remove enet and lasso from regular use (#224)

Author: ethanglaser

configs/regular/linear_model.json

@@ -60,23 +60,11 @@
                 "regression datasets"
             ]
         },
-        "sklearn lasso": {
-            "SETS": ["sklearn-ex[cpu] implementations", "common lasso parameters", "regression datasets"]
-        },
-        "sklearn elasticnet": {
-            "SETS": ["sklearn-ex[cpu] implementations", "common elasticnet parameters", "regression datasets"]
-        },
         "cuml linear": {
             "SETS": ["cuml implementation", "common linear parameters", "cuml L2 parameters", "regression datasets"]
         },
         "cuml ridge": {
             "SETS": ["cuml implementation", "common ridge parameters", "cuml L2 parameters", "regression datasets"]
-        },
-        "cuml lasso": {
-            "SETS": ["cuml implementation", "common lasso parameters", "cuml L1 parameters", "regression datasets"]
-        },
-        "cuml elasticnet": {
-            "SETS": ["cuml implementation", "common elasticnet parameters", "cuml L1 parameters", "regression datasets"]
         }
     }
 }

configs/testing/azure-pipelines-ci.json

@@ -91,7 +91,7 @@
             },
             {
                 "algorithm": {
-                    "estimator": ["LinearRegression", "Ridge", "Lasso", "ElasticNet"]
+                    "estimator": ["LinearRegression", "Ridge"]
                 }
             },
             {

configs/weekly/linear_model.json

@@ -56,23 +56,11 @@
                 "regression datasets"
             ]
         },
-        "sklearn lasso": {
-            "SETS": ["sklearn-ex[cpu] implementations", "common lasso parameters", "regression datasets"]
-        },
-        "sklearn elasticnet": {
-            "SETS": ["sklearn-ex[cpu] implementations", "common elasticnet parameters", "regression datasets"]
-        },
         "cuml linear": {
             "SETS": ["cuml implementation", "common linear parameters", "cuml L2 parameters", "regression datasets"]
         },
         "cuml ridge": {
             "SETS": ["cuml implementation", "common ridge parameters", "cuml L2 parameters", "regression datasets"]
-        },
-        "cuml lasso": {
-            "SETS": ["cuml implementation", "common lasso parameters", "cuml L1 parameters", "regression datasets"]
-        },
-        "cuml elasticnet": {
-            "SETS": ["cuml implementation", "common elasticnet parameters", "cuml L1 parameters", "regression datasets"]
         }
     }
 }

test-configuration-linux.yml

@@ -38,7 +38,7 @@ steps:
   - script: |
       source /usr/share/miniconda/etc/profile.d/conda.sh
       conda activate bench-env
-      python -m sklbench --report -l DEBUG --report -p algorithm:library=sklearn,sklearnex algorithm:estimator=PCA,KMeans,ElasticNet,KNeighborsClassifier data:source=make_classification data:generation_kwargs:n_samples=10000 data:generation_kwargs:n_classes=2 data:split_kwargs:train_size=0.5 data:split_kwargs:test_size=0.5
+      python -m sklbench --report -l DEBUG --report -p algorithm:library=sklearn,sklearnex algorithm:estimator=PCA,KMeans,KNeighborsClassifier data:source=make_classification data:generation_kwargs:n_samples=10000 data:generation_kwargs:n_classes=2 data:split_kwargs:train_size=0.5 data:split_kwargs:test_size=0.5
     displayName: CLI arguments example run
   - script: |
       source /usr/share/miniconda/etc/profile.d/conda.sh

test-configuration-win.yml

@@ -37,7 +37,7 @@ steps:
     condition: eq(variables['PKG_MANAGER'], 'conda')
   - script: |
       call activate bench-env
-      python -m sklbench --report -l DEBUG --report -p algorithm:library=sklearn,sklearnex algorithm:estimator=PCA,KMeans,ElasticNet,KNeighborsClassifier data:source=make_classification data:generation_kwargs:n_samples=10000 data:generation_kwargs:n_classes=2 data:split_kwargs:train_size=0.5 data:split_kwargs:test_size=0.5
+      python -m sklbench --report -l DEBUG --report -p algorithm:library=sklearn,sklearnex algorithm:estimator=PCA,KMeans,KNeighborsClassifier data:source=make_classification data:generation_kwargs:n_samples=10000 data:generation_kwargs:n_classes=2 data:split_kwargs:train_size=0.5 data:split_kwargs:test_size=0.5
     displayName: CLI arguments example run
   - script: |
       call activate bench-env