added adasyn test, decision tree viz

configs/complete_emb.yaml

@@ -11,7 +11,5 @@ train_ratio: 0.8
 batch_size: 32
 num_epochs: 5
 verbose: True
-
-# Network Hyperparams --------------------------------------
-
-network_layers: [128]
\ No newline at end of file
+network_layers: [128]
+optimizer: "Adam"
\ No newline at end of file

configs/compliance_emb.yaml

@@ -11,7 +11,5 @@ train_ratio: 0.8
 batch_size: 32
 num_epochs: 20
 verbose: True
-
-# Network Hyperparams --------------------------------------
-
-network_layers: [128]
\ No newline at end of file
+network_layers: [128]
+optimizer: "Adam"
\ No newline at end of file

configs/fall_emb.yaml

@@ -11,7 +11,5 @@ train_ratio: 0.8
 batch_size: 32
 num_epochs: 10
 verbose: True
-
-# Network Hyperparams --------------------------------------
-
-network_layers: [128]
\ No newline at end of file
+network_layers: [128]
+optimizer: "Adam"
\ No newline at end of file

configs/settings.yaml

@@ -13,4 +13,4 @@ fall_exercises: ['8058','8062','8066','8077','8074','8059','8071','8067']
 # Settings for dataset -------------------------------------------------
 #
 
-use_real_ats_names: False
+use_real_ats_names: True

src/analysis/find_best_shap_features.py

@@ -26,7 +26,6 @@ def main():
         dl = data_loader.FallDataLoader(FALL_FILENAME).load_data()
     
     X, y = dl.get_data()
-    #X['Random'] = np.random.rand(len(X),1) # add random noise col
     cols = X.columns
     X = np.array(X)
     y = np.array(y)

src/analysis/tune_rf_grid_search.py

+import numpy as np
+import pandas as pd
+import paths as pt
+from sklearn.model_selection import GridSearchCV
+from xgboost import XGBClassifier
+from tools import data_loader, file_writer
+from sklearn.model_selection import StratifiedKFold
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import roc_auc_score, accuracy_score
+
+FILENAME = "fall_emb.csv"
+
+def main():
+    dl = data_loader.FallDataLoader(FILENAME).load_data()
+    features = dl.get_features()
+    X_train, X_test, y_train, y_test = dl.prepare_data_split(test_size=0.7,
+                                                             scaling_strategy="Standard")
+            
+    param_grid = { 
+        'n_estimators': [200, 400, 600, 800, 1000],
+        'max_features': ['auto'],
+        'max_depth' : [3],
+        'min_samples_split' : [2],
+        'min_samples_leaf': [3],
+        'criterion' :['gini']
+    }
+    
+    model = RandomForestClassifier(random_state=0,
+                                   class_weight="balanced")
+
+    cv_rfc = GridSearchCV(estimator=model, param_grid=param_grid,
+                          cv=5, verbose=2, n_jobs=-1, scoring='roc_auc')
+    cv_rfc.fit(X_train, y_train)
+    
+    print("Best: %f using %s" % (cv_rfc.best_score_, cv_rfc.best_params_))
+        
+    rf = cv_rfc.best_estimator_
+    print(f"\nACC_TEST: {accuracy_score(y_test, rf.predict(X_test))}")
+    print(f"ROC_AUC_TEST: {roc_auc_score(y_test, rf.predict_proba(X_test)[:,1])}")
+        
+if __name__ == "__main__":
+    main()
\ No newline at end of file

