Merge pull request #61 from UDST/release

[0.2] Release

.gitignore

@@ -1,3 +1,5 @@
+data/
+
 # Jupyter checkpoints
 **/.ipynb_checkpoints
 .pytest_cache/*

CHANGELOG.md

@@ -1,5 +1,9 @@
 # ChoiceModels change log
 
+### 0.2 (2019-01-25)
+
+- production release
+
 ### 0.2.dev10 (2019-01-25)
 
 - moves the `choicemodels.tools.distancematrix` functions directly into `choicemodels.tools`

choicemodels/__init__.py

@@ -3,4 +3,4 @@
 
 from .mnl import MultinomialLogit, MultinomialLogitResults
 
-version = __version__ = '0.2.dev10'
+version = __version__ = '0.2'

choicemodels/mnl.py

@@ -76,9 +76,6 @@ class MultinomialLogit(object):
     and the alternatives. Attributes of a particular alternative may vary for different
     choosers (distance, for example), but this must be set up manually in the input data.
 
-    [TO DO: comparison of the estimation engines]
-    [TO DO: testing and input validation]
-
     Note that prediction methods are in a separate class: see MultinomialLogitResults().
 
     Parameters
@@ -250,7 +247,7 @@ class MultinomialLogitResults(object):
         If not provided, these will be extracted from the raw results.
 
     estimation_engine : str, optional
-        'ChoiceModels' (default) or 'PyLogit'.  # TO DO - infer from model_expression?
+        'ChoiceModels' (default) or 'PyLogit'.
 
     """
     def __init__(self, model_expression, results=None, fitted_parameters=None, 
@@ -287,11 +284,6 @@ def probabilities(self, data):
         Generate predicted probabilities for a table of choice scenarios, using the fitted
         parameters stored in the results object.
         
-        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
-        
         Parameters
         ----------
         data : choicemodels.tools.MergedChoiceTable
@@ -307,6 +299,11 @@ def probabilities(self, data):
         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
 

choicemodels/tools/simulation.py

@@ -139,9 +139,9 @@ def iterative_lottery_choices(choosers, alternatives, mct_callable, probs_callab
         all choosers are matched or no alternatives remain.
 
     chooser_batch_size : int or None, optional
-        Size of the batches for processing smaller groups of choosers one at a time. Useful
-        when the anticipated size of the merged choice tables (choosers X alternatives
-        X covariates) will be too large for python/pandas to handle. 
+        Size of the batches for processing smaller groups of choosers one at a time. 
+        Useful when the anticipated size of the merged choice tables (choosers X 
+        alternatives X covariates) will be too large for python/pandas to handle. 
 
     Returns
     -------

docs/source/index.rst

@@ -8,7 +8,7 @@ ChoiceModels
 
 ChoiceModels is a Python library for discrete choice modeling, with utilities for sampling, simulation, and other ancillary tasks. It's part of the `Urban Data Science Toolkit <https://github.com/udst>`__ (UDST).
 
-v0.2.dev10, released January 25, 2019
+v0.2, released January 25, 2019
 
 
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