feat(Metalearner): Added Metalearner Gaussian Process

aucmedi/ensemble/metalearner/__init__.py

@@ -49,12 +49,14 @@
 from aucmedi.ensemble.metalearner.logistic_regression import Logistic_Regression
 from aucmedi.ensemble.metalearner.naive_bayes import Naive_Bayes
 from aucmedi.ensemble.metalearner.support_vector_machine import SupportVectorMachine
+from aucmedi.ensemble.metalearner.gaussian_process import Gaussian_Process
 
 #-----------------------------------------------------#
 #           Access Functions to Metalearners          #
 #-----------------------------------------------------#
 metalearner_dict = {"logistic_regression": Logistic_Regression,
                     "naive_bayes": Naive_Bayes,
                     "support_vector_machine": SupportVectorMachine,
+                    "gaussian_process": Gaussian_Process,
 }
 """ Dictionary of implemented Metalearners. """

aucmedi/ensemble/metalearner/gaussian_process.py

@@ -0,0 +1,81 @@
+#==============================================================================#
+#  Author:       Dominik Müller                                                #
+#  Copyright:    2022 IT-Infrastructure for Translational Medical Research,    #
+#                University of Augsburg                                        #
+#                                                                              #
+#  This program is free software: you can redistribute it and/or modify        #
+#  it under the terms of the GNU General Public License as published by        #
+#  the Free Software Foundation, either version 3 of the License, or           #
+#  (at your option) any later version.                                         #
+#                                                                              #
+#  This program is distributed in the hope that it will be useful,             #
+#  but WITHOUT ANY WARRANTY; without even the implied warranty of              #
+#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the               #
+#  GNU General Public License for more details.                                #
+#                                                                              #
+#  You should have received a copy of the GNU General Public License           #
+#  along with this program.  If not, see <http://www.gnu.org/licenses/>.       #
+#==============================================================================#
+#-----------------------------------------------------#
+#                   Library imports                   #
+#-----------------------------------------------------#
+# External libraries
+import pickle
+from sklearn.gaussian_process import GaussianProcessClassifier
+import numpy as np
+# Internal libraries/scripts
+from aucmedi.ensemble.metalearner.ml_base import Metalearner_Base
+
+#-----------------------------------------------------#
+#            Metalearner: Gaussian Process            #
+#-----------------------------------------------------#
+class Gaussian_Process(Metalearner_Base):
+    """ A Gaussian Process based Metalearner.
+
+    This class should be passed to a Ensemble function like Stacking for combining predictions.
+
+    !!! warning
+        Can only be utilized for binary and multi-class tasks.
+
+        Does not work on multi-label annotations!
+    """
+    #---------------------------------------------#
+    #                Initialization               #
+    #---------------------------------------------#
+    def __init__(self):
+        self.model = GaussianProcessClassifier(random_state=0,
+                                               multi_class="one_vs_rest")
+
+    #---------------------------------------------#
+    #                  Training                   #
+    #---------------------------------------------#
+    def train(self, x, y):
+        # Preprocess to sparse encoding
+        y = np.argmax(y, axis=-1)
+        # Train model
+        self.model = self.model.fit(x, y)
+
+    #---------------------------------------------#
+    #                  Prediction                 #
+    #---------------------------------------------#
+    def predict(self, data):
+        # Compute prediction probabilities via fitted model
+        pred = self.model.predict_proba(data)
+        # Return results as NumPy array
+        return pred
+
+    #---------------------------------------------#
+    #              Dump Model to Disk             #
+    #---------------------------------------------#
+    def dump(self, path):
+        # Dump model to disk via pickle
+        with open(path, "wb") as pickle_writer:
+            pickle.dump(self.model, pickle_writer)
+
+    #---------------------------------------------#
+    #             Load Model from Disk            #
+    #---------------------------------------------#
+    def load(self, path):
+        # Load model from disk via pickle
+        with open(path, "rb") as pickle_reader:
+            self.model = pickle.load(pickle_reader)

tests/test_metalearner.py

@@ -138,3 +138,34 @@ def test_SVM_usage(self):
         preds = ml.predict(data=self.pred_data)
         # Check
         self.assertTrue(np.array_equal(preds.shape, (25,4)))
+
+    #-------------------------------------------------#
+    #                 Gaussian Process                #
+    #-------------------------------------------------#
+    def test_Gaussian_Process_create(self):
+        # Initializations
+        ml = Gaussian_Process()
+        self.assertTrue("gaussian_process" in metalearner_dict)
+        ml = metalearner_dict["gaussian_process"]()
+        # Storage
+        model_path = os.path.join(self.tmp_data.name, "ml_model.pickle")
+        self.assertFalse(os.path.exists(model_path))
+        ml.dump(model_path)
+        self.assertTrue(os.path.exists(model_path))
+        # Loading
+        ml.model = None
+        self.assertTrue(ml.model is None)
+        ml.load(model_path)
+        self.assertFalse(ml.model is None)
+        # Cleanup
+        os.remove(model_path)
+
+    def test_Gaussian_Process_usage(self):
+        # Initializations
+        ml = Gaussian_Process()
+        # Training
+        ml.train(x=self.pred_data, y=self.labels_ohe)
+        # Inference
+        preds = ml.predict(data=self.pred_data)
+        # Check
+        self.assertTrue(np.array_equal(preds.shape, (25,4)))