| from datetime import datetime, timedelta | |
| import numpy as np | |
| import pandas as pd | |
| import plotly.express as px | |
| from plotly.graph_objs import Figure | |
| # Dummy data creation | |
| def dummy_data_for_plot(metrics, num_days=30): | |
| dates = [datetime.now() - timedelta(days=i) for i in range(num_days)] | |
| data = [] | |
| for metric in metrics: | |
| for date in dates: | |
| model = f"Model_{metric}" | |
| score = np.random.uniform(50, 55) | |
| data.append([date, metric, score, model]) | |
| df = pd.DataFrame(data, columns=["date", "task", "score", "model"]) | |
| return df | |
| def create_metric_plot_obj_1( | |
| df: pd.DataFrame, metrics: list[str], 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 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["task"].isin(metrics)] | |
| # Filter the human baselines based on the specified metrics | |
| # filtered_human_baselines = {k: v for k, v in HUMAN_BASELINE.items() if k in metrics} | |
| # Create a line figure using plotly express with specified markers and custom data | |
| fig = px.line( | |
| df, | |
| x="date", | |
| y="score", | |
| color="task", | |
| markers=True, | |
| custom_data=["task", "score", "model"], | |
| title=title, | |
| ) | |
| # Update hovertemplate for better hover interaction experience | |
| fig.update_traces( | |
| hovertemplate="<br>".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 | |
| def dummydf(): | |
| # data = [{"Model": "gpt-35-turbo-1106", | |
| # "Agent": "prompt agent", | |
| # "Opponent Model": "gpt-4", | |
| # "Opponent Agent": "prompt agent", | |
| # 'Breakthrough': 0, | |
| # 'Connect Four': 0, | |
| # 'Blind Auction': 0, | |
| # 'Kuhn Poker': 0, | |
| # "Liar's Dice": 0, | |
| # 'Negotiation': 0, | |
| # 'Nim': 0, | |
| # 'Pig': 0, | |
| # 'Iterated Prisoners Dilemma': 0, | |
| # 'Tic-Tac-Toe': 0 | |
| # }, | |
| # {"Model": "Llama-2-70b-chat-hf", | |
| # "Agent": "prompt agent", | |
| # "Opponent Model": "gpt-4", | |
| # "Opponent Agent": "prompt agent", | |
| # 'Breakthrough': 1, | |
| # 'Connect Four': 0, | |
| # 'Blind Auction': 0, | |
| # 'Kuhn Poker': 0, | |
| # "Liar's Dice": 0, | |
| # 'Negotiation': 0, | |
| # 'Nim': 0, | |
| # 'Pig': 0, | |
| # 'Iterated Prisoners Dilemma': 0, | |
| # 'Tic-Tac-Toe': 0 | |
| # }, | |
| # {"Model": "gpt-35-turbo-1106", | |
| # "Agent": "ToT agent", | |
| # "Opponent Model": "gpt-4", | |
| # "Opponent Agent": "prompt agent", | |
| # 'Breakthrough': 0, | |
| # 'Connect Four': 0, | |
| # 'Blind Auction': 0, | |
| # 'Kuhn Poker': 0, | |
| # "Liar's Dice": 0, | |
| # 'Negotiation': 0, | |
| # 'Nim': 0, | |
| # 'Pig': 0, | |
| # 'Iterated Prisoners Dilemma': 0, | |
| # 'Tic-Tac-Toe': 0 | |
| # }, | |
| # {"Model": "Llama-2-70b-chat-hf", | |
| # "Agent": "CoT agent", | |
| # "Opponent Model": "gpt-4", | |
| # "Opponent Agent": "prompt agent", | |
| # 'Breakthrough': 0, | |
| # 'Connect Four': 0, | |
| # 'Blind Auction': 0, | |
| # 'Kuhn Poker': 0, | |
| # "Liar's Dice": 0, | |
| # 'Negotiation': 0, | |
| # 'Nim': 0, | |
| # 'Pig': 0, | |
| # 'Iterated Prisoners Dilemma': 0, | |
| # 'Tic-Tac-Toe': 0 | |
| # }] | |
| df = pd.read_csv('./assets/uc_result.csv') | |
| return df | |