Cleaned Up PHOTON

Reassured all examples are working

Author: RLeenings

photonai/base/PhotonBase.py

@@ -1635,7 +1635,7 @@ def transform(self, X, y=None, **kwargs):
             elif hasattr(self.base_element, 'predict', **kwargs):
                 # Logger().warn("used prediction instead of transform " + self.name)
                 # raise Warning()
-                return self.base_element.predict(X)
+                return self.base_element.predict(X), y, kwargs
             else:
                 Logger().error('BaseException: transform-predict-mess')
                 raise BaseException('transform-predict-mess')
@@ -1956,7 +1956,7 @@ def predict(self, data, targets=None, **kwargs):
         # todo: parallelize prediction
         predicted_data = np.array([])
         for name, element in self.pipe_elements.items():
-            element_transform, _, _ = element.predict(data, **kwargs)
+            element_transform = element.predict(data, **kwargs)
             predicted_data = PipelineStacking.stack_data(predicted_data, element_transform)
         if self.voting:
             if hasattr(predicted_data, 'shape'):
@@ -2217,6 +2217,14 @@ def set_params(self, **kwargs):
                 self.base_element.set_params(**unnamed_config)
         return self
 
+    def copy_me(self):
+
+        ps = PipelineSwitch(self.name)
+        for element in self.pipeline_element_list:
+            new_element = element.copy_me()
+            ps += new_element
+        return ps
+
     def prettify_config_output(self, config_name, config_value, return_dict=False):
 
         """

photonai/base/PhotonBatchElement.py

@@ -1,5 +1,5 @@
 from .PhotonBase import PipelineElement
-from ..photonlogger import Logger
+from ..photonlogger.Logger import Logger
 import numpy as np
 
 

photonai/configuration/PhotonCore.json

@@ -419,58 +419,14 @@
     "photonai.modelwrapper.KerasDNNRegressor.KerasDNNRegressor",
     "Estimator"
   ],
-  "SiameseDNNClassifier":[
-    "photonai.modelwrapper.SiameseDNNClassifier.SiameseDNNClassifier",
-    "Estimator"
-  ],
-  "PretrainedCNNClassifier":[
-    "photonai.modelwrapper.PretrainedCNN.PretrainedCNNClassifier",
-    "Estimator"
-  ],
-  "CNN1dClassifier":[
-    "photonai.modelwrapper.CNN1d.CNN1d",
-    "Estimator"
-  ],
-  "TensorFlowDNNClassifier":[
-    "photonai.modelwrapper.TFDNNClassifier.TFDNNClassifier",
-    "Estimator"
-  ],
   "KerasDNNMultiOutput":[
     "photonai.modelwrapper.KerasDNNMultiOutput.KerasDNNMultiOutput",
     "Estimator"
   ],
-  "SimpleAutoencoder":[
-    "photonai.modelwrapper.KerasAutoencoder.SimpleAutoencoder",
-    "Estimator"
-  ],
-  "RLCNN":[
-    "photonai.modelwrapper.RLCNN.RLCNN",
-    "Estimator"
-  ],
-  "WrapperModel":[
-    "photonai.modelwrapper.WrapperModel.WrapperModel",
-    "Estimator"
-  ],
-  "LogisticGWASFeatureSelection":[
-    "photonai.modelwrapper.FeatureSelection.LogisticGWASFeatureSelection",
-    "Transformer"
-  ],
   "SourceSplitter":[
     "photonai.modelwrapper.SourceSplitter.SourceSplitter",
     "Transformer"
   ],
-  "PyESNClassifier":[
-    "photonai.modelwrapper.PyESNWrapper.PyESNClassifier",
-    "Estimator"
-  ],
-  "PyESNRegressor":[
-    "photonai.modelwrapper.PyESNWrapper.PyESNRegressor",
-    "Estimator"
-  ],
-  "TestWrapper":[
-    "photonai.modelwrapper.TestWrapper.WrapperTestElement",
-    "Estimator"
-  ],
   "CategorialANOVASelectPercentile":[
     "photonai.modelwrapper.FeatureSelection.AnovaSelectPercentile",
     "Transformer"
@@ -499,18 +455,10 @@
     "photonai.modelwrapper.PhotonOneClassSVM.PhotonOneClassSVM",
     "Estimator"
   ],
-  "Selectphotonai.modelwrapper":[
-    "photonai.modelwrapper.FeatureSelection.ModelSelector",
-    "Transformer"
-  ],
   "ImbalancedDataTransform":[
     "photonai.modelwrapper.ImbalancedWrapper.ImbalancedDataTransform",
     "Transformer"
   ],
-  "GPR_AM":[
-    "photonai.modelwrapper.GPR_AM.GPR_AM",
-    "Transformer"
-  ],
   "AnomalyDetectorWrapper":[
     "photonai.modelwrapper.AnomalyDetectorWrapper.AnomalyDetectorWrapper",
     "Estimator"
@@ -526,9 +474,6 @@
   "SamplePairingClassification": [
     "photonai.modelwrapper.SamplePairing.SamplePairingClassification",
     "Transformer"
-  ],
-  "PretrainedCNNRegressor": [
-    "photonai.modelwrapper.PretrainedCNN.PretrainedCNNRegressor",
-    "Estimator"
   ]
+
 }