from os.path import abspath
from os.path import split
from simdeep.simdeep_boosting import SimDeepBoosting
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'
# Optional metadata FILE
OPTIONAL_METADATA = "metadata_dummy.tsv"
# Subsetting training set with only males from metadata:
SUBSET_TRAINING_WITH_META = {'stage': ['I', 'II', 'III']}
PROJECT_NAME = 'TestProject'
SEED = 3
nb_it = 5 # 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
#########################################################
################ ADDITIONAL PARAMETERS ##################
# PATH_TO_SAVE_MODEL = '/home/username/deepprog'
# PVALUE_THRESHOLD = 0.01
# NB_SELECTED_FEATURES = 10
# STACK_MULTI_OMIC = False
#########################################################
# IT is possible to define a custom normalisation
# 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,
# metadata_tsv=OPTIONAL_METADATA, # optional
path_data=PATH_DATA,
project_name=PROJECT_NAME,
path_results=PATH_DATA,
use_r_packages=False, # to use R functions from the survival and survcomp packages
epochs=EPOCHS,
seed=SEED,
# normalization=norm,
cluster_method='coxPH',
metadata_usage='labels',
use_autoencoders=True,
feature_surv_analysis=True,
feature_selection_usage="lasso",
# subset_training_with_meta=SUBSET_TRAINING_WITH_META,
# stack_multi_omic=True,
# path_to_save_model=PATH_TO_SAVE_MODEL,
# pvalue_threshold=PVALUE_THRESHOLD,
# nb_selected_features=NB_SELECTED_FEATURES,
)
boosting.fit()
boosting.predict_labels_on_full_dataset()
boosting.save_models_classes()
boosting.save_cv_models_classes()
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.compute_survival_feature_scores_per_cluster(pval_thres=0.10)
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(
tsv_dict={'RNA': 'rna_dummy.tsv'}, # OMIC file of the test set. It doesnt have to be the same as for training
path_survival_file='survival_dummy.tsv', # Optional survival file of the test set for computing validation log-rank pvalue
fname_key='dummy', # Name of the test test to be used
)
boosting.predict_labels_on_test_dataset()
boosting.save_test_models_classes()
boosting.compute_c_indexes_for_test_dataset()
boosting.compute_clusters_consistency_for_test_labels()
# Experimental method to plot the test dataset amongst the class kernel densities
boosting.plot_supervised_kernel_for_test_sets()
boosting.plot_supervised_predicted_labels_for_test_sets()
boosting.load_new_test_dataset(
tsv_dict={'METH': 'meth_dummy.tsv'}, # OMIC file of the second test set.
path_survival_file='survival_dummy.tsv', # Survival file of the test set
fname_key='dummy_METH', # Name of the second test test
metadata_file="metadata_dummy.tsv" # Optional metadata
)
boosting.predict_labels_on_test_dataset()
boosting.compute_c_indexes_for_test_dataset()
boosting.compute_clusters_consistency_for_test_labels()
# Experimental method to plot the test dataset amongst the class kernel densities
boosting.plot_supervised_kernel_for_test_sets()
boosting.plot_supervised_predicted_labels_for_test_sets()
if __name__ == '__main__':
test_instance()
Datasets
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