Source code for getml.preprocessors.mapping

# Copyright 2022 The SQLNet Company GmbH
# This file is licensed under the Elastic License 2.0 (ELv2).
# Refer to the LICENSE.txt file in the root of the repository
# for details.

Contains routines for preprocessing data frames.

from dataclasses import dataclass, field
from typing import ClassVar, List

from getml.feature_learning.aggregations import mapping as mapping_aggregations
from getml.feature_learning.aggregations import _Aggregations

from .validate import _validate
from .preprocessor import _Preprocessor

[docs]@dataclass(repr=False) class Mapping(_Preprocessor): """ A mapping preprocessor maps categorical values, discrete values and individual words in a text field to numerical values. These numerical values are retrieved by aggregating targets in the relational neighbourhood. You are particularly encouraged to use the mapping preprocessor in combination with :class:`~getml.feature_learning.FastProp`. Refer to the :ref:`User guide <mappings>` for more information. Args: aggregation (List[:class:`~getml.feature_learning.aggregations`], optional): The aggregation function to use over the targets. Must be from :mod:`~getml.feature_learning.aggregations`. min_freq (int, optional): The minimum number of targets required for a value to be included in the mapping. Range: [0, :math:`\\infty`] multithreading (bool, optional): Whether you want to apply multithreading. Example: .. code-block:: python mapping = getml.preprocessors.Mapping() pipe = getml.Pipeline( population=population_placeholder, peripheral=[order_placeholder, trans_placeholder], preprocessors=[mapping], feature_learners=[feature_learner_1, feature_learner_2], feature_selectors=feature_selector, predictors=predictor, share_selected_features=0.5 ) Note: Not supported in the getML community edition. """ agg_sets: ClassVar[_Aggregations] = mapping_aggregations aggregation: List[str] = field(default_factory=lambda: mapping_aggregations.Default) min_freq: int = 30 multithreading: bool = True
[docs] def validate(self, params=None): """Checks both the types and the values of all instance variables and raises an exception if something is off. Args: params (dict, optional): A dictionary containing the parameters to validate. If not is passed, the own parameters will be validated. """ params = _validate(self, params) if not all([agg in mapping_aggregations.All for agg in params["aggregation"]]): raise ValueError( "'aggregation' must be from Mapping.agg_sets.All, " + "meaning from the following set: " + str(mapping_aggregations.All) + "." ) if not isinstance(params["min_freq"], int): raise TypeError("'min_freq' must be an int.") if params["min_freq"] < 0: raise TypeError("'min_freq' cannot be negative.") if not isinstance(params["multithreading"], bool): raise TypeError("'multithreading' must be a bool.")