wip

golem/core/optimisers/genetic/gp_optimizer_new.py

+from typing import Sequence, Union, Any
+
+from golem.core.dag.graph import Graph
+from golem.core.optimisers.genetic.gp_params import GPAlgorithmParameters
+from golem.core.optimisers.genetic.operators.crossover import Crossover, SinglePredefinedGraphCrossover
+from golem.core.optimisers.genetic.operators.elitism import Elitism
+from golem.core.optimisers.genetic.operators.inheritance import Inheritance
+from golem.core.optimisers.genetic.operators.mutation import Mutation, SinglePredefinedGraphMutation
+from golem.core.optimisers.genetic.operators.operator import PopulationT, EvaluationOperator
+from golem.core.optimisers.genetic.operators.regularization import Regularization
+from golem.core.optimisers.genetic.operators.reproduction import ReproductionController
+from golem.core.optimisers.genetic.operators.selection import Selection
+from golem.core.optimisers.genetic.parameters.graph_depth import AdaptiveGraphDepth
+from golem.core.optimisers.genetic.parameters.operators_prob import init_adaptive_operators_prob
+from golem.core.optimisers.genetic.parameters.population_size import init_adaptive_pop_size, PopulationSize
+from golem.core.optimisers.objective.objective import Objective
+from golem.core.optimisers.opt_history_objects.individual import Individual
+from golem.core.optimisers.optimization_parameters import GraphRequirements
+from golem.core.optimisers.optimizer import GraphGenerationParams
+from golem.core.optimisers.populational_optimizer import PopulationalOptimizer
+
+
+class EvoGraphOptimizer(PopulationalOptimizer):
+    """
+    Multi-objective evolutionary graph optimizer named GPComp
+    """
+
+    def __init__(self,
+                 objective: Objective,
+                 initial_graphs: Sequence[Union[Graph, Any]],
+                 requirements: GraphRequirements,
+                 graph_generation_params: GraphGenerationParams,
+                 graph_optimizer_params: GPAlgorithmParameters):
+        super().__init__(objective, initial_graphs, requirements, graph_generation_params, graph_optimizer_params)
+        # Define genetic operators
+        self.regularization = Regularization(graph_optimizer_params, graph_generation_params)
+        self.selection = Selection(graph_optimizer_params)
+        self.crossover = SinglePredefinedGraphCrossover(graph_optimizer_params, requirements, graph_generation_params)
+        self.mutation = SinglePredefinedGraphMutation(graph_optimizer_params, requirements, graph_generation_params)
+        self.inheritance = Inheritance(graph_optimizer_params, self.selection)
+        self.elitism = Elitism(graph_optimizer_params)
+        self.operators = [self.regularization, self.selection, self.crossover,
+                          self.mutation, self.inheritance, self.elitism]
+
+        self.reproducer = ReproductionController(parameters=graph_optimizer_params,
+                                                 selection=self.selection,
+                                                 mutation=self.mutation,
+                                                 crossover=self.crossover,
+                                                 verifier=self.graph_generation_params.verifier)
+
+        # Define adaptive parameters
+        self._pop_size: PopulationSize = init_adaptive_pop_size(graph_optimizer_params, self.generations)
+        self._operators_prob = init_adaptive_operators_prob(graph_optimizer_params)
+        self._graph_depth = AdaptiveGraphDepth(self.generations,
+                                               start_depth=requirements.start_depth,
+                                               max_depth=requirements.max_depth,
+                                               max_stagnation_gens=graph_optimizer_params.adaptive_depth_max_stagnation,
+                                               adaptive=graph_optimizer_params.adaptive_depth)
+
+        # Define initial parameters
+        self.requirements.max_depth = self._graph_depth.initial
+        self.graph_optimizer_params.pop_size = self._pop_size.initial
+        self.initial_individuals = [Individual(graph, metadata=requirements.static_individual_metadata)
+                                    for graph in self.initial_graphs]
+
+    def _initial_population(self, evaluator: EvaluationOperator):
+        """ Initializes the initial population """
+        # Adding of initial assumptions to history as zero generation
+        self._update_population(evaluator(self.initial_individuals), 'initial_assumptions')
+        # pop_size = self.graph_optimizer_params.pop_size
+        #
+        # if len(self.initial_individuals) < pop_size:
+        #     self.initial_individuals += self.reproducer._reproduce(population=self.initial_individuals,
+        #                                                            evaluator=evaluator)
+        #     # Adding of extended population to history
+        #     self._update_population(self.initial_individuals, 'extended_initial_assumptions')
+
+    def _evolve_population(self, evaluator: EvaluationOperator) -> PopulationT:
+        """ Method realizing full evolution cycle """
+
+        # Defines adaptive changes to algorithm parameters
+        #  like pop_size and operator probabilities
+        self._update_requirements()
+
+        # Regularize previous population
+        individuals_to_select = self.regularization(self.population, evaluator)
+        # Reproduce from previous pop to get next population
+        new_population = self.reproducer.reproduce(individuals_to_select, evaluator)
+
+        # Adaptive agent experience collection & learning
+        # Must be called after reproduction (that collects the new experience)
+        experience = self.mutation.agent_experience
+        experience.collect_results(new_population)
+        self.mutation.agent.partial_fit(experience)
+
+        # Use some part of previous pop in the next pop
+        new_population = self.inheritance(self.population, new_population)
+        new_population = self.elitism(self.generations.best_individuals, new_population)
+        return new_population
+
+    def _update_requirements(self):
+        if not self.generations.is_any_improved:
+            self.graph_optimizer_params.mutation_prob, self.graph_optimizer_params.crossover_prob = \
+                self._operators_prob.next(self.population)
+            self.log.info(
+                f'Next mutation proba: {self.graph_optimizer_params.mutation_prob}; '
+                f'Next crossover proba: {self.graph_optimizer_params.crossover_prob}')
+        self.graph_optimizer_params.pop_size = self._pop_size.next(self.population)
+        self.requirements.max_depth = self._graph_depth.next()
+        self.log.info(
+            f'Next population size: {self.graph_optimizer_params.pop_size}; '
+            f'max graph depth: {self.requirements.max_depth}')
+
+        # update requirements in operators
+        for operator in self.operators:
+            operator.update_requirements(self.graph_optimizer_params, self.requirements)

