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Figure3_new.py
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Figure3_new.py
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""" 比较在力变化时，手势精度的分类情况,
单独将不同等级的力作为独立的输入，监测在不同独立力等级输入下手势分类"""
import numpy as np
from sklearn.decomposition import PCA
import pandas as pd
from User_modify import *
from Tool_Dataprocess import produce_sample_label
import matplotlib.pyplot as plt
from sklearn import preprocessing
from sklearn.neighbors import KNeighborsClassifier
from sklearn import svm
from sklearn.ensemble import RandomForestClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.neural_network import MLPClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import StratifiedKFold
import sklearn.metrics as sm
import numpy as np
from Tool_Visualization import *
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.model_selection import LeaveOneGroupOut
from Tool_Functionset import *
# 数据处理程序
sub_num = 14
# gesture_oversub_score = []
# for sub_index in range(1,sub_num+1):
#
# path1 = './Feature_2/data' + str(sub_index) + '3.csv'
# path2 = './Feature_2/label' + str(sub_index) + '3.csv'
#
# dataset = pd.read_csv(path1, header=None)
# label = pd.read_csv(path2, header=None)
# dataset = np.array(dataset)
# label = np.array(label)
# label = label.astype(int)
#
# # 打乱数据，为各个训练器准备训练数据
# data_pool = np.zeros([dataset.shape[0], dataset.shape[1] + label.shape[1]])
# data_pool[:, 0:dataset.shape[1]] = dataset
# data_pool[:, dataset.shape[1]:] = label
# np.random.seed(0) # 控制随机数产生的种子
# np.random.shuffle(data_pool)
# dataset = data_pool[:, 0:dataset.shape[1]]
# label = data_pool[:, dataset.shape[1]:]
# label = label.astype(int)
#
# # 构造分类器的集合
# knn = KNeighborsClassifier() # KNN Modeling
# clf = svm.SVC(kernel='rbf', C=10)
# rf = RandomForestClassifier(n_estimators=20) # RF Modeling
# lda = LinearDiscriminantAnalysis()
# selected_model = [rf, lda, knn, clf]
#
#
# # 探究不同力变化时，对于手势分类精度的影响
# gesture_score_list = np.zeros((3,3))
# for force_index_tr in range(3):
# for force_index_te in range(3):
# train_dataset = dataset[label[:, 2] == force_index_tr]
# train_label = label[label[:, 2] == force_index_tr]
# test_dataset = dataset[label[:, 2] == force_index_te]
# test_label = label[label[:, 2] == force_index_te]
#
# # 循环选择模型
# model_index = 3
# candidate_model_a = selected_model[model_index]
# gesture_score = gesture_lot_validate_multi_level(train_dataset, train_label, test_dataset, test_label,
# candidate_model_a)
# # 理解失误，还是得改成lot的形式
# gesture_score_list[force_index_tr, force_index_te] = gesture_score
# gesture_oversub_score.append(gesture_score_list)
# print(sub_index)
#
# gesture_oversub_score = np.array(gesture_oversub_score)#dim0= sub, dim1 = train_index , dim2 = test_index
# mean_gesture_oversub_score = np.mean(gesture_oversub_score, axis=0)
#
# np.save('./new_figure/gesture_oversub_score_svm.npy', gesture_oversub_score)
#
# print(1)
RA_flag = 5
if RA_flag == 4:
gesture_score_rf = np.load('./new_figure/gesture_oversub_score_rf.npy')
gesture_score_lda = np.load('./new_figure/gesture_oversub_score_lda.npy')
gesture_score_knn = np.load('./new_figure/gesture_oversub_score_knn.npy')
gesture_score_svm = np.load('./new_figure/gesture_oversub_score_svm.npy')
gesture_score_list = [gesture_score_rf, gesture_score_lda, gesture_score_knn, gesture_score_svm]
for i in range(len(gesture_score_list)):
gesture_score_list[i] = np.mean(gesture_score_list[i], axis=0)
plt.rcParams['font.family'] = 'Arial'
plt.rcParams.update({'font.size': 9})
fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(2,2), dpi=800)
labels_name = ['10%MVC', '40%MVC', '70%MVC']
title_list = ['RF', 'LDA', 'KNN', 'SVM']
# plt.xlabel('Testing')
# plt.ylabel('Training')
fig.text(0.5,0.005,'Testing force level', ha = 'center', fontsize = 10)
