def plot_confusion_matrix(cm, fig, counter, num_plots, title='Confusion matrix', cmap=plt.cm.Blues):
ax = fig.add_subplot(1, num_plots, counter+1)
ax.imshow(cm, interpolation='nearest', cmap=cmap)
ax.set_title(title)
tick_marks = np.arange(len(np.unique(test_labels_df['cat'])))
plt.set_xticks(tick_marks, np.unique(test_labels_df['cat']), rotation=45)
plt.set_yticks(tick_marks, np.unique(test_labels_df['cat']))
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
def plot_boxplots(data):
"""
:param data:
:return:
"""
all_data = data.values
labels = data.columns
plt.boxplot(all_data)
plt.set_title('box plot')
plt.yaxis.grid(True)
plt.set_xticks([y + 1 for y in range(len(all_data))])
plt.set_xlabel('feature')
plt.set_ylabel('ylabel')
plt.setp(xticks=[y + 1 for y in range(len(all_data))], xticklabels=labels)
plt.show()
def plot_2D_scores(plt,fig,filename_data_pos,filename_data_neg,filename_data_grid,filename_scores_grid,th,noice_needed=0,caption='',filename_out=None):
if not os.path.isfile(filename_data_pos): return
if not os.path.isfile(filename_data_neg): return
if not os.path.isfile(filename_data_grid): return
data = tools_IO.load_mat(filename_scores_grid, dtype=numpy.chararray, delim='\t')
data = data[1:,:]
grid_scores = data[:, 1:].astype('float32')
data = tools_IO.load_mat(filename_data_grid, dtype=numpy.chararray, delim='\t')
data_grid = data[:,1:].astype('float32')
data = tools_IO.load_mat(filename_data_pos, dtype=numpy.chararray, delim='\t')
l1 = (data[:, 0]).astype('float32')
x1 = data[:,1:].astype('float32')
data = tools_IO.load_mat(filename_data_neg, dtype=numpy.chararray, delim='\t')
l2 = (data[:, 0]).astype('float32')
x2 = data[:,1:].astype('float32')
X = numpy.vstack((x1,x2))
labels = numpy.hstack((l1, l2)).astype(int)
X1 = X[labels > 0]
X0 = X[labels <= 0]
#'''
max = numpy.max(grid_scores)
min = numpy.min(grid_scores)
for i in range(0,grid_scores.shape[0]):
if(grid_scores[i]>th):
grid_scores[i]=(grid_scores[i]-th)/(max-th)
else:
grid_scores[i] = (grid_scores[i] - th) / (th-min)
#'''
S=int(math.sqrt(grid_scores.shape[0]))
grid_scores=numpy.reshape(grid_scores,(S,S))
minx=numpy.min(data_grid[:, 0])
maxx=numpy.max(data_grid[:, 0])
miny=numpy.min(data_grid[:, 1])
maxy=numpy.max(data_grid[:, 1])
if noice_needed>0:
noice1 = 0.05-0.2*numpy.random.random_sample(X1.shape)
noice0 = 0.05-0.2*numpy.random.random_sample(X0.shape)
X1+=noice1
X0+=noice0
plt.set_title(caption)
xx, yy = numpy.meshgrid(numpy.linspace(minx, maxx, num=S), numpy.linspace(miny, maxy,num=S))
plt.contourf(xx, yy, numpy.flip(grid_scores,0), cmap=cm.coolwarm, alpha=.8)
#plt.imshow(grid_scores, interpolation='bicubic',cmap=cm.coolwarm,extent=[minx,maxx,miny,maxy],aspect='auto')
plt.plot(X0[:, 0], X0[:, 1], 'ro', color='blue', alpha=0.4)
plt.plot(X1[: ,0], X1[:, 1], 'ro' ,color='red' , alpha=0.4)
plt.grid()
plt.set_xticks(())
plt.set_yticks(())
#fig.subplots_adjust(hspace=0.001,wspace =0.001)
if filename_out is not None:
plt.savefig(filename_out)
return
def Plot(self):
self.delPlot()
listSalePlot = []
listLabel = []
listDate = []
tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
(44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
(148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
(227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
(188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]
for i in range(len(tableau20)):
r, g, b = tableau20[i]
tableau20[i] = (r / 255., g / 255., b / 255.)
