Remove check for NaN flags in MNLLocationChoiceModel

When doing LCM prediction, it will be up to the
user to pass in only the choosers who are picking new
locations. Both RelcationModel and TransitionModel return
indexes of choosers, those can be concatenated to to make
an indexer of choosers.

urbansim/models/lcm.py

@@ -79,11 +79,6 @@ class MNLLocationChoiceModel(object):
         A patsy model expression. Should contain only a right-hand side.
     sample_size : int
         Number of choices to sample for estimating the model.
-    location_id_col : str, optional
-        Name of a column in the choosers table that corresponds to the
-        index of the location being chosen. If given, this is used to
-        make sure that during prediction only choosers that have nan
-        in this column choose new alternatives.
     choosers_fit_filters : list of str, optional
         Filters applied to choosers table before fitting the model.
     choosers_predict_filters : list of str, optional
@@ -108,15 +103,14 @@ class MNLLocationChoiceModel(object):
         in output.
 
     """
-    def __init__(self, model_expression, sample_size, location_id_col=None,
+    def __init__(self, model_expression, sample_size,
                  choosers_fit_filters=None, choosers_predict_filters=None,
                  alts_fit_filters=None, alts_predict_filters=None,
                  interaction_predict_filters=None,
                  estimation_sample_size=None,
                  choice_column=None, name=None):
         self.model_expression = model_expression
         self.sample_size = sample_size
-        self.location_id_col = location_id_col
         self.choosers_fit_filters = choosers_fit_filters
         self.choosers_predict_filters = choosers_predict_filters
         self.alts_fit_filters = alts_fit_filters
@@ -152,7 +146,6 @@ def from_yaml(cls, yaml_str=None, str_or_buffer=None):
         model = cls(
             cfg['model_expression'],
             cfg['sample_size'],
-            location_id_col=cfg.get('location_id_col', None),
             choosers_fit_filters=cfg.get('choosers_fit_filters', None),
             choosers_predict_filters=cfg.get('choosers_predict_filters', None),
             alts_fit_filters=cfg.get('alts_fit_filters', None),
@@ -290,8 +283,6 @@ def predict(self, choosers, alternatives):
         """
         self.assert_fitted()
 
-        if self.location_id_col:
-            choosers = choosers[choosers[self.location_id_col].isnull()]
         choosers = util.apply_filter_query(
             choosers, self.choosers_predict_filters)
         alternatives = util.apply_filter_query(
@@ -331,7 +322,6 @@ def to_dict(self):
             'model_expression': self.model_expression,
             'sample_size': self.sample_size,
             'name': self.name,
-            'location_id_col': self.location_id_col,
             'choosers_fit_filters': self.choosers_fit_filters,
             'choosers_predict_filters': self.choosers_predict_filters,
             'alts_fit_filters': self.alts_fit_filters,

urbansim/models/tests/test_lcm.py

@@ -58,7 +58,6 @@ def test_unit_choice_none_available(choosers, alternatives):
 def test_mnl_lcm(choosers, alternatives):
     model_exp = 'var2 + var1:var3'
     sample_size = 5
-    location_id_col = 'thing_id'
     choosers_fit_filters = ['var1 != 5']
     choosers_predict_filters = ['var1 != 7']
     alts_fit_filters = ['var3 != 15']
@@ -69,7 +68,7 @@ def test_mnl_lcm(choosers, alternatives):
     name = 'Test LCM'
 
     model = lcm.MNLLocationChoiceModel(
-        model_exp, sample_size, location_id_col,
+        model_exp, sample_size,
         choosers_fit_filters, choosers_predict_filters,
         alts_fit_filters, alts_predict_filters,
         interaction_predict_filters, estimation_sample_size,
@@ -83,10 +82,7 @@ def test_mnl_lcm(choosers, alternatives):
     assert len(model.fit_parameters) == 2
     assert len(model.fit_parameters.columns) == 3
 
-    choosers.thing_id = np.nan
-    choosers.thing_id.iloc[0] = 'a'
-
-    choices = model.predict(choosers, alternatives)
+    choices = model.predict(choosers.iloc[1:], alternatives)
 
     pdt.assert_index_equal(choices.index, pd.Index([1, 3, 4]))
     assert choices.isin(alternatives.index).all()
@@ -102,7 +98,6 @@ def test_mnl_lcm(choosers, alternatives):
 def test_mnl_lcm_repeated_alts(choosers, alternatives):
     model_exp = 'var2 + var1:var3'
     sample_size = 5
-    location_id_col = None
     choosers_fit_filters = ['var1 != 5']
     choosers_predict_filters = ['var1 != 7']
     alts_fit_filters = ['var3 != 15']
@@ -113,7 +108,7 @@ def test_mnl_lcm_repeated_alts(choosers, alternatives):
     name = 'Test LCM'
 
     model = lcm.MNLLocationChoiceModel(
-        model_exp, sample_size, location_id_col,
+        model_exp, sample_size,
         choosers_fit_filters, choosers_predict_filters,
         alts_fit_filters, alts_predict_filters,
         interaction_predict_filters, estimation_sample_size,