Merge branch 'cb_dm_semantics' of github.com:JohnLangford/vowpal_wabb…

…it into cb_dm_semantics

python/examples/covington.py

@@ -79,7 +79,10 @@ def _run(self, sentence):
         output = [-1 for i in range(N)]
         for n in range(N):
             # make LDF examples
-            examples = [ lambda: self.makeExample(sentence,n=n,m=m) for m in range(-1,N) if n != m ]
+            examples = []
+            for m in range(-1, N):
+                if n != m:
+                    examples.append(self.makeExample(sentence=sentence, n=n, m=m))
 
             # truth
             parN = sentence[n][1]

python/setup.py

@@ -103,11 +103,8 @@ def run(self):
 
 class Sdist(_sdist):
     def run(self):
-        # try to run prep if needed
-        try:
-            prep()
-        except:
-            pass
+        # run prep if needed
+        prep()
         _sdist.run(self)
 
 

python/vowpalwabbit/__init__.py

@@ -1 +1,2 @@
 # -*- coding: utf-8 -*-
+"""Python interfaces for VW"""

python/vowpalwabbit/pyvw.py

@@ -1,8 +1,12 @@
+# -*- coding: utf-8 -*-
+"""Python binding for pylibvw class"""
+
 from __future__ import division
 import pylibvw
 
 
 class SearchTask():
+    """Search task class"""
     def __init__(self, vw, sch, num_actions):
         self.vw = vw
         self.sch = sch
@@ -16,7 +20,7 @@ def __del__(self):
     def _run(self, your_own_input_example):
         pass
 
-    def _call_vw(self, my_example, isTest, useOracle=False): # run_fn, setup_fn, takedown_fn, isTest):
+    def _call_vw(self, my_example, isTest, useOracle=False):  # run_fn, setup_fn, takedown_fn, isTest):
         self._output = None
         self.bogus_example.set_test_only(isTest)
         def run(): self._output = self._run(my_example)
@@ -30,22 +34,27 @@ def run(): self._output = self._run(my_example)
         self.vw.learn(self.blank_line) # this will cause our ._run hook to get called
 
     def learn(self, data_iterator):
+        """Train search task by providing an iterator of examples"""
         for my_example in data_iterator.__iter__():
             self._call_vw(my_example, isTest=False);
 
     def example(self, initStringOrDict=None, labelType=pylibvw.vw.lDefault):
-        """TODO"""
+        """Create an example
+            initStringOrDict can specify example as VW formatted string, or a dictionary
+            labelType can specify the desire label type"""
         if self.sch.predict_needs_example():
             return self.vw.example(initStringOrDict, labelType)
         else:
             return self.vw.example(None, labelType)
 
     def predict(self, my_example, useOracle=False):
+        """Return prediction"""
         self._call_vw(my_example, isTest=True, useOracle=useOracle);
         return self._output
 
 
 def get_prediction(ec, prediction_type):
+    """Get specified type of prediction from example"""
     switch_prediction_type = {
         pylibvw.vw.pSCALAR: ec.get_simplelabel_prediction,
         pylibvw.vw.pSCALARS: ec.get_scalars,
@@ -383,6 +392,7 @@ def from_example(self, ex):
 
 
 class simple_label(abstract_label):
+    """Class for simple VW label"""
     def __init__(self, label=0., weight=1., initial=0., prediction=0.):
         abstract_label.__init__(self)
         if isinstance(label, example):
@@ -407,6 +417,7 @@ def __str__(self):
 
 
 class multiclass_label(abstract_label):
+    """Class for multiclass VW label with prediction"""
     def __init__(self, label=1, weight=1., prediction=1):
         abstract_label.__init__(self)
         if isinstance(label, example):
@@ -429,6 +440,7 @@ def __str__(self):
 
 
 class multiclass_probabilities_label(abstract_label):
+    """Class for multiclass VW label with probabilities"""
     def __init__(self, label, prediction=None):
         abstract_label.__init__(self)
         if isinstance(label, example):
@@ -447,6 +459,7 @@ def __str__(self):
 
 
 class cost_sensitive_label(abstract_label):
+    """Class for cost sensative VW label"""
     def __init__(self, costs=[], prediction=0):
         abstract_label.__init__(self)
         if isinstance(costs, example):
@@ -477,6 +490,7 @@ def __str__(self):
 
 
 class cbandits_label(abstract_label):
+    """Class for contextual bandits VW label"""
     def __init__(self, costs=[], prediction=0):
         abstract_label.__init__(self)
         if isinstance(costs, example):

python/vowpalwabbit/sklearn_vw.py

@@ -1,9 +1,6 @@
 # -*- coding: utf-8 -*-
 # pylint: unused-argument, invalid-name, too-many-arguments, too-many-locals
-
-"""
-Utilities to support integration of Vowpal Wabbit and scikit-learn
-"""
+"""Utilities to support integration of Vowpal Wabbit and scikit-learn"""
 
 import numpy as np
 import re
@@ -447,9 +444,11 @@ def get_intercept(self):
         return self.get_vw().get_weight(CONSTANT_HASH)
 
     def save(self, filename):
+        """Save model to file"""
         joblib.dump(dict(params=self.get_params(), coefs=self.get_coefs(), fit=self.fit_), filename=filename)
 
     def load(self, filename):
+        """Load model from file"""
         obj = joblib.load(filename=filename)
         self.set_params(**obj['params'])
         self.set_coefs(obj['coefs'])