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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/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
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