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# -*- coding: utf-8 -*- | |
import numpy as np | |
import pandas as pd | |
from scipy.signal import butter, lfilter | |
from sklearn.linear_model import LogisticRegression | |
from sklearn.lda import LDA | |
from sklearn.qda import QDA | |
from sklearn.preprocessing import StandardScaler | |
from sklearn.ensemble import RandomForestClassifier | |
from sklearn.metrics import roc_auc_score | |
#############function to read data########### | |
FNAME = "input/{0}/subj{1}_series{2}_{3}.csv" | |
def load_data(subj, series=range(1,9), prefix = 'train'): | |
data = [pd.read_csv(FNAME.format(prefix,subject,s,'data'), index_col=0) for s in series] | |
idx = [d.index for d in data] | |
data = [d.values.astype(float) for d in data] | |
if prefix == 'train': | |
events = [pd.read_csv(FNAME.format(prefix,subject,s,'events'), index_col=0).values for s in series] | |
return data, events | |
else: | |
return data, idx | |
def compute_features(X, scale=None): | |
X0 = [x[:,0] for x in X] | |
X = np.concatenate(X, axis=0) | |
F = []; | |
for fc in np.linspace(0,1,11)[1:]: | |
b,a = butter(3,fc/250.0,btype='lowpass') | |
F.append(np.concatenate([lfilter(b,a,x0) for x0 in X0], axis=0)[:,np.newaxis]) | |
F = np.concatenate(F, axis=1) | |
F = np.concatenate((X,F,F**2), axis=1) | |
if scale is None: | |
scale = StandardScaler() | |
F = scale.fit_transform(F) | |
return F, scale | |
else: | |
F = scale.transform(F) | |
return F | |
#%%########### Initialize #################################################### | |
cols = ['HandStart','FirstDigitTouch', | |
'BothStartLoadPhase','LiftOff', | |
'Replace','BothReleased'] | |
subjects = range(1,13) | |
idx_tot = [] | |
scores_tot = [] | |
###loop on subjects and 8 series for train data + 2 series for test data | |
for subject in subjects: | |
X_train, y = load_data(subject) | |
X_test, idx = load_data(subject,[9,10],'test') | |
################ Train classifiers ########################################### | |
lda = LDA() | |
rf = RandomForestClassifier(n_estimators=200, n_jobs=-1, criterion="entropy", random_state=1) | |
lr = LogisticRegression() | |
X_train, scaler = compute_features(X_train) | |
X_test = compute_features(X_test, scaler) #pass the learned mean and std to normalized test data | |
y = np.concatenate(y,axis=0) | |
scores = np.empty((X_test.shape[0],6)) | |
downsample = 40 | |
for i in range(6): | |
print('Train subject %d, class %s' % (subject, cols[i])) | |
rf.fit(X_train[::downsample,:], y[::downsample,i]) | |
lda.fit(X_train[::downsample,:], y[::downsample,i]) | |
lr.fit(X_train[::downsample,:], y[::downsample,i]) | |
scores[:,i] = (rf.predict_proba(X_test)[:,1]*0.3 + | |
lda.predict_proba(X_test)[:,1]*0.4 + | |
lr.predict_proba(X_test)[:,1]*0.3) | |
scores_tot.append(scores) | |
idx_tot.append(idx) | |
#idx_tot.append(np.concatenate(idx)) | |
# get AUC | |
auc = [roc_auc_score(idx[:,i],scores[:,i]) for i in range(6)] | |
auc_tot.append(auc) | |
print(auc) | |
scores_tot = np.concatenate(scores_tot) | |
idx_tot = np.concatenate(idx_tot) | |
global_auc = [roc_auc_score(idx_tot[:,i],scores_tot[:,i]) for i in range(6)] | |
print('Global AUC : %.4f' % np.mean(global_auc)) | |
#%%########### submission file ################################################ | |
submission_file = 'Submission.csv' | |
# create pandas object for submission | |
submission = pd.DataFrame(index=np.concatenate(idx_tot), | |
columns=cols, | |
data=np.concatenate(scores_tot)) | |
# write file | |
submission.to_csv(submission_file,index_label='id',float_format='%.3f') |
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