def genotype_factory(self,
max_depth: int = None,
n_estimators: int = None):
return OrderedDict({
"max_depth":
RandPoissonChromosome(
value=max_depth if max_depth is not None else 3,
min_val=2,
max_val=None,
output_dtype=int),
"n_estimators":
RandPoissonChromosome(
value=n_estimators if n_estimators is not None else 10,
min_val=2,
max_val=None,
output_dtype=int),
"criterion":
RandOptionsChromosome(options=["mse", "mae"]),
"max_features":
RandOptionsChromosome(options=["auto", "sqrt", "log2"]),
"warm_start":
RandUniformBooleanChromosome(),
"bootstrap":
RandUniformBooleanChromosome()
})
def genotype_factory(self):
return OrderedDict({
"n_neighbors" : RandPoissonChromosome(
value=4,
min_val=2,
max_val=None,
output_dtype=int
),
"weights": RandOptionsChromosome(options=[
"uniform",
"distance"
]),
"algorithm": RandOptionsChromosome(options=[
"ball_tree",
"kd_tree",
"brute"
]),
"metric": RandOptionsChromosome(options=[
"euclidean",
"manhattan",
"chebyshev"
]),
"leaf_size": RandPoissonChromosome(
value=rand_int(2, 32),
min_val=3,
max_val=None,
output_dtype=int
)
})
def genotype_factory(
self,
regressor_chromosome=None,
n_estimators: int = None,
):
return OrderedDict({
"base_estimator":
regressor_chromosome if regressor_chromosome is not None else
DecisionTreeRegressorChromosome(),
"n_estimators":
RandPoissonChromosome(
value=n_estimators if n_estimators is not None else 50,
min_val=2,
max_val=None,
output_dtype=int),
"max_samples":
RandGaussChromosome(
value=rand_real(),
min_val=0.1,
max_val=1.,
),
"max_features":
RandGaussChromosome(value=rand_real(), min_val=0.1, max_val=1.0),
"warm_start":
RandUniformBooleanChromosome()
})
def genotype_factory(self,
layers: int = 6,
min_layers: int = 2,
max_layers=512):
return OrderedDict({
"hidden_layer_sizes":
RandArrayChromosome(length=layers,
fixed=False,
generator=RandPoissonChromosome(
value=rand_int(min_val=min_layers,
max_val=max_layers),
min_val=min_layers,
max_val=max_layers,
output_dtype=int)),
"activation":
RandOptionsChromosome(
options=["logistic", "tanh", "relu", "identity"]),
"solver":
RandOptionsChromosome(options=["lbfgs", "sgd", "adam"]),
"learning_rate":
RandOptionsChromosome(
options=["constant", "invscaling", "adaptive"]),
"shuffle":
RandUniformBooleanChromosome(),
"warm_start":
RandUniformBooleanChromosome(),
"max_iter":
RandOptionsChromosome(options=[256, 512, 1024, 2048, 4096]),
})
def genotype_factory(self,
max_depth: int = None,
learning_rate: float = None,
n_estimators: int = None):
return OrderedDict({
"max_depth":
RandPoissonChromosome(
value=max_depth if max_depth is not None else rand_int(2, 16),
min_val=2,
max_val=None,
rounding=None,
output_dtype=int),
"learning_rate":
RandGaussChromosome(
value=learning_rate if learning_rate is not None else 0.1,
min_val=0.01,
max_val=0.99,
rounding=2),
"n_estimators":
RandPoissonChromosome(value=n_estimators if n_estimators
is not None else rand_int(2, 128),
min_val=2,
max_val=None,
output_dtype=int),
"objective":
RandOptionsChromosome(
options=["reg:linear", "reg:gamma", "reg:tweedie"]),
"booster":
RandOptionsChromosome(options=["gbtree", "gblinear", "dart"]),
"base_score":
RandGaussChromosome(value=0.5,
min_val=0.01,
max_val=0.99,
rounding=3),
"gamma":
RandGaussChromosome(value=rand_real(),
min_val=0.01,
max_val=0.99,
rounding=3)
})
def genotype_factory(self,
n_estimators: int = None,
learning_rate: float = None,
max_depth: int = None,
alpha: float = None):
return OrderedDict({
"n_estimators":
RandPoissonChromosome(
value=n_estimators if n_estimators is not None else 128,
min_val=2),
"loss":
RandOptionsChromosome(options=["ls", "lad", "huber", "quantile"]),
"learning_rate":
RandGaussChromosome(
value=learning_rate if learning_rate is not None else 0.1,
min_val=0.001,
max_val=0.999,
rounding=3,
output_dtype=np.float64),
"max_features":
RandOptionsChromosome(options=["auto", "sqrt", "log2"]),
"max_depth":
RandPoissonChromosome(
value=max_depth if max_depth is not None else 6,
min_val=2,
max_val=None,
rounding=None,
output_dtype=np.int64),
"criterion":
RandOptionsChromosome(options=["mse", "mae", "friedman_mse"]),
"warm_start":
RandUniformBooleanChromosome(),
"alpha":
RandGaussChromosome(value=alpha if alpha is not None else 0.9,
min_val=0.01,
max_val=0.99,
rounding=2,
output_dtype=np.float64)
})
def genotype_factory(self, max_depth: int = None):
return OrderedDict({
"splitter":
RandOptionsChromosome(options=["best", "random"]),
"criterion":
RandOptionsChromosome(options=["mse", "mae", "friedman_mse"]),
"max_depth":
RandPoissonChromosome(
value=max_depth if max_depth is not None else rand_int(2, 16),
min_val=2,
max_val=None,
rounding=None,
output_dtype=int),
"max_features":
RandOptionsChromosome(options=["auto", "sqrt", "log2"])
})
def genotype_factory(self,
n_estimators: int = None,
learning_rate: float = None,
regressor_chromosome=None):
return OrderedDict({
"base_estimator":
regressor_chromosome if regressor_chromosome is not None else
DecisionTreeRegressorChromosome(),
"n_estimators":
RandPoissonChromosome(
value=n_estimators if n_estimators is not None else 50,
min_val=2,
max_val=None,
output_dtype=int),
"learning_rate":
RandGaussChromosome(value=learning_rate
if learning_rate is not None else rand_real(),
min_val=0.01,
max_val=1.0,
rounding=3,
output_dtype=np.float32),
"loss":
RandOptionsChromosome(options=["linear", "square", "exponential"])
})