if (len(self.listDimensionsPlot)) == 1 :
listInDimensions = self.read_CSV_InDimensions(self.listHeadShow[0])
for i in range(len(listInDimensions)):
filename = listInDimensions[i]
listLabel.append(filename)
##เพิ่ม Date ตรงนี้
with open(""+str(self.listDatePlot[0])+str(filename)+str(self.listMeasuresPlot[0])+".csv", newline='') as f:
reader = csv.reader(f)
row1 = next(reader)
row2 = next(reader)
listSalePlot.append(row2)
listDate = row1
elif (len(self.listDimensionsPlot)) == 0:
for i in range(len(self.listInDimensionsPlot)):
filename = self.listInDimensionsPlot[i]
listLabel.append(filename)
with open(""+str(self.listDatePlot[0])+str(filename)+str(self.listMeasuresPlot[0])+".csv", newline='') as f:
reader = csv.reader(f)
row1 = next(reader)
row2 = next(reader)
listSalePlot.append(row2)
listDate = row1
print (listDate)
print (listLabel)
print (listSalePlot)
canvasPlot = 0
print (len(self.canvas))
for plot in range(len(self.plotType)):
self.canvas.append("canvas"+str(plot))
if (self.plotType[plot]=="Plot"):
if (len(self.listMeasuresPlot)==1) and (len(self.listDatePlot)==1):
f = Figure(figsize=(6, 5), dpi=80)
f.set_facecolor("w")
plt = f.add_subplot(111)
for i in range(len(listSalePlot)):
print (listSalePlot[i])
plt.plot(listDate,listSalePlot[i],lw=2.5,color=tableau20[i],label=listLabel[i])
minX = (int(min(listDate)))
maxX = (int(max(listDate)))
x = range(minX, (maxX+1))
plt.set_xticks(x)
plt.set_xticklabels(listDate)
plt.set_title(str(self.listMeasuresPlot[0]))
plt.legend(loc='upper left', bbox_to_anchor=(0, 1),ncol=2, fancybox=True, shadow=True)
self.canvas[plot] = FigureCanvasTkAgg(f, master=self.f2)
self.canvas[plot].show()
self.canvas[plot].get_tk_widget().place(x = 300*canvasPlot + 350, y = 10)
self.canvas[plot]._tkcanvas.place(x = 300*canvasPlot + 350, y = 10)
elif (self.plotType[plot]=="Pie"):
if (len(self.listMeasuresPlot)==1) and (len(self.listDatePlot)==1):
f = Figure(figsize=(6, 5), dpi=80)
f.set_facecolor("w")
plt = f.add_subplot(111)
plt.set_title(str(self.listMeasuresPlot[0]))
saleCounty = []
colors = []
for i in range(len(listSalePlot)):
saleCountySum = 0
colors.append(tableau20[i])
for j in range(len(listSalePlot[i])):
saleCountySum = saleCountySum + float(listSalePlot[i][j])
saleCounty.append(saleCountySum)
print (saleCounty)
plt.pie(saleCounty, labels=listLabel, colors=colors,
autopct='%1.1f%%', shadow=True, startangle=90)
plt.axis('equal')
self.canvas[plot] = FigureCanvasTkAgg(f, master=self.f2)
self.canvas[plot].show()
self.canvas[plot].get_tk_widget().place(x = 300*canvasPlot + 350, y = 10)
self.canvas[plot]._tkcanvas.place(x = 300*canvasPlot + 350, y = 10)
elif (self.plotType[plot]=="Bar"):
if (len(self.listMeasuresPlot)==1) and (len(self.listDatePlot)==1):
f = Figure(figsize=(6, 5), dpi=80)
f.set_facecolor("w")
plt = f.add_subplot(111)
print (len(listDate))
width = 0.2
ind = np.arange(len(listDate))
for i in range(len(listSalePlot)):
listSalePlot[i] = [float(j) for j in listSalePlot[i]]
plt.bar(ind+(i*width),listSalePlot[i],width,color=tableau20[i],label=listLabel[i])
plt.set_xticks(ind+((width/2)*(len(listSalePlot))))
plt.set_xticklabels(listDate)
plt.set_title(str(self.listMeasuresPlot[0]))
plt.legend(loc='upper left', bbox_to_anchor=(0, 1),ncol=2, fancybox=True, shadow=True)
self.canvas[plot] = FigureCanvasTkAgg(f, master=self.f2)
self.canvas[plot].show()
self.canvas[plot].get_tk_widget().place(x = 300*canvasPlot + 350, y = 10)
self.canvas[plot]._tkcanvas.place(x = 300*canvasPlot + 350, y = 10)
canvasPlot = canvasPlot + 1.5
self.plotTypeOlt.append(canvasPlot)
plt.legend
plt.show()
#Kernel Density Estimate (KDE). KDE is a fundamental data smoothing problem where inferences about
#the population are made, based on a finite data sample.
