<jupyter_start><jupyter_text>Exploratory Data Analysis of Novice Ebru DatasetIn this notebook we are trying to explore the data and plot some prior results.<jupyter_code># Import libraries necessary for this project
import pandas as pd
import matplotlib.pyplot as plt
# Pretty display for notebooks
%matplotlib inline
df = pd.read_csv('results.csv')

from IPython.display import display
display(df.head())
alpha_beta_1 = df[ (df['Content_Weight']==1) & (df['Style_Weight']==1000.0)]
alpha_beta_2 = df[ (df['Content_Weight']==8) & (df['Style_Weight']==10000.0)]
alpha_beta_3 = df[ (df['Content_Weight']==5) & (df['Style_Weight']==1000.0)]
alpha_beta_4 = df[ (df['Content_Weight']==5) & (df['Style_Weight']==10000.0)]
alpha_beta_1["Content_Loss"].plot(label = 'α/β 1/$10^3$')
alpha_beta_2["Content_Loss"].plot(label = 'α/β 8/$10^4$')
alpha_beta_3["Content_Loss"].plot(label = 'α/β 5/$10^3$')
alpha_beta_4["Content_Loss"].plot(label = 'α/β 5/$10^4$')
plt.title("Content Loss α/β ")
plt.xlabel('Target Image')
plt.ylabel('Content Loss')
plt.legend()
plt.show()
alpha_beta_1["Style_Loss"].plot( label = 'α/β 1/$10^3$')
alpha_beta_2["Style_Loss"].plot(label = 'α/β 8/$10^4$')
alpha_beta_3["Style_Loss"].plot(label = 'α/β 5/$10^3$')
alpha_beta_4["Style_Loss"].plot(label = 'α/β 5/$10^4$')
plt.title("Style loss  α/β")
plt.xlabel('Target Image')
plt.ylabel('Style Loss')
plt.legend()
plt.show()
alpha_beta_1["Total_Loss"].plot(label = 'α/β 1/$10^3$')
alpha_beta_2["Total_Loss"].plot(label = 'α/β 8/$10^4$')
alpha_beta_3["Total_Loss"].plot(label = 'α/β 5/$10^3$')
alpha_beta_4["Total_Loss"].plot(label = 'α/β 5/$10^4$')
plt.title("Total Loss  α/β")
plt.xlabel('Target Image')
plt.ylabel('Total Loss')
plt.legend()
plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.20515759312320916, 'fraction_numerical': 0.05157593123209169, 'mean_word_length': 7.009174311926605, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '221131', 'license': 'MIT', 'n_tokens_mistral': 758, 'n_tokens_neox': 687, 'n_words': 128, 'path': 'Data Visualization.ipynb', 'repo_name': 'Ayshine/Novice-Ebru-Dataset', 'size': '1745'}