optimize neuro

add new example
debug pipeline stacking

photonai/base/PhotonBase.py

@@ -1633,7 +1633,8 @@ def transform(self, X, y=None, **kwargs):
             if hasattr(self.base_element, 'transform'):
                 return self.adjusted_delegate_call(self.base_element.transform, X, y, **kwargs)
             elif hasattr(self.base_element, 'predict', **kwargs):
-                raise Warning("used prediction instead of transform " + self.name)
+                # Logger().warn("used prediction instead of transform " + self.name)
+                # raise Warning()
                 return self.base_element.predict(X)
             else:
                 Logger().error('BaseException: transform-predict-mess')
@@ -1863,7 +1864,7 @@ def __init__(self, name: str, stacking_elements=None, voting: bool=False):
                 self.__iadd__(item_to_stack)
 
         # in case any of the children needs y or the covariates, we have to request them
-        self.needs_y = True
+        self.needs_y = False
         self.needs_covariates = True
 
     def __iadd__(self, item):
@@ -1944,7 +1945,7 @@ def fit(self, data, targets=None, **kwargs):
         """
         for name, element in self.pipe_elements.items():
             # Todo: parallellize fitting
-            element.fit(data, targets)
+            element.fit(data, targets, **kwargs)
         return self
 
     def predict(self, data, targets=None, **kwargs):
@@ -2009,7 +2010,7 @@ def stack_data(cls, a, b):
         New matrix, that is a and b horizontally joined
 
         """
-        if a.size == 0:
+        if a is None or (isinstance(a, np.ndarray) and a.size == 0):
             a = b
         else:
             # Todo: check for right dimensions!

photonai/examples/neuro_image_transformation.py

@@ -11,7 +11,7 @@
                     best_config_metric='mean_absolute_error',  # after hyperparameter search, the metric declares the winner config
                     outer_cv=KFold(n_splits=2),  # repeat hyperparameter search three times
                     inner_cv=KFold(n_splits=2),  # test each configuration ten times respectively,
-                    verbosity=1)
+                    verbosity=2)
 
 
 root_folder = "/spm-data/Scratch/spielwiese_ramona/test_photon_neuro/*.nii"

photonai/examples/stacking_example.py

@@ -1,42 +1,28 @@
-from photonai.base.PhotonBase import Hyperpipe, PipelineElement, PipelineStacking, PipelineBranch, OutputSettings
+from photonai.base.PhotonBase import Hyperpipe, PipelineElement, PipelineStacking,
 from photonai.optimization.Hyperparameters import FloatRange, IntegerRange, Categorical
-from photonai.investigator.Investigator import Investigator
-from photonai.configuration.Register import PhotonRegister
 from sklearn.model_selection import KFold
 
 from sklearn.datasets import load_breast_cancer
 X, y = load_breast_cancer(True)
 
 
 my_pipe = Hyperpipe('basic_stacking',
-                    optimizer='grid_search',
+                    optimizer='sk_opt',
                     metrics=['accuracy', 'precision', 'recall'],
                     best_config_metric='accuracy',
                     outer_cv=KFold(n_splits=3),
                     inner_cv=KFold(n_splits=10),
                     verbosity=1)
 
-tree_branch = PipelineBranch('first_branch')
-tree_branch += PipelineElement('StandardScaler')
-tree_branch += PipelineElement('DecisionTreeClassifier', {'criterion': ['gini'],
-                                                          'min_samples_split': IntegerRange(2, 4)})
-
-svm_branch = PipelineBranch('svm_branch')
-svm_branch += PipelineElement('StandardScaler')
-svm_branch += PipelineElement('SVC', {'kernel': Categorical(['rbf', 'linear']),
-                                      'C': FloatRange(0.5, 2, "linspace", num=3)})
-
-
+my_pipe += PipelineElement('StandardScaler')
 my_pipe_stack = PipelineStacking('final_stack', voting=False)
-my_pipe_stack += svm_branch
-my_pipe_stack += tree_branch
+my_pipe_stack += PipelineElement('DecisionTreeClassifier', hyperparameters={'criterion': ['gini'],
+                                                                            'min_samples_split': IntegerRange(2, 4)})
 
+my_pipe_stack += PipelineElement('LinearSVC', hyperparameters={'C': FloatRange(0.5, 25)})
 my_pipe += my_pipe_stack
 
 my_pipe += PipelineElement('SVC', {'kernel': Categorical(['rbf', 'linear'])})
-
 my_pipe.fit(X, y)
 
-# Investigator.load_from_file("basic_svm_pipe", 'my_tree.p')
-
 debug = True

photonai/examples/subpipelines.py

@@ -0,0 +1,45 @@
+from photonai.base.PhotonBase import Hyperpipe, PipelineElement, PipelineStacking, PipelineBranch
+from photonai.optimization.Hyperparameters import FloatRange, IntegerRange, Categorical
+from photonai.investigator.Investigator import Investigator
+from photonai.configuration.Register import PhotonRegister
+from sklearn.model_selection import KFold
+from sklearn.datasets import load_breast_cancer
+
+X, y = load_breast_cancer(True)
+
+
+my_pipe = Hyperpipe('basic_stacking',
+                    optimizer='grid_search',
+                    metrics=['accuracy', 'precision', 'recall'],
+                    best_config_metric='f1_score',
+                    outer_cv=KFold(n_splits=3),
+                    inner_cv=KFold(n_splits=10),
+                    verbosity=1)
+
+# BRANCH WITH QUANTILTRANSFORMER AND DECISIONTREECLASSIFIER
+tree_qua_branch = PipelineBranch('tree_branch')
+tree_qua_branch += PipelineElement('QuantileTransformer')
+tree_qua_branch += PipelineElement('DecisionTreeClassifier',{'min_samples_split': IntegerRange(2, 4)},criterion='gini')
+
+# BRANCH WITH MinMaxScaler AND DecisionTreeClassifier
+svm_mima_branch = PipelineBranch('svm_branch')
+svm_mima_branch += PipelineElement('MinMaxScaler')
+svm_mima_branch += PipelineElement('SVC', {'kernel': Categorical(['rbf', 'linear']),
+                                      'C':2.0},gamma='auto')
+
+# BRANCH WITH StandardScaler AND KNeighborsClassifier
+knn_sta_branch = PipelineBranch('neighbour_branch')
+knn_sta_branch += PipelineElement('StandardScaler')
+knn_sta_branch += PipelineElement('KNeighborsClassifier')
+
+# voting = True to mean the result of every branch
+my_pipe_stack = PipelineStacking('final_stack', voting=True)
+my_pipe_stack += tree_qua_branch
+my_pipe_stack += svm_mima_branch
+my_pipe_stack += knn_sta_branch
+
+my_pipe += my_pipe_stack
+
+my_pipe += PipelineElement('LogisticRegression', solver='lbfgs')
+
+my_pipe.fit(X, y)

setup.py

@@ -6,7 +6,7 @@
     from setuptools import setup, find_packages
 
 
-__version__ = '0.4.0'
+__version__ = '0.5.0'
 
 setup(
   name='photonai',