[dask] test that Dask automatically treats 'category' columns as cate…

…gorical features (#3932)
microsoft · Feb 9, 2021 · 18d5793 · 18d5793
1 parent 06ed433
commit 18d5793
Showing 1 changed file with 0 additions and 30 deletions.
diff --git a/tests/python_package_test/test_dask.py b/tests/python_package_test/test_dask.py
@@ -210,11 +210,6 @@ def test_classifier(output, centers, client, listen_port):
         "num_leaves": 10
     }
 
-    if output == 'dataframe-with-categorical':
-        params["categorical_feature"] = [
-            i for i, col in enumerate(dX.columns) if col.startswith('cat_')
-        ]
-
     dask_classifier = lgb.DaskLGBMClassifier(
         client=client,
         time_out=5,
@@ -283,11 +278,6 @@ def test_classifier_pred_contrib(output, centers, client, listen_port):
         "num_leaves": 10
     }
 
-    if output == 'dataframe-with-categorical':
-        params["categorical_feature"] = [
-            i for i, col in enumerate(dX.columns) if col.startswith('cat_')
-        ]
-
     dask_classifier = lgb.DaskLGBMClassifier(
         client=client,
         time_out=5,
@@ -379,11 +369,6 @@ def test_regressor(output, client, listen_port):
         "num_leaves": 10
     }
 
-    if output == 'dataframe-with-categorical':
-        params["categorical_feature"] = [
-            i for i, col in enumerate(dX.columns) if col.startswith('cat_')
-        ]
-
     dask_regressor = lgb.DaskLGBMRegressor(
         client=client,
         time_out=5,
@@ -460,11 +445,6 @@ def test_regressor_pred_contrib(output, client, listen_port):
         "num_leaves": 10
     }
 
-    if output == 'dataframe-with-categorical':
-        params["categorical_feature"] = [
-            i for i, col in enumerate(dX.columns) if col.startswith('cat_')
-        ]
-
     dask_regressor = lgb.DaskLGBMRegressor(
         client=client,
         time_out=5,
@@ -519,11 +499,6 @@ def test_regressor_quantile(output, client, listen_port, alpha):
         "num_leaves": 10
     }
 
-    if output == 'dataframe-with-categorical':
-        params["categorical_feature"] = [
-            i for i, col in enumerate(dX.columns) if col.startswith('cat_')
-        ]
-
     dask_regressor = lgb.DaskLGBMRegressor(
         client=client,
         local_listen_port=listen_port,
@@ -593,11 +568,6 @@ def test_ranker(output, client, listen_port, group):
         "min_child_samples": 1
     }
 
-    if output == 'dataframe-with-categorical':
-        params["categorical_feature"] = [
-            i for i, col in enumerate(dX.columns) if col.startswith('cat_')
-        ]
-
     dask_ranker = lgb.DaskLGBMRanker(
         client=client,
         time_out=5,