import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(style="darkgrid")
sns.set_palette("tab10")
### Plotting
[docs]def plot_accuracies_per_tag(title, **kwargs):
if kwargs is None:
print("Need accuracies to plot")
return
classes = set(sum([list(kwargs[exp].keys()) for exp in kwargs], []))
classes = list(classes)
classes.remove('__OVERALL__')
classes = ['Overall'] + sorted(classes)
experiments = {}
for exp in kwargs:
experiments[exp] = {k: kwargs[exp][k] for k in kwargs[exp] if k != '__OVERALL__'}
experiments[exp]['Overall'] = kwargs[exp]['__OVERALL__']
df = pd.DataFrame([{
'experiment': exp,
**experiments[exp]
} for exp in experiments])
df = pd.melt(df, id_vars="experiment", var_name="type", value_name="accuracy")
fig, ax = plt.subplots(1,1, figsize=(16,6))
plt.sca(ax)
ax.tick_params(axis='x', rotation=90)
ax.set_ylim((0.5, 1.05))
sns.factorplot(ax=ax, x='type', y='accuracy', hue='experiment',
data=df, kind='bar', order=classes, legend_out=True)
handles, labels = ax.get_legend_handles_labels()
l = ax.legend(handles=handles, labels=labels, title=title, frameon=True, fancybox=True, framealpha=0.8, facecolor="#FFFFFF")
return fig
[docs]def plot_distributedness(title, top_neurons_per_tag):
fig, ax = plt.subplots(1,1, figsize=(16,6))
tags = sorted(top_neurons_per_tag.keys())
number_of_neurons = [len(top_neurons_per_tag[tag]) for tag in tags]
ax = sns.barplot(x=tags, y=number_of_neurons, color="yellowgreen")
ax.tick_params(axis='x', rotation=90)
ax.set_ylabel("Number of Important Neurons")
ax.set_title(title)
[docs]def plot_accuracies(title, overall_acc,
top_10_acc, random_10_acc, bottom_10_acc,
top_15_acc, random_15_acc, bottom_15_acc,
top_20_acc, random_20_acc, bottom_20_acc):
exps = ["Full", " ",
"Top 10%", "Random 10%", "Bottom 10%", " ",
"Top 15%", "Random 15%", "Bottom 15%", " ",
"Top 20%", "Random 20%", "Bottom 20%"]
concise_exps = ["", "Full", " ",
" ", "10% Neurons", " ", " ",
" ", "15% Neurons", " ", " ",
" ", "20% Neurons", " ", " "]
accs = np.array([0, overall_acc, 0,
top_10_acc, random_10_acc, bottom_10_acc, 0,
top_15_acc, random_15_acc, bottom_15_acc, 0,
top_20_acc, random_20_acc, bottom_20_acc, 0])
min_value = np.min(accs[accs>0.01])
colors = [(0,0,0), 'limegreen', (0,0,0),
'dodgerblue', 'gold', 'orangered', 'g',
'dodgerblue', 'gold', 'orangered', 'g',
'dodgerblue', 'gold', 'orangered', 'g', (0,0,0)]
ax = sns.barplot(x=concise_exps, y=accs, palette=colors)
ax.set_ylim((max(min_value-0.1,0), 1.04))
ax.set_title(title)
handles = [ax.patches[1], ax.patches[3], ax.patches[4], ax.patches[5]]
labels = ["All Neurons", "Top Neurons", "Random Neurons", "Bottom Neurons"]
ax.legend(handles=handles, labels=labels, frameon=True, fancybox=True, ncol=2, framealpha=0.8, facecolor="#FFFFFF")
