rename DeepRankQuery back to Query

README.md

@@ -105,9 +105,9 @@ For more details, see the [extended documentation](https://deeprank2.rtfd.io/).
 
 ### Data generation
 
-For each protein-protein complex (or protein structure containing a missense variant), a `DeepRankQuery` can be created and added to the `QueryCollection` object, to be processed later on. Two subtypes of `DeepRankQuery` exist: `ProteinProteinInterfaceQuery` and `SingleResidueVariantQuery`.
+For each protein-protein complex (or protein structure containing a missense variant), a `Query` can be created and added to the `QueryCollection` object, to be processed later on. Two subtypes of `Query` exist: `ProteinProteinInterfaceQuery` and `SingleResidueVariantQuery`.
 
-A `DeepRankQuery` takes as inputs:
+A `Query` takes as inputs:
 - a `.pdb` file, representing the protein-protein structure,
 - the resolution (`"residue"` or `"atom"`), i.e. whether each node should represent an amino acid residue or an atom,
 - the ids of the chains composing the structure, and

deeprank2/dataset.py

@@ -441,7 +441,7 @@ def __init__( # pylint: disable=too-many-arguments
                 Value will be ignored and inherited from `dataset_train` if `train` is set as False and `dataset_train` is assigned.
                 Defaults to "all".
             target (Optional[str], optional): Default options are irmsd, lrmsd, fnat, binary, capri_class, and dockq. It can also be
-                a custom-defined target given to the DeepRankQuery class as input (see: `deeprank2.query`); in this case,
+                a custom-defined target given to the Query class as input (see: `deeprank2.query`); in this case,
                 the task parameter needs to be explicitly specified as well.
                 Only numerical target variables are supported, not categorical.
                 If the latter is your case, please convert the categorical classes into
@@ -680,7 +680,7 @@ def __init__( # noqa: MC0001, pylint: disable=too-many-arguments, too-many-local
                 :class:`Datasets` belong to the "cluster" Group. They are saved in the .HDF5 file to make them available to networks
                 that make use of clustering methods. Defaults to None.
             target (Optional[str], optional): Default options are irmsd, lrmsd, fnat, binary, capri_class, and dockq.
-                It can also be a custom-defined target given to the DeepRankQuery class as input (see: `deeprank2.query`);
+                It can also be a custom-defined target given to the Query class as input (see: `deeprank2.query`);
                 in this case, the task parameter needs to be explicitly specified as well.
                 Only numerical target variables are supported, not categorical.
                 If the latter is your case, please convert the categorical classes into

deeprank2/query.py

@@ -38,10 +38,10 @@
 # TODO: consider whether we want to use the built-in repr and eq, or define it ourselves
 # if built-in: consider which arguments to include in either.
 @dataclass(repr=False, kw_only=True)
-class DeepRankQuery:
+class Query:
     """Represents one entity of interest: a single residue variant (SRV) or a protein-protein interface (PPI).
 
-    :class:`DeepRankQuery` objects are used to generate graphs from structures, and they should be created before any model is loaded.
+    :class:`Query` objects are used to generate graphs from structures, and they should be created before any model is loaded.
     They can have target values associated with them, which will be stored with the resulting graph.
 
     Args:
@@ -216,10 +216,10 @@ def get_query_id(self) -> str:
 
 
 @dataclass(kw_only=True)
-class SingleResidueVariantQuery(DeepRankQuery):
+class SingleResidueVariantQuery(Query):
     """A query that builds a single residue variant graph.
 
-    Args (common for `DeepRankQuery`):
+    Args (common for `Query`):
         pdb_path (str): the path to the PDB file to query.
         resolution (Literal['residue', 'atom']): sets whether each node is a residue or atom.
         chain_ids (list[str] | str): the chain identifier(s) in the pdb file (generally a single capital letter).
@@ -314,7 +314,7 @@ def _build_helper(self) -> Graph:
 
 
 @dataclass(kw_only=True)
-class ProteinProteinInterfaceQuery(DeepRankQuery):
+class ProteinProteinInterfaceQuery(Query):
     """A query that builds a protein-protein interface graph.
 
     Args:
@@ -389,14 +389,14 @@ def __init__(self):
 
     def add(
         self,
-        query: DeepRankQuery,
+        query: Query,
         verbose: bool = False,
         warn_duplicate: bool = True,
     ):
         """Add a new query to the collection.
 