golem/core/optimisers/genetic/pool.py

+from dataclasses import dataclass
+from enum import Enum, auto
+from typing import Optional, List, Any, Callable
+
+
+class ParametersTypesEnum(Enum):
+    UNKNOWN = auto()
+    OPTIMIZER = auto()
+    POOL = auto()
+    NODE = auto()
+
+    def __ge__(self, other):
+        if self.__class__ is other.__class__:
+            return self.value >= other.value
+        return NotImplemented
+
+    def __gt__(self, other):
+        if self.__class__ is other.__class__:
+            return self.value > other.value
+        return NotImplemented
+
+    def __next__(self):
+        return ParametersTypesEnum(self.value + 1)
+
+
+# class Parameters:
+#     def __init__(self, type_: ParametersTypesEnum, data: Optional[dict] = None):
+#         data = data or dict()
+#
+#         for k in data:
+#             if isinstance(data[k], dict):
+#                 data[k] = Parameters(next(type_), data[k])
+#         self.type = type_
+#         self.__data = data
+#
+#     def __getitem__(self, keys):
+#         data = self.__data
+#         for key in keys:
+#             data = data[key]
+#         return data
+#
+#     def __setitem__(self, keys, value):
+#         data = self.__data
+#         for key in keys[:-1]:
+#             if key not in data:
+#                 data[key] = Parameters(next(self.type))
+#             data = data[key]
+#         data[keys[-1]] = value
+#
+#     def __repr__(self):
+#         def pp(parameters, indent=0):
+#             return '\n' + '\n'.join(f"{' ' * indent}'{key}': {value.type.name + pp(value, indent + 2) if isinstance(value, self.__class__) else value}"
+#                              for key, value in parameters.__data.items())
+#         return self.type.name + pp(self)
+#
+#     def __iter__(self):
+#         return (x for x in self.__data.keys())
+#
+#     def items(self):
+#         return (x for x in self.__data.items())
+#
+#     def filter_by_type(self, type_: ParametersTypesEnum):
+#         return [pars for name, pars in self.items()
+#                 if isinstance(pars, Parameters) and pars.type is type_]
+
+
+class Parameters:
+    pass
+
+
+@dataclass
+class OptimizerParameters(Parameters):
+    pool_parameters: List['PoolParameters']
+    n_jobs: int = -1
+
+
+@dataclass
+class PoolParameters(Parameters):
+    name: str
+    constructor: Callable
+    n_jobs: int
+    nodes: List['Node']
+    scheme: 'Scheme'
+    task_constructor: Callable
+    task_history: List[Any]
+
+
+class Optimizer:
+    def __init__(self, parameters: OptimizerParameters):
+        self.parameters = parameters
+
+    def _evolve_population(self):
+        common_parameters = self.parameters
+        for pool_params in common_parameters.pool_parameters:
+            pool = pool_params.constructor(pool_params, common_parameters)
+            common_parameters.update(pool.run())
+
+
+class Pool:
+    """ Pool of nodes """
+
+    def __init__(self, pool_parameters: PoolParameters, parameters: OptimizerParameters):
+        self.name = pool_parameters.name
+        self.nodes_map = {node.name: node for node in pool_parameters.nodes}
+        self.task = pool_parameters.task
+        self.scheme = pool_parameters.scheme
+
+        # TODO error if there are some nodes with same name
+
+    def __call__(self, task: Task):
+        if not task.next in self.nodes_map:
+            raise ValueError((f"Pool {self.name}. Unknown node {task.next}. "
+                              f"Existing nodes: {', '.join(self.nodes_map)}."))
+        processed_task = task.run_on_node(self.nodes_map[task.next])
+        return processed_task
+
+
+class Node:
+    """ Node with operation """
+
+    def __init__(self, name: str, operation: Callable):
+        self.name = name
+        self.operation = operation
+
+    def __call__(self, *args, **kwargs):
+        return self.operation(*args, **kwargs)
+
+
+class Task:
+    """ Data with parameters for operation """
+
+    def __init__(self, data: Any, parameters: Any):
+        self.data = data
+        self.parameters = parameters
+
+    def run_on_node(self, node: Node):
+        result = node(self.data, self.parameters)