#It helps generate estimations of the overall distributions.
#estimations of the overall distributions
pd.plotting.scatter_matrix(retscomp, diagonal='kde', figsize=(10,10));
#heat maps to visualize the correlation ranges among the competing stocks.
#The lighter the color, the more correlated the two stocks are
mk=plt.subplot()
plt.imshow(corr, cmap='hot', interpolation='none')
plt.colorbar()
plt.set_xticks(range(len(corr)), corr.columns)
plt.set_yticks(range(len(corr)), corr.columns)
plt.show()
#this might not show causality, and could just show the trend in the technology
#industry rather than show how competing stocks
#Stock RETURNS n RISKS
#average of returns (Return Rate) and the standard deviation of returns (Risk).
plt.scatter(retscomp.mean(), retscomp.std())
plt.xlabel("Expected returns")
plt.ylabel("Risk")
plt.legend
for label, x, y in zip(retscomp.columns, retscomp.mean(), retscomp.std()):
plt.annotate(
#ax = fig.add_subplot(131)
#rects1 = ax.bar(x, stand_alone, width, label='Stand Alone')
#rects1 = ax.bar(x - width/2, stand_alone, width, color='cornflowerblue')
#rects1 = ax.bar(x,time, width)
rects1 = ax.bar(x, read, width, hatch='....', color='white', edgecolor='black')
#rects1 = ax.bar(x - width/2, stand_alone, width, label='Stand Alone')
#rects2 = ax.bar(x, collocated, width, label='Collocated')
#rects2 = ax.bar(x + width/2, collocated, width, color='orange')
#rects2 = ax.bar(x + width/2, collocated, width, label='Collocated')
# Add some text for labels, title and custom x-axis tick labels, etc.
plt.set_ylabel('Traffic in Million', fontsize=12)
plt.set_title('Read', fontsize=14)
plt.set_xticks(x)
plt.set_xticklabels(
labels,
fontsize=12,
)
#ax.set_xticklabels(labels, fontsize=12, rotation='vertical')
#ax.legend(loc=1, bbox_to_anchor=(0.5, 0., 0.5, 0.99))
plt.set_yticks(np.arange(0, max(read) + 2, 3))
x = np.arange(len(labels)) # the label locations
fig.subplots_adjust(wspace=.5)
plt.tight_layout()
plt.show()
fig.savefig('method-stream.png', dpi=100)
print('Done users matrix')
ticks = [0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0]
colours = ['r', 'g', 'b', 'y']
shapes = ['+', 'o', 'x']
print(colours)
with open('pickles/precisions_map.pickle', 'wb') as handle:
pickle.dump(precisions_map, handle, protocol=pickle.HIGHEST_PROTOCOL)
with open('pickles/super_result.pickle', 'wb') as handle:
pickle.dump(super_result, handle, protocol=pickle.HIGHEST_PROTOCOL)
fig = plt.figure(figsize=(10, 15))
#fig.subplots_adjust(bottom = 10, top =20 ,wspace = 50 ,hspace = 50
for i, key in enumerate(precisions_map.keys()):
plt.plot(ticks,
precisions_map[key],
str(colours[i % len(colours)]) + str(shapes[i % 3]),
label=str(key))
plt.set_xticklabels(ticks)
plt.set_xticks([0.5 * a for a in range(len(ticks))])
plt.set_title('Precision for different distance metrics')
plt.legend(loc='lower left')
plt.grid(axis='both')
plt.savefig('figs/comparsion.jpg')
plt.clf()
plt.cla()
def make_figure_markers(means_mo, stddevs_mo, \
means_ma, stddevs_ma, \
means_w0, stddevs_w0, \
means_w1, stddevs_w1, \
means_w2, stddevs_w2, \
means_w3, stddevs_w3, \
means_w4, stddevs_w4, \
means_w5, stddevs_w5, \
title, xlabel, ylabel, ylim):
fig1 = plt.figure(figsize=(10, 10))
#plt.title(title)
plt.ylabel(xlabel)
plt.xlabel(ylabel)
plt.ylim(ylim)
start, end = plt.get_xlim()
plt.set_xticks(np.arange(start, end, 0.1))
plt.errorbar(range(len(means_mo)),
means_mo,
stddevs_mo,
capsize=5,
errorevery=2,
label='no attenuation')
plt.errorbar(range(len(means_ma)),
means_ma,
stddevs_ma,
capsize=5,
errorevery=3,
label='model')
plt.errorbar(range(len(means_w0)),
means_w0,
stddevs_w0,
capsize=5,
errorevery=4,
label='w0 set')
plt.errorbar(range(len(means_w1)),
means_w1,
stddevs_w1,
capsize=5,
errorevery=5,
label='w1 set')
plt.errorbar(range(len(means_w2)),
means_w2,
stddevs_w2,
capsize=5,
errorevery=6,
label='w2 set')
plt.errorbar(range(len(means_w3)),
means_w3,
stddevs_w3,
capsize=5,
errorevery=7,
label='w3 set')
plt.errorbar(range(len(means_w4)),
means_w4,
stddevs_w4,
capsize=5,
errorevery=8,
label='w4 set')
plt.errorbar(range(len(means_w5)),
means_w5,
stddevs_w5,
capsize=5,
errorevery=9,
label='w5 set')
plt.legend(ncol=2, loc='lower right')
