scikit-learn · henridwyer · Jun 30, 2015 · Jun 30, 2015 · Jun 30, 2015 · Jun 30, 2015
diff --git a/...linear_model/plot_lasso_and_elasticnet.py → ...ot_lasso_adaptive_lasso_and_elasticnet.py b/...linear_model/plot_lasso_and_elasticnet.py → ...ot_lasso_adaptive_lasso_and_elasticnet.py
@@ -1,11 +1,11 @@
 """
-========================================
-Lasso and Elastic Net for Sparse Signals
-========================================
+========================================================
+Lasso, Adaptive Lasso and Elastic Net for Sparse Signals
+========================================================
 
-Estimates Lasso and Elastic-Net regression models on a manually generated
-sparse signal corrupted with an additive noise. Estimated coefficients are
-compared with the ground-truth.
+Estimates Lasso, Adaptive Lasso and Elastic-Net regression models on a
+manually generated sparse signal corrupted with an additive noise.
+Estimated coefficients are compared with the ground-truth.
 
 """
 print(__doc__)
@@ -47,6 +47,17 @@
 print(lasso)
 print("r^2 on test data : %f" % r2_score_lasso)
 
+###############################################################################
+# Adaptive Lasso
+from sklearn.linear_model import AdaptiveLasso
+
+ada_lasso = AdaptiveLasso(alpha=alpha)
+
+y_pred_ada_lasso = ada_lasso.fit(X_train, y_train).predict(X_test)
+r2_score_ada_lasso = r2_score(y_test, y_pred_ada_lasso)
+print(ada_lasso)
+print("r^2 on test data : %f" % r2_score_ada_lasso)
+
 ###############################################################################
 # ElasticNet
 from sklearn.linear_model import ElasticNet
@@ -62,8 +73,10 @@
          label='Elastic net coefficients')
 plt.plot(lasso.coef_, color='gold', linewidth=2,
          label='Lasso coefficients')
+plt.plot(ada_lasso.coef_, color='red', linewidth=2,
+         label='Adaptive Lasso coefficients')
 plt.plot(coef, '--', color='navy', label='original coefficients')
 plt.legend(loc='best')
-plt.title("Lasso R^2: %f, Elastic Net R^2: %f"
-          % (r2_score_lasso, r2_score_enet))
+plt.title("Lasso R^2: %f, Adaptive Lasso R^2: %f, Elastic Net R^2: %f"
+          % (r2_score_lasso, r2_score_ada_lasso, r2_score_enet))
 plt.show()
diff --git a/sklearn/linear_model/__init__.py b/sklearn/linear_model/__init__.py
@@ -14,10 +14,10 @@
 from .bayes import BayesianRidge, ARDRegression
 from .least_angle import (Lars, LassoLars, lars_path, LarsCV, LassoLarsCV,
                           LassoLarsIC)
-from .coordinate_descent import (Lasso, ElasticNet, LassoCV, ElasticNetCV,
-                                 lasso_path, enet_path, MultiTaskLasso,
-                                 MultiTaskElasticNet, MultiTaskElasticNetCV,
-                                 MultiTaskLassoCV)
+from .coordinate_descent import (Lasso, ElasticNet, AdaptiveLasso,
+                                 LassoCV, ElasticNetCV, lasso_path, enet_path,
+                                 MultiTaskLasso, MultiTaskElasticNet,
+                                 MultiTaskElasticNetCV, MultiTaskLassoCV)
 from .huber import HuberRegressor
 from .sgd_fast import Hinge, Log, ModifiedHuber, SquaredLoss, Huber
 from .stochastic_gradient import SGDClassifier, SGDRegressor
@@ -36,6 +36,7 @@
 from .theil_sen import TheilSenRegressor
 
 __all__ = ['ARDRegression',
+           'AdaptiveLasso',
            'BayesianRidge',
            'ElasticNet',
            'ElasticNetCV',