src/analysis/tune_rf_grid_search_imbl.py

+import numpy as np
+import pandas as pd
+import paths as pt
+from sklearn.model_selection import GridSearchCV
+from xgboost import XGBClassifier
+from tools import data_loader, file_writer
+from sklearn.model_selection import StratifiedKFold
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import roc_auc_score, accuracy_score
+from imblearn.pipeline import make_pipeline, Pipeline
+from imblearn.over_sampling import ADASYN
+
+FILENAME = "fall_emb.csv"
+
+def main():
+    dl = data_loader.FallDataLoader(FILENAME).load_data()
+    features = dl.get_features()
+    X_train, X_test, y_train, y_test = dl.prepare_data_split(test_size=0.7,
+                                                             scaling_strategy="Standard")
+            
+    param_grid = { 
+        'model__n_estimators': [200, 400, 600, 800, 1000],
+        'model__max_features': ['auto'],
+        'model__max_depth' : [3],
+        'model__min_samples_split' : [2],
+        'model__min_samples_leaf': [3],
+        'model__criterion' :['gini']
+    }
+        
+    rf = RandomForestClassifier(random_state=0)
+    adasyn = ADASYN(random_state=0)
+    pipeline = Pipeline([('sampling', adasyn), ('model', rf)])
+
+    cv_rfc = GridSearchCV(estimator=pipeline, param_grid=param_grid,
+                          cv=5, verbose=2, n_jobs=-1, scoring='roc_auc')
+    cv_rfc.fit(X_train, y_train)
+    
+    print("Best: %f using %s" % (cv_rfc.best_score_, cv_rfc.best_params_))
+        
+    rf = cv_rfc.best_estimator_
+    print(f"\nACC_TEST: {accuracy_score(y_test, rf.predict(X_test))}")
+    print(f"ROC_AUC_TEST: {roc_auc_score(y_test, rf.predict_proba(X_test)[:,1])}")
+        
+if __name__ == "__main__":
+    main()
\ No newline at end of file

src/analysis/tune_xgb_random_search.py

-import numpy as np
-import pandas as pd
-import paths as pt
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.pipeline import Pipeline
-from sklearn.model_selection import RandomizedSearchCV
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.linear_model import LogisticRegression          
-from sklearn.svm import SVC
-from sklearn.naive_bayes import GaussianNB
-from xgboost import XGBClassifier
-from tools import data_loader, file_writer
-from sklearn.model_selection import StratifiedKFold
-
-CASE = "Complete"
-COMPLETE_FILENAME = "complete_count.csv"
-FALL_FILENAME = "fall_count.csv"
-SCALING_STRATEGY = "Standard"
-
-def main():
-    if CASE == "Complete":
-        X, y = data_loader.CompleteDataLoader(COMPLETE_FILENAME) \
-            .load_data().prepare_data(SCALING_STRATEGY)
-    else:
-        X, y = data_loader.FallDataLoader(FALL_FILENAME) \
-            .load_data().prepare_data(SCALING_STRATEGY)
-            
-    params = {
-        'min_child_weight': [1, 5, 10, 20],
-        'gamma': [0.1, 0.2, 0.5, 1, 1.5, 2, 5, 10],
-        'subsample': [0.2, 0.4, 0.6, 0.8, 1.0],
-        'colsample_bytree': [0.2, 0.4, 0.6, 0.8, 1.0],
-        'max_depth': [1, 2, 3, 4, 5, 10, 20, 50]
-        }
-    
-    neg, pos = np.bincount(y)
-    scale_pos_weight = neg / pos
-    xgb = XGBClassifier(learning_rate=0.1,
-                        n_estimators=400,
-                        scale_pos_weight=scale_pos_weight,
-                        objective='binary:logistic',
-                        eval_metric='logloss',
-                        use_label_encoder=False,
-                        seed=0)
-
-    skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=0)
-    random_search = RandomizedSearchCV(xgb,
-                                       param_distributions=params,
-                                       n_iter=10,
-                                       scoring='neg_log_loss',
-                                       n_jobs=-1,
-                                       cv=skf,
-                                       verbose=3,
-                                       random_state=0)
-    random_search.fit(X, y)
-    
-    print('\n All results:')
-    print(random_search.cv_results_)
-    print('\n Best estimator:')
-    print(random_search.best_estimator_)
-    print('\n Best normalized gini score for %d-fold search with %d parameter combinations:' % (5, 5))
-    print(random_search.best_score_ * 2 - 1)
-    print('\n Best hyperparameters:')
-    print(random_search.best_params_)
-    results = pd.DataFrame(random_search.cv_results_)
-    file_writer.write_csv(results, pt.REPORTS_DIR, 'xgb-random-grid-search-results.csv')
-        
-if __name__ == "__main__":
-    main()
\ No newline at end of file

src/data/make_dataset_count.py

@@ -28,6 +28,8 @@ def make_complete_count():
     unique_ats = list(set(np.concatenate(unique_ats)))
     
     df_ats = preprocessor.extract_cat_count(df, unique_ats, cols_ats, 'Ats_')
+    df_ats = df_ats.drop(['Ats_0'], axis=1)
+    
     df = df.drop(cols_ats, axis=1)
     df = pd.concat([df.drop(case, axis=1), df_ats, df[[case]]], axis=1)
 
