Experiments
- class survivors.experiments.grid.Experiments(folds=5, except_stop=False, dataset_name='NONE_NAME', mode='CV', bins_sch='')
- Class receives methods, metrics and grids,
produces cross-validation experiments, stores table of results : name, params, time, metrics (by sample and mean)
Attributes
- methodslist
- Must have predicting methods according to metrics:
IBS - survival func IAUC - cumulative hazard func CI - occurred time CI_CENS - occurred time
- methods_gridlist
Each grid is dictionary: key - param name, values - list
- metricslist
Each metric is string, which must be in METRIC_DICT
- is_tableboolean
Flag of calculation ending
- foldsint
Quantity of cross-validate folds.
- except_stopbool
Mode of ending because of exception. True - stop experiments with current method False - continue experiments
- dataset_namestr
Unique name of current dataset (used for saving)
Methods
add_method : append method and its grid set_metrics : check and set list of metric name run : start experiments with data X, y get_agg_results : choose for each method aggregated params by metric and aggreg save : export table as xlsx
- survivors.experiments.grid.generate_sample(X, y, folds, mode='CV')
Generate cross-validate samples with StratifiedKFold.
Parameters
- XPandas dataframe
Contain input features of events.
- ystructured array
Contain censuring flag and time of events.
- foldsint
Quantity of cross-validate folds.
- modestr
Validation scenario.
Yields
- X_trainPandas dataframe
Contain input features of train sample.
- y_trainarray-like
Contain censuring flag and time of train sample.
- X_testPandas dataframe
Contain input features of test sample.
- y_testarray-like
Contain censuring flag and time of test sample.
- binsarray-like
Points of timeline.
- survivors.experiments.grid.get_fit_eval_func(method, X, y, folds, metrics_names=['CI'], mode='CV', dir_path=None)
Return function, which on sample X, y apply cross-validation and calculate metrics for each fold.
Parameters
- methodclass
Must have methods for fitting, predicting time, hazard and survival func
- XPandas dataframe
Contain input features of events.
- ystructured array
Contain censuring flag and time of events.
- foldsint
Quantity of cross-validate folds.
- metrics_namesTYPE, optional
DESCRIPTION. The default is [‘CI’].
- modestr
Validation scenario.
- dir_pathstr
Path to cache directory (for loading pretrained models).
Returns
- functions
Recieve hyperparameters and return list of metrics arrays. Allow to use in ParameterGrid.
- survivors.experiments.grid.get_name_file(method, params, mode, fold)
Creating a name to cache the model without considering variables independent for reproducibility.
- survivors.experiments.grid.prepare_sample(X, y, train_index, test_index)
Constructing a set of bins on target variables and clipping