new weekly data and new global accuracy script

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/cloud_storage.py

@@ -13,6 +13,9 @@ BUCKET_NAME = "weekly-stats"
 FOLDER_NAME = "historical_data"
 FILES_IN_TWO_MONTHS = 16  # 2 files per week
 FILES_IN_FOUR_MONTHS = 30  # four months ago we did not have two files per week but one
+FILES_IN_SIX_MONTHS = 40  # 1 file per week
+FILES_IN_EIGHT_MONTHS = 48
+FILES_IN_TEN_MONTHS = 56
 
 
 def initialize_client():

scripts/global_tool_accuracy.py

@@ -0,0 +1,231 @@
+import os
+import pandas as pd
+from typing import Tuple, List, Dict
+import ipfshttpclient
+from utils import INC_TOOLS
+from typing import List
+from utils import TMP_DIR, ROOT_DIR
+from cloud_storage import (
+    initialize_client,
+    download_tools_historical_files,
+    FILES_IN_TWO_MONTHS,
+    FILES_IN_FOUR_MONTHS,
+    FILES_IN_SIX_MONTHS,
+    FILES_IN_EIGHT_MONTHS,
+    FILES_IN_TEN_MONTHS,
+)
+
+MAX_ATTEMPTS = 5
+historical_files_count_map = {
+    1: FILES_IN_TWO_MONTHS,
+    2: FILES_IN_FOUR_MONTHS,
+    3: FILES_IN_SIX_MONTHS,
+    4: FILES_IN_EIGHT_MONTHS,
+    5: FILES_IN_TEN_MONTHS,
+}
+
+
+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 / filename)
+    print(f"HASH of the tools accuracy file: {result['Hash']}")
+    return result["Hash"]
+
+
+def clean_tools_dataset(tools_df: pd.DataFrame) -> pd.DataFrame:
+
+    # Remove tool_name and TEMP_TOOL
+    tools_non_error = tools_df[
+        tools_df["tool"].isin(["tool_name", "TEMP_TOOL"]) == False
+    ].copy()
+    # Remove errors
+    tools_non_error = tools_non_error[tools_non_error["error"] == 0]
+    tools_non_error.loc[:, "currentAnswer"] = tools_non_error["currentAnswer"].replace(
+        {"no": "No", "yes": "Yes"}
+    )
+    tools_non_error = tools_non_error[
+        tools_non_error["currentAnswer"].isin(["Yes", "No"])
+    ]
+    tools_non_error = tools_non_error[tools_non_error["vote"].isin(["Yes", "No"])]
+    tools_non_error["win"] = (
+        tools_non_error["currentAnswer"] == tools_non_error["vote"]
+    ).astype(int)
+    tools_non_error.columns = tools_non_error.columns.astype(str)
+    return tools_non_error
+
+
+def take_toptool_name(tools_df: pd.DataFrame) -> str:
+    volumes = tools_df.tool.value_counts().reset_index()
+    return volumes.iloc[0].tool
+
+
+def keep_last_answer_per_question_per_tool(clean_tools_df: pd.DataFrame) -> None:
+    for tool in INC_TOOLS:
+        print(f"checking answers from tool {tool}")
+        tool_data = clean_tools_df[clean_tools_df["tool"] == tool]
+        # sort tool_data by request date in ascending order
+        tool_data = tool_data.sort_values(by="request_time", ascending=True)
+
+        unique_questions = tool_data.title.unique()
+        for question in unique_questions:
+            market_data = tool_data[tool_data["title"] == question]
+            market_data = market_data.sort_values(by="request_time", ascending=True)
+
+
+def compute_nr_questions_per_tool(clean_tools_df: pd.DataFrame) -> dict:
+    answered_questions = {}
+
+    for tool in INC_TOOLS:
+        print(f"processing tool {tool}")
+        tool_data = clean_tools_df[clean_tools_df["tool"] == tool]
+        # sort tool_data by request date in ascending order
+        tool_data = tool_data.sort_values(by="request_time", ascending=True)
+        # count unique prediction markets
+        unique_questions = tool_data.title.unique()
+        answered_questions[tool] = {}
+        answered_questions[tool]["total_answered_questions"] = len(unique_questions)
+        markets_different_answer = {}
+        for question in unique_questions:
+            market_data = tool_data[tool_data["title"] == question]
+            different_responses = market_data.currentAnswer.value_counts()
+            # Extract yes and no counts, defaulting to 0 if not present
+            yes_count = different_responses.get("Yes", 0)
+            no_count = different_responses.get("No", 0)
+            if yes_count > 0 and no_count > 0:
+                # print(f"found a market {question} with different answers")
+                # found a market with different responses from the same tool
+                markets_different_answer[question] = {
+                    "yes_responses": yes_count,
+                    "no_responses": no_count,
+                }
+
+        answered_questions[tool]["markets_different_answers"] = markets_different_answer
+    return answered_questions
+
+
+def classify_tools_by_responses(
+    answered_questions: dict, ref_nr_questions: int
+) -> Tuple:
+    enough_questions_tools = []
+    more_questions_tools = []
+    total_tools = answered_questions.keys()
