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Copy pathplotUtils.py
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1123 lines (878 loc) · 44.3 KB
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import os;
import numpy as np;
import matplotlib.pyplot as plt;
from resultUtils import matrixAccuracy;
def autolabel(rects, plt, means, maxValue):
"""
Attach a text label above each bar displaying its height
"""
if maxValue > 70:
offset = 5.7;
increase1 = 5;
increase2 = -3;
elif maxValue > 50:
offset = 4.5;
increase1 = 5;
increase2 = 0;
else:
offset = 3;
increase1 = -5;
increase2 = -3;
i = 0;
for rect in rects:
height = rect.get_height();
if 0:
if i == 0 and height < 11:
textHeight = 12+increase1;
if i == 0 and height < 15:
textHeight = 18+increase1;
elif i == 0:
textHeight = 10+increase2;
else:
textHeight = 10+increase2;
if height > 12:
plt.text(rect.get_x() + rect.get_width()/2., textHeight-6,'%3.1f%c' % (height, '%'), ha='center', va='bottom', fontsize=14)
#plt.text(rect.get_x() + rect.get_width()/2., textHeight-offset,'+-%3.1f' % (means[1][i]), ha='center', va='bottom', fontsize=14)
else:
plt.text(rect.get_x() + rect.get_width()/2., textHeight-8,'%3.1f%c' % (height, '%'), ha='center', va='bottom', fontsize=14)
#plt.text(rect.get_x() + rect.get_width()/2., textHeight-8-offset,'+-%3.1f' % (means[1][i]), ha='center', va='bottom', fontsize=14)
else:
plt.text(rect.get_x() + rect.get_width()/2., -9,'%3.1f%c' % (height, '%'), ha='center', va='bottom', fontsize=14)
i += 1;
def plotBarGraphCC(means, errors, title, filename):
labels = ['Top 1', 'Top 5', 'Top 10', 'Kamitani Score'];
barWidth = 0.15 # the width of the bars
# Set position of bar on X axis
r1 = np.arange(len(means[0]));
r2 = [x + barWidth for x in r1];
r3 = [x + barWidth for x in r2];
r4 = [x + barWidth for x in r3];
r5 = [x + barWidth for x in r4];
r6 = [x + barWidth for x in r5];
r7 = [x + barWidth for x in r6];
plt.figure(figsize = (14, 4));
# Make the plot
plt.bar(r1, means[0], yerr=errors[0], width=barWidth, edgecolor='white', label='Single volume/200 classes/Mean prototype', align='center', alpha=0.5, ecolor='black', capsize=10);
plt.bar(r2, means[1], yerr=errors[1], width=barWidth, edgecolor='white', label='Single volume/50 classes/Mean prototype',align='center', alpha=0.5, ecolor='black', capsize=10);
plt.bar(r3, means[2], yerr=errors[2], width=barWidth, edgecolor='white', label='Mean volume/200 classes/Mean prototype',align='center', alpha=0.5, ecolor='black', capsize=10);
plt.bar(r4, means[3], yerr=errors[3], width=barWidth, edgecolor='white', label='Mean volume/50 classes/Mean prototype',align='center', alpha=0.5, ecolor='black', capsize=10);
plt.bar(r5, means[4], yerr=errors[4], width=barWidth, edgecolor='white', label='Single Volume/50 classes/Image prototype',align='center', alpha=0.5, ecolor='black', capsize=10);
plt.bar(r6, means[5], yerr=errors[5], width=barWidth, edgecolor='white', label='Mean volume/50 classes/Image Prototype',align='center', alpha=0.5, ecolor='black', capsize=10);
plt.bar(r7, means[6], yerr=errors[6], width=barWidth, edgecolor='white', label='[Horikawa 2017]',align='center', alpha=0.5, ecolor='black', capsize=10);
# Add xticks on the middle of the group bars
plt.xlabel('Accuracy scores', fontweight='bold')
plt.xticks([r + barWidth for r in range(len(r1))], labels);
plt.grid(True, axis='y');
plt.ylabel('Accuracy');
plt.title(title);
# Create legend & Show graphic
plt.legend();
#plt.show();
plt.savefig(filename);
def plotBarGraphCC2(means, errors, title, filename):
barNames = ['Single volume', 'Mean volume'];
plotBarGraphDefault(means, errors, title, filename, barNames);
def plotBarGraphCC2A(means, errors, title, filename):
labels = ['Top 1', 'Top 5', 'Top 10'];
barWidth = 0.22; # the width of the bars
# Set position of bar on X axis
r1 = np.arange(len(means[0]));
r2 = [x + barWidth for x in r1];
r3 = [x + barWidth + 0.0125 for x in r2];
r4 = [x + barWidth for x in r3];
plt.figure(figsize = (14, 4));
# Make the plot
b1 = plt.bar(r1, means[0], yerr=errors[0], width=barWidth, edgecolor='white', label='Single volume - fc7', align='center', alpha=0.5, ecolor='black');
b2 = plt.bar(r2, means[1], yerr=errors[1], width=barWidth, edgecolor='white', label='Mean volume - fc7',align='center', alpha=0.5, ecolor='black');
