updating daily data

active_traders.parquet

@@ -1,3 +1,3 @@
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-size 17464854
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+size 17691664

all_trades_profitability.parquet.gz

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+size 17823081

closed_market_metrics.parquet

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+size 149870

closed_markets_div.parquet

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+size 89735

daily_info.parquet

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+size 3678287

daily_mech_requests.parquet

@@ -1,3 +1,3 @@
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+oid sha256:e5da96c306d6255ec5f6062e515ecadcb0c55f3bf106ef98ddf7a04031b9d2f1
+size 8037

daily_mech_requests_by_pearl_agents.parquet

@@ -1,3 +1,3 @@
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-size 4532
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+size 4551

error_by_markets.parquet

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-size 11440
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+size 11736

errors_by_mech.parquet

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-size 6112
+oid sha256:c4604e6043191bad48b966531574aea05039e50aab27eb75bdaba5bd057916c1
+size 6116

invalid_trades.parquet

@@ -1,3 +1,3 @@
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+size 334820

latest_result_DAA_Pearl.parquet

@@ -1,3 +1,3 @@
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+size 5520

latest_result_DAA_QS.parquet

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-size 6128
+oid sha256:3ab2f0716f02958a98132e469347af21cf5675aac510f35063f09b5116880b6b
+size 6141

old_tools_accuracy.csv

@@ -1,13 +0,0 @@
-tool,tool_accuracy,total_requests,min,max
-claude-prediction-offline,72.53057384760113,383346,2025-04-02 05:16:45,2025-06-06 00:13:05
-claude-prediction-online,62.65060240963856,166266,2025-04-02 00:01:00,2025-05-21 18:47:35
-prediction-offline,62.179688199982074,2767227,2025-04-02 00:00:05,2025-06-09 07:28:55
-prediction-offline-sme,61.504424778761056,20986,2025-04-02 00:57:25,2025-06-07 08:53:25
-prediction-online,55.565397106584,153914,2025-04-02 07:32:50,2025-06-08 22:40:55
-prediction-online-sme,54.13848631239936,89155,2025-04-02 07:33:25,2025-06-09 04:59:30
-prediction-request-rag,33.33333333333333,1547,2025-04-02 14:01:55,2025-06-03 18:59:40
-prediction-request-rag-claude,25.0,1776,2025-04-02 12:05:10,2025-06-03 17:51:10
-prediction-request-reasoning,51.56896642045157,1200639,2025-04-02 07:03:25,2025-06-08 23:46:20
-prediction-request-reasoning-claude,66.66666666666666,1341,2025-04-02 12:25:50,2025-05-28 09:25:40
-prediction-url-cot-claude,55.55555555555556,5230,2025-04-02 05:46:40,2025-05-20 15:21:20
-superforcaster,60.26234567901234,119300,2025-04-02 00:02:10,2025-06-08 21:00:40

pearl_agents.parquet

@@ -1,3 +1,3 @@
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-size 47450
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+size 47490

retention_activity.parquet.gz

@@ -1,3 +1,3 @@
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+size 4354408

scripts/get_mech_info.py

@@ -324,10 +324,6 @@ def get_mech_events_since_last_run(logger, mech_sandbox: bool = False):
     try:
         all_trades = read_all_trades_profitability()
         latest_timestamp = max(all_trades.creation_timestamp)
-        # cutoff_date = "2025-01-13"
-        # latest_timestamp = pd.Timestamp(
-        #     datetime.strptime(cutoff_date, "%Y-%m-%d")
-        # ).tz_localize("UTC")
         print(f"Updating data since {latest_timestamp}")
     except Exception:
         print("Error while reading the profitability parquet file")

scripts/mech_request_utils.py

@@ -624,6 +624,7 @@ def get_ipfs_data(input_filename: str, output_filename: str, logger):
             updated_mech_requests.update(partial_dict)
 
     save_json_file(updated_mech_requests, output_filename)
+
     logger.info(f"NUMBER OF MECH REQUEST IPFS ERRORS={nr_errors}")
 
