Source code for getml.feature_learning.multirel

# 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.

Feature learning based on Multi-Relational Decision Tree Learning.

from dataclasses import dataclass, field
from typing import Any, ClassVar, Dict, List, Optional, Union

from .aggregations import _Aggregations
from .aggregations import multirel as multirel_aggregations
from .fastprop import FastProp
from .feature_learner import _FeatureLearner
from .validation import _validate_multirel_parameters

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[docs]@dataclass(repr=False) class Multirel(_FeatureLearner): """ Feature learning based on Multi-Relational Decision Tree Learning. :class:`~getml.feature_learning.Multirel` automates feature learning for relational data and time series. It is based on an efficient variation of the Multi-Relational Decision Tree Learning (MRDTL). For more information on the underlying feature learning algorithm, check out the :ref:`User guide <feature_learning_algorithms_multirel>`. Args: aggregation (List[:class:`~getml.feature_learning.aggregations`], optional): Mathematical operations used by the automated feature learning algorithm to create new features. Must be from :mod:`~getml.feature_learning.aggregations`. allow_sets (bool, optional): Multirel can summarize different categories into sets for producing conditions. When expressed as SQL statements these sets might look like this: .. code-block:: sql t2.category IN ( 'value_1', 'value_2', ... ) This can be very powerful, but it can also produce features that are hard to read and might be prone to overfitting when the `sampling_factor` is too low. delta_t (float, optional): Frequency with which lag variables will be explored in a time series setting. When set to 0.0, there will be no lag variables. For more information please refer to :ref:`data_model_time_series`. Range: [0, :math:`\\infty`] grid_factor (float, optional): Multirel will try a grid of critical values for your numerical features. A higher `grid_factor` will lead to a larger number of critical values being considered. This can increase the training time, but also lead to more accurate features. Range: (0, :math:`\\infty`] loss_function (:class:`~getml.feature_learning.loss_functions`, optional): Objective function used by the feature learning algorithm to optimize your features. For regression problems use :class:`~getml.feature_learning.loss_functions.SquareLoss` and for classification problems use :class:`~getml.feature_learning.loss_functions.CrossEntropyLoss`. max_length (int, optional): The maximum length a subcondition might have. Multirel will create conditions in the form .. code-block:: sql (condition 1.1 AND condition 1.2 AND condition 1.3 ) OR ( condition 2.1 AND condition 2.2 AND condition 2.3 ) ... Using this parameter you can set the maximum number of conditions allowed in the brackets. Range: [0, :math:`\\infty`] min_df (int, optional): Only relevant for columns with role :const:``. The minimum number of fields (i.e. rows) in :const:`` column a given word is required to appear in to be included in the bag of words. Range: [1, :math:`\\infty`] min_num_samples (int, optional): Determines the minimum number of samples a subcondition should apply to in order for it to be considered. Higher values lead to less complex statements and less danger of overfitting. Range: [1, :math:`\\infty`] num_features (int, optional): Number of features generated by the feature learning algorithm. Range: [1, :math:`\\infty`] num_subfeatures (int, optional): The number of subfeatures you would like to extract in a subensemble (for snowflake data model only). See :ref:`data_model_snowflake_schema` for more information. Range: [1, :math:`\\infty`] num_threads (int, optional): Number of threads used by the feature learning algorithm. If set to zero or a negative value, the number of threads will be determined automatically by the getML engine. Range: [:math:`0`, :math:`\\infty`] propositionalization (:class:`~getml.feature_learning.FastProp`, optional): The feature learner used for joins which are flagged to be propositionalized (by setting a join's `relationship` parameter to :const:``) regularization (float, optional): Most important regularization parameter for the quality of the features produced by Multirel. Higher values will lead to less complex features and less danger of overfitting. A `regularization` of 1.0 is very strong and allows no conditions. Range: [0, 1] round_robin (bool, optional): If True, the Multirel picks a different `aggregation` every time a new feature is generated. sampling_factor (float, optional): Multirel uses a bootstrapping procedure (sampling with replacement) to train each of the features. The sampling factor is proportional to the share of the samples randomly drawn from the population table every time Multirel generates a new feature. A lower sampling factor (but still greater than 0.0), will lead to less danger of overfitting, less complex statements and faster training. When set to 1.0, roughly 20,000 samples are drawn from the population table. If the population table contains less than 20,000 samples, it will use standard bagging. When set to 0.0, there will be no sampling at all. Range: [0, :math:`\\infty`] seed (Union[int,None], optional): Seed used for the random number generator that underlies the sampling procedure to make the calculation reproducible. Internally, a `seed` of None will be mapped to 5543. Range: [0, :math:`\\infty`] share_aggregations (float, optional): Every time a new feature is generated, the `aggregation` will be taken from a random subsample of possible aggregations and values to be aggregated. This parameter determines the size of that subsample. Only relevant when `round_robin` is False. Range: [0, 1] share_conditions (float, optional): Every time a new column is tested for applying conditions, it might be skipped at random. This parameter determines the probability that a column will *not* be skipped. Range: [0, 1] shrinkage (float, optional): Since Multirel works using a gradient-boosting-like algorithm, `shrinkage` (or learning rate) scales down the weights and thus the impact of each new tree. This gives more room for future ones to improve the overall performance of the model in this greedy algorithm. Higher values will lead to more danger of overfitting. Range: [0, 1] silent (bool, optional): Controls the logging during training. vocab_size (int, optional): Determines the maximum number of words that are extracted in total from :const:`` columns. This can be interpreted as the maximum size of the bag of words. Range: [0, :math:`\\infty`] Note: Not supported in the getML community edition. """ # ---------------------------------------------------------------- agg_sets: ClassVar[_Aggregations] = multirel_aggregations # ---------------------------------------------------------------- aggregation: List[str] = field( default_factory=lambda: multirel_aggregations.Default ) allow_sets: bool = True delta_t: float = 0.0 grid_factor: float = 1.0 loss_function: Optional[str] = None max_length: int = 4 min_df: int = 30 min_num_samples: int = 1 num_features: int = 100 num_subfeatures: int = 5 num_threads: int = 0 propositionalization: FastProp = field(default_factory=FastProp) regularization: float = 0.01 round_robin: bool = False sampling_factor: float = 1.0 seed: int = 5543 share_aggregations: float = 0.0 share_conditions: float = 1.0 shrinkage: float = 0.0 silent: bool = True vocab_size: int = 500 # ----------------------------------------------------------------
[docs] def validate(self, params: Optional[Dict[str, Any]] = None) -> 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. """ # ------------------------------------------------------------ if params is None: params = self.__dict__ else: params = {**self.__dict__, **params} # ------------------------------------------------------------ if not isinstance(params, dict): raise ValueError("params must be None or a dictionary!") # ------------------------------------------------------------ for kkey in params: if kkey not in type(self)._supported_params: raise KeyError( f"Instance variable '{kkey}' is not supported in {self.type}." ) # ------------------------------------------------------------ _validate_multirel_parameters(**params)
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