Merge pull request #561 from dirac-institute/batch

Remove the BatchFilter abstract data type

src/kbmod/filters/base_filter.py

@@ -37,43 +37,3 @@ def keep_row(self, row: ResultRow):
            An indicator of whether to keep the row.
         """
         pass
-
-
-class BatchFilter(abc.ABC):
-    """The base class for derived filters on the ResultList
-    that operate on the results in a single batch.
-
-    Batching should be used when the user needs greater control
-    over how the filter is run, such as using aggregate statistics
-    from all candidates or running batch computations on GPUs.
-    """
-
-    def __init__(self, *args, **kwargs):
-        pass
-
-    @abc.abstractmethod
-    def get_filter_name(self):
-        """Get the name of the filter.
-
-        Returns
-        -------
-        str
-            The filter name.
-        """
-        pass
-
-    @abc.abstractmethod
-    def keep_indices(self, results: ResultList):
-        """Determine which of the ResultList's indices to keep.
-
-        Parameters
-        ----------
-        results: ResultList
-            The set of results to filter.
-
-        Returns
-        -------
-        list
-           A list of indices (int) indicating which rows to keep.
-        """
-        pass

src/kbmod/filters/clustering_filters.py

@@ -1,11 +1,10 @@
 import numpy as np
 from sklearn.cluster import DBSCAN
 
-from kbmod.filters.base_filter import BatchFilter
 from kbmod.result_list import ResultList, ResultRow
 
 
-class DBSCANFilter(BatchFilter):
+class DBSCANFilter:
     """Cluster the candidates using DBSCAN and only keep a
     single representative trajectory from each cluster."""
 
@@ -17,8 +16,6 @@ def __init__(self, eps, *args, **kwargs):
         eps : `float`
             The clustering threshold.
         """
-        super().__init__(*args, **kwargs)
-
         self.eps = eps
         self.cluster_type = ""
         self.cluster_args = dict(eps=self.eps, min_samples=1, n_jobs=-1)
@@ -272,4 +269,6 @@ def apply_clustering(result_list, cluster_params):
         filt = ClusterMidPosFilter(**cluster_params)
     else:
         raise ValueError(f"Unknown clustering type: {cluster_type}")
-    result_list.apply_batch_filter(filt)
+
+    indices_to_keep = filt.keep_indices(result_list)
+    result_list.filter_results(indices_to_keep, filt.get_filter_name())

src/kbmod/result_list.py

@@ -800,25 +800,6 @@ def apply_filter(self, filter_obj, num_threads=1):
 
         return self
 
-    def apply_batch_filter(self, filter_obj):
-        """Apply the given batch filter object to the ResultList.
-
-        Modifies the ResultList in place.
-
-        Parameters
-        ----------
-        filter_obj : BatchFilter
-            The filtering object to use.
-
-        Returns
-        -------
-        self : ResultList
-            Returns a reference to itself to allow chaining.
-        """
-        indices_to_keep = filter_obj.keep_indices(self)
-        self.filter_results(indices_to_keep, filter_obj.get_filter_name())
-        return self
-
     def get_filtered(self, label=None):
         """Get the results filtered at a given stage or all stages.