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ÇĽîdocutils.nodesöîdocumentöôö)üö}ö(î	rawsourceöîöîchildrenö]öhîsectionöôö)üö}ö(hhh]ö(hîtitleöôö)üö}ö(hîMaui Utilitiesöh]öhîTextöôöîMaui Utilitiesöůöüö}ö(hhîparentöhhhîsourceöNîlineöNubaî
attributesö}ö(îidsö]öîclassesö]öînamesö]öîdupnamesö]öîbackrefsö]öuîtagnameöhhhhhhî+/home/jona/work/phd/maui/maui/doc/utils.rstöhKubîsphinx.addnodesöîindexöôö)üö}ö(hhh]öh}ö(h]öh!]öh#]öh%]öh']öîentriesö]ö(îsingleöîmaui.utils (module)öîmodule-maui.utilsöhNtöauh)h,hhhhhîC/home/jona/work/phd/maui/maui/maui/utils.py:docstring of maui.utilsöhKubhî	paragraphöôö)üö}ö(hîTThe maui.utils model contains utility functions for multi-omics analysis
using maui.öh]öhîTThe maui.utils model contains utility functions for multi-omics analysis
using maui.öůöüö}ö(hhBhh@hhhNhNubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hîC/home/jona/work/phd/maui/maui/maui/utils.py:docstring of maui.utilsöhKhhhhubh-)üö}ö(hhh]öh}ö(h]öh!]öh#]öh%]öh']öîentriesö]ö(h9î+compute_harrells_c() (in module maui.utils)öîmaui.utils.compute_harrells_cöhNtöauh)h,hhhhhNhNubh+îdescöôö)üö}ö(hhh]ö(h+îdesc_signatureöôö)üö}ö(hîĹcompute_harrells_c(z, survival, duration_column='duration', observed_column='observed', cox_penalties=[0.1, 1, 10, 100, 1000, 10000], cv_folds=5)öh]ö(h+îdesc_addnameöôö)üö}ö(hîmaui.utils.öh]öhîmaui.utils.öůöüö}ö(hhhhjhhhNhNubah}ö(h]öh!]öh#]öh%]öh']öî	xml:spaceöîpreserveöuh)hhhhdhhhîV/home/jona/work/phd/maui/maui/maui/utils.py:docstring of maui.utils.compute_harrells_cöhNubh+î	desc_nameöôö)üö}ö(hîcompute_harrells_cöh]öhîcompute_harrells_cöůöüö}ö(hhhh}hhhNhNubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)h{hhdhhhhzhNubh+îdesc_parameterlistöôö)üö}ö(hî}z, survival, duration_column='duration', observed_column='observed', cox_penalties=[0.1, 1, 10, 100, 1000, 10000], cv_folds=5öh]öh+îdesc_parameteröôö)üö}ö(hî}z, survival, duration_column='duration', observed_column='observed', cox_penalties=[0.1, 1, 10, 100, 1000, 10000], cv_folds=5öh]öhî}z, survival, duration_column='duration', observed_column='observed', cox_penalties=[0.1, 1, 10, 100, 1000, 10000], cv_folds=5öůöüö}ö(hhhhôhhhNhNubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhhŹhhhhzhNubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hőhhdhhhhzhNubh+îonlyöôö)üö}ö(hhh]öh+îpending_xreföôö)üö}ö(hhh]öhîinlineöôö)üö}ö(hhh]öhî[source]öůöüö}ö(hhhh│ubah}ö(h]öh!]öî
viewcode-linköah#]öh%]öh']öuh)h▒hh«ubah}ö(h]öh!]öh#]öh%]öh']öîreftypeöîviewcodeöî	refdomainöîstdöîrefexplicitöëî	reftargetöî_modules/maui/utilsöîrefidöhîrefdocöîutilsöuh)hČhhęubah}ö(h]öh!]öh#]öh%]öh']öîexpröîhtmlöuh)hžhhdhhhNhNubeh}ö(h]öh[ah!]öh#]öh[ah%]öh']öîfirstöëîmoduleöî
maui.utilsöîclassöhîfullnameöhuh)hbhh_hhhhzhNubh+îdesc_contentöôö)üö}ö(hhh]ö(h?)üö}ö(hîsCompute's Harrell's c-Index for a Cox Proportional Hazards regression modeling
survival by the latent factors in z.öh]öhîwComputeÔÇÖs HarrellÔÇÖs c-Index for a Cox Proportional Hazards regression modeling
survival by the latent factors in z.öůöüö}ö(hhŰhhÚhhhNhNubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hîV/home/jona/work/phd/maui/maui/maui/utils.py:docstring of maui.utils.compute_harrells_cöhKhhŠhhubh?)üö}ö(hîĆz:                  pd.DataFrame (n_samples, n_latent factors)
survival:           pd.DataFrame of survival information and relevant covariatesöh]öhîĆz:                  pd.DataFrame (n_samples, n_latent factors)
survival:           pd.DataFrame of survival information and relevant covariatesöůöüö}ö(hh˙hh°hhhNhNubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hh¸hKhhŠhhubhîblock_quoteöôö)üö}ö(hhh]öh?)üö}ö(hî/(such as sex, age at diagnosis, or tumor stage)öh]öhî/(such as sex, age at diagnosis, or tumor stage)öůöüö}ö(hj
hjubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hh¸hKhjubah}ö(h]öh!]öh#]öh%]öh']öuh)jhhŠhhhh¸hNubhîdefinition_listöôö)üö}ö(hhh]ö(hîdefinition_list_itemöôö)üö}ö(hîćduration_column:    the name of the column in ``survival`` containing the
duration (time between diagnosis and death or last followup)öh]ö(hîtermöôö)üö}ö(hîIduration_column:    the name of the column in ``survival`` containing theöh]ö(hî.duration_column:    the name of the column in öůöüö}ö(hî.duration_column:    the name of the column in öhj,ubhîliteralöôö)üö}ö(hî``survival``öh]öhîsurvivalöůöüö}ö(hhhj7ubah}ö(h]öh!]öh#]öh%]öh']öuh)j5hj,ubhî containing theöůöüö}ö(hî containing theöhj,ubeh}ö(h]öh!]öh#]öh%]öh']öuh)j*hh¸hK	hj&ubhî
definitionöôö)üö}ö(hhh]öh?)üö}ö(hî<duration (time between diagnosis and death or last followup)öh]öhî<duration (time between diagnosis and death or last followup)öůöüö}ö(hjWhjUubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hh¸hK
hjRubah}ö(h]öh!]öh#]öh%]öh']öuh)jPhj&ubeh}ö(h]öh!]öh#]öh%]öh']öuh)j$hh¸hK	hj!ubj%)üö}ö(hîoobserved_column:    the name of the column in ``survival`` containing
indicating whether time of death is knownöh]ö(j+)üö}ö(hîEobserved_column:    the name of the column in ``survival`` containingöh]ö(hî.observed_column:    the name of the column in öůöüö}ö(hî.observed_column:    the name of the column in öhjsubj6)üö}ö(hî``survival``öh]öhîsurvivalöůöüö}ö(hhhj|ubah}ö(h]öh!]öh#]öh%]öh']öuh)j5hjsubhî containingöůöüö}ö(hî containingöhjsubeh}ö(h]öh!]öh#]öh%]öh']öuh)j*hh¸hKhjoubjQ)üö}ö(hhh]öh?)üö}ö(hî)indicating whether time of death is knownöh]öhî)indicating whether time of death is knownöůöüö}ö(hjÜhjśubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hh¸hKhjĽubah}ö(h]öh!]öh#]öh%]öh']öuh)jPhjoubeh}ö(h]öh!]öh#]öh%]öh']öuh)j$hh¸hKhj!hhubj%)üö}ö(hî¬cox_penalties:      penalty coefficient in Cox PH solver (see ``lifelines.CoxPHFitter``)
to try. Returns the best c given by the different penalties
(by cross-validation)öh]ö(j+)üö}ö(hîXcox_penalties:      penalty coefficient in Cox PH solver (see ``lifelines.CoxPHFitter``)öh]ö(hî>cox_penalties:      penalty coefficient in Cox PH solver (see öůöüö}ö(hî>cox_penalties:      penalty coefficient in Cox PH solver (see öhjÂubj6)üö}ö(hî``lifelines.CoxPHFitter``öh]öhîlifelines.CoxPHFitteröůöüö}ö(hhhj┐ubah}ö(h]öh!]öh#]öh%]öh']öuh)j5hjÂubhî)öůöüö}ö(hî)öhjÂubeh}ö(h]öh!]öh#]öh%]öh']öuh)j*hh¸hKhj▓ubjQ)üö}ö(hhh]öh?)üö}ö(hîQto try. Returns the best c given by the different penalties
(by cross-validation)öh]öhîQto try. Returns the best c given by the different penalties
(by cross-validation)öůöüö}ö(hjŢhj█ubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hh¸hKhjěubah}ö(h]öh!]öh#]öh%]öh']öuh)jPhj▓ubeh}ö(h]öh!]öh#]öh%]öh']öuh)j$hh¸hKhj!hhubeh}ö(h]öh!]öh#]öh%]öh']öuh)jhhŠhhhh¸hNubh?)üö}ö(hîAcv_folds:           number of cross-validation folds to compute Cöh]öhîAcv_folds:           number of cross-validation folds to compute Cöůöüö}ö(hjřhjűhhhNhNubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hh¸hKhhŠhhubj )üö}ö(hhh]öj%)üö}ö(hîhcs: array, Harrell's c-Index, an auc-like metric for survival prediction accuracy.