         Args:
-            query(:class:`DeepRankQuery`): The `DeepRankQuery` to add to the collection.
+            query(:class:`Query`): The `Query` to add to the collection.
             verbose(bool): For logging query IDs added. Defaults to `False`.
             warn_duplicate (bool): Log a warning before renaming if a duplicate query is identified. Defaults to `True`.
         """
@@ -412,7 +412,7 @@ def add(
             new_id = query.model_id + "_" + str(self._ids_count[query_id])
             query.model_id = new_id
             if warn_duplicate:
-                _log.warning(f'DeepRankQuery with ID {query_id} has already been added to the collection. Renaming it as {query.get_query_id()}')
+                _log.warning(f'Query with ID {query_id} has already been added to the collection. Renaming it as {query.get_query_id()}')
 
         self._queries.append(query)
 
@@ -427,20 +427,20 @@ def export_dict(self, dataset_path: str):
             pickle.dump(self, pkl_file)
 
     @property
-    def queries(self) -> list[DeepRankQuery]:
+    def queries(self) -> list[Query]:
         """The list of queries added to the collection."""
         return self._queries
 
-    def __contains__(self, query: DeepRankQuery) -> bool:
+    def __contains__(self, query: Query) -> bool:
         return query in self._queries
 
-    def __iter__(self) -> Iterator[DeepRankQuery]:
+    def __iter__(self) -> Iterator[Query]:
         return iter(self._queries)
 
     def __len__(self) -> int:
         return len(self._queries)
 
-    def _process_one_query(self, query: DeepRankQuery):
+    def _process_one_query(self, query: Query):
         """Only one process may access an hdf5 file at a time"""
 
         try:
@@ -458,7 +458,7 @@ def _process_one_query(self, query: DeepRankQuery):
                     graph.write_as_grid_to_hdf5(output_path, self._grid_settings, self._grid_map_method, augmentation)
 
         except (ValueError, AttributeError, KeyError, TimeoutError) as e:
-            _log.warning(f'\nGraph/DeepRankQuery with ID {query.get_query_id()} ran into an Exception ({e.__class__.__name__}: {e}),'
+            _log.warning(f'\nGraph/Query with ID {query.get_query_id()} ran into an Exception ({e.__class__.__name__}: {e}),'
             ' and it has not been written to the hdf5 file. More details below:')
             _log.exception(e)
 

docs/getstarted.md

@@ -6,9 +6,9 @@ For more details, see the [extended documentation](https://deeprank2.rtfd.io/).
 
 ## Data generation
 
-For each protein-protein complex (or protein structure containing a missense variant), a `DeepRankQuery` can be created and added to the `QueryCollection` object, to be processed later on. Two subtypes of `DeepRankQuery` exist: `ProteinProteinInterfaceQuery` and `SingleResidueVariantQuery`.
+For each protein-protein complex (or protein structure containing a missense variant), a `Query` can be created and added to the `QueryCollection` object, to be processed later on. Two subtypes of `Query` exist: `ProteinProteinInterfaceQuery` and `SingleResidueVariantQuery`.
 
-A `DeepRankQuery` takes as inputs:
+A `Query` takes as inputs:
 - a `.pdb` file, representing the protein-protein structure,
 - the resolution (`"residue"` or `"atom"`), i.e. whether each node should represent an amino acid residue or an atom,
 - the ids of the chains composing the structure, and

tests/test_querycollection.py

@@ -12,7 +12,7 @@
 from deeprank2.domain import nodestorage as Nfeat
 from deeprank2.domain.aminoacidlist import alanine, phenylalanine
 from deeprank2.features import components, contact, surfacearea
-from deeprank2.query import (DeepRankQuery, ProteinProteinInterfaceQuery,
+from deeprank2.query import (ProteinProteinInterfaceQuery, Query,
                              QueryCollection, SingleResidueVariantQuery)
 from deeprank2.tools.target import compute_ppi_scores
 
@@ -131,7 +131,7 @@ def test_querycollection_process():
         assert isinstance(collection.queries, list)
         assert len(collection.queries) == n_queries
         for query in collection.queries:
-            assert issubclass(type(query), DeepRankQuery)
+            assert issubclass(type(query), Query)
 
         rmtree(output_directory)
 
@@ -224,9 +224,8 @@ def test_querycollection_process_combine_output_false():
 
 
 def test_querycollection_duplicates_add():
-    """
-    Tests add method of QueryCollection class.
-    """
+    """Tests add method of QueryCollection class."""
+
     ref_path = "tests/data/ref/1ATN/1ATN.pdb"
     pssm_path1 = "tests/data/pssm/1ATN/1ATN.A.pdb.pssm"
     pssm_path2 = "tests/data/pssm/1ATN/1ATN.B.pdb.pssm"

tutorials/data_generation_ppi.ipynb

@@ -147,7 +147,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## `QueryCollection` and `DeepRankQuery` objects"
+    "## `QueryCollection` and `Query` objects"
    ]
   },
   {

tutorials/data_generation_srv.ipynb

@@ -166,7 +166,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## `QueryCollection` and `DeepRankQuery` objects"
+    "## `QueryCollection` and `Query` objects"
    ]
   },
   {