b3 = plt.bar(r3, means[2], yerr=errors[2], width=barWidth, edgecolor='white', label='Single volume - conv5',align='center', alpha=0.5, ecolor='black');
b4 = plt.bar(r4, means[3], yerr=errors[3], width=barWidth, edgecolor='white', label='Mean volume - conv5',align='center', alpha=0.5, ecolor='black');
# Add xticks on the middle of the group bars
plt.xlabel('Accuracy scores', fontweight='bold', fontsize = 12);
plt.xticks([r + barWidth for r in range(len(r1))], labels, fontsize = 12);
plt.grid(True, axis='y');
plt.ylabel('Accuracy', fontsize = 12);
plt.title(title, fontsize = 12);
# Create legend & Show graphic
plt.legend(fontsize = 12);
maxValue = 0;
for i in range(4):
for j in range(len(means[0])):
maxValue = max(maxValue, means[i][j] + errors[i][1][j]);
plt.ylim(bottom=-10, top=maxValue+10);
# dotted lines
plt.hlines(100/50, r1[0] - 0.7 * barWidth, r1[0] + 3.7*barWidth, colors='k', linestyles = 'dashed');
plt.hlines(500/50, r1[1] - 0.7 * barWidth, r1[1] + 3.7*barWidth, colors='k', linestyles = 'dashed');
plt.hlines(1000/50, r1[2] - 0.7 * barWidth, r1[2] + 3.7*barWidth, colors='k', linestyles = 'dashed');
autolabel(b1, plt, errors[0], maxValue);
autolabel(b2, plt, errors[1], maxValue);
autolabel(b3, plt, errors[2], maxValue);
autolabel(b4, plt, errors[3], maxValue);
#plt.show();
plt.savefig(filename);
def plotBarGraphCC3(means, errors, title, filename):
barNames = ['Avg. prototype', 'Image prototype'];
plotBarGraphDefault(means, errors, title, filename, barNames);
def plotBarGraphCC3A(means, errors, title, filename):
labels = ['Top 1', 'Top 5', 'Top 10'];
barWidth = 0.22; # the width of the bars
# Set position of bar on X axis
r1 = np.arange(len(means[0]));
r2 = [x + barWidth for x in r1];
r3 = [x + barWidth + 0.0125 for x in r2];
r4 = [x + barWidth for x in r3];
plt.figure(figsize = (14, 4));
# Make the plot
b1 = plt.bar(r1, means[0], yerr=errors[0], width=barWidth, edgecolor='white', label='Avg. prototype - fc7', align='center', alpha=0.5, ecolor='black');
b2 = plt.bar(r2, means[1], yerr=errors[1], width=barWidth, edgecolor='white', label='Image prototype - fc7',align='center', alpha=0.5, ecolor='black');
b3 = plt.bar(r3, means[2], yerr=errors[2], width=barWidth, edgecolor='white', label='Avg. prototype - conv5',align='center', alpha=0.5, ecolor='black');
b4 = plt.bar(r4, means[3], yerr=errors[3], width=barWidth, edgecolor='white', label='Image prototype - conv5',align='center', alpha=0.5, ecolor='black');
# Add xticks on the middle of the group bars
plt.xlabel('Accuracy scores', fontweight='bold', fontsize = 12);
plt.xticks([r + barWidth for r in range(len(r1))], labels, fontsize = 12);
plt.grid(True, axis='y');
plt.ylabel('Accuracy', fontsize = 12);
plt.title(title, fontsize = 12);
# Create legend & Show graphic
plt.legend(fontsize = 12);
maxValue = 0;
for i in range(4):
for j in range(len(means[0])):
maxValue = max(maxValue, means[i][j] + errors[i][1][j]);
plt.ylim(bottom=-10, top=maxValue+10);
# dotted lines
plt.hlines(100/50, r1[0] - 0.7 * barWidth, r1[0] + 3.7*barWidth, colors='k', linestyles = 'dashed');
plt.hlines(500/50, r1[1] - 0.7 * barWidth, r1[1] + 3.7*barWidth, colors='k', linestyles = 'dashed');
plt.hlines(1000/50, r1[2] - 0.7 * barWidth, r1[2] + 3.7*barWidth, colors='k', linestyles = 'dashed');
autolabel(b1, plt, errors[0], maxValue);
autolabel(b2, plt, errors[1], maxValue);
autolabel(b3, plt, errors[2], maxValue);
autolabel(b4, plt, errors[3], maxValue);
#plt.show();
plt.savefig(filename);
def plotBarGraphNN(means, errors, title, filename):
labels = ['Top 1', 'Top 5', 'Top 10', 'Kamitani Score'];
barWidth = 0.20 # the width of the bars
# Set position of bar on X axis
r1 = np.arange(len(means[0]));
r2 = [x + barWidth for x in r1];
r3 = [x + barWidth for x in r2];
r4 = [x + barWidth for x in r3];
#plt.figure(figsize = (10, 8));
# Make the plot
b1 = plt.bar(r1, means[0], yerr=errors[0], width=barWidth, edgecolor='white', label='Single volume/200 classes', align='center', alpha=0.5, ecolor='black');
b2 = plt.bar(r2, means[1], yerr=errors[1], width=barWidth, edgecolor='white', label='Single volume/50 classes',align='center', alpha=0.5, ecolor='black');
b3 = plt.bar(r3, means[2], yerr=errors[2], width=barWidth, edgecolor='white', label='Mean volume/200 classes',align='center', alpha=0.5, ecolor='black');
b4 = plt.bar(r4, means[3], yerr=errors[3], width=barWidth, edgecolor='white', label='Mean volume/50 classes',align='center', alpha=0.5, ecolor='black');