one value per cv_foldöh]ö(j+)üö}ö(hîRcs: array, Harrell's c-Index, an auc-like metric for survival prediction accuracy.öh]öhîTcs: array, HarrellÔÇÖs c-Index, an auc-like metric for survival prediction accuracy.öůöüö}ö(hjhjubah}ö(h]öh!]öh#]öh%]öh']öuh)j*hh¸hKhjubjQ)üö}ö(hhh]öh?)üö}ö(hîone value per cv_foldöh]öhîone value per cv_foldöůöüö}ö(hj#hj!ubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hh¸hKhjubah}ö(h]öh!]öh#]öh%]öh']öuh)jPhjubeh}ö(h]öh!]öh#]öh%]öh']öuh)j$hh¸hKhj	ubah}ö(h]öh!]öh#]öh%]öh']öuh)jhhŠhhhh¸hNubeh}ö(h]öh!]öh#]öh%]öh']öuh)hńhh_hhhhzhNubeh}ö(h]öh!]öh#]öh%]öh']öîdomainöîpyöîobjtypeöîfunctionöîdesctypeöjPînoindexöëuh)h]hhhhhNhNubh-)üö}ö(hhh]öh}ö(h]öh!]öh#]öh%]öh']öîentriesö]ö(h9î$compute_roc() (in module maui.utils)öîmaui.utils.compute_rocöhNtöauh)h,hhhhhNhNubh^)üö}ö(hhh]ö(hc)üö}ö(hXcompute_roc(z, y, classifier=LinearSVC(C=0.001, class_weight=None, dual=True, fit_intercept=True,      intercept_scaling=1, loss='squared_hinge', max_iter=1000,      multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,      verbose=0), cv_folds=10)öh]ö(hi)üö}ö(hîmaui.utils.öh]öhîmaui.utils.öůöüö}ö(hhhjhhhhNhNubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hhhjdhhhîO/home/jona/work/phd/maui/maui/maui/utils.py:docstring of maui.utils.compute_rocöhNubh|)üö}ö(hîcompute_rocöh]öhîcompute_rocöůöüö}ö(hhhjwhhhNhNubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)h{hjdhhhjvhNubhî)üö}ö(hîšz, y, classifier=LinearSVC(C=0.001, class_weight=None, dual=True, fit_intercept=True, intercept_scaling=1, loss='squared_hinge', max_iter=1000, multi_class='ovr', penalty='l2', random_state=None, tol=0.0001, verbose=0), cv_folds=10öh]ö(hĺ)üö}ö(hîzöh]öhîzöůöüö}ö(hhhjëubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjůubhĺ)üö}ö(hîyöh]öhîyöůöüö}ö(hhhjŚubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjůubhĺ)üö}ö(hîclassifier=LinearSVC(C=0.001öh]öhîclassifier=LinearSVC(C=0.001öůöüö}ö(hhhjąubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjůubhĺ)üö}ö(hîclass_weight=Noneöh]öhîclass_weight=Noneöůöüö}ö(hhhj│ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjůubhĺ)üö}ö(hî	dual=Trueöh]öhî	dual=Trueöůöüö}ö(hhhj┴ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjůubhĺ)üö}ö(hîfit_intercept=Trueöh]öhîfit_intercept=Trueöůöüö}ö(hhhj¤ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjůubhĺ)üö}ö(hîintercept_scaling=1öh]öhîintercept_scaling=1öůöüö}ö(hhhjŢubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjůubhĺ)üö}ö(hîloss='squared_hinge'öh]öhîloss='squared_hinge'öůöüö}ö(hhhjŰubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjůubhĺ)üö}ö(hî
max_iter=1000öh]öhî
max_iter=1000öůöüö}ö(hhhj¨ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjůubhĺ)üö}ö(hîmulti_class='ovr'öh]öhîmulti_class='ovr'öůöüö}ö(hhhjubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjůubhĺ)üö}ö(hîpenalty='l2'öh]öhîpenalty='l2'öůöüö}ö(hhhjubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjůubhĺ)üö}ö(hîrandom_state=Noneöh]öhîrandom_state=Noneöůöüö}ö(hhhj#ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjůubhĺ)üö}ö(hî
tol=0.0001öh]öhî
tol=0.0001öůöüö}ö(hhhj1ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjůubhĺ)üö}ö(hî
verbose=0)öh]öhî
verbose=0)öůöüö}ö(hhhj?ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjůubhĺ)üö}ö(hîcv_folds=10öh]öhîcv_folds=10öůöüö}ö(hhhjMubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjůubeh}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hőhjdhhhjvhNubhĘ)üö}ö(hhh]öhş)üö}ö(hhh]öh▓)üö}ö(hhh]öhî[source]öůöüö}ö(hhhjgubah}ö(h]öh!]öhŻah#]öh%]öh']öuh)h▒hjdubah}ö(h]öh!]öh#]öh%]öh']öîreftypeöh╚î	refdomainöh╩îrefexplicitöëî	reftargetöî_modules/maui/utilsöîrefidöjyîrefdocöhđuh)hČhjaubah}ö(h]öh!]öh#]öh%]öh']öîexpröhěuh)hžhjdhhhNhNubeh}ö(h]öj_ah!]öh#]öj_ah%]öh']öh▀ëhÓî
maui.utilsöhÔhhŃjyuh)hbhjahhhjvhNubhň)üö}ö(hhh]ö(h?)üö}ö(hîQCompute the ROC (false positive rate, true positive rate) using cross-validation.öh]öhîQCompute the ROC (false positive rate, true positive rate) using cross-validation.öůöüö}ö(hjöhjĺhhhNhNubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hîO/home/jona/work/phd/maui/maui/maui/utils.py:docstring of maui.utils.compute_rocöhKhjĆhhubh?)üö}ö(hîěz:          DataFrame (n_samples, n_latent_factors) of latent factor values
y:          Series (n_samples,) of ground-truth labels to try to predict
classifier: Classifier object to use, default ``LinearSVC(C=.001)``öh]ö(hî├z:          DataFrame (n_samples, n_latent_factors) of latent factor values
y:          Series (n_samples,) of ground-truth labels to try to predict
classifier: Classifier object to use, default öůöüö}ö(hî├z:          DataFrame (n_samples, n_latent_factors) of latent factor values
y:          Series (n_samples,) of ground-truth labels to try to predict
classifier: Classifier object to use, default öhjíhhhNhNubj6)üö}ö(hî``LinearSVC(C=.001)``öh]öhîLinearSVC(C=.001)öůöüö}ö(hhhj¬ubah}ö(h]öh!]öh#]öh%]öh']öuh)j5hjíubeh}ö(h]öh!]öh#]öh%]öh']öuh)h>hjáhKhjĆhhubj )üö}ö(hhh]öj%)üö}ö(hîŻroc_curves: dict, one key per class as well as "mean", each value is a dataframe
containing the tpr (true positive rate) and fpr (falce positive rate)
defining that class (or the mean) ROC.öh]ö(j+)üö}ö(hîProc_curves: dict, one key per class as well as "mean", each value is a dataframeöh]öhîTroc_curves: dict, one key per class as well as ÔÇťmeanÔÇŁ, each value is a dataframeöůöüö}ö(hjăhj┼ubah}ö(h]öh!]öh#]öh%]öh']öuh)j*hjáhKhj┴ubjQ)üö}ö(hhh]öh?)üö}ö(hîlcontaining the tpr (true positive rate) and fpr (falce positive rate)
defining that class (or the mean) ROC.öh]öhîlcontaining the tpr (true positive rate) and fpr (falce positive rate)
defining that class (or the mean) ROC.öůöüö}ö(hjěhjÍubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjáhKhjËubah}ö(h]öh!]öh#]öh%]öh']öuh)jPhj┴ubeh}ö(h]öh!]öh#]öh%]öh']öuh)j$hjáhKhjżubah}ö(h]öh!]öh#]öh%]öh']öuh)jhjĆhhhjáhNubeh}ö(h]öh!]öh#]öh%]öh']öuh)hńhjahhhjvhNubeh}ö(h]öh!]öh#]öh%]öh']öjMîpyöjOîfunctionöjQjjRëuh)h]hhhhhNhNubh-)üö}ö(hhh]öh}ö(h]öh!]öh#]öh%]öh']öîentriesö]ö(h9î7correlate_factors_and_features() (in module maui.utils)öî)maui.utils.correlate_factors_and_featuresöhNtöauh)h,hhhhhNhNubh^)üö}ö(hhh]ö(hc)üö}ö(hîJcorrelate_factors_and_features(z, concatenated_data, pval_threshold=0.001)öh]ö(hi)üö}ö(hîmaui.utils.öh]öhîmaui.utils.öůöüö}ö(hhhjhhhNhNubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hhhjhhhîb/home/jona/work/phd/maui/maui/maui/utils.py:docstring of maui.utils.correlate_factors_and_featuresöhNubh|)üö}ö(hîcorrelate_factors_and_featuresöh]öhîcorrelate_factors_and_featuresöůöüö}ö(hhhj(hhhNhNubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)h{hjhhhj'hNubhî)üö}ö(hî*z, concatenated_data, pval_threshold=0.001öh]ö(hĺ)üö}ö(hjőh]öhîzöůöüö}ö(hhhj:ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhj6ubhĺ)üö}ö(hîconcatenated_dataöh]öhîconcatenated_dataöůöüö}ö(hhhjGubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhj6ubhĺ)üö}ö(hîpval_threshold=0.001öh]öhîpval_threshold=0.001öůöüö}ö(hhhjUubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhj6ubeh}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hőhjhhhj'hNubhĘ)üö}ö(hhh]öhş)üö}ö(hhh]öh▓)üö}ö(hhh]öhî[source]öůöüö}ö(hhhjoubah}ö(h]öh!]öhŻah#]öh%]öh']öuh)h▒hjlubah}ö(h]öh!]öh#]öh%]öh']öîreftypeöh╚î	refdomainöh╩îrefexplicitöëî	reftargetöî_modules/maui/utilsöîrefidöj*îrefdocöhđuh)hČhjiubah}ö(h]öh!]öh#]öh%]öh']öîexpröhěuh)hžhjhhhNhNubeh}ö(h]öjah!]öh#]öjah%]öh']öh▀ëhÓî
