[Update]dummydatagen file and assets folder

assets/uc_result.csv

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+Method,Chruch,Parachute,Tench,Garbage Turch,Van Gogh,Violence,Illegal Activity,Nudity
+ESD,98.58%,80.97%,93.96%,92.15%,55.78%,44.23%,65.55,6163,17.8
+FMN,88.48%,56.77%,46.60%,45.64%,90.63%,73.46%,131.37,350,17.9
+UCE,98.40%,60.22%,47.71%,94.31%,39.35%,34.67%,182.01,434,5.1
+CA,60.82%,96.01%,92.70%,46.67%,90.11%,81.97%,54.21,734,10.1
+SalUn,86.26%,90.39%,95.08%,86.91%,96.35%,99.59%,61.05,667,30.8

dummydatagen.py

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+
+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