Source code for immuneML.preprocessing.filters.MetadataRepertoireFilter

from pathlib import Path

import numpy as np
import pandas as pd

from immuneML.analysis.criteria_matches.CriteriaMatcher import CriteriaMatcher
from immuneML.analysis.criteria_matches.CriteriaTypeInstantiator import CriteriaTypeInstantiator
from immuneML.data_model.dataset.RepertoireDataset import RepertoireDataset
from immuneML.preprocessing.filters.Filter import Filter

[docs] class MetadataRepertoireFilter(Filter): """ Removes repertoires from a RepertoireDataset based on information stored in the metadata_file. Note that this filters out repertoires, not individual sequences, and can thus only be applied to RepertoireDatasets. Since this filter changes the number of repertoires (examples for the machine learning task), it cannot be used with :ref:`TrainMLModel` instruction. To filter out repertoires, use preprocessing from the :ref:`DatasetExport` instruction that will create a new dataset ready to be used for training machine learning models. Arguments: criteria (dict): a nested dictionary that specifies the criteria for keeping certain columns. See :py:obj:`~immuneML.analysis.criteria_matches.CriteriaMatcher.CriteriaMatcher` for a more detailed explanation. YAML specification: .. indent with spaces .. code-block:: yaml preprocessing_sequences: my_preprocessing: - my_filter: # Example filter that keeps repertoires with values greater than 1 in the "my_column_name" column of the metadata_file MetadataRepertoireFilter: type: GREATER_THAN value: type: COLUMN name: my_column_name threshold: 1 """ def __init__(self, criteria: dict, result_path: Path = None): super().__init__(result_path) self.criteria = CriteriaTypeInstantiator.instantiate(criteria)
[docs] def keeps_example_count(self) -> bool: return False
[docs] def process_dataset(self, dataset: RepertoireDataset, result_path: Path, number_of_processes=1): self.check_dataset_type(dataset, [RepertoireDataset], "MetadataRepertoireFilter") self.result_path = result_path if result_path is not None else self.result_path processed_dataset = dataset.clone() original_repertoires = processed_dataset.get_data() indices = self._get_matching_indices(processed_dataset) processed_dataset.repertoires = [original_repertoires[i] for i in indices] processed_dataset.metadata_file = self._build_new_metadata(dataset, indices) self.check_dataset_not_empty(processed_dataset, "MetadataRepertoireFilter") return processed_dataset
def _get_matching_indices(self, dataset: RepertoireDataset): metadata = pd.DataFrame(dataset.get_metadata(None)) matches = CriteriaMatcher().match(self.criteria, metadata) indices = np.where(matches)[0] return indices