This PR closes #49 and adds function ModelBuilder.fov_filter to filter datasets based on lists that contain the fov names. The actual train/test/validation splits are saved individually for each dataset in e.g. configs/tonic_split.json and loaded in ModelBuilder.prep_data.
How did you implement your changes
If we supply params["data_splits"] = ["path_to_/dataset_split.json"] to ModelBuilder or PromixNaive objects, calling class method prep_data loads the .json files into dicts with three keys each (train, validation and test). The associated values are lists of FOVs that should be used for train / validation / test datasets. To do this, class function ModelBuilder.fov_filter(self, dataset, fov_list, fov_key) is called to construct the train / validation and test datasets. ModelBuilder.fov_filter simply takes fov_list, constructs a predicate and maps it onto the full dataset to filter out all samples showing FOVs that are not in fov_list.
What is the purpose of this PR?
This PR closes #49 and adds function
ModelBuilder.fov_filterto filter datasets based on lists that contain the fov names. The actual train/test/validation splits are saved individually for each dataset in e.g.configs/tonic_split.jsonand loaded inModelBuilder.prep_data.How did you implement your changes If we supply
params["data_splits"] = ["path_to_/dataset_split.json"]toModelBuilderorPromixNaiveobjects, calling class methodprep_dataloads the .json files into dicts with three keys each (train,validationandtest). The associated values are lists of FOVs that should be used for train / validation / test datasets. To do this, class functionModelBuilder.fov_filter(self, dataset, fov_list, fov_key)is called to construct the train / validation and test datasets.ModelBuilder.fov_filtersimply takesfov_list, constructs a predicate and maps it onto the full dataset to filter out all samples showing FOVs that are not infov_list.Remaining issues
None