updating weekly data

active_traders.parquet

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all_trades_profitability.parquet.gz

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closed_market_metrics.parquet

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closed_markets_div.parquet

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daily_info.parquet

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daily_mech_requests.parquet

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daily_mech_requests_by_pearl_agents.parquet

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error_by_markets.parquet

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errors_by_mech.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/global_tool_accuracy.py

@@ -17,7 +17,7 @@ from cloud_storage import (
 
 ACCURACY_FILENAME = "tools_accuracy.csv"
 IPFS_SERVER = "/dns/registry.autonolas.tech/tcp/443/https"
-NR_ANSWERED_MARKETS = 800  # In two months the max we can reach is 1000 for top tools
+NR_ANSWERED_MARKETS = 1000  # In two months the max we can reach is 1000 for top tools
 MAX_ATTEMPTS = 5
 historical_files_count_map = {
     1: FILES_IN_TWO_MONTHS,
@@ -27,6 +27,7 @@ historical_files_count_map = {
     5: FILES_IN_TEN_MONTHS,
 }
 DEFAULT_ACCURACY = 0.50
+DEFAULT_BAD_ACCURACY = 0.00
 
 
 def keep_last_answer_per_question_per_tool(
@@ -314,8 +315,6 @@ def get_accuracy_values(tools_df: pd.DataFrame, more_q_tools: list) -> list:
     # print("Using evenly distributed sampling for accuracy computation")
     for tool in tools:
         print(f"Processing tool: {tool}")
-        if tool == "prediction-offline-sme" or tool == "prediction-url-cot-claude":
-            continue
         tools_data = tools_df[tools_df["tool"] == tool]
         min_timestamp = tools_data.request_time.min().strftime("%Y-%m-%d %H:%M:%S")
         max_timestamp = tools_data.request_time.max().strftime("%Y-%m-%d %H:%M:%S")
@@ -323,7 +322,7 @@ def get_accuracy_values(tools_df: pd.DataFrame, more_q_tools: list) -> list:
             global_accuracies.append(
                 {
                     "tool": tool,
-                    "accuracy": None,
+                    "accuracy": DEFAULT_ACCURACY,
                     "nr_responses": NR_ANSWERED_MARKETS,
                     "min": min_timestamp,
                     "max": max_timestamp,
@@ -332,14 +331,14 @@ def get_accuracy_values(tools_df: pd.DataFrame, more_q_tools: list) -> list:
             continue
 
         # tool_accuracy = sampled_accuracy(tools_data, sampling_percentage=0.50)
-        tool_accuracy = sampled_accuracy(tools_data, n=500)
+        # tool_accuracy = sampled_accuracy(tools_data, n=500)
 
         # sampled_data = evenly_distributed_sampling(
         #     tools_data, group_size=5, sampling_percentage=0.30
         # )
         # print(f"length of sampled data for tool {tool}: {len(sampled_data)}")
-        # correct_answers = int(tools_data.win.sum())
-        # tool_accuracy = round(correct_answers / len(tools_data), 5)
+        correct_answers = int(tools_data.win.sum())
+        tool_accuracy = round(correct_answers / len(tools_data), 5)
 
         global_accuracies.append(
             {
@@ -355,56 +354,56 @@ def get_accuracy_values(tools_df: pd.DataFrame, more_q_tools: list) -> list:
 
