def load_model(path_file):
""" Load the model and its parameters from a .zip file
Parameters:
-----------
* path_file, str
address of the file where the model will be loaded from
Returns:
--------
* pysurvival_model : Pysurvival object
Pysurvival model
"""
# Initializing a base model
from pysurvival.models import BaseModel
base_model = BaseModel()
# Temporary loading the model
base_model.load(path_file)
model_name = base_model.name
# Loading the actual Pysurvival model - Kaplan-Meier
if 'kaplanmeier' in model_name.lower():
if 'smooth' in model_name.lower():
from pysurvival.models.non_parametric import SmoothKaplanMeierModel
pysurvival_model = SmoothKaplanMeierModel()
else:
from pysurvival.models.non_parametric import KaplanMeierModel
pysurvival_model = KaplanMeierModel()
elif 'linearmultitask' in model_name.lower():
from pysurvival.models.multi_task import LinearMultiTaskModel
pysurvival_model = LinearMultiTaskModel()
elif 'neuralmultitask' in model_name.lower():
from pysurvival.models.multi_task import NeuralMultiTaskModel
structure = [
{
'activation': 'relu',
'num_units': 128
},
]
pysurvival_model = NeuralMultiTaskModel(structure=structure)
elif 'exponential' in model_name.lower():
from pysurvival.models.parametric import ExponentialModel
pysurvival_model = ExponentialModel()
elif 'weibull' in model_name.lower():
from pysurvival.models.parametric import WeibullModel
pysurvival_model = WeibullModel()
elif 'gompertz' in model_name.lower():
from pysurvival.models.parametric import GompertzModel
pysurvival_model = GompertzModel()
elif 'loglogistic' in model_name.lower():
from pysurvival.models.parametric import LogLogisticModel
pysurvival_model = LogLogisticModel()
elif 'lognormal' in model_name.lower():
from pysurvival.models.parametric import LogNormalModel
pysurvival_model = LogNormalModel()
elif 'simulation' in model_name.lower():
from pysurvival.models.simulations import SimulationModel
pysurvival_model = SimulationModel()
elif 'coxph' in model_name.lower():
if 'nonlinear' in model_name.lower():
from pysurvival.models.semi_parametric import NonLinearCoxPHModel
pysurvival_model = NonLinearCoxPHModel()
else:
from pysurvival.models.semi_parametric import CoxPHModel
pysurvival_model = CoxPHModel()
elif 'random' in model_name.lower() and 'survival' in model_name.lower():
from pysurvival.models.survival_forest import RandomSurvivalForestModel
pysurvival_model = RandomSurvivalForestModel()
elif 'extra' in model_name.lower() and 'survival' in model_name.lower():
from pysurvival.models.survival_forest import ExtraSurvivalTreesModel
pysurvival_model = ExtraSurvivalTreesModel()
elif 'condi' in model_name.lower() and 'survival' in model_name.lower():
from pysurvival.models.survival_forest import ConditionalSurvivalForestModel
pysurvival_model = ConditionalSurvivalForestModel()
elif 'svm' in model_name.lower():
if 'linear' in model_name.lower():
from pysurvival.models.svm import LinearSVMModel
pysurvival_model = LinearSVMModel()
elif 'kernel' in model_name.lower():
from pysurvival.models.svm import KernelSVMModel
pysurvival_model = KernelSVMModel()
else:
raise NotImplementedError(
'{} is not a valid pysurvival model.'.format(model_name))
# Transferring the components
pysurvival_model.__dict__.update(copy.deepcopy(base_model.__dict__))
del base_model
return pysurvival_model
brier_scores = brier_score(model,
table[variables],
table['PFS'],
table['disease_progress'],
t_max=84,
figure_size=(20, 6.5))
return c_indexes, brier_scores
# In[ ]:
# In[36]:
nlcph = NonLinearCoxPHModel(structure=[{
