Maint/isort

.isort.cfg

@@ -0,0 +1,4 @@
+[settings]
+line_length = 99
+multi_line_output = 3
+include_trailing_comma = True

.pre-commit-config.yaml

@@ -0,0 +1,16 @@
+repos:
+-   repo: https://github.com/ambv/black
+    rev: stable
+    hooks:
+    - id: black
+      language_version: python3.8
+      args: [--config=pyproject.toml]
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v1.2.3
+    hooks:
+    - id: flake8
+      args: [--config=.flake8]
+-   repo: https://github.com/pre-commit/mirrors-isort
+    rev: v4.3.21
+    hooks:
+    - id: isort

.travis.yml

@@ -30,6 +30,7 @@ script:
   - black --check .
   - flake8 --config=.flake8 .
   - mypy cgp
+  - isort --check-only cgp examples test
   - if [ "$DEP" = "[all]" -a $TRAVIS_PYTHON_VERSION = 3.8 ]; then
        pip install gym || exit 1;
        make -C docs/ html || exit 1;

cgp/__init__.py

@@ -6,16 +6,11 @@
 __url__ = "https://happy-algorithms-league.github.io/hal-cgp/"
 __doc__ = f"{__description__} <{__url__}>"
 
-from .genome import Genome
+from . import ea, local_search, utils
 from .cartesian_graph import CartesianGraph
+from .genome import Genome
+from .hl_api import evolve
+from .individual import IndividualMultiGenome, IndividualSingleGenome
 from .node import OperatorNode
 from .node_impl import Add, ConstantFloat, Div, Mul, Parameter, Pow, Sub
 from .population import Population
-
-from .hl_api import evolve
-
-from . import utils
-from . import ea
-from . import local_search
-
-from .individual import IndividualSingleGenome, IndividualMultiGenome

cgp/cartesian_graph.py

@@ -1,8 +1,13 @@
 import collections
 import copy
 import math  # noqa: F401
-import numpy as np  # noqa: F401
 import re
+from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Set
+
+import numpy as np  # noqa: F401
+
+from .node import Node, OperatorNode
+from .node_input_output import InputNode, OutputNode
 
 try:
     import sympy
@@ -19,10 +24,6 @@
 except ModuleNotFoundError:
     torch_available = False
 
-from typing import Callable, Dict, List, Optional, Set, TYPE_CHECKING
-
-from .node import Node, OperatorNode
-from .node_input_output import InputNode, OutputNode
 
 if TYPE_CHECKING:
     from .genome import Genome
@@ -372,7 +373,7 @@ def _format_output_str_sympy_of_all_nodes(self):
             for node in active_nodes[hidden_column_idx]:
                 node.format_output_str_sympy(self)
 
-    def to_sympy(self, simplify: Optional[bool] = True,) -> List["sympy_expr.Expr"]:
+    def to_sympy(self, simplify: Optional[bool] = True) -> List["sympy_expr.Expr"]:
         """Compile the function(s) represented by the graph to a SymPy expression.
 
         Generates one SymPy expression for each output node.

cgp/ea/mu_plus_lambda.py

@@ -1,8 +1,8 @@
 import concurrent.futures
-import numpy as np
-
 from typing import Callable, List, Union
 
+import numpy as np
+
 from ..individual import IndividualBase
 from ..population import Population
 

cgp/genome.py

@@ -1,18 +1,18 @@
+from typing import Dict, Generator, List, Optional, Tuple, Type, Union
+
 import numpy as np
 
+from .cartesian_graph import CartesianGraph
+from .node import Node, OperatorNode
+from .primitives import Primitives
+
 try:
     import torch  # noqa: F401
 
     torch_available = True
 except ModuleNotFoundError:
     torch_available = False
 
-from typing import Dict, Generator, List, Optional, Tuple, Type, Union
-
-from .cartesian_graph import CartesianGraph
-from .node import Node, OperatorNode
-from .primitives import Primitives
-
 