     # delivers

scripts/pull_data.py

@@ -136,12 +136,12 @@ def only_new_weekly_analysis():
 
     save_historical_data()
     try:
-        clean_old_data_from_parquet_files("2025-06-03")
+        clean_old_data_from_parquet_files("2025-06-06")
         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_accuracy()
     compute_tools_based_datasets()
     # move to tmp folder the new generated files
     move_files()

scripts/update_tools_accuracy.py

@@ -17,12 +17,19 @@ import time
 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"
-SAMPLING_POPULATION_SIZE = 300
+SAMPLING_POPULATION_SIZE = 500
 RECENTS_SAMPLES_SIZE = 5000
 NR_SUBSETS = 100
 SAMPLES_THRESHOLD = 50
 DEFAULT_ACCURACY = 51.0
 LAST_MODEL_UPDATE = "2025-06-03"
+CLAUDE_TOOLS = [
+    "claude-prediction-online",
+    "claude-prediction-offline",
+    "prediction-request-rag-claude",
+    "prediction-request-reasoning-claude",
+    "prediction-url-cot-claude",
+]
 
 
 def mean_and_std(numbers):
@@ -270,6 +277,70 @@ def update_global_accuracy(
     return global_accuracies
 
 
+def check_upgrade_dates(
+    tools_df,
+    tools_list,
+    new_tools,
+    claude_upgrade_date="30-07-2025",
+    gpt_upgrade_date="03-06-2025",
+) -> None:
+    # Convert upgrade dates to datetime
+    claude_upgrade_date = pd.to_datetime(claude_upgrade_date, format="%d-%m-%Y").date()
+    gpt_upgrade_date = pd.to_datetime(gpt_upgrade_date, format="%d-%m-%Y").date()
+    for tool in tools_list.keys():
+        print(f"checking tool {tool}")
+        # take the RECENT_SAMPLES from tools_df
+        tool_data = tools_df[tools_df["tool"] == tool]
+        # sort tool_data by request date in ascending order
+        tool_data = tool_data.sort_values(by="request_date", ascending=True)
+        if len(tool_data) < RECENTS_SAMPLES_SIZE:
+            new_tools.append(tool)
+            continue
+        recent_samples = get_recent_samples(
+            tool_data, recent_samples_size=RECENTS_SAMPLES_SIZE
+        )
+        recent_samples = recent_samples.sort_values(by="request_date", ascending=True)
+        print(recent_samples.head())
+        oldest_sample_date = recent_samples.iloc[0].request_date
+        if isinstance(oldest_sample_date, str):
+            oldest_sample_date = pd.to_datetime(oldest_sample_date).date()
+        print(f"tool {tool}: oldest sample date {oldest_sample_date}")
+        if tool in CLAUDE_TOOLS:
+            # if oldest_sample_date is before claude_upgrade_date then remove the tool
+            # from valid_tools and add it to the list of other_tools
+            if oldest_sample_date < claude_upgrade_date:
+                print(f"the oldest sample found is older than {claude_upgrade_date}")
+                new_tools.append(tool)
+        elif oldest_sample_date < gpt_upgrade_date:
+            print(f"the oldest sample found is older than {gpt_upgrade_date}")
+            new_tools.append(tool)
+    return
+
+
+def check_upgraded_tools(
+    tools_df,
+    valid_tools,
+    other_tools,
+):
+    """
+    Function to update the input lists and remove from valid tools any tools whose oldest date is before the upgrade dates
+    """
+    new_tools = []
+    # Check and remove tools from valid_tools
+    check_upgrade_dates(tools_df, valid_tools, new_tools)
+    for tool in new_tools:
+        if tool in valid_tools.keys():
+            print(f"removing tool {tool} from valid tools")
+            del valid_tools[tool]
+    # Check and remove tools from other_tools
+    check_upgrade_dates(tools_df, other_tools, new_tools)
+    for tool in new_tools:
+        if tool in other_tools.keys():
+            print(f"removing tool {tool} from other tools")
+            del other_tools[tool]
+    return new_tools
+
+
 def add_historical_data(
     tools_historical_file: str,
     tools_df: pd.DataFrame,
@@ -317,6 +388,7 @@ def add_historical_data(
         recent_samples = get_recent_samples(historical_tool_data, needed_samples)
         # Combine the current tools with the historical ones
         tools_df = pd.concat([tools_df, recent_samples], ignore_index=True)
+        tools_df = tools_df.sort_values(by="request_date", ascending=True)
         valid_tools[tool] = count + needed_samples
         completed_tools.append(tool)
         # Remove the tool from more_sample_tools
@@ -326,34 +398,9 @@ def add_historical_data(
     return tools_df
 
