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Copy pathlinear.py
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executable file
·62 lines (46 loc) · 1.74 KB
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"""
Adapt linear regression models to the setting of ordinal regression.
In practice this means override the predict method so that it predicts
ordinal (not continuous) labels. Also the score method is overwritten to
use the mean absolute error.
"""
from sklearn import linear_model, metrics, svm
import numpy as np
import metrics as m # avoid name clash with sklearn.metrics
METRIC = lambda x, y: - metrics.mean_absolute_error(x, y)
# METRIC = lambda x, y: - metrics.zero_one_loss(x, y, normalize=True)
# METRIC = lambda x, y: - m.pairwise_disagreement(x, y)
class RidgeOR(linear_model.Ridge):
"""
Overwrite Ridge from scikit-learn to use
the (minus) absolute error as score function.
(see https://github.com/scikit-learn/scikit-learn/issues/3848
on why this cannot be accomplished using a GridSearchCV object)
"""
def fit(self, X, y):
self.unique_y_ = np.unique(y)
super(linear_model.Ridge, self).fit(X, y)
return self
def predict(self, X):
pred = np.round(super(linear_model.Ridge, self).predict(X))
pred = np.clip(pred, 0, self.unique_y_.max())
return pred
def score(self, X, y):
pred = self.predict(X)
return METRIC(y, pred)
if hasattr(svm, 'LinearSVR'):
class LAD(svm.LinearSVR):
"""
Least Absolute Deviation
"""
def fit(self, X, y):
self.unique_y_ = np.unique(y)
svm.LinearSVR.fit(self, X, y)
return self
def predict(self, X):
pred = np.round(super(svm.LinearSVR, self).predict(X))
pred = np.clip(pred, 0, self.unique_y_.max())
return pred
def score(self, X, y):
pred = self.predict(X)
return METRIC(y, pred)