remove AtomMappingScorer references for now

will still ducktype fine

this isn't in a released gufe, so can't use (yet)

src/konnektor/network_planners/NetworkPlanner.py

@@ -2,7 +2,7 @@
 import logging
 from typing import Iterable
 
-from gufe import AtomMapper, AtomMappingScorer
+from gufe import AtomMapper
 from gufe import LigandNetwork, Component
 
 log = logging.getLogger(__name__)
@@ -12,7 +12,7 @@
 
 class NetworkPlanner(abc.ABC):
 
-    def __init__(self, mapper: AtomMapper, scorer: AtomMappingScorer):
+    def __init__(self, mapper: AtomMapper, scorer):
         """This class is an implementation for the LigandNetworkPlanner interface.
         It defines the std. class for a Konnektor LigandNetworkPlanner
 

src/konnektor/network_planners/concatenator/_abstract_network_concatenator.py

@@ -2,7 +2,7 @@
 import logging
 from typing import Iterable
 
-from gufe import AtomMapper, AtomMappingScorer
+from gufe import AtomMapper
 from gufe import LigandNetwork, Component
 
 from .._networkx_implementations._abstract_network_generator import _AbstractNetworkGenerator
@@ -15,7 +15,7 @@ class NetworkConcatenator(NetworkPlanner):
     progress: bool = False
     nprocesses: int
 
-    def __init__(self, mapper: AtomMapper, scorer: AtomMappingScorer,
+    def __init__(self, mapper: AtomMapper, scorer,
                  network_generator: _AbstractNetworkGenerator,
                  nprocesses: int = 1,
                  _initial_edge_lister=None):

src/konnektor/network_planners/concatenator/mst_concatenator.py

@@ -2,7 +2,7 @@
 import logging
 from typing import Iterable
 
-from gufe import AtomMapper, AtomMappingScorer, LigandNetwork
+from gufe import AtomMapper, LigandNetwork
 from ._abstract_network_concatenator import NetworkConcatenator
 from .._networkx_implementations import MstNetworkGenerator
 
@@ -12,7 +12,7 @@
 # Todo: check this algorithm again
 
 class MstConcatenate(NetworkConcatenator):
-    def __init__(self, mapper: AtomMapper, scorer: AtomMappingScorer, n_connecting_edges: int = 3, nprocesses: int = 1):
+    def __init__(self, mapper: AtomMapper, scorer, n_connecting_edges: int = 3, nprocesses: int = 1):
         """
         This concatenator is connnecting two Networks with a kruskal like approach up to the number of connecting edges.
 

src/konnektor/network_planners/generators/cyclic_network_planner.py

@@ -1,6 +1,6 @@
 from typing import Union, List, Iterable
 
-from gufe import Component, LigandNetwork, AtomMapper, AtomMappingScorer
+from gufe import Component, LigandNetwork, AtomMapper
 from konnektor.network_planners._networkx_implementations import CyclicNetworkGenerator as nx_CNG
 
 from ._abstract_network_generator import NetworkGenerator
@@ -11,7 +11,7 @@
 
 class CyclicNetworkGenerator(NetworkGenerator):
 
-    def __init__(self, mapper: AtomMapper, scorer: AtomMappingScorer,
+    def __init__(self, mapper: AtomMapper, scorer,
                  node_present_in_cycles: int = 2, cycle_sizes: Union[int, List[int]] = 3,
                  nprocesses: int = 1, _initial_edge_lister: NetworkGenerator = None):
         """

src/konnektor/network_planners/generators/heuristic_maximal_network_planner.py

@@ -3,7 +3,7 @@
 from typing import Iterable
 
 import numpy as np
-from gufe import Component, LigandNetwork, AtomMapper, AtomMappingScorer
+from gufe import Component, LigandNetwork, AtomMapper
 from tqdm.auto import tqdm
 
 from ._abstract_network_generator import NetworkGenerator
@@ -12,7 +12,7 @@
 #Todo: is graph connectivity ensured?
 
 class HeuristicMaximalNetworkGenerator(NetworkGenerator):
-    def __init__(self, mapper: AtomMapper, scorer: AtomMappingScorer, n_samples: int = 100, progress: bool = False,
+    def __init__(self, mapper: AtomMapper, scorer, n_samples: int = 100, progress: bool = False,
                  nprocesses: int = 1):
         """
         The Heuristic Maximal Network planner builds for given set of compounds a set of edges per node build graph under the assumption each component can be connected to another.

