Readme + correction Ordinal encode

README.md

@@ -5,12 +5,12 @@ This work was achieved by Lucile Dierckx, Rosana Veroneze and Siegfried Nijssen
 
 The neural network can be fully translated to an ordered list of decision rules once trained.
 
-To train and test the model, run the *train_and_test_model.py* or *train_and_test_model_ML.py* file.
+To train and test the model, run the *train_and_test_model.py* file for binary or multi-class classification, and run the *train_and_test_model_ML.py* file for multi-label classification.
 
 ### Dependencies
 1. numpy
 2. pandas
-3. scitkit-learn
+3. scikit-learn
 4. pytorch
 
 
@@ -21,8 +21,8 @@ In case you want to use our work as part of your research please consider citing
 author="Dierckx, Lucile and Veroneze, Rosana and Nijssen, Siegfried",
 editor="Kashima, Hisashi and Peng, Wen-Chih and Ide, Tsuyoshi",
 title="RL-Net: Interpretable Rule Learning with Neural Networks",
-booktitle="Advances in Knowledge Discovery and Data Mining",
+booktitle="PAKDD 2023: Advances in Knowledge Discovery and Data Mining",
 year="2023",
 publisher="Springer International Publishing"
 }
-```
+```

dataset_processing.py

@@ -1,7 +1,7 @@
 import pandas as pd
 import numpy as np
 from sklearn.impute import SimpleImputer
-from sklearn.preprocessing import OrdinalEncoder, LabelEncoder
+from sklearn.preprocessing import OneHotEncoder, LabelEncoder
 from datasets.FeatureBinarizer import FeatureBinarizer
 from sklearn.model_selection import train_test_split
 
@@ -59,9 +59,6 @@ def load_and_transform_data(dataset, doFeatureBinarizer=True, doOrdinalEncode=Fa
     elif dataset == "drive": 
         ds = pd.read_csv(ds_folder+"Drive/Sensorless_drive_diagnosis.txt", header=None, skipinitialspace=True, sep=' ')
         lb = ds.iloc[:,48]; ds = ds.iloc[:,:48]
-    elif dataset == "covtype": 
-        ds = pd.read_csv(ds_folder+"Covtype/covtype.data", header=None, skipinitialspace=True)
-        lb = ds.iloc[:,54]; ds = ds.iloc[:,:54]
     elif dataset == "yeast": 
         ds = pd.read_csv(ds_folder+"Yeast/yeast-train.csv", header=0, skipinitialspace=True)
         lb = ds.iloc[:,-14:]; ds = ds.iloc[:,:-14]
@@ -112,9 +109,9 @@ def load_and_transform_data(dataset, doFeatureBinarizer=True, doOrdinalEncode=Fa
         X_tst = fb.transform(X_tst)
 
     elif doOrdinalEncode:
-        enc = OrdinalEncoder()
-        X[categorical_cols] = enc.fit_transform(X[categorical_cols])
-        X_tst[categorical_cols] = enc.transform(X_tst[categorical_cols])
+        enc = OneHotEncoder(sparse=False)
+        X = pd.concat((X[numerical_cols],pd.DataFrame(enc.fit_transform(X[categorical_cols])).set_index(X.index)),axis=1)
+        X_tst = pd.concat((X_tst[numerical_cols], pd.DataFrame(enc.transform(X_tst[categorical_cols])).set_index(X_tst.index)),axis=1)
 
     if dataset not in ["yeast", "scene"]:
         le = LabelEncoder()
@@ -128,5 +125,5 @@ def load_and_transform_data(dataset, doFeatureBinarizer=True, doOrdinalEncode=Fa
 
 if __name__ == "__main__":  
     dataset = "yeast" 
-    # adult, magic, house, heloc, mushroom, chess, ads, nursery, car, pageblocks, pendigits, contraceptivemc, drive, covtype, yeast, scene
+    # adult, magic, house, heloc, mushroom, chess, ads, nursery, car, pageblocks, pendigits, contraceptivemc, drive, yeast, scene
     X, Y, X_tst, Y_tst = load_and_transform_data(dataset, doFeatureBinarizer=True, doOrdinalEncode=False)

networkTorch_multiClass.py

@@ -223,7 +223,6 @@ def train(model, X, Y, X_val, Y_val, batch_size, nbOutput, learning_rate=1e-2, l
     acc = accuracy_score(y, y_predr)
     print('init acc', acc)
     print_rules(model)
-    print(model.and_layer.loc[0])
     best_loss = sys.maxsize
     best_epoch = -1
     best_w = None

train_and_test_model.py

@@ -68,5 +68,5 @@ def worker(name):
 
 
 name = "mushroom"
-#["adult", "magic", "house", "heloc", "mushroom", "chess", "ads", "nursery", "car", "pageblocks", "pendigits", "contraceptivemc", "drive", "covtype"]
+#["adult", "magic", "house", "heloc", "mushroom", "chess", "ads", "nursery", "car", "pageblocks", "pendigits", "contraceptivemc", "drive"]
 worker(name)