Whitespace fixes

choicemodels/mnl.py

@@ -99,8 +99,8 @@ class MultinomialLogit(object):
         https://github.com/timothyb0912/pylogit/blob/master/pylogit/pylogit.py#L116-L130
 
     observation_id_col : str, optional
-        Name of column or index containing the observation id. This should uniquely 
-        identify each distinct choice scenario. Not required if data is passed as a 
+        Name of column or index containing the observation id. This should uniquely
+        identify each distinct choice scenario. Not required if data is passed as a
         MergedChoiceTable.
 
     choice_col : str, optional
@@ -114,8 +114,8 @@ class MultinomialLogit(object):
         https://github.com/timothyb0912/pylogit/blob/master/pylogit/pylogit.py#L151-L165
 
     alternative_id_col : str, optional
-        Name of column or index containing the alternative id. This is only required if 
-        the model expression varies for different alternatives. Not required if data is 
+        Name of column or index containing the alternative id. This is only required if
+        the model expression varies for different alternatives. Not required if data is
         passed as a MergedChoiceTable.
 
     initial_coefs : numeric or list-like of numerics, optional
@@ -144,10 +144,10 @@ def __init__(self, data, model_expression, observation_id_col=None, choice_col=N
             self._observation_id_col = self._data.observation_id_col
             self._alternative_id_col = self._data.alternative_id_col
             self._choice_col = self._data.choice_col
-        
+
         else:
             self._df = self._data
-        
+
         if isinstance(self._model_expression, OrderedDict):
             self._estimation_engine = 'PyLogit'
 
@@ -170,7 +170,7 @@ def __init__(self, data, model_expression, observation_id_col=None, choice_col=N
 
         return
 
-    
+
     def fit(self):
         """
         Fit the model using maximum likelihood estimation. Uses either the ChoiceModels
@@ -229,7 +229,7 @@ class MultinomialLogitResults(object):
     """
     The results class represents a fitted model. It can report the model fit, generate
     choice probabilties, etc.
-    
+
     A full-featured results object is returned by MultinomialLogit.fit(). A results object
     with more limited functionality can also be built directly from fitted parameters and
     a model expression.
@@ -238,7 +238,7 @@ class MultinomialLogitResults(object):
     ----------
     model_expression : str or OrderedDict
         Patsy 'formula-like' (str) or PyLogit 'specification' (OrderedDict).
-    
+
     results : dict or object, optional
         Raw results as currently provided by the estimation engine. This should be
         replaced with a more consistent and comprehensive set of inputs.
@@ -250,9 +250,9 @@ class MultinomialLogitResults(object):
         'ChoiceModels' (default) or 'PyLogit'.
 
     """
-    def __init__(self, model_expression, results=None, fitted_parameters=None, 
+    def __init__(self, model_expression, results=None, fitted_parameters=None,
                  estimation_engine='ChoiceModels'):
-        
+
         if (fitted_parameters is None) & (results is not None):
             if (estimation_engine == 'ChoiceModels'):
                 fitted_parameters = results['fit_parameters']['Coefficient'].tolist()
@@ -261,76 +261,76 @@ def __init__(self, model_expression, results=None, fitted_parameters=None,
         self.model_expression = model_expression
         self.results = results
         self.fitted_parameters = fitted_parameters
-        
-    
+
+
     def __repr__(self):
     	return self.report_fit()
 
-    
+
     def __str__(self):
         return self.report_fit()
 
-    
+
     def get_raw_results(self):
         """
         Return the raw results as provided by the estimation engine. Dict or object.
 
         """
         return self.results
 
-    
+
     def probabilities(self, data):
         """
         Generate predicted probabilities for a table of choice scenarios, using the fitted
         parameters stored in the results object.
-        
+
         Parameters
         ----------
         data : choicemodels.tools.MergedChoiceTable
             Long-format table of choice scenarios. TO DO - accept other data formats.
-        
+
         Expected class parameters
         -------------------------
         self.model_expression : patsy string
         self.fitted_parameters : list of floats
-        
+
         Returns
         -------
         pandas.Series with indexes matching the input
-        
+
         """
         # TO DO - make sure this handles pylogit case
-        
+
         # TO DO - does MergedChoiceTable guarantee that alternatives for a single scenario
         # are consecutive? seems like a requirement here; should document it
-        
+
         df = data.to_frame()
         numalts = data.sample_size  # TO DO - make this an official MCT param
-        
+
         dm = dmatrix(self.model_expression, data=df)
-        
+
         # utility is sum of data values times fitted betas
         u = np.dot(self.fitted_parameters, np.transpose(dm))
-        
+
         # reshape so axis 0 lists alternatives and axis 1 lists choosers
         u = np.reshape(u, (numalts, u.size // numalts), order='F')
-    
+
         # scale the utilities to make exponentiation easier
         # https://stats.stackexchange.com/questions/304758/softmax-overflow
         u = u - u.max(axis=0)
-        
+
         exponentiated_utility = np.exp(u)
         sum_exponentiated_utility = np.sum(exponentiated_utility, axis=0)
-        
+
         probs = exponentiated_utility / sum_exponentiated_utility
-        
+
         # convert back to ordering of the input data
         probs = probs.flatten(order='F')
-        
+
         df['prob'] = probs  # adds indexes
         return df.prob
-    
-    
+
+
     def report_fit(self):
         """
         Print a report of the model estimation results.
@@ -617,15 +617,15 @@ def mnl_estimate(data, chosen, numalts, GPU=False, coeffrange=(None, None),
     log_likelihood : dict
         Dictionary of log-likelihood values describing the quality of
         the model fit. The  following keys is entered into `log_likelihood`.
-        
+
         aic : float
-            Akaike information criterion for an estimated model. 
-            aic =  -2 * log_likelihood + 2 * num_estimated_parameters 
-        
+            Akaike information criterion for an estimated model.
+            aic =  -2 * log_likelihood + 2 * num_estimated_parameters
+
         bic : float
-            Bayesian information criterion for an estimated model. 
+            Bayesian information criterion for an estimated model.
             bic = -2 * log_likelihood + log(num_observations) * num_parameters
-        
+
         num_obs : int
             Number of observations in the model.
 
@@ -637,12 +637,12 @@ def mnl_estimate(data, chosen, numalts, GPU=False, coeffrange=(None, None),
 
         llnull : float
             Value of the constant-only loglikelihood
-        
+
         ll : float
             Value of the loglikelihood
 
         rho_squared  : float
-            McFadden's pseudo-R-squared. 
+            McFadden's pseudo-R-squared.
             rho_squared = 1.0 - final_log_likelihood / null_log_likelihood
 
         rho_bar_squared : float
@@ -696,7 +696,7 @@ def mnl_estimate(data, chosen, numalts, GPU=False, coeffrange=(None, None),
 
     if bfgs_result[2]['warnflag'] > 0:
         logger.warning("mnl did not converge correctly: %s",  bfgs_result)
-    
+
     beta = bfgs_result[0]
     stderr = mnl_loglik(
         beta, data, chosen, numalts, weights, stderr=1, lcgrad=lcgrad)

choicemodels/tools/__init__.py

@@ -3,4 +3,4 @@
 
 from .distancematrix import *
 from .mergedchoicetable import *
-from .simulation import *
+from .simulation import *