disable dummy encoder in knob identification

server/website/website/tasks/async_tasks.py

@@ -482,6 +482,8 @@ def configuration_recommendation(recommendation_input):
         workload=mapped_workload,
         task_type=PipelineTaskType.RANKED_KNOBS)
     ranked_knobs = JSONUtil.loads(ranked_knobs.data)[:IMPORTANT_KNOB_NUMBER]
+    # Sort the important knobs to fix the columns of input X
+    ranked_knobs = sorted(ranked_knobs)
     ranked_knob_idxs = [i for i, cl in enumerate(X_columnlabels) if cl in ranked_knobs]
     X_workload = X_workload[:, ranked_knob_idxs]
     X_target = X_target[:, ranked_knob_idxs]

server/website/website/tasks/periodic_tasks.py

@@ -18,7 +18,7 @@ from analysis.preprocessing import (Bin, get_shuffle_indices,
                                     DummyEncoder,
                                     consolidate_columnlabels)
 from website.models import PipelineData, PipelineRun, Result, Workload
-from website.settings import RUN_EVERY
+from website.settings import RUN_EVERY, ENABLE_DUMMY_ENCODER
 from website.types import PipelineTaskType, WorkloadStatusType
 from website.utils import DataUtil, JSONUtil
 
@@ -296,6 +296,7 @@ def run_knob_identification(knob_data, metric_data, dbms):
             nonconst_metric_columnlabels.append(cl)
     nonconst_metric_matrix = np.hstack(nonconst_metric_matrix)
 
+    if ENABLE_DUMMY_ENCODER:
         # determine which knobs need encoding (enums with >2 possible values)
 
         categorical_info = DataUtil.dummy_encoder_helper(nonconst_knob_columnlabels,
@@ -308,6 +309,8 @@ def run_knob_identification(knob_data, metric_data, dbms):
         encoded_knob_matrix = dummy_encoder.fit_transform(
             nonconst_knob_matrix)
         encoded_knob_columnlabels = dummy_encoder.new_labels
+    else:
+        encoded_knob_columnlabels = nonconst_knob_columnlabels
 
     # standardize values in each column to N(0, 1)
     standardizer = StandardScaler()