Fix imports: No global imports, all relative

gp/cartesian_graph.py

@@ -12,8 +12,8 @@
     torch = None
 
 
-from .node import InputNode, OutputNode
-import gp
+from .node import Node, InputNode, OutputNode
+from .genome import Genome
 from typing import Callable, List, Optional, Dict
 
 
@@ -22,7 +22,7 @@ class CartesianGraph:
     Genome.
     """
 
-    def __init__(self, genome: gp.Genome) -> None:
+    def __init__(self, genome: Genome) -> None:
         """Init function.
 
         Parameters
@@ -52,7 +52,7 @@ def pretty_str(self) -> str:
         """
         n_characters = 24
 
-        def pretty_node_str(node: gp.Node) -> str:
+        def pretty_node_str(node: Node) -> str:
             s = node.pretty_str(n_characters)
             assert len(s) == n_characters
             return s
@@ -87,7 +87,7 @@ def empty_node_str() -> str:
 
         return s
 
-    def parse_genome(self, genome: gp.Genome) -> None:
+    def parse_genome(self, genome: Genome) -> None:
         if genome.dna is None:
             raise RuntimeError("dna not initialized")
 
@@ -119,15 +119,15 @@ def _hidden_column_idx(self, idx: int) -> int:
         return (idx - self._n_inputs) // self._n_rows
 
     @property
-    def input_nodes(self) -> List[gp.Node]:
+    def input_nodes(self) -> List[Node]:
         return self._nodes[: self._n_inputs]
 
     @property
-    def hidden_nodes(self) -> List[gp.Node]:
+    def hidden_nodes(self) -> List[Node]:
         return self._nodes[self._n_inputs : -self._n_outputs]
 
     @property
-    def output_nodes(self) -> List[gp.Node]:
+    def output_nodes(self) -> List[Node]:
         return self._nodes[-self._n_outputs :]
 
     def _determine_active_nodes(self) -> Dict[int, set]:
@@ -195,7 +195,7 @@ def __call__(self, x: List[float]) -> List[float]:
 
         return [node._output for node in self.output_nodes]
 
-    def __getitem__(self, key: int) -> gp.Node:
+    def __getitem__(self, key: int) -> Node:
         return self._nodes[key]
 
     def to_str(self) -> str:

gp/genome.py

@@ -1,8 +1,10 @@
-import gp
+from __future__ import annotations
+
 import numpy as np
 
 from typing import Generator, List, Optional, Tuple
 
+from .node import Node
 from .primitives import Primitives
 
 
@@ -17,7 +19,7 @@ def __init__(
         n_columns: int,
         n_rows: int,
         levels_back: int,
-        primitives: List[gp.Node],
+        primitives: List[Node],
     ) -> None:
         """Init function.
 
@@ -34,7 +36,7 @@ def __init__(
         levels_back : int
             Number of previous columns that an entry in the genome can be
             connected with.
-        primitives : List[gp.Node]
+        primitives : List[Node]
            List of primitives that the genome can refer to.
         """
         if n_inputs <= 0:
@@ -410,10 +412,10 @@ def _mutate_hidden_region(
             return silent_mutation
 
     @property
-    def primitives(self) -> gp.Primitives:
+    def primitives(self) -> Primitives:
         return self._primitives
 
-    def clone(self) -> gp.Genome:
+    def clone(self) -> Genome:
         """Clone the genome.
 
         Returns

gp/node.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from typing import List, Union
-from cartesian_graph import CartesianGraph
+from .cartesian_graph import CartesianGraph
 
 primitives_dict = {}  # maps string of class names to classes
 

gp/population.py

@@ -1,4 +1,3 @@
-import gp
 import numpy as np
 
 from .individual import Individual, IndividualMultiGenome
@@ -48,28 +47,28 @@ def __init__(
         self._generate_random_parent_population()
 
     @property
-    def champion(self) -> gp.Individual:
+    def champion(self) -> Individual:
         """Return parent with the highest fitness.
         """
         return max(self._parents, key=lambda ind: ind.fitness)
 
     @property
-    def parents(self) -> List[gp.Individual]:
+    def parents(self) -> List[Individual]:
         return self._parents
 
     @parents.setter
-    def parents(self, new_parents: List[gp.Individual]) -> None:
+    def parents(self, new_parents: List[Individual]) -> None:
         self.generation += 1
         self._parents = new_parents
 
-    def __getitem__(self, idx: int) -> gp.Individual:
+    def __getitem__(self, idx: int) -> Individual:
         return self._parents[idx]
 
     def _generate_random_parent_population(self) -> None:
         self._parents = self._generate_random_individuals(self.n_parents)
         self._label_new_individuals(self._parents)
 
-    def _label_new_individuals(self, individuals: List[gp.Individual]) -> None:
+    def _label_new_individuals(self, individuals: List[Individual]) -> None:
         for ind in individuals:
             ind.idx = self._max_idx + 1
             self._max_idx += 1
@@ -90,9 +89,7 @@ def _generate_random_individuals(self, n: int) -> List[Individual]:
 
         return individuals
 
-    def crossover(
-        self, breeding_pool: List[gp.Individual], n_offsprings: int
-    ) -> List[gp.Individual]:
+    def crossover(self, breeding_pool: List[Individual], n_offsprings: int) -> List[Individual]:
         """Create an offspring population via crossover.
 
         Parameters
@@ -127,7 +124,7 @@ def crossover(
         assert len(breeding_pool) >= n_offsprings
         return sorted(breeding_pool, key=lambda x: -x.fitness)[:n_offsprings]
 
-    def mutate(self, offsprings: List[gp.Individual]) -> List[gp.Individual]:
+    def mutate(self, offsprings: List[Individual]) -> List[Individual]:
         """Mutate a list of offspring invididuals.
 
         Parameters