 def global_tool_accuracy():
     # read the tools df
-    # print("Reading tools parquet file")
-    # tools_df = pd.read_parquet(TMP_DIR / "tools.parquet")
-
-    # # clean the tools df
-    # clean_tools_df = clean_tools_dataset(tools_df)
-    # print("Current count of answered questions per tool after cleaning:")
-    # print(clean_tools_df.groupby("tool")["title"].nunique())
-
-    # # extract the number of questions answered from the top tool
-    # answered_questions = compute_nr_questions_per_tool(clean_tools_df=clean_tools_df)
-    # ref_nr_questions = NR_ANSWERED_MARKETS
-
-    # # classify tools between those with enough questions and those that need more data
-    # more_q_tools = classify_tools_by_responses(answered_questions, ref_nr_questions)
-
-    # clean_tools_df = get_unique_recent_samples(tools_df=clean_tools_df)
-
-    # print(
-    #     "Current count of answered questions per tool after selecting the global population:"
-    # )
-    # print(clean_tools_df.groupby("tool")["title"].nunique())
-
-    # # go for historical data if needed up to a maximum of 5 attempts
-    # nr_attempts = 0
-    # client = initialize_client()
-    # while len(more_q_tools) > 0 and nr_attempts < MAX_ATTEMPTS:
-    #     nr_attempts += 1
-    #     print(f"Attempt {nr_attempts} to reach the reference number of questions")
-    #     clean_tools_df, updated_tools = check_historical_samples(
-    #         client=client,
-    #         tools_df=clean_tools_df,
-    #         more_questions_tools=more_q_tools,
-    #         ref_nr_questions=ref_nr_questions,
-    #         attempt_nr=nr_attempts,
-    #     )
-    #     print(f"Tools that were completed with historical data {updated_tools}")
-    #     print(f"More tools with not enough questions {more_q_tools}")
-
-    # # save cleaned tools df into a parquet file
-    # try:
-    #     if "request_block" in clean_tools_df.columns:
-    #         clean_tools_df["request_block"] = pd.to_numeric(
-    #             clean_tools_df["request_block"], errors="coerce"
-    #         ).astype("Int64")
-    #     clean_tools_df.to_parquet(TMP_DIR / "clean_tools.parquet", index=False)
-    # except Exception as e:
-    #     print(f"Error saving clean tools parquet file: {e}")
+    print("Reading tools parquet file")
+    tools_df = pd.read_parquet(TMP_DIR / "tools.parquet")
+
+    # clean the tools df
+    clean_tools_df = clean_tools_dataset(tools_df)
+    print("Current count of answered questions per tool after cleaning:")
+    print(clean_tools_df.groupby("tool")["title"].nunique())
+
+    # extract the number of questions answered from each tool
+    answered_questions = compute_nr_questions_per_tool(clean_tools_df=clean_tools_df)
+    ref_nr_questions = NR_ANSWERED_MARKETS
+
+    # classify tools between those with enough questions and those that need more data
+    more_q_tools = classify_tools_by_responses(answered_questions, ref_nr_questions)
+
+    clean_tools_df = get_unique_recent_samples(tools_df=clean_tools_df)
+
+    print(
+        "Current count of answered questions per tool after selecting the global population:"
+    )
+    print(clean_tools_df.groupby("tool")["title"].nunique())
+
+    # go for historical data if needed up to a maximum of 5 attempts
+    nr_attempts = 0
+    client = initialize_client()
+    while len(more_q_tools) > 0 and nr_attempts < MAX_ATTEMPTS:
+        nr_attempts += 1
+        print(f"Attempt {nr_attempts} to reach the reference number of questions")
+        clean_tools_df, updated_tools = check_historical_samples(
+            client=client,
+            tools_df=clean_tools_df,
+            more_questions_tools=more_q_tools,
+            ref_nr_questions=ref_nr_questions,
+            attempt_nr=nr_attempts,
+        )
+        print(f"Tools that were completed with historical data {updated_tools}")
+        print(f"More tools with not enough questions {more_q_tools}")
+
+    # save cleaned tools df into a parquet file
+    try:
+        if "request_block" in clean_tools_df.columns:
+            clean_tools_df["request_block"] = pd.to_numeric(
+                clean_tools_df["request_block"], errors="coerce"
+            ).astype("Int64")
+        clean_tools_df.to_parquet(TMP_DIR / "clean_tools.parquet", index=False)
+    except Exception as e:
+        print(f"Error saving clean tools parquet file: {e}")
 
     # read the cleaned tools df
-    clean_tools_df = pd.read_parquet(TMP_DIR / "clean_tools.parquet")
+    # clean_tools_df = pd.read_parquet(TMP_DIR / "clean_tools.parquet")
 
     print(
         "Current count of answered questions per tool after reading the parquet file:"
@@ -413,86 +412,88 @@ def global_tool_accuracy():
         clean_tools_df.groupby("tool")["title"].nunique().sort_values(ascending=False)
     )
     # check the last tool
-    # if len(more_q_tools) > 0:
-    #     raise ValueError(
-    #         f"Not enough data for the tools: {more_q_tools}. "
-    #         "Please check the historical data or increase the number of attempts."
-    #     )
+    if len(more_q_tools) > 0:
+        raise ValueError(
+            f"Not enough data for the tools: {more_q_tools}. "
+            "Please check the historical data or increase the number of attempts."
+        )
 