filename = title + '.jpg'
plt.savefig(filename)
plt.close()
_table_stats = []
_table_p = []
_line_s = []
_line_p = []
_line_s.append('MO')
_line_p.append('MO')
_line_s.append('-')
_line_p.append('-')
stat, p = stats.ttest_ind(means_mo, means_ma)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_mo, means_w0)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_mo, means_w1)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_mo, means_w2)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_mo, means_w3)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_mo, means_w4)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_mo, means_w5)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
_table_stats.append(_line_s)
_table_p.append(_line_p)
_line_s = []
_line_p = []
_line_s.append('MA')
_line_p.append('MA')
stat, p = stats.ttest_ind(means_ma, means_mo)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
# ma ma
_line_s.append('-')
_line_p.append('-')
stat, p = stats.ttest_ind(means_ma, means_w0)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_ma, means_w1)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_ma, means_w2)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_ma, means_w3)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_ma, means_w4)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_ma, means_w5)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
_table_stats.append(_line_s)
_table_p.append(_line_p)
_line_s = []
_line_p = []
_line_s.append('W0')
_line_p.append('W0')
stat, p = stats.ttest_ind(means_w0, means_mo)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w0, means_ma)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
# w0 w0
_line_s.append('-')
_line_p.append('-')
stat, p = stats.ttest_ind(means_w0, means_w1)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w0, means_w2)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w0, means_w3)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w0, means_w4)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w0, means_w5)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
_table_stats.append(_line_s)
_table_p.append(_line_p)
_line_s = []
_line_p = []
_line_s.append('W1')
_line_p.append('W1')
stat, p = stats.ttest_ind(means_w1, means_mo)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w1, means_ma)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w1, means_w0)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
# w1 w1
_line_s.append('-')
_line_p.append('-')
stat, p = stats.ttest_ind(means_w1, means_w2)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w1, means_w3)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w1, means_w4)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w1, means_w5)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
_table_stats.append(_line_s)
_table_p.append(_line_p)
_line_s = []
_line_p = []
_line_s.append('W2')
_line_p.append('W2')
stat, p = stats.ttest_ind(means_w2, means_mo)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w2, means_ma)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w2, means_w0)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w2, means_w1)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
# w2 w2
_line_s.append('-')
_line_p.append('-')
stat, p = stats.ttest_ind(means_w2, means_w3)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w2, means_w4)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w2, means_w5)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
_table_stats.append(_line_s)
_table_p.append(_line_p)
_line_s = []
_line_p = []
_line_s.append('W3')
_line_p.append('W3')
stat, p = stats.ttest_ind(means_w3, means_mo)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w3, means_ma)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w3, means_w0)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w3, means_w1)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w3, means_w2)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
# w3 w3
_line_s.append('-')
_line_p.append('-')
stat, p = stats.ttest_ind(means_w3, means_w4)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w3, means_w5)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
