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HaChoi_2016.py
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HaChoi_2016.py
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import sys
import custom_model as cm
import numpy as np
import random
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from sklearn.metrics.classification import accuracy_score, recall_score, f1_score
import scipy.stats as st
from keras.layers import Input, Dense, Dropout, Conv2D, Flatten, MaxPooling2D, Activation, Concatenate, merge
from keras.callbacks import ReduceLROnPlateau, EarlyStopping, Callback
from keras.models import Model
from keras import backend as K
K.set_image_data_format('channels_first')
def custom_model(X_shape, idx_modalities, n_classes):
# Architecture for PAMAP and MHEALTH datasets
img_cols1 = idx_modalities[0]
img_cols2 = idx_modalities[1] - idx_modalities[0]
img_cols3 = idx_modalities[2] - idx_modalities[1]
img_cols4 = X.shape[3] - idx_modalities[2]
_, _, img_rows, img_cols = X.shape
inp_modality1 = Input((1, img_rows, img_cols1))
inp_modality2 = Input((1, img_rows, img_cols2))
inp_modality3 = Input((1, img_rows, img_cols3))
inp_modality4 = Input((1, img_rows, img_cols4))
H1 = Conv2D(filters=5, kernel_size=(5, 5))(inp_modality1)
H1 = Activation('relu')(H1)
H1 = MaxPooling2D(pool_size=(4, 4))(H1)
H2 = Conv2D(filters=5, kernel_size=(5, 5))(inp_modality2)
H2 = Activation('relu')(H2)
H2 = MaxPooling2D(pool_size=(4, 4))(H2)
H3 = Conv2D(filters=5, kernel_size=(5, 5))(inp_modality3)
H3 = Activation('relu')(H3)
H3 = MaxPooling2D(pool_size=(4, 4))(H3)
H4 = Conv2D(filters=5, kernel_size=(5, 3))(inp_modality4)#For PAMAP
#H4 = Conv2D(filters=5, kernel_size=(5, 2))(inp_modality4) # For MEHEALTH
H4 = Activation('relu')(H4)
H4 = MaxPooling2D(pool_size=(4, 1))(H4)
shape_1 = int(H2.shape[1].value)
shape_2 = int(H2.shape[2].value)
shape_3 = int(H2.shape[3].value)
inp_zeros = Input((shape_1, shape_2, shape_3)) # Here is the features map shape
H = merge([H1, inp_zeros, H2, inp_zeros, H3, inp_zeros, H4], mode='concat', concat_axis=3)
H = Conv2D(filters=10, kernel_size=(5, 5))(H)
H = Activation('relu')(H)
H = MaxPooling2D(pool_size=(2, 2))(H)
H = Flatten()(H)
H = Dense(120)(H)
H = Activation('relu')(H)
H = Dense(n_classes)(H)
H = Activation('softmax')(H)
model = Model([inp_modality1, inp_modality2, inp_modality3, inp_modality4, inp_zeros], H)
return model, (shape_1, shape_2, shape_3)
def zero_padding_PAMAP(X):
# Groups the heterogenous sensors for PAMAP
idx_modalities = []
idx_acc = [1, 2, 3, 4, 5, 6, 14, 15, 16, 17, 18, 19, 27, 28, 29, 30, 31, 32]
idx_gyro = [7, 8, 9, 20, 21, 22, 33, 34, 35]
idx_mag = [10, 11, 12, 23, 24, 25, 36, 37, 38]
idx_temp = [0, 13, 26]
X_acc = X[:, :, :, idx_acc]
X_gyro = X[:, :, :, idx_gyro]
X_mag = X[:, :, :, idx_mag]
X_temp = X[:, :, :, idx_temp]
X_zeros = np.zeros((X.shape[0], X.shape[1], X.shape[2], 2)) # Vertical Kernel-1 = 2
X = X_acc
X = np.concatenate((X, X_zeros), axis=3)
idx_modalities.append(X.shape[3])
X = np.concatenate((X, X_gyro), axis=3)
X = np.concatenate((X, X_zeros),axis=3)
idx_modalities.append(X.shape[3])
X = np.concatenate((X, X_mag),axis=3)
X = np.concatenate((X, X_zeros),axis=3)
idx_modalities.append(X.shape[3])
X = np.concatenate((X, X_temp),axis=3)
return X, idx_modalities
def zero_padding_MHEALTH(X):
# Groups the heterogenous sensors for MHEALTH
idx_modalities = []