     # take the name of the last tool in the completed_tools list
-    # if len(updated_tools) > 0:
-    #     last_tool = updated_tools[-1]
-    #     print(f"last tool with enough questions: {last_tool}")
-    # else:
-    #     # the last tool from the one with the lowest count of titles
-    #     last_tool = (
-    #         clean_tools_df.groupby("tool")["title"]
-    #         .nunique()
-    #         .sort_values(ascending=True)
-    #         .index[0]
-    #     )
-    # last_tool = "prediction-request-reasoning-claude"
-    # # take only the last recent_samples_size for the last tool
-    # last_tool_data = clean_tools_df[clean_tools_df["tool"] == last_tool].copy()
-    # # sort by request_time in descending order
-    # last_tool_data = last_tool_data.sort_values(by="request_time", ascending=False)
-    # # take the last recent_samples_size rows
-    # last_tool_data = last_tool_data.head(NR_ANSWERED_MARKETS)
-    # print("Extracting the final list of market questions from the last tool")
-    # # extract the title values in last_tool_data
-    # last_tool_titles = last_tool_data.title.unique()
-    # print("Current count of answered questions per tool before filtering:")
-    # print(clean_tools_df.groupby("tool")["title"].nunique())
-    # common_titles = []
-    # for tool in clean_tools_df.tool.unique():
-    #     if tool in INC_TOOLS == False:
-    #         continue
-    #     # if tool == "prediction-request-reasoning-claude":
-    #     #     continue  # skip the last tool as we already have its titles
-    #     tool_data = clean_tools_df[clean_tools_df["tool"] == tool]
-    #     tool_titles_set = set(
-    #         tool_data[tool_data["title"].isin(last_tool_titles)]["title"].unique()
-    #     )
-    #     print(f"Tool: {tool}, Count of titles: {len(tool_titles_set)}")
-    #     common_titles.append(tool_titles_set)
-
-    # # create a list with the titles that appear in all sets
-    # common_titles_set = set.intersection(*common_titles)
+    if len(updated_tools) > 0:
+        last_tool = updated_tools[-1]
+        print(f"last tool with enough questions: {last_tool}")
+    else:
+        # the last tool from the one with the lowest count of titles
+        last_tool = (
+            clean_tools_df.groupby("tool")["title"]
+            .nunique()
+            .sort_values(ascending=True)
+            .index[0]
+        )
+
+    # Remove non relevant tools
+    clean_tools_df = clean_tools_df[clean_tools_df["tool"].isin(INC_TOOLS) == True]
+    # take only the last recent_samples_size for the last tool
+    last_tool_data = clean_tools_df[clean_tools_df["tool"] == last_tool].copy()
+    # sort by request_time in descending order
+    last_tool_data = last_tool_data.sort_values(by="request_time", ascending=False)
+    # take the last recent_samples_size rows
+    last_tool_data = last_tool_data.head(NR_ANSWERED_MARKETS)
+    print("Extracting the final list of market questions from the last tool")
+    # extract the title values in last_tool_data
+    last_tool_titles = last_tool_data.title.unique()
+    print("Current count of answered questions per tool before filtering:")
+    print(clean_tools_df.groupby("tool")["title"].nunique())
+    common_titles = []
+    for tool in clean_tools_df.tool.unique():
+        tool_data = clean_tools_df[clean_tools_df["tool"] == tool]
+        tool_titles_set = set(
+            tool_data[tool_data["title"].isin(last_tool_titles)]["title"].unique()
+        )
+        print(f"Tool: {tool}, Count of titles: {len(tool_titles_set)}")
+        common_titles.append(tool_titles_set)
+
+    # create a list with the titles that appear in all sets
+    common_titles_set = set.intersection(*common_titles)
+    print(f"Common titles across all tools: {len(common_titles_set)}")
 
     # filter clean_tools_df to include only the titles from the common set
-    # clean_tools_df = clean_tools_df[clean_tools_df["title"].isin(common_titles_set)]
+    clean_tools_df = clean_tools_df[clean_tools_df["title"].isin(common_titles_set)]
 
-    # getting the most NR_ANSWERED_MARKETS answered questions per tool
-    clean_tools_df = get_unique_recent_samples(
-        clean_tools_df, recent_samples_size=NR_ANSWERED_MARKETS
+    print(
+        f"Current count of answered questions per tool after selecting common titles:"
     )
-    # print(
-    #     f"Current count of answered questions per tool after selecting common titles {len(common_titles_set)}:"
-    # )
     print(clean_tools_df.groupby("tool")["title"].nunique())
     # compute the accuracy
     print("Computing the global accuracies for the tools")
-    global_accuracies = get_accuracy_values(tools_df=clean_tools_df, more_q_tools=[])
+    global_accuracies = get_accuracy_values(
+        tools_df=clean_tools_df,
+        more_q_tools=["prediction-offline-sme", "prediction-url-cot-claude"],
+    )
     # new tools
-    # if len(more_q_tools) > 0:
-    #     # compute the average accuracy for the new tools
-    #     total_accuracy = sum(item["accuracy"] for item in global_accuracies)
-    #     avg_accuracy = (
-    #         round(total_accuracy / len(global_accuracies), 5)
-    #         if len(global_accuracies) > 0
-    #         else DEFAULT_ACCURACY
-    #     )
-    #     for tool in more_q_tools:
-    #         global_accuracies[tool]["accuracy"] = avg_accuracy
+    if len(more_q_tools) > 0:
+        # compute the average accuracy for the new tools
+        total_accuracy = sum(item["accuracy"] for item in global_accuracies)
+        avg_accuracy = (
+            round(total_accuracy / len(global_accuracies), 5)
+            if len(global_accuracies) > 0
+            else DEFAULT_ACCURACY
+        )
+        for tool in more_q_tools:
+            global_accuracies[tool]["accuracy"] = avg_accuracy
 
