def getTransition(self, stack, buff, leftmostChildren, rightmostChildren, arcs, labeled):
"""This function should return a Transition object representing the correct action to
to take according to the oracle."""
if len(stack) > 1:
top = stack[-1]
pre_top = stack[-2]
rmc_top = rightmostChildren.get(p.get_id(top), -1)
rmc_pre_top = rightmostChildren.get(p.get_id(pre_top), -1)
lmc_top = leftmostChildren.get(p.get_id(top), p.INFINITY)
lmc_pre_top = leftmostChildren.get(p.get_id(pre_top), p.INFINITY)
if ( p.get_head(pre_top) == p.get_id(top) and self.is_removable(pre_top, arcs, lmc_pre_top, rmc_pre_top) ):
if labeled:
return(Transition(Transition.LeftArc, p.get_deprel(pre_top)))
else:
return(Transition(Transition.LeftArc, None))
elif ( p.get_head(top) == p.get_id(pre_top) and self.is_removable(top, arcs, lmc_top, rmc_top) ):
if labeled:
return(Transition(Transition.RightArc, p.get_deprel(top)))
else:
return(Transition(Transition.RightArc, None))
else:
return(Transition(Transition.Shift, None))
else:
if len(buff) >= 1:
return(Transition(Transition.Shift, None))
else:
return(None)
def __prepare_for_first_contour_connecting(self):
# cv2.imshow("first", self.cv_image)
dilated_image = Helper.dilate_image(self.cv_image)
# cv2.imshow("first dilate", dilated_image)
# self.steps.append(("con_dilated", dilated_image))
dilated_mask = Helper.convert_image_to_mask(dilated_image)
# cv2.imshow("mask", dilated_mask)
self.steps.append(("con_gray", dilated_mask))
gray_denoised_image = cv2.fastNlMeansDenoising(dilated_mask, None, 5, 7, 21)
self.steps.append(("con_denoised", gray_denoised_image))
# cv2.imshow("denoised", gray_denoised_image)
threshold_image = cv2.adaptiveThreshold(gray_denoised_image,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
self.steps.append(("con_threshold", threshold_image))
# cv2.imshow("threshold", threshold_image)
# ERODED = cv2.erode(threshold_image, (2,2), iterations=1)
# cv2.imshow("ERODED", ERODED)
# DILATED = cv2.dilate(ERODED, (2,2), iterations=1)
# cv2.imshow("DILATED", DILATED)
# DENOISED = cv2.fastNlMeansDenoising(threshold_image, None, 5, 7, 21)
# cv2.imshow("DENOISED", DENOISED)
prepared_mask = cv2.bitwise_and(threshold_image, threshold_image, mask=self.image_masks.topo_mask)
self.steps.append(("con_mask", prepared_mask))
cv2.imshow('prepared', prepared_mask)
return prepared_mask
def getTransition(self, stack, buff, leftmostChildren, rightmostChildren,
arcs, labeled):
"""This function should return a Transition object representing the correct action to
to take according to the oracle."""
if len(stack) > 1:
top = stack[-1]
pre_top = stack[-2]
rmc_top = rightmostChildren.get(p.get_id(top), -1)
rmc_pre_top = rightmostChildren.get(p.get_id(pre_top), -1)
lmc_top = leftmostChildren.get(p.get_id(top), p.INFINITY)
lmc_pre_top = leftmostChildren.get(p.get_id(pre_top), p.INFINITY)
if (p.get_head(pre_top) == p.get_id(top) and self.is_removable(
pre_top, arcs, lmc_pre_top, rmc_pre_top)):
if labeled:
return (Transition(Transition.LeftArc,
p.get_deprel(pre_top)))
else:
return (Transition(Transition.LeftArc, None))
elif (p.get_head(top) == p.get_id(pre_top)
and self.is_removable(top, arcs, lmc_top, rmc_top)):
if labeled:
return (Transition(Transition.RightArc, p.get_deprel(top)))
else:
return (Transition(Transition.RightArc, None))
else:
return (Transition(Transition.Shift, None))
else:
if len(buff) >= 1:
return (Transition(Transition.Shift, None))
else:
return (None)
def get_input_offset_token(self, token, input_sentence,
input_offset): #CONFIRM THIS!!!