maui.utilsöhÔhhŃj*uh)hbhjhhhj'hNubhň)üö}ö(hhh]ö(h?)üö}ö(hîBCompute pearson correlation of latent factors with input features.öh]öhîBCompute pearson correlation of latent factors with input features.öůöüö}ö(hjťhjÜhhhNhNubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hîb/home/jona/work/phd/maui/maui/maui/utils.py:docstring of maui.utils.correlate_factors_and_featuresöhKhjŚhhubh?)üö}ö(hî╣z:                  (n_samples, n_factors) DataFrame of latent factor values, output of maui model
concatenated_data:  (n_samples, n_features) DataFrame of concatenated multi-omics dataöh]öhî╣z:                  (n_samples, n_factors) DataFrame of latent factor values, output of maui model
concatenated_data:  (n_samples, n_features) DataFrame of concatenated multi-omics dataöůöüö}ö(hjźhjęhhhNhNubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjĘhKhjŚhhubj )üö}ö(hhh]öj%)üö}ö(hîafeature_s:  DataFrame (n_features, n_latent_factors)
Latent factors representation of the data X.öh]ö(j+)üö}ö(hî4feature_s:  DataFrame (n_features, n_latent_factors)öh]öhî4feature_s:  DataFrame (n_features, n_latent_factors)öůöüö}ö(hj└hjżubah}ö(h]öh!]öh#]öh%]öh']öuh)j*hjĘhK
hj║ubjQ)üö}ö(hhh]öh?)üö}ö(hî,Latent factors representation of the data X.öh]öhî,Latent factors representation of the data X.öůöüö}ö(hjĐhj¤ubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjĘhKhj╠ubah}ö(h]öh!]öh#]öh%]öh']öuh)jPhj║ubeh}ö(h]öh!]öh#]öh%]öh']öuh)j$hjĘhK
hjĚubah}ö(h]öh!]öh#]öh%]öh']öuh)jhjŚhhhjĘhNubeh}ö(h]öh!]öh#]öh%]öh']öuh)hńhjhhhj'hNubeh}ö(h]öh!]öh#]öh%]öh']öjMîpyöjOîfunctionöjQjŘjRëuh)h]hhhhhNhNubh-)üö}ö(hhh]öh}ö(h]öh!]öh#]öh%]öh']öîentriesö]ö(h9î.estimate_kaplan_meier() (in module maui.utils)öî maui.utils.estimate_kaplan_meieröhNtöauh)h,hhhhhNhNubh^)üö}ö(hhh]ö(hc)üö}ö(hîZestimate_kaplan_meier(y, survival, duration_column='duration', observed_column='observed')öh]ö(hi)üö}ö(hîmaui.utils.öh]öhîmaui.utils.öůöüö}ö(hhhjhhhNhNubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hhhjhhhîY/home/jona/work/phd/maui/maui/maui/utils.py:docstring of maui.utils.estimate_kaplan_meieröhNubh|)üö}ö(hîestimate_kaplan_meieröh]öhîestimate_kaplan_meieröůöüö}ö(hhhj!hhhNhNubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)h{hjhhhj hNubhî)üö}ö(hîCy, survival, duration_column='duration', observed_column='observed'öh]ö(hĺ)üö}ö(hjÖh]öhîyöůöüö}ö(hhhj3ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhj/ubhĺ)üö}ö(hîsurvivalöh]öhîsurvivalöůöüö}ö(hhhj@ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhj/ubhĺ)üö}ö(hîduration_column='duration'öh]öhîduration_column='duration'öůöüö}ö(hhhjNubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhj/ubhĺ)üö}ö(hîobserved_column='observed'öh]öhîobserved_column='observed'öůöüö}ö(hhhj\ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhj/ubeh}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hőhjhhhj hNubhĘ)üö}ö(hhh]öhş)üö}ö(hhh]öh▓)üö}ö(hhh]öhî[source]öůöüö}ö(hhhjvubah}ö(h]öh!]öhŻah#]öh%]öh']öuh)h▒hjsubah}ö(h]öh!]öh#]öh%]öh']öîreftypeöh╚î	refdomainöh╩îrefexplicitöëî	reftargetöî_modules/maui/utilsöîrefidöj#îrefdocöhđuh)hČhjpubah}ö(h]öh!]öh#]öh%]öh']öîexpröhěuh)hžhjhhhNhNubeh}ö(h]öj	ah!]öh#]öj	ah%]öh']öh▀ëhÓî
maui.utilsöhÔhhŃj#uh)hbhjhhhj hNubhň)üö}ö(hhh]ö(h?)üö}ö(hîWEstimate survival curves for groups defined in y based on survival data in ``survival``öh]ö(hîKEstimate survival curves for groups defined in y based on survival data in öůöüö}ö(hîKEstimate survival curves for groups defined in y based on survival data in öhjíhhhNhNubj6)üö}ö(hî``survival``öh]öhîsurvivalöůöüö}ö(hhhj¬ubah}ö(h]öh!]öh#]öh%]öh']öuh)j5hjíubeh}ö(h]öh!]öh#]öh%]öh']öuh)h>hîY/home/jona/work/phd/maui/maui/maui/utils.py:docstring of maui.utils.estimate_kaplan_meieröhKhj×hhubj )üö}ö(hhh]ö(j%)üö}ö(hîYy:                  pd.Series, groups (clusters, subtypes). the index is
the sample namesöh]ö(j+)üö}ö(hîHy:                  pd.Series, groups (clusters, subtypes). the index isöh]öhîHy:                  pd.Series, groups (clusters, subtypes). the index isöůöüö}ö(hj╚hjĂubah}ö(h]öh!]öh#]öh%]öh']öuh)j*hjżhKhj┬ubjQ)üö}ö(hhh]öh?)üö}ö(hîthe sample namesöh]öhîthe sample namesöůöüö}ö(hj┘hjÎubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjżhKhjďubah}ö(h]öh!]öh#]öh%]öh']öuh)jPhj┬ubeh}ö(h]öh!]öh#]öh%]öh']öuh)j$hjżhKhj┐ubj%)üö}ö(hîřsurvival:           pd.DataFrame with the same index as y, with columns for
the duration (survival time for each patient) and whether
or not the death was observed. If the death was not
observed (sensored), the duration is the time of the last
followup.öh]ö(j+)üö}ö(hîKsurvival:           pd.DataFrame with the same index as y, with columns foröh]öhîKsurvival:           pd.DataFrame with the same index as y, with columns foröůöüö}ö(hj¸hj§ubah}ö(h]öh!]öh#]öh%]öh']öuh)j*hjżhK
hj˝ubjQ)üö}ö(hhh]öh?)üö}ö(hî▒the duration (survival time for each patient) and whether
or not the death was observed. If the death was not
observed (sensored), the duration is the time of the last
followup.öh]öhî▒the duration (survival time for each patient) and whether
or not the death was observed. If the death was not
observed (sensored), the duration is the time of the last
followup.öůöüö}ö(hjhjubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjżhKhjubah}ö(h]öh!]öh#]öh%]öh']öuh)jPhj˝ubeh}ö(h]öh!]öh#]öh%]öh']öuh)j$hjżhK
hj┐hhubeh}ö(h]öh!]öh#]öh%]öh']öuh)jhj×hhhjżhNubh?)üö}ö(hîúduration_column:        the name of the column in  ``survival`` with the duration
observed_column:    the name of the column in ``survival`` with True/False valuesöh]ö(hî3duration_column:        the name of the column in  öůöüö}ö(hî3duration_column:        the name of the column in  öhj&hhhNhNubj6)üö}ö(hî``survival``öh]öhîsurvivalöůöüö}ö(hhhj/ubah}ö(h]öh!]öh#]öh%]öh']öuh)j5hj&ubhîA with the duration
