import pandas as pd import plotly.express as px from plotly.graph_objs import Figure import pickle from datetime import datetime, timezone from typing import List, Dict, Tuple, Any # Average ⬆️ human baseline is 0.897 (source: averaging human baselines below) # ARC human baseline is 0.80 (source: https://lab42.global/arc/) # HellaSwag human baseline is 0.95 (source: https://deepgram.com/learn/hellaswag-llm-benchmark-guide) # MMLU human baseline is 0.898 (source: https://openreview.net/forum?id=d7KBjmI3GmQ) # TruthfulQA human baseline is 0.94(source: https://arxiv.org/pdf/2109.07958.pdf) # Define the human baselines HUMAN_BASELINES = { "Average ⬆️": 0.897 * 100, "ARC": 0.80 * 100, "HellaSwag": 0.95 * 100, "MMLU": 0.898 * 100, "TruthfulQA": 0.94 * 100, } def to_datetime(model_info: Tuple[str, Any]) -> datetime: """ Converts the lastModified attribute of the object to datetime. :param model_info: A tuple containing the name and object. The object must have a lastModified attribute with a string representing the date and time. :return: A datetime object converted from the lastModified attribute of the input object. """ name, obj = model_info return datetime.strptime(obj.lastModified, "%Y-%m-%dT%H:%M:%S.%fZ").replace(tzinfo=timezone.utc) def join_model_info_with_results(results_df: pd.DataFrame) -> pd.DataFrame: """ Integrates model information with the results DataFrame by matching 'Model sha'. :param results_df: A DataFrame containing results information including 'Model sha' column. :return: A DataFrame with updated 'Results Date' columns, which are synchronized with model information. """ # load cache from disk try: with open("model_info_cache.pkl", "rb") as f: model_info_cache = pickle.load(f) except (EOFError, FileNotFoundError): model_info_cache = {} # Sort date strings using datetime objects as keys sorted_dates = sorted(list(model_info_cache.items()), key=to_datetime, reverse=True) results_df["Results Date"] = datetime.now().replace(tzinfo=timezone.utc) # Define the date format string date_format = "%Y-%m-%dT%H:%M:%S.%fZ" # Iterate over sorted_dates and update the dataframe for name, obj in sorted_dates: # Convert the lastModified string to a datetime object last_modified_datetime = datetime.strptime(obj.lastModified, date_format).replace(tzinfo=timezone.utc) # Update the "Results Date" column where "Model sha" equals obj.sha results_df.loc[results_df["Model sha"] == obj.sha, "Results Date"] = last_modified_datetime return results_df def create_scores_df(results_df: pd.DataFrame) -> pd.DataFrame: """ Generates a DataFrame containing the maximum scores until each result date. :param results_df: A DataFrame containing result information including metric scores and result dates. :return: A new DataFrame containing the maximum scores until each result date for every metric. """ # Step 1: Ensure 'Results Date' is in datetime format and sort the DataFrame by it results_df["Results Date"] = pd.to_datetime(results_df["Results Date"]) results_df.sort_values(by="Results Date", inplace=True) # Step 2: Initialize the scores dictionary scores = { "Average ⬆️": [], "ARC": [], "HellaSwag": [], "MMLU": [], "TruthfulQA": [], "Result Date": [], "Model Name": [], } # Step 3: Iterate over the rows of the DataFrame and update the scores dictionary for i, row in results_df.iterrows(): date = row["Results Date"] for column in scores.keys(): if column == "Result Date": if not scores[column] or scores[column][-1] <= date: scores[column].append(date) continue if column == "Model Name": scores[column].append(row["model_name_for_query"]) continue current_max = scores[column][-1] if scores[column] else float("-inf") scores[column].append(max(current_max, row[column])) # Step 4: Convert the dictionary to a DataFrame return pd.DataFrame(scores) def create_plot_df(scores_df: pd.DataFrame) -> pd.DataFrame: """ Transforms the scores DataFrame into a new format suitable for plotting. :param scores_df: A DataFrame containing metric scores and result dates. :return: A new DataFrame reshaped for plotting purposes. """ # Sample columns cols = ["Average ⬆️", "ARC", "HellaSwag", "MMLU", "TruthfulQA"] # Initialize the list to store DataFrames dfs = [] # Iterate over the cols and create a new DataFrame for each column for col in cols: d = scores_df[[col, "Model Name", "Result Date"]].copy().reset_index(drop=True) d["Metric Name"] = col d.rename(columns={col: "Metric Value"}, inplace=True) dfs.append(d) # Concatenate all the created DataFrames concat_df = pd.concat(dfs, ignore_index=True) # Sort values by 'Result Date' concat_df.sort_values(by="Result Date", inplace=True) concat_df.reset_index(drop=True, inplace=True) # Drop duplicates based on 'Metric Name' and 'Metric Value' and keep the first (earliest) occurrence concat_df.drop_duplicates(subset=["Metric Name", "Metric Value"], keep="first", inplace=True) concat_df.reset_index(drop=True, inplace=True) return concat_df def create_metric_plot_obj( df: pd.DataFrame, metrics: List[str], human_baselines: Dict[str, float], title: str ) -> Figure: """ Create a Plotly figure object with lines representing different metrics and horizontal dotted lines representing human baselines. :param df: The DataFrame containing the metric values, names, and dates. :param metrics: A list of strings representing the names of the metrics to be included in the plot. :param human_baselines: A dictionary where keys are metric names and values are human baseline values for the metrics. :param title: A string representing the title of the plot. :return: A Plotly figure object with lines representing metrics and horizontal dotted lines representing human baselines. """ # Filter the DataFrame based on the specified metrics df = df[df["Metric Name"].isin(metrics)] # Filter the human baselines based on the specified metrics filtered_human_baselines = {k: v for k, v in human_baselines.items() if k in metrics} # Create a line figure using plotly express with specified markers and custom data fig = px.line( df, x="Result Date", y="Metric Value", color="Metric Name", markers=True, custom_data=["Metric Name", "Metric Value", "Model Name"], title=title, ) # Update hovertemplate for better hover interaction experience fig.update_traces( hovertemplate="
".join( [ "Model Name: %{customdata[2]}", "Metric Name: %{customdata[0]}", "Date: %{x}", "Metric Value: %{y}", ] ) ) # Update the range of the y-axis fig.update_layout(yaxis_range=[0, 100]) # Create a dictionary to hold the color mapping for each metric metric_color_mapping = {} # Map each metric name to its color in the figure for trace in fig.data: metric_color_mapping[trace.name] = trace.line.color # Iterate over filtered human baselines and add horizontal lines to the figure for metric, value in filtered_human_baselines.items(): color = metric_color_mapping.get(metric, "blue") # Retrieve color from mapping; default to blue if not found location = "top left" if metric == "HellaSwag" else "bottom left" # Set annotation position # Add horizontal line with matched color and positioned annotation fig.add_hline( y=value, line_dash="dot", annotation_text=f"{metric} human baseline", annotation_position=location, annotation_font_size=10, annotation_font_color=color, line_color=color, ) return fig # Example Usage: # human_baselines dictionary is defined. # chart = create_metric_plot_obj(scores_df, ["ARC", "HellaSwag", "MMLU", "TruthfulQA"], human_baselines, "Graph Title")