# Add xticks on the middle of the group bars
plt.xlabel('Accuracy scores', fontweight='bold')
plt.xticks([r + barWidth for r in range(len(r1))], labels);
plt.grid(True, axis='y');
plt.ylabel('Accuracy');
plt.title(title);
# Create legend & Show graphic
plt.legend();
#plt.show();
plt.savefig(filename);
def plotBarGraphNN2A(means, errors, title, filename):
labels = ['Top 1', 'Top 5', 'Top 10'];
barWidth = 0.22 # the width of the bars
plt.figure(figsize = (14, 4));
# Set position of bar on X axis
r1 = np.arange(len(means[0]));
r2 = [x + barWidth for x in r1];
r3 = [x + barWidth + 0.0125 for x in r2];
r4 = [x + barWidth for x in r3];
#plt.figure(figsize = (10, 8));
# Make the plot
b1 = plt.bar(r1, means[0], yerr=errors[0], width=barWidth, edgecolor='white', label='Single volume - fc7', align='center', alpha=0.5, ecolor='black', capsize=0);
b2 = plt.bar(r2, means[1], yerr=errors[1], width=barWidth, edgecolor='white', label='Mean volume - fc7',align='center', alpha=0.5, ecolor='black', capsize=0);
b3 = plt.bar(r3, means[2], yerr=errors[2], width=barWidth, edgecolor='white', label='Single volume - conv5',align='center', alpha=0.5, ecolor='black', capsize=0);
b4 = plt.bar(r4, means[3], yerr=errors[3], width=barWidth, edgecolor='white', label='Mean volume - conv5',align='center', alpha=0.5, ecolor='black', capsize=0);
# Add xticks on the middle of the group bars
plt.xlabel('Accuracy scores', fontsize = 12, fontweight='bold')
plt.xticks([r + 1.5*barWidth for r in range(len(r1))], labels, fontsize=12);
plt.grid(True, axis='y');
plt.ylabel('Accuracy', fontsize=12);
plt.title(title, fontsize = 14);
# Create legend & Show graphic
plt.legend(loc = 'upper left', fontsize=12);
maxValue = 0;
for i in range(4):
for j in range(len(means[0])):
maxValue = max(maxValue, means[i][j] + errors[i][1][j]);
plt.ylim(bottom=-10, top=maxValue+10);
# dotted lines
plt.hlines(100/50, r1[0] - 0.7 * barWidth, r1[0] + 3.7*barWidth, colors='k', linestyles = 'dashed');
plt.hlines(500/50, r1[1] - 0.7 * barWidth, r1[1] + 3.7*barWidth, colors='k', linestyles = 'dashed');
plt.hlines(1000/50, r1[2] - 0.7 * barWidth, r1[2] + 3.7*barWidth, colors='k', linestyles = 'dashed');
autolabel(b1, plt, errors[0], maxValue);
autolabel(b2, plt, errors[1], maxValue);
autolabel(b3, plt, errors[2], maxValue);
autolabel(b4, plt, errors[3], maxValue);
#plt.show();
plt.savefig(filename);
def plotBarGraphDefault(means, errors, title, filename, barNames):
imagined = 'Imagined' in title;
labels = ['Top 1', 'Top 5', 'Top 10'];
barWidth = 0.4 # the width of the bars
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5));
fig.subplots_adjust(top=0.85, bottom=0.1, wspace=0.0)
fig.suptitle(title, fontsize = 14);
# Set position of bar on X axis
r1 = np.arange(len(means[0]));
r2 = [x + barWidth for x in r1];
maxValue = 0;
for i in range(4):
for j in range(len(means[0])):
maxValue = max(maxValue, means[i][j] + errors[i][1][j]);
#plt.figure(figsize = (10, 8));
# Make the plot
prop_cycle = plt.rcParams['axes.prop_cycle'];
colors = prop_cycle.by_key()['color'];
ax2.get_yaxis().set_ticklabels([]);
if imagined:
c1 = colors[4];
c2 = colors[5];
else:
c1 = colors[0];
c2 = colors[1];
b1 = ax1.bar(r1, means[0], yerr=errors[0], width=barWidth, edgecolor='white', label=barNames[0], align='center', alpha=0.5, ecolor='black', capsize=0, color=c1);
b2 = ax1.bar(r2, means[1], yerr=errors[1], width=barWidth, edgecolor='white', label=barNames[1], align='center', alpha=0.5, ecolor='black', capsize=0, color=c2);
autolabel(b1, ax1, errors[0], maxValue);
autolabel(b2, ax1, errors[1], maxValue);
b3 = ax2.bar(r1, means[2], yerr=errors[2], width=barWidth, edgecolor='white', label=barNames[0], align='center', alpha=0.5, ecolor='black', capsize=0, color=c1);
b4 = ax2.bar(r2, means[3], yerr=errors[3], width=barWidth, edgecolor='white', label=barNames[1], align='center', alpha=0.5, ecolor='black', capsize=0, color=c2);
autolabel(b3, ax2, errors[2], maxValue);
autolabel(b4, ax2, errors[3], maxValue);
# Add xticks on the middle of the group bars
ax1.set_title('FC7', fontsize = 15, fontweight='bold');
ax2.set_title('Conv5', fontsize = 15, fontweight='bold');
ax1.set_xticks([r + .5*barWidth for r in range(len(r1))]);
ax1.set_xticklabels(labels, fontsize=15);
ax1.grid(True, axis='y');
ax2.set_xticks([r + .5*barWidth for r in range(len(r1))]);