'activation': 'ReLU',
'num_units': 64
}],
auto_scaler=True)
nlcph.fit(train[features],
train['PFS'],
train['disease_progress'],
init_method='glorot_uniform',
lr=0.0001)
c_index = concordance_index(nlcph, test[features], test['PFS'],
test['disease_progress'])
print('C-index: {:.2f}'.format(c_index))
ibs = integrated_brier_score(nlcph,
test[features],
test['PFS'],
for train_index, test_index in kf.split(MOF):
data_train = MOF.loc[train_index].reset_index(drop=True)
data_test = MOF.loc[test_index].reset_index(drop=True)
X_train, X_test = data_train[features], data_test[features]
T_train, T_test = data_train['time'].values, data_test[
'time'].values
E_train, E_test = data_train['event'].values, data_test[
'event'].values
structure = [{
'activation': 'SELU',
'num_units': num_units
}, {
'activation': 'SELU',
'num_units': num_units2
}]
nonlinear_coxph = NonLinearCoxPHModel(structure=structure)
nonlinear_coxph.fit(X_train,
T_train,
E_train,
lr=lr,
init_method='xav_uniform',
dropout=dropout)
c_index = concordance_index(nonlinear_coxph, X_test,
T_test, E_test)
c_index_df = c_index_df.append({"c_index": c_index},
ignore_index=True)
print(c_index_df)
if len(c_index_df) == 10:
mean = c_index_df["c_index"].mean()
dataframe_hp = dataframe_hp.append(
{
def run_pysurvival_with_repetitions(data,
features,
survival,
event,
models,
test_ratio,
repetitions=10):
num_samples = len(data.index)
print('Number of Samples:', num_samples)
''' Initialize Outputs '''
outputs = initialize_outputs(models, features)
''' Run Survival Model N times '''
for _ in range(repetitions):
''' Dataset Splitting '''
index_train, index_test = train_test_split(range(num_samples),
test_size=test_ratio)
data_train = data.loc[index_train].reset_index(drop=True)
data_test = data.loc[index_test].reset_index(drop=True)
X_train, X_test = data_train[features], data_test[features]
T_train, T_test = data_train[survival].values, data_test[
survival].values
E_train, E_test = data_train[event].values, data_test[event].values
''' Run Cox '''
if 'cox' in models:
coxph = CoxPHModel()
coxph.fit(X_train,
T_train,
E_train,
lr=0.0001,
l2_reg=1e-2,
init_method='zeros',
verbose=False)
c_index = concordance_index(coxph, X_test, T_test, E_test)
outputs['cox']['c_index'].append(c_index)
ibs = integrated_brier_score(coxph,
X_test,
T_test,
E_test,
t_max=None)
outputs['cox']['ibs'].append(ibs)
for idx, i in enumerate(features):
outputs['cox']['weights'][i].append(coxph.weights[idx])
''' Run RSF '''
if 'rsf' in models:
rsf = RandomSurvivalForestModel(num_trees=200)
rsf.fit(X_train,
T_train,
E_train,
max_features="sqrt",
max_depth=5,
min_node_size=20)
c_index = concordance_index(rsf, X_test, T_test, E_test)
outputs['rsf']['c_index'].append(c_index)
ibs = integrated_brier_score(rsf,
X_test,
T_test,
E_test,
t_max=None)
outputs['rsf']['ibs'].append(ibs)
for key, value in rsf.variable_importance.items():
outputs['rsf']['importance'][key].append(value)
''' Run Deepsurv '''
if 'deepsurv' in models:
structure = [{
'activation': 'ReLU',
'num_units': 128
}, {
'activation': 'ReLU',
'num_units': 128
}, {
'activation': 'ReLU',
'num_units': 128
}]
nonlinear_coxph = NonLinearCoxPHModel(structure=structure)
nonlinear_coxph.fit(X_train,
T_train,
E_train,
lr=1e-4,
init_method='xav_uniform',
verbose=False)
c_index = concordance_index(nonlinear_coxph, X_test, T_test,
E_test)
outputs['deepsurv']['c_index'].append(c_index)
ibs = integrated_brier_score(nonlinear_coxph,
X_test,
T_test,
E_test,
t_max=None)
outputs['deepsurv']['ibs'].append(ibs)
return outputs