I following readme.txt file,when i run py.test ,i meet some problem.How deal with it?There, I copy these problems.
ccf@ccf-Lenovo-Product:~/CCF/DeepSurv-master$ py.test
============================= test session starts ==============================
platform linux2 -- Python 2.7.6, pytest-3.1.2, py-1.4.34, pluggy-0.4.0
rootdir: /home/ccf/CCF/DeepSurv-master, inifile:
collected 8 items
tests/test_deepsurv.py .F.FEEEE
==================================== ERRORS ====================================
________________ ERROR at setup of TestDeepSurvTrain.test_train ________________
self = <class test_deepsurv.TestDeepSurvTrain at 0x7f9bffc83120>
@classmethod
def setup_class(self):
self.train, self.valid, self.test = generate_data(treatment_group=True)
hyperparams = {
'n_in': 10,
'learning_rate': 1e-5,
'hidden_layers_sizes': [10]
}
network = DeepSurv(**hyperparams)
log = network.train(self.train, self.valid,
n_epochs=10,validation_frequency=1)
tests/test_deepsurv.py:63:
deepsurv/deep_surv.py:397: in train
update_fn = update_fn, **kwargs
deepsurv/deep_surv.py:243: in _get_train_valid_fn
learning_rate=learning_rate, **kwargs
self = <deepsurv.deep_surv.DeepSurv instance at 0x7f9bfa3d5b90>, L1_reg = 0.0
L2_reg = 0.0, update_fn = <function nesterov_momentum at 0x7f9c04ebfaa0>
max_norm = None, deterministic = False
kwargs = {'learning_rate': <TensorType(float32, scalar)>, 'momentum': array(0.0, dtype=float32)}
loss = Elemwise{add,no_inplace}.0
updates = OrderedDict([(W, Elemwise{add,no_inplace}.0), (b, Elemwise{add,no_inplace}.0),...b,no_inplace}.0), (<TensorType(float64, vector)>, Elemwise{sub,no_inplace}.0)])
def _get_loss_updates(self,
L1_reg = 0.0, L2_reg = 0.001,
update_fn = lasagne.updates.nesterov_momentum,
max_norm = None, deterministic = False,
**kwargs):
"""
Returns Theano expressions for the network's loss function and parameter
updates.
Parameters:
L1_reg: float for L1 weight regularization coefficient.
L2_reg: float for L2 weight regularization coefficient.
max_norm: If not None, constraints the norm of gradients to be less
than max_norm.
deterministic: True or False. Determines if the output of the network
is calculated determinsitically.
update_fn: lasagne update function.
Default: Stochastic Gradient Descent with Nesterov momentum
**kwargs: additional parameters to provide to update_fn.
For example: momentum
Returns:
loss: Theano expression for a penalized negative log likelihood.
updates: Theano expression to update the parameters using update_fn.
"""
loss = (
self._negative_log_likelihood(self.E, deterministic)
+ regularize_layer_params(self.network,l1) * L1_reg
+ regularize_layer_params(self.network, l2) * L2_reg
)
if max_norm:
grads = T.grad(loss,self.params)
scaled_grads = lasagne.updates.total_norm_constraint(grads, max_norm)
updates = update_fn(
grads, self.params, **kwargs
)
return loss, updates
updates = update_fn(
loss, self.params, **kwargs
)
# If the model was loaded from file, reload params
if self.restored_update_params:
E AttributeError: DeepSurv instance has no attribute 'restored_update_params'
deepsurv/deep_surv.py:209: AttributeError
---------------------------- Captured stdout setup -----------------------------
[ 1. 0. 1. ..., 1. 0. 0.]
[ 1. 1. 1. ..., 1. 1. 1.]
[ 0. 1. 1. ..., 1. 0. 1.]
