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getml.feature_learning.Relboost dataclass

Relboost(
    allow_null_weights: bool = False,
    delta_t: float = 0.0,
    gamma: float = 0.0,
    loss_function: Optional[
        Union[CrossEntropyLossType, SquareLossType]
    ] = None,
    max_depth: int = 3,
    min_df: int = 30,
    min_num_samples: int = 1,
    num_features: int = 100,
    num_subfeatures: int = 100,
    num_threads: int = 0,
    propositionalization: FastProp = FastProp(),
    reg_lambda: float = 0.0,
    sampling_factor: float = 1.0,
    seed: int = 5543,
    shrinkage: float = 0.1,
    silent: bool = True,
    vocab_size: int = 500,
)

Bases: _FeatureLearner

Feature learning based on Gradient Boosting.

Relboost automates feature learning for relational data and time series. It is based on a generalization of the XGBoost algorithm to relational data, hence the name.

For more information on the underlying feature learning algorithm, check out the User Guide: Relboost.

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PARAMETER DESCRIPTION
allow_null_weights

Whether you want to allow Relboost to set weights to NULL.

TYPE: bool DEFAULT: False

delta_t

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 Time Series in the User Guide. Range: [0, ∞]

TYPE: float DEFAULT: 0.0

gamma

During the training of Relboost, which is based on gradient tree boosting, this value serves as the minimum improvement in terms of the loss_function required for a split of the tree to be applied. Larger gamma will lead to fewer partitions of the tree and a more conservative algorithm. Range: [0, ∞]

TYPE: float DEFAULT: 0.0

loss_function

Objective function used by the feature learning algorithm to optimize your features. For regression problems use SquareLoss and for classification problems use CrossEntropyLoss.

TYPE: Optional[Union[CrossEntropyLossType, SquareLossType]] DEFAULT: None

max_depth

Maximum depth of the trees generated during the gradient tree boosting. Deeper trees will result in more complex models and increase the risk of overfitting. Range: [0, ∞]

TYPE: int DEFAULT: 3

min_df

Only relevant for columns with role text. The minimum number of fields (i.e. rows) in text column a given word is required to appear in to be included in the bag of words. Range: [1, ∞]

TYPE: int DEFAULT: 30

min_num_samples

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, ∞]

TYPE: int DEFAULT: 1

num_features

Number of features generated by the feature learning algorithm. Range: [1, ∞]

TYPE: int DEFAULT: 100

num_subfeatures

The number of subfeatures you would like to extract in a subensemble (for snowflake data model only). See Snowflake Schema for more information. Range: [1, ∞]

TYPE: int DEFAULT: 100

num_threads

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: [0, ∞]

TYPE: int DEFAULT: 0

propositionalization

The feature learner used for joins which are flagged to be propositionalized (by setting a join's relationship parameter to propositionalization)

TYPE: FastProp DEFAULT: FastProp()

reg_lambda

L2 regularization on the weights in the gradient boosting routine. This is one of the most important hyperparameters in the Relboost as it allows for the most direct regularization. Larger values will make the resulting model more conservative. Range: [0, ∞]

TYPE: float DEFAULT: 0.0

sampling_factor

Relboost 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 Relboost 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, ∞]

TYPE: float DEFAULT: 1.0

seed

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, ∞]

TYPE: int DEFAULT: 5543

shrinkage

Since Relboost 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. It must be between 0.0 and 1.0 with higher values leading to a higher danger of overfitting. Range: [0, 1]

TYPE: float DEFAULT: 0.1

silent

Controls the logging during training.

TYPE: bool DEFAULT: True

vocab_size

Determines the maximum number of words that are extracted in total from text columns. This can be interpreted as the maximum size of the bag of words. Range: [0, ∞]

TYPE: int DEFAULT: 500

validate

validate(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.

PARAMETER DESCRIPTION
params

A dictionary containing the parameters to validate. If not is passed, the own parameters will be validated.

TYPE: Optional[Dict[str, Any]] DEFAULT: None

Source code in getml/feature_learning/relboost.py
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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: 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}."
            )

    # ------------------------------------------------------------

    if not isinstance(params["silent"], bool):
        raise TypeError("'silent' must be of type bool")

    # ------------------------------------------------------------

    _validate_relboost_parameters(**params)