abstract_data_manager.py

import abc
from typing import Any, Dict, Union

import numpy as np
import scipy.sparse

from autosklearn.pipeline.components.data_preprocessing.feature_type import (
    FeatTypeSplit,
)


class AbstractDataManager:
    __metaclass__ = abc.ABCMeta

    def __init__(self, name: str):

        self._data = dict()  # type: Dict
        self._info = dict()  # type: Dict
        self._name = name

    @property
    def name(self) -> str:
        return self._name

    @property
    def data(self) -> Dict[str, np.ndarray]:
        return self._data

    @property
    def info(self) -> Dict[str, Any]:
        return self._info

    @property
    def feat_type(self) -> Dict[Union[str, int], str]:
        return self._feat_type

    @feat_type.setter
    def feat_type(self, value: Dict[Union[str, int], str]) -> None:
        self._feat_type = value

    @property
    def encoder(self) -> FeatTypeSplit:
        return self._encoder

    @encoder.setter
    def encoder(self, value: FeatTypeSplit) -> FeatTypeSplit:
        self._encoder = value

    def __repr__(self) -> str:
        return "DataManager : " + self.name

    def __str__(self) -> str:
        val = "DataManager : " + self.name + "\ninfo:\n"
        for item in self.info:
            val = val + "\t" + item + " = " + str(self.info[item]) + "\n"
        val = val + "data:\n"

        for subset in self.data:
            val = val + "\t%s = %s %s %s\n" % (
                subset,
                type(self.data[subset]),
                str(self.data[subset].shape),
                str(self.data[subset].dtype),
            )
            if isinstance(self.data[subset], scipy.sparse.spmatrix):
                val = val + "\tdensity: %f\n" % (
                    float(len(self.data[subset].data))
                    / self.data[subset].shape[0]
                    / self.data[subset].shape[1]
                )
        val = val + "feat_type:\t" + str(self.feat_type) + "\n"
        return val