# 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 propositionalization.
"""
from dataclasses import dataclass, field
from typing import Any, ClassVar, Dict, List, Optional, Union
from .aggregations import _Aggregations
from .aggregations import fastprop as fastprop_aggregations
from .feature_learner import _FeatureLearner
from .validation import _validate_dfs_model_parameters
[docs]@dataclass(repr=False)
class FastProp(_FeatureLearner):
"""
Generates simple features based on propositionalization.
:class:`~getml.feature_learning.FastProp` generates simple and easily
interpretable features for relational data and time series. It is based on a
propositionalization approach and has been optimized for speed and memory efficiency.
:class:`~getml.feature_learning.FastProp` generates a large number
of features and selects the most relevant ones based on the pair-wise correlation
with the target(s).
It is recommended to combine :class:`~getml.feature_learning.FastProp` with
the :class:`~getml.preprocessors.Mapping` and :class:`~getml.preprocessors.Seasonal`
preprocessors, which can drastically improve predictive accuracy.
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`.
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. Please note that you must also pass a value to
max_lag.
For more information please refer to
:ref:`data_model_time_series`. 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_lag (int, optional):
Maximum number of steps taken into the past to form lag variables. The
step size is determined by delta_t. Please note that you must also pass
a value to delta_t.
For more information please refer to
:ref:`data_model_time_series`. 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`]
num_features (int, optional):
Number of features generated by the feature learning
algorithm. Range: [1, :math:`\\infty`]
n_most_frequent (int, optional):
:class:`~getml.feature_learning.FastProp` can find the N most frequent
categories in a categorical column and derive features from them.
The parameter determines how many categories should be used.
Range: [0, :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`]
sampling_factor (float, optional):
FastProp 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 2,000 samples are drawn from the
population table. If the population table contains less than 2,000 samples,
it will use standard bagging. When set to 0.0, there will be no sampling at
all. Range: [0, :math:`\\infty`]
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`]
"""
agg_sets: ClassVar[_Aggregations] = fastprop_aggregations
aggregation: List[str] = field(
default_factory=lambda: fastprop_aggregations.Default
)
delta_t: float = 0.0
loss_function: Optional[str] = None
max_lag: int = 0
min_df: int = 30
n_most_frequent: int = 0
num_features: int = 200
num_threads: int = 0
sampling_factor: float = 1.0
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.
params can hold the full set or a subset of the
parameters explained in
:class:`~getml.feature_learning.FastProp`.
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!")
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_dfs_model_parameters(**params)