 ID_INPUT_NODE: int = -1
 ID_OUTPUT_NODE: int = -2
@@ -170,7 +170,7 @@ def determine_permissible_values(self) -> List[np.ndarray]:
             permissible_values.append(permissible_values_per_gene)
         return permissible_values
 
-    def _determine_permissible_values_input_region(self, gene_idx: int,) -> np.ndarray:
+    def _determine_permissible_values_input_region(self, gene_idx: int) -> np.ndarray:
 
         if self._is_function_gene(gene_idx):
             return np.array(self._id_input_node)
@@ -190,7 +190,7 @@ def _determine_permissible_values_hidden_region(
         else:
             assert False  # should never be reached
 
-    def _determine_permissible_values_output_region(self, gene_idx: int,) -> np.ndarray:
+    def _determine_permissible_values_output_region(self, gene_idx: int) -> np.ndarray:
 
         if self._is_function_gene(gene_idx):
             return np.array(self._id_output_node)

cgp/hl_api.py

@@ -1,9 +1,10 @@
+from typing import Callable, Optional
+
 import numpy as np
-from typing import Optional, Callable
 
-from .population import Population
-from .individual import IndividualBase
 from .ea import MuPlusLambda
+from .individual import IndividualBase
+from .population import Population
 
 
 def evolve(

cgp/individual.py

@@ -1,6 +1,10 @@
-import numpy as np
 from typing import Callable, List, Union
 
+import numpy as np
+
+from .cartesian_graph import CartesianGraph
+from .genome import Genome
+
 try:
     import sympy  # noqa: F401
     from sympy.core import expr as sympy_expr  # noqa: F401
@@ -16,9 +20,6 @@
 except ModuleNotFoundError:
     torch_available = False
 
-from .genome import Genome
-from .cartesian_graph import CartesianGraph
-
 
 class IndividualBase:
     """Base class for all individuals.

cgp/local_search/gradient_based.py

@@ -1,5 +1,9 @@
+from typing import Callable, List, Optional, Union
+
 import numpy as np
 
+from ..individual import IndividualBase
+
 try:
     import torch  # noqa: F401
     from torch.optim.optimizer import Optimizer  # noqa: F401
@@ -8,11 +12,6 @@
 except ModuleNotFoundError:
     torch_available = False
 
-from typing import Callable, List, Optional, Union
-
-
-from ..individual import IndividualBase
-
 
 def gradient_based(
     individual: IndividualBase,

cgp/node.py

@@ -1,7 +1,6 @@
 import math  # noqa: F401
 import re
-
-from typing import Callable, Dict, List, Tuple, Type, TYPE_CHECKING
+from typing import TYPE_CHECKING, Callable, Dict, List, Tuple, Type
 
 from . import node_validation
 

cgp/node_input_output.py

@@ -1,4 +1,4 @@
-from typing import List, TYPE_CHECKING
+from typing import TYPE_CHECKING, List
 
 from .node import Node
 

cgp/node_validation.py

@@ -1,3 +1,5 @@
+from typing import TYPE_CHECKING, Type
+
 import numpy as np
 
 try:
@@ -15,16 +17,15 @@
 except ModuleNotFoundError:
     torch_available = False
 
-from typing import Type, TYPE_CHECKING
 
 if TYPE_CHECKING:
-    from .node import OperatorNode  # noqa: F401
     from .genome import Genome  # noqa: F401
+    from .node import OperatorNode  # noqa: F401
 
 
 def _create_genome(cls: Type["OperatorNode"]) -> "Genome":
     # delayed imports to avoid circular imports
-    from .genome import Genome, ID_INPUT_NODE, ID_OUTPUT_NODE, ID_NON_CODING_GENE
+    from .genome import ID_INPUT_NODE, ID_NON_CODING_GENE, ID_OUTPUT_NODE, Genome
 
     primitives = (cls,)
     genome = Genome(1, 1, 1, 1, 1, primitives)

cgp/population.py

@@ -1,9 +1,9 @@
-import numpy as np
-
 from typing import List, Union
 
+import numpy as np
+
 from .genome import Genome
-from .individual import IndividualBase, IndividualSingleGenome, IndividualMultiGenome
+from .individual import IndividualBase, IndividualMultiGenome, IndividualSingleGenome
 
 
 class Population:

cgp/primitives.py

@@ -1,8 +1,8 @@
 from dataclasses import dataclass, field
-import numpy as np
-
 from typing import Iterator, Tuple, Type
 
