Spaces:
Sleeping
Sleeping
File size: 3,856 Bytes
dab7e6b 7bb2c40 dab7e6b c41a81d dab7e6b bd62fcd e0a8d2a bd62fcd e0a8d2a bd62fcd e0a8d2a bd62fcd e0a8d2a 7bb2c40 e0a8d2a 7bb2c40 e0a8d2a |
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 |
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() |