import pyarrow as pa
from sklearn.kernel_approximation import Nystroem as NystroemOperation
from DashAI.back.api.utils import create_random_state, parse_string_to_dict
from DashAI.back.converters.category.dimensionality_reduction import (
DimensionalityReductionConverter,
)
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,
string_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 NystroemSchema(BaseSchema):
kernel: schema_field(
none_type(string_field()),
"rbf",
description=MultilingualString(
en="The kernel to use for the approximation.",
es="El kernel a usar para la aproximación.",
),
) # type: ignore
gamma: schema_field(
none_type(float_field(gt=0)),
None,
description=MultilingualString(
en=(
"Gamma parameter for RBF, laplacian, polynomial, exp chi2 "
"and sigmoid kernels."
),
es=(
"Parámetro gamma para los kernels RBF, laplaciano, polinomial, "
"chi2 exponencial y sigmoide."
),
),
) # type: ignore
coef0: schema_field(
none_type(float_field()),
None,
description=MultilingualString(
en="The coef0 parameter for polynomial and sigmoid kernels.",
es="Parámetro coef0 para los kernels polinomial y sigmoide.",
),
) # type: ignore
degree: schema_field(
none_type(float_field(ge=1)),
None,
description=MultilingualString(
en="The degree of the polynomial kernel.",
es="El grado del kernel polinomial.",
),
) # type: ignore
kernel_params: schema_field(
none_type(string_field()),
None,
description=MultilingualString(
en="Additional parameters (kwargs) for the kernel function.",
es="Parámetros adicionales (kwargs) para la función kernel.",
),
) # type: ignore
n_components: schema_field(
int_field(ge=1),
2,
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"]))),
None,
description=MultilingualString(
en=(
"Seed of the pseudo random number generator to use when "
"shuffling the data."
),
es=("Semilla del generador pseudoaleatorio usado al mezclar los datos."),
),
) # type: ignore
n_jobs: schema_field(
none_type(int_field()),
None,
description=MultilingualString(
en="Number of parallel jobs to run.",
es="Número de trabajos paralelos a ejecutar.",
),
) # type: ignore
[docs]
class Nystroem(DimensionalityReductionConverter, SklearnWrapper, NystroemOperation):
"""Scikit-learn's Nystroem wrapper for DashAI."""
SCHEMA = NystroemSchema
DESCRIPTION = MultilingualString(
en=(
"Approximate a kernel map using a subset of the training data. "
"Constructs an approximate feature map for an arbitrary kernel "
"using a subset of the data as basis."
),
es=(
"Aproxima un mapa de kernel usando un subconjunto de los datos de "
"entrenamiento. Construye un mapa de características aproximado para "
"un kernel arbitrario usando un subconjunto de datos como base."
),
)
DISPLAY_NAME = MultilingualString(
en="Nystroem Approximation", es="Aproximación Nystroem"
)
IMAGE_PREVIEW = "nystroem.png"
[docs]
def __init__(self, **kwargs):
self.kernel_params = kwargs.pop("kernel_params", None)
if self.kernel_params is not None:
self.kernel_params = parse_string_to_dict(self.kernel_params)
kwargs["kernel_params"] = self.kernel_params
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())