________ ERROR at setup of TestDeepSurvTrain.test_network_predict_risk _________
self = <class test_deepsurv.TestDeepSurvTrain at 0x7f9bffc83120>
@classmethod
def setup_class(self):
self.train, self.valid, self.test = generate_data(treatment_group=True)
hyperparams = {
'n_in': 10,
'learning_rate': 1e-5,
'hidden_layers_sizes': [10]
}
network = DeepSurv(**hyperparams)
log = network.train(self.train, self.valid,
n_epochs=10,validation_frequency=1)
tests/test_deepsurv.py:63:
deepsurv/deep_surv.py:397: in train
update_fn = update_fn, **kwargs
deepsurv/deep_surv.py:243: in _get_train_valid_fn
learning_rate=learning_rate, **kwargs
self = <deepsurv.deep_surv.DeepSurv instance at 0x7f9bfa3d5b90>, L1_reg = 0.0
L2_reg = 0.0, update_fn = <function nesterov_momentum at 0x7f9c04ebfaa0>
max_norm = None, deterministic = False
kwargs = {'learning_rate': <TensorType(float32, scalar)>, 'momentum': array(0.0, dtype=float32)}
loss = Elemwise{add,no_inplace}.0
updates = OrderedDict([(W, Elemwise{add,no_inplace}.0), (b, Elemwise{add,no_inplace}.0),...b,no_inplace}.0), (<TensorType(float64, vector)>, Elemwise{sub,no_inplace}.0)])
def _get_loss_updates(self,
L1_reg = 0.0, L2_reg = 0.001,
update_fn = lasagne.updates.nesterov_momentum,
max_norm = None, deterministic = False,
**kwargs):
"""
Returns Theano expressions for the network's loss function and parameter
updates.
Parameters:
L1_reg: float for L1 weight regularization coefficient.
L2_reg: float for L2 weight regularization coefficient.
max_norm: If not None, constraints the norm of gradients to be less
than max_norm.
deterministic: True or False. Determines if the output of the network
is calculated determinsitically.
update_fn: lasagne update function.
Default: Stochastic Gradient Descent with Nesterov momentum
**kwargs: additional parameters to provide to update_fn.
For example: momentum
Returns:
loss: Theano expression for a penalized negative log likelihood.
updates: Theano expression to update the parameters using update_fn.
"""
loss = (
self._negative_log_likelihood(self.E, deterministic)
+ regularize_layer_params(self.network,l1) * L1_reg
+ regularize_layer_params(self.network, l2) * L2_reg
)
if max_norm:
grads = T.grad(loss,self.params)
scaled_grads = lasagne.updates.total_norm_constraint(grads, max_norm)
updates = update_fn(
grads, self.params, **kwargs
)
return loss, updates
updates = update_fn(
loss, self.params, **kwargs
)
# If the model was loaded from file, reload params
if self.restored_update_params:
E AttributeError: DeepSurv instance has no attribute 'restored_update_params'
deepsurv/deep_surv.py:209: AttributeError
________ ERROR at setup of TestDeepSurvTrain.test_get_concordance_index ________
self = <class test_deepsurv.TestDeepSurvTrain at 0x7f9bffc83120>
@classmethod
def setup_class(self):
self.train, self.valid, self.test = generate_data(treatment_group=True)
hyperparams = {
'n_in': 10,
'learning_rate': 1e-5,
'hidden_layers_sizes': [10]
}
network = DeepSurv(**hyperparams)
log = network.train(self.train, self.valid,
n_epochs=10,validation_frequency=1)
tests/test_deepsurv.py:63:
deepsurv/deep_surv.py:397: in train
update_fn = update_fn, **kwargs
deepsurv/deep_surv.py:243: in _get_train_valid_fn
learning_rate=learning_rate, **kwargs
self = <deepsurv.deep_surv.DeepSurv instance at 0x7f9bfa3d5b90>, L1_reg = 0.0
L2_reg = 0.0, update_fn = <function nesterov_momentum at 0x7f9c04ebfaa0>
max_norm = None, deterministic = False
kwargs = {'learning_rate': <TensorType(float32, scalar)>, 'momentum': array(0.0, dtype=float32)}
loss = Elemwise{add,no_inplace}.0
updates = OrderedDict([(W, Elemwise{add,no_inplace}.0), (b, Elemwise{add,no_inplace}.0),...b,no_inplace}.0), (<TensorType(float64, vector)>, Elemwise{sub,no_inplace}.0)])
def _get_loss_updates(self,
L1_reg = 0.0, L2_reg = 0.001,
update_fn = lasagne.updates.nesterov_momentum,
max_norm = None, deterministic = False,
**kwargs):
"""
Returns Theano expressions for the network's loss function and parameter
updates.
Parameters:
L1_reg: float for L1 weight regularization coefficient.
L2_reg: float for L2 weight regularization coefficient.
max_norm: If not None, constraints the norm of gradients to be less
than max_norm.
deterministic: True or False. Determines if the output of the network
is calculated determinsitically.
update_fn: lasagne update function.
Default: Stochastic Gradient Descent with Nesterov momentum
**kwargs: additional parameters to provide to update_fn.
For example: momentum
Returns:
loss: Theano expression for a penalized negative log likelihood.
updates: Theano expression to update the parameters using update_fn.
