def plot(data, fname):
plt.clf()
width = 10
data = np.array(data)
bins = np.arange(0, max(data), step=width)
sns.distplot(data, norm_hist=True, kde=True, bins=bins, label='No ones')
plt.ylim((0, 0.001))
plt.xlabel('First passage time (ps)')
plt.text(
1000, 0.0005,
'MFPT = {0:4.2f} +/- {1:4.2f} ps'.format(data.mean(), 2 * data.std()))
plt.savefig(fname, transparanet=True)
def plot_events(real,pred,meta,real_,pred_, label=None):
from matplotlib.pylab import plt
import random
fig,ax = plt.subplots(figsize=(15, .8))
ax.set_title(label)
plt.xlim(0,max(meta[1],10))
ax.set_xticks(np.arange(0,max(real[-1][1],10),.1),minor=True)
maxsize=20
# random.random()/4
for i in range(min(maxsize,len(pred_))):
d = pred_[i]
plt.axvspan(d[0], d[1], 0, 0.6,linewidth=0,edgecolor='k',facecolor='#edb4b4', alpha=.6)
plt.text((d[1] + d[0]) / 2, 0.2,f'{i}' , horizontalalignment='center', verticalalignment='center')
for i in range(min(maxsize,len(pred))):
d = pred[i]
plt.axvspan(d[0], d[1], 0.0, 0.6,linewidth=0,edgecolor='k',facecolor='#a31f1f', alpha=.6)
plt.text((d[1] + d[0]) / 2, 0.2,f'{i}' , horizontalalignment='center', verticalalignment='center')
# maxsize=len(real)
for i in range(min(maxsize,len(real_))):
gt = real_[i]
plt.axvspan(gt[0], gt[1], 0.4, 1,linewidth=0,edgecolor='k',facecolor='#d2f57a', alpha=.6)
plt.text((gt[1] + gt[0]) / 2, 0.8,f'{i}' , horizontalalignment='center', verticalalignment='center')
for i in range(min(maxsize,len(real))):
gt = real[i]
plt.axvspan(gt[0], gt[1], 0.4, 1,linewidth=0,edgecolor='k',facecolor='#1fa331', alpha=.6)
plt.text((gt[1] + gt[0]) / 2, 0.8,f'{i}' , horizontalalignment='center', verticalalignment='center')
# plt.grid(True)
plt.minorticks_on()
ax.set(yticks=[.25,.75], yticklabels=['P','R'])
# plt.grid(b=True, which='minor', color='#999999', linestyle='-', alpha=0.2)
plt.show()
def plot_events_with_event_scores(gt_event_scores,
detected_event_scores,
ground_truth_events,
detected_events,
label=None):
fig, ax = plt.subplots(figsize=(10, 2))
ax.set_title(label)
maxsize = 10
maxsize = len(detected_events)
for i in range(min(maxsize, len(detected_event_scores))):
d = detected_events[i]
plt.axvspan(d[0],
d[1],
0,
0.5,
linewidth=1,
edgecolor='k',
facecolor='r',
alpha=.6)
txt = detected_event_scores[i]
if type(txt) == int or type(txt) == float:
txt = "%g" % round(detected_event_scores[i],
2 if detected_event_scores[i] < 10 else 0)
plt.text((d[1] + d[0]) / 2,
0.1 + random.random() / 4,
txt,
horizontalalignment='center',
verticalalignment='center')
maxsize = len(ground_truth_events)
for i in range(min(maxsize, len(gt_event_scores))):
gt = ground_truth_events[i]
plt.axvspan(gt[0],
gt[1],
0.5,
1,
linewidth=1,
edgecolor='k',
facecolor='g',
alpha=.6)
txt = gt_event_scores[i]
if type(txt) == int or type(txt) == float:
txt = "%g" % round(gt_event_scores[i],
2 if gt_event_scores[i] < 10 else 0)
plt.text((gt[1] + gt[0]) / 2,
0.6 + random.random() / 4,
txt,
horizontalalignment='center',
verticalalignment='center')
# plt.savefig('result/choice.png')
# plt.show()
# plt.close()
feature_name = ['time','background','body','eye','lefteye','righteye','face','mouth','nose']
dot_data = tree.export_graphviz(clf
,feature_names=feature_name
,class_names=['正常儿童','自闭症儿童']
#,class_names= ['0','1','2']
,filled=True
,rounded=True
)
graph = graphviz.Source(dot_data.replace('helvetica','"Microsoft YaHei"'))
graph.render(filename='decision', directory='result_random', format='png', cleanup=True, quiet=True) #保存
#graph.view(filename='result',cleanup=True,quiet=True)
eigenvalue = list(zip(feature_name,clf.feature_importances_))
eigenvalue = sorted(eigenvalue, key=lambda x:x[1], reverse=True)
value_label = list(zip(*eigenvalue))
from pylab import mpl
mpl.rcParams['font.sans-serif'] = ['SimHei'] #设置字体为黑体
plt.title('准确率-%s'%sco)
r = plt.bar(range(len(eigenvalue)), value_label[1], color='rgb',tick_label=value_label[0])
totol_eigenvalue = 0
for i,value in zip(r, value_label[1]):
totol_eigenvalue += value
plt.text(i.get_xy()[0]+i.get_width()/2, i.get_height() + 1/pow(10, len(str(value).split('.')[-1]))/10, '%s'%value, ha='center')
plt.legend()
plt.savefig('result_random/eigenvalueSort.png')
plt.close()
#plt.show()
else:
weights[ed] = 1
edg.append([edges[l][0],edges[l][1]])
import matplotlib.patches as patches
style = "Simple, tail_width=2, head_width=0, head_length=1"
kw = dict(arrowstyle=style, color="k")
for l in range(len(edges)):
# psy = (pos[edges[l][1]][0], 0.01+pos[edges[l][1]][1])
a = patches.FancyArrowPatch(pos[edges[l][0]], pos[edges[l][1]],#psy,
connectionstyle="arc3,rad=.43",
**kw)
ax.add_patch(a)
for l in range(len(edg)):
we = str(edg[l][0]) + str(edg[l][1])
if(we=='14'):
ytext = pos[edges[l][0]][1]-0.04
xtext = (edg[l][0] + edg[l][1]) / 2 -1
elif(we=='24'):
ytext = pos[edges[l][0]][1]-0.015
xtext = (edg[l][0] + edg[l][1]) / 2 -1
else:
ytext = pos[edges[l][0]][1]-+0.005
xtext = (edg[l][0] + edg[l][1]) / 2 -1.1#pos[edges[l][0]][1] +0.5#- pos[edges[l][0]][1]
print(xtext,ytext)
plt.text(xtext, ytext, str(weights[we]), fontdict={'size':fs, 'color':'k'})
plt.savefig('illustrationBP.png', bbox_inches='tight', dpi=300)
plt.show()