from sklearn.neural_network import MLPClassifier
from typing import Mapping, Iterable, Literal
from .base import BaseC, HyperparameterSearcher
from optuna.distributions import (
FloatDistribution,
CategoricalDistribution,
BaseDistribution,
)
from ....feature_selection import BaseFSC
[docs]
class MLPC(BaseC):
"""Multi-layer Perceptron classifier.
Hyperparameter optimization is performed automatically during training.
The hyperparameter search process can be modified by the user.
"""
[docs]
def __init__(
self,
hyperparam_search_method: Literal["optuna", "grid"] | None = None,
hyperparam_search_space: (
Mapping[str, Iterable | BaseDistribution] | None
) = None,
feature_selectors: list[BaseFSC] | None = None,
max_n_features: int | None = 10,
model_random_state: int = 42,
name: str | None = None,
threshold_strategy: Literal["f1", "roc"] | None = "roc",
**kwargs,
):
"""
Initializes an MLPC object.
Parameters
----------
hyperparam_search_method : Literal[None, 'grid', 'optuna']
Default: None. If None, a model-specific default hyperparameter search
is conducted.
hyperparam_search_space : Mapping[str, Iterable | BaseDistribution]
Default: None. If None, a model-specific default hyperparameter search
is conducted.
feature_selectors : list[BaseFSC]
Default: None. If not None, specifies the feature selectors for the
VotingSelectionReport.
max_n_features : int | None
Default: None.
Only useful if feature_selectors is not None.
If None, then all features with at least 50% support are selected.
model_random_state : int
Default: 42. Random seed for the model.
name : str
Default: None. Determines how the model shows up in the reports.
If None, a default name is set based on the type of the model.
threshold_strategy : Literal['f1', 'roc'] | None
Default: 'f1'. Determines the decision threshold optimization strategy.
'f1' uses the F1 score, 'roc' uses the ROC curve.
If None, no threshold optimization is performed.
Only considered if model yields probabilities.
**kwargs : dict
Key word arguments are passed directly into the intialization of the
HyperparameterSearcher class. See below for options.
inner_cv : int | BaseCrossValidator
Default: 5. Number of inner cross validation folds. Inner
cross validation is used for hyperparameter optimization.
inner_cv_seed : int
Default: 42. Random seed for inner cross validation.
n_jobs : int
Default: 1. Number of parallel jobs to run.
verbose : int
Default: 0. Sets the sklearn verbosity level for the sklearn estimator.
2 is the most verbose.
n_trials : int
Default: 100. Number of trials for hyperparameter optimization. Only
used if hyperparam_search_method is 'optuna'.
"""
super().__init__(threshold_strategy=threshold_strategy)
self._feature_selectors = feature_selectors
self._max_n_features = max_n_features
self._type = type
if name is None:
self._name = "MLPC"
else:
self._name = name
self._best_estimator = MLPClassifier(random_state=model_random_state)
if (hyperparam_search_method is None) or (hyperparam_search_space is None):
hyperparam_search_method = "optuna"
hyperparam_search_space = {
"hidden_layer_sizes": CategoricalDistribution(
[
(50,),
(100,),
(
50,
25,
),
(
50,
50,
),
(
100,
50,
),
(
100,
50,
25,
),
]
),
"activation": CategoricalDistribution(["relu", "tanh"]),
"alpha": FloatDistribution(1e-5, 1e0, log=True),
"learning_rate": CategoricalDistribution(["constant", "adaptive"]),
"learning_rate_init": FloatDistribution(1e-5, 1e-1, log=True),
}
self._hyperparam_searcher = HyperparameterSearcher(
estimator=self._best_estimator,
method=hyperparam_search_method,
hyperparam_grid=hyperparam_search_space,
estimator_name=self._name,
**kwargs,
)
self._validate_inputs()