if h.get_id(token) + input_offset <= 0: #To prevent wraparound
return (None)
return (self.try_get_token(input_sentence,
-(h.get_id(token) + input_offset))
) # No +1 since the token id starts from 1 instead of 0
def get_all_siblings(self, token, input_sentence, arcs):
if arcs.get(h.get_id(token), None) is None:
return([])
all_siblings = []
for word in input_sentence:
if h.get_id(word) != 0 and h.get_id(word) != h.get_id(token) and arcs.get(h.get_id(word), None) is not None and arcs[h.get_id(word)] == arcs[h.get_id(token)]:
all_siblings += [word]
return(all_siblings)
def __connect_contours_by_distances(self, mask, distances, min_contour_area): contour_connector = ContourConnector(mask) for distance in distances: contour_connector.connect_contours_within_distance(distance) Helper.reduce_image_contours(contour_connector.connected_contours_mask, min_contour_area) return contour_connector.connected_contours_mask
def output(self, sentence):
for token in sentence:
head = self.arcs.get(h.get_id(token), '0')
label = self.labels.get(h.get_id(token), '_')
label = label if label is not None else '_'
token[6] = str(head)
token[7] = str(label)
print '\t'.join(token)
print
def initialize(self, sentence):
self.root = ['0', 'ROOT', 'ROOT', 'ROOT', 'ROOT', 'ROOT', '-1', 'ROOT', 'ROOT', 'ROOT']
self.buff = [self.root] + list(reversed(sentence))
self.stack = list()
self.arcs = {} #arcs is actually a mapping from child to parent (child's head)
self.labels = {}
self.transitions = list()
self.leftmostChildren = h.get_leftmost_children(sentence) #Map from parent to leftmost child
self.rightmostChildren = h.get_rightmost_children(sentence) #Map from parent to rightmost child
def __prepare_for_second_contour_connecting(self, mask):
reduced_mask = Helper.reduce_image_contours(mask, 1)
self.steps.append(("con_reduced", reduced_mask))
# cv2.imshow("reduced", reduced_mask)
dilated_mask = Helper.dilate_image(reduced_mask)
# self.steps.append(("con_dilated", dilated_mask))
# cv2.imshow("second dilated", dilated_mask)
# Helper.show_images_and_wait([])
return dilated_mask
def output(self, sentence):
for token in sentence:
head = self.arcs.get(h.get_id(token), '0')
label = self.labels.get(h.get_id(token), '_')
label = label if label is not None else '_'
token[6] = str(head)
token[7] = str(label)
print '\t'.join(token)
print
def __generate_general_color_lines_mask(self, low_range, high_range):
color_range = self.__get_image_in_range_from_hsv(low_range, high_range)
filled_contours = self.__get_filled_contours_from_image(color_range)
contour_mask = Helper.convert_image_to_contour_mask(filled_contours)
dilated = Helper.dilate_image(contour_mask, array=(2, 2))
mask = Helper.reduce_image_contours(dilated,
6,
line_thickness=cv2.FILLED)
return mask
def __generate_green_mask(self):
green_range = self.__get_image_in_range_from_hsv(
MaskGenerator.low_green, MaskGenerator.high_green)
filled_green_contours = self.__get_filled_contours_from_image(
green_range)
green_mask = Helper.convert_image_to_mask(filled_green_contours)
green_mask_reduced = Helper.reduce_image_contours(
green_mask, 200, line_thickness=cv2.FILLED)
return green_mask_reduced
def get_all_children(self, token, input_sentence, arcs):
all_children = {}
head_id = h.get_id(token)
for tail in arcs.keys():
if arcs[tail] == head_id:
child_token = self.try_get_token(input_sentence, -(tail))
if child_token is not None:
all_children[h.get_id(child_token)] = child_token
else:
print >> sys.stderr, 'Non-existent child, should NOT happen!!!'