 
-def compute_global_accuracy_same_population(
-    tools_df: pd.DataFrame,
-    sample_size: int = SAMPLING_POPULATION_SIZE,
-    n_subsets: int = NR_SUBSETS,
-) -> Tuple[Dict, Dict]:
-    """
-    For the tools in the dataset, it creates different subsets of the same size (using downsampling or upsampling) and
-    computes the accuracy for each subset. Finally it averages the accuracies across all subsets.
-
-    Args:
-        tools_df: DataFrame containing the tools data
-        sample_size: Target number of samples per tool
-        n_subsets: Number of balanced datasets to create
-
-    Returns:
-        List of global accuracies for the tools
-    """
-    valid_tools, more_sample_tools = classify_tools(
-        tools_df, recent_samples_size=RECENTS_SAMPLES_SIZE
-    )
-    global_accuracies = {}
-    if len(valid_tools) > 0:
-        # Compute the accuracy for tools in valid_tools
-        update_global_accuracy(
-            valid_tools, tools_df, global_accuracies, sample_size, n_subsets
-        )
-
-    # Check historical files for tools that need more samples
+def check_historical_samples(
+    global_accuracies, more_sample_tools, valid_tools, sample_size, n_subsets
+):
     client = initialize_client()
     # first attempt: historical file download
     tool_names = list(more_sample_tools.keys())
@@ -361,7 +408,7 @@ def compute_global_accuracy_same_population(
     completed_tools = []
     if len(tool_names) > 0:
         print("First attempt to complete the population size")
-        # first attempt: historical file from two months ago
+        # first attempt: historical file from 4 months ago
         tools_historical_file = download_tools_historical_files(
             client, skip_files_count=FILES_IN_TWO_MONTHS
         )
@@ -375,7 +422,7 @@ def compute_global_accuracy_same_population(
             completed_tools,
         )
         print(more_sample_tools)
-        # second attempt: historical file from 4 months ago
+        # second attempt: historical file from 6 months ago
         if len(more_sample_tools) > 0:
             print("Second attempt to complete the population size")
             # second historical file download
@@ -403,25 +450,101 @@ def compute_global_accuracy_same_population(
                 n_subsets,
                 one_tool=tool,
             )
-    # 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():
-        # tool but not reaching yet the population size so treated as a new tool
-        new_tools.append(tool)
+    return
+
+
+def compute_global_accuracy_same_population(
+    tools_df: pd.DataFrame,
+    recent_samples_size: int = RECENTS_SAMPLES_SIZE,
+    sample_size: int = SAMPLING_POPULATION_SIZE,
+    n_subsets: int = NR_SUBSETS,
+) -> Tuple[Dict, Dict]:
+    """
+    For the tools in the dataset, it creates different subsets of the same size (using downsampling or upsampling) and
+    computes the accuracy for each subset. Finally it averages the accuracies across all subsets.
+
+    Args:
+        tools_df: DataFrame containing the tools data
+        sample_size: Target number of samples per tool
+        n_subsets: Number of balanced datasets to create
+
+    Returns:
+        List of global accuracies for the tools
+    """
+
+    valid_tools, more_sample_tools = classify_tools(tools_df, recent_samples_size)
+    # check tools that were upgraded recently
+    # otherwise they will be moved as new_tools
+    new_tools = check_upgraded_tools(tools_df, valid_tools, more_sample_tools)
+    print(f"new tools {new_tools} after checking the upgraded tools")
+    global_accuracies = {}
+    if len(valid_tools) > 0:
+        # Compute the accuracy for tools in valid_tools
+        update_global_accuracy(
+            valid_tools, tools_df, global_accuracies, sample_size, n_subsets
+        )
+
+    # Check historical files for tools that need more samples
+    if len(more_sample_tools) > 0:
+        new_tools.extend(more_sample_tools.keys())
+        # check_historical_samples(
+        #     global_accuracies, more_sample_tools, valid_tools, sample_size, n_subsets
+        # )
+        # for tool in more_sample_tools.keys():
+        #     # tool but not reaching yet the population size so treated as a new tool
+        #     new_tools.append(tool)
     return global_accuracies, new_tools
 