+import numpy as np
+
 from .node import Node
 
 

cgp/utils.py

@@ -1,12 +1,12 @@
 import functools
 import hashlib
-import numpy as np
 import os
 import pickle
-
 from typing import Any, Callable, Dict, List, Tuple, Type, Union
 
-from .node import primitives_dict, Node
+import numpy as np
+
+from .node import Node, primitives_dict
 
 
 def __check_cache_consistency(fn: str, func: Callable[..., float]) -> None:
@@ -53,11 +53,7 @@ def __store_new_cache_entry(
 ) -> None:
     with open(fn, "ab") as f:
 
-        result = {
-            "args": args,
-            "kwargs": kwargs,
-            "return_value": return_value,
-        }
+        result = {"args": args, "kwargs": kwargs, "return_value": return_value}
 
         pickle.dump({key: result}, f)
 

examples/example_caching.py

@@ -8,9 +8,10 @@
 second time and when you comment out the decorator on
 `inner_objective`."""
 
-import numpy as np
 import time
 
+import numpy as np
+
 import cgp
 
 # %%

examples/example_differential_evo_regression.py

@@ -21,6 +21,7 @@
 """
 
 import functools
+
 import matplotlib.pyplot as plt
 import numpy as np
 import scipy.constants
@@ -89,12 +90,7 @@ def objective(individual, seed):
     "primitives": (cgp.Add, cgp.Sub, cgp.Mul, cgp.Parameter),
 }
 
-ea_params = {
-    "n_offsprings": 4,
-    "tournament_size": 1,
-    "n_processes": 1,
-    "k_local_search": 2,
-}
+ea_params = {"n_offsprings": 4, "tournament_size": 1, "n_processes": 1, "k_local_search": 2}
 
 evolve_params = {"max_generations": 2000, "min_fitness": 0.0}
 

examples/example_evo_regression.py

@@ -8,10 +8,11 @@
 
 
 import functools
+import warnings
+
 import matplotlib.pyplot as plt
 import numpy as np
 import scipy.constants
-import warnings
 
 import cgp
 

examples/example_mountain_car.py

@@ -14,6 +14,13 @@
 
 
 import functools
+import warnings
+
+import matplotlib.pyplot as plt
+import numpy as np
+import sympy
+
+import cgp
 
 try:
     import gym
@@ -22,12 +29,6 @@
         "Failed to import the OpenAI Gym package. Please install it via `pip install gym`."
     )
 
-import matplotlib.pyplot as plt
-import numpy as np
-import sympy
-import warnings
-
-import cgp
 
 # %%
 # For more flexibility in the evolved expressions, we define two

examples/example_parametrized_nodes.py

@@ -11,14 +11,14 @@
 
 import functools
 import math
+
 import matplotlib.pyplot as plt
 import numpy as np
 import scipy.constants
 import torch
 
 import cgp
 
-
 # %%
 # We first define a new node that adds two inputs then scales and
 # finally shifts the result. The scale ("w") and shift factors ("b")
@@ -138,9 +138,7 @@ def recording_callback(pop):
 
 obj = functools.partial(objective, seed=population_params["seed"])
 
-cgp.evolve(
-    pop, obj, ea, **evolve_params, print_progress=True, callback=recording_callback,
-)
+cgp.evolve(pop, obj, ea, **evolve_params, print_progress=True, callback=recording_callback)
 
 # %%
 # After the evolutionary search has ended, we print the expression