_table_stats.append(_line_s)
_table_p.append(_line_p)
_line_s = []
_line_p = []
_line_s.append('W4')
_line_p.append('W4')
stat, p = stats.ttest_ind(means_w4, means_mo)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w4, means_ma)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w4, means_w0)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w4, means_w1)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w4, means_w2)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w4, means_w3)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
# w4 w4
_line_s.append('-')
_line_p.append('-')
stat, p = stats.ttest_ind(means_w4, means_w5)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
_table_stats.append(_line_s)
_table_p.append(_line_p)
_line_s = []
_line_p = []
_line_s.append('W5')
_line_p.append('W5')
stat, p = stats.ttest_ind(means_w5, means_mo)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w5, means_ma)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w5, means_w0)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w5, means_w1)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w5, means_w2)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w5, means_w3)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
stat, p = stats.ttest_ind(means_w5, means_w4)
_line_s.append(round(stat, 1))
_line_p.append(round(p, 4))
# w5 w5
_line_s.append('-')
_line_p.append('-')
_table_stats.append(_line_s)
_table_p.append(_line_p)
_line_s = []
_line_p = []
# print('Statistics=%.3f, p=%.3f' % (stat, p))
headers = ['', 'MO', 'MA', 'W0', 'W1', 'W2', 'W3', 'W4', 'W5']
np.savetxt(
title + '_stat.txt',
["%s" % tabulate.tabulate(_table_stats, headers, tablefmt="latex")],
fmt='%s')
np.savetxt(title + '_p.txt',
["%s" % tabulate.tabulate(_table_p, headers, tablefmt="latex")],
fmt='%s')
dir_path, "kitti_squeezeDet_config.json")
base_dir = json.load(open(args["ext_config_file_path"]))["BASE_DIR"]
if not os.path.exists(base_dir):
os.makedirs(base_dir)
start(args)
# get training directory
train_dir = os.path.join(base_dir, "train")
# plot results
with open(os.path.join(train_dir, "step_times.json"), "r") as f:
J = json.load(f)
steps = np.arange(J["MAX_STEPS"])
times = np.array(J["TIMES"])
fig = plt.figure()
plt.plot(steps, times, color="xkcd:green", linewidth=2)
"""
For comparison export sec/batch of the [old implementation](https://github.com/BichenWuUCB/squeezeDet/blob/master/src/train.py)
and plot it for the same number of steps with the same way
with color "xkcd:purple".
"""
plt.grid()
plt.set_xticks(np.arange(0, J["MAX_STEPS"], 5000))
plt.set_xlabel("steps")
plt.set_ylabel("total_time")
# save figure and plot it
plt.savefig(SAVE_FIG_TO_PATH)
plt.show()
def Pilot_Plot():
# Creating big figure to hold all
fig = plt.figure(figsize=(20, 16))
# setting font size
plt.rcParams.update({'font.size': 8})
# # Turn off axis lines and ticks of the big subplot
# ax.spines['top'].set_color('none')
# ax.spines['bottom'].set_color('none')
# ax.spines['left'].set_color('none')
# ax.spines['right'].set_color('none')
# Plotting estimated mu vs. response mu
ax0 = fig.add_subplot(231)
ax0.scatter(res_mu, est_mu, marker='.', label=f'n={len(res_mu)}')
ax0.legend()
ax0.set_title(
'FOR VALIDATION: Participant response as a function of estimated mu')
ax0.set_xlabel('response mu (radians)')
ax0.set_ylabel('estimated mu (radians)')
plt.set_xticks(card_direc)
plt.set_yticks(card_direc)
# Plotting MSE across presentation times
ax1 = fig.add_subplot(232)
ax1.set_title('MSE across stimulus presentation times')
ax1.set_xlabel('Presentation Times (ms)')
ax1.set_ylabel('MSE')
# plt.bar(np.arange(8), durMSE, width=0.2)
ax1.plot(np.arange(8), durMSE)
ax1.set_xticks(np.arange(8), labels=durVal)
#######################################################################################################