ax2.set_xticklabels( labels, fontsize=15);
ax2.grid(True, axis='y');
ax1.set_ylabel('Accuracy', fontsize=15);
# Create legend & Show graphic
ax1.legend(loc = 'upper left', fontsize=15);
ax1.set_ylim(bottom=-10, top=maxValue+10);
ax2.set_ylim(bottom=-10, top=maxValue+10);
# dotted lines
ax1.hlines(100/50, r1[0] - 0.7 * barWidth, r1[0] + 1.7*barWidth, colors='k', linestyles = 'dashed');
ax1.hlines(500/50, r1[1] - 0.7 * barWidth, r1[1] + 1.7*barWidth, colors='k', linestyles = 'dashed');
ax1.hlines(1000/50, r1[2] - 0.7 * barWidth, r1[2] + 1.7*barWidth, colors='k', linestyles = 'dashed');
ax2.hlines(100/50, r1[0] - 0.7 * barWidth, r1[0] + 1.7*barWidth, colors='k', linestyles = 'dashed');
ax2.hlines(500/50, r1[1] - 0.7 * barWidth, r1[1] + 1.7*barWidth, colors='k', linestyles = 'dashed');
ax2.hlines(1000/50, r1[2] - 0.7 * barWidth, r1[2] + 1.7*barWidth, colors='k', linestyles = 'dashed');
plt.savefig(filename);
def plotBarGraphNN2(means, errors, title, filename):
barNames = ['Single volume', 'Mean volume'];
plotBarGraphDefault(means, errors, title, filename, barNames);
def plotBarGraphNN3A(means, errors, title, filename):
labels = ['Top 1', 'Top 5', 'Top 10'];
barWidth = 0.22 # the width of the bars
plt.figure(figsize = (14, 4));
# Set position of bar on X axis
r1 = np.arange(len(means[0]));
r2 = [x + barWidth for x in r1];
r3 = [x + barWidth + 0.0125 for x in r2];
r4 = [x + barWidth for x in r3];
#plt.figure(figsize = (10, 8));
# Make the plot
b1 = plt.bar(r1, means[0], yerr=errors[0], width=barWidth, edgecolor='white', label='Neural netwroks - fc7', align='center', alpha=0.5, ecolor='black', capsize=0);
b2 = plt.bar(r2, means[1], yerr=errors[1], width=barWidth, edgecolor='white', label='Correlation rank - fc7',align='center', alpha=0.5, ecolor='black', capsize=0);
b3 = plt.bar(r3, means[2], yerr=errors[2], width=barWidth, edgecolor='white', label='Neural networks - conv5',align='center', alpha=0.5, ecolor='black', capsize=0);
b4 = plt.bar(r4, means[3], yerr=errors[3], width=barWidth, edgecolor='white', label='Correlation Rank - conv5',align='center', alpha=0.5, ecolor='black', capsize=0);
# Add xticks on the middle of the group bars
plt.xlabel('Accuracy scores', fontsize = 12, fontweight='bold')
plt.xticks([r + 1.5*barWidth for r in range(len(r1))], labels, fontsize=12);
plt.grid(True, axis='y');
plt.ylabel('Accuracy', fontsize=12);
plt.title(title, fontsize = 14);
# Create legend & Show graphic
plt.legend(loc = 'upper left', fontsize=12);
maxValue = 0;
for i in range(4):
for j in range(len(means[0])):
maxValue = max(maxValue, means[i][j] + errors[i][1][j]);
plt.ylim(bottom=-10, top=maxValue+10);
# dotted lines
plt.hlines(100/50, r1[0] - 0.7 * barWidth, r1[0] + 3.7*barWidth, colors='k', linestyles = 'dashed');
plt.hlines(500/50, r1[1] - 0.7 * barWidth, r1[1] + 3.7*barWidth, colors='k', linestyles = 'dashed');
plt.hlines(1000/50, r1[2] - 0.7 * barWidth, r1[2] + 3.7*barWidth, colors='k', linestyles = 'dashed');
autolabel(b1, plt, errors[0], maxValue);
autolabel(b2, plt, errors[1], maxValue);
autolabel(b3, plt, errors[2], maxValue);
autolabel(b4, plt, errors[3], maxValue);
#plt.show();
plt.savefig(filename);
def plotBarGraphNN3(means, errors, title, filename):
barNames = ['Neural Networks', 'Correlation Rank'];
plotBarGraphDefault(means, errors, title, filename, barNames);
def plotBarGraphHuge(means, errors, title, filename):
labels = ['Top 1', 'Top 5', 'Top 10', 'Kamitani Score'];
barWidth = 0.20 # the width of the bars
# Set position of bar on X axis
r1 = np.arange(len(means[0]));
r2 = [x + barWidth for x in r1];
r3 = [x + barWidth for x in r2];
#plt.figure(figsize = (10, 8));
# Make the plot
plt.bar(r1, means[0], yerr=errors[0], width=barWidth, edgecolor='white', label='Single volume/15225 classes', align='center', alpha=0.5, ecolor='black', capsize=10);
plt.bar(r2, means[1], yerr=errors[1], width=barWidth, edgecolor='white', label='Mean volume/15225 classes',align='center', alpha=0.5, ecolor='black', capsize=10);
plt.bar(r3, means[2], yerr=errors[2], width=barWidth, edgecolor='white', label='[Horikawa 2017]',align='center', alpha=0.5, ecolor='black', capsize=10);
# Add xticks on the middle of the group bars
plt.xlabel('Accuracy scores', fontweight='bold')
plt.xticks([r + barWidth for r in range(len(r1))], labels);
plt.grid(True, axis='y');
plt.ylabel('Accuracy');
plt.title(title);
