viisar#14 refactored combine method to support weighted combination r…

…ules

brew/combination/combiner.py

@@ -4,6 +4,11 @@
 
 class Combiner(object):
 
+    __VALID_WEIGHTED_COMBINATION_RULES = [
+        rules.majority_vote_rule,
+        rules.mean_rule,
+    ]
+
     def __init__(self, rule='majority_vote'):
         self.combination_rule = rule
 
@@ -25,13 +30,38 @@ def __init__(self, rule='majority_vote'):
         else:
             raise Exception('invalid argument rule for Combiner class')
 
-    def combine(self, results):
+    def combine(self, results, weights=None):
+        """
+        This method puts together the results of all classifiers
+        based on a pre-selected combination rule.
+
+        Parameters
+        ----------
+        results: array-like, shape = [n_samples, n_classes, n_classifiers]
+                    If combination rule is 'majority_vote' results should be Ensemble.output(X, mode='votes')
+                    Otherwise, Ensemble.output(X, mode='probs')
+        weights: array-like, optional(default=None)
+                    Weights of the classifiers. Must have the same size of n_classifiers.
+                    Applies only to 'majority_vote' and 'mean' combination rules.
+        """
+
+        nresults = results.copy().astype(float)
+        n_samples = nresults.shape[0]
+        y_pred = np.zeros((n_samples,))
 
-        n_samples = results.shape[0]
+        if weights is not None:
+            # verify valid combination rules
+            if self.rule in __VALID_WEIGHTED_COMBINATION_RULES:
+                # verify shapes
+                if weights.shape[0] != nresults.shape[2]:
+                    raise Exception(
+                        'weights and classifiers must have same size')
 
-        out = np.zeros((n_samples,))
+                # apply weights
+                for i in range(nresults.shape[2]):
+                    nresults[:, :, i] = nresults[:, :, i] * weights[i]
 
         for i in range(n_samples):
-            out[i] = self.rule(results[i, :, :])
+            y_pred[i] = self.rule(nresults[i, :, :])
 
-        return out
+        return y_pred