Example

Example/output.txt

+perception_severity/socialSupport_emotionality 
+norm_fulfillment/empowerment_abilities 
+perception_vulnerability/empowerment_desires 

Example/sobar-72.csv

+,behavior_sexualRisk,behavior_eating,behavior_personalHygine,intention_aggregation,intention_commitment,attitude_consistency,attitude_spontaneity,norm_significantPerson,norm_fulfillment,perception_vulnerability,perception_severity,motivation_strength,motivation_willingness,socialSupport_emotionality,socialSupport_appreciation,socialSupport_instrumental,empowerment_knowledge,empowerment_abilities,empowerment_desires,ca_cervix
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README.md

@@ -35,6 +35,15 @@ python3 run_mem.py -h
 ```
 
 
+### Example
+In the Example subrepository, you can find a dataset and the output files that you would obtain by running the following command line:
+```python
+python3 run_mem.py sobar-72.csv  --nbcpus 12 --target ca_cervix
+```
+The dataset comes from the UCI Machine Learning Repository ([dataset](https://archive.ics.uci.edu/ml/datasets/Cervical+Cancer+Behavior+Risk)). It dataset contains 19 attributes regarding cervical cancer behavior risk with class label is ca_cervix with 1 and 0 as values which means the respondent with and without cervical cancer, respectively.
+
+
+
 ## References
 [1] Stout, Q.F. Isotonic Regression via Partitioning. Algorithmica 66, 93–112 (2013). https://doi.org/10.1007/s00453-012-9628-4
 

run_mem.py

@@ -12,6 +12,7 @@ import numpy as np
 import pandas as pd
 import sys
 import argparse
+import os
 
 def parse_args():
     parser=argparse.ArgumentParser(description="Monotonic Ensemble Model approach")
@@ -39,10 +40,10 @@ def verify_nb_classes_dataset(df, target):
 
 
 def modify_label_classes_dataset(df, target):
-    if not set(df[target]) == {0,1}:
-        tt = list(set(df[target]))
-        df[target] = df[target].map({tt[0]:0,tt[1]:1})
-        df.rename({target:'target'}, axis=1, inplace=True)
+    df.rename({target:'target'}, axis=1, inplace=True)
+    if not set(df['target']) == {0,1}:
+        tt = list(set(df['target']))
+        df[target] = df['target'].map({tt[0]:0,tt[1]:1})
     return df
 
 
@@ -60,10 +61,15 @@ def main():
         df.reset_index(drop=True, inplace=True)
     except:
         print("Can't open the file {}. Check the format.\n".format(inputs.dataset))
+        
+    if not os.path.isdir(inputs.outdir):
+        os.makedirs(inputs.outdir)
+        print("Outdir created")
+
 
     if not verify_nb_classes_dataset(df, inputs.target):
         sys.exit()
-
+        
     df = modify_label_classes_dataset(df, inputs.target)