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Figure1.py
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Figure1.py
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""" 使用一般的机器学习模型进行 "综合分类" 的评估，出混淆矩阵的图,K-fold validation"""
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
""" 构造分类器的集合(这是经过网格调优之后的)"""
knn = KNeighborsClassifier(n_neighbors = 10) #KNN Modeling
clf = svm.SVC(kernel='rbf', C=10)
rf = RandomForestClassifier(n_estimators=20) #RF Modeling
rf = RandomForestClassifier()
lda = LinearDiscriminantAnalysis()
#selected_model = [rf, lda, knn, clf]
selected_model = [clf]
#绘图选项
CM_flag = 1 #0表示不绘制混淆矩阵，1表示绘制混淆矩阵
RA_flag = 1 #0表示不会绘制，1表示会绘制
#
model_num = 1
sub_num = 14
feature_num = 1 #表示选择了几种信号
# ACC_score_list = np.zeros((model_num,feature_num*sub_num))
# for model_index in range(model_num):
# # 选择对应的模型
# #feature_set = {'PPG': range(0, 18), 'PPG_R': range(0, 6), 'PPG_IR': range(6, 12), 'PPG_G': range(12, 18)}
# feature_set = {'PPG': range(0, 15), 'PPG_R': range(0, 5), 'PPG_IR': range(5, 10), 'PPG_G': range(10, 15),"PPG_R+IR": range(0,10), "PPG_R+G": [0,1,2,3,4,10,11,12,13,14],"PPG_IR+G": range(5,15)}
# feature_set_cata = ['PPG', 'PPG_R', 'PPG_IR', 'PPG_G',"PPG_R+IR", "PPG_R+G","PPG_IR+G"]
#
# #选择用哪些通道的特征进行训练
# for feature_index in range(feature_num):
# feature = feature_set[feature_set_cata[feature_index]]
#
# # 从头开始遍历所有的被试
# test_label_oversub_list = []
# predicted_label_oversub_list = []
# 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)
# #
# dataset = dataset[:, feature]
# # 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)
# 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"""
#
# fold = 10 # 总共进行5次验证结果
# #kf = KFold(n_splits=fold)
# predicted_label_overkfold_list = []
# test_label_overkfold_list = []
# for i in range(fold):
# j = i
# test_index = label[:, 3] == i
# test_index_1 = label[:, 3] == j
# for m in range(test_index_1.shape[0]):
# test_index[m] = test_index[m] or test_index_1[m]
# train_index = ~ test_index
# #for train_index, test_index in kf.split(dataset):
# train_dataset = dataset[train_index, :]
# train_label = label[train_index, :]
# train_label = train_label[:, 4]
# test_dataset = dataset[test_index, :]
# test_label = label[test_index, :]
# test_label = test_label[:, 4]
# # 选择模型，实际上，在绘制FIG_1时，只需要选择一个模型就可以
# model = selected_model[model_index]
#
# # 进行归一化
# scaler = preprocessing.MinMaxScaler(feature_range=(0, 1)).fit(train_dataset)
# train_dataset = scaler.transform(train_dataset)
# test_dataset = scaler.transform(test_dataset)
# model.fit(train_dataset, train_label)
# predicted_label = model.predict(test_dataset)
# test_label_overkfold_list.append(list(test_label))
# predicted_label_overkfold_list.append(list(predicted_label))
#
# test_label_overkfold_list = np.array(test_label_overkfold_list)
# predicted_label_overkfold_list = np.array(predicted_label_overkfold_list)
# test_label_overkfold_list = np.ravel(test_label_overkfold_list)
# predicted_label_overkfold_list = np.ravel(predicted_label_overkfold_list)
# test_label_oversub_list.append(list(test_label_overkfold_list))
# predicted_label_oversub_list.append(list(predicted_label_overkfold_list))
#
# scores = sm.accuracy_score(test_label_overkfold_list, predicted_label_overkfold_list)#把所有的交叉验证平均值存为accuracy score
# ACC_score_list[model_index, feature_index * sub_num +(sub_index-1)] = scores
#
# test_label_oversub_list = np.array(test_label_oversub_list)
# test_label_oversub_list = np.ravel(test_label_oversub_list)
#
# predicted_label_oversub_list = np.array(predicted_label_oversub_list)
# predicted_label_oversub_list = np.ravel(predicted_label_oversub_list)
#
# np.save('./new_figure/test_label_oversub_list.npy', test_label_oversub_list)
