How to use the vowpalwabbit.pyvw.SearchTask function in vowpalwabbit
To help you get started, we’ve selected a few vowpalwabbit examples, based on popular ways it is used in public projects.
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VowpalWabbit / vowpal_wabbit / python / examples / test_search.py View on Github 
from __future__ import print_function
import sys
from vowpalwabbit import pyvw
class SequenceLabeler(pyvw.SearchTask):
def __init__(self, vw, sch, num_actions):
# you must must must initialize the parent class
# this will automatically store self.sch <- sch, self.vw <- vw
pyvw.SearchTask.__init__(self, vw, sch, num_actions)
# you can test program options with sch.po_exists
# and get their values with sch.po_get -> string and
# sch.po_get_int -> int
if sch.po_exists('search'):
print('found --search')
print('--search value =', sch.po_get('search'), ', type =', type(sch.po_get('search')))
# set whatever options you want
sch.set_options( sch.AUTO_HAMMING_LOSS | sch.AUTO_CONDITION_FEATURES )
def _run(self, sentence): # it's called _run to remind you that you shouldn't call it directly!(NOUN, 'monster'),
(VERB, 'ate'),
(DET , 'a'),
(ADJ , 'big'),
(NOUN, 'sandwich')],
[(DET , 'the'),
(NOUN, 'sandwich'),
(VERB, 'was'),
(ADJ , 'tasty')],
[(NOUN, 'it'),
(VERB, 'ate'),
(NOUN, 'it'),
(ADJ , 'all')] ]
class SequenceLabeler(pyvw.SearchTask):
def __init__(self, vw, sch, num_actions):
# you must must must initialize the parent class
# this will automatically store self.sch <- sch, self.vw <- vw
pyvw.SearchTask.__init__(self, vw, sch, num_actions)
# set whatever options you want
sch.set_options( sch.AUTO_HAMMING_LOSS | sch.AUTO_CONDITION_FEATURES | sch.IS_LDF )
def makeExample(self, word, p):
ex = self.example({'w': [word + '_' + str(p)]}, labelType=self.vw.lCostSensitive)
ex.set_label_string(str(p) + ':0')
return ex
def _run(self, sentence): # it's called _run to remind you that you shouldn't call it directly!
output = []
for n in range(len(sentence)):from vowpalwabbit import pyvw
class LatentVariableClassifier(pyvw.SearchTask):
def __init__(self, vw, sch, num_actions):
pyvw.SearchTask.__init__(self, vw, sch, num_actions)
sch.set_options( sch.AUTO_CONDITION_FEATURES )
def _run(self, y_x):
y,(x0,x1) = y_x
ex = self.example({'x': [('x0',x0), ('x1',x1)]})
h = self.sch.predict(examples=ex, my_tag=1, oracle=None) * 2 - 3
ex = self.example({'x': [('x0',x0), ('x1',x1), ('x0h',x0*h)]})
p = self.sch.predict(examples=ex, my_tag=2, oracle=y, condition=(1,'h'))
self.sch.loss( 0. if p == y else 1. )
return p'b': [wordM, dir + '_' + wordN],
'p': [wordN + '_' + wordM, dir + '_' + wordN + '_' + wordM],
'd': [ str(m-n <= d) + '<=' + str(d) for d in [-8, -4, -2, -1, 1, 2, 4, 8] ] +
[ str(m-n >= d) + '>=' + str(d) for d in [-8, -4, -2, -1, 1, 2, 4, 8] ] })
pred = self.sch.predict(examples = ex,
my_tag = (m+1)*N + n + 1,
oracle = isParent,
condition = [ (max(0, (m )*N + n + 1), 'p'),
(max(0, (m+1)*N + n ), 'q') ])
vw.finish_example([ex]) # must pass the example in as a list because search is a MultiEx reduction
if pred == 2:
output[n] = m
break
return output
class CovingtonDepParserLDF(pyvw.SearchTask):
def __init__(self, vw, sch, num_actions):
pyvw.SearchTask.__init__(self, vw, sch, num_actions)
sch.set_options( sch.AUTO_HAMMING_LOSS | sch.IS_LDF | sch.AUTO_CONDITION_FEATURES )
def makeExample(self, sentence, n, m):
wordN = sentence[n][0]
wordM = sentence[m][0] if m >= 0 else '*ROOT*'
dir = 'l' if m < n else 'r'
ex = self.vw.example( { 'a': [wordN, dir + '_' + wordN],
'b': [wordM, dir + '_' + wordM],
'p': [wordN + '_' + wordM, dir + '_' + wordN + '_' + wordM],
'd': [ str(m-n <= d) + '<=' + str(d) for d in [-8, -4, -2, -1, 1, 2, 4, 8] ] +
[ str(m-n >= d) + '>=' + str(d) for d in [-8, -4, -2, -1, 1, 2, 4, 8] ] },
labelType=self.vw.lCostSensitive)
# the label string is (m+2):0. The :0 means cost zero (this is
# irrelevant and could be any number). +2 ensures >= 1
