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"""
Feature learning based on Gradient Boosting.
"""
from dataclasses import dataclass
from typing import ClassVar, List, Union
from .fastprop import FastProp
from .feature_learner import _FeatureLearner
from .loss_functions import SquareLoss
from .validation import _validate_relboost_parameters
# --------------------------------------------------------------------
[docs]@dataclass(repr=False)
class RelMT(_FeatureLearner):
"""Feature learning based on relational linear model trees.
:class:`~getml.feature_learning.RelMT` automates feature learning
for relational data and time series. It is based on a
generalization of linear model trees to relational data, hence
the name. A linear model tree is a decision tree
with linear models on its leaves.
Args:
allow_avg (bool, optional):
Whether to allow an AVG aggregation. Particularly for time
series problems, AVG aggregations are not necessary and you
can save some time by taking them out.
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`]
gamma (float, optional):
During the training of RelMT, 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_df (int, optional):
Only relevant for columns with role :const:`~getml.data.roles.text`.
The minimum
number of fields (i.e. rows) in :const:`~getml.data.roles.text` 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:`\\infty`, :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:`getml.data.relationship.propositionalization`)
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.RelMT` as it allows
for the most direct regularization. Larger values will
make the resulting model more conservative. Range: [0,
:math:`\\infty`]
sampling_factor (float, optional):
RelMT 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
RelMT 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`]
shrinkage (float, optional):
Since RelMT 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 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:`getml.data.roles.text`
columns. This can be interpreted as the maximum size of the bag of words.
Range: [0, :math:`\\infty`]
"""
# ----------------------------------------------------------------
allow_avg: bool = True
delta_t: float = 0.0
gamma: float = 0.0
loss_function: Union[str, None] = None
max_depth: int = 2
min_df: int = 30
min_num_samples: int = 1
num_features: int = 30
num_subfeatures: int = 30
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
# ----------------------------------------------------------------
[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.
"""
# ------------------------------------------------------------
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)
# ------------------------------------------------------------