     print(f"global accuracies {global_accuracies}")
     # create a dataframe from global_accuracies
     computed_accuracy_df = pd.DataFrame(global_accuracies)
+    # sort by accuracy descending
+    computed_accuracy_df = computed_accuracy_df.sort_values(
+        by="accuracy", ascending=False, ignore_index=True
+    )
     print(computed_accuracy_df.head())
     print("Saving into a csv file")
     computed_accuracy_df.to_csv(ROOT_DIR / ACCURACY_FILENAME, index=False)
     # save the data into IPFS
-    # push_csv_file_to_ipfs()
+    push_csv_file_to_ipfs()
 
 
 if __name__ == "__main__":

scripts/pull_data.py

@@ -22,7 +22,7 @@ from get_mech_info import (
     get_mech_events_since_last_run,
     update_json_files,
 )
-from update_tools_accuracy import compute_tools_accuracy
+from global_tool_accuracy import global_tool_accuracy
 from cleaning_old_info import clean_old_data_from_parquet_files
 from web3_utils import get_timestamp_two_weeks_ago
 from manage_space_files import move_files
@@ -136,16 +136,17 @@ def only_new_weekly_analysis():
 
     save_historical_data()
     try:
-        clean_old_data_from_parquet_files("2025-06-10")
+        clean_old_data_from_parquet_files("2025-06-19")
         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_files()
     prepare_daa_data()
+    global_tool_accuracy()
     logging.info("Weekly analysis files generated and saved")
 
 

scripts/utils.py

@@ -41,11 +41,11 @@ INC_TOOLS = [
     "prediction-offline",
     "claude-prediction-online",
     "claude-prediction-offline",
-    "prediction-offline-sme",
+    # "prediction-offline-sme",
     "prediction-online-sme",
     "prediction-request-rag",
     "prediction-request-reasoning",
-    "prediction-url-cot-claude",
+    # "prediction-url-cot-claude",
     "prediction-request-rag-claude",
     "prediction-request-reasoning-claude",
     "superforcaster",

service_map.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8a8e1dc04106d040f27211a539da58807fe72cdb1142eef615bd2c32eb5ae770
-size 173007
+oid sha256:67cabc3346e428edad8119d8bdba4fde1190c39f50c5cc960977945bf62ba143
+size 173883

tools_accuracy.csv

@@ -1,13 +1,11 @@
-tool,tool_accuracy,nr_responses,min,max
-claude-prediction-offline,0.59785,800,2025-06-11 22:43:20,2025-08-10 23:28:40
-claude-prediction-online,0.57552,800,2025-06-12 07:00:35,2025-08-11 02:47:25
-prediction-offline,0.65566,800,2025-06-10 19:23:15,2025-08-10 23:44:45
-prediction-offline-sme,0.5,800,2025-03-18 00:34:45,2025-08-09 09:24:30
-prediction-online,0.65628,800,2025-06-10 09:02:55,2025-08-10 23:30:00
-prediction-online-sme,0.56816,800,2025-06-10 04:40:00,2025-08-10 23:39:40
-prediction-request-rag,0.59852,800,2025-06-11 02:24:45,2025-08-10 23:38:35
-prediction-request-rag-claude,0.5,800,2025-03-18 00:08:15,2025-08-10 19:05:15
-prediction-request-reasoning,0.66068,800,2025-06-10 00:56:45,2025-08-11 02:47:50
-prediction-request-reasoning-claude,0.5,800,2025-03-18 16:35:45,2025-08-10 22:41:50
-prediction-url-cot-claude,0.5,800,2025-01-12 01:30:50,2025-07-27 08:15:15
-superforcaster,0.66273,800,2025-06-10 02:00:30,2025-08-10 19:59:40
+tool,accuracy,nr_responses,min,max
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