observed_column:    the name of the column in öůöüö}ö(hîA with the duration
observed_column:    the name of the column in öhj&hhhNhNubj6)üö}ö(hî``survival``öh]öhîsurvivalöůöüö}ö(hhhjBubah}ö(h]öh!]öh#]öh%]öh']öuh)j5hj&ubhî with True/False valuesöůöüö}ö(hî with True/False valuesöhj&hhhNhNubeh}ö(h]öh!]öh#]öh%]öh']öuh)h>hjżhKhj×hhubj)üö}ö(hhh]öh?)üö}ö(hî%for whether death was observed or notöh]öhî%for whether death was observed or notöůöüö}ö(hj`hj^ubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjżhKhj[ubah}ö(h]öh!]öh#]öh%]öh']öuh)jhj×hhhjżhNubj )üö}ö(hhh]öj%)üö}ö(hîśkm_estimates:       pd.DataFrame, index is the timeline, columns are survival
functions (estimated by Kaplan-Meier) for each class, as
defined in ``y``.öh]ö(j+)üö}ö(hîMkm_estimates:       pd.DataFrame, index is the timeline, columns are survivalöh]öhîMkm_estimates:       pd.DataFrame, index is the timeline, columns are survivalöůöüö}ö(hj{hjyubah}ö(h]öh!]öh#]öh%]öh']öuh)j*hjżhKhjuubjQ)üö}ö(hhh]öh?)üö}ö(hîJfunctions (estimated by Kaplan-Meier) for each class, as
defined in ``y``.öh]ö(hîDfunctions (estimated by Kaplan-Meier) for each class, as
defined in öůöüö}ö(hîDfunctions (estimated by Kaplan-Meier) for each class, as
defined in öhjŐubj6)üö}ö(hî``y``öh]öhîyöůöüö}ö(hhhjôubah}ö(h]öh!]öh#]öh%]öh']öuh)j5hjŐubhî.öůöüö}ö(hî.öhjŐubeh}ö(h]öh!]öh#]öh%]öh']öuh)h>hjżhKhjçubah}ö(h]öh!]öh#]öh%]öh']öuh)jPhjuubeh}ö(h]öh!]öh#]öh%]öh']öuh)j$hjżhKhjrubah}ö(h]öh!]öh#]öh%]öh']öuh)jhj×hhhjżhNubeh}ö(h]öh!]öh#]öh%]öh']öuh)hńhjhhhj hNubeh}ö(h]öh!]öh#]öh%]öh']öjMîpyöjOîfunctionöjQj╦jRëuh)h]hhhhhNhNubh-)üö}ö(hhh]öh}ö(h]öh!]öh#]öh%]öh']öîentriesö]ö(h9î-filter_factors_by_r2() (in module maui.utils)öîmaui.utils.filter_factors_by_r2öhNtöauh)h,hhhhhNhNubh^)üö}ö(hhh]ö(hc)üö}ö(hî*filter_factors_by_r2(z, x, threshold=0.02)öh]ö(hi)üö}ö(hîmaui.utils.öh]öhîmaui.utils.öůöüö}ö(hhhjßhhhNhNubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hhhjŢhhhîX/home/jona/work/phd/maui/maui/maui/utils.py:docstring of maui.utils.filter_factors_by_r2öhNubh|)üö}ö(hîfilter_factors_by_r2öh]öhîfilter_factors_by_r2öůöüö}ö(hhhjhhhNhNubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)h{hjŢhhhj´hNubhî)üö}ö(hîz, x, threshold=0.02öh]ö(hĺ)üö}ö(hjőh]öhîzöůöüö}ö(hhhjubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhj■ubhĺ)üö}ö(hîxöh]öhîxöůöüö}ö(hhhjubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhj■ubhĺ)üö}ö(hîthreshold=0.02öh]öhîthreshold=0.02öůöüö}ö(hhhjubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhj■ubeh}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hőhjŢhhhj´hNubhĘ)üö}ö(hhh]öhş)üö}ö(hhh]öh▓)üö}ö(hhh]öhî[source]öůöüö}ö(hhhj7ubah}ö(h]öh!]öhŻah#]öh%]öh']öuh)h▒hj4ubah}ö(h]öh!]öh#]öh%]öh']öîreftypeöh╚î	refdomainöh╩îrefexplicitöëî	reftargetöî_modules/maui/utilsöîrefidöj˛îrefdocöhđuh)hČhj1ubah}ö(h]öh!]öh#]öh%]öh']öîexpröhěuh)hžhjŢhhhNhNubeh}ö(h]öjěah!]öh#]öjěah%]öh']öh▀ëhÓî
maui.utilsöhÔhhŃj˛uh)hbhj┌hhhj´hNubhň)üö}ö(hhh]ö(h?)üö}ö(hî_Filter latent factors by the R^2 of a linear model predicting features x
from latent factors z.öh]öhî_Filter latent factors by the R^2 of a linear model predicting features x
from latent factors z.öůöüö}ö(hjdhjbhhhNhNubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hîX/home/jona/work/phd/maui/maui/maui/utils.py:docstring of maui.utils.filter_factors_by_r2öhKhj_hhubh?)üö}ö(hîŤz:  (n_samples, n_factors) DataFrame of latent factor values, output of a maui model
x:  (n_samples, n_features) DataFrame of concatenated multi-omics dataöh]öhîŤz:  (n_samples, n_factors) DataFrame of latent factor values, output of a maui model
x:  (n_samples, n_features) DataFrame of concatenated multi-omics dataöůöüö}ö(hjshjqhhhNhNubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjphKhj_hhubj )üö}ö(hhh]öj%)üö}ö(hî█z_filtered: (n_samples, n_factors) DataFrame of latent factor values,
with only those columns from the input `z` which have an R^2
above the threshold when using that column as an input
to a linear model predicting `x`.öh]ö(j+)üö}ö(hîEz_filtered: (n_samples, n_factors) DataFrame of latent factor values,öh]öhîEz_filtered: (n_samples, n_factors) DataFrame of latent factor values,öůöüö}ö(hjłhjćubah}ö(h]öh!]öh#]öh%]öh']öuh)j*hjphK
hjéubjQ)üö}ö(hhh]öh?)üö}ö(hîĽwith only those columns from the input `z` which have an R^2
above the threshold when using that column as an input
to a linear model predicting `x`.öh]ö(hî'with only those columns from the input öůöüö}ö(hî'with only those columns from the input öhjŚubhîtitle_referenceöôö)üö}ö(hî`z`öh]öhîzöůöüö}ö(hhhjóubah}ö(h]öh!]öh#]öh%]öh']öuh)jáhjŚubhîg which have an R^2
above the threshold when using that column as an input
to a linear model predicting öůöüö}ö(hîg which have an R^2
above the threshold when using that column as an input
to a linear model predicting öhjŚubjí)üö}ö(hî`x`öh]öhîxöůöüö}ö(hhhjÁubah}ö(h]öh!]öh#]öh%]öh']öuh)jáhjŚubhî.öůöüö}ö(hjąhjŚubeh}ö(h]öh!]öh#]öh%]öh']öuh)h>hjphKhjöubah}ö(h]öh!]öh#]öh%]öh']öuh)jPhjéubeh}ö(h]öh!]öh#]öh%]öh']öuh)j$hjphK
hjubah}ö(h]öh!]öh#]öh%]öh']öuh)jhj_hhhjphNubeh}ö(h]öh!]öh#]öh%]öh']öuh)hńhj┌hhhj´hNubeh}ö(h]öh!]öh#]öh%]öh']öjMîpyöjOîfunctionöjQjýjRëuh)h]hhhhhNhNubh-)üö}ö(hhh]öh}ö(h]öh!]öh#]öh%]öh']öîentriesö]ö(h9îCmap_factors_to_feaures_using_linear_models() (in module maui.utils)öî5maui.utils.map_factors_to_feaures_using_linear_modelsöhNtöauh)h,hhhhhNhNubh^)üö}ö(hhh]ö(hc)üö}ö(hî0map_factors_to_feaures_using_linear_models(z, x)öh]ö(hi)üö}ö(hîmaui.utils.öh]öhîmaui.utils.öůöüö}ö(hhhjhhhNhNubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hhhj■hhhîn/home/jona/work/phd/maui/maui/maui/utils.py:docstring of maui.utils.map_factors_to_feaures_using_linear_modelsöhNubh|)üö}ö(hî*map_factors_to_feaures_using_linear_modelsöh]öhî*map_factors_to_feaures_using_linear_modelsöůöüö}ö(hhhjhhhNhNubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)h{hj■hhhjhNubhî)üö}ö(hîz, xöh]ö(hĺ)üö}ö(hjőh]öhîzöůöüö}ö(hhhj#ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjubhĺ)üö}ö(hjh]öhîxöůöüö}ö(hhhj0ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjubeh}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hőhj■hhhjhNubhĘ)üö}ö(hhh]öhş)üö}ö(hhh]öh▓)üö}ö(hhh]öhî[source]öůöüö}ö(hhhjIubah}ö(h]öh!]öhŻah#]öh%]öh']öuh)h▒hjFubah}ö(h]öh!]öh#]öh%]öh']öîreftypeöh╚î	refdomainöh╩îrefexplicitöëî	reftargetöî_modules/maui/utilsöîrefidöjîrefdocöhđuh)hČhjCubah}ö(h]öh!]öh#]öh%]öh']öîexpröhěuh)hžhj■hhhNhNubeh}ö(h]öj¨ah!]öh#]öj¨ah%]öh']öh▀ëhÓî
maui.utilsöhÔhhŃjuh)hbhjűhhhjhNubhň)üö}ö(hhh]ö(h?)üö}ö(hîŃGet feature <-> latent factors mapping from linear models.