# # Plotting response time vs. estimated mu, vs. response_mu
# plt.figure(figsize=(13,6))
#
# plt.subplot(211)
# plt.scatter(est_mu, data[colnames.index('keyRT')], marker='.', label=f'n={len(est_mu)}')
# plt.legend()
# plt.title('Response time as a function of estimated mean location')
# plt.xlabel('est_mu (radians)')
# plt.ylabel('response time (ms)')
#
# plt.subplot(212)
# plt.scatter(res_mu, data[colnames.index('keyRT')], marker='.',label=f'n={len(res_mu)}')
# plt.legend()
# plt.title('Response time as a function of participant-observed mean location')
# plt.xlabel('response mu (radians)')
# plt.ylabel('response time (ms)')
#
# plt.tight_layout()
# plt.show()
# #######################################################################################################
# ## Plotting cardinal vs. oblique MSE
# plt.figure(figsize=(4,3))
# plt.title('MSE, by orientation of MSE \n(each direction padded by pi/8 radians')
# plt.bar([0, 1], [card_MSE, obli_MSE])
# plt.ylabel('MSE')
# plt.xticks([0,1], ('cardinal','oblique'))
# plt.show()
#######################################################################################################
## Plotting relationship of accuracy with RT and PT
## TODO: Plot SAT curve after fitting logistic function
plt.figure(figsize=(14, 4))
plt.rcParams.update({'font.size': 6})
ax2 = fig.add_subplot(233)
# plt.title('Response time-Accuracy')
# plt.xlabel('Response time (ms)')
# plt.ylabel('Accuracy (percent distance of response mu from est_mu)')
# plt.scatter(keyRT, ERxTrials * 100, marker='.', s=1)
# plt.xticks(durVal, labels=durVal)
ax2.set_title('Presentation Time vs. Accuracy')
ax2.set_xlabel('Presentation time (ms)')
ax2.set_ylabel('Accuracy (percent distance of response mu from est_mu)')
ax2.scatter(vizDur, res_acc * 100)
ax2.set_xticks(durVal, labels=durVal)
# Boxplot, Response time median, mean, IQR for each stimulus duration
ax3 = fig.add_subplot(234)
ax3.set_title(
'Response time, by stimulus duration \n(orange = median, green = mean)'
)
ax3.set_ylabel('Response time (ms)')
ax3.set_xlabel('Presentation duration (ms)')
ax3.boxplot(dur_RT, meanline=True, showmeans=True)
ax3.set_xticks(np.arange(1, len(durVal) + 1), labels=durVal)
#######################################################################################################
# ## Accuracy as a function of distance of initial probe to (1) response mu and (2) estimated mu
# fig = plt.figure(figsize=(10,5))
#
# ax = fig.add_subplot(111) # The big subplot
# ax1 = fig.add_subplot(211)
# ax2 = fig.add_subplot(212)
#
# # Turn off axis lines and ticks of the big subplot
# ax.spines['top'].set_color('none')
# ax.spines['bottom'].set_color('none')
# ax.spines['left'].set_color('none')
# ax.spines['right'].set_color('none')
# ax.tick_params(labelcolor='w', top='off', bottom='off', left='off', right='off')
# ax.set_ylabel('Trial Accuracy')
#
# # fig.suptitle('Accuracy vs. probe locations')
# ax1.scatter(probe2est, ERxTrials, marker='.', s=1)
# ax1.set_xlabel('distance from initial probe to estimated mu (radians)')
# ax2.scatter(probe2res, ERxTrials, marker='.', s=1)
# ax2.set_xlabel('distance from initial probe to response mu (radians)')
# plt.tight_layout()
#
# plt.figure(figsize=(14, 4))
# plt.rcParams.update({'font.size': 6})
ax4 = fig.add_subplot(235)
plt.title('Presentation Time-Accuracy Curve')
plt.xlabel('Presentation time (ms)')
plt.ylabel('Accuracy (percent distance of response mu from est_mu)')
plt.scatter(vizDur, res_acc * 100)
plt.xticks(durVal, labels=durVal)
plt.title('Error vs. Propmix')
plt.xlabel('Response time (ms)')
plt.ylabel('Accuracy (percent distance of response mu from est_mu)')
plt.scatter(keyRT, res_acc * 100, marker='.', s=1)
# plt.xticks(durVal, labels=durVal)
plt.figure(figsize=(12, 4))
plt.rcParams.update({'font.size': 12})
plt.suptitle('Parameter dependency on response time')
plt.rcParams.update({'font.size': 8})
plt.subplot(121)
plt.title(
'Mean response time as a function of kappa (concentration) of stimulus'
)
for i, x in enumerate(propVal):
plt.plot(kpVal[i], kp_RT_mean[i], label=f'propmix = {x}')
plt.legend()
plt.xlabel('estimated kappa')
plt.ylabel('mean RT (ms)')
plt.title('RT by kappa')
## All kappa
# plt.plot(kapVal, kap_RT_mean)
# plt.xlabel('kappa')
# plt.ylabel('mean RT (ms)')
plt.subplot(122)
plt.bar(propVal, prop_RT_mean)
plt.title('Mean response time as a function of noise present')
plt.xlabel(
'Proportion of uniform noise to von Mises distribution in stimulus')
plt.ylabel('mean RT (ms)')
plt.xticks(propVal)