import os
os.environ['PYTHONHASHSEED']=str(2020)
import random
random.seed(2020)
from os.path import abspath
from os.path import split
from simdeep.simdeep_boosting import SimDeepBoosting
from simdeep.simdeep_utils import save_model
from simdeep.simdeep_utils import load_model
def test_instance():
"""
example of SimDeepBoosting
"""
PATH_DATA = '{0}/../examples/data/'.format(split(abspath(__file__))[0])
#Input file
TRAINING_TSV = {'RNA': 'rna_dummy.tsv', 'METH': 'meth_dummy.tsv'}
SURVIVAL_TSV = 'survival_dummy.tsv'
PROJECT_NAME = 'TestProject'
SEED = 3
nb_it = 2 # Number of models to be built
nb_threads = 2 # Number of processes to be used to fit individual survival models
################ AUTOENCODER PARAMETERS ################
EPOCHS = 10
## Additional parameters for the autoencoders can be defined, see config.py file for details
# LEVEL_DIMS_IN = [250]
# LEVEL_DIMS_OUT = [250]
# LOSS = 'binary_crossentropy'
# OPTIMIZER = 'adam'
# ACT_REG = 0
# W_REG = 0
# DROPOUT = 0.5
# DATA_SPLIT = 0
# ACTIVATION = 'tanh'
#########################################################
################ ADDITIONAL PARAMETERS ##################
# PATH_TO_SAVE_MODEL = '/home/username/deepprog'
# PVALUE_THRESHOLD = 0.01
# NB_SELECTED_FEATURES = 10
# STACK_MULTI_OMIC = False
#########################################################
from sklearn.preprocessing import RobustScaler
norm = {
'CUSTOM': RobustScaler,
}
boosting = SimDeepBoosting(
nb_threads=nb_threads,
nb_it=nb_it,
split_n_fold=3,
survival_tsv=SURVIVAL_TSV,
training_tsv=TRAINING_TSV,
path_data=PATH_DATA,
project_name=PROJECT_NAME,
path_results=PATH_DATA,
epochs=EPOCHS,
seed=SEED,
normalization=norm,
cluster_method='mixture',
use_autoencoders=True,
feature_surv_analysis=True,
distribute=False
# stack_multi_omic=STACK_MULTI_OMIC,
# level_dims_in=LEVEL_DIMS_IN,
# level_dims_out=LEVEL_DIMS_OUT,
# loss=LOSS,
# optimizer=OPTIMIZER,
# act_reg=ACT_REG,
# w_reg=W_REG,
# dropout=DROPOUT,
# data_split=DATA_SPLIT,
# activation=ACTIVATION,
# path_to_save_model=PATH_TO_SAVE_MODEL,
# pvalue_threshold=PVALUE_THRESHOLD,
# nb_selected_features=NB_SELECTED_FEATURES,
)
boosting.fit()
save_model(boosting, "./test_saved_model")
del boosting
boosting = load_model("TestProject", "./test_saved_model")
boosting.predict_labels_on_full_dataset()
boosting.compute_clusters_consistency_for_full_labels()
boosting.evalutate_cluster_performance()
boosting.collect_cindex_for_test_fold()
boosting.collect_cindex_for_full_dataset()
boosting.compute_feature_scores_per_cluster()
boosting.write_feature_score_per_cluster()
boosting.collect_number_of_features_per_omic()
boosting.compute_pvalue_for_merged_test_fold()
boosting.load_new_test_dataset(
{'RNA': 'rna_dummy.tsv'}, # OMIC file of the test set. It doesnt have to be the same as for training
'dummy', # Name of the test test to be used
'survival_dummy.tsv', # Survival file of the test set Optional
)
boosting.predict_labels_on_test_dataset()
boosting.compute_c_indexes_for_test_dataset()
boosting.compute_clusters_consistency_for_test_labels()
boosting.load_new_test_dataset(
{'METH': 'meth_dummy.tsv'}, # OMIC file of the second test set.
'dummy_METH', # Name of the second test test
'survival_dummy.tsv', # Survival file of the test set Optional
)
boosting.predict_labels_on_test_dataset()
boosting.compute_c_indexes_for_test_dataset()
boosting.compute_clusters_consistency_for_test_labels()
if __name__ == '__main__':
test_instance()
Datasets
Models
