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| import pandas as pd | |
| from fuzzywuzzy import fuzz | |
| from collections import Counter | |
| from nltk.stem import PorterStemmer | |
| from ast import literal_eval | |
| from typing import Union, List | |
| import streamlit as st | |
| from my_model.results.evaluation import KBVQAEvaluator | |
| from my_model.config import evaluation_config as config | |
| class ResultDemonstrator(KBVQAEvaluator): | |
| def run(self): | |
| import pandas as pd | |
| import altair as alt | |
| # Sample data | |
| data = pd.DataFrame({ | |
| 'x': range(10), | |
| 'y': [2, 1, 4, 3, 5, 6, 9, 7, 10, 8] | |
| }) | |
| # Create a scatter plot | |
| chart = alt.Chart(data).mark_point().encode( | |
| x='x', | |
| y='y' | |
| ) | |
| # Display the chart in Streamlit | |
| st.altair_chart(chart, use_container_width=True) | |
| # Display the chart in Streamlit | |
| st.altair_chart(chart, use_container_width=True) | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| # Data | |
| x = np.random.randn(100) | |
| y = np.random.randn(100) | |
| # Create a scatter plot | |
| fig, ax = plt.subplots() | |
| ax.scatter(x, y) | |
| # Display the plot in Streamlit | |
| st.pyplot(fig) | |
| ############ | |
| # Load data from Excel | |
| d = pd.read_excel('my_model/results/evaluation_results.xlsx', sheet_name="Main Data") | |
| # Assume 'accuracies' and 'token_counts' need to be computed or are columns in the DataFrame | |
| # Compute colors and labels for the plot (assuming these columns are already in the DataFrame) | |
| d['color'] = d['accuracy'].apply(lambda x: 'green' if x == 1 else 'orange' if round(x, 2) == 0.67 else 'red') | |
| d['label'] = d['accuracy'].apply(lambda x: 'Correct' if x == 1 else 'Partially Correct' if x == 0.67 else 'Incorrect') | |
| # Creating the scatter plot | |
| scatter_chart = alt.Chart(d).mark_circle(size=20).encode( | |
| x=alt.X('index:Q', title='Index'), # Assuming 'index' is a column or can be created | |
| y=alt.Y('token_counts:Q', title='Number of Tokens'), | |
| color=alt.Color('color:N', legend=alt.Legend(title="VQA Score")), | |
| tooltip=['index', 'token_counts', 'label'] | |
| ).interactive() | |
| # Display the chart | |
| st.altair_chart(scatter_chart, use_container_width=True) | |
| #################### | |
| scores = d['vqa_score_13b_caption+detic'] | |
| token_counts = d['trimmed_tokens_count_caption_detic'] | |
| # Define colors and labels for the legend | |
| colors = ['green' if score == 1 else 'orange' if round(score,2) == 0.67 else 'red' for score in scores] | |
| labels = ['Correct' if score == 1 else 'Partially Correct' if score == 0.67 else 'Incorrect' for score in scores] | |
| plt.figure(figsize=(10, 6)) | |
| # Create a scatter plot with smaller dots using the 's' parameter | |
| scatter = plt.scatter(range(len(token_counts)), token_counts, c=colors, s=20, label=labels) | |
| # Create a custom legend | |
| from matplotlib.lines import Line2D | |
| legend_elements = [Line2D([0], [0], marker='o', color='w', label='Full VQA Score', markerfacecolor='green', markersize=10), | |
| Line2D([0], [0], marker='o', color='w', label='Partial VQA Score', markerfacecolor='orange', markersize=10), | |
| Line2D([0], [0], marker='o', color='w', label='Zero VQA Score', markerfacecolor='red', markersize=10)] | |
| #plt.legend(handles=legend_elements, loc='upper right') | |
| plt.legend(handles=legend_elements, loc='best', bbox_to_anchor=(1, 1)) | |
| # Set the title and labels | |
| plt.title('Token Counts VS VQA Score') | |
| plt.xlabel('Index') | |
| plt.ylabel('Number of Tokens') | |
| # Display the plot | |
| plt.show() |