transform target for regression

toolkit.py

@@ -379,6 +379,7 @@ class ClassificationProject(object):
         self._b_eventlist_train = None
 
         self._scaler = None
+        self._scaler_target = None
         self._class_weight = None
         self._balanced_class_weight = None
         self._model = None
@@ -621,6 +622,39 @@ class ClassificationProject(object):
         return self._scaler
 
 
+    @property
+    def scaler_target(self):
+        "same as scaler, but for scaling regression targets"
+        # create the scaler (and fit to training data) if not existent
+        if self._scaler_target is None:
+            filename = os.path.join(self.project_dir, "scaler_target.pkl")
+            try:
+                self._scaler_target = joblib.load(filename)
+                logger.info("Loaded existing scaler from {}".format(filename))
+            except IOError:
+                logger.info("Creating new {} for scaling the targets".format(self.scaler_type))
+                scaler_fit_kwargs = dict()
+                if self.scaler_type == "StandardScaler":
+                    self._scaler_target = StandardScaler()
+                elif self.scaler_type == "RobustScaler":
+                    self._scaler_target = RobustScaler()
+                elif self.scaler_type == "WeightedRobustScaler":
+                    self._scaler_target = WeightedRobustScaler()
+                    scaler_fit_kwargs["weights"] = self.w_train_tot
+                else:
+                    raise ValueError("Scaler type {} unknown".format(self.scaler_type))
+                logger.info("Fitting {} to training data".format(self.scaler_type))
+                orig_copy_setting = self.scaler.copy
+                self.scaler.copy = False
+                self._scaler_target.fit(self.y_train, **scaler_fit_kwargs)
+                # i don't want to scale the classification target here
+                self._scaler_target.center_[0] = 0.
+                self._scaler_target.scale_[0] = 1.
+                self.scaler.copy = orig_copy_setting
+                joblib.dump(self._scaler_target, filename)
+        return self._scaler_target
+
+
     def _batch_transform(self, x, fn, batch_size):
         "Transform array in batches, temporarily setting mask_values to nan"
         transformed = np.empty(x.shape, dtype=x.dtype)
@@ -648,6 +682,24 @@ class ClassificationProject(object):
             return self.scaler.inverse_transform(x)
 
 
+    def transform_target(self, y, batch_size=10000):
+        if not self.target_fields:
+            return y
+        if self.mask_value is not None:
+            return self._batch_transform(y, self.scaler_target.transform, batch_size)
+        else:
+            return self.scaler_target.transform(y)
+
+
+    def inverse_transform_target(self, y, batch_size=10000):
+        if not self.target_fields:
+            return y
+        if self.mask_value is not None:
+            return self._batch_transform(y, self.scaler_target.inverse_transform, batch_size)
+        else:
+            return self.scaler_target.inverse_transform(y)
+
+
     @property
     def history(self):
         params_file = os.path.join(self.project_dir, "history_params.json")
@@ -873,7 +925,7 @@ class ClassificationProject(object):
         idx = self.train_val_idx[1]
         x_val, y_val, w_val = self.x_train[idx], self.y_train[idx], self.w_train_tot[idx]
         x_val_input = self.get_input_list(self.transform(x_val))
-        y_val_output = self.get_output_list(y_val)
+        y_val_output = self.get_output_list(self.transform_target(y_val))
         w_val_list = self.get_weight_list(w_val)
         return x_val_input, y_val_output, w_val_list
 
@@ -884,7 +936,7 @@ class ClassificationProject(object):
         idx = self.train_val_idx[0]
         x_train, y_train, w_train =  self.x_train[idx], self.y_train[idx], self.w_train_tot[idx]
         x_train_input = self.get_input_list(self.transform(x_train))
-        y_train_output = self.get_output_list(y_train)
+        y_train_output = self.get_output_list(self.transform_target(y_train))
         w_train_list = self.get_weight_list(w_train)
         return x_train_input, y_train_output, w_train_list
 
@@ -955,7 +1007,7 @@ class ClassificationProject(object):
                 y_batch = y_train[shuffled_idx[start:start+int(self.batch_size)]]
                 w_batch = w_train[shuffled_idx[start:start+int(self.batch_size)]]
                 x_input = self.get_input_list(self.transform(x_batch))
-                y_output = self.get_output_list(y_batch)
+                y_output = self.get_output_list(self.transform_target(y_batch))
                 w_list = self.get_weight_list(w_batch)
                 yield (x_input, y_output, w_list)