Runs one univariate (multi-output) linear model per latent factor in `z`,
predicting the values of the features `x`, in order to get weights
between inputs and outputs.öh]ö(hîÇGet feature <-> latent factors mapping from linear models.
Runs one univariate (multi-output) linear model per latent factor in öůöüö}ö(hîÇGet feature <-> latent factors mapping from linear models.
Runs one univariate (multi-output) linear model per latent factor in öhjthhhNhNubjí)üö}ö(hî`z`öh]öhîzöůöüö}ö(hhhj}ubah}ö(h]öh!]öh#]öh%]öh']öuh)jáhjtubhî(,
predicting the values of the features öůöüö}ö(hî(,
predicting the values of the features öhjthhhNhNubjí)üö}ö(hî`x`öh]öhîxöůöüö}ö(hhhjÉubah}ö(h]öh!]öh#]öh%]öh']öuh)jáhjtubhî5, in order to get weights
between inputs and outputs.öůöüö}ö(hî5, in order to get weights
between inputs and outputs.öhjthhhNhNubeh}ö(h]öh!]öh#]öh%]öh']öuh)h>hîn/home/jona/work/phd/maui/maui/maui/utils.py:docstring of maui.utils.map_factors_to_feaures_using_linear_modelsöhKhjqhhubh?)üö}ö(hîŤz:  (n_samples, n_factors) DataFrame of latent factor values, output of a maui model
x:  (n_samples, n_features) DataFrame of concatenated multi-omics dataöh]öhîŤz:  (n_samples, n_factors) DataFrame of latent factor values, output of a maui model
x:  (n_samples, n_features) DataFrame of concatenated multi-omics dataöůöüö}ö(hjČhj¬hhhNhNubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjęhKhjqhhubj )üö}ö(hhh]öj%)üö}ö(hîÜW:  (n_features, n_latent_factors) DataFrame
w_{ij} is the coefficient associated with feature `i` in a linear model
predicting it from latent factor `j`.öh]ö(j+)üö}ö(hî,W:  (n_features, n_latent_factors) DataFrameöh]öhî,W:  (n_features, n_latent_factors) DataFrameöůöüö}ö(hj┴hj┐ubah}ö(h]öh!]öh#]öh%]öh']öuh)j*hjęhKhj╗ubjQ)üö}ö(hhh]öh?)üö}ö(hîmw_{ij} is the coefficient associated with feature `i` in a linear model
predicting it from latent factor `j`.öh]ö(hî2w_{ij} is the coefficient associated with feature öůöüö}ö(hî2w_{ij} is the coefficient associated with feature öhjđubjí)üö}ö(hî`i`öh]öhîiöůöüö}ö(hhhj┘ubah}ö(h]öh!]öh#]öh%]öh']öuh)jáhjđubhî4 in a linear model
predicting it from latent factor öůöüö}ö(hî4 in a linear model
predicting it from latent factor öhjđubjí)üö}ö(hî`j`öh]öhîjöůöüö}ö(hhhjýubah}ö(h]öh!]öh#]öh%]öh']öuh)jáhjđubhî.öůöüö}ö(hjąhjđubeh}ö(h]öh!]öh#]öh%]öh']öuh)h>hjęhKhj═ubah}ö(h]öh!]öh#]öh%]öh']öuh)jPhj╗ubeh}ö(h]öh!]öh#]öh%]öh']öuh)j$hjęhKhjŞubah}ö(h]öh!]öh#]öh%]öh']öuh)jhjqhhhjęhNubeh}ö(h]öh!]öh#]öh%]öh']öuh)hńhjűhhhjhNubeh}ö(h]öh!]öh#]öh%]öh']öjMîpyöjOîfunctionöjQj#	jRëuh)h]hhhhhNhNubh-)üö}ö(hhh]öh}ö(h]öh!]öh#]öh%]öh']öîentriesö]ö(h9î&merge_factors() (in module maui.utils)öîmaui.utils.merge_factorsöhNtöauh)h,hhhhhNhNubh^)üö}ö(hhh]ö(hc)üö}ö(hîĹmerge_factors(z, l=None, threshold=0.17, merge_fn=<function mean>, metric='correlation', linkage='single', plot_dendro=True, plot_dendro_ax=None)öh]ö(hi)üö}ö(hîmaui.utils.öh]öhîmaui.utils.öůöüö}ö(hhhj9	hhhNhNubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hhhj5	hhhîQ/home/jona/work/phd/maui/maui/maui/utils.py:docstring of maui.utils.merge_factorsöhNubh|)üö}ö(hî
merge_factorsöh]öhî
merge_factorsöůöüö}ö(hhhjH	hhhNhNubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)h{hj5	hhhjG	hNubhî)üö}ö(hîéz, l=None, threshold=0.17, merge_fn=<function mean>, metric='correlation', linkage='single', plot_dendro=True, plot_dendro_ax=Noneöh]ö(hĺ)üö}ö(hjőh]öhîzöůöüö}ö(hhhjZ	ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjV	ubhĺ)üö}ö(hîl=Noneöh]öhîl=Noneöůöüö}ö(hhhjg	ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjV	ubhĺ)üö}ö(hîthreshold=0.17öh]öhîthreshold=0.17öůöüö}ö(hhhju	ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjV	ubhĺ)üö}ö(hîmerge_fn=<function mean>öh]öhîmerge_fn=<function mean>öůöüö}ö(hhhjâ	ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjV	ubhĺ)üö}ö(hîmetric='correlation'öh]öhîmetric='correlation'öůöüö}ö(hhhjĹ	ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjV	ubhĺ)üö}ö(hîlinkage='single'öh]öhîlinkage='single'öůöüö}ö(hhhjč	ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjV	ubhĺ)üö}ö(hîplot_dendro=Trueöh]öhîplot_dendro=Trueöůöüö}ö(hhhjş	ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjV	ubhĺ)üö}ö(hîplot_dendro_ax=Noneöh]öhîplot_dendro_ax=Noneöůöüö}ö(hhhj╗	ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhjV	ubeh}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hőhj5	hhhjG	hNubhĘ)üö}ö(hhh]öhş)üö}ö(hhh]öh▓)üö}ö(hhh]öhî[source]öůöüö}ö(hhhjŇ	ubah}ö(h]öh!]öhŻah#]öh%]öh']öuh)h▒hjĎ	ubah}ö(h]öh!]öh#]öh%]öh']öîreftypeöh╚î	refdomainöh╩îrefexplicitöëî	reftargetöî_modules/maui/utilsöîrefidöjJ	îrefdocöhđuh)hČhj¤	ubah}ö(h]öh!]öh#]öh%]öh']öîexpröhěuh)hžhj5	hhhNhNubeh}ö(h]öj0	ah!]öh#]öj0	ah%]öh']öh▀ëhÓî
maui.utilsöhÔhhŃjJ	uh)hbhj2	hhhjG	hNubhň)üö}ö(hhh]ö(h?)üö}ö(hîŢMerge latent factors in `z` which form clusters, as defined by hierarchical
clustering where a cluster is formed by cutting at a pre-set threshold, i.e.
merge factors if their distance to one-another is below `threshold`.öh]ö(hîMerge latent factors in öůöüö}ö(hîMerge latent factors in öhj
hhhNhNubjí)üö}ö(hî`z`öh]öhîzöůöüö}ö(hhhj	
ubah}ö(h]öh!]öh#]öh%]öh']öuh)jáhj
ubhîÂ which form clusters, as defined by hierarchical
clustering where a cluster is formed by cutting at a pre-set threshold, i.e.
merge factors if their distance to one-another is below öůöüö}ö(hîÂ which form clusters, as defined by hierarchical
clustering where a cluster is formed by cutting at a pre-set threshold, i.e.
