sumpos_test.py

import pickle
import numpy as np
import pandas as pd
import torch
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm.auto import tqdm

with open('sts_attributions/shelf_approx_attr_l-9_N-100.pkl', 'rb') as inp:
    shelf_approx = pickle.load(inp)
with open('sts_attributions/tuned_approx_attr_l-9_N-100.pkl', 'rb') as inp:
    tuned_approx = pickle.load(inp)
with open('sts_attributions/exact_attr_l-9_N-100.pkl', 'rb') as inp:
    exact = pickle.load(inp)

fig, axs = plt.subplots(1, 3, figsize=(15,5))
for ax_i, (attr_set, label) in enumerate(zip(
    [shelf_approx, tuned_approx, exact],
    ['ShelfApprox', 'TunedApprox', 'Exact']
)):
    xs = np.zeros(attr_set.shape[0])
    ys = np.zeros(attr_set.shape[0])
    for i in tqdm(range(attr_set.shape[0])):
        A = torch.tensor(attr_set.iloc[i].attributions)
        xs[i] = torch.relu(A).sum().item()
        ys[i] = -torch.relu(-A).sum().item()
    df = pd.DataFrame({'SumPositive': xs, 'SumNegative': ys})
    sns.regplot(data=df, x='SumPositive', y='SumNegative', lowess=True, 
                scatter_kws={'s': 1, 'alpha': 0.3}, 
                line_kws={'linewidth': 1}, ax=axs[ax_i])
    axs[ax_i].set_title(label)
plt.savefig('sumpos_vs_sumneg.pdf')
# plt.show()

fig, axs = plt.subplots(1, 3, figsize=(15,5))
for ax_i, (attr_set, label) in enumerate(zip(
    [shelf_approx, tuned_approx, exact],
    ['ShelfApprox', 'TunedApprox', 'Exact']
)):
    xs = np.zeros(attr_set.shape[0])
    for i in tqdm(range(attr_set.shape[0])):
        A = torch.tensor(attr_set.iloc[i].attributions)
        xs[i] = torch.relu(A).sum().item()
    axs[ax_i].hist(xs, bins=50)
    axs[ax_i].set_title(label)
    axs[ax_i].set_xlim(0, 6)
plt.savefig('sumpos_hist.pdf')
# plt.show()