@@ -46,6 +48,8 @@ def make_compliance_count():
     unique_ats = list(set(np.concatenate(unique_ats)))
     
     df_ats = preprocessor.extract_cat_count(df, unique_ats, cols_ats, 'Ats_')
+    df_ats = df_ats.drop(['Ats_0'], axis=1)
+    
     df = df.drop(cols_ats, axis=1)
     df = pd.concat([df.drop(case, axis=1), df_ats, df[[case]]], axis=1)
         
@@ -65,6 +69,8 @@ def make_fall_count():
     unique_ats = list(set(np.concatenate(unique_ats)))
     
     df_ats = preprocessor.extract_cat_count(df, unique_ats, cols_ats, 'Ats_')
+    df_ats = df_ats.drop(['Ats_0'], axis=1)
+    
     df = df.drop(cols_ats, axis=1)
     df = pd.concat([df.drop(case, axis=1), df_ats, df[[case]]], axis=1)
         

src/data/make_dataset_emb.py

@@ -44,13 +44,14 @@ def main(ats_resolution: int = None):
         # Encode dataframe given params
         model_path = Path.joinpath(pt.ROOT_DIR, emb_cfg['model_path'])
         df_enc = encode_dataframe(df=df_to_enc,
-                                target_name=emb_cfg['target_name'],                              
-                                batch_size=emb_cfg['batch_size'],
-                                train_ratio=emb_cfg['train_ratio'],
-                                epochs=emb_cfg['num_epochs'],
-                                network_layers=emb_cfg['network_layers'],
-                                verbose=emb_cfg['verbose'],
-                                model_path=model_path)
+                                  target_name=emb_cfg['target_name'],                              
+                                  batch_size=emb_cfg['batch_size'],
+                                  train_ratio=emb_cfg['train_ratio'],
+                                  epochs=emb_cfg['num_epochs'],
+                                  optimizer=emb_cfg['optimizer'],
+                                  network_layers=emb_cfg['network_layers'],
+                                  verbose=emb_cfg['verbose'],
+                                  model_path=model_path)
     
         df_rand = pd.DataFrame(np.random.rand(len(df),1), columns=['Rand'])
         df = pd.concat([df.drop(target_name, axis=1), df_rand, df_enc, df.pop(target_name)], axis=1)
@@ -81,14 +82,14 @@ def make_fall_test_emb(ats_resolution):
     df = pd.concat([df.drop('Fall', axis=1), ats_enc, ex_enc, df.pop('Fall')], axis=1)
     file_writer.write_csv(df, pt.PROCESSED_DATA_DIR, 'fall_test_emb.csv')
 
-def encode_dataframe(df, target_name, batch_size, train_ratio,
-                     epochs, network_layers, verbose, model_path):
+def encode_dataframe(df, target_name, batch_size, train_ratio, epochs,
+                     optimizer, network_layers, verbose, model_path):
     X_train, X_val, y_train, y_val, labels = preprocessor.prepare_data_for_embedder(df,
                                                                                     target_name,
                                                                                     train_ratio)
     network = neural_embedder.NeuralEmbedder(df=df, target_name=target_name, epochs=epochs,
                                              batch_size=batch_size, network_layers=network_layers,
-                                             verbose=verbose, model_path=model_path)
+                                             optimizer_fn=optimizer, verbose=verbose, model_path=model_path)
     network.fit(X_train, y_train, X_val, y_val)
     network.save_model()
     embedded_weights = network.get_embedded_weights()
@@ -114,14 +115,16 @@ def encode_dataframe(df, target_name, batch_size, train_ratio,
         
     return df_to_enc
 
-def encode_dataframe_cv(df_to_enc, target_name, artifacts_path):
-    params = get_config(df_to_enc, target_name, artifacts_path)
-        
-    X, y = preprocessor.get_X_y(df_to_enc, target_name)
+def encode_dataframe_cv(df, target_name, batch_size, train_ratio,
+                        epochs, network_layers, verbose, model_path):        
+    X, y = preprocessor.get_X_y(df, target_name)
     X, labels = preprocessor.encode_vector_label(X)
     y = np.array(y)
+
+    network = neural_embedder.NeuralEmbedder(df=df, target_name=target_name, epochs=epochs,
+                                             batch_size=batch_size, network_layers=network_layers,
+                                             verbose=verbose, model_path=model_path)
     
