Adding option to match without replacement.

pymatch/Matcher.py

@@ -141,7 +141,7 @@ def predict_scores(self):
             scores += m.predict(self.X[m.params.index])
         self.data['scores'] = scores/self.nmodels
 
-    def match(self, threshold=0.001, nmatches=1, method='min', max_rand=10):
+    def match(self, threshold=0.001, nmatches=1, method='min', max_rand=10, with_replacement=True):
         """
         Finds suitable match(es) for each record in the minority
         dataset, if one exists. Records are exlcuded from the final
@@ -165,36 +165,45 @@ def match(self, threshold=0.001, nmatches=1, method='min', max_rand=10):
             "min" - choose the profile with the closest score
         max_rand : int
             max number of profiles to consider when using random tie-breaks
+        with_replacement : bool
+            True - matching is performed with replacement, in the 
+            majority group. The same entry from the majority group can be 
+            matched to multiple entries from the minority group
+            False - matching is performed without replacement, in 
+            the majority group. All matches consist of unique entries. 
+            Matching order is randomized.
 
         Returns
         -------
         None
         """
+
         if 'scores' not in self.data.columns:
             print("Propensity Scores have not been calculated. Using defaults...")
             self.fit_scores()
             self.predict_scores()
         test_scores = self.data[self.data[self.yvar]==True][['scores']]
         ctrl_scores = self.data[self.data[self.yvar]==False][['scores']]
         result, match_ids = [], []
+        if with_replacement==False:
+            test_scores=test_scores.reindex(np.random.permutation(test_scores.index))
         for i in range(len(test_scores)):
             # uf.progress(i+1, len(test_scores), 'Matching Control to Test...')
             match_id = i
             score = test_scores.iloc[i]
-            if method == 'random':
-                bool_match = abs(ctrl_scores - score) <= threshold
-                matches = ctrl_scores.loc[bool_match[bool_match.scores].index]
-            elif method == 'min':
-                matches = abs(ctrl_scores - score).sort_values('scores').head(nmatches)
-            else:
-                raise(AssertionError, "Invalid method parameter, use ('random', 'min')")
+
+            bool_match = abs(ctrl_scores - score) <= threshold
+            matches = ctrl_scores.loc[bool_match[bool_match.scores].index]
+
             if len(matches) == 0:
                 continue
             # randomly choose nmatches indices, if len(matches) > nmatches
             select = nmatches if method != 'random' else np.random.choice(range(1, max_rand+1), 1)
             chosen = np.random.choice(matches.index, min(select, nmatches), replace=False)
             result.extend([test_scores.index[i]] + list(chosen))
             match_ids.extend([i] * (len(chosen)+1))
+            if with_replacement==False:
+                ctrl_scores['scores'].iloc[list(chosen-len(test_scores))]=999
         self.matched_data = self.data.loc[result]
         self.matched_data['match_id'] = match_ids
         self.matched_data['record_id'] = self.matched_data.index