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ML_binary.py
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# +
# +
import pandas as pd
import numpy as np
import csv
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn import tree
from sklearn import svm
from sklearn.preprocessing import StandardScaler
from sklearn import metrics
from sklearn.metrics import roc_curve, auc, roc_auc_score, f1_score
from sklearn.metrics import recall_score, precision_score, accuracy_score
from sklearn.metrics import confusion_matrix
feats = ['HR_mean','HR_std','meanNN','SDNN','medianNN','meanSD','SDSD','RMSSD','pNN20','pNN50','TINN','LF','HF','ULF','VLF','LFHF',
'total_power','lfp','hfp','SD1','SD2','pA','pQ','ApEn','shanEn','D2','subject','label']
WINDOW_SIZE = '120'
NOISE = ['bp_time_ens']
subjects = [2,3,4,5,6,7,8,9,10,11,13,14,15,16,17]
# +
from collections import Counter
def read_csv(path, feats, testset_num):
#print("testset num: ",testset_num)
df = pd.read_csv(path, index_col = 0)
df = df[feats]
train_df = df.loc[df['subject'] != testset_num]
test_df = df.loc[df['subject'] == testset_num]
del train_df['subject']
del test_df['subject']
del df['subject']
X_train = train_df.drop('label', axis=1).values
y_train = train_df['label'].values
X_test = test_df.drop('label', axis=1).values
y_test = test_df['label'].values
return df, X_train, y_train, X_test, y_test
# -
# # Machine learning
# +
def DT_model(X_train, y_train, X_test, y_test):
model = tree.DecisionTreeClassifier(random_state=0)
model.fit(X_train,y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
AUC = roc_auc_score(y_test, y_pred)
F1 = f1_score(y_test, y_pred)
return AUC, F1, accuracy
def RF_model(X_train, y_train, X_test, y_test):
model = RandomForestClassifier(max_depth=4, random_state=0)
model.fit(X_train,y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
AUC = roc_auc_score(y_test, y_pred)
F1 = f1_score(y_test, y_pred)
return AUC, F1, accuracy
def AB_model(X_train, y_train, X_test, y_test):
model = AdaBoostClassifier(random_state=0)
model.fit(X_train,y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
AUC = roc_auc_score(y_test, y_pred)
F1 = f1_score(y_test, y_pred)
return AUC, F1, accuracy
def KN_model(X_train, y_train, X_test, y_test):
model = KNeighborsClassifier(n_neighbors=9)
model.fit(X_train,y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
AUC = roc_auc_score(y_test, y_pred)
F1 = f1_score(y_test, y_pred)
return AUC, F1, accuracy
def LDA_model(X_train, y_train, X_test, y_test):
model = LinearDiscriminantAnalysis()
model.fit(X_train,y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
AUC = roc_auc_score(y_test, y_pred)
F1 = f1_score(y_test, y_pred)
return AUC, F1, accuracy
def SVM_model(X_train, y_train, X_test, y_test):
model = svm.SVC()
model.fit(X_train,y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
AUC = roc_auc_score(y_test, y_pred)
F1 = f1_score(y_test, y_pred)
return AUC, F1, accuracy
def GB_model(X_train, y_train, X_test, y_test):
model = GradientBoostingClassifier(random_state=0)
model.fit(X_train,y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