 
-def get_accuracy_info(clean_tools_df: pd.DataFrame) -> [pd.DataFrame, bool, Dict]:
+def compute_global_weekly_accuracy(clean_tools_df):
+    """
+    Compute accuracy following version 5.0 of spec"""
+    # get the information in clean_tools_df from last two weeks only, timestamp column is request_time
+
+    three_weeks_ago = pd.Timestamp.now(tz="UTC") - pd.Timedelta(days=21)
+    recent_df = clean_tools_df[clean_tools_df["request_time"] >= three_weeks_ago]
+
+    # compute at the tool level (using "tool" column) the volume of requests per tool
+    tool_volumes = (
+        recent_df.groupby("tool")["request_id"].count().reset_index(name="volume")
+    )
+    min_volume = tool_volumes["volume"].min()
+    max_volume = tool_volumes["volume"].max()
+
+    # compute the average volume of tool requests in the last two weeks, excluding min and max
+    filtered_volumes = tool_volumes[
+        (tool_volumes["volume"] != min_volume) & (tool_volumes["volume"] != max_volume)
+    ]
+    avg_volume = filtered_volumes["volume"].mean()
+
+    print("Tool volumes in last three weeks:")
+    print(tool_volumes)
+    print(
+        f"Average volume of tool requests in last three weeks excluding min and max: {avg_volume}"
+    )
+    sampling_size = int(avg_volume / 2)
+    print(f"Sampling size = {sampling_size}")
+
+    return compute_global_accuracy_same_population(
+        tools_df=recent_df,
+        recent_samples_size=avg_volume,
+        sample_size=sampling_size,
+        n_subsets=50,
+    )
+
+
+def get_accuracy_info(clean_tools_df: pd.DataFrame) -> [pd.DataFrame, bool, List]:
     """
     Extracts accuracy information from the tools DataFrame.
     """
 
     # compute global accuracy information for the tools
-    global_accuracies, new_tools = compute_global_accuracy_same_population(
-        tools_df=clean_tools_df, n_subsets=NR_SUBSETS
+    # global_accuracies, new_tools = compute_global_accuracy_same_population(
+    #     tools_df=clean_tools_df,
+    # )
+    global_accuracies, new_tools = compute_global_weekly_accuracy(
+        clean_tools_df=clean_tools_df
     )
-
     # transform the dictionary global_accuracies into a DataFrame
     wins = pd.DataFrame(
         [
@@ -472,18 +595,17 @@ def update_tools_accuracy_same_model(
     acc_info["max"] = acc_info["max"].dt.strftime("%Y-%m-%d %H:%M:%S")
     all_accuracies = []
     final_acc_df = pd.DataFrame(columns=tools_acc.columns)
+    # accuracy has been computed over the same population size
+    new_volume = SAMPLING_POPULATION_SIZE
     for tool in tools_to_update:
-        if tool in new_tools:
-            continue
-        if tool not in existing_tools:
+        if tool in new_tools or tool not in existing_tools:
             new_tools.append(tool)
             continue
         new_accuracy = round(
             acc_info[acc_info["tool"] == tool]["tool_accuracy"].values[0], 2
         )
         all_accuracies.append(new_accuracy)
-        # accuracy has been computed over the same population size
-        new_volume = SAMPLING_POPULATION_SIZE
+
         if no_timeline_info:
             new_min_timeline = None
             new_max_timeline = None
@@ -548,49 +670,29 @@ def update_tools_accuracy_same_model(
     return final_acc_df
 
 
-def update_tools_accuracy(
-    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 3rd of June when the gpt 4.1 update happened 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 the 3rd of June
-    split_date = pd.to_datetime(LAST_MODEL_UPDATE).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)}")
-
-    acc_info_after = update_tools_accuracy_same_model(tools_acc, after_split, inc_tools)
-    # return the two different dataframes
-    return acc_info_after
-
-
 def compute_tools_accuracy():
+    """To compute/update the latest accuracy information. Relevant dates
+    -- 3rd of June 2025 when the gpt 4.1 update happened
+    -- 30th of July 2025 when the Claude 4 update happened"""
     print("Computing accuracy of tools")
     print("Reading tools parquet file")
-    tools = pd.read_parquet(TMP_DIR / "tools.parquet")
-    # Computing tools accuracy information
-    print("Computing tool accuracy information")
+    tools_df = pd.read_parquet(TMP_DIR / "tools.parquet")
+
     # Check if the file exists
-    acc_data = old_acc_data = None
+    acc_data = None
     if os.path.exists(ROOT_DIR / ACCURACY_FILENAME):
         acc_data = pd.read_csv(ROOT_DIR / ACCURACY_FILENAME)
 