-    network = neural_embedder.NeuralEmbedder(**params)
     skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=0)
     es_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss',
                                                    mode='min',
@@ -150,7 +153,7 @@ def encode_dataframe_cv(df_to_enc, target_name, artifacts_path):
     if ENABLE_EMB_VIZ:
         network.make_visualizations_from_network(extension='png')
 
-    df_to_enc = df_to_enc.drop(target_name, axis=1)
+    df_to_enc = df.drop(target_name, axis=1)
     for index in range(df_to_enc.shape[1]):
         column = df_to_enc.columns[index]
         labels_column = labels[index]

src/data/make_screenings.py

@@ -138,7 +138,7 @@ def get_screenings_by_id(data, id, settings):
 
             single_screening['Ats'] = feature_maker.get_ats(citizen_data.ats, end_date, settings)
             single_screening['NumberAts'] = feature_maker.get_number_ats(citizen_data.ats, end_date)
-            single_screening['LoanPeriod'] = feature_maker.get_loan_period(citizen_data.ats, end_date)
+            single_screening['LoanPeriod'] = feature_maker.get_avg_loan_period(citizen_data.ats, end_date)
 
             single_screening['Needs'] = screening.NeedForHelpScore
             single_screening['NeedsReason'] = screening.NeedForHelpReason

src/model/train_xgb_models.py

@@ -11,7 +11,7 @@ from sklearn.model_selection import train_test_split
 import xgboost as xgb
 
 DATA_DIR = pt.PROCESSED_DATA_DIR
-CASES = ["Complete", "Compliance", "Fall", "Fall_test"]
+CASES = ["Complete", "Compliance", "Fall"]
 
 def main(dataset_version : str = 'emb'):
     for case in CASES:
@@ -47,7 +47,7 @@ def main(dataset_version : str = 'emb'):
         df = df.sample(frac=1, random_state=0).reset_index(drop=True)
         
         #df['Random'] = np.random.rand(len(df),1) # add random noise col
-    
+            
         X = df.drop([target_name], axis=1)
         y = df[target_name]
         X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,

src/tools/data_loader.py

@@ -6,8 +6,8 @@ from sklearn.preprocessing import StandardScaler, MinMaxScaler
 from sklearn.model_selection import train_test_split
 
 class BaseDataLoader(ABC):
-    def __init__(self, file_name=None, converters=None):
-        """Initilizer method that takes a file name"""
+    def __init__(self, file_name, converters=None):
+        """Initilizer method that takes a file name and optionally a converter"""
         self.file_name = file_name
         self.converters = converters
         
@@ -15,62 +15,50 @@ class BaseDataLoader(ABC):
     def load_data(self):
         """Loads the data from a data set at startup"""
         
-    @abstractmethod
-    def prepare_data(self, scaling_strategy=None):
-        """Prepares the data from a data set"""
-        
-    @abstractmethod
-    def prepare_data_split(self, scaling_strategy=None, test_size=None):
-        """Prepares and splits the data from a data set"""
-
-class ComplianceDataLoader(BaseDataLoader):
-    def load_data(self):
-        df = file_reader.read_csv(pt.PROCESSED_DATA_DIR,
-                                  self.file_name,
-                                  converters=self.converters)
-        X = df.drop(['Compliance'], axis=1)
-        y = df['Compliance']
-        self.X = X
-        self.y = y
-        return self
-
     def get_data(self):
+        """Returns the features and target"""
         return self.X, self.y
     
     def get_features(self):
+        """Returns the feature names"""
         return self.X.columns
-
-    def prepare_data(self, scaling_strategy: str = "Standard"):
+        
+    def prepare_data(self, scaling_strategy=None):
+        """Prepares the data from a data set"""
         X = np.array(self.X)
         y = np.array(self.y)
-        emb_cols = self.X.filter(regex='((\d+)[Ats])\w+', axis=1)
-        n_scale_cols = self.X.shape[1] - emb_cols.shape[1]
-        
-        if scaling_strategy == "Standard":
-            scaler = StandardScaler()
-            X_sc = scaler.fit_transform(X[:,:n_scale_cols])
-            X = np.concatenate([X_sc, X[:,n_scale_cols:]], axis=1)
-        else:
-            scaler = MinMaxScaler()
-            X_sc = scaler.fit_transform(X[:,:n_scale_cols])
-            X = np.concatenate([X_sc, X[:,n_scale_cols:]], axis=1)
         