"""
loss = (
self._negative_log_likelihood(self.E, deterministic)
+ regularize_layer_params(self.network,l1) * L1_reg
+ regularize_layer_params(self.network, l2) * L2_reg
)
if max_norm:
grads = T.grad(loss,self.params)
scaled_grads = lasagne.updates.total_norm_constraint(grads, max_norm)
updates = update_fn(
grads, self.params, **kwargs
)
return loss, updates
updates = update_fn(
loss, self.params, **kwargs
)
# If the model was loaded from file, reload params
if self.restored_update_params:
E AttributeError: DeepSurv instance has no attribute 'restored_update_params'
deepsurv/deep_surv.py:209: AttributeError
_________ ERROR at setup of TestDeepSurvTrain.test_recommend_treatment _________
self = <class test_deepsurv.TestDeepSurvTrain at 0x7f9bffc83120>
@classmethod
def setup_class(self):
self.train, self.valid, self.test = generate_data(treatment_group=True)
hyperparams = {
'n_in': 10,
'learning_rate': 1e-5,
'hidden_layers_sizes': [10]
}
network = DeepSurv(**hyperparams)
log = network.train(self.train, self.valid,
n_epochs=10,validation_frequency=1)
tests/test_deepsurv.py:63:
deepsurv/deep_surv.py:397: in train
update_fn = update_fn, **kwargs
deepsurv/deep_surv.py:243: in _get_train_valid_fn
learning_rate=learning_rate, **kwargs
self = <deepsurv.deep_surv.DeepSurv instance at 0x7f9bfa3d5b90>, L1_reg = 0.0
L2_reg = 0.0, update_fn = <function nesterov_momentum at 0x7f9c04ebfaa0>
max_norm = None, deterministic = False
kwargs = {'learning_rate': <TensorType(float32, scalar)>, 'momentum': array(0.0, dtype=float32)}
loss = Elemwise{add,no_inplace}.0
updates = OrderedDict([(W, Elemwise{add,no_inplace}.0), (b, Elemwise{add,no_inplace}.0),...b,no_inplace}.0), (<TensorType(float64, vector)>, Elemwise{sub,no_inplace}.0)])
def _get_loss_updates(self,
L1_reg = 0.0, L2_reg = 0.001,
update_fn = lasagne.updates.nesterov_momentum,
max_norm = None, deterministic = False,
**kwargs):
"""
Returns Theano expressions for the network's loss function and parameter
updates.
Parameters:
L1_reg: float for L1 weight regularization coefficient.
L2_reg: float for L2 weight regularization coefficient.
max_norm: If not None, constraints the norm of gradients to be less
than max_norm.
deterministic: True or False. Determines if the output of the network
is calculated determinsitically.
update_fn: lasagne update function.
Default: Stochastic Gradient Descent with Nesterov momentum
**kwargs: additional parameters to provide to update_fn.
For example: momentum
Returns:
loss: Theano expression for a penalized negative log likelihood.
updates: Theano expression to update the parameters using update_fn.
"""
loss = (
self._negative_log_likelihood(self.E, deterministic)
+ regularize_layer_params(self.network,l1) * L1_reg
+ regularize_layer_params(self.network, l2) * L2_reg
)
if max_norm:
grads = T.grad(loss,self.params)
scaled_grads = lasagne.updates.total_norm_constraint(grads, max_norm)
updates = update_fn(
grads, self.params, **kwargs
)
return loss, updates
updates = update_fn(
loss, self.params, **kwargs
)
# If the model was loaded from file, reload params
if self.restored_update_params:
E AttributeError: DeepSurv instance has no attribute 'restored_update_params'
deepsurv/deep_surv.py:209: AttributeError
=================================== FAILURES ===================================
_____________ TestDeepSurvInit.test_deepsurv_initialize_batch_norm _____________
self = <test_deepsurv.TestDeepSurvInit instance at 0x7f9bfa41f488>
def test_deepsurv_initialize_batch_norm(self):
network = DeepSurv(batch_norm = True, **self.hyperparams)
tests/test_deepsurv.py:39:
self = <deepsurv.deep_surv.DeepSurv instance at 0x7f9bfa41fc68>, n_in = 10
learning_rate = 1e-05, hidden_layers_sizes = [10, 10], lr_decay = 0.0
momentum = 0.9, L2_reg = 0.0, L1_reg = 0.0, activation = 'rectify'
dropout = None, batch_norm = True, standardize = False
def __init__(self, n_in,
learning_rate, hidden_layers_sizes = None,
lr_decay = 0.0, momentum = 0.9,
L2_reg = 0.0, L1_reg = 0.0,
activation = "rectify",
dropout = None,
batch_norm = False,
standardize = False,
):
"""
This class implements and trains a DeepSurv model.
