dummydatagen.py

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