+        if scaling_strategy != None:
+            emb_cols = self.X.filter(regex='((\d+)[Ats])\w+', axis=1)
+            n_scale_cols = self.X.shape[1] - emb_cols.shape[1]
+            
+            if scaling_strategy == "Standard":
+                scaler = StandardScaler()
+                X_sc = scaler.fit_transform(X[:,:n_scale_cols])
+                X = np.concatenate([X_sc, X[:,n_scale_cols:]], axis=1)
+            else:
+                scaler = MinMaxScaler()
+                X_sc = scaler.fit_transform(X[:,:n_scale_cols])
+                X = np.concatenate([X_sc, X[:,n_scale_cols:]], axis=1)
+            
         return X, y
-    
-    def prepare_data_split(self, scaling_strategy: str = "Standard", test_size: float = 0.3):
+        
+    def prepare_data_split(self, test_size, scaling_strategy=None):
+        """Prepares and splits the data from a data set"""
         X = np.array(self.X)
         y = np.array(self.y)
-        emb_cols = self.X.filter(regex='((\d+)[Ats])\w+', axis=1)
-        n_scale_cols = self.X.shape[1] - emb_cols.shape[1]
-        
-        if scaling_strategy == "Standard":
-            scaler = StandardScaler()
-            X_sc = scaler.fit_transform(X[:,:n_scale_cols])
-            X = np.concatenate([X_sc, X[:,n_scale_cols:]], axis=1)
-        else:
-            scaler = MinMaxScaler()
-            X_sc = scaler.fit_transform(X[:,:n_scale_cols])
-            X = np.concatenate([X_sc, X[:,n_scale_cols:]], axis=1)
+
+        if scaling_strategy != None:
+            emb_cols = self.X.filter(regex='((\d+)[Ats])\w+', axis=1)
+            n_scale_cols = self.X.shape[1] - emb_cols.shape[1]
+            if scaling_strategy == "Standard":
+                scaler = StandardScaler()
+                X_sc = scaler.fit_transform(X[:,:n_scale_cols])
+                X = np.concatenate([X_sc, X[:,n_scale_cols:]], axis=1)
+            else:
+                scaler = MinMaxScaler()
+                X_sc = scaler.fit_transform(X[:,:n_scale_cols])
+                X = np.concatenate([X_sc, X[:,n_scale_cols:]], axis=1)
         
         X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size,
                                                     stratify=y, random_state=0)
@@ -87,49 +75,17 @@ class CompleteDataLoader(BaseDataLoader):
         self.X = X
         self.y = y
         return self
-
-    def get_data(self):
-        return self.X, self.y
     
-    def get_features(self):
-        return self.X.columns
-    
-    def prepare_data(self, scaling_strategy: str = "Standard"):
-        X = np.array(self.X)
-        y = np.array(self.y)
-        emb_cols = self.X.filter(regex='((\d+)[Ats])\w+', axis=1)
-        n_scale_cols = self.X.shape[1] - emb_cols.shape[1]
-        
-        if scaling_strategy == "Standard":
-            scaler = StandardScaler()
-            X_sc = scaler.fit_transform(X[:,:n_scale_cols])
-            X = np.concatenate([X_sc, X[:,n_scale_cols:]], axis=1)
-        else:
-            scaler = MinMaxScaler()
-            X_sc = scaler.fit_transform(X[:,:n_scale_cols])
-            X = np.concatenate([X_sc, X[:,n_scale_cols:]], axis=1)
-        
-        return X, y
-    
-    def prepare_data_split(self, scaling_strategy: str = "Standard", test_size: float = 0.3):
-        X = np.array(self.X)
-        y = np.array(self.y)
-        emb_cols = self.X.filter(regex='((\d+)[Ats])\w+', axis=1)
-        n_scale_cols = self.X.shape[1] - emb_cols.shape[1]
-        
-        if scaling_strategy == "Standard":
-            scaler = StandardScaler()
-            X_sc = scaler.fit_transform(X[:,:n_scale_cols])
-            X = np.concatenate([X_sc, X[:,n_scale_cols:]], axis=1)
-        else:
-            scaler = MinMaxScaler()
-            X_sc = scaler.fit_transform(X[:,:n_scale_cols])
-            X = np.concatenate([X_sc, X[:,n_scale_cols:]], axis=1)
-        
-        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size,
-                                                            stratify=y, random_state=0)
-        
-        return X_train, X_test, y_train, y_test
+class ComplianceDataLoader(BaseDataLoader):
+    def load_data(self):
+        df = file_reader.read_csv(pt.PROCESSED_DATA_DIR,
+                                  self.file_name,
+                                  converters=self.converters)
+        X = df.drop(['Compliance'], axis=1)
+        y = df['Compliance']
+        self.X = X
+        self.y = y
+        return self
 