return (all_children)
def get_all_children(self, token, input_sentence, arcs):
all_children = {}
head_id = h.get_id(token)
for tail in arcs.keys():
if arcs[tail] == head_id:
child_token = self.try_get_token(input_sentence, -(tail))
if child_token is not None:
all_children[h.get_id(child_token)] = child_token
else:
print >>sys.stderr, 'Non-existent child, should NOT happen!!!'
return(all_children)
def __generate_black_mask(self):
black_range = self.__get_image_in_range_from_hsv(
MaskGenerator.low_black, MaskGenerator.high_black)
filled_contours = self.__get_filled_contours_from_image(black_range)
contours_mask = Helper.convert_image_to_mask(filled_contours)
dilated = Helper.dilate_image(contours_mask, array=self.dilate_array)
black_mask = Helper.reduce_image_contours(dilated,
6,
line_thickness=cv2.FILLED)
return black_mask
def __generate_blue_mask(self):
blue_range = self.__get_image_in_range_from_hsv(
MaskGenerator.low_blue, MaskGenerator.high_blue)
self.image_masks.steps.append(("blue_range", blue_range))
filled_blue_contours = self.__get_filled_contours_from_image(
blue_range)
self.image_masks.steps.append(("blue_filled", filled_blue_contours))
blue_mask = Helper.convert_image_to_mask(filled_blue_contours)
dilated = Helper.dilate_image(blue_mask, array=(2, 2))
self.image_masks.steps.append(("blue_dilated", dilated))
blue_mask = Helper.reduce_image_contours(dilated,
15,
line_thickness=cv2.FILLED)
return blue_mask
def initialize(self, sentence):
self.root = [
'0', 'ROOT', 'ROOT', 'ROOT', 'ROOT', 'ROOT', '-1', 'ROOT', 'ROOT',
'ROOT'
]
self.buff = [self.root] + list(reversed(sentence))
self.stack = list()
self.arcs = {
} #arcs is actually a mapping from child to parent (child's head)
self.labels = {}
self.transitions = list()
self.leftmostChildren = h.get_leftmost_children(
sentence) #Map from parent to leftmost child
self.rightmostChildren = h.get_rightmost_children(
sentence) #Map from parent to rightmost child
def __generate_red_mask(self):
red_range = self.__get_image_in_range_from_hsv(MaskGenerator.low_red,
MaskGenerator.high_red)
# cv2.imshow("red range", red_range)
filled_contours = self.__get_filled_contours_from_image(red_range)
# cv2.imshow("filled contours", filled_contours)
contours_mask = Helper.convert_image_to_mask(filled_contours)
# cv2.imshow("contours mask", contours_mask)
dilated = Helper.dilate_image(contours_mask, array=self.dilate_array)
# cv2.imshow("dilated", dilated)
reduced = Helper.reduce_image_contours(dilated,
6,
line_thickness=cv2.FILLED)
# cv2.imshow("red range", red_range)
return reduced
def __get_min_contour_dist(self):
angle = Helper.convert_grade_to_angle(self.user_settings.max_grade)
min_feet_dist = self.user_settings.get_contour_interval_dist(
) / math.tan(math.radians(angle))
min_pixel_dist = int(
min_feet_dist / self.user_settings.get_feet_per_pixel()) + 1
return min_pixel_dist
def is_removable(self, word, arcs, leftmost_child, rightmost_child):
if ((leftmost_child != p.INFINITY
and not arcs.get(leftmost_child, False))
or (rightmost_child != -1 and not arcs.get(
rightmost_child, False))): #CHeck if thie value is correct
return (False)
if (p.is_root(word)):
return (False)
return True
def get_model7_params(self, stack, buff, input_sentence, arcs, labels, tType, feat_type, source_type, source_offset = 0, input_offset = 0, head_multiplier = 0, left_rightmost_multiplier = 0, left_right_sibling_specifier = 0, suffix_len = 0): #Described here: http://stp.lingfil.uu.se/~nivre/docs/maltparser.pdf
assert feat_type in [self.DEP_FEAT, self.POS_FEAT, self.LEX_FEAT], "Invalid feat_type specified"
