def __init__(self, model_fn, eval_fn, x_train, y_train, x_test, y_test): self.x_train = x_train self.y_train = y_train self.x_test = x_test self.y_test = y_test self.model_fn = model_fn self.eval_fn = eval_fn Benchmark.__init__(self, 0, 1)
def __init__(self, X, y): r"""Initialize feature selection benchmark. Arguments: X (pandas.core.frame.DataFrame): Features. y (pandas.core.series.Series) Expected classifier results. """ self.__best_fitness = float('inf') self.__best_solution = None Benchmark.__init__(self, 0.0, 1.0) self.train_X, self.test_X, self.train_y, self.test_y = train_test_split( X, y, test_size=0.2)
def __init__(self, parent, fitness_name): r"""Initialize pipeline optimizer benchmark. Arguments: parent (PipelineOptimizer): Parent instance of PipelineOptimizer. fitness_name (str): Name of the fitness class to use as a function. """ self._parent = parent self._current_best_fitness = float('inf') self._current_best_pipeline = None self._fitness_name = fitness_name self._evals = 0 self._logger = self._parent.get_logger() Benchmark.__init__(self, 0.0, 1.0)
def __init__(self, x, y, parent, population_size, fitness_function): r"""Initialize pipeline benchmark. Arguments: parent (Pipeline): Parent instance of Pipeline. population_size (uint): Number of individuals in the hiperparameter optimization process. fitness_function (str): Name of the fitness function to use. """ self.__parent = parent self.__x = x self.__y = y self.__population_size = population_size self.__current_best_fitness = float('inf') self.__fitness_function = FitnessFactory().get_result(fitness_function) self.__evals = 0 self.__logger = self.__parent.get_logger() Benchmark.__init__(self, 0.0, 1.0)
def __init__(self, model_fn, eval_fn, x_train, y_train, cv=3, random_state=None): self.x_train = x_train self.y_train = y_train self.model_fn = model_fn self.eval_fn = eval_fn self.k_fold = KFold(n_splits=cv, random_state=random_state, shuffle=True) self.cache = {} Benchmark.__init__(self, 0, 1)
def __init__(self): Benchmark.__init__(self, -5.12, 5.12)
def __init__(self): Benchmark.__init__(self, -10, 10)
def __init__(self): Benchmark.__init__(self, -11, 11)
def __init__(self, evaluate_candidates, param_grid): self.evaluate_candidates = evaluate_candidates self.param_grid = param_grid self.cache = {} Benchmark.__init__(self, 0, 1)