Visualizing and Correlating Scores from GPT4 API and ChatGPT in Python

What will you learn?

In this engaging tutorial, you will master the art of visualizing scores obtained from GPT4 API and ChatGPT models. Learn how to correlate these scores using Python, gaining valuable insights into their performance.

Introduction to the Problem and Solution

Imagine having access to data containing scores generated by two powerful models – GPT4 API and ChatGPT. The challenge lies in effectively analyzing and understanding these scores visually. By leveraging Python libraries such as matplotlib and seaborn, we can create insightful graphs that bring these scores to life.

Our goal is not just limited to visualization; we aim to delve deeper into the correlation between these scores. This correlation analysis enables us to uncover relationships between the outputs of GPT4 API and ChatGPT, providing a comprehensive view of their performance metrics.


# Import necessary libraries
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

# Sample data (replace with actual data)
gpt4_scores = [85, 90, 88, 92, 87]
chat_gpt_scores = [78, 85, 80, 86, 82]

# Visualizing scores using a bar plot
plt.figure(figsize=(10,6)), gpt4_scores, color='b', alpha=0.7, label='GPT4 API')[x + 0.2 for x in range(len(chat_gpt_scores))], chat_gpt_scores,
        color='r', alpha=0.7,label='ChatGPT')
plt.xlabel('Sample ID')
plt.title('Comparison of Scores between GPT4 API and ChatGPT')

# Correlating the scores
correlation = np.corrcoef(gpt4_scores , chat_gpt_scores)[0][1]
print(f"Correlation between GPT4 API and ChatGPT Scores: {correlation}")

# Copyright PHD

Note: This code snippet serves as a foundational example using sample data.


In this solution: – We import matplotlib.pyplot as plt for graph plotting. – Sample data for GPT4 API and ChatGPT is initialized. – A bar plot is created to visually compare the scores from both models. – The correlation coefficient between the score sets is calculated using numpy.corrcoef().

Visualizing the scores allows for a graphical comparison of individual model performances while correlation analysis quantifies their relationship numerically.

Frequently Asked Questions

How can I access real-time score data from GTP APIs?

To access real-time score data from GTP APIs like GTP4 or ChatGTP in Python…

Can I customize the colors used in the bar plot?

Certainly! You can customize colors by specifying values in color parameter when plotting…

What does correlation coefficient value indicate?

The correlation coefficient value ranges between -1 to +1 where…


In conclusion…

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