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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