fig.text(0, 0.5, 'Training force level', va='center', rotation = 'vertical',fontsize=10)
for r in range(2):
for c in range(2):
data_index=r*2+c
data = gesture_score_list[data_index]
im = ax[r,c].imshow(data, interpolation='nearest',cmap=plt.cm.Blues)
num_local = np.array(range(len(labels_name)))
if r==1:
ax[r, c].set_xticks(num_local, labels_name, rotation = 30)
else:
ax[r, c].set_xticks([])
if c ==0:
ax[r,c].set_yticks(num_local, labels_name, rotation = 30)
else:
ax[r,c].set_yticks([])
ax[r,c].set_title(title_list[data_index], fontsize = 14)
#ax[r,c].set_ylabel('Training force level')
for first_index in range(data.shape[0]): # 第几行
for second_index in range(data.shape[1]): # 第几列
a = data[first_index][second_index]
b = "%.1f" % (a * 100)
if first_index == second_index: # and first_index < 5:
ax[r,c].text(first_index, second_index, b, fontsize=16, color="w", va='center', ha='center',
weight='medium')
else:
ax[r,c].text(first_index, second_index, b, size=16, va='center', ha='center', weight='medium')
fig.colorbar(im, ax=[ax[0,0], ax[0,1], ax[1,0], ax[1,1]], fraction=0.03, pad=0.05)
#plt.tight_layout()
plt.savefig('C:\\Users\\86156\\Desktop\\f3_new.png', bbox_inches='tight')
if RA_flag == 5:
gesture_score_rf = np.load('./new_figure/gesture_oversub_score_rf.npy')
gesture_score_lda = np.load('./new_figure/gesture_oversub_score_lda.npy')
gesture_score_knn = np.load('./new_figure/gesture_oversub_score_knn.npy')
gesture_score_svm = np.load('./new_figure/gesture_oversub_score_svm.npy')
gesture_score_list = [gesture_score_rf, gesture_score_lda, gesture_score_knn, gesture_score_svm]
for i in range(len(gesture_score_list)):
gesture_score_list[i] = np.mean(gesture_score_list[i], axis=0)
plt.rcParams['font.family'] = 'Arial'
#plt.tick_params(width=0.1) # 修改刻度线线粗细width参数，修改刻度字体labelsize参数
plt.rcParams.update({'font.size': 7})
fig= plt.figure(figsize=(2.5,2.5), dpi=800)
ax = plt.gca()
labels_name = ['10%MVC', '40%MVC', '70%MVC']
plt.xlabel('Testing force level')
plt.ylabel('Training force level')
data_index = 3
data = gesture_score_list[data_index]
ax.spines['bottom'].set_linewidth(0)
ax.spines['left'].set_linewidth(0)
ax.spines['top'].set_linewidth(0)
ax.spines['right'].set_linewidth(0)
im = ax.imshow(data*100, interpolation='nearest', cmap=plt.cm.YlOrRd)
num_local = np.array(range(len(labels_name)))
ax.set_xticks(num_local, labels_name, rotation=0)
ax.set_yticks(num_local-0.2, labels_name, rotation=90)
for first_index in range(data.shape[0]): # 第几行
for second_index in range(data.shape[1]): # 第几列
a = data[first_index][second_index]
b = "%.1f" % (a * 100)
if first_index == second_index: # and first_index < 5:
ax.text(first_index, second_index, b, fontsize=12, color="w", va='center', ha='center',
weight='medium')
else:
ax.text(first_index, second_index, b, size=12, va='center', ha='center', weight='medium')
fc = fig.colorbar(im, fraction=0.04, pad=0.05)
fc.outline.set_visible(False)
ax2 = fc.ax
#fc.tick_params(width=0) # 修改刻度线线粗细width参数，修改刻度字体labelsize参数
plt.tick_params(width=0) # 修改刻度线线粗细width参数，修改刻度字体labelsize参数
# ax2.spines['bottom'].set_linewidth(0)
# ax2.spines['left'].set_linewidth(0)
# ax2.spines['top'].set_linewidth(0)
# ax2.spines['right'].set_linewidth(0)
#plt.tight_layout()
plt.savefig('C:\\Users\\Lenovo\\Desktop\\fig\\f3_new_3.png', bbox_inches='tight')
