# Source code for getml.feature_learning.fastboost

```
# 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 fast gradient boosting.
"""
from dataclasses import dataclass
from typing import Any, Dict, Literal, Optional
from .feature_learner import _FeatureLearner
from .validation import _validate_fastboost_parameters
[docs]@dataclass(repr=False)
class Fastboost(_FeatureLearner):
"""
Feature learning based on Gradient Boosting.
:class:`~getml.feature_learning.Fastboost` automates feature learning
for relational data and time series. The algorithm used is slighly
simpler than :class:`~getml.feature_learning.Relboost` and much faster.
Args:
gamma (float, optional):
During the training of Fastboost, 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, :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_depth (int, optional):
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, :math:`\\infty`]
min_child_weights (float, optional):
Determines the minimum sum of the weights 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_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`]
reg_lambda (float, optional):
L2 regularization on the weights in the gradient boosting
routine. This is one of the most important hyperparameters
in the :class:`~getml.feature_learning.Relboost` as it allows
for the most direct regularization. Larger values will
make the resulting model more conservative. 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`]
shrinkage (float, optional):
Since Fastboost 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]
silent (bool, optional):
Controls the logging during training.
subsample (float, optional):
Fastboost 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
Fastboost 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, the number of samples drawn will
be identical to the size of the population table.
When set to 0.0, there will be no sampling at all.
Range: [0, 1]
Note:
Not supported in the getML community edition.
"""
gamma: float = 0.0
loss_function: Optional[Literal["CrossEntropyLoss", "SquareLoss"]] = None
max_depth: int = 5
min_child_weights: float = 1.0
num_features: int = 100
num_threads: int = 1
reg_lambda: float = 1.0
seed: int = 5543
shrinkage: float = 0.1
silent: bool = True
subsample: float = 1.0
[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.
params can hold the full set or a subset of the
parameters explained in
:class:`~getml.feature_learning.Fastboost`.
If params is None, the
current set of parameters in the
instance dictionary 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!")
unsupported_params = [
k for k in params if k not in type(self)._supported_params
]
if unsupported_params:
raise KeyError(
"The following instance variables are not supported "
+ f"in {self.type}: {unsupported_params}"
)
_validate_fastboost_parameters(**params)
```