-    new_acc_data = update_tools_accuracy(acc_data, tools, INC_TOOLS)
+    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")
+    new_acc_data = update_tools_accuracy_same_model(acc_data, tools_df, INC_TOOLS)
 
-    # save acc_data into a CSV file
-    print("Saving into a csv files")
+    print("Saving into a csv file")
     new_acc_data.to_csv(ROOT_DIR / ACCURACY_FILENAME, index=False)
     # save the data into IPFS
-    push_csv_file_to_ipfs()
+    # push_csv_file_to_ipfs()
 
 
 def push_csv_file_to_ipfs(filename: str = ACCURACY_FILENAME) -> str:

service_map.pkl

@@ -1,3 +1,3 @@
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tools_accuracy.csv

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 tool,tool_accuracy,total_requests,min,max
-prediction-offline,61.52,300,2025-06-03 00:00:05,2025-07-30 23:44:15
-prediction-request-reasoning,55.59,300,2025-06-03 00:00:30,2025-07-30 23:43:05
-prediction-online,60.18,300,2025-06-03 00:00:05,2025-07-30 23:23:30
-prediction-online-sme,54.13,300,2025-06-03 00:04:30,2025-07-30 23:43:35
-superforcaster,63.67,300,2025-06-03 01:15:10,2025-07-30 23:41:00
-claude-prediction-offline,59.72,300,2025-06-06 00:13:05,2025-07-30 23:24:35
-prediction-request-rag,49.97,300,2025-06-03 18:59:40,2025-07-30 23:41:15
-claude-prediction-online,51.46,300,2025-06-11 07:23:05,2025-07-30 23:41:30
-prediction-offline-sme,54.5,300,2025-06-03 11:55:10,2025-07-29 11:56:35
-prediction-request-rag-claude,56.75,300,2025-06-03 17:51:10,2025-07-30 22:26:00
-prediction-request-reasoning-claude,56.75,300,2025-06-16 11:02:15,2025-07-30 20:25:20
-prediction-url-cot-claude,56.75,300,2025-06-12 20:36:25,2025-07-27 08:15:15
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+prediction-online-sme,52.33,500,2025-06-03 00:04:30,2025-08-03 22:49:45
+prediction-online,59.38,500,2025-06-03 00:00:05,2025-08-03 22:31:35
+prediction-request-reasoning,58.02,500,2025-06-03 00:00:30,2025-08-03 23:44:40
+claude-prediction-offline,57.92,500,2025-06-06 00:13:05,2025-08-03 21:46:10
+claude-prediction-online,57.92,500,2025-06-11 07:23:05,2025-08-03 23:02:15
+superforcaster,57.92,500,2025-06-03 01:15:10,2025-08-03 22:50:05
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+prediction-request-rag-claude,57.92,500,2025-06-03 17:51:10,2025-08-03 22:52:10
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+prediction-request-rag,57.92,500,2025-06-03 18:59:40,2025-08-03 21:23:40

tools_accuracy_version3_0.csv

@@ -0,0 +1,13 @@
+tool,tool_accuracy,total_requests,min,max
+prediction-offline,61.52,300,2025-06-03 00:00:05,2025-07-30 23:44:15
+prediction-request-reasoning,55.59,300,2025-06-03 00:00:30,2025-07-30 23:43:05
+prediction-online,60.18,300,2025-06-03 00:00:05,2025-07-30 23:23:30
+prediction-online-sme,54.13,300,2025-06-03 00:04:30,2025-07-30 23:43:35
+superforcaster,63.67,300,2025-06-03 01:15:10,2025-07-30 23:41:00
+claude-prediction-offline,59.72,300,2025-06-06 00:13:05,2025-07-30 23:24:35
+prediction-request-rag,49.97,300,2025-06-03 18:59:40,2025-07-30 23:41:15
+claude-prediction-online,51.46,300,2025-06-11 07:23:05,2025-07-30 23:41:30
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+prediction-url-cot-claude,56.75,300,2025-06-12 20:36:25,2025-07-27 08:15:15

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