Update sklearn regression example (#2330)

examples/trials/sklearn/regression/main.py

@@ -24,9 +24,9 @@ import numpy as np
 from sklearn.metrics import r2_score
 from sklearn.preprocessing import StandardScaler
 from sklearn.linear_model import LinearRegression
-from sklearn.svm import SVR
-from sklearn.neighbors import KNeighborsRegressor
-from sklearn.tree import DecisionTreeRegressor
+from sklearn.linear_model import Ridge
+from sklearn.linear_model import Lars
+from sklearn.linear_model import ARDRegression
 
 LOG = logging.getLogger('sklearn_regression')
 
@@ -55,24 +55,18 @@ def get_model(PARAMS):
     '''Get model according to parameters'''
     model_dict = {
         'LinearRegression': LinearRegression(),
-        'SVR': SVR(),
-        'KNeighborsRegressor': KNeighborsRegressor(),
-        'DecisionTreeRegressor': DecisionTreeRegressor()
+        'Ridge': Ridge(),
+        'Lars': Lars(),
+        'ARDRegression': ARDRegression()
+
     }
     if not model_dict.get(PARAMS['model_name']):
         LOG.exception('Not supported model!')
         exit(1)
 
     model = model_dict[PARAMS['model_name']]
+    model.normalize = bool(PARAMS['normalize'])
 
-    try:
-        if PARAMS['model_name'] == 'SVR':
-            model.kernel = PARAMS['svr_kernel']
-        elif PARAMS['model_name'] == 'KNeighborsRegressor':
-            model.weights = PARAMS['knr_weights']
-    except Exception as exception:
-        LOG.exception(exception)
-        raise
     return model
 
 def run(X_train, X_test, y_train, y_test, model):

examples/trials/sklearn/regression/search_space.json

 {
-    "model_name":{"_type":"choice","_value":["LinearRegression", "SVR", "KNeighborsRegressor", "DecisionTreeRegressor"]},
-    "svr_kernel": {"_type":"choice","_value":["linear", "poly", "rbf"]},
-    "knr_weights": {"_type":"choice","_value":["uniform", "distance"]}
+    "model_name":{"_type":"choice","_value":["LinearRegression", "Lars", "Ridge", "ARDRegression"]},
+    "normalize": {"_type":"choice","_value":["true", "false"]}
 }
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