idx_acc = [0, 1, 2, 5, 6, 7, 14, 15, 16]
idx_gyro = [8, 9, 10, 17, 18, 19]
idx_mag = [11, 12, 13, 20, 21, 22]
idx_ele = [3, 4]
X_acc = X[:, :, :, idx_acc]
X_gyro = X[:, :, :, idx_gyro]
X_mag = X[:, :, :, idx_mag]
X_ele = X[:, :, :, idx_ele]
X_zeros = np.zeros((X.shape[0], X.shape[1], X.shape[2], 2)) # Vertical Kernel-1 = 2
X = X_acc
X = np.concatenate((X, X_zeros), axis=3)
idx_modalities.append(X.shape[3])
X = np.concatenate((X, X_gyro), axis=3)
X = np.concatenate((X, X_zeros),axis=3)
idx_modalities.append(X.shape[3])
X = np.concatenate((X, X_mag),axis=3)
X = np.concatenate((X, X_zeros),axis=3)
idx_modalities.append(X.shape[3])
X = np.concatenate((X, X_ele),axis=3)
return X, idx_modalities
def split_X(X, idx_modalities, zeros):
X_tmp = []
X_tmp.append(X[:, :, :, 0:idx_modalities[0]])
X_tmp.append(X[:, :, :, idx_modalities[0]:idx_modalities[1]])
X_tmp.append(X[:, :, :, idx_modalities[1]:idx_modalities[2]])
X_tmp.append(X[:, :, :, idx_modalities[2]:X.shape[3]])
X_tmp.append(zeros)
return X_tmp
if __name__ == '__main__':
#Paper: Multi-modal convolutional neural networks for activity recognition
np.random.seed(12227)
if (len(sys.argv) > 1):
data_input_file = sys.argv[1]
else:
data_input_file = 'data/LOSO/MHEALTH.npz'
tmp = np.load(data_input_file)
X = tmp['X']
y = tmp['y']
folds = tmp['folds']
n_class = y.shape[1]
#####Groups the heterogenous sensors for PAMAP2P##################
#X, idx_modalities = zero_padding_PAMAP(X)
X, idx_modalities = zero_padding_MHEALTH(X)
_, _, img_rows, img_cols = X.shape
avg_acc = []
avg_recall = []
avg_f1 = []
print('Ha and Choi 2016 {}'.format(data_input_file))
for i in range(0, len(folds)):
train_idx = folds[i][0]
test_idx = folds[i][1]
X_train = X[train_idx]
X_test = X[test_idx]
model, inp_zeros = custom_model(X.shape, idx_modalities, n_classes=n_class)
zeros_mat = np.zeros((X_train.shape[0], inp_zeros[0], inp_zeros[1], inp_zeros[2]))
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='Adadelta')
X_train = split_X(X_train, idx_modalities, zeros_mat)
model.fit(X_train, y[train_idx], batch_size=cm.bs, epochs=cm.n_ep,
verbose=0, callbacks=[cm.custom_stopping(value=cm.loss, verbose=1)], validation_data=(X_train, y[train_idx]))
X_test = split_X(X_test, idx_modalities, zeros_mat)
y_pred = model.predict(X_test)
y_pred = np.argmax(y_pred, axis=1)
y_true = np.argmax(y[test_idx], axis=1)
acc_fold = accuracy_score(y_true, y_pred)
avg_acc.append(acc_fold)
recall_fold = recall_score(y_true, y_pred, average='macro')
avg_recall.append(recall_fold)
f1_fold = f1_score(y_true, y_pred, average='macro')
avg_f1.append(f1_fold)
print('Accuracy[{:.4f}] Recall[{:.4f}] F1[{:.4f}] at fold[{}]'.format(acc_fold, recall_fold, f1_fold, i))
print('______________________________________________________')
del model
ic_acc = st.t.interval(0.9, len(avg_acc) - 1, loc=np.mean(avg_acc), scale=st.sem(avg_acc))
ic_recall = st.t.interval(0.9, len(avg_recall) - 1, loc=np.mean(avg_recall), scale=st.sem(avg_recall))
ic_f1 = st.t.interval(0.9, len(avg_f1) - 1, loc=np.mean(avg_f1), scale=st.sem(avg_f1))
print('Mean Accuracy[{:.4f}] IC [{:.4f}, {:.4f}]'.format(np.mean(avg_acc), ic_acc[0], ic_acc[1]))
print('Mean Recall[{:.4f}] IC [{:.4f}, {:.4f}]'.format(np.mean(avg_recall), ic_recall[0], ic_recall[1]))
print('Mean F1[{:.4f}] IC [{:.4f}, {:.4f}]'.format(np.mean(avg_f1), ic_f1[0], ic_f1[1]))