# np.save('./new_figure/predicted_label_oversub_list.npy', predicted_label_oversub_list)
#
#
#
# if (CM_flag ==1) and (model_index==0):
# m = sm.confusion_matrix(test_label_oversub_list, predicted_label_oversub_list)
# print(m)
# label_strlist = ['TIP10', 'TIP40', 'TIP70', 'TMP10', 'TMP40', 'TMP70', 'TRP10',
# 'TRP40',
# 'TRP70', 'KP10', 'KP40', 'KP70' ]
# plot_confusion_matrix(m, label_strlist,
# 'Confusion Matrix for hand Gestures at 3 Force levels' + ' (' + feature_set_cata[
# feature_index] + ') ')
# plt.show()
# plt.savefig('C:\\Users\\86156\\Desktop\\fc1.png')
# print(1)
# print(model_index)
#
# np.save('./new_figure/figure_1.npy', ACC_score_list)
# print(ACC_score_list)
# np.savetxt('./figure_note/ACC_score_list', ACC_score_list, delimiter=',')
#选择是否绘制识别准确率的图片
# test_label_oversub_list = np.load('./new_figure/test_label_oversub_list.npy')
# predicted_label_oversub_list = np.load('./new_figure/predicted_label_oversub_list.npy')
#
# m = sm.confusion_matrix(test_label_oversub_list, predicted_label_oversub_list)
# print(m)
# label_strlist = ['TIP10', 'TIP40', 'TIP70', 'TMP10', 'TMP40', 'TMP70', 'TRP10',
# 'TRP40',
# 'TRP70', 'KP10', 'KP40', 'KP70' ]
# plot_confusion_matrix(m, label_strlist,
# 'Confusion Matrix for hand Gestures at 3 Force levels')
# plt.savefig('C:\\Users\\86156\\Desktop\\fc1_1.png')
#直接设置双框3.5英寸的标准大小,绘制多种模式PPG对比的图片
plt.rcParams['font.family'] = 'Arial'
plt.rcParams.update({'font.size': 8})
ACC_score_list = np.load('./new_figure/figure_1.npy')
feature_num = 7
RA_flag =1
if RA_flag==1:
PPG = ACC_score_list[:, range(sub_num)]
PPG_R = ACC_score_list[:, range(sub_num, 2*sub_num)]
PPG_IR = ACC_score_list[:, range(2*sub_num, 3*sub_num)]
PPG_G = ACC_score_list[:, range(3*sub_num, 4*sub_num)]
PPG_RIR = ACC_score_list[:, range(4*sub_num, 5*sub_num)]
PPG_RG = ACC_score_list[:, range(5*sub_num, 6*sub_num)]
PPG_IRG = ACC_score_list[:, range(6*sub_num, 7*sub_num)]
print(1)
feature_set_cata = [PPG, PPG_R, PPG_IR, PPG_G, PPG_RIR, PPG_RG, PPG_IRG]
labels_name = ['RF', 'LDA', 'KNN', 'SVM']
sensor_name = ['PPG-ALL', 'PPG-R', 'PPG-IR', 'PPG-G', ' PPG-R+IR','PPG-R+G', 'PPG-IR+G' ]
#color_list = ['skyblue', 'lightcoral', 'navajowhite', 'limegreen','peru','royalblue','#ffc6ff']ffadad
#color_list = ['skyblue', '#ffadad', '#ffd6a5', '#caffbf', '#fdffb6', '#a0c4ff', '#ffc6ff']
#color_list = ['geekblue', '#44c489', '#28a9ae', '#28a2b7', '#4c7788', '#6c4f63', '#432c39']
color_list = ['#e6f7ff', '#bae7ff', '#91d5ff', '#69c0ff', '#40a9ff', '#1890ff', '#096dd9']
plt.figure(figsize=(3.5,3.5), 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)
#, alpha=0.6
for i in range(feature_num):
x = np.arange(4)*1.6
y = np.mean(feature_set_cata[i], axis=1)
error = np.std(feature_set_cata[i], axis=1, ddof=1)
y_errormin = [0,0,0,0]
plt.bar(x + 0.2 * i, y * 100, width=0.18, lw=3, label=sensor_name[i], color=color_list[i])
plt.errorbar(x + 0.2 * i, y * 100, yerr=[y_errormin, error * 100], fmt='.', ecolor='black',
elinewidth=0.3, ms=0.000001, mfc='wheat', mec='salmon', capsize=1, capthick=0.2)
plt.xticks(x + 0.6, labels_name, fontsize=8)
plt.xlabel('Machine learning Model', loc='center', fontsize=8, weight='medium')
plt.ylabel('Recognition Accuracy (%) ', fontsize=8)
plt.tick_params(width=0.5)#设置刻度线条的粗细
handles, labels = ax.get_legend_handles_labels()
handles = [handles[0], handles[4], handles[1],handles[5], handles[2], handles[6],handles[3]]
labels = [labels[0], labels[4], labels[1],labels[5], labels[2], labels[6],labels[3]]
leg = ax.legend(handles, labels, loc = 'upper left',bbox_to_anchor = (-0.05,-0.15) ,fontsize = 6, ncol = 4)
leg.get_frame().set_linewidth(0.1)
plt.ylim(0, 90)
plt.yticks([0,10,20,30,40,50,60,70,80,90])
plt.tight_layout()
plt.savefig('C:\\Users\\86156\\Desktop\\f1_1.png')
#plt.savefig('C:/Users/Lenovo/Desktop/New_Figure')
plt.show()