Use "regression" instead of "hedonic".

urbansim/models/__init__.py

@@ -1,2 +1,2 @@
-from .hedonic import HedonicModel, HedonicModelGroup
+from .regression import RegressionModel, RegressionModelGroup
 from .lcm import LocationChoiceModel

urbansim/models/hedonic.py → urbansim/models/regression.py

@@ -71,7 +71,7 @@ def predict(df, filters, model_fit, ytransform=None):
     return pd.Series(sim_data, index=df.index)
 
 
-class HedonicModel(object):
+class RegressionModel(object):
     """
     A hedonic (regression) model with the ability to store an
     estimated model and predict new data based on the model.
@@ -103,7 +103,7 @@ def __init__(self, fit_filters, predict_filters, model_expression,
         self.predict_filters = predict_filters
         self.model_expression = model_expression
         self.ytransform = ytransform
-        self.name = name or 'HedonicModel'
+        self.name = name or 'RegressionModel'
         self.model_fit = None
 
     def fit_model(self, data):
@@ -150,9 +150,9 @@ def predict(self, data):
             data, self.predict_filters, self.model_fit, self.ytransform)
 
 
-class HedonicModelGroup(object):
+class RegressionModelGroup(object):
     """
-    Manages a group of hedonic models that refer to different segments
+    Manages a group of regression models that refer to different segments
     within a single table.
 
     Model names must match the segment names after doing a Pandas groupby.
@@ -169,11 +169,11 @@ def __init__(self, segmentation_col):
 
     def add_model(self, model):
         """
-        Add a `HedonicModel` instance.
+        Add a `RegressionModel` instance.
 
         Parameters
         ----------
-        model : `HedonicModel`
+        model : `RegressionModel`
             Should have a ``.name`` attribute matching one of
             the groupby segments.
 
@@ -183,7 +183,7 @@ def add_model(self, model):
     def add_model_from_params(self, name, fit_filters, predict_filters,
                               model_expression, ytransform=None):
         """
-        Add a model by passing arguments through to `HedonicModel`.
+        Add a model by passing arguments through to `RegressionModel`.
 
         Parameters
         ----------
@@ -205,7 +205,7 @@ def add_model_from_params(self, name, fit_filters, predict_filters,
             By default no transformation is applied.
 
         """
-        model = HedonicModel(
+        model = RegressionModel(
             fit_filters, predict_filters, model_expression, ytransform, name)
         self.models[name] = model
 

urbansim/models/tests/test_hedonic.py → urbansim/models/tests/test_regression.py

@@ -4,7 +4,7 @@
 
 from statsmodels.regression.linear_model import RegressionResultsWrapper
 
-from .. import hedonic
+from .. import regression
 from ...exceptions import ModelEvaluationError
 
 
@@ -25,15 +25,15 @@ def groupby_df(test_df):
 def test_fit_model(test_df):
     filters = []
     model_exp = 'col1 ~ col2'
-    fit = hedonic.fit_model(test_df, filters, model_exp)
+    fit = regression.fit_model(test_df, filters, model_exp)
     assert isinstance(fit, RegressionResultsWrapper)
 
 
 def test_predict(test_df):
     filters = ['col1 in [0, 2, 4]']
     model_exp = 'col1 ~ col2'
-    fit = hedonic.fit_model(test_df, filters, model_exp)
-    predicted = hedonic.predict(
+    fit = regression.fit_model(test_df, filters, model_exp)
+    predicted = regression.predict(
         test_df.query('col1 in [1, 3]'), None, fit)
     expected = pd.Series([1., 3.], index=['b', 'd'])
     pdt.assert_series_equal(predicted, expected)
@@ -43,8 +43,8 @@ def test_predict_ytransform(test_df):
     yt = lambda x: x / 2.
     filters = ['col1 in [0, 2, 4]']
     model_exp = 'col1 ~ col2'
-    fit = hedonic.fit_model(test_df, filters, model_exp)
-    predicted = hedonic.predict(
+    fit = regression.fit_model(test_df, filters, model_exp)
+    predicted = regression.predict(
         test_df.query('col1 in [1, 3]'), None, fit, ytransform=yt)
     expected = pd.Series([0.5, 1.5], index=['b', 'd'])
     pdt.assert_series_equal(predicted, expected)
@@ -55,21 +55,21 @@ def test_predict_with_nans():
         {'col1': range(5),
          'col2': [5, 6, pd.np.nan, 8, 9]},
         index=['a', 'b', 'c', 'd', 'e'])
-    fit = hedonic.fit_model(df.loc[['a', 'b', 'e']], None, 'col1 ~ col2')
+    fit = regression.fit_model(df.loc[['a', 'b', 'e']], None, 'col1 ~ col2')
 
     with pytest.raises(ModelEvaluationError):
-        hedonic.predict(
+        regression.predict(
             df.loc[['c', 'd']], None, fit)
 
 
-def test_HedonicModel(test_df):
+def test_RegressionModel(test_df):
     fit_filters = ['col1 in [0, 2, 4]']
     predict_filters = ['col1 in [1, 3]']
     model_exp = 'col1 ~ col2'
     ytransform = lambda x: x / 2.
     name = 'test hedonic'
 
-    model = hedonic.HedonicModel(
+    model = regression.RegressionModel(
         fit_filters, predict_filters, model_exp, ytransform, name)
     assert model.fit_filters == fit_filters
     assert model.predict_filters == predict_filters
@@ -91,17 +91,17 @@ def test_HedonicModel(test_df):
     pdt.assert_series_equal(predicted, expected)
 
 
-def test_HedonicModelGroup(groupby_df):
+def test_RegressionModelGroup(groupby_df):
     model_exp = 'col1 ~ col2'
 
-    hmg = hedonic.HedonicModelGroup('group')
+    hmg = regression.RegressionModelGroup('group')
 
-    xmodel = hedonic.HedonicModel(None, None, model_exp, name='x')
+    xmodel = regression.RegressionModel(None, None, model_exp, name='x')
     hmg.add_model(xmodel)
-    assert isinstance(hmg.models['x'], hedonic.HedonicModel)
+    assert isinstance(hmg.models['x'], regression.RegressionModel)
 
     hmg.add_model_from_params('y', None, None, model_exp)
-    assert isinstance(hmg.models['y'], hedonic.HedonicModel)
+    assert isinstance(hmg.models['y'], regression.RegressionModel)
     assert hmg.models['y'].name == 'y'
 
     fits = hmg.fit_models(groupby_df)