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Figure5.py
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Figure5.py
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""" 对比不同的subject在综合分类中的鲁棒性"""
""" 将总体作为研究对象"""
import numpy as np
from sklearn.decomposition import PCA
import pandas as pd
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 *
import warnings
warnings.filterwarnings("ignore")
sub_num = 14
# sub_score_array = []
# for sub_index in range(1,sub_num+1):
# print(sub_index)
# 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(n_neighbors=10) # KNN Modeling
# clf = svm.SVC(kernel='rbf', C=10)
# rf = RandomForestClassifier(n_estimators=20) # RF Modeling
# lda = LinearDiscriminantAnalysis()
#
# selected_model = [rf, lda, knn, clf]
#
# # 循环选择模型
# output_flag_list = [0]
# for output_flag in output_flag_list:
# model_index = 2
# candidate_model_a = selected_model[model_index] # 选择只针对手势分类的分类器
# candidate_model_b = selected_model[model_index] # 选择只针对力分类的分类器
# candidate_model_c = selected_model[model_index] # 选择总共的分类器
#
# acc_score, recall_score, f1_score = k_fold_multiaim_validate(dataset, label, candidate_model_a, candidate_model_b, candidate_model_c, flag=output_flag)
# score_list = [acc_score, recall_score, f1_score]
# model_score_array = np.array(score_list)#二维数组（评估类型×任务）
# sub_score_array.append(model_score_array)
# print(sub_index)
#
# sub_score_array = np.array(sub_score_array)#0维：subject 1维度:acc recall f1 2维：3个g , f, all
# sub_score_array = np.squeeze(sub_score_array)
# np.save('./new_figure/sub_score_array_knn.npy', sub_score_array)
# print(1)
RA_flag = 2
if RA_flag == 1:
#绘制手势分类关于分类器和subject的变化图 F6
sub_num = 14
rf_score = np.load('./new_figure/sub_score_array_rf.npy')
lda_score = np.load('./new_figure/sub_score_array_lda.npy')
knn_score = np.load('./new_figure/sub_score_array_knn.npy')
svm_score = np.load('./new_figure/sub_score_array_svm.npy')
model_score_list = [rf_score, lda_score, knn_score, svm_score]
gesture_acc_list = []
for i in range(4):
gesture_acc_list.append(model_score_list[i][:,0,0])
gesture_acc_list = np.array(gesture_acc_list)
print(1)
plt.rcParams['font.family'] = 'Arial'
plt.rcParams.update({'font.size': 6})
fig = plt.figure(figsize=(3.5, 2), dpi=800)
ax = plt.gca()
bwith = 0.5
ax.spines['bottom'].set_linewidth(bwith)
ax.spines['left'].set_linewidth(bwith)
ax.spines['top'].set_linewidth(0)
ax.spines['right'].set_linewidth(0)
#color_list = ['#bae7ff', '#91d5ff', '#69c0ff', '#40a9ff']
color_list = ['#cdb4db','#ffc8dd','#ffafcc','#bde0fe']
#color_list = ['#2a9d8f', '#e9c46a', '#f4a261', '#e76f51']
marker_list = ['o', '^', 's','*']
model_name = ['RF', 'LDA', 'KNN', 'SVM']
subjects_name = ['S1', 'S2', 'S3', 'S4', 'S5', 'S6', 'S7', 'S8', 'S9','S10','S11','S12','S13','S14','Ave']
ax.set_ylim(0,100)
ax.set_ylabel('Recognition Accuracy (%)')
for i in range(4):
x = np.arange(1, sub_num+1)
y = gesture_acc_list[i,:]
ax.plot(x,y*100, color = color_list[i], marker = marker_list[i], label = model_name[i], markersize = 2)
leg = plt.legend()
leg.get_frame().set_linewidth(0.1)
mean_z = np.mean(gesture_acc_list, axis=1)
std_z = np.std(gesture_acc_list, axis=1, ddof=1)
for m in range(4):
ax.bar([15 + 0.5 * m], mean_z[m] * 100, width=0.4, lw=3, color=color_list[m])
ax.errorbar(15 + 0.5 * m, mean_z[m] * 100, yerr=[[0], [std_z[m] * 100]], fmt='.', ecolor='black',
elinewidth=0.3, ms=0.001, mfc='wheat', mec='salmon', capsize=1, capthick=0.2)
ax.scatter([15 + 0.5 * m], 5, marker=marker_list[m], s=4, c = '#FFFFFF')
x = np.arange(1,sub_num+1)
tic = list(x)
tic.append(15.75)
ax.set_xticks(tic, subjects_name)
plt.tight_layout()
#plt.savefig('C:\\Users\\86156\\Desktop\\f5_1.png')
plt.savefig('C:\\Users\\Lenovo\\Desktop\\fig\\f5_2.png')
if RA_flag == 2:
#绘制力分类精度关于分类器和subject的图f51
sub_num = 14
rf_score = np.load('./new_figure/sub_score_array_rf.npy')
lda_score = np.load('./new_figure/sub_score_array_lda.npy')
knn_score = np.load('./new_figure/sub_score_array_knn.npy')
svm_score = np.load('./new_figure/sub_score_array_svm.npy')