merge factors if their distance to one-another is below öhj
hhhNhNubjí)üö}ö(hî`threshold`öh]öhî	thresholdöůöüö}ö(hhhj
ubah}ö(h]öh!]öh#]öh%]öh']öuh)jáhj
ubhî.öůöüö}ö(hjąhj
hhhNhNubeh}ö(h]öh!]öh#]öh%]öh']öuh)h>hîQ/home/jona/work/phd/maui/maui/maui/utils.py:docstring of maui.utils.merge_factorsöhKhjř	hhubh?)üö}ö(hî»z:              (n_samples, n_factors) DataFrame of latent factor values, output of a maui model
metric:         Distance metric to merge factors by, one which is supported byöh]öhî»z:              (n_samples, n_factors) DataFrame of latent factor values, output of a maui model
metric:         Distance metric to merge factors by, one which is supported byöůöüö}ö(hj7
hj5
hhhNhNubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hj4
hKhjř	hhubj)üö}ö(hhh]öh?)üö}ö(hî$:func:`scipy.spatial.distance.pdist`öh]öhş)üö}ö(hjH
h]öj6)üö}ö(hjH
h]öhîscipy.spatial.distance.pdist()öůöüö}ö(hhhjM
ubah}ö(h]öh!]ö(îxreföîpyöîpy-funcöeh#]öh%]öh']öuh)j5hjJ
ubah}ö(h]öh!]öh#]öh%]öh']öîreftypeöîfuncöî	refdomainöjX
îrefexplicitöëî	py:moduleöjŘ	îpy:classöNî	reftargetöîscipy.spatial.distance.pdistöîrefdocöhđîrefwarnöëuh)hČhj4
hK	hjF
ubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hj4
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ubah}ö(h]öh!]öh#]öh%]öh']öuh)jhjř	hhhj4
hNubj )üö}ö(hhh]ö(j%)üö}ö(hîćlinkage:        The kind of linkage to form hierarchical clustering, one which is
supported by :func:`scipy.cluster.hierarchy.linkage`öh]ö(j+)üö}ö(hîQlinkage:        The kind of linkage to form hierarchical clustering, one which isöh]öhîQlinkage:        The kind of linkage to form hierarchical clustering, one which isöůöüö}ö(hjé
hjÇ
ubah}ö(h]öh!]öh#]öh%]öh']öuh)j*hj4
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ubjQ)üö}ö(hhh]öh?)üö}ö(hî4supported by :func:`scipy.cluster.hierarchy.linkage`öh]ö(hî
supported by öůöüö}ö(hî
supported by öhjĹ
ubhş)üö}ö(hî':func:`scipy.cluster.hierarchy.linkage`öh]öj6)üö}ö(hjť
h]öhî!scipy.cluster.hierarchy.linkage()öůöüö}ö(hhhj×
ubah}ö(h]öh!]ö(jW
îpyöîpy-funcöeh#]öh%]öh']öuh)j5hjÜ
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îrefexplicitöëjg
jŘ	jh
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îscipy.cluster.hierarchy.linkageöjk
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ubeh}ö(h]öh!]öh#]öh%]öh']öuh)j$hj4
hK
hjy
ubj%)üö}ö(hî▓l:              As an alternative to supplying `metric` and `linkage`, supply a
linkage matrix of your own choice, such as one computed by
:func:`scipy.cluster.hierarchy.linkage`öh]ö(j+)üö}ö(hîOl:              As an alternative to supplying `metric` and `linkage`, supply aöh]ö(hî/l:              As an alternative to supplying öůöüö}ö(hî/l:              As an alternative to supplying öhj╬
ubjí)üö}ö(hî`metric`öh]öhîmetricöůöüö}ö(hhhjÎ
ubah}ö(h]öh!]öh#]öh%]öh']öuh)jáhj╬
ubhî and öůöüö}ö(hî and öhj╬
ubjí)üö}ö(hî	`linkage`öh]öhîlinkageöůöüö}ö(hhhjŕ
ubah}ö(h]öh!]öh#]öh%]öh']öuh)jáhj╬
ubhî
, supply aöůöüö}ö(hî
, supply aöhj╬
ubeh}ö(h]öh!]öh#]öh%]öh']öuh)j*hj4
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ubjQ)üö}ö(hhh]öh?)üö}ö(hîblinkage matrix of your own choice, such as one computed by
:func:`scipy.cluster.hierarchy.linkage`öh]ö(hî;linkage matrix of your own choice, such as one computed by
öůöüö}ö(hî;linkage matrix of your own choice, such as one computed by
öhjubhş)üö}ö(hî':func:`scipy.cluster.hierarchy.linkage`öh]öj6)üö}ö(hjh]öhî!scipy.cluster.hierarchy.linkage()öůöüö}ö(hhhjubah}ö(h]öh!]ö(jW
îpyöîpy-funcöeh#]öh%]öh']öuh)j5hjubah}ö(h]öh!]öh#]öh%]öh']öîreftypeöîfuncöî	refdomainöjîrefexplicitöëjg
jŘ	jh
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îscipy.cluster.hierarchy.linkageöjk
hđjl
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ubeh}ö(h]öh!]öh#]öh%]öh']öuh)j$hj4
hK
hjy
hhubj%)üö}ö(hîçthreshold:      The distance threshold. latent factors with similarity below the
threshold will be merged to form single latent facatoröh]ö(j+)üö}ö(hîPthreshold:      The distance threshold. latent factors with similarity below theöh]öhîPthreshold:      The distance threshold. latent factors with similarity below theöůöüö}ö(hjEhjCubah}ö(h]öh!]öh#]öh%]öh']öuh)j*hj4
hKhj?ubjQ)üö}ö(hhh]öh?)üö}ö(hî6threshold will be merged to form single latent facatoröh]öhî6threshold will be merged to form single latent facatoröůöüö}ö(hjVhjTubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hj4
hKhjQubah}ö(h]öh!]öh#]öh%]öh']öuh)jPhj?ubeh}ö(h]öh!]öh#]öh%]öh']öuh)j$hj4
hKhjy
hhubj%)üö}ö(hXmerge_fn:       A function which will be used to merge latent factors. The default
is :func:`numpy.mean`, i.e. the newly formed (merged) latent factor
will be the mean of the merged ones. Supply any function here which
has the same interface, i.e. takes a matrix and an axis.öh]ö(j+)üö}ö(hîRmerge_fn:       A function which will be used to merge latent factors. The defaultöh]öhîRmerge_fn:       A function which will be used to merge latent factors. The defaultöůöüö}ö(hjthjrubah}ö(h]öh!]öh#]öh%]öh']öuh)j*hj4
hKhjnubjQ)üö}ö(hhh]öh?)üö}ö(hî└is :func:`numpy.mean`, i.e. the newly formed (merged) latent factor
will be the mean of the merged ones. Supply any function here which
has the same interface, i.e. takes a matrix and an axis.öh]ö(hîis öůöüö}ö(hîis öhjâubhş)üö}ö(hî:func:`numpy.mean`öh]öj6)üö}ö(hjÄh]öhînumpy.mean()öůöüö}ö(hhhjÉubah}ö(h]öh!]ö(jW
îpyöîpy-funcöeh#]öh%]öh']öuh)j5hjîubah}ö(h]öh!]öh#]öh%]öh']öîreftypeöîfuncöî	refdomainöjÜîrefexplicitöëjg
jŘ	jh
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numpy.meanöjk
hđjl
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hKhjâubhîź, i.e. the newly formed (merged) latent factor
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will be the mean of the merged ones. Supply any function here which
has the same interface, i.e. takes a matrix and an axis.öhjâubeh}ö(h]öh!]öh#]öh%]öh']öuh)h>hj4
hKhjÇubah}ö(h]öh!]öh#]öh%]öh']öuh)jPhjnubeh}ö(h]öh!]öh#]öh%]öh']öuh)j$hj4
hKhjy
hhubj%)üö}ö(hîÇplot_dendro:    Boolean. If True, the function will plot a dendrogram showing
which latent factors are merged and the threshold.öh]ö(j+)üö}ö(hîMplot_dendro:    Boolean. If True, the function will plot a dendrogram showingöh]öhîMplot_dendro:    Boolean. If True, the function will plot a dendrogram showingöůöüö}ö(hjăhj┼ubah}ö(h]öh!]öh#]öh%]öh']öuh)j*hj4
hKhj┴ubjQ)üö}ö(hhh]öh?)üö}ö(hî2which latent factors are merged and the threshold.öh]öhî2which latent factors are merged and the threshold.öůöüö}ö(hjěhjÍubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hj4
hKhjËubah}ö(h]öh!]öh#]öh%]öh']öuh)jPhj┴ubeh}ö(h]öh!]öh#]öh%]öh']öuh)j$hj4
hKhjy
hhubeh}ö(h]öh!]öh#]öh%]öh']öuh)jhjř	hhhj4
hNubeh}ö(h]öh!]öh#]öh%]öh']öuh)hńhj2	hhhjG	hNubeh}ö(h]öh!]öh#]öh%]öh']öjMîpyöjOîfunctionöjQjjRëuh)h]hhhhhNhNubh-)üö}ö(hhh]öh}ö(h]öh!]öh#]öh%]öh']öîentriesö]ö(h9î2multivariate_logrank_test() (in module maui.utils)öî$maui.utils.multivariate_logrank_testöhNtöauh)h,hhhhhî]/home/jona/work/phd/maui/maui/maui/utils.py:docstring of maui.utils.multivariate_logrank_testöhNubh^)üö}ö(hhh]ö(hc)üö}ö(hî^multivariate_logrank_test(y, survival, duration_column='duration', observed_column='observed')öh]ö(hi)üö}ö(hîmaui.utils.öh]öhîmaui.utils.öůöüö}ö(hhhjhhhNhNubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hhhjhhhî]/home/jona/work/phd/maui/maui/maui/utils.py:docstring of maui.utils.multivariate_logrank_testöhNubh|)üö}ö(hîmultivariate_logrank_testöh]öhîmultivariate_logrank_testöůöüö}ö(hhhj)hhhNhNubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)h{hjhhhj(hNubhî)üö}ö(hîCy, survival, duration_column='duration', observed_column='observed'öh]ö(hĺ)üö}ö(hjÖh]öhîyöůöüö}ö(hhhj;ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhj7ubhĺ)üö}ö(hîsurvivalöh]öhîsurvivalöůöüö}ö(hhhjHubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhj7ubhĺ)üö}ö(hîduration_column='duration'öh]öhîduration_column='duration'öůöüö}ö(hhhjVubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhj7ubhĺ)üö}ö(hîobserved_column='observed'öh]öhîobserved_column='observed'öůöüö}ö(hhhjdubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hĹhj7ubeh}ö(h]öh!]öh#]öh%]öh']öhxhyuh)hőhjhhhj(hNubhĘ)üö}ö(hhh]öhş)üö}ö(hhh]öh▓)üö}ö(hhh]öhî[source]öůöüö}ö(hhhj~ubah}ö(h]öh!]öhŻah#]öh%]öh']öuh)h▒hj{ubah}ö(h]öh!]öh#]öh%]öh']öîreftypeöh╚î	refdomainöh╩îrefexplicitöëî	reftargetöî_modules/maui/utilsöîrefidöj+îrefdocöhđuh)hČhjxubah}ö(h]öh!]öh#]öh%]öh']öîexpröhěuh)hžhjhhhNhNubeh}ö(h]öjah!]öh#]öjah%]öh']öh▀ëhÓî
