def classification_participant_dependent(csv, normalizerMethod, normalizer1,
normalizer2, totalDays,
leave_one_patient):
X_train = []
Y_train = []
X_val = []
Y_val = []
X_test = []
Y_test = []
for i in range(totalDays - 1, len(csv)):
days = data_collector.collectDayData(csv, i, totalDays)
userid = days[0][1]
if data_collector.isSameUserAcross(days):
x = transform_to_train_vec.transform(days, True, "SVM", totalDays,
"z-score", normalizer1,
normalizer2)
if days[0][EMA_INDEX] != '':
X_train, Y_train, X_val, Y_val, X_test, Y_test = assign_data.dependent_assign(
False, 0.60, 0.75, X_train, Y_train, X_val, Y_val, X_test,
Y_test, x, days[0][EMA_INDEX], leave_one_patient, userid)
# FIX class_weight
clf = LinearSVC(random_state=0, C=1)
#clf = svm.SVC(random_state=0, class_weight={0:0.3, 1:0.25, 2:0.2, 3:0.2})
clf.fit(X_train, Y_train)
preds = clf.predict(X_test)
return preds, Y_test
def classify_participant_independent(csv, trainingid, validationid, testingid, normalizer_type, normalizer1, normalizer2, totalDays):
X_train = []
Y_train = []
X_val = []
Y_val = []
X_test = []
Y_test = []
for i in range(totalDays, len(csv)):
days = data_collector.collectDayData(csv, i, totalDays)
userid = days[0][1]
if data_collector.isSameUserAcross(days):
x = transform_to_train_vec.transform(
days,
False,
"LSTM",
totalDays,
normalizer_type,
normalizer1,
normalizer2
)
# put the x vector into the appropriate set (i.e. training, validation, testing)
if days[0][EMA_INDEX] != '':
X_train, Y_train, X_val, Y_val, X_test, Y_test = assign_data.independent_assign(
True,
days[0][1],
trainingid,
validationid,
testingid,
X_train,
Y_train,
X_val,
Y_val,
X_test,
Y_test,
x,
days[0][EMA_INDEX]
)
model = Sequential()
model.add(LSTM(64, return_sequences=True, input_shape=(totalDays, len(X_train[0][0]))))
model.add(LSTM(64, return_sequences=True, recurrent_dropout=0.2))
model.add(LSTM(64))
model.add(Dense(4, activation='softmax', kernel_regularizer=L1L2(l1=0.0, l2=0.0)))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['categorical_accuracy'])
if len(X_val) == 0:
model.fit(X_train, Y_train, epochs=5)
else:
model.fit(X_train, Y_train, epochs=5, validation_data=(X_val, Y_val))
y_pred = model.predict(X_test)
return prediction_utilities.convert_preds_into_ema(y_pred), Y_test
def regression_participant_independent(csv, trainingid, validationid, testingid, normalizerMethod, normalizer1, normalizer2, totalDays):
X_train = []
Y_train = []
X_val = []
Y_val = []
X_test = []
Y_test = []
for i in range(totalDays, len(csv)):
days = data_collector.collectDayData(csv, i, totalDays)
if data_collector.isSameUserAcross(days):
x = transform_to_train_vec.transform(
days,
False,
"LSTM",
totalDays,
normalizerMethod,
normalizer1,
normalizer2
)
# put the x vector into the appropriate set (i.e. training, validation, testing)
if days[0][EMA_INDEX] != '':
X_train, Y_train, X_val, Y_val, X_test, Y_test = assign_data.independent_assign(
False,
days[0][1],
trainingid,
validationid,
testingid,
X_train,
Y_train,
X_val,
Y_val,
X_test,
Y_test,
x,
days[0][EMA_INDEX]
)
model = Sequential()
model.add(LSTM(128, return_sequences=True, input_shape=(totalDays, len(X_train[0][0]))))
model.add(LSTM(128, return_sequences=True, recurrent_dropout=0.2))
model.add(LSTM(64))
model.add(Dense(128, activation='tanh'))
model.add(Dense(1, activation='softmax', kernel_regularizer=L1L2(l1=0.0, l2=0.0)))
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['acc'])
if len(X_val) == 0:
model.fit(X_train, Y_train, epochs=30)
else:
model.fit(X_train, Y_train, epochs=30, validation_data=(X_val, Y_val))
y_pred = model.predict(X_test)
return y_pred, Y_test
def classification_participant_dependent(csv, normalizerMethod, normalizer1,
normalizer2, totalDays,
leave_one_patient):
X_train = []
Y_train = []
X_val = []
Y_val = []
X_test = []
Y_test = []
for i in range(totalDays - 1, len(csv)):
days = data_collector.collectDayData(csv, i, totalDays)
userid = days[0][1]
if data_collector.isSameUserAcross(days):
x = transform_to_train_vec.transform(days, True, "simpleNN",
totalDays, "z-score",
normalizer1, normalizer2)
