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="
".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/object_parachute.csv') print(df) # length = len(df) # for i in range(length): # df.loc[i,"Method_string"]=df.loc[i, "Method"] # df.loc[i,"Method"]=df.loc[i, "Method_string"] # df.drop(columns=["Method_string"]) return df