 class FallDataLoader(BaseDataLoader):
     def load_data(self):
@@ -141,49 +97,6 @@ class FallDataLoader(BaseDataLoader):
         self.X = X
         self.y = y
         return self
-    
-    def get_data(self):
-        return self.X, self.y
-    
-    def get_features(self):
-        return self.X.columns
-    
-    def prepare_data(self, scaling_strategy: str = "Standard"):
-        X = np.array(self.X)
-        y = np.array(self.y)
-        emb_cols = self.X.filter(regex='((\d+)[Ats])\w+', axis=1)
-        n_scale_cols = self.X.shape[1] - emb_cols.shape[1]
-        
-        if scaling_strategy == "Standard":
-            scaler = StandardScaler()
-            X_sc = scaler.fit_transform(X[:,:n_scale_cols])
-            X = np.concatenate([X_sc, X[:,n_scale_cols:]], axis=1)
-        else:
-            scaler = MinMaxScaler()
-            X_sc = scaler.fit_transform(X[:,:n_scale_cols])
-            X = np.concatenate([X_sc, X[:,n_scale_cols:]], axis=1)
-        
-        return X, y
-    
-    def prepare_data_split(self, scaling_strategy: str, test_size: float):
-        X = np.array(self.X)
-        y = np.array(self.y)
-        emb_cols = self.X.filter(regex='((\d+)[Ats])\w+', axis=1)
-        n_scale_cols = self.X.shape[1] - emb_cols.shape[1]
-        
-        if scaling_strategy == "Standard":
-            scaler = StandardScaler()
-            X_sc = scaler.fit_transform(X[:,:n_scale_cols])
-            X = np.concatenate([X_sc, X[:,n_scale_cols:]], axis=1)
-        else:
-            scaler = MinMaxScaler()
-            X_sc = scaler.fit_transform(X[:,:n_scale_cols])
-            X = np.concatenate([X_sc, X[:,n_scale_cols:]], axis=1)
-        
-        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size,
-                                                    stratify=y, random_state=0)
-        
-        return X_train, X_test, y_train, y_test
 
 class FallTestDataLoader(BaseDataLoader):
     def load_data(self):
@@ -196,40 +109,40 @@ class FallTestDataLoader(BaseDataLoader):
         self.y = y
         return self
     
-    def get_data(self):
-        return self.X, self.y
-    
-    def prepare_data(self, scaling_strategy: str = "Standard"):
+    def prepare_data(self, scaling_strategy: str = None):
         X = np.array(self.X)
         y = np.array(self.y)
-        emb_cols = self.X.filter(regex='((\d+)[Ats|Ex])\w+', axis=1)
-        n_scale_cols = self.X.shape[1] - emb_cols.shape[1]
         
-        if scaling_strategy == "Standard":
-            scaler = StandardScaler()
-            X_sc = scaler.fit_transform(X[:,:n_scale_cols])
-            X = np.concatenate([X_sc, X[:,n_scale_cols:]], axis=1)
-        else:
-            scaler = MinMaxScaler()
-            X_sc = scaler.fit_transform(X[:,:n_scale_cols])
-            X = np.concatenate([X_sc, X[:,n_scale_cols:]], axis=1)
+        if scaling_strategy != None:
+            emb_cols = self.X.filter(regex='((\d+)[Ats|Ex])\w+', axis=1)
+            n_scale_cols = self.X.shape[1] - emb_cols.shape[1]
+            if scaling_strategy == "Standard":
+                scaler = StandardScaler()
+                X_sc = scaler.fit_transform(X[:,:n_scale_cols])
+                X = np.concatenate([X_sc, X[:,n_scale_cols:]], axis=1)
+            else:
+                scaler = MinMaxScaler()
+                X_sc = scaler.fit_transform(X[:,:n_scale_cols])
+                X = np.concatenate([X_sc, X[:,n_scale_cols:]], axis=1)
         
         return X</