if days[0][EMA_INDEX] != '':
X_train, Y_train, X_val, Y_val, X_test, Y_test = assign_data.dependent_assign(
True, 0.60, 0.75, X_train, Y_train, X_val, Y_val, X_test,
Y_test, x, days[0][EMA_INDEX], leave_one_patient, userid)
model = Sequential()
model.add(Dense(1024, input_dim=len(X_train[0]), activation='relu'))
model.add(Dense(512, activation='relu'))
model.add(Dense(512, activation='relu'))
model.add(Dense(32, activation='relu'))
#model.add(Dropout(0.4))
model.add(
Dense(4,
activation='softmax',
kernel_regularizer=L1L2(l1=0.0, l2=0.25)))
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['categorical_accuracy'])
if len(X_val) == 0:
model.fit(X_train, Y_train, epochs=4)
else:
model.fit(X_train, Y_train, epochs=4, validation_data=(X_val, Y_val))
y_pred = model.predict(X_test)
return prediction_utilities.convert_preds_into_ema(y_pred), Y_test
def classification_participant_independent(csv, trainingid, validationid,
testingid, normalizerMethod,
normalizer1, normalizer2,
totalDays):
X_train = []
Y_train = []
X_val = []
Y_val = []
X_test = []
Y_test = []
for i in range(totalDays - 1, len(csv)):
days = data_collector.collectDayData(csv, i, totalDays)
if data_collector.isSameUserAcross(days):
x = transform_to_train_vec.transform(days, True, "simpleNN",
totalDays, "z-score",
normalizer1, normalizer2)
# put the x vector into the appropriate set (i.e. training, validation, testing)
if days[0][EMA_INDEX] != '':
X_train, Y_train, X_val, Y_val, X_test, Y_test = assign_data.independent_assign(
True, days[0][1], trainingid, validationid, testingid,
X_train, Y_train, X_val, Y_val, X_test, Y_test, x,
days[0][EMA_INDEX])
model = Sequential()
model.add(Dense(1024, input_dim=len(X_train[0]), activation='relu'))
model.add(Dense(256, activation='relu'))
model.add(
Dense(4,
activation='softmax',
kernel_regularizer=L1L2(l1=0.0, l2=0.15)))
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(X_train, Y_train, epochs=30, validation_data=(X_val, Y_val))
y_pred = model.predict(X_test)
return prediction_utilities.convert_preds_into_ema(y_pred), Y_test
def regression_participant_independent(csv, trainingid, validationid,
testingid, normalizerMethod,
normalizer1, normalizer2, totalDays):
X_train = []
Y_train = []
X_val = []
Y_val = []
X_test = []
Y_test = []
for i in range(totalDays - 1, len(csv)):
days = data_collector.collectDayData(csv, i, totalDays)
if data_collector.isSameUserAcross(days):
x = transform_to_train_vec.transform(days, True, "simpleNN",
totalDays, normalizerMethod,
normalizer1, normalizer2)
# put the x vector into the appropriate set (i.e. training, validation, testing)
if days[0][EMA_INDEX] != '':
X_train, Y_train, X_val, Y_val, X_test, Y_test = assign_data.independent_assign(
False, days[0][1], trainingid, validationid, testingid,
X_train, Y_train, X_val, Y_val, X_test, Y_test, x,
days[0][EMA_INDEX])
model = Sequential()
model.add(Dense(1024, input_dim=len(X_train[0]), activation='relu'))
model.add(Dense(1024, activation='relu'))
model.add(
Dense(1,
activation='softmax',
kernel_regularizer=L1L2(l1=0.0, l2=0.15)))
model.compile(optimizer='rmsprop',
loss='mean_squared_error',
metrics=['acc'])
model.fit(X_train, Y_train, epochs=15, validation_data=(X_val, Y_val))
y_pred = model.predict(X_test)
return y_pred, Y_test
def regression_participant_dependent(csv, normalizerMethod, normalizer1,
normalizer2, totalDays,
leave_one_patient):
X_train = []
Y_train = []
X_val = []
Y_val = []
X_test = []
Y_test = []
for i in range(totalDays - 1, len(csv)):
days = data_collector.collectDayData(csv, i, totalDays)
userid = days[0][1]
if data_collector.isSameUserAcross(days):
x = transform_to_train_vec.transform(days, True, "simpleNN",
totalDays, normalizerMethod,
normalizer1, normalizer2)
if days[0][EMA_INDEX] != '':
X_train, Y_train, X_val, Y_val, X_test, Y_test = assign_data.dependent_assign(
False, 0.60, 0.75, X_train, Y_train, X_val, Y_val, X_test,
Y_test, x, days[0][EMA_INDEX], leave_one_patient, userid)
model = Sequential()
model.add(Dense(1024, input_dim=len(X_train[0]), activation='relu'))
model.add(Dense(1024, activation='relu'))
