def fit(self, X: np.ndarray, y: np.ndarray):
if isinstance(X, (pd.DataFrame, pd.Series)):
X = X.values
if isinstance(y, (pd.DataFrame, pd.Series)):
y = y.values.reshape(-1, 1)
if X.shape[0] != y.shape[0]:
msg = (f"Ensure that the number of samples in X and y are the same"
f"Number of samples in X = {X.shape[0]}"
f"Number of samples in y = {y.shape[0]}")
raise ModelError(msg)
self.X = X
self.y = y
if self.subsets is None:
self.subsets = []
# Create random subsets of decision variables for each subnet
for i in range(self.num_subnets):
n = random.randint(1, self.X.shape[1])
self.subsets.append(random.sample(range(self.X.shape[1]), n))
# Ensure that each decision variable is used as an input in at least one subnet
for n in list(range(self.X.shape[1])):
if not any(n in k for k in self.subsets):
self.subsets[random.randint(0, self.num_subnets -
1)].append(n)
# Create problem
problem = surrogateProblem(
performance_evaluator=self._model_performance)
problem.n_of_objectives = 2
# Create Population
initial_pop = self._create_individuals()
population = SurrogatePopulation(problem, self.pop_size, initial_pop,
None, None, None)
# Do evolution
evolver = self.training_algorithm(problem,
initial_population=population)
recombinator = EvoDN2Recombination(evolver=evolver)
evolver.population.recombination = recombinator
figure = animate_init_(evolver.population.objectives,
filename="EvoDN2.html")
while evolver.continue_evolution():
evolver.iterate()
figure = animate_next_(
evolver.population.objectives,
figure,
filename="EvoDN2.html",
generation=evolver._iteration_counter,
)
self.model_population = evolver.population
# Selection
self.select()
self.model_trained = True
def fit(self, X: np.ndarray, y: np.ndarray):
if isinstance(X, (pd.DataFrame, pd.Series)):
X = X.values
if isinstance(y, (pd.DataFrame, pd.Series)):
y = y.values.reshape(-1, 1)
if X.shape[0] != y.shape[0]:
msg = (f"Ensure that the number of samples in X and y are the same"
f"Number of samples in X = {X.shape[0]}"
f"Number of samples in y = {y.shape[0]}")
raise ModelError(msg)
self.X = X
self.y = y
# Create problem
problem = surrogateProblem(
performance_evaluator=self._model_performance)
problem.n_of_objectives = 2
# Create Population
initial_pop = self._create_individuals()
population = SurrogatePopulation(problem, self.pop_size, initial_pop,
None, None, None)
# Do evolution
evolver = self.training_algorithm(problem,
initial_population=population)
recombinator = EvoNNRecombination(evolver=evolver,
mutation_type=self.mutation_type)
evolver.population.recombination = recombinator
figure = animate_init_(evolver.population.objectives,
filename="EvoNN.html")
while evolver.continue_evolution():
evolver.iterate()
figure = animate_next_(
evolver.population.objectives,
figure,
filename="EvoNN.html",
generation=evolver._iteration_counter,
)
self.model_population = evolver.population
# Selection
self.select()
self.model_trained = True
def fit(self, X: pd.DataFrame, y: pd.DataFrame):
if X.shape[0] != y.shape[0]:
msg = (f"Ensure that the number of samples in X and y are the same"
f"Number of samples in X = {X.shape[0]}"
f"Number of samples in y = {y.shape[0]}")
raise ModelError(msg)
self.X = X
self.y = y
if self.terminal_set is None:
self.terminal_set = X.columns.tolist()
# Create problem
problem = surrogateProblem(
performance_evaluator=self._model_performance)
problem.n_of_objectives = 2
# Create Population
initial_pop = self._create_individuals()
population = SurrogatePopulation(problem, self.pop_size, initial_pop,
None, None, None)
population.xover = BioGP_xover(
probability_crossover=self.probability_crossover)
population.mutation = BioGP_mutation(
probability_mutation=self.probability_mutation)
# Do single objective evolution
tournament_evolver = TournamentEA(
problem,
initial_population=population,
population_size=self.pop_size,
n_gen_per_iter=self.single_obj_generations,
n_iterations=1,
)
figure = animate_init_(tournament_evolver.population.objectives,
filename="BioGP.html")
while tournament_evolver.continue_evolution():
tournament_evolver.iterate()
figure = animate_next_(
tournament_evolver.population.objectives,
figure,
filename="BioGP.html",
generation=tournament_evolver._iteration_counter,
)
population = tournament_evolver.population
# Do bi-objective evolution
evolver = self.training_algorithm(
problem,
initial_population=population,
population_size=self.pop_size,
n_gen_per_iter=10,
n_iterations=10,
)
while evolver.continue_evolution():
evolver.iterate()
figure = animate_next_(
evolver.population.objectives,
figure,
filename="BioGP.html",
generation=evolver._iteration_counter +
tournament_evolver._iteration_counter,
)
self.model_population = evolver.population
# Selection
self.select()
self.model_trained = True
from desdeo_problem.testproblems.TestProblems import test_problem_builder
from desdeo_emo.EAs.RVEA import RVEA
from desdeo_emo.EAs.NSGAIII import NSGAIII
from desdeo_emo.othertools.plotlyanimate import animate_init_, animate_next_
dtlz3 = test_problem_builder("DTLZ3", n_of_variables=12, n_of_objectives=11)
evolver = RVEA(dtlz3, n_iterations=10)
figure = animate_init_(evolver.population.objectives, filename="dtlz3.html")
while evolver.continue_evolution():
evolver.iterate()
figure = animate_next_(
evolver.population.objectives,
figure,
filename="dtlz3.html",
generation=evolver._iteration_counter,
)