AUC = roc_auc_score(y_test, y_pred)
F1= f1_score(y_test, y_pred)
return AUC, F1, accuracy
# +
for n in NOISE:
path = '27_features_ppg_test/bi/ens/3/data_merged_' + n + '.csv'
result_path_all = 'result/bi/ens/3/all_features_' + n + '.csv'
result_path_all = 'bb.csv'
DT_AUC, DT_F1, DT_ACC = [], [], []
RF_AUC, RF_F1, RF_ACC = [], [], []
AB_AUC, AB_F1, AB_ACC = [], [], []
KN_AUC, KN_F1, KN_ACC = [], [], []
LDA_AUC, LDA_F1, LDA_ACC = [], [], []
SVM_AUC, SVM_F1, SVM_ACC = [], [], []
GB_AUC, GB_F1, GB_ACC = [], [], []
for sub in subjects:
df, X_train, y_train, X_test, y_test = read_csv(path, feats, sub)
df.fillna(0)
# Normalization
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
auc_dt, f1_dt, acc_dt = DT_model(X_train, y_train, X_test, y_test)
auc_rf, f1_rf, acc_rf = RF_model(X_train, y_train, X_test, y_test)
auc_ab, f1_ab, acc_ab = AB_model(X_train, y_train, X_test, y_test)
auc_kn, f1_kn, acc_kn = KN_model(X_train, y_train, X_test, y_test)
auc_lda, f1_lda, acc_lda = LDA_model(X_train, y_train, X_test, y_test)
auc_svm, f1_svm, acc_svm = SVM_model(X_train, y_train, X_test, y_test)
auc_gb, f1_gb, acc_gb = GB_model(X_train, y_train, X_test, y_test)
DT_AUC.append(auc_dt)
DT_F1.append(f1_dt)
DT_ACC.append(acc_dt)
RF_AUC.append(auc_rf)
RF_F1.append(f1_rf)
RF_ACC.append(acc_rf)
AB_AUC.append(auc_ab)
AB_F1.append(f1_ab)
AB_ACC.append(acc_ab)
KN_AUC.append(auc_kn)
KN_F1.append(f1_kn)
KN_ACC.append(f1_kn)
LDA_AUC.append(auc_lda)
LDA_F1.append(f1_lda)
LDA_ACC.append(acc_lda)
SVM_AUC.append(auc_svm)
SVM_F1.append(f1_svm)
SVM_ACC.append(acc_svm)
GB_AUC.append(auc_gb)
GB_F1.append(f1_gb)
GB_ACC.append(acc_gb)
with open(result_path_all, 'w', newline='') as file:
writer = csv.writer(file)
writer.writerow(['subject','S2','S3','S4','S5','S6','S7','S8','S9','S10','S11','S13','S14','S15','S16','S17','total'])
writer.writerow(['DT_AUC'] + DT_AUC + [np.mean(DT_AUC)])
writer.writerow(['RF_AUC'] + RF_AUC + [np.mean(RF_AUC)])
writer.writerow(['AB_AUC'] + AB_AUC + [np.mean(AB_AUC)])
writer.writerow(['KN_AUC'] + KN_AUC + [np.mean(KN_AUC)])
writer.writerow(['LDA_AUC'] + LDA_AUC + [np.mean(LDA_AUC)])
writer.writerow(['SVM_AUC'] + SVM_AUC + [np.mean(SVM_AUC)])
writer.writerow(['GB_AUC'] + GB_AUC + [np.mean(GB_AUC)])
writer.writerow(['DT_F1'] + DT_F1 + [np.mean(DT_F1)])
writer.writerow(['RF_F1'] + RF_F1 + [np.mean(RF_F1)])
writer.writerow(['AB_F1'] + AB_F1 + [np.mean(AB_F1)])
writer.writerow(['KN_F1'] + KN_F1 + [np.mean(KN_F1)])
writer.writerow(['LDA_F1'] + LDA_F1 + [np.mean(LDA_F1)])
writer.writerow(['SVM_F1'] + SVM_F1 + [np.mean(SVM_F1)])
writer.writerow(['GB_F1'] + GB_F1 + [np.mean(GB_F1)])
writer.writerow(['DT_ACC'] + DT_ACC + [np.mean(DT_ACC)])
writer.writerow(['RF_ACC'] + RF_ACC + [np.mean(RF_ACC)])
writer.writerow(['AB_ACC'] + AB_ACC + [np.mean(AB_ACC)])
writer.writerow(['KN_ACC'] + KN_ACC + [np.mean(KN_ACC)])
writer.writerow(['LDA_ACC'] + LDA_ACC + [np.mean(LDA_ACC)])
writer.writerow(['SVM_ACC'] + SVM_ACC + [np.mean(SVM_ACC)])
writer.writerow(['GB_ACC'] + GB_ACC + [np.mean(GB_ACC)])
file.close()
print("DONE: ",n)
# -
# ####