maui.utilsöhÔhhŃj+uh)hbhjhhhj(hNubhň)üö}ö(hhh]ö(h?)üö}ö(hXCompute the multivariate log-rank test for differential survival
among the groups defined by ``y`` in the survival data in ``survival``,
under the null-hypothesis that all groups have the same survival function
(i.e. test whether at least one group has different survival rates)öh]ö(hî]Compute the multivariate log-rank test for differential survival
among the groups defined by öůöüö}ö(hî]Compute the multivariate log-rank test for differential survival
among the groups defined by öhjęhhhNhNubj6)üö}ö(hî``y``öh]öhîyöůöüö}ö(hhhj▓ubah}ö(h]öh!]öh#]öh%]öh']öuh)j5hjęubhî in the survival data in öůöüö}ö(hî in the survival data in öhjęhhhNhNubj6)üö}ö(hî``survival``öh]öhîsurvivalöůöüö}ö(hhhj┼ubah}ö(h]öh!]öh#]öh%]öh']öuh)j5hjęubhîĆ,
under the null-hypothesis that all groups have the same survival function
(i.e. test whether at least one group has different survival rates)öůöüö}ö(hîĆ,
under the null-hypothesis that all groups have the same survival function
(i.e. test whether at least one group has different survival rates)öhjęhhhNhNubeh}ö(h]öh!]öh#]öh%]öh']öuh)h>hjhKhjŽhhubj )üö}ö(hhh]ö(j%)üö}ö(hîYy:                  pd.Series, groups (clusters, subtypes). the index is
the sample namesöh]ö(j+)üö}ö(hîHy:                  pd.Series, groups (clusters, subtypes). the index isöh]öhîHy:                  pd.Series, groups (clusters, subtypes). the index isöůöüö}ö(hjšhjňubah}ö(h]öh!]öh#]öh%]öh']öuh)j*hjhKhjßubjQ)üö}ö(hhh]öh?)üö}ö(hîthe sample namesöh]öhîthe sample namesöůöüö}ö(hj°hj÷ubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjhK	hjˇubah}ö(h]öh!]öh#]öh%]öh']öuh)jPhjßubeh}ö(h]öh!]öh#]öh%]öh']öuh)j$hjhKhjŮubj%)üö}ö(hîřsurvival:           pd.DataFrame with the same index as y, with columns for
the duration (survival time for each patient) and whether
or not the death was observed. If the death was not
observed (sensored), the duration is the time of the last
followup.öh]ö(j+)üö}ö(hîKsurvival:           pd.DataFrame with the same index as y, with columns foröh]öhîKsurvival:           pd.DataFrame with the same index as y, with columns foröůöüö}ö(hj
hj
ubah}ö(h]öh!]öh#]öh%]öh']öuh)j*hjhK
hj
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or not the death was observed. If the death was not
observed (sensored), the duration is the time of the last
followup.öh]öhî▒the duration (survival time for each patient) and whether
or not the death was observed. If the death was not
observed (sensored), the duration is the time of the last
followup.öůöüö}ö(hj'
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ubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjhKhj"
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hjŮhhubeh}ö(h]öh!]öh#]öh%]öh']öuh)jhjŽhhhjhNubh?)üö}ö(hîúduration_column:        the name of the column in  ``survival`` with the duration
observed_column:    the name of the column in ``survival`` with True/False valuesöh]ö(hî3duration_column:        the name of the column in  öůöüö}ö(hî3duration_column:        the name of the column in  öhjE
hhhNhNubj6)üö}ö(hî``survival``öh]öhîsurvivalöůöüö}ö(hhhjN
ubah}ö(h]öh!]öh#]öh%]öh']öuh)j5hjE
ubhîA with the duration
observed_column:    the name of the column in öůöüö}ö(hîA with the duration
observed_column:    the name of the column in öhjE
hhhNhNubj6)üö}ö(hî``survival``öh]öhîsurvivalöůöüö}ö(hhhja
ubah}ö(h]öh!]öh#]öh%]öh']öuh)j5hjE
ubhî with True/False valuesöůöüö}ö(hî with True/False valuesöhjE
hhhNhNubeh}ö(h]öh!]öh#]öh%]öh']öuh)h>hjhKhjŽhhubj)üö}ö(hhh]öh?)üö}ö(hî%for whether death was observed or notöh]öhî%for whether death was observed or notöůöüö}ö(hj
hj}
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hj0hhubj%)üö}ö(hîualpha:              threshold for p-value of CPH coefficients to call a latent
factor clinically relevant (p < alpha)öh]ö(j+)üö}ö(hîNalpha:              threshold for p-value of CPH coefficients to call a latentöh]öhîNalpha:              threshold for p-value of CPH coefficients to call a latentöůöüö}ö(hjţhjýubah}ö(h]öh!]öh#]öh%]öh']öuh)j*hj/hKhjŔubjQ)üö}ö(hhh]öh?)üö}ö(hî&factor clinically relevant (p < alpha)öh]öhî&factor clinically relevant (p < alpha)öůöüö}ö(hj hjřubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hj/hKhj˙ubah}ö(h]öh!]öh#]öh%]öh']öuh)jPhjŔubeh}ö(h]öh!]öh#]öh%]öh']öuh)j$hj/hKhj0hhubeh}ö(h]öh!]öh#]öh%]öh']öuh)jhjhhhj/hNubh?)üö}ö(hîXcox_penalizer:      penalty coefficient in Cox PH solver (see ``lifelines.CoxPHFitter``)öh]ö(hî>cox_penalizer:      penalty coefficient in Cox PH solver (see öůöüö}ö(hî>cox_penalizer:      penalty coefficient in Cox PH solver (see öhjhhhNhNubj6)üö}ö(hî``lifelines.CoxPHFitter``öh]öhîlifelines.CoxPHFitteröůöüö}ö(hhhj&ubah}ö(h]öh!]öh#]öh%]öh']öuh)j5hjubhî)öůöüö}ö(hjĐhjhhhNhNubeh}ö(h]öh!]öh#]öh%]öh']öuh)h>hj/hKhjhhubj )üö}ö(hhh]öj%)üö}ö(hî«z_clinical: pd.DataFrame, subset of the latent factors which have been
determined to have clinical value (are individually predictive
of survival, controlling for covariates)öh]ö(j+)üö}ö(hîFz_clinical: pd.DataFrame, subset of the latent factors which have beenöh]öhîFz_clinical: pd.DataFrame, subset of the latent factors which have beenöůöüö}ö(hjGhjEubah}ö(h]öh!]öh#]öh%]öh']öuh)j*hj/hKhjAubjQ)üö}ö(hhh]öh?)üö}ö(hîgdetermined to have clinical value (are individually predictive
of survival, controlling for covariates)öh]öhîgdetermined to have clinical value (are individually predictive
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----------öh]öhîParameters
----------öůöüö}ö(hhhj(ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)j&hjhh¸ubeh}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöîSEVEREöîsourceöh¸îlineöKuh)jhhŠhhhh¸hKubj)üö}ö(hhh]öh?)üö}ö(hîUnexpected indentation.öh]öhîUnexpected indentation.öůöüö}ö(hhhjDubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjAubah}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöîERRORöîsourceöh¸îlineöKuh)jhhŠhhhh¸hKubj)üö}ö(hhh]öh?)üö}ö(hî;Block quote ends without a blank line; unexpected unindent.öh]öhî;Block quote ends without a blank line; unexpected unindent.öůöüö}ö(hhhj`ubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hj]ubah}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöîWARNINGöîlineöK	îsourceöh¸uh)jhhŠhhhh¸hNubj)üö}ö(hhh]öh?)üö}ö(hî?Definition list ends without a blank line; unexpected unindent.öh]öhî?Definition list ends without a blank line; unexpected unindent.öůöüö}ö(hhhj|ubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjyubah}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöjvîlineöKîsourceöh¸uh)jhhŠhhhh¸hKubj)üö}ö(hhh]ö(h?)üö}ö(hîUnexpected section title.öh]öhîUnexpected section title.öůöüö}ö(hhhjŚubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjöubj')üö}ö(hîReturns
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----------öh]öhîParameters
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----------öh]öhîParameters