assert source_type in [self.BUFF_SOURCE, self.STACK_SOURCE, self.INPUT_SOURCE], "Invalid source type specified"
assert source_offset >= 0, "Invalid source_offset"
assert head_multiplier >= 0, "Invalid head multiplier specified"
rev_input_sentence = input_sentence[::-1]
source = self.get_source(stack, buff, input_sentence, source_type)#Reverse the input sentence if it isn't already reversed by get_source
token = self.try_get_token(source, -(source_offset + 1))
if token is None:
return(None)
if input_offset != 0:
token = self.get_input_offset_token(token, rev_input_sentence, input_offset)
if token is None:
return(None)
token = self.get_head_offset_token(token, rev_input_sentence, head_multiplier, arcs)
if token is None:
return(None)
token = self.get_left_rightmost_child(token, rev_input_sentence, arcs, left_rightmost_multiplier)
if token is None:
return(None)
token = self.get_left_right_sibling(token, rev_input_sentence, arcs, left_right_sibling_specifier)
if token is None:
return(None)
ret_str = 'transition=%d,feat_type=%d,source_type=%d,source_offset=%d,input_offset=%d,head_multiplier=%d,left_rightmost_multiplier=%d,left_right_sibling_specifier=%d' % (tType, feat_type, source_type, source_offset, input_offset, head_multiplier , left_rightmost_multiplier, left_right_sibling_specifier)
if feat_type == self.LEX_FEAT:
#suffix len can be specified via argument
lex_feat = h.get_word(token)
if suffix_len > 0:
ret_str += 'lex_feat=%s' %(lex_feat[-suffix_len:])
else:
ret_str += 'lex_feat=%s' %(lex_feat)
elif feat_type == self.DEP_FEAT:
dep_feat = labels.get(h.get_id(token), None)
if dep_feat is not None:
ret_str += 'dep_feat=%s' %(dep_feat)
else:
return(None)
elif feat_type == self.POS_FEAT:
pos_feat = h.get_postag(token)
ret_str += 'pos_feat=%s' % (pos_feat)
else:
return(None)
return(ret_str)
def get_head_offset_token(self, token, input_sentence, head_multiplier, arcs):
while (token is not None and head_multiplier > 0):
head_multiplier -= 1
token_id = h.get_id(token)
head_id = arcs.get(token_id, None)
if head_id == None:
token = None
else:
token = self.try_get_token(input_sentence, -head_id)
return(token)
def __get_grade_for_pixel_distance(self, pixel_dist):
feet_dist = (pixel_dist * self.user_settings.get_feet_per_pixel())
theta = math.atan(self.user_settings.get_contour_interval_dist() /
feet_dist)
angle = math.degrees(theta)
grade = Helper.convert_angle_to_grade(angle)
return grade
def get_head_offset_token(self, token, input_sentence, head_multiplier,
arcs):
while (token is not None and head_multiplier > 0):
head_multiplier -= 1
token_id = h.get_id(token)
head_id = arcs.get(token_id, None)
if head_id == None:
token = None
else:
token = self.try_get_token(input_sentence, -head_id)
return (token)
def execute_transition(self, transition):
"""This function should take a transition object and apply to the
current parser state. It need not return anything."""
self.transitions.append(transition.transitionType)
if (transition.transitionType == Transition.Shift):
self.stack.append(self.buff.pop())
elif (transition.transitionType == Transition.LeftArc):
top = self.stack.pop()
top_id = h.get_id(top)
pre_top = self.stack.pop()
pre_top_id = h.get_id(pre_top)
self.stack.append(top)
self.arcs[pre_top_id] = top_id
self.labels[pre_top_id] = transition.label
else:
top = self.stack.pop()
top_id = h.get_id(top)
pre_top = self.stack[-1]
pre_top_id = h.get_id(pre_top)
self.arcs[top_id] = pre_top_id
self.labels[top_id] = transition.label
def execute_transition(self, transition):
"""This function should take a transition object and apply to the
current parser state. It need not return anything."""
