allow lambdas for examples for lazy computation

1 files changed, 37 insertions, 10 deletions

diff --git a/python/pyvw.py b/python/pyvw.py
index d1a8fa90..3f8caa9c 100644
--- a/python/pyvw.py
+++ b/python/pyvw.py
@@ -90,8 +90,13 @@ class vw(pylibvw.vw):
             """The basic (via-reduction) prediction mechanism. Several
             variants are supported through this overloaded function:
             
-              'examples' can be a single example (interpreted as non-LDF
-                 mode) or a list of examples (interpreted as LDF mode).
+              'examples' can be a single example (interpreted as
+                 non-LDF mode) or a list of examples (interpreted as
+                 LDF mode).  it can also be a lambda function that
+                 returns a single example or list of examples, and in
+                 that list, each element can also be a lambda function
+                 that returns an example. this is done for lazy
+                 example construction (aka speed).
 
               'my_tag' should be an integer id, specifying this prediction
                  
@@ -114,17 +119,39 @@ class vw(pylibvw.vw):
               'learner_id' specifies the underlying learner id
 
             Returns a single prediction.
+
             """
-            if (isinstance(examples, list) and all([isinstance(ex, example) or isinstance(ex, pylibvw.example) for ex in examples])) or \
-               isinstance(examples, example) or isinstance(examples, pylibvw.example):
-                P = sch.get_predictor(my_tag)
-                if isinstance(examples, list): # LDF
-                    P.set_input_length(len(examples))
+
+            P = sch.get_predictor(my_tag)
+            if sch.is_ldf():
+                # we need to know how many actions there are, even if we don't know their identities
+                while hasattr(examples, '__call__'): examples = examples()
+                if not isinstance(examples, list): raise TypeError('expected example _list_ in LDF mode for SearchTask.predict()')
+                P.set_input_length(len(examples))
+                if sch.predict_needs_example():
                     for n in range(len(examples)):
-                        P.set_input_at(n, examples[n])
-                else: # non-LDF
+                        ec = examples[n]
+                        while hasattr(ec, '__call__'): ec = ec()   # unfold the lambdas
+                        if not isinstance(ec, example) and not isinstance(ec, pylibvw.example): raise TypeError('non-example in LDF example list in SearchTask.predict()')
+                        P.set_input_at(n, ec)
+                else:
+                    pass # TODO: do we need to set the examples even though they're not used?
+            else:
+                if sch.predict_needs_example():
+                    while hasattr(examples, '__call__'): examples = examples()
                     P.set_input(examples)
-
+                else:
+                    pass # TODO: do we need to set the examples even though they're not used?
+            
+            # if (isinstance(examples, list) and all([isinstance(ex, example) or isinstance(ex, pylibvw.example) for ex in examples])) or \
+            #    isinstance(examples, example) or isinstance(examples, pylibvw.example):
+            #     if isinstance(examples, list): # LDF
+            #         P.set_input_length(len(examples))
+            #         for n in range(len(examples)):
+            #             P.set_input_at(n, examples[n])
+            #     else: # non-LDF
+            #         P.set_input(examples)
+            if True:   # TODO: get rid of this
                 if oracle is None: pass
                 elif isinstance(oracle, list):
                     if len(oracle) > 0: P.set_oracles(oracle)