simdeep package

Submodules

simdeep.config module

simdeep.coxph_from_r module

simdeep.coxph_from_r.c_index(values, isdead, nbdays, values_test, isdead_test, nbdays_test, isfactor=False, use_r_packages=False, seed=None)

simdeep.coxph_from_r.c_index_from_python(values, isdead, nbdays, values_test, isdead_test, nbdays_test, isfactor=False)

simdeep.coxph_from_r.c_index_from_r(values, isdead, nbdays, values_test, isdead_test, nbdays_test, isfactor=False)

simdeep.coxph_from_r.c_index_multiple(values, isdead, nbdays, values_test, isdead_test, nbdays_test, isfactor=False, use_r_packages=False, seed=None)

simdeep.coxph_from_r.c_index_multiple_from_python(matrix, isdead, nbdays, matrix_test, isdead_test, nbdays_test, isfactor=False)

simdeep.coxph_from_r.c_index_multiple_from_r(matrix, isdead, nbdays, matrix_test, isdead_test, nbdays_test, lambda_val=None, isfactor=False)

simdeep.coxph_from_r.convert_to_rmatrix(data)

simdeep.coxph_from_r.coxph(values, isdead, nbdays, do_KM_plot=False, metadata_mat=None, png_path='./', dichotomize_afterward=False, fig_name='KM_plot.png', isfactor=False, use_r_packages=False, seed=None)

simdeep.coxph_from_r.coxph_from_python(values, isdead, nbdays, do_KM_plot=False, png_path='./', metadata_mat=None, dichotomize_afterward=False, fig_name='KM_plot.pdf', penalizer=0.01, l1_ratio=0.0, isfactor=False)

simdeep.coxph_from_r.coxph_from_r(values, isdead, nbdays, do_KM_plot=False, metadata_mat=None, png_path='./', dichotomize_afterward=False, fig_name='KM_plot.png', isfactor=False)

input:

values

array values of activities

isdead

array <binary> Event occured int boolean: 0/1

nbdays

array <int>

return:

pvalues from wald test

simdeep.coxph_from_r.main()

DEBUG

simdeep.coxph_from_r.predict_with_coxph_glmnet(matrix, isdead, nbdays, matrix_test, alpha=0.5, lambda_val=None)

simdeep.coxph_from_r.surv_mean(isdead, nbdays, use_r_packages=False)

simdeep.coxph_from_r.surv_mean_from_python(isdead, nbdays)

simdeep.coxph_from_r.surv_mean_from_r(isdead, nbdays)

simdeep.coxph_from_r.surv_median(isdead, nbdays, use_r_packages=False)

simdeep.coxph_from_r.surv_median_from_python(isdead, nbdays)

simdeep.coxph_from_r.surv_median_from_r(isdead, nbdays)

simdeep.deepmodel_base module

simdeep.extract_data module

class simdeep.extract_data.LoadData(path_data='/home/docs/checkouts/readthedocs.org/user_builds/deepprog-garmires-lab/checkouts/stable/simdeep/../examples/data/', training_tsv={'GE': 'rna_dummy.tsv', 'METH': 'meth_dummy.tsv', 'MIR': 'mir_dummy.tsv'}, survival_tsv='survival_dummy.tsv', metadata_tsv=None, metadata_test_tsv=None, test_tsv={'MIR': 'mir_test_dummy.tsv'}, survival_tsv_test='survival_test_dummy.tsv', cross_validation_instance=KFold(n_splits=5, random_state=1, shuffle=True), test_fold=0, stack_multi_omic=False, fill_unkown_feature_with_0=True, normalization={'NB_FEATURES_TO_KEEP': 100, 'TRAIN_CORR_RANK_NORM': True, 'TRAIN_CORR_REDUCTION': True, 'TRAIN_MAD_SCALE': False, 'TRAIN_MIN_MAX': False, 'TRAIN_NORM_SCALE': False, 'TRAIN_QUANTILE_TRANSFORM': False, 'TRAIN_RANK_NORM': True, 'TRAIN_ROBUST_SCALE': False, 'TRAIN_ROBUST_SCALE_TWO_WAY': False}, survival_flag={'event': 'recurrence', 'patient_id': 'barcode', 'survival': 'days'}, subset_training_with_meta={}, _autoencoder_parameters={}, verbose=True)