self.transitions.append(transition.transitionType)
if (transition.transitionType == Transition.Shift):
self.stack.append(self.buff.pop())
elif (transition.transitionType == Transition.LeftArc):
top = self.stack.pop()
top_id= h.get_id(top)
pre_top = self.stack.pop()
pre_top_id = h.get_id(pre_top)
self.stack.append(top)
self.arcs[pre_top_id] = top_id
self.labels[pre_top_id] = transition.label
else:
top = self.stack.pop()
top_id = h.get_id(top)
pre_top = self.stack[-1]
pre_top_id = h.get_id(pre_top)
self.arcs[top_id] = pre_top_id
self.labels[top_id] = transition.label
def get_leftmost_child(self, token, input_sentence, arcs, leftmost_multiplier):
assert leftmost_multiplier < 0, "Invalid leftmost_multiplier passed"
while token is not None and leftmost_multiplier < 0:
leftmost_multiplier += 1
all_children = self.get_all_children(token, input_sentence, arcs)
if len(all_children) == 0 :
return(None)
min_candidate = min(all_children.keys())
if (min_candidate < h.get_id(token)):
token = all_children[min_candidate]
else:
return(None)
return(token)
def get_rightmost_child(self, token, input_sentence, arcs, rightmost_multiplier):
assert rightmost_multiplier > 0, "Invalid rightmost_multiplier passed"
while token is not None and rightmost_multiplier > 0:
rightmost_multiplier -= 1
all_children = self.get_all_children(token, input_sentence, arcs)
if len(all_children) == 0 :
return(None)
max_candidate = max(all_children.keys())
if (max_candidate > h.get_id(token)):
token = all_children[max_candidate]
else:
return(None)
return(token)
def get_all_siblings(self, token, input_sentence, arcs):
if arcs.get(h.get_id(token), None) is None:
return ([])
all_siblings = []
for word in input_sentence:
if h.get_id(word) != 0 and h.get_id(
word) != h.get_id(token) and arcs.get(
h.get_id(word), None) is not None and arcs[h.get_id(
word)] == arcs[h.get_id(token)]:
all_siblings += [word]
return (all_siblings)
def get_leftmost_child(self, token, input_sentence, arcs,
leftmost_multiplier):
assert leftmost_multiplier < 0, "Invalid leftmost_multiplier passed"
while token is not None and leftmost_multiplier < 0:
leftmost_multiplier += 1
all_children = self.get_all_children(token, input_sentence, arcs)
if len(all_children) == 0:
return (None)
min_candidate = min(all_children.keys())
if (min_candidate < h.get_id(token)):
token = all_children[min_candidate]
else:
return (None)
return (token)
def get_rightmost_child(self, token, input_sentence, arcs,
rightmost_multiplier):
assert rightmost_multiplier > 0, "Invalid rightmost_multiplier passed"
while token is not None and rightmost_multiplier > 0:
rightmost_multiplier -= 1
all_children = self.get_all_children(token, input_sentence, arcs)
if len(all_children) == 0:
return (None)
max_candidate = max(all_children.keys())
if (max_candidate > h.get_id(token)):
token = all_children[max_candidate]
else:
return (None)
return (token)
def get_right_sibling(self, token, input_sentence, arcs, right_sibling_multiplier):
assert right_sibling_multiplier > 0, "Invalid right sibling multiplier"
all_siblings = self.get_all_siblings(token, input_sentence, arcs)
while(token is not None and right_sibling_multiplier > 0):
right_sibling_multiplier -= 1
if len(all_siblings) == 0:
return(None)
min_dist = h.INFINITY
nearest_sibling = None
for sibling in all_siblings:
if h.get_id(sibling) > h.get_id(token) and abs(h.get_id(sibling) - h.get_id(token)) < min_dist:
min_dist = abs(h.get_id(sibling) - h.get_id(token))