src/konnektor/network_planners/generators/maximal_network_planner.py

@@ -2,7 +2,7 @@
 import itertools
 from typing import Iterable
 
-from gufe import AtomMapper, AtomMappingScorer
+from gufe import AtomMapper
 from gufe import LigandNetwork, Component
 from tqdm.auto import tqdm
 
@@ -11,7 +11,7 @@
 
 
 class MaximalNetworkGenerator(NetworkGenerator):
-    def __init__(self, mapper: AtomMapper, scorer: AtomMappingScorer, progress: bool = False, nprocesses: int = 1):
+    def __init__(self, mapper: AtomMapper, scorer, progress: bool = False, nprocesses: int = 1):
         """
         The Maximal Network planner builds for given set of compounds a fully connected graph under the assumption each component can be connected to another.
         The edges of this graph are realized as atom mappings of pairwise components. If not all mappings can be created, it will ignore the mapping failure, and return a nearly fully connected graph.

src/konnektor/network_planners/generators/minimal_spanning_tree_network_planner.py

@@ -1,6 +1,6 @@
 from typing import Iterable
 
-from gufe import LigandNetwork, Component, AtomMapper, AtomMappingScorer
+from gufe import LigandNetwork, Component, AtomMapper
 from konnektor.network_planners._networkx_implementations import MstNetworkGenerator
 
 from ._abstract_network_generator import NetworkGenerator
@@ -9,7 +9,7 @@
 
 class MinimalSpanningTreeNetworkGenerator(NetworkGenerator):
 
-    def __init__(self, mapper: AtomMapper, scorer: AtomMappingScorer,
+    def __init__(self, mapper: AtomMapper, scorer,
                  nprocesses: int = 1, _initial_edge_lister: NetworkGenerator = None):
         """
         The minimal spanning tree ligand network planner, builds an MST for a given set of ligands. The edges of the the graph,

src/konnektor/network_planners/generators/n_node_edges_network_planner.py

@@ -1,6 +1,6 @@
 from typing import Iterable
 
-from gufe import Component, LigandNetwork, AtomMapper, AtomMappingScorer
+from gufe import Component, LigandNetwork, AtomMapper
 from konnektor.network_planners._networkx_implementations import NNodeEdgesNetworkGenerator
 
 from ._abstract_network_generator import NetworkGenerator
@@ -9,7 +9,7 @@
 
 class NNodeEdgesNetworkGenerator(NetworkGenerator):
 
-    def __init__(self, mapper: AtomMapper, scorer: AtomMappingScorer,
+    def __init__(self, mapper: AtomMapper, scorer,
                  target_node_connectivity: int = 3,
                  nprocesses: int = 1,
                  _initial_edge_lister: NetworkGenerator = None):

src/konnektor/network_planners/generators/redundant_minimal_spanning_tree_network_planner.py

@@ -1,6 +1,6 @@
 from typing import Iterable
 
-from gufe import Component, LigandNetwork, AtomMapper, AtomMappingScorer
+from gufe import Component, LigandNetwork, AtomMapper
 from konnektor.network_planners._networkx_implementations import MstNetworkGenerator
 
 from ._abstract_network_generator import NetworkGenerator
@@ -9,7 +9,7 @@
 
 class RedundantMinimalSpanningTreeNetworkGenerator(NetworkGenerator):
 
-    def __init__(self, mapper: AtomMapper, scorer: AtomMappingScorer, n_redundancy: int = 2, nprocesses: int = 1,
+    def __init__(self, mapper: AtomMapper, scorer, n_redundancy: int = 2, nprocesses: int = 1,
                  _initial_edge_lister: NetworkGenerator = None):
         """Plan a Network which connects all ligands n times with minimal cost.
         This planner uses n_redundancy times the MST algorithm on the full

src/konnektor/network_planners/generators/star_network_planner.py

@@ -1,6 +1,6 @@
 from typing import Iterable
 
-from gufe import Component, LigandNetwork, AtomMapper, AtomMappingScorer
+from gufe import Component, LigandNetwork, AtomMapper
 from konnektor.network_planners._networkx_implementations import RadialNetworkGenerator
 
 from ._abstract_network_generator import NetworkGenerator
@@ -9,7 +9,7 @@
 
 class StarNetworkGenerator(NetworkGenerator):
 
-    def __init__(self, mapper: AtomMapper, scorer: AtomMappingScorer,
+    def __init__(self, mapper: AtomMapper, scorer,
                  nprocesses: int = 1, _initial_edge_lister: NetworkGenerator = None):
         """
         The Star Ligand Network Planner or Radial Ligand Network Planner, set's one ligand into the center of a graph and connects all other ligands to it.

