plot_java_after.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

color = sns.color_palette('colorblind', n_colors=3)
# dist - acc
# dist_grouped = pd.read_csv('figures/java_dist_correctness.csv')

# conditions = [
#     (dist_grouped['locality'] == 0),
#     (dist_grouped['locality'] == 1),
#     (dist_grouped['locality'] == 2)]
choices = ['no locality',
           'same project, different subdir',
           'same subdir']
#
# dist_grouped['Locality'] = np.select(conditions, choices)
#
# dist_grouped['Accuracy'] = dist_grouped['correctness']
# dist_grouped['Neg. Distance'] = dist_grouped['dist_right']

fig, ax = plt.subplots(1, 1, figsize=(5, 4))
# sns.scatterplot(x='Neg. Distance', y='Accuracy', hue='Locality', data=dist_grouped, s=8,
#                 palette=color, ax=ax[0], legend=False)

grouped = pd.read_csv('figures/java_rank_after.csv')
grouped = grouped.loc[grouped['rank'] <= 200]
conditions = [
    (grouped['locality'] == 0),
    (grouped['locality'] == 1),
    (grouped['locality'] == 2)]

grouped['Locality'] = np.select(conditions, choices)
grouped['Rank'] = grouped['rank']

grouped['Accuracy'] = grouped['correctness']
grouped['Neg. Distance (Modified)'] = grouped['dist']

# rank - acc
# sns.scatterplot(x='Rank', y='Accuracy', hue='Locality', data=grouped, s=8,
#                 palette=color, ax=ax[1], legend=False)

# rank - dist
sns.scatterplot(x='Rank', y='Neg. Distance (Modified)', hue='Locality', data=grouped, s=8,
                palette=color)

fig.tight_layout()
plt.savefig('figures/java_after.pdf')