# #画图程序
# gesture_oversub_score = np.load('./new_figure/gesture_oversub_score.npy')
# RA_flag = 3
# #绘制柱状图
# if RA_flag==1:
# MVC_10 = gesture_oversub_score[:,0,:]
# MVC_40 = gesture_oversub_score[:,1,:]
# MVC_70 = gesture_oversub_score[:,2,:]
# MVC_all = gesture_oversub_score[:,3,:]
#
# print(1)
# feature_set_cata = [MVC_10, MVC_40, MVC_70, MVC_all]
# labels_name = ['RF', 'LDA', 'KNN', 'SVM']
# MVC_level = ['MVC_10', 'MVC_40', 'MVC_70', 'MVC_all']
#
# color_list = ['skyblue', 'lightcoral', 'navajowhite', 'limegreen']
#
# for i in range(4):
# x = np.arange(4)
# y = np.mean(feature_set_cata[i], axis=0)
# error = np.std(feature_set_cata[i], axis=0)
# plt.bar(x + 0.2 * i, y * 100, alpha=0.6, width=0.2, lw=3, label=MVC_level[i], color=color_list[i])
# plt.errorbar(x + 0.2 * i, y * 100, yerr=error * 100, fmt='.', ecolor='black',
# elinewidth=1, ms=5, mfc='wheat', mec='salmon', capsize=5)
# plt.xticks(x + 0.3, labels_name, fontsize=12)
# plt.xlabel('Machine learning Model', loc='center', fontsize=12, weight='medium')
# plt.ylabel('Recognition Accuracy (%) ', fontsize=12)
# plt.legend()
# plt.ylim(0, 100)
# plt.tight_layout()
# plt.savefig('C:\\Users\\86156\\Desktop\\f31.png')
# plt.show()
#
# #改进柱状图
# RA_flag =3
# if RA_flag==3:
# MVC_10 = gesture_oversub_score[:, 0, :]
# MVC_40 = gesture_oversub_score[:, 1, :]
# MVC_70 = gesture_oversub_score[:, 2, :]
# MVC_all = gesture_oversub_score[:, 3, :]
# print(1)
#
# feature_set_cata = [MVC_10, MVC_40, MVC_70, MVC_all]
# labels_name = ['RF', 'LDA', 'KNN', 'SVM']
# MVC_level = ['MVC_10', 'MVC_40', 'MVC_70', 'MVC_all']
#
# color_list = ['skyblue', '#ffadad', '#ffd6a5', '#caffbf']
# plt.rcParams['font.family'] = 'Arial'
# plt.rcParams.update({'font.size': 6})
# plt.figure(figsize=(3.5,3), dpi=600)
#
# ax = plt.gca()
# bwith = 0.5
# ax.spines['bottom'].set_linewidth(bwith)
# ax.spines['left'].set_linewidth(bwith)
# ax.spines['top'].set_linewidth(bwith)
# ax.spines['right'].set_linewidth(bwith)
# #, alpha=0.6
# for i in range(4):
# x = np.arange(4)*1.2
# y = np.mean(feature_set_cata[i], axis=0)
# error = np.std(feature_set_cata[i], axis=0, ddof=1)
# plt.bar(x + 0.2 * i, y * 100, width=0.18, lw=3, label=MVC_level[i], color=color_list[i])
# plt.errorbar(x + 0.2 * i, y * 100, yerr=error * 100, fmt='.', ecolor='black',
# elinewidth=0.3, ms=0.1, mfc='wheat', mec='salmon', capsize=1, capthick = 0.2)
# plt.xticks(x + 0.3, labels_name, fontsize=6)
# plt.xlabel('Machine learning Model', loc='center', fontsize=6, weight='medium')
# plt.ylabel('Recognition Accuracy (%) ', fontsize=6)
# plt.tick_params(width=0.5)#设置刻度线条的粗细
# plt.legend()
# #plt.legend(loc = 'upper left',bbox_to_anchor = (-0.02,-0.15) ,fontsize = 6, ncol = 4)
# plt.ylim(0, 102)
# plt.tight_layout()
# plt.savefig('C:\\Users\\86156\\Desktop\\f32.png')
# #plt.savefig('C:/Users/Lenovo/Desktop/New_Figure')
# plt.show()
#
#
#
#
# #绘制箱线图
#
# RA_flag = 1
# if RA_flag==2:
# RF = gesture_oversub_score[:, :, 0]
# LDA = gesture_oversub_score[:, :, 1]
# KNN = gesture_oversub_score[:, :, 2]
# SVM = gesture_oversub_score[:, :, 3]
# print(1)
#
# data_list = [RF, LDA, KNN, SVM]
# # 首先有图（fig），然后有轴（ax）
# posi = np.arange(4)
#
# title_list = ['RF', 'LDA', 'KNN', 'SVM']
# colors_list = ['pink', 'lightblue', 'lightgreen','lightcoral']
# label_list = ['MVC_10','MVC_40','MVC_70','MVC_all']
# plt.rcParams['font.family'] = 'Arial'
# plt.rcParams.update({'font.size': 6})
# fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(3.5,3), dpi=200)
# for r in range(2):
# for c in range(2):
# data_index=r*2+c
# data = data_list[data_index]
# bplot1 = ax[r, c].boxplot(data*100, vert=True, showfliers=False,
# patch_artist=True)
# ax[r, c].set_xticks(posi+1, label_list, rotation = -30)
# #plt.xticks(x + 0.6, labels_name, fontsize=6)
# ax[r, c].set_title(title_list[data_index])
# ax[r,c].set_ylim([50,105])
# ax[r,c].set_yticks([50,60,70,80,90,100])
# if c==0:
# ax[r, c].set_ylabel('Recognition Accuracy(%)')
# #plt.ylim([50,100])
# # ax.set_label("a")
# for i in range(len(colors_list)):
# bplot1['boxes'][i].set_facecolor(colors_list[i])
# plt.tight_layout()
# plt.savefig('C:\\Users\\86156\\Desktop\\f3.png')
# plt.show()