Add TEMP lime_survey cols

exploration/ml_pipeline_classification.py

@@ -51,6 +51,13 @@ import machine_learning.model
 # %% jupyter={"source_hidden": true}
 model_input = pd.read_csv("../data/intradaily_30_min_all_targets/input_JCQ_job_demand_mean.csv")
 
+lime_cols = [col for col in model_input if col.startswith('limesurvey_demand')]
+model_input['limesurvey_demand_control_ratio'].describe()
+lime_cols
+
+# TODO: prek lime_cols ustvari klastre, ki jih nato kasneje ločeno preveriš z modeli (npr. k=5). Potrebno bo trikrat ponoviti spodnji postopek. 
+# Pomisli, če gre kaj zavizi v for loop (npr. modeli v seznamu)
+
 # %% jupyter={"source_hidden": true}
 index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
 model_input.set_index(index_columns, inplace=True)