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)
class BenchmarkTestCase(TestCase):
def setUp(self):
self.Lower, self.Upper = [-19, -10], [19, 5]
self.b = Benchmark(self.Lower, self.Upper)
def test_lower_fine(self):
self.assertEqual(self.Lower, self.b.Lower)
def test_upper_fine(self):
self.assertEqual(self.Upper, self.b.Upper)
def test_function_eval_fine(self):
f = self.b.function()
self.assertTrue(callable(f))
self.assertEqual(inf, f(1, self.Upper))
def __init__(self):
Benchmark.__init__(self, -5.12, 5.12)
def __init__(self): Benchmark.__init__(self, -10, 10)
def setUp(self):
self.Lower, self.Upper = [-19, -10], [19, 5]
self.b = Benchmark(self.Lower, self.Upper)
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)
