add example code

photonai/examples/Investigator.py

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+
+from photonai.investigator.Investigator import Investigator
+
+# from mongodb
+Investigator.load_from_db('mongodb://server:27017/photon_results', 'my_pipe')
+
+# from working memory
+my_pipe = Hyperpipe(...)
+my_pipe.fit(X, y)
+Investigator.show(my_pipe)
+
+# from file
+Investigator.load_from_file('my_pipe_name', '/home/usr123/proj45/my_pipe_results2019-08-01/photon_result_file.p')

photonai/examples/custom_estimator.py

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+from sklearn.base import BaseEstimator
+
+
+class CustomEstimator(BaseEstimator):
+
+    def __init__(self, param1=0, param2=None):
+        # it is important that you name your params the same in the constructor
+        #  stub as well as in your class variables!
+        self.param1 = param1
+        self.param2 = param2
+
+    def fit(self, data, targets=None, **kwargs):
+        """
+        Adjust the underlying model or method to the data.
+
+        Returns
+        -------
+        IMPORTANT: must return self!
+        """
+        return self
+
+    def predict(self, data):
+        """
+        Use the learned model to make predictions.
+        """
+        my_predictions = []
+        return my_predictions
+
+

photonai/examples/custom_transformer.py

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+from sklearn.base import BaseEstimator
+
+
+class CustomTransformer(BaseEstimator):
+
+    def __init__(self, param1=0, param2=None):
+        # it is important that you name your params the same in the constructor
+        #  stub as well as in your class variables!
+        self.param1 = param1
+        self.param2 = param2
+
+    def fit(self, data, targets=None, **kwargs):
+        """
+        Adjust the underlying model or method to the data.
+
+        Returns
+        -------
+        IMPORTANT: must return self!
+        """
+        return self
+
+    def transform(self, data, targets=None, **kwargs):
+        """
+        Apply the method's logic to the data.
+        """
+        transformed_data = []
+        return transformed_data
+