nearest_sibling = sibling
token = nearest_sibling
# NOTE: It is possible NOT that we keep cycling between siblings.
return(token)
def __get_sub_image(self):
rows, cols, chan = self.image.shape
sub_image = self.image[int(self.__bottom_thresh *
rows):rows, # bottom rows
int(self.__left_thresh *
cols):int(self.__right_thresh *
cols) # middle rows
]
sub_image = cv2.resize(sub_image,
None,
fx=self.__resize_factor,
fy=self.__resize_factor,
interpolation=cv2.INTER_LINEAR)
sub_image = Helper.convert_image_to_mask(sub_image)
gray_denoised_image = cv2.fastNlMeansDenoising(sub_image, None, 5, 7,
21)
threshold_image = cv2.threshold(gray_denoised_image, 225, 255,
cv2.THRESH_BINARY_INV)[1]
return sub_image
def get_right_sibling(self, token, input_sentence, arcs,
right_sibling_multiplier):
assert right_sibling_multiplier > 0, "Invalid right sibling multiplier"
all_siblings = self.get_all_siblings(token, input_sentence, arcs)
while (token is not None and right_sibling_multiplier > 0):
right_sibling_multiplier -= 1
if len(all_siblings) == 0:
return (None)
min_dist = h.INFINITY
nearest_sibling = None
for sibling in all_siblings:
if h.get_id(sibling) > h.get_id(token) and abs(
h.get_id(sibling) - h.get_id(token)) < min_dist:
min_dist = abs(h.get_id(sibling) - h.get_id(token))
nearest_sibling = sibling
token = nearest_sibling
# NOTE: It is possible NOT that we keep cycling between siblings.
return (token)
def get_input_offset_token(self, token, input_sentence, input_offset):
if h.get_id(token) + input_offset <= 0: #To prevent wraparound
return(None)
return(self.try_get_token(input_sentence, -(h.get_id(token) + input_offset))) # No +1 since the token id starts from 1 instead of 0
def get_model7_params(
self,
stack,
buff,
input_sentence,
arcs,
labels,
tType,
feat_type,
source_type,
source_offset=0,
input_offset=0,
head_multiplier=0,
left_rightmost_multiplier=0,
left_right_sibling_specifier=0,
suffix_len=0
): #Described here: http://stp.lingfil.uu.se/~nivre/docs/maltparser.pdf
assert feat_type in [self.DEP_FEAT, self.POS_FEAT,
self.LEX_FEAT], "Invalid feat_type specified"
assert source_type in [
self.BUFF_SOURCE, self.STACK_SOURCE, self.INPUT_SOURCE
], "Invalid source type specified"
assert source_offset >= 0, "Invalid source_offset"
assert head_multiplier >= 0, "Invalid head multiplier specified"
rev_input_sentence = input_sentence[::-1]
source = self.get_source(
stack, buff, input_sentence, source_type
) #Reverse the input sentence if it isn't already reversed by get_source
token = self.try_get_token(source, -(source_offset + 1))
if token is None:
return (None)
if input_offset != 0:
token = self.get_input_offset_token(token, rev_input_sentence,
input_offset)
if token is None:
return (None)
token = self.get_head_offset_token(token, rev_input_sentence,
head_multiplier, arcs)
if token is None:
return (None)
token = self.get_left_rightmost_child(token, rev_input_sentence, arcs,
left_rightmost_multiplier)
if token is None:
return (None)
token = self.get_left_right_sibling(token, rev_input_sentence, arcs,
left_right_sibling_specifier)
if token is None:
return (None)
ret_str = 'transition=%d,feat_type=%d,source_type=%d,source_offset=%d,input_offset=%d,head_multiplier=%d,left_rightmost_multiplier=%d,left_right_sibling_specifier=%d' % (
tType, feat_type, source_type, source_offset, input_offset,
head_multiplier, left_rightmost_multiplier,
left_right_sibling_specifier)
if feat_type == self.LEX_FEAT:
#suffix len can be specified via argument
lex_feat = h.get_word(token)
if suffix_len > 0:
ret_str += 'lex_feat=%s' % (lex_feat[-suffix_len:])
else:
ret_str += 'lex_feat=%s' % (lex_feat)
elif feat_type == self.DEP_FEAT:
dep_feat = labels.get(h.get_id(token), None)
if dep_feat is not None:
ret_str += 'dep_feat=%s' % (dep_feat)
else:
