls.parquet

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winning_df.parquet

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