# Create legend & Show graphic
plt.legend();
#plt.show();
plt.savefig(filename);
def plotBarGraphHuge2(means, errors, title, filename):
imagined = 'Imagined' in title;
labels = ['FC7', 'Conv5'];
barWidth = 0.22; # the width of the bars
plt.figure(figsize = (8, 4));
# Set position of bar on X axis
r1 = np.arange(len(means[0]));
r2 = [x + barWidth for x in r1];
#plt.figure(figsize = (10, 8));
# Make the plot
prop_cycle = plt.rcParams['axes.prop_cycle'];
colors = prop_cycle.by_key()['color'];
if imagined:
c1 = colors[4];
c2 = colors[5];
else:
c1 = colors[0];
c2 = colors[1];
b1 = plt.bar(r1, means[0], yerr=errors[0], width=barWidth, edgecolor='white', label='Our result',align='center', alpha=0.5, ecolor='black', color=c1);
b2 = plt.bar(r2, means[1], yerr=errors[1], width=barWidth, edgecolor='white', label='[Horikawa 2017]',align='center', alpha=0.5, ecolor='black', color=c2);
# Add xticks on the middle of the group bars
plt.xlabel('Accuracy scores', fontweight='bold', fontsize = 12)
plt.xticks([r + barWidth for r in range(len(r1))], labels, fontsize = 12);
plt.grid(True, axis='y');
plt.ylabel('Accuracy', fontsize = 12);
plt.title(title, fontsize = 12);
# Create legend & Show graphic
plt.legend(loc = 'lower center', fontsize = 12);
plt.ylim(bottom=-10, top=100);
plt.yticks(np.arange(0, 100, 25.0));
# dotted lines
plt.hlines(50, r1[0] - 0.7 * barWidth, r1[0] + 1.7*barWidth, colors='k', linestyles = 'dashed');
plt.hlines(50, r1[1] - 0.7 * barWidth, r1[1] + 1.7*barWidth, colors='k', linestyles = 'dashed');
maxValue = 0;
for i in range(2):
for j in range(len(means[0])):
maxValue = max(maxValue, means[i][j] + errors[i][1][j]);
autolabel(b1, plt, errors[0], maxValue);
autolabel(b2, plt, errors[1], maxValue);
#plt.show();
plt.savefig(filename);
def plotBarGraphHuge3(means, errors, title, filename):
labels = ['FC7', 'Conv5'];
barWidth = 0.20 # the width of the bars
# Set position of bar on X axis
r1 = np.arange(len(means[0]));
r2 = [x + barWidth for x in r1];
r3 = [x + barWidth for x in r2];
#plt.figure(figsize = (10, 8));
# Make the plot
plt.bar(r1, means[0], yerr=errors[0], width=barWidth, edgecolor='white', label='Single volume', align='center', alpha=0.5, ecolor='black', capsize=10);
plt.bar(r2, means[1], yerr=errors[1], width=barWidth, edgecolor='white', label='Mean volume',align='center', alpha=0.5, ecolor='black', capsize=10);
plt.bar(r3, means[2], yerr=errors[2], width=barWidth, edgecolor='white', label='[Horikawa 2017]',align='center', alpha=0.5, ecolor='black', capsize=10);
# Add xticks on the middle of the group bars
plt.xlabel('Accuracy scores', fontweight='bold')
plt.xticks([r + barWidth for r in range(len(r1))], labels);
plt.grid(True, axis='y');
plt.ylabel('Accuracy');
plt.title(title);
# Create legend & Show graphic
plt.legend();
#plt.show();
plt.savefig(filename);
def plotBarGraphMulti(means, errors, title, filename):
labels = ['Top 1', 'Top 5', 'Top 10', 'Kamitani Score'];
barWidth = 0.20 # the width of the bars
# Set position of bar on X axis
r1 = np.arange(len(means[0]));
r2 = [x + barWidth for x in r1];
plt.figure(figsize = (10, 8));
# Make the plot
plt.bar(r1, means[0], yerr=errors[0], width=barWidth, edgecolor='white', label='200 classes/single data', align='center', alpha=0.5, ecolor='black', capsize=10);
plt.bar(r2, means[1], yerr=errors[1], width=barWidth, edgecolor='white', label='150 classes Leave one out/single data',align='center', alpha=0.5, ecolor='black', capsize=10);
# Add xticks on the middle of the group bars
plt.xlabel('Accuracy scores', fontweight='bold')
plt.xticks([r + barWidth for r in range(len(r1))], labels);
plt.grid(True, axis='y');
plt.ylabel('Accuracy');
plt.title(title);
# Create legend & Show graphic
plt.legend();
#plt.show();
plt.savefig(filename);
def normalizeAccuracies(acc, numClasses):
chance = 100.0 / numClasses;
return 100.0 * (acc-chance) / (100.0-chance);
def plotResultsCC(excelfile, outputfile, layer, seenAcc, imagAcc):
import xlrd;
wb = xlrd.open_workbook(excelfile);
barsSeen = np.zeros((7,4));
errorsSeen = np.zeros((7, 2, 4));
barsImagined = np.zeros((7,4));
errorsImagined = np.zeros((7, 2, 4));
normbarsSeen = np.zeros((7,4));
normerrorsSeen = np.zeros((7, 2, 4));
normbarsImagined = np.zeros((7,4));
normerrorsImagined = np.zeros((7, 2, 4));
sheetIdxs = [0, 1, 3, 4, 2, 5];
numclasses = [200, 50, 200, 50, 50, 50];
for i in range(len(sheetIdxs)):
