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Fix linear selection on Python 3.12 #25

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Fix linear selection on Python 3.12 #25

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21 changes: 14 additions & 7 deletions feature/linear.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,9 +4,11 @@

from typing import NoReturn, Tuple

import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression, Lasso, Ridge
from sklearn.linear_model import LogisticRegression, RidgeClassifier
from sklearn.multiclass import OneVsRestClassifier

from feature.base import _BaseSupervisedSelector, _BaseDispatcher
from feature.utils import Num, get_task_string
Expand All @@ -29,10 +31,10 @@ def __init__(self, seed: int, num_features: Num, regularization: str, alpha:Num)
"regression_lasso": Lasso(random_state=self.seed),
"regression_ridge": Ridge(random_state=self.seed),
# "classification_none": LogisticRegression(penalty="none"), # won't converge most times
"classification_none": LogisticRegression(random_state=self.seed,
multi_class="auto", solver="liblinear"),
"classification_lasso": LogisticRegression(random_state=self.seed, penalty='l1',
multi_class="auto", solver="liblinear"),
"classification_none": OneVsRestClassifier(
LogisticRegression(random_state=self.seed, solver="liblinear")),
"classification_lasso": OneVsRestClassifier(
LogisticRegression(random_state=self.seed, penalty='l1', solver="liblinear")),
"classification_ridge": RidgeClassifier(random_state=self.seed)}

def get_model_args(self, selection_method) -> Tuple:
Expand Down Expand Up @@ -60,13 +62,18 @@ def fit(self, data: pd.DataFrame, labels: pd.Series) -> NoReturn:
# But that does not necessarily mean they are more important
# See more discussion here:
# https://scikit-learn.org/stable/auto_examples/inspection/plot_linear_model_coefficient_interpretation.html#sphx-glr-auto-examples-inspection-plot-linear-model-coefficient-interpretation-py
self.abs_scores = abs(self.imp.coef_)
if isinstance(self.imp, OneVsRestClassifier):
coefficients = np.vstack([estimator.coef_ for estimator in self.imp.estimators_])
else:
coefficients = self.imp.coef_

# LogisticRegression/RidgeClassifier returns a coef_ array of (n_classes, n_features)
# These coefficients map the importance of the feature for a specific class.
# One approach is to average the importances
if isinstance(self.imp, LogisticRegression) or isinstance(self.imp, RidgeClassifier):
self.abs_scores = abs(self.imp.coef_.mean(0))
if isinstance(self.imp, (OneVsRestClassifier, LogisticRegression, RidgeClassifier)):
self.abs_scores = abs(coefficients.mean(0))
else:
self.abs_scores = abs(coefficients)

def transform(self, data: pd.DataFrame) -> pd.DataFrame:

Expand Down