+    for tool in total_tools:
+        if answered_questions[tool] >= ref_nr_questions:
+            enough_questions_tools.append(tool)
+        else:
+            more_questions_tools.append(tool)
+    return enough_questions_tools, more_questions_tools
+
+
+def add_historical_data(
+    tools_historical_file: str,
+    tools_df: pd.DataFrame,
+    more_questions_tools: list,
+    recent_nr_questions: int,
+    completed_tools: List[str],
+) -> pd.DataFrame:
+    """
+    It searches into the historical cloud files to get more samples for the tools.
+    """
+    if not tools_historical_file:
+        raise ValueError(
+            "No historical tools file found, skipping adding historical data."
+        )
+
+    # get the historical tools data
+    print(f"Downloaded historical file into the tmp folder: {tools_historical_file}")
+    # Load the historical tools data
+    historical_tools_df = pd.read_parquet(TMP_DIR / tools_historical_file)
+    # check if the historical tools data has samples from the tools that need more samples
+    historical_tools_df = historical_tools_df[
+        historical_tools_df["tool"].isin(more_questions_tools)
+    ]
+    # check the volume of questions for the tools in the historical data
+    tools_df = pd.concat([tools_df, historical_tools_df], ignore_index=True)
+    # remove duplicates
+    tools_df.drop_duplicates(
+        subset=["request_id", "request_block"], keep="last", inplace=True
+    )
+    # check the new total of answered questions per tool
+    answered_questions = compute_nr_questions_per_tool(clean_tools_df=tools_df)
+    for tool in more_questions_tools:
+        new_count = answered_questions[tool]["total_answered_questions"]
+        if new_count >= recent_nr_questions:
+            completed_tools.append(tool)
+    # TODO remove the tools in completed_tools list from more_questions_tools
+    return tools_df
+
+
+def check_historical_samples(
+    client,
+    tools_df: pd.DataFrame,
+    more_questions_tools: list,
+    ref_nr_questions: int,
+    attempt_nr: int,
+) -> Tuple:
+    """
+    Function to download historical data from tools and to update the list
+    of tools that need more questions. It returns a list of the tools that we
+    managed to complete the requirement
+    """
+    print(f"Tools with not enough samples: {more_questions_tools}")
+    completed_tools = []
+
+    files_count = historical_files_count_map[attempt_nr]
+    tools_historical_file = download_tools_historical_files(
+        client, skip_files_count=files_count
+    )
+
+    tools_df = add_historical_data(
+        tools_historical_file,
+        tools_df,
+        more_questions_tools,
+        ref_nr_questions,
+        completed_tools,
+    )
+    # TODO for each tool in tools_df, take the last answer only for each question based on request_time
+    return tools_df, completed_tools
+
+
+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)
+
+    # extract the top tool
+    top_tool = take_toptool_name(tools_df=clean_tools_df)
+
+    # 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 = answered_questions[top_tool]["total_answered_questions"]
+
+    # classify tools between those with enough questions and those that need more data
+    enough_q_tools, more_q_tools = classify_tools_by_responses(
+        answered_questions, ref_nr_questions
+    )
+
+    # TODO for each tool in clean_tools_df, take the last answer only for each question based on request_time
+
+    # 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=tools_df,
+            more_questions_tools=more_q_tools,
+            ref_nr_questions=ref_nr_questions,
+            attempt_nr=nr_attempts,
+        )
+        print(f"Updated tools {updated_tools}")
+        print(f"more tools with missing data {more_q_tools}")
+
+
+if __name__ == "__main__":
+    global_tool_accuracy()

scripts/pull_data.py

@@ -136,7 +136,7 @@ def only_new_weekly_analysis():
 
     save_historical_data()
     try:
-        clean_old_data_from_parquet_files("2025-06-06")
+        clean_old_data_from_parquet_files("2025-06-10")
         clean_old_data_from_json_files()
     except Exception as e:
         print("Error cleaning the oldest information from parquet files")

service_map.pkl

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traders_weekly_metrics.parquet

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unknown_traders.parquet

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weekly_mech_calls.parquet

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winning_df.parquet

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