add custom scaler steps

preprocessing.py

@@ -0,0 +1,87 @@
+import keras
+print("Running Keras in version {}".format(keras.__version__))
+
+import h5py
+import numpy as np
+import pandas as pd
+import time
+import sklearn.model_selection as sk
+import matplotlib.pyplot as plt
+from sklearn.cluster import KMeans
+from imblearn.over_sampling import SMOTE
+from imblearn.under_sampling import RandomUnderSampler
+from imblearn.over_sampling import RandomOverSampler
+from collections import Counter
+import os
+
+# preprocessing pipeline
+# 
+
+def Safelog(val):
+    # get range of vector
+    if val > 0:
+        return np.log10(val)
+    elif val < 0:
+        return -np.log10(-val)
+    else:
+        return 0
+
+def Safeexp(val):
+    if val > 0:
+        return -10 ** -val
+    elif val < 0:
+        return 10 ** val
+    else:
+        return 0
+    
+
+class FuncTransform():
+    '''
+    Class to transform and inverse transform data with given functions.
+    Transform and inverse transform functions have to be given as dictionaries in the following format:
+    {'key1': function1, 'key2': function2, ...}
+    '''
+
+    def __init__(self, func_transform, func_inverse):
+        self.func_transform = func_transform
+        self.func_inverse = func_inverse
+
+    def fit(self, X):
+        return self
+    
+    def transform(self, X):
+        X = X.copy()
+        for key in X.keys():   
+            if "Class" not in key:
+                X[key] = X[key].apply(self.func_transform[key])
+        return X
+    
+    def fit_transform(self, X):
+        return self.fit(X).transform(X)
+    
+    def inverse_transform(self, X_log):
+        X_log = X_log.copy()
+        for key in X_log.keys():
+            if "Class" not in key:
+                X_log[key] = X_log[key].apply(self.func_inverse[key])
+        return X_log
+    
+class DataSetSampling():
+    
+    def __init__(self, X, y, sampling_strategy):
+        self.X = X
+        self.y = y
+        self.sampling_strategy = sampling_strategy
+        
+    def fit(self, X):
+        return self
+    
+    def transform(self):
+        return self
+    
+    
+class Scaling():
+    
+        
+    
+