sheet = wb.sheet_by_index(sheetIdxs[i]);
for j in range(4):
barsSeen[i, j] = sheet.cell_value(1+j, 6);
normbarsSeen[i, j] = normalizeAccuracies(sheet.cell_value(1+j, 6), numclasses[i]);
minValue = 0;
maxValue = 0;
for k in range(4):
value = sheet.cell_value(1+j, k+1) - barsSeen[i, j];
if k == 0:
minValue = value;
maxValue = value;
else:
minValue = min(minValue, value);
maxValue = max(maxValue, value);
errorsSeen[i, 0, j] = abs(minValue);
errorsSeen[i, 1, j] = abs(maxValue);
minValue = 0;
maxValue = 0;
for k in range(4):
value = normalizeAccuracies(sheet.cell_value(1+j, k+1), numclasses[i]) - normbarsSeen[i, j];
if k == 0:
minValue = value;
maxValue = value;
else:
minValue = min(minValue, value);
maxValue = max(maxValue, value);
normerrorsSeen[i, 0, j] = abs(minValue);
normerrorsSeen[i, 1, j] = abs(maxValue);
for j in range(4):
barsImagined[i, j] = sheet.cell_value(7+j, 6);
normbarsImagined[i, j] = normalizeAccuracies(sheet.cell_value(7+j, 6), numclasses[i]);
minValue = 0;
maxValue = 0;
for k in range(4):
value = sheet.cell_value(7+j, k+1) - barsImagined[i, j];
if k == 0:
minValue = value;
maxValue = value;
else:
minValue = min(minValue, value);
maxValue = max(maxValue, value);
errorsImagined[i, 0, j] = abs(minValue);
errorsImagined[i, 1, j] = abs(maxValue);
minValue = 0;
maxValue = 0;
for k in range(4):
value = normalizeAccuracies(sheet.cell_value(7+j, k+1), numclasses[i]) - normbarsImagined[i, j];
if k == 0:
minValue = value;
maxValue = value;
else:
minValue = min(minValue, value);
maxValue = max(maxValue, value);
normerrorsImagined[i, 0, j] = abs(minValue);
normerrorsImagined[i, 1, j] = abs(maxValue);
barsSeen[6, 3] = seenAcc;
barsImagined[6, 3] = imagAcc;
plotBarGraphCC(barsSeen, errorsSeen, 'Seen image classification correlation ' + layer, os.path.splitext(outputfile)[0] + '_seen.png');
plt.pause(0.001);
plotBarGraphCC(barsImagined, errorsImagined, 'Imagined image classification correlation ' + layer, os.path.splitext(outputfile)[0] + '_imagined.png');
def plotResultsCC2(excel5, excel7, outputfile):
from openpyxl import load_workbook;
nBars = 3;
nScores = 4;
barsSeen = np.zeros((nScores, nBars));
errorsSeen = np.zeros((nScores, 2, nBars));
barsImagined = np.zeros((nScores, nBars));
errorsImagined = np.zeros((nScores, 2, nBars));
sheetIdxs = [1, 4];
for p in range(2):
if p == 0:
wb = load_workbook(excel7, data_only=True);
else:
wb = load_workbook(excel5, data_only=True);
for i in range(len(sheetIdxs)):
sheet = wb[wb.sheetnames[sheetIdxs[i]]];
for j in range(nBars):
barsSeen[i+p*2, j] = sheet.cell(1+j+1, 6+1).value;
if sheet.cell(1+j+1, 7+1).value == 0:
errorsSeen[i+p*2, 0, j] = 0;
errorsSeen[i+p*2, 1, j] = 0;
else:
errorsSeen[i+p*2, 0, j] = sheet.cell(1+j+1, 8+1).value;
errorsSeen[i+p*2, 1, j] = sheet.cell(1+j+1, 8+1).value;
for j in range(nBars):
barsImagined[i+p*2, j] = sheet.cell(7+j+1, 6+1).value;
if sheet.cell(7+j+1, 7+1).value == 0:
errorsImagined[i+p*2, 0, j] = 0;
errorsImagined[i+p*2, 1, j] = 0;
else:
errorsImagined[i+p*2, 0, j] = sheet.cell(7+j+1, 8+1).value;
errorsImagined[i+p*2, 1, j] = sheet.cell(7+j+1, 8+1).value;
plotBarGraphCC2(barsSeen, errorsSeen, 'Seen stimuli classification by correlation rank with 50 classes', os.path.splitext(outputfile)[0] + '_seen.png');
plt.pause(0.001);
plotBarGraphCC2(barsImagined, errorsImagined, 'Imagined stimuli classification by correlation rank with 50 classes', os.path.splitext(outputfile)[0] + '_imagined.png');
def plotResultsCC3(excel5, excel7, outputfile):
from openpyxl import load_workbook;
nBars = 3;
nScores = 4;
barsSeen = np.zeros((nScores, nBars));
errorsSeen = np.zeros((nScores, 2, nBars));
barsImagined = np.zeros((nScores, nBars));
errorsImagined = np.zeros((nScores, 2, nBars));
sheetIdxs = [4, 5];
for p in range(2):
if p == 0:
wb = load_workbook(excel7, data_only=True);
else:
wb = load_workbook(excel5, data_only=True);
for i in range(len(sheetIdxs)):
sheet = wb[wb.sheetnames[sheetIdxs[i]]];
for j in range(nBars):
barsSeen[i+p*2, j] = sheet.cell(1+j+1, 6+1).value;
if sheet.cell(1+j+1, 7+1) == 0:
errorsSeen[i+p*2, 0, j] = 0;
errorsSeen[i+p*2, 1, j] = 0;
else:
errorsSeen[i+p*2, 0, j] = sheet.cell(1+j+1, 8+1).value;
errorsSeen[i+p*2, 1, j] = sheet.cell(1+j+1, 8+1).value;
for j in range(nBars):
barsImagined[i+p*2, j] = sheet.cell(7+j+1, 6+1).value;
if sheet.cell(7+j+1, 7+1).value == 0:
errorsImagined[i+p*2, 0, j] = 0;