src/konnektor/network_planners/generators/two_dimensional_network_planners.py

@@ -4,7 +4,7 @@
 from tqdm import tqdm
 from typing import Iterable
 
-from gufe import Component, LigandNetwork, AtomMapper, AtomMappingScorer
+from gufe import Component, LigandNetwork, AtomMapper
 
 # Clustering
 from scikit_mol.fingerprints import RDKitFingerprintTransformer, MorganFingerprintTransformer
@@ -29,7 +29,7 @@ def __init__(self,
                  concatenator: MstConcatenate = MstConcatenate,
                  clusterer: ComponentsDiversityClustering = ComponentsDiversityClustering(
                      featurize=RDKitFingerprintTransformer(), cluster=KMeans(n_clusters=3)),
-                 mapper: AtomMapper = None, scorer: AtomMappingScorer = None,
+                 mapper: AtomMapper = None, scorer = None,
                  nprocesses: int = 1, progress: bool = False
                  ):
         ''' Implements the general concept of multidimensional networks.
@@ -150,7 +150,7 @@ def __init__(self,
                  clusterer: ComponentsDiversityClustering = ComponentsDiversityClustering(
                      featurize=MorganFingerprintTransformer(), cluster=HDBSCAN(metric="jaccard", min_cluster_size=3, alpha=1/2048)),
                  mapper: AtomMapper = None,
-                 scorer: AtomMappingScorer = None,
+                 scorer = None,
                  nprocesses: int = 1, progress: bool = False
                  ):
         '''  The StarrySkyNetworkGenerator is an advanced network algorithm,

src/konnektor/tests/test_toy_data.py

@@ -3,7 +3,7 @@
 from rdkit import Chem
 from rdkit.Chem import AllChem
 
-from gufe import AtomMapper, AtomMapping, AtomMappingScorer, SmallMoleculeComponent, LigandNetwork
+from gufe import AtomMapper, AtomMapping, SmallMoleculeComponent, LigandNetwork
 from konnektor.utils.toy_data import genMapper, genScorer, build_random_dataset, build_random_mst_network, build_random_fully_connected_network
 
 def test_genMapper():
@@ -28,7 +28,7 @@ def test_genScorer():
     n_scores = 10
     scorer = genScorer(n_scores=n_scores, rand_seed=42)
 
-    assert isinstance(scorer, AtomMappingScorer)
+    # assert isinstance(scorer, AtomMappingScorer)
     assert len(scorer.vals) ==n_scores
     assert scorer.i == 0
 
@@ -67,7 +67,7 @@ def test_build_random_dataset():
     assert len(compounds) == n_compounds
     assert all(isinstance(c, SmallMoleculeComponent) for c in compounds)
     assert isinstance(mapper, AtomMapper)
-    assert isinstance(scorer, AtomMappingScorer)
+    # assert isinstance(scorer, AtomMappingScorer)
     assert len(scorer.vals) == n_compounds
 
 def test_build_random_mst_network():

src/konnektor/utils/toy_data.py

@@ -4,7 +4,7 @@
 from rdkit.Chem import AllChem
 from gufe import SmallMoleculeComponent, LigandNetwork
 
-from gufe import LigandAtomMapping, AtomMapper, AtomMappingScorer, AtomMapping
+from gufe import LigandAtomMapping, AtomMapper, AtomMapping
 import numpy as np
 
 
@@ -32,7 +32,7 @@ def _to_dict(self):
         return vars(self)
 
 
-class genScorer(AtomMappingScorer):
+class genScorer:  # (AtomMappingScorer):
     def __init__(self, n_scores: int, rand_seed: int = None):
         """
         Builds a scorer that contains a predefined sequence of scores, n_scores long and each score is initially randomly uniformly picked between 1 and 0.
@@ -54,6 +54,10 @@ def __init__(self, n_scores: int, rand_seed: int = None):
         self.n_scores = n_scores
         self.i = 0
 
+    def __call__(self, mapping):
+        # todo: remove once subclassed from gufe
+        return self.get_score(mapping)
+
     def get_score(self, mapping: AtomMapping) -> float:
         """
         return the score, at position self.i
@@ -74,8 +78,7 @@ def get_score(self, mapping: AtomMapping) -> float:
         return v
 
 
-def build_random_dataset(n_compounds: int = 20, rand_seed: int = None) -> (
-        Iterable[SmallMoleculeComponent], AtomMapper, AtomMappingScorer):
+def build_random_dataset(n_compounds: int = 20, rand_seed: int = None):
     """
     This function builds a random dataset of n_compounds artificial molecules.
     Additionally the generic scorer and mapper matching the compounds is returned.