Parameters:
n_in: number of input nodes.
learning_rate: learning rate for training.
lr_decay: coefficient for Power learning rate decay.
L2_reg: coefficient for L2 weight decay regularization. Used to help
prevent the model from overfitting.
L1_reg: coefficient for L1 weight decay regularization
momentum: coefficient for momentum. Can be 0 or None to disable.
hidden_layer_sizes: a list of integers to determine the size of
each hidden layer.
activation: a lasagne activation class.
Default: lasagne.nonlinearities.rectify
batch_norm: True or False. Include batch normalization layers.
dropout: if not None or 0, the percentage of dropout to include
after each hidden layer. Default: None
standardize: True or False. Include standardization layer after
input layer.
"""
self.X = T.fmatrix('x') # patients covariates
self.E = T.ivector('e') # the observations vector
# Default Standardization Values: mean = 0, std = 1
self.offset = theano.shared(numpy.zeros(shape = n_in, dtype=numpy.float32))
self.scale = theano.shared(numpy.ones(shape = n_in, dtype=numpy.float32))
network = lasagne.layers.InputLayer(shape=(None,n_in),
input_var = self.X)
if standardize:
network = lasagne.layers.standardize(network,self.offset,
self.scale,
shared_axes = 0)
self.standardize = standardize
if activation == 'rectify':
activation_fn = lasagne.nonlinearities.rectify
else:
raise IllegalArgumentException("Unknown activation function: %s" % activation)
# Construct Neural Network
for n_layer in (hidden_layers_sizes or []):
if activation_fn == lasagne.nonlinearities.rectify:
W_init = lasagne.init.GlorotUniform()
else:
# TODO: implement other initializations
W_init = lasagne.init.GlorotUniform()
network = lasagne.layers.DenseLayer(
network, num_units = n_layer,
nonlinearity = activation_fn,
W = W_init
)
if batch_norm:
network = lasagne.layers.po(network)
E AttributeError: 'module' object has no attribute 'po'
deepsurv/deep_surv.py:86: AttributeError
_________ TestDeepSurvInit.test_deepsurv_initialize_standardize_layer __________
self = <test_deepsurv.TestDeepSurvInit instance at 0x7f9bfa37c9e0>
def test_deepsurv_initialize_standardize_layer(self):
network = DeepSurv(standardize = True, **self.hyperparams)
tests/test_deepsurv.py:47:
self = <deepsurv.deep_surv.DeepSurv instance at 0x7f9bfa3f7b00>, n_in = 10
learning_rate = 1e-05, hidden_layers_sizes = [10, 10], lr_decay = 0.0
momentum = 0.9, L2_reg = 0.0, L1_reg = 0.0, activation = 'rectify'
dropout = None, batch_norm = False, standardize = True
def __init__(self, n_in,
learning_rate, hidden_layers_sizes = None,
lr_decay = 0.0, momentum = 0.9,
L2_reg = 0.0, L1_reg = 0.0,
activation = "rectify",
dropout = None,
batch_norm = False,
standardize = False,
):
"""
This class implements and trains a DeepSurv model.
Parameters:
n_in: number of input nodes.
learning_rate: learning rate for training.
lr_decay: coefficient for Power learning rate decay.
L2_reg: coefficient for L2 weight decay regularization. Used to help
prevent the model from overfitting.
L1_reg: coefficient for L1 weight decay regularization
momentum: coefficient for momentum. Can be 0 or None to disable.
hidden_layer_sizes: a list of integers to determine the size of
each hidden layer.
activation: a lasagne activation class.
Default: lasagne.nonlinearities.rectify
batch_norm: True or False. Include batch normalization layers.
dropout: if not None or 0, the percentage of dropout to include
after each hidden layer. Default: None
standardize: True or False. Include standardization layer after
input layer.
"""
self.X = T.fmatrix('x') # patients covariates
self.E = T.ivector('e') # the observations vector
# Default Standardization Values: mean = 0, std = 1
self.offset = theano.shared(numpy.zeros(shape = n_in, dtype=numpy.float32))
self.scale = theano.shared(numpy.ones(shape = n_in, dtype=numpy.float32))
network = lasagne.layers.InputLayer(shape=(None,n_in),
input_var = self.X)
if standardize:
network = lasagne.layers.standardize(network,self.offset,
self.scale,
shared_axes = 0)
E AttributeError: 'module' object has no attribute 'standardize'
deepsurv/deep_surv.py:60: AttributeError
================= 2 failed, 2 passed, 4 error in 5.42 seconds ==================
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