[0.2.dev7] MergedChoiceTable check for duplicate column names

choicemodels/__init__.py

@@ -3,4 +3,4 @@
 
 from .mnl import MultinomialLogit, MultinomialLogitResults
 
-version = __version__ = '0.2.dev6'
+version = __version__ = '0.2.dev7'

choicemodels/tools/mergedchoicetable.py

@@ -31,12 +31,14 @@ class MergedChoiceTable(object):
     observations : pandas.DataFrame
         Table with one row for each chooser or choice scenario, with unique ID's in the 
         index field. Additional columns can contain fixed attributes of the choosers. 
-        Index name is set to 'obs_id' if none provided.
+        Index name is set to 'obs_id' if none provided. All observation/alternative 
+        column names must be unique except for the join key.
 
     alternatives : pandas.DataFrame
         Table with one row for each alternative, with unique ID's in the index field.
         Additional columns can contain fixed attributes of the alternatives. Index name
-        is set to 'alt_id' if none provided.
+        is set to 'alt_id' if none provided. All observation/alternative column names
+        must be unique except for the join key.
 
     chosen_alternatives : str or pandas.Series, optional
         List of the alternative ID selected in each choice scenario. (This is required for
@@ -121,12 +123,25 @@ def __init__(self, observations, alternatives, chosen_alternatives=None,
         # TO DO - check that dfs have unique indexes
         # TO DO - check that chosen_alternatives correspond correctly to other dfs
         # TO DO - same with weights (could join onto other tables and then split off)
-        # TO DO - check for overlapping column names
 
         # Normalize chosen_alternatives to a pd.Series
         if (chosen_alternatives is not None) & isinstance(chosen_alternatives, str):
-            chosen_alternatives = observations[chosen_alternatives]
+            chosen_alternatives = observations[chosen_alternatives].copy()
             observations = observations.drop(chosen_alternatives.name, axis='columns')
+            chosen_alternatives.name = '_' + alternatives.index.name  # avoids conflicts
+
+        # Check for duplicate column names
+        obs_cols = list(observations.columns) + list(observations.index.names)
+        alt_cols = list(alternatives.columns) + list(alternatives.index.names)
+        dupes = [c for c in obs_cols if c in alt_cols]
+
+        if len(dupes) == 1:
+            raise ValueError("Column '{}' appears in both input tables. Please ensure "
+                             "column names are unique before merging".format(dupes[0]))
+        elif len(dupes) > 1:
+            raise ValueError("Columns '{}' appear in both input tables. Please ensure "
+                             "column names are unique before merging"\
+                             .format("', '".join(dupes)))
 
         # Normalize weights to a pd.Series
         if (weights is not None) & isinstance(weights, str):

setup.py

@@ -14,7 +14,7 @@
 
 setup(
     name='choicemodels',
-    version='0.2.dev6',
+    version='0.2.dev7',
     description='Tools for discrete choice estimation',
     long_description=long_description,
     author='UDST',

tests/test_mct.py

@@ -8,19 +8,25 @@
 import choicemodels
 from choicemodels.tools import MergedChoiceTable
 
-d1 = {'oid': [0,1], 
-      'obsval': [6,8],
-      'choice': [1,2]}
 
-d2 = {'aid': [0,1,2], 
-      'altval': [10,20,30],
-      'w': [1,1,100]}
+@pytest.fixture
+def obs():
+    d1 = {'oid': [0,1], 
+          'obsval': [6,8],
+          'choice': [1,2]}
+
+    return pd.DataFrame(d1).set_index('oid')
+
+@pytest.fixture
+def alts():
+    d2 = {'aid': [0,1,2], 
+          'altval': [10,20,30],
+          'w': [1,1,100]}
 
-obs = pd.DataFrame(d1).set_index('oid')
-alts = pd.DataFrame(d2).set_index('aid')
+    return pd.DataFrame(d2).set_index('aid')
 
 
-def test_mergedchoicetable():
+def test_mergedchoicetable(obs, alts):
     # NO SAMPLING, TABLE FOR SIMULATION
 
     mct = choicemodels.tools.MergedChoiceTable(obs, alts).to_frame()
@@ -163,7 +169,7 @@ def test_mergedchoicetable():
                                  chosen_alternatives = 'choice').to_frame()
 
 
-def test_no_alternatives():
+def test_no_alternatives(obs, alts):
     """
     Empty alternatives should produce empty choice table.
 
@@ -172,12 +178,53 @@ def test_no_alternatives():
     assert len(mct) == 0
 
 
-def test_no_choosers():
+def test_no_choosers(obs, alts):
     """
     Empty observations should produce empty choice table.
 
     """
     mct = MergedChoiceTable(pd.DataFrame(), alts).to_frame()
     assert len(mct) == 0
 
+
+def test_dupe_column(obs, alts):
+    """
+    Duplicate column names should raise an error.
+
+    """
+    obs['save_the_whales'] = None
+    alts['save_the_whales'] = None
+
+    try:
+        MergedChoiceTable(obs, alts)
+    except ValueError as e:
+        print(e)
+
+
+def test_multiple_dupe_columns(obs, alts):
+    """
+    Duplicate column names should raise an error. This covers the case of multiple
+    columns, and the case of an index conflicting with a non-index.
 
+    """
+    obs['save_the_whales'] = None
+    alts['save_the_whales'] = None
+    alts[obs.index.name] = None
+
+    try:
+        MergedChoiceTable(obs, alts)
+    except ValueError as e:
+        print(e)
+
+
+def test_join_key_name_conflict(obs, alts):
+    """
+    Duplicate column names are not allowed, except for the join key -- it's fine for the 
+    chosen_alternatives column in the observations to have the same name as the index of
+    the alternatives. This test should run without raising an error.
+
+    """
+    obs[alts.index.name] = obs.choice
+    MergedChoiceTable(obs, alts, chosen_alternatives=alts.index.name)
+
+