Initial commit for pluggable query_strategies. (#59)

src/resspect/database.py

@@ -27,7 +27,7 @@
 from resspect.query_strategies import *
 from resspect.query_budget_strategies import *
 from resspect.metrics import get_snpcc_metric
-from resspect.plugin_utils import fetch_classifier_class
+from resspect.plugin_utils import fetch_classifier_class, fetch_query_strategy_class
 
 __all__ = ['DataBase']
 
@@ -1265,7 +1265,7 @@ def make_query_budget(self, budgets, strategy='UncSampling', screen=False) -> li
         return query_indx
 
     def make_query(self, strategy='UncSampling', batch=1,
-                   screen=False, queryable=False, query_thre=1.0) -> list:
+                   screen=False, queryable=False, query_threshold=1.0) -> list:
         """Identify new object to be added to the training sample.
 
         Parameters
@@ -1282,7 +1282,7 @@ def make_query(self, strategy='UncSampling', batch=1,
         queryable: bool (optional)
             If True, consider only queryable objects.
             Default is False.
-        query_thre: float (optional)
+        query_threshold: float (optional)
             Percentile threshold where a query is considered worth it.
             Default is 1 (no limit).
         screen: bool (optional)
@@ -1305,57 +1305,24 @@ def make_query(self, strategy='UncSampling', batch=1,
 
         id_name = self.identify_keywords()
 
-        if strategy == 'UncSampling':
-            query_indx = uncertainty_sampling(class_prob=self.classprob,
-                                              queryable_ids=self.queryable_ids,
-                                              test_ids=self.pool_metadata[id_name].values,
-                                              batch=batch, screen=screen,
-                                              query_thre=query_thre)
-
-
-        elif strategy == 'UncSamplingEntropy':
-            query_indx = uncertainty_sampling_entropy(class_prob=self.classprob,
-                                              queryable_ids=self.queryable_ids,
-                                              test_ids=self.pool_metadata[id_name].values,
-                                              batch=batch, screen=screen,
-                                              query_thre=query_thre)
-
-        elif strategy == 'UncSamplingLeastConfident':
-            query_indx = uncertainty_sampling_least_confident(class_prob=self.classprob,
-                                              queryable_ids=self.queryable_ids,
-                                              test_ids=self.pool_metadata[id_name].values,
-                                              batch=batch, screen=screen,
-                                              query_thre=query_thre)
-
-        elif strategy == 'UncSamplingMargin':
-            query_indx = uncertainty_sampling_margin(class_prob=self.classprob,
-                                              queryable_ids=self.queryable_ids,
-                                              test_ids=self.pool_metadata[id_name].values,
-                                              batch=batch, screen=screen,
-                                              query_thre=query_thre)
-            return query_indx
-        elif strategy == 'QBDMI':
-            query_indx = qbd_mi(ensemble_probs=self.ensemble_probs,
-                                queryable_ids=self.queryable_ids,
-                                test_ids=self.pool_metadata[id_name].values,
-                                batch=batch, screen=screen,
-                                query_thre=query_thre)
-
-        elif strategy =='QBDEntropy':
-            query_indx = qbd_entropy(ensemble_probs=self.ensemble_probs,
-                                    queryable_ids=self.queryable_ids,
-                                    test_ids=self.pool_metadata[id_name].values,
-                                    batch=batch, screen=screen,
-                                    query_thre=query_thre)
-
-        elif strategy == 'RandomSampling':
-            query_indx = random_sampling(queryable_ids=self.queryable_ids,
-                                         test_ids=self.pool_metadata[id_name].values,
-                                         queryable=queryable, batch=batch,
-                                         query_thre=query_thre, screen=screen)
-
-        else:
-            raise ValueError('Invalid strategy.')
+        # retrieve and instantiate the query strategy class
+        query_strategy_class = fetch_query_strategy_class(strategy)
+        query_strategy = query_strategy_class(
+            queryable_ids=self.queryable_ids,
+            test_ids=self.pool_metadata[id_name].values,
+            batch=batch,
+            screen=screen,
+            query_threshold=query_threshold,
+            queryable=queryable
+        )
+
+        # Use the `requires_ensemble` flag to determine which probabilities to use
+        input_probabilities = self.classprob
+        if query_strategy.requires_ensemble:
+            input_probabilities = self.ensemble_probs
+
+        # get the query index from the strategy
+        query_indx = query_strategy.sample(input_probabilities)
 
         if screen:
             print('       ... queried obj id: ', self.pool_metadata[id_name].values[query_indx[0]])

src/resspect/plugin_utils.py

@@ -1,5 +1,6 @@
 import importlib
-from resspect.classifiers import CLASSIFIER_REGISTRY
+from resspect.classifiers import CLASSIFIER_REGISTRY, ResspectClassifier
+from resspect.query_strategies import QUERY_STRATEGY_REGISTRY, QueryStrategy
 
 def get_or_load_class(class_name: str, registry: dict) -> type:
     """Given the name of a class and a registry dictionary, attempt to return
@@ -24,10 +25,15 @@ def get_or_load_class(class_name: str, registry: dict) -> type:
         `name` key was found in the config.
     """
 
-    if class_name in registry:
-        returned_class = registry[class_name]
-    else:
-        returned_class = import_module_from_string(class_name)
+    returned_class = None
+
+    try:
+        if class_name in registry:
+            returned_class = registry[class_name]
+        else:
+            returned_class = import_module_from_string(class_name)
+    except ValueError as exc:
+        raise ValueError(f"Error fetching class: {class_name}") from exc
 
     return returned_class
 
@@ -84,7 +90,7 @@ def import_module_from_string(module_path: str) -> type:
     return returned_cls
 
 
-def fetch_classifier_class(classifier_name: str) -> type:
+def fetch_classifier_class(classifier_name: str) -> ResspectClassifier:
     """Fetch the classifier class from the registry.
 
     Parameters
@@ -95,8 +101,8 @@ def fetch_classifier_class(classifier_name: str) -> type:
 
     Returns
     -------
-    type
-        The classifier class.
+    ResspectClassifier
+        The subclass of ResspectClassifier.
 
     Raises
     ------
@@ -106,11 +112,29 @@ def fetch_classifier_class(classifier_name: str) -> type:
         If no classifier was specified in the runtime configuration.
     """
 
-    clf_class = None
+    return get_or_load_class(classifier_name, CLASSIFIER_REGISTRY)
 
-    try:
-        clf_class = get_or_load_class(classifier_name, CLASSIFIER_REGISTRY)
-    except ValueError as exc:
-        raise ValueError(f"Error fetching class: {classifier_name}") from exc
 
-    return clf_class
+def fetch_query_strategy_class(query_strategy_name: str) -> QueryStrategy:
+    """Fetch the query strategy class from the registry.
+
+    Parameters
+    ----------
+    query_strategy_name : str
+        The name of the query strategy class to retrieve. This should either be the
+    name of the class or the import specification for the class.
+
+    Returns
+    -------
+    QueryStrategy
+        The subclass of QueryStrategy.
+
+    Raises
+    ------
+    ValueError
+        If a built-in query strategy was requested, but not found in the registry.
+    ValueError
+        If no query strategy was specified in the runtime configuration.
+    """
+
+    return get_or_load_class(query_strategy_name, QUERY_STRATEGY_REGISTRY)