Spaces:
Sleeping
Sleeping
File size: 5,588 Bytes
2b8ce29 5755a0f 8de39ca 2b8ce29 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 |
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')
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
|