WIp something

shadow_model.py

@@ -2,6 +2,7 @@
 
 import numpy as np
 from sklearn.model_selection import train_test_split
+from sklearn.base import clone
 
 
 class ShadowModels:
@@ -15,20 +16,41 @@ class ShadowModels:
         - Instance n models (must be similar to the target model)
         - Train each model on training splited data
         - Run prediction on both training and test splited data
-        - Label the resulting prediction vector with "in"/"out" 
+        - Label the resulting prediction vector with "in"/"out"
           if it was train or test
         - drop mic
     """
 
-    def __init__(self, n_models: int, data: np.ndarray, target_classes: int):
+    def __init__(self, n_models: int, data: np.ndarray,
+                 target_classes: int, learner):
+
         self.n_models = n_models
         self.data = data
         self.target_classes = target_classes
-        self.splits = self.split_data(self.data, self.n_models)
+        self.splits = self._split_data(self.data, self.n_models)
+        self.learner = learner
+        self.models = self._get_models(self.learner, self.n_models)
 
     @staticmethod
-    def split_data(data, n_splits, shuffle=False) ->List[np.ndarray]:
-        if shuffle:    
+    def _split_data(data, n_splits, shuffle=False) -> List[np.ndarray]:
+        if shuffle:
             np.random.shuffle(data)  # shufle data just in case
         splited = np.vsplit(data, n_splits)
         return splited
+
+    @staticmethod
+    def _get_model_list(learner, n) -> List:
+        """
+        Intances n shadow models, copies of the input parameter learner
+        """
+        models = [clone(learner) for _ in range(n)]
+        return models
+
+    def fit(self):
+
+        # TRAIN
+        for model, data_subset in zip(self.models, self.splits):
+            X = data_subset[:, :-1]
+            y = data_subset[:, -1]
+            X_train, X_test, y_train, y_test = train_test_split(X, y)
+            model.fit(X_train, y_train)