import pyarrow as pa
from sklearn.kernel_approximation import RBFSampler as RBFSamplerOperation
from DashAI.back.api.utils import create_random_state
from DashAI.back.converters.category.polynomial_kernel import PolynomialKernelConverter
from DashAI.back.converters.sklearn_wrapper import SklearnWrapper
from DashAI.back.core.schema_fields import (
enum_field,
float_field,
int_field,
none_type,
schema_field,
union_type,
)
from DashAI.back.core.schema_fields.base_schema import BaseSchema
from DashAI.back.core.utils import MultilingualString
from DashAI.back.types.dashai_data_type import DashAIDataType
from DashAI.back.types.value_types import Float
class RBFSamplerSchema(BaseSchema):
gamma: schema_field(
union_type(enum_field(["scale"]), float_field(gt=0)),
"scale",
description=MultilingualString(
en="Parameter of the RBF kernel.",
es="Parámetro del kernel RBF.",
),
) # type: ignore
n_components: schema_field(
int_field(ge=1),
100,
description=MultilingualString(
en="The number of features to construct.",
es="El número de características a construir.",
),
) # type: ignore
random_state: schema_field(
none_type(union_type(int_field(), enum_field(["RandomState"]))),
0,
description=MultilingualString(
en=(
"Pseudo-random number generator to control the generation of the "
"random weights and random offset when fitting the training data. "
"Pass an int for reproducible output across multiple function calls."
),
es=(
"Generador pseudoaleatorio para controlar pesos y desplazamientos "
"aleatorios al ajustar los datos. Pasa un entero para obtener "
"resultados reproducibles."
),
),
) # type: ignore
[docs]
class RBFSampler(PolynomialKernelConverter, SklearnWrapper, RBFSamplerOperation):
"""Scikit-learn's RBFSampler wrapper for DashAI."""
SCHEMA = RBFSamplerSchema
DESCRIPTION = MultilingualString(
en=(
"Approximates the feature map of an RBF kernel by Monte Carlo "
"approximation of its Fourier transform."
),
es=(
"Aproxima el mapa de características de un kernel RBF mediante "
"la aproximación de Monte Carlo de su transformada de Fourier."
),
)
DISPLAY_NAME = MultilingualString(en="RBF Sampler", es="Muestreador RBF")
IMAGE_PREVIEW = "rbf_sampler.png"
[docs]
def __init__(self, **kwargs):
self.random_state = kwargs.pop("random_state", None)
if self.random_state == "RandomState":
self.random_state = create_random_state()
kwargs["random_state"] = self.random_state
super().__init__(**kwargs)
def get_output_type(self, column_name: str = None) -> DashAIDataType:
"""Returns Float64 as the output type for transformed data."""
return Float(arrow_type=pa.float64())