t2 = np.random.exponential(scale=1 / (np.log(2) / halflife2),
size=int(n_samples / 2))
t = np.concatenate((t1, t2))
censtime = np.random.exponential(scale=1 / (np.log(2) / (halflife_cens)),
size=n_samples)
f = t < censtime
t[~f] = censtime[~f]
y_train = nnet_survival.make_surv_array(t, f, breaks)
x_train = np.zeros(n_samples)
x_train[int(n_samples / 2):] = 1
model = Sequential()
#Hidden layers would go here. For this example, using simple linear model with no hidden layers.
model.add(Dense(1, input_dim=1, use_bias=0, kernel_initializer='zeros'))
model.add(nnet_survival.PropHazards(n_intervals))
model.compile(loss=nnet_survival.surv_likelihood(n_intervals),
optimizer=optimizers.RMSprop())
#model.summary()
early_stopping = EarlyStopping(monitor='loss', patience=2)
history = model.fit(x_train,
y_train,
batch_size=32,
epochs=1000,
callbacks=[early_stopping])
y_pred = model.predict_proba(x_train, verbose=0)
kmf = KaplanMeierFitter()
kmf.fit(t[0:int(n_samples / 2)], event_observed=f[0:int(n_samples / 2)])
plt.plot(breaks, np.concatenate(([1], np.cumprod(y_pred[0, :]))), 'bo-')
plt.plot(kmf.survival_function_.index.values,
def architecture(self,n_intervals):
#mrna_input
input_1 = Input(shape = (122,122,1))
mrna_conv_1 = Convolution2D(256, (3, 3), kernel_initializer='glorot_normal')(input_1)
mrna_bn_1 = BatchNormalization()(mrna_conv_1)
mrna_act_1 = Activation('relu')(mrna_bn_1)
mrna_pool_1 = MaxPooling2D(pool_size = (2,2))(mrna_act_1)
mrna_conv_2 = Convolution2D(256, (3, 3), kernel_initializer='glorot_normal')(mrna_pool_1)
mrna_bn_2 = BatchNormalization()(mrna_conv_2)
mrna_act_2 = Activation('relu')(mrna_bn_2)
mrna_pool_2 = MaxPooling2D(pool_size = (2,2))(mrna_act_2)
flat_1 = Flatten()(mrna_pool_2)
#meth_input
input_2 = Input(shape = (122,122,1))
meth_conv_1 = Convolution2D(256, (3, 3), kernel_initializer='glorot_normal')(input_2)
meth_bn_1 = BatchNormalization()(meth_conv_1)
meth_act_1 = Activation('relu')(meth_bn_1)
meth_pool_1 = MaxPooling2D(pool_size = (2,2))(meth_act_1)
meth_conv_2 = Convolution2D(256, (3, 3), kernel_initializer='glorot_normal')(meth_pool_1)
meth_bn_2 = BatchNormalization()(meth_conv_2)
meth_act_2 = Activation('relu')(meth_bn_2)
meth_pool_2 = MaxPooling2D(pool_size = (2,2))(meth_act_2)
flat_2 = Flatten()(meth_pool_2)
#mirna_input
input_3 = Input(shape = (42,42,1))
mirna_conv_1 = Convolution2D(256, (3, 3), kernel_initializer='glorot_normal')(input_3)
mirna_bn_1 = BatchNormalization()(mirna_conv_1)
mirna_act_1 = Activation('relu')(mirna_bn_1)
mirna_pool_1 = MaxPooling2D(pool_size = (2,2))(mirna_act_1)
mirna_conv_2 = Convolution2D(256, (3, 3), kernel_initializer='glorot_normal')(mirna_pool_1)
mirna_bn_2 = BatchNormalization()(mirna_conv_2)
mirna_act_2 = Activation('relu')(mirna_bn_2)
mirna_pool_2 = MaxPooling2D(pool_size = (2,2))(mirna_act_2)
flat_3 = Flatten()(mirna_pool_2)
#clinical_input
input_4 = Input(shape=(22, ), name='clinical')
dense = Dense(1, activation='relu', kernel_initializer='glorot_normal')(input_4)
#flat4 = Flatten()(dense)
if self.omics == 'mrna':
if self.clinical:
concat = Concatenate()([flat_1, dense])
else:
concat = flat_1
dense_1 = Dense(512, activation = 'relu',kernel_initializer='glorot_normal')(concat)
dense_1_dropout = Dropout(0.5)(dense_1)
dense_2 = Dense(128, activation = 'relu',kernel_initializer='glorot_normal')(dense_1_dropout)
dense_2_dropout = Dropout(0.1)(dense_2)
if self.PH:
