embiggen/edge_prediction/edge_prediction_sklearn/ridge_classifier_cv_edge_prediction.py
"""Submodule wrapping Ridge Classifier Cross Validator for Edge prediction."""
from typing import Dict, Any, Tuple, Union, Optional, Union, List
from sklearn.linear_model import RidgeClassifierCV
from embiggen.edge_prediction.edge_prediction_sklearn.sklearn_edge_prediction_adapter import (
SklearnEdgePredictionAdapter,
)
from embiggen.utils import normalize_kwargs
class RidgeClassifierCVEdgePrediction(SklearnEdgePredictionAdapter):
"""Create wrapper over Sklearn Ridge Classifier Cross Validator for Edge prediction."""
def __init__(
self,
alphas: Tuple[float] = (0.1, 1.0, 10.0),
fit_intercept: bool = True,
scoring: Optional[str] = "f1_macro",
cv: int=10,
class_weight: Union[Dict, str] = "balanced",
store_cv_values: bool = False,
edge_embedding_methods: Union[List[str], str] = "Concatenate",
training_unbalance_rate: float = 1.0,
use_edge_metrics: bool = False,
use_scale_free_distribution: bool = True,
prediction_batch_size: int = 2**12,
random_state: int = 42,
):
"""Create the Ridge Classifier Cross Validator for Edge prediction."""
self._kwargs = normalize_kwargs(
self,
dict(
alphas=alphas,
fit_intercept=fit_intercept,
scoring=scoring,
cv=cv,
class_weight=class_weight,
store_cv_values=store_cv_values,
)
)
super().__init__(
RidgeClassifierCV(**self._kwargs),
edge_embedding_methods=edge_embedding_methods,
training_unbalance_rate=training_unbalance_rate,
use_edge_metrics=use_edge_metrics,
use_scale_free_distribution=use_scale_free_distribution,
prediction_batch_size=prediction_batch_size,
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(cv=2)
@classmethod
def model_name(cls) -> str:
return "Ridge Classifier Cross Validator"