From 2be53f4fa3f59acd069a5c3817db2a85b907b6b0 Mon Sep 17 00:00:00 2001 From: srajanpaliwal Date: Thu, 26 Oct 2017 18:17:57 -0400 Subject: [PATCH] [MRG] Fix LogisticRegression see also should include LogisticRegressionCV(#9995) - Added reference to LogisticRegressionCV in LogisticRegression. - Added reference to OrthogonalMatchingPursuitCV in OrthogonalMatchingPursuit. - Added references for ElasticNetCV, MultiTaskElasticNet, MultiTaskLasso. - Cross-referenced RFE and RFECV in each others docstring. - Cross-referenced CalibratedClassifierCV in _CalibrationClassifier - Added reference to LassoLarsIC in docstring of LassoLars. - Added description to references in See also section of Ridge, RidgeClassifier. --- sklearn/calibration.py | 4 ++++ sklearn/feature_selection/rfe.py | 9 +++++++++ sklearn/linear_model/coordinate_descent.py | 11 +++++++++-- sklearn/linear_model/least_angle.py | 1 + sklearn/linear_model/logistic.py | 1 + sklearn/linear_model/omp.py | 2 +- sklearn/linear_model/ridge.py | 20 ++++++++++++-------- 7 files changed, 37 insertions(+), 11 deletions(-) diff --git a/sklearn/calibration.py b/sklearn/calibration.py index 0d2f76cd12239..3c09d5c02f13d 100644 --- a/sklearn/calibration.py +++ b/sklearn/calibration.py @@ -265,6 +265,10 @@ class _CalibratedClassifier(object): if None, then classes is extracted from the given target values in fit(). + See also + -------- + CalibratedClassifierCV + References ---------- .. [1] Obtaining calibrated probability estimates from decision trees diff --git a/sklearn/feature_selection/rfe.py b/sklearn/feature_selection/rfe.py index 1b95c92fdb5bb..5bde9e57c3f9f 100644 --- a/sklearn/feature_selection/rfe.py +++ b/sklearn/feature_selection/rfe.py @@ -101,6 +101,11 @@ class RFE(BaseEstimator, MetaEstimatorMixin, SelectorMixin): >>> selector.ranking_ array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5]) + See also + -------- + RFECV : Recursive feature elimination with built-in cross-validated + selection of the best number of features + References ---------- @@ -365,6 +370,10 @@ class RFECV(RFE, MetaEstimatorMixin): >>> selector.ranking_ array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5]) + See also + -------- + RFE : Recursive feature elimination + References ---------- diff --git a/sklearn/linear_model/coordinate_descent.py b/sklearn/linear_model/coordinate_descent.py index e03aece7f2762..388c6ca49bed7 100644 --- a/sklearn/linear_model/coordinate_descent.py +++ b/sklearn/linear_model/coordinate_descent.py @@ -640,6 +640,8 @@ class ElasticNet(LinearModel, RegressorMixin): See also -------- + ElasticNetCV : Elastic net model with best model selection by + cross-validation. SGDRegressor: implements elastic net regression with incremental training. SGDClassifier: implements logistic regression with elastic net penalty (``SGDClassifier(loss="log", penalty="elasticnet")``). @@ -1688,7 +1690,10 @@ class MultiTaskElasticNet(Lasso): See also -------- - ElasticNet, MultiTaskLasso + MultiTaskElasticNet : Multi-task L1/L2 ElasticNet with built-in + cross-validation. + ElasticNet + MultiTaskLasso Notes ----- @@ -1873,7 +1878,9 @@ class MultiTaskLasso(MultiTaskElasticNet): See also -------- - Lasso, MultiTaskElasticNet + MultiTaskLasso : Multi-task L1/L2 Lasso with built-in cross-validation + Lasso + MultiTaskElasticNet Notes ----- diff --git a/sklearn/linear_model/least_angle.py b/sklearn/linear_model/least_angle.py index bb7c12ab601a2..88fae8aa72934 100644 --- a/sklearn/linear_model/least_angle.py +++ b/sklearn/linear_model/least_angle.py @@ -824,6 +824,7 @@ class LassoLars(Lars): Lasso LassoCV LassoLarsCV + LassoLarsIC sklearn.decomposition.sparse_encode """ diff --git a/sklearn/linear_model/logistic.py b/sklearn/linear_model/logistic.py index 7c8a8d9ae4614..3de13a86b508a 100644 --- a/sklearn/linear_model/logistic.py +++ b/sklearn/linear_model/logistic.py @@ -1120,6 +1120,7 @@ class LogisticRegression(BaseEstimator, LinearClassifierMixin, SGDClassifier : incrementally trained logistic regression (when given the parameter ``loss="log"``). sklearn.svm.LinearSVC : learns SVM models using the same algorithm. + LogisticRegressionCV : Logistic regression with built-in cross validation Notes ----- diff --git a/sklearn/linear_model/omp.py b/sklearn/linear_model/omp.py index 8fcbd4e211af9..9870105580797 100644 --- a/sklearn/linear_model/omp.py +++ b/sklearn/linear_model/omp.py @@ -598,7 +598,7 @@ class OrthogonalMatchingPursuit(LinearModel, RegressorMixin): Lars LassoLars decomposition.sparse_encode - + OrthogonalMatchingPursuitCV """ def __init__(self, n_nonzero_coefs=None, tol=None, fit_intercept=True, normalize=True, precompute='auto'): diff --git a/sklearn/linear_model/ridge.py b/sklearn/linear_model/ridge.py index 8a48cef65ce5e..c46cdff7da2d3 100644 --- a/sklearn/linear_model/ridge.py +++ b/sklearn/linear_model/ridge.py @@ -624,7 +624,10 @@ class Ridge(_BaseRidge, RegressorMixin): See also -------- - RidgeClassifier, RidgeCV, :class:`sklearn.kernel_ridge.KernelRidge` + RidgeClassifier : Ridge classifier + RidgeCV : Ridge regression with built-in cross validation + :class:`sklearn.kernel_ridge.KernelRidge` : Kernel ridge regression + combines ridge regression with the kernel trick Examples -------- @@ -770,7 +773,8 @@ class RidgeClassifier(LinearClassifierMixin, _BaseRidge): See also -------- - Ridge, RidgeClassifierCV + Ridge : Ridge regression + RidgeClassifierCV : Ridge classifier with built-in cross validation Notes ----- @@ -1233,9 +1237,9 @@ class RidgeCV(_BaseRidgeCV, RegressorMixin): See also -------- - Ridge: Ridge regression - RidgeClassifier: Ridge classifier - RidgeClassifierCV: Ridge classifier with built-in cross validation + Ridge : Ridge regression + RidgeClassifier : Ridge classifier + RidgeClassifierCV : Ridge classifier with built-in cross validation """ pass @@ -1318,9 +1322,9 @@ class RidgeClassifierCV(LinearClassifierMixin, _BaseRidgeCV): See also -------- - Ridge: Ridge regression - RidgeClassifier: Ridge classifier - RidgeCV: Ridge regression with built-in cross validation + Ridge : Ridge regression + RidgeClassifier : Ridge classifier + RidgeCV : Ridge regression with built-in cross validation Notes -----