----------öůöüö}ö(hhhjrubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)j&hjahjżubeh}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöj>îsourceöjżîlineöKuh)jhj×hhhjżhKubj)üö}ö(hhh]öh?)üö}ö(hî?Definition list ends without a blank line; unexpected unindent.öh]öhî?Definition list ends without a blank line; unexpected unindent.öůöüö}ö(hhhjŹubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjŐubah}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöjvîlineöKîsourceöjżuh)jhj×hhhjżhKubj)üö}ö(hhh]öh?)üö}ö(hîUnexpected indentation.öh]öhîUnexpected indentation.öůöüö}ö(hhhjĘubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjąubah}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöjZîsourceöjżîlineöKuh)jhj×hhhjżhK
ubj)üö}ö(hhh]ö(h?)üö}ö(hîUnexpected section title.öh]öhîUnexpected section title.öůöüö}ö(hhhj├ubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hj└ubj')üö}ö(hîReturns
-------öh]öhîReturns
-------öůöüö}ö(hhhjĐubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)j&hj└hjżubeh}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöj>îsourceöjżîlineöKuh)jhj×hhhjżhKubj)üö}ö(hhh]öh?)üö}ö(hhh]öhî╝duplicate object description of maui.utils.filter_factors_by_r2, other instance in /home/jona/work/phd/maui/maui/doc/filtering-and-merging-latent-factors.rst, use :noindex: for one of themöůöüö}ö(hhhjýubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjÚubah}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöjvîlineöKîsourceöj´uh)jubj)üö}ö(hhh]ö(h?)üö}ö(hîUnexpected section title.öh]öhîUnexpected section title.öůöüö}ö(hhhjubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjubj')üö}ö(hîParameters
----------öh]öhîParameters
----------öůöüö}ö(hhhjubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)j&hjhjpubeh}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöj>îsourceöjpîlineöKuh)jhj_hhhjphKubj)üö}ö(hhh]ö(h?)üö}ö(hîUnexpected section title.öh]öhîUnexpected section title.öůöüö}ö(hhhj/ubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hj,ubj')üö}ö(hîReturns
-------öh]öhîReturns
-------öůöüö}ö(hhhj=ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)j&hj,hjpubeh}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöj>îsourceöjpîlineöK
uh)jhj_hhhjphK
ubj)üö}ö(hhh]ö(h?)üö}ö(hîUnexpected section title.öh]öhîUnexpected section title.öůöüö}ö(hhhjXubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjUubj')üö}ö(hîParameters
----------öh]öhîParameters
----------öůöüö}ö(hhhjfubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)j&hjUhjęubeh}ö(h]öh!]öh#]öĽŞh%]öh']öîlevelöKîtypeöj>îsourceöjęîlineöKuh)jhjqhhhjęhKubj)üö}ö(hhh]ö(h?)üö}ö(hîUnexpected section title.öh]öhîUnexpected section title.öůöüö}ö(hhhjüubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hj~ubj')üö}ö(hîReturns
-------öh]öhîReturns
-------öůöüö}ö(hhhjĆubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)j&hj~hjęubeh}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöj>îsourceöjęîlineöKuh)jhjqhhhjęhKubj)üö}ö(hhh]öh?)üö}ö(hhh]öhîÁduplicate object description of maui.utils.merge_factors, other instance in /home/jona/work/phd/maui/maui/doc/filtering-and-merging-latent-factors.rst, use :noindex: for one of themöůöüö}ö(hhhj¬ubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjžubah}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöjvîlineöKîsourceöjG	uh)jubj)üö}ö(hhh]ö(h?)üö}ö(hîUnexpected section title.öh]öhîUnexpected section title.öůöüö}ö(hhhj─ubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hj┴ubj')üö}ö(hîParameters
----------öh]öhîParameters
----------öůöüö}ö(hhhjĎubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)j&hj┴hj4
ubeh}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöj>îsourceöj4
îlineöKuh)jhjř	hhhj4
hKubj)üö}ö(hhh]öh?)üö}ö(hîUnexpected indentation.öh]öhîUnexpected indentation.öůöüö}ö(hhhjÝubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjŕubah}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöjZîsourceöj4
îlineöK	uh)jhjř	hhhj4
hKubj)üö}ö(hhh]öh?)üö}ö(hî;Block quote ends without a blank line; unexpected unindent.öh]öhî;Block quote ends without a blank line; unexpected unindent.öůöüö}ö(hhhjubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjubah}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöjvîlineöK
îsourceöj4
uh)jhjř	hhhj4
hNubj)üö}ö(hhh]ö(h?)üö}ö(hîUnexpected section title.öh]öhîUnexpected section title.öůöüö}ö(hhhj#ubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hj ubj')üö}ö(hîParameters
----------öh]öhîParameters
----------öůöüö}ö(hhhj1ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)j&hj hjubeh}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöj>îsourceöjîlineöKuh)jhjŽhhhjhKubj)üö}ö(hhh]öh?)üö}ö(hî?Definition list ends without a blank line; unexpected unindent.öh]öhî?Definition list ends without a blank line; unexpected unindent.öůöüö}ö(hhhjLubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjIubah}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöjvîlineöKîsourceöjuh)jhjŽhhhjhKubj)üö}ö(hhh]öh?)üö}ö(hîUnexpected indentation.öh]öhîUnexpected indentation.öůöüö}ö(hhhjgubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjdubah}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöjZîsourceöjîlineöKuh)jhjŽhhhjhKubj)üö}ö(hhh]ö(h?)üö}ö(hîUnexpected section title.öh]öhîUnexpected section title.öůöüö}ö(hhhjéubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjubj')üö}ö(hîReturns
-------öh]öhîReturns
-------öůöüö}ö(hhhjÉubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)j&hjhjubeh}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöj>îsourceöjîlineöKuh)jhjŽhhhjhKubj)üö}ö(hhh]öh?)üö}ö(hhh]öhîŤduplicate object description of maui.utils.scale, other instance in /home/jona/work/phd/maui/maui/doc/data-normalization.rst, use :noindex: for one of themöůöüö}ö(hhhjźubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjĘubah}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöjvîlineöKîsourceöjĐ
uh)jubj)üö}ö(hhh]ö(h?)üö}ö(hîUnexpected section title.öh]öhîUnexpected section title.öůöüö}ö(hhhj┼ubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hj┬ubj')üö}ö(hîParameters
----------öh]öhîParameters
----------öůöüö}ö(hhhjËubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)j&hj┬hj╗
ubeh}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöj>îsourceöj╗
îlineöKuh)jhj&hhhj╗
hKubj)üö}ö(hhh]ö(h?)üö}ö(hîUnexpected section title.öh]öhîUnexpected section title.öůöüö}ö(hhhjţubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjŰubj')üö}ö(hîReturns
-------öh]öhîReturns
-------öůöüö}ö(hhhjŘubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)j&hjŰhj╗
ubeh}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöj>îsourceöj╗
îlineöKuh)jhj&hhhj╗
hKubj)üö}ö(hhh]öh?)üö}ö(hhh]öhî┐duplicate object description of maui.utils.select_clinical_factors, other instance in /home/jona/work/phd/maui/maui/doc/filtering-and-merging-latent-factors.rst, use :noindex: for one of themöůöüö}ö(hhhjubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjubah}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöjvîlineöKîsourceöjäuh)jubj)üö}ö(hhh]ö(h?)üö}ö(hîUnexpected section title.öh]öhîUnexpected section title.öůöüö}ö(hhhj1ubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hj.ubj')üö}ö(hîParameters
----------öh]öhîParameters
----------öůöüö}ö(hhhj?ubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)j&hj.hj/ubeh}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöj>îsourceöj/îlineöKuh)jhjhhhj/hKubj)üö}ö(hhh]öh?)üö}ö(hî?Definition list ends without a blank line; unexpected unindent.öh]öhî?Definition list ends without a blank line; unexpected unindent.öůöüö}ö(hhhjZubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjWubah}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöjvîlineöKîsourceöj/uh)jhjhhhj/hKubj)üö}ö(hhh]ö(h?)üö}ö(hîUnexpected section title.öh]öhîUnexpected section title.öůöüö}ö(hhhjuubah}ö(h]öh!]öh#]öh%]öh']öuh)h>hjrubj')üö}ö(hîReturns
-------öh]öhîReturns
-------öůöüö}ö(hhhjâubah}ö(h]öh!]öh#]öh%]öh']öhxhyuh)j&hjrhj/ubeh}ö(h]öh!]öh#]öh%]öh']öîlevelöKîtypeöj>îsourceöj/îlineöKuh)jhjhhhj/hKubeîtransform_messagesö]öîtransformeröNî
decorationöNhhub.