[patch:lib] Add __repr__ function to all classifiers

docs/_includes/examples/classifiers/knn_classifier.py

@@ -6,7 +6,7 @@
 y = ['class0', 'class1', 'class1']
 
 # Create and fit the classifier
-clf = KNNClassifier(k=1, classes=set(y))
+clf = KNNClassifier(k=1, classes=list(set(y)))
 clf.fit(X, y)
 
 # Predict labels for the training data (just as an example)

lib/sequentia/classifiers/hmm/gmmhmm.py

@@ -177,6 +177,13 @@ def n_seqs(self):
         except AttributeError as e:
             raise AttributeError('The model has not been fitted and has not seen any observation sequences') from e
 
+    @property
+    def model(self):
+        try:
+            return self._model
+        except AttributeError as e:
+            raise AttributeError('The model must be fitted first') from e
+
     @property
     def initial(self):
         try:
@@ -199,4 +206,24 @@ def transitions(self):
     @transitions.setter
     def transitions(self, probabilities):
         self._topology.validate_transitions(probabilities)
-        self._transitions = probabilities
+        self._transitions = probabilities
+
+    def __repr__(self):
+        module = self.__class__.__module__
+        out = '{}{}('.format('' if module == '__main__' else '{}.'.format(module), self.__class__.__name__)
+        attrs = [
+            ('label', repr(self._label)),
+            ('n_states', repr(self._n_states)),
+            ('n_components', repr(self._n_components)),
+            ('covariance_type', repr(self._covariance_type))
+        ]
+        try:
+            self._initial
+            attrs.append(('initial', 'array([...])'))
+            self._transitions
+            attrs.append(('transitions', 'array([...])'))
+            self._model
+            attrs.append(('n_seqs', repr(self._n_seqs)))
+        except AttributeError:
+            pass
+        return out + ', '.join('{}={}'.format(name, val) for name, val in attrs) + ')'

lib/sequentia/classifiers/hmm/hmm_classifier.py

@@ -199,4 +199,16 @@ def encoder(self):
 
     @property
     def classes(self):
-        return self.encoder.classes_
+        return self.encoder.classes_
+
+    def __repr__(self):
+        module = self.__class__.__module__
+        out = '{}{}('.format('' if module == '__main__' else '{}.'.format(module), self.__class__.__name__)
+        try:
+            self._models
+            out += 'models=[\n  '
+            out += ',\n  '.join(repr(model) for model in self._models)
+            out += '\n]'
+        except AttributeError:
+            pass
+        return out + ')'

lib/sequentia/classifiers/knn/knn_classifier.py

@@ -368,4 +368,34 @@ def encoder(self):
 
     @property
     def classes(self):
-        return self._encoder.classes_
+        return self._encoder.classes_
+
+    @property
+    def X(self):
+        try:
+            return self._X
+        except AttributeError:
+            raise RuntimeError('The classifier needs to be fitted first')
+
+    @property
+    def y(self):
+        try:
+            return self._y
+        except AttributeError:
+            raise RuntimeError('The classifier needs to be fitted first')
+
+    def __repr__(self):
+        module = self.__class__.__module__
+        out = '{}{}('.format('' if module == '__main__' else '{}.'.format(module), self.__class__.__name__)
+        attrs = [
+            ('k', repr(self._k)),
+            ('window', repr(self._window)),
+            ('use_c', repr(self._use_c)),
+            ('classes', repr(list(self._encoder.classes_)))
+        ]
+        try:
+            (self._X, self._y)
+            attrs.extend([('X', '[...]'), ('y', 'array([...])')])
+        except AttributeError:
+            pass
+        return out + ', '.join('{}={}'.format(name, val) for name, val in attrs) + ')'