dense_3 = Dense(1, use_bias=0, kernel_initializer='zeros')(dense_2_dropout)
output = nnet_survival.PropHazards(n_intervals)(dense_3)
else:
output = Dense(n_intervals, activation='sigmoid', kernel_initializer='he_normal')(dense_2_dropout)
if self.clinical:
model = Model(inputs=[input_1, input_4], outputs=[output])
else:
model = Model(inputs=[input_1], outputs=[output])
if self.omics == 'meth':
if self.clinical:
concat = Concatenate()([flat_2, dense])
else:
concat = flat_2
dense_1 = Dense(512, activation = 'relu',kernel_initializer='glorot_normal')(concat)
dense_1_dropout = Dropout(0.5)(dense_1)
dense_2 = Dense(128, activation = 'relu',kernel_initializer='glorot_normal')(dense_1_dropout)
dense_2_dropout = Dropout(0.1)(dense_2)
if self.PH:
dense_3 = Dense(1, use_bias=0, kernel_initializer='zeros')(dense_2_dropout)
output = nnet_survival.PropHazards(n_intervals)(dense_3)
else:
output = Dense(n_intervals, activation='sigmoid', kernel_initializer='he_normal')(dense_2_dropout)
if self.clinical:
model = Model(inputs=[input_2, input_4], outputs=[output])
else:
model = Model(inputs=[input_2], outputs=[output])
if self.omics == 'mirna':
if self.clinical:
concat = Concatenate()([flat_3, dense])
else:
concat = flat_3
dense_1 = Dense(512, activation = 'relu',kernel_initializer='glorot_normal')(concat)
dense_1_dropout = Dropout(0.5)(dense_1)
dense_2 = Dense(128, activation = 'relu',kernel_initializer='glorot_normal')(dense_1_dropout)
dense_2_dropout = Dropout(0.1)(dense_2)
if self.PH:
dense_3 = Dense(1, use_bias=0, kernel_initializer='zeros')(dense_2_dropout)
output = nnet_survival.PropHazards(n_intervals)(dense_3)
else:
output = Dense(n_intervals, activation='sigmoid', kernel_initializer='he_normal')(dense_2_dropout)
if self.clinical:
model = Model(inputs=[input_3,input_4], outputs=[output])
else:
model = Model(inputs=[input_3], outputs=[output])
if self.omics == 'mrna_meth':
if self.clinical:
concat = Concatenate()([flat_1, flat_2, dense])
else:
concat = Concatenate()([flat_1,flat_2])
dense_1 = Dense(512, activation = 'relu',kernel_initializer='glorot_normal')(concat)
dense_1_dropout = Dropout(0.5)(dense_1)
dense_2 = Dense(128, activation = 'relu',kernel_initializer='glorot_normal')(dense_1_dropout)
dense_2_dropout = Dropout(0.1)(dense_2)
if self.PH:
dense_3 = Dense(1, use_bias=0, kernel_initializer='zeros')(dense_2_dropout)
output = nnet_survival.PropHazards(n_intervals)(dense_3)
else:
output = Dense(n_intervals, activation='sigmoid', kernel_initializer='he_normal')(dense_2_dropout)
if self.clinical:
model = Model(inputs=[input_1,input_2,input_4], outputs=[output])
else:
model = Model(inputs=[input_1, input_2], outputs=[output])
if self.omics == 'mrna_mirna':
if self.clinical:
concat = Concatenate()([flat_1, flat_3, dense])
else:
concat = Concatenate()([flat_1,flat_3])
dense_1 = Dense(512, activation = 'relu',kernel_initializer='glorot_normal')(concat)
dense_1_dropout = Dropout(0.5)(dense_1)
dense_2 = Dense(128, activation = 'relu',kernel_initializer='glorot_normal')(dense_1_dropout)
dense_2_dropout = Dropout(0.1)(dense_2)
if self.PH:
dense_3 = Dense(1, use_bias=0, kernel_initializer='zeros')(dense_2_dropout)
output = nnet_survival.PropHazards(n_intervals)(dense_3)
else:
output = Dense(n_intervals, activation='sigmoid', kernel_initializer='he_normal')(dense_2_dropout)
if self.clinical:
model = Model(inputs=[input_1,input_3,input_4], outputs=[output])