model_score_list = [rf_score, lda_score, knn_score, svm_score]
gesture_acc_list = []
for i in range(4):
gesture_acc_list.append(model_score_list[i][:,0,1])
gesture_acc_list = np.array(gesture_acc_list)
print(1)
plt.rcParams['font.family'] = 'Arial'
plt.rcParams.update({'font.size': 6})
fig = plt.figure(figsize=(3.5, 2), dpi=800)
ax = plt.gca()
bwith = 0.5
ax.spines['bottom'].set_linewidth(bwith)
ax.spines['left'].set_linewidth(bwith)
ax.spines['top'].set_linewidth(0)
ax.spines['right'].set_linewidth(0)
#color_list = ['#bae7ff', '#91d5ff', '#69c0ff', '#40a9ff']
#color_list = ['#e6f7ff', '#bae7ff', '#91d5ff', '#69c0ff']
#color_list = ['#2a9d8f', '#e9c46a', '#f4a261', '#e76f51']
color_list = ['#cdb4db', '#ffc8dd', '#ffafcc', '#bde0fe']
marker_list = ['o', '^', 's','*']
model_name = ['RF', 'LDA', 'KNN', 'SVM']
subjects_name = ['S1', 'S2', 'S3', 'S4', 'S5', 'S6', 'S7', 'S8', 'S9','S10','S11','S12','S13','S14','Ave']
ax.set_ylim(0,100)
ax.set_ylabel('Recognition Accuracy (%)')
for i in range(4):
x = np.arange(1, sub_num+1)
y = gesture_acc_list[i,:]
ax.plot(x,y*100, color = color_list[i], marker = marker_list[i], label = model_name[i], markersize = 2)
leg = plt.legend()
leg.get_frame().set_linewidth(0.1)
mean_z = np.mean(gesture_acc_list, axis=1)
std_z = np.std(gesture_acc_list, axis=1, ddof=1)
for m in range(4):
ax.bar([15 + 0.5 * m], mean_z[m] * 100, width=0.4, lw=3, color=color_list[m])
ax.errorbar(15 + 0.5 * m, mean_z[m] * 100, yerr=[[0], [std_z[m] * 100]], fmt='.', ecolor='black',
elinewidth=0.3, ms=0.001, mfc='wheat', mec='salmon', capsize=1, capthick=0.2)
ax.scatter([15 + 0.5 * m], 5, marker=marker_list[m], s=4, c = '#FFFFFF')
x = np.arange(1,sub_num+1)
tic = list(x)
tic.append(15.75)
ax.set_xticks(tic, subjects_name)
plt.tight_layout()
plt.savefig('C:\\Users\\86156\\Desktop\\f5_2.png')#关于力分类在所有人上的图
#plt.savefig('C:\\Users\\Lenovo\\Desktop\\fig\\f5_2.png')
# sub_num = 9
# sub_score_array = np.load('./new_figure/sub_score_array.npy')
# plt.rcParams['font.family'] = 'Arial'
# plt.rcParams.update({'font.size': 6})
# fig, ax = plt.subplots(nrows=3, ncols=1, figsize = (3.5, 4.5), dpi = 400)
# x = np.arange(10)
# y = np.arange(10)
# color_list = ['skyblue', 'lightcoral', 'navajowhite']
# task_name = ['Gesture Recognition', 'Force Level Recognition', 'Gesture and Force level Recognition ']
# marker_list = ['o','^','s']
# subjects_name = ['S1', 'S2','S3', 'S4','S5', 'S6','S7','S8', 'S9', 'Ave']
# label_name = ['Precision (%)','Recall (%)','F1-score (%)' ]
# for i in range(3):
# x = np.arange(1,sub_num+1)
# y = sub_score_array[:, i, :]
# ax[i].set_ylim(0, 100)
# ax[i].set_ylabel(label_name[i])
# for j in range(3):
# ax[i].plot(x,y[:,j]*100,color = color_list[j], marker = marker_list[j], label = task_name[j], markersize = 2)
# if i==2:
# ax[i].legend()
# mean_z = np.mean(y,axis=0)
# std_z = np.std(y, axis = 0, ddof=1)
# for m in range(3):
# ax[i].bar([10+0.5*m], mean_z[m] * 100, width=0.4, lw=3, color=color_list[m])
# ax[i].errorbar([10+0.5*m], mean_z[m] * 100, yerr=std_z[m] * 100, fmt='.', ecolor='black',
# elinewidth=0.3, ms=0.1, mfc='wheat', mec='salmon', capsize=2.5, capthick=0.4)
# tic = list(x)
# tic.append(10.5)
# ax[i].set_xticks(tic, subjects_name)
# #ax[i].set_xticklabels(tic, subjects_name)
# #ax[i].set_sticklabels(tic, subjects_name)
# plt.tight_layout()
# plt.savefig('C:\\Users\\86156\\Desktop\\f5.png')
# plt.show()
#
#
#
# RA_flag =0
# if RA_flag==1:
# labels_name = ['Sub 1', 'Sub 2','Sub 3', 'Sub 4','Sub 5', 'Sub 6']
# task_name = ['Gesture Recognition', 'Force Level Recognition', 'Gesture and Force level Recognition ']
# color_list = ['skyblue', 'lightcoral', 'navajowhite']
# for i in range(3):
# x = np.arange(sub_num)
# y = sub_score_array[:, i]
# plt.bar(x + 0.2 * i, y * 100, alpha=0.6, width=0.2, lw=3, label=task_name[i], color=color_list[i])
#
# plt.xticks(x + 0.2, labels_name, fontsize=12)
# plt.xlabel('Subject', loc='center', fontsize=12, weight='medium')
# plt.ylabel('Recall (%) ', fontsize=12)
# plt.legend(loc = 'lower right')
# plt.ylim(0, 100)
# plt.show()