errorsImagined[i+p*2, 1, j] = 0;
else:
errorsImagined[i+p*2, 0, j] = sheet.cell(7+j+1, 8+1).value;
errorsImagined[i+p*2, 1, j] = sheet.cell(7+j+1, 8+1).value;
plotBarGraphCC3(barsSeen, errorsSeen, 'Seen stimuli classification by correlation rank with 50 classes', os.path.splitext(outputfile)[0] + '_seen.png');
plt.pause(0.001);
plotBarGraphCC3(barsImagined, errorsImagined, 'Imagined stimuli classification by correlation rank with 50 classes', os.path.splitext(outputfile)[0] + '_imagined.png');
def plotResultsCC4(outputfile):
def calculateMatrixResult(filename, bars, idx):
top1, top5, top10 = matrixAccuracy(filename);
bars[idx, 0] = top1;
bars[idx, 1] = top5;
bars[idx, 2] = top10;
return bars;
nBars = 3;
nScores = 4;
barsSeen = np.zeros((nScores, nBars));
errorsSeen = np.zeros((nScores, 2, nBars));
barsImagined = np.zeros((nScores, nBars));
errorsImagined = np.zeros((nScores, 2, nBars));
# fc7
filename = 'CMs/AverageMatrix_4096_allData_50Classes_mean';
barsSeen = calculateMatrixResult(filename, barsSeen, 0);
filename = 'CMs/AverageMatrix_4096_allData_50Classes_ip';
barsSeen = calculateMatrixResult(filename, barsSeen, 1);
filename = 'CMs/AverageMatrix_4096_img_allData_50Classes_mean';
barsImagined = calculateMatrixResult(filename, barsImagined, 0);
filename = 'CMs/AverageMatrix_4096_img_allData_50Classes_ip';
barsImagined = calculateMatrixResult(filename, barsImagined, 1);
# conv5
filename = 'CMs/AverageMatrix_25088_allData_50Classes_mean';
barsSeen = calculateMatrixResult(filename, barsSeen, 2);
filename = 'CMs/AverageMatrix_25088_allData_50Classes_ip';
barsSeen = calculateMatrixResult(filename, barsSeen, 3);
filename = 'CMs/AverageMatrix_25088_img_allData_50Classes_mean';
barsImagined = calculateMatrixResult(filename, barsImagined, 2);
filename = 'CMs/AverageMatrix_25088_img_allData_50Classes_ip';
barsImagined = calculateMatrixResult(filename, barsImagined, 3);
plotBarGraphCC3(barsSeen, errorsSeen, 'Seen stimuli classification based on matrices', os.path.splitext(outputfile)[0] + '_seen.png');
plt.pause(0.001);
plotBarGraphCC3(barsImagined, errorsImagined, 'Imagined stimuli classification based on matrices', os.path.splitext(outputfile)[0] + '_imagined.png');
def plotResultsNN(excelfile, outputfile, layer):
import xlrd;
wb = xlrd.open_workbook(excelfile);
barsSeen = np.zeros((4,4));
errorsSeen = np.zeros((4, 2, 4));
barsImagined = np.zeros((4,4));
errorsImagined = np.zeros((4, 2, 4));
sheetIdxs = [1, 3, 2, 4];
for i in range(4):
sheet = wb.sheet_by_index(sheetIdxs[i]);
for j in range(4):
barsSeen[i, j] = sheet.cell_value(1+j, 6);
minValue = 0;
maxValue = 0;
for k in range(4):
value = sheet.cell_value(1+j, k+1) - barsSeen[i, j];
if k == 0:
minValue = value;
maxValue = value;
else:
minValue = min(minValue, value);
maxValue = max(maxValue, value);
errorsSeen[i, 0, j] = abs(minValue);
errorsSeen[i, 1, j] = abs(maxValue);
for j in range(4):
barsImagined[i, j] = sheet.cell_value(7+j, 6);
minValue = 0;
maxValue = 0;
for k in range(4):
value = sheet.cell_value(7+j, k+1) - barsImagined[i, j];
if k == 0:
minValue = value;
maxValue = value;
else:
minValue = min(minValue, value);
maxValue = max(maxValue, value);
errorsImagined[i, 0, j] = abs(minValue);
errorsImagined[i, 1, j] = abs(maxValue);
plotBarGraphNN(barsSeen, errorsSeen, 'Seen image classification Neural Networks ' + layer, os.path.splitext(outputfile)[0] + '_seen.png');
plt.pause(0.001);
plotBarGraphNN(barsImagined, errorsImagined, 'Imagined image classification Neural Networks ' + layer, os.path.splitext(outputfile)[0] + '_imagined.png');
def plotResultsNN2(excel5, excel7, outputfile):
import xlrd;
nBars = 3;
nScores = 4;
barsSeen = np.zeros((nScores, nBars));
errorsSeen = np.zeros((nScores, 2, nBars));
barsImagined = np.zeros((nScores, nBars));
errorsImagined = np.zeros((nScores, 2, nBars));
sheetIdxs = [3, 4];
for p in range(2):
if p == 0:
wb = xlrd.open_workbook(excel7);
else:
wb = xlrd.open_workbook(excel5);
for i in range(len(sheetIdxs)):
sheet = wb.sheet_by_index(sheetIdxs[i]);
for j in range(nBars):
barsSeen[i+p*2, j] = sheet.cell_value(1+j, 6);
if sheet.cell_value(1+j, 7) == 0:
errorsSeen[i+p*2, 0, j] = 0;
errorsSeen[i+p*2, 1, j] = 0;
else:
errorsSeen[i+p*2, 0, j] = min(sheet.cell_value(1+j, 6), sheet.cell_value(1+j, 8));
errorsSeen[i+p*2, 1, j] = sheet.cell_value(1+j, 8);
for j in range(nBars):