model.add(
Dense(1, activation='relu', kernel_regularizer=L1L2(l1=0.0, l2=0.2)))
model.compile(optimizer='adam', loss='mean_squared_error', metrics=['acc'])
model.fit(X_train, Y_train, epochs=15, validation_data=(X_val, Y_val))
y_pred = model.predict(X_test)
return y_pred, Y_test
def classification_participant_independent(csv, trainingid, validationid,
testingid, normalizerMethod,
normalizer1, normalizer2,
totalDays):
X_train = []
Y_train = []
X_val = []
Y_val = []
X_test = []
Y_test = []
for i in range(totalDays - 1, len(csv)):
days = data_collector.collectDayData(csv, i, totalDays)
if data_collector.isSameUserAcross(days):
x = transform_to_train_vec.transform(days, True, "SVM", totalDays,
"z-score", normalizer1,
normalizer2)
# put the x vector into the appropriate set (i.e. training, validation, testing)
if days[0][EMA_INDEX] != '':
X_train, Y_train, X_val, Y_val, X_test, Y_test = assign_data.independent_assign(
False, days[0][1], trainingid, validationid, testingid,
X_train, Y_train, X_val, Y_val, X_test, Y_test, x,
days[0][EMA_INDEX])
clf = LinearSVC(random_state=0,
class_weight={
0: 0.3,
1: 0.25,
2: 0.25,
3: 0.20
})
clf.fit(X_train, Y_train)
preds = clf.predict(X_test)
return preds, Y_test
def classify_participant_dependent(csv, normalizer_type, normalizer1, normalizer2, totalDays, leave_one_patient):
X_train = []
Y_train = []
X_val = []
Y_val = []
X_test = []
Y_test = []
for i in range(totalDays - 1, len(csv)):
days = data_collector.collectDayData(csv, i, totalDays)
userid = days[0][1]
if data_collector.isSameUserAcross(days):
x = transform_to_train_vec.transform(
days,
False,
"LSTM",
totalDays,
normalizer_type,
normalizer1,
normalizer2
)
if days[0][EMA_INDEX] != '':
X_train, Y_train, X_val, Y_val, X_test, Y_test = assign_data.dependent_assign(
True,
0.60,
0.75,
X_train,
Y_train,
X_val,
Y_val,
X_test,
Y_test,
x,
days[0][EMA_INDEX],
leave_one_patient,
userid
)
model = Sequential()
model.add(Bidirectional(LSTM(150, return_sequences=False, recurrent_dropout=0.2, bias_initializer = keras.initializers.Constant(value=0.15)), input_shape=(len(X_train[0]), len(X_train[0][0]))))
model.add(Dense(4, activation='softmax', kernel_regularizer=L1L2(l1=0.0, l2=0.0)))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['categorical_accuracy'])
if leave_one_patient is None:
model.fit(X_train, Y_train, epochs=9, validation_data=(X_val, Y_val))
else:
model.fit(X_train, Y_train, epochs=9)
y_pred = model.predict(X_test)
# return the predictions and the truth values
return prediction_utilities.convert_preds_into_ema(y_pred), Y_test
def regression_participant_dependent(csv, normalizerMethod, normalizer1, normalizer2, totalDays, leave_one_patient):
X_train = []
Y_train = []
X_val = []
Y_val = []
X_test = []
Y_test = []
for i in range(totalDays - 1, len(csv)):
days = data_collector.collectDayData(csv, i, totalDays)
userid = days[0][1]
if data_collector.isSameUserAcross(days):
x = transform_to_train_vec.transform(
days,
False,
"LSTM",
totalDays,
normalizerMethod,
normalizer1,
normalizer2
)
if days[0][EMA_INDEX] != '':
X_train, Y_train, X_val, Y_val, X_test, Y_test = assign_data.dependent_assign(
False,
0.60,
0.75,
X_train,
Y_train,
X_val,
Y_val,
X_test,
Y_test,
x,
days[0][EMA_INDEX],
leave_one_patient,
userid
)
print(X_test.shape)
model = Sequential()
# model.add(Bidirectional(LSTM(128, return_sequences=True), input_shape=(totalDays, len(X_train[0][0]))))
# model.add(LSTM(128, return_sequences=True, recurrent_dropout=0.20))
# model.add(LSTM(64))
# model.add(Dropout(0.4))
model.add(LSTM(128, return_sequences=True, input_shape=(totalDays, len(X_train[0][0]))))
model.add(LSTM(128, return_sequences=True, recurrent_dropout=0.2))
model.add(LSTM(64))
model.add(Dense(128, activation='tanh'))
model.add(Dense(1, activation='relu'))
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['acc'])
if leave_one_patient is None:
model.fit(X_train, Y_train, epochs=25, validation_data=(X_val, Y_val))
else:
model.fit(X_train, Y_train, epochs=25)
y_pred = model.predict(X_test)
# return the predictions and the truth values
return y_pred, Y_test