from vowpalwabbit import pyvw
# the label for each word is its parent, or -1 for root
my_dataset = [ [("the", 1), # 0
("monster", 2), # 1
("ate", -1), # 2
("a", 5), # 3
("big", 5), # 4
("sandwich", 2)] # 5
,
[("the", 1), # 0
("sandwich", 2), # 1
("is", -1), # 2
("tasty", 2)] # 3
,
[("a", 1), # 0
("sandwich", 2), # 1
("ate", -1), # 2
("itself", 2)] # 3
]
class CovingtonDepParser(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.AUTO_CONDITION_FEATURES )
def _run(self, sentence):
N = len(sentence)
# initialize our output so everything is a root
output = [-1 for i in range(N)]
for n in range(N):
wordN,parN = sentence[n]
for m in range(-1,N):
if m == n: continue
wordM = sentence[m][0] if m > 0 else "*root*"
# ask the question: is m the parent of n?
isParent = 2 if m == parN else 1
# construct an example
dir = 'l' if m < n else 'r'
ex = self.vw.example({'a': [wordN, dir + '_' + wordN],
'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
ex.set_label_string(str(100 + n - m) + ":0")
return ex
def _run(self, sentence):
N = len(sentence)
# initialize our output so everything is a root
output = [-1 for i in range(N)]
for n in range(N):
# make LDF examples
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]
# Mapping:
# -1 => 1
# 0...n-1 => 2...n+1
# n+1...N => n+2 ...N+1
oracle = parN+2 if parN < n else parN + 1 # have to -1 because we excluded n==m from list
# make a prediction
pred = self.sch.predict(examples = examples,
my_tag = n+1,
oracle = oracle,
condition = [ (n, 'p'), (n-1, 'q') ] )
vw.finish_example(examples)
# Reverse mapping:
# 1 => -1
# 2...n+1 => 0...n-1
# n+2...N+1 => n+1...N
output[n] = pred-2 if pred <= n + 1 else pred - 1 # have to +1 because n==m excluded
return output
# TODO: if they make sure search=0 <==> ldf <==> csoaa_ldf
# demo the non-ldf version:
print('training non-LDF')
vw = pyvw.Workspace("--search 2 --search_task hook", quiet=True)
task = vw.init_search_task(CovingtonDepParser)
for p in range(2): # do two passes over the training data
task.learn(my_dataset)
print('testing non-LDF')
print(task.predict( [(w,-1) for w in "the monster ate a sandwich".split()] ))
print('should have printed [ 1 2 -1 4 2 ]')
print('training LDF')
vw = pyvw.Workspace("--search 0 --csoaa_ldf m --search_task hook", quiet=True)
task = vw.init_search_task(CovingtonDepParserLDF)
for p in range(100): # do two passes over the training data
task.learn(my_dataset)
print('testing LDF')
print(task.predict( [(w,-1) for w in "the monster ate a sandwich".split()] ))
print('should have printed [ 1 2 -1 4 2 ]')