Bases: object

create_a_cv_split()

load_array()

load_matrix_full()

load_matrix_test(normalization=None)

load_matrix_test_fold()

load_meta_data(sep='\t')

load_meta_data_test(metadata_file='', sep='\t')

load_new_test_dataset(tsv_dict, path_survival_file=None, survival_flag=None, normalization=None, metadata_file=None)

load_survival()

load_survival_test(survival_flag=None)

normalize_training_array()

reorder_matrix_array(new_sample_ids)

save_ref_matrix(path_folder, project_name)

subset_training_sets(change_cv=False)

transform_matrices(matrix_ref, matrix, key, normalization=None)

simdeep.plot_utils module

class simdeep.plot_utils.SampleHTML(name, label, proba, survival)

Bases: object

simdeep.plot_utils.make_color_dict(id_list)

According to an id_list define a color gradient return {id:color}

simdeep.plot_utils.make_color_dict_from_r(labels)

simdeep.plot_utils.make_color_list(id_list)

According to an id_list define a color gradient return {id:color}

simdeep.plot_utils.plot_kernel_plots(test_labels, test_labels_proba, labels, activities, activities_test, dataset, path_html, metadata_frame=None)

perform a html kernel plot

simdeep.simdeep_analysis module

simdeep.simdeep_boosting module

simdeep.simdeep_distributed module

simdeep.simdeep_multiple_dataset module

simdeep.simdeep_utils module

simdeep.simdeep_utils.feature_selection_usage_type(value)

simdeep.simdeep_utils.load_labels_file(path_labels, sep='\t')

simdeep.simdeep_utils.load_model(project_name, path_model='./')

simdeep.simdeep_utils.metadata_usage_type(value)

simdeep.simdeep_utils.save_model(boosting, path_to_save_model='./')

simdeep.survival_utils module

class simdeep.survival_utils.CorrelationReducer(distance='correlation', threshold=None)

Bases: object

fit(dataset)

fit_transform(dataset)

transform(dataset)

class simdeep.survival_utils.MadScaler

Bases: object

fit_transform(X)

class simdeep.survival_utils.RankCorrNorm(dataset)

Bases: object

class simdeep.survival_utils.RankNorm

Bases: object

fit_transform(X)

class simdeep.survival_utils.SampleReducer(perc_sample_to_keep=0.9)

Bases: object

sample_to_keep(datasets, index=None)

class simdeep.survival_utils.VarianceReducer(nb_features=200)

Bases: object

fit(dataset)

fit_transform(dataset)

transform(dataset)

simdeep.survival_utils.convert_metadata_frame_to_matrix(frame)

simdeep.survival_utils.load_data_from_tsv(use_transpose=False, **kwargs)

simdeep.survival_utils.load_entrezID_to_ensg()

simdeep.survival_utils.load_survival_file(f_name, path_data='/home/docs/checkouts/readthedocs.org/user_builds/deepprog-garmires-lab/checkouts/stable/simdeep/../examples/data/', sep='\t', survival_flag={'event': 'recurrence', 'patient_id': 'barcode', 'survival': 'days'})

simdeep.survival_utils.return_intersection_indexes(ids_1, ids_2)

simdeep.survival_utils.save_matrix(matrix, feature_array, sample_array, path_folder, project_name, key='', sep='\t')

simdeep.survival_utils.select_best_classif_params(clf)

select best classifier parameters based uniquely on test errors

simdeep.survival_utils.translate_index(original_ids, new_ids)

Module contents