else:
model = Model(inputs=[input_1, input_3], outputs=[output])
if self.omics == 'mrna_meth_mirna':
if self.clinical:
concat = Concatenate()([flat_1, flat_2, flat_3, dense])
else:
concat = Concatenate()([flat_1, flat_2, flat_3])
dense_1 = Dense(512, activation = 'relu',kernel_initializer='glorot_normal')(concat)
dense_1_dropout = Dropout(0.5)(dense_1)
dense_2 = Dense(128, activation = 'relu',kernel_initializer='glorot_normal')(dense_1_dropout)
dense_2_dropout = Dropout(0.1)(dense_2)
if self.PH:
dense_3 = Dense(1, use_bias=0, kernel_initializer='zeros')(dense_2_dropout)
output = nnet_survival.PropHazards(n_intervals)(dense_3)
else:
output = Dense(n_intervals, activation='sigmoid', kernel_initializer='he_normal')(dense_2)
if self.clinical:
model = Model(inputs=[input_1,input_2,input_3,input_4], outputs=[output])
else:
model = Model(inputs=[input_1,input_2,input_3], outputs=[output])
return model
def binary_ANN_surviavl():
breaks = np.arange(0, 5000, 50)
n_intervals = len(breaks) - 1
timegap = breaks[1:] - breaks[:-1]
halflife1 = 200
halflife2 = 400
halflife_cens = 400
n_samples = 5000
np.random.seed(seed=0)
t1 = np.random.exponential(scale=1 / (np.log(2) / halflife1),
size=int(n_samples / 2))
t2 = np.random.exponential(scale=1 / (np.log(2) / halflife2),
size=int(n_samples / 2))
t = np.concatenate((t1, t2))
censtime = np.random.exponential(scale=1 / (np.log(2) / (halflife_cens)),
size=n_samples)
f = t < censtime
t[~f] = censtime[~f]
y_train = nnet_survival.make_surv_array(t, f, breaks)
x_train = np.zeros(n_samples)
x_train[int(n_samples / 2):] = 1
model = Sequential()
# Hidden layers would go here. For this example, using simple linear model with no hidden layers.
model.add(Dense(1, input_dim=1, use_bias=0, kernel_initializer='zeros'))
model.add(nnet_survival.PropHazards(n_intervals))
model.compile(loss=nnet_survival.surv_likelihood(n_intervals),
optimizer=optimizers.RMSprop())
# model.summary()
early_stopping = EarlyStopping(monitor='loss', patience=2)
history = model.fit(x_train,
y_train,
batch_size=32,
epochs=1000,
callbacks=[early_stopping])
y_pred = model.predict_proba(x_train, verbose=0)
kmf = KaplanMeierFitter()
kmf.fit(t[0:int(n_samples / 2)], event_observed=f[0:int(n_samples / 2)])
plt.plot(breaks, np.concatenate(([1], np.cumprod(y_pred[0, :]))), 'bo-')
plt.plot(kmf.survival_function_.index.values,
kmf.survival_function_.KM_estimate,
color='k')
kmf.fit(t[int(n_samples / 2) + 1:],
event_observed=f[int(n_samples / 2) + 1:])
plt.plot(breaks, np.concatenate(([1], np.cumprod(y_pred[-1, :]))), 'ro-')
plt.plot(kmf.survival_function_.index.values,
kmf.survival_function_.KM_estimate,
color='k')
plt.xticks(np.arange(0, 2000.0001, 200))
plt.yticks(np.arange(0, 1.0001, 0.125))
plt.xlim([0, 2000])
plt.ylim([0, 1])
plt.xlabel('Follow-up time (days)')
plt.ylabel('Proportion surviving')
plt.title('One covariate. Actual=black, predicted=blue/red.')
plt.show()
myData = pd.DataFrame({'x_train': x_train, 't': t, 'f': f})
cf = CoxPHFitter()
cf.fit(myData, 't', event_col='f')
# x_train = x_train.astype(np.float64)
# cox_coef = cf.hazards_.x_train.values[0]
cox_coef = cf.hazards_.x_train
nn_coef = model.get_weights()[0][0][0]
print('Cox model coefficient:')
print(cox_coef)
print('Cox model hazard ratio:')
print(np.exp(cox_coef))
print('Neural network coefficient:')
print(nn_coef)
print('Neural network hazard ratio:')
print(np.exp(nn_coef))