Use numeric indexing and .take instead of .loc

.loc is really slow with large indexes.
We can do things much faster using location based indexing
instead of label based indexing.
Here I'm replacing a .loc with a .take.

urbansim/urbanchoice/interaction.py

@@ -52,16 +52,18 @@ def mnl_interaction_dataset(choosers, alternatives, SAMPLE_SIZE,
     # SAMPLE_SIZE >= numalts. That may not happen often in
     # practical situations but it should be supported
     # because a) why not? and b) testing.
+    alts_idx = np.arange(len(alternatives))
     if SAMPLE_SIZE < numalts:
-        sample = np.random.choice(
-            alternatives.index.values, SAMPLE_SIZE * numchoosers)
+        sample = np.random.choice(alts_idx, SAMPLE_SIZE * numchoosers)
         if chosenalts is not None:
             # replace the first row for each chooser with
             # the currently chosen alternative.
-            sample[::SAMPLE_SIZE] = chosenalts
+            # chosenalts -> integer position
+            sample[::SAMPLE_SIZE] = pd.Series(
+                alts_idx, index=alternatives.index).loc[chosenalts].values
     else:
         assert chosenalts is None  # if not sampling, must be simulating
-        sample = np.tile(alternatives.index.values, numchoosers)
+        sample = np.tile(alts_idx, numchoosers)
 
     if not choosers.index.is_unique:
         raise Exception(
@@ -72,7 +74,7 @@ def mnl_interaction_dataset(choosers, alternatives, SAMPLE_SIZE,
             "ERROR: alternatives index is not unique, "
             "sample will not work correctly")
 
-    alts_sample = alternatives.loc[sample]
+    alts_sample = alternatives.take(sample)
     assert len(alts_sample.index) == SAMPLE_SIZE * len(choosers.index)
     alts_sample['join_index'] = np.repeat(choosers.index.values, SAMPLE_SIZE)
 
@@ -84,4 +86,4 @@ def mnl_interaction_dataset(choosers, alternatives, SAMPLE_SIZE,
     chosen[:, 0] = 1
 
     logger.debug('finish: compute MNL interaction dataset')
-    return sample, alts_sample, chosen
+    return alternatives.index.values[sample], alts_sample, chosen