barsImagined[i+p*2, j] = sheet.cell_value(7+j, 6);
if sheet.cell_value(7+j, 7) == 0:
errorsImagined[i+p*2, 0, j] = 0;
errorsImagined[i+p*2, 1, j] = 0;
else:
errorsImagined[i+p*2, 0, j] = min(sheet.cell_value(7+j, 6), sheet.cell_value(7+j, 8));
errorsImagined[i+p*2, 1, j] = sheet.cell_value(7+j, 8);
plotBarGraphNN2(barsSeen, errorsSeen, 'Seen stimuli classification by neural networks with 50 classes', os.path.splitext(outputfile)[0] + '_seen.png');
plt.pause(0.001);
plotBarGraphNN2(barsImagined, errorsImagined, 'Imagined stimuli classification neural networks with 50 classes', os.path.splitext(outputfile)[0] + '_imagined.png');
def plotResultsNN3(excelNN7, excelCC7, excelNN5, excelCC5, outputfile):
import xlrd;
nBars = 3;
nScores = 4;
barsSeen = np.zeros((nScores, nBars));
errorsSeen = np.zeros((nScores, 2, nBars));
barsImagined = np.zeros((nScores, nBars));
errorsImagined = np.zeros((nScores, 2, nBars));
for barIdx in range(4):
if barIdx == 0:
wb = xlrd.open_workbook(excelNN7);
sheetN = 4;
elif barIdx == 1:
wb = xlrd.open_workbook(excelCC7);
sheetN = 4;
elif barIdx == 2:
wb = xlrd.open_workbook(excelNN5);
sheetN = 4;
elif barIdx == 3:
wb = xlrd.open_workbook(excelCC5);
sheetN = 4;
sheet = wb.sheet_by_index(sheetN);
for j in range(nBars):
barsSeen[barIdx, j] = sheet.cell_value(1+j, 6);
if sheet.cell_value(1+j, 7) == 0:
errorsSeen[barIdx, 0, j] = 0;
errorsSeen[barIdx, 1, j] = 0;
else:
errorsSeen[barIdx, 0, j] = min(sheet.cell_value(1+j, 6), sheet.cell_value(1+j, 8));
errorsSeen[barIdx, 1, j] = sheet.cell_value(1+j, 8);
for j in range(nBars):
barsImagined[barIdx, j] = sheet.cell_value(7+j, 6);
if sheet.cell_value(7+j, 7) == 0:
errorsImagined[barIdx, 0, j] = 0;
errorsImagined[barIdx, 1, j] = 0;
else:
errorsImagined[barIdx, 0, j] = min(sheet.cell_value(7+j, 6), sheet.cell_value(7+j, 8));
errorsImagined[barIdx, 1, j] = sheet.cell_value(7+j, 8);
plotBarGraphNN3(barsSeen, errorsSeen, 'Seen stimuli classification with 50 classes', os.path.splitext(outputfile)[0] + '_seen.png');
plt.pause(0.001);
plotBarGraphNN3(barsImagined, errorsImagined, 'Imagined stimuli classification with 50 classes', os.path.splitext(outputfile)[0] + '_imagined.png');
def plotResultsHuge2(excel7, excel5, outputfile, seenAcc7, imagAcc7, seenAcc5, imagAcc5):
from openpyxl import load_workbook;
barsSeen = np.zeros((2,2));
errorsSeen = np.zeros((2, 2, 2));
barsImagined = np.zeros((2,2));
errorsImagined = np.zeros((2, 2, 2));
# FC7 Horikawa accuracies
barsSeen[1, 0] = seenAcc7;
barsImagined[1, 0] = imagAcc7;
errorsSeen[1, 0, 0] = 1.57;
errorsSeen[1, 1, 0] = 1.57;
errorsSeen[1, 0, 1] = 1.52;
errorsSeen[1, 1, 1] = 1.52;
# Conv5 Horikawa accuracies
barsSeen[1, 1] = seenAcc5;
barsImagined[1, 1] = imagAcc5;
errorsImagined[1, 0, 0] = 6.44;
errorsImagined[1, 1, 0] = 6.44;
errorsImagined[1, 0, 1] = 2.35;
errorsImagined[1, 1, 1] = 2.35;
sheetIdxs = [0];
for i in range(len(sheetIdxs)):
for j in range(2):
if j == 0:
wb = load_workbook(excel7, data_only=True);
else:
wb = load_workbook(excel5, data_only=True);
sheet = wb.active;
barsSeen[i, j] = sheet.cell(5, 7).value;
if sheet.cell(5, 9).value == 0:
errorsSeen[i, 0, j] = 0;
errorsSeen[i, 1, j] = 0;
else:
errorsSeen[i, 0, j] = sheet.cell(5, 9).value;
errorsSeen[i, 1, j] = sheet.cell(5, 9).value;
barsImagined[i, j] = sheet.cell(11, 7).value;
if sheet.cell(10, 8).value == 0:
errorsImagined[i, 0, j] = 0;
errorsImagined[i, 1, j] = 0;
else:
errorsImagined[i, 0, j] = sheet.cell(11, 9).value;
errorsImagined[i, 1, j] = sheet.cell(11, 9).value;
plotBarGraphHuge2(barsSeen, errorsSeen, 'Seen image classification correlation 14018 classes ', os.path.splitext(outputfile)[0] + '_seen.png');
plt.pause(0.001);
plotBarGraphHuge2(barsImagined, errorsImagined, 'Imagined image classification correlation 14018 classes ', os.path.splitext(outputfile)[0] + '_imagined.png');
def plotResultsHuge3(excel7, excel5, outputfile, seenAcc7, imagAcc7, seenAcc5, imagAcc5):
import xlrd;
barsSeen = np.zeros((3,2));
errorsSeen = np.zeros((3, 2, 2));
barsImagined = np.zeros((3,2));
errorsImagined = np.zeros((3, 2, 2));
# FC7 Horikawa accuracies
barsSeen[2, 0] = seenAcc7;
barsImagined[2, 0] = imagAcc7;
# Conv5 Horikawa accuracies
barsSeen[2, 1] = seenAcc5;
barsImagined[2, 1] = imagAcc5;
sheetIdxs = [0, 1];
for i in range(len(sheetIdxs)):
for j in range(2):
if j == 0:
wb = xlrd.open_workbook(excel7);
else:
wb = xlrd.open_workbook(excel5);