Add ElasticNet as the linear regression model for TPOT-MDR-regression

optional-requirements.txt

@@ -1,3 +1,3 @@
 xgboost==0.6
-scikit-mdr==0.4.2
+scikit-mdr==0.4.4
 skrebate==0.3.4

setup.py

@@ -39,7 +39,7 @@ def calculate_version():
     extras_require={
         'xgboost': ['xgboost>=0.6'],
         'skrebate': ['skrebate>=0.3.4'],
-        'mdr': ['scikit-mdr>=0.4.2']
+        'mdr': ['scikit-mdr>=0.4.4']
     },
     classifiers=[
         'Intended Audience :: Science/Research',

tests.py

@@ -31,6 +31,7 @@
 from tpot.config_classifier_light import classifier_config_dict_light
 from tpot.config_regressor_light import regressor_config_dict_light
 from tpot.config_classifier_mdr import tpot_mdr_classifier_config_dict
+from tpot.config_regressor_mdr import tpot_mdr_regressor_config_dict
 
 import numpy as np
 import inspect
@@ -193,7 +194,8 @@ def test_lite_params():
     tpot_obj = TPOTRegressor(config_dict='TPOT light')
     assert tpot_obj.config_dict == regressor_config_dict_light
 
-    assert_raises(TypeError, TPOTRegressor, config_dict='TPOT MDR')
+    tpot_obj = TPOTRegressor(config_dict='TPOT MDR')
+    assert tpot_obj.config_dict == tpot_mdr_regressor_config_dict
 
 
 def test_random_ind():

tpot/config_regressor_mdr.py

@@ -17,12 +17,21 @@
 License along with TPOT. If not, see <http://www.gnu.org/licenses/>.
 """
 
+import numpy as np
+
 # Check the TPOT documentation for information on the structure of config dicts
 
 tpot_mdr_regressor_config_dict = {
 
     # Regressors
 
+    'sklearn.linear_model.ElasticNetCV': {
+        'l1_ratio': np.arange(0.0, 1.01, 0.05),
+        'tol': [1e-5, 1e-4, 1e-3, 1e-2, 1e-1]
+    },
+
+    # Feature Constructors
+
     'mdr.ContinuousMDR': {
         'tie_break': [0, 1],
         'default_label': [0, 1]