`relevanceai.vector_tools.dim_reduction`

Module Contents

Classes

`DimReductionBase`	Using verbose loguru as base logger for now
`PCA`	Using verbose loguru as base logger for now
`TSNE`	Using verbose loguru as base logger for now
`UMAP`	Using verbose loguru as base logger for now
`Ivis`	Using verbose loguru as base logger for now
`DimReduction`	Base class for all relevanceai client utilities

class relevanceai.vector_tools.dim_reduction.DimReductionBase

Bases: relevanceai.logger.LoguruLogger

Using verbose loguru as base logger for now

__call__(self, *args, **kwargs)

abstract fit_transform(self, vectors: numpy.ndarray, dr_args: Dict[Any, Any], dims: int) → numpy.ndarray

class relevanceai.vector_tools.dim_reduction.PCA

Bases: DimReductionBase

Using verbose loguru as base logger for now

fit_transform(self, vectors: numpy.ndarray, dr_args: Optional[Dict[Any, Any]] = DIM_REDUCTION_DEFAULT_ARGS['pca'], dims: int = 3) → numpy.ndarray

class relevanceai.vector_tools.dim_reduction.TSNE

Bases: DimReductionBase

Using verbose loguru as base logger for now

fit_transform(self, vectors: numpy.ndarray, dr_args: Optional[Dict[Any, Any]] = DIM_REDUCTION_DEFAULT_ARGS['tsne'], dims: int = 3) → numpy.ndarray

class relevanceai.vector_tools.dim_reduction.UMAP

Bases: DimReductionBase

Using verbose loguru as base logger for now

fit_transform(self, vectors: numpy.ndarray, dr_args: Optional[Dict[Any, Any]] = DIM_REDUCTION_DEFAULT_ARGS['umap'], dims: int = 3) → numpy.ndarray

class relevanceai.vector_tools.dim_reduction.Ivis

Bases: DimReductionBase

Using verbose loguru as base logger for now

fit_transform(self, vectors: numpy.ndarray, dr_args: Optional[Dict[Any, Any]] = DIM_REDUCTION_DEFAULT_ARGS['tsne'], dims: int = 3) → numpy.ndarray

class relevanceai.vector_tools.dim_reduction.DimReduction(project, api_key)

Bases: relevanceai.base._Base, DimReductionBase

Base class for all relevanceai client utilities

static dim_reduce(vectors: numpy.ndarray, dr: Union[relevanceai.vector_tools.constants.DIM_REDUCTION, DimReductionBase], dr_args: Union[None, dict], dims: typing_extensions.Literal[2, 3]) → numpy.ndarray: Dimensionality reduction