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[ENH] Implement GaussianRegressor using scikit-learn's LinearRegression adapter #216

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[ENH] Implement GaussianRegressor using scikit-learn's LinearRegression adapter #216

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2fb1a60
implements gaussian regression
sanjayk0508 Mar 23, 2024
fcec0d0
fixed - sphinx-build error
sanjayk0508 Mar 23, 2024
017e1ab
update - doctest
sanjayk0508 Mar 23, 2024
adb2aca
updated
sanjayk0508 Mar 23, 2024
5a7484a
updated
sanjayk0508 Mar 23, 2024
fb44d8a
updated
sanjayk0508 Mar 23, 2024
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update - doctest 
Signed-off-by: Sanjay <[email protected]>
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@sanjayk0508
sanjayk0508 committed Mar 23, 2024
commit 017e1ab17a89ee07038bf40c46c0e38dc15b850d
3 changes: 2 additions & 1 deletion .all-contributorsrc
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"avatar_url": "https://avatars.githubusercontent.com/u/102804548?v=4",
"profile": "https://github.com/sanjayk0508",
"contributions": [
"code"
"code",
"doc"
]
},
{
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2 changes: 1 addition & 1 deletion skpro/regression/gr/__init__.py
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@@ -1,6 +1,6 @@
"""Gaussian regression"""

# License: BSD-3-Clause (see LICENSE file)
# copyright: skpro developers, BSD-3-Clause License (see LICENSE file)

from skpro.regression.gr._gaussian import GaussianRegressor

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9 changes: 0 additions & 9 deletions skpro/regression/gr/_gaussian.py
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Expand Up @@ -7,8 +7,6 @@
import numpy as np
from scipy.stats import norm

from skpro.utils.validation import check_X_y, check_is_fitted, check_array

from skpro.regression.adapters.sklearn import SklearnProbaReg
from skpro.regression.base.adapters import _DelegateWithFittedParamForwarding

Expand Down Expand Up @@ -127,8 +125,6 @@ def _fit(self, X, y):
self : GaussianRegressor
Fitted regressor.
"""
X, y = check_X_y(X, y)
check_is_fitted(self, ["_estimator"])

self._estimator.fit(X, y)
self.residuals_ = y - self.predict(X)
Expand Down Expand Up @@ -157,7 +153,6 @@ def score(self, X, y):
score : float
Negative log-likelihood of the Gaussian distribution.
"""
check_is_fitted(self, ["_estimator"])

residuals = y - self.predict(X)
return -norm.logpdf(residuals).sum()
Expand All @@ -178,8 +173,6 @@ def predict_proba(self, X):
the mean of the distribution, and the second column represents the
standard deviation.
"""
check_is_fitted(self, ["_estimator"])
X = check_array(X)

y_mean = self.predict(X)

Expand All @@ -206,8 +199,6 @@ def predict(self, X):
y_pred : array-like of shape (n_samples,) or (n_samples, n_targets)
Predicted values.
"""
check_is_fitted(self, ["_estimator"])
X = check_array(X)

return self._estimator.predict(X)

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