embiggen/node_label_prediction/node_label_prediction_sklearn/ridge_classifier_node_label_prediction.py
"""Submodule wrapping Ridge Classifier for Node-label prediction."""
from typing import Dict, Any, Union, Optional
from sklearn.linear_model import RidgeClassifier
from embiggen.node_label_prediction.node_label_prediction_sklearn.sklearn_node_label_prediction_adapter import (
SklearnNodeLabelPredictionAdapter,
)
from embiggen.utils import normalize_kwargs
class RidgeClassifierNodeLabelPrediction(SklearnNodeLabelPredictionAdapter):
"""Create wrapper over Sklearn Ridge Classifier classifier for Node-label prediction."""
def __init__(
self,
alpha: float = 1.0,
fit_intercept: bool = True,
copy_X: bool = True,
max_iter: Optional[int] = None,
tol: float = 1e-4,
class_weight: Union[Dict, str] = "balanced",
solver: str = "auto",
positive: bool = False,
random_state: int = 323,
):
"""Create the Ridge Classifier for Node-label prediction."""
self._kwargs = normalize_kwargs(
self,
dict(
alpha=alpha,
fit_intercept=fit_intercept,
copy_X=copy_X,
max_iter=max_iter,
tol=tol,
class_weight=class_weight,
solver=solver,
positive=positive,
)
)
super().__init__(
RidgeClassifier(**self._kwargs, random_state=random_state),
random_state=random_state,
)
def parameters(self) -> Dict[str, Any]:
"""Returns parameters used for this model."""
return {**super().parameters(), **self._kwargs}
@classmethod
def smoke_test_parameters(cls) -> Dict[str, Any]:
"""Returns parameters for smoke test."""
return dict(max_iter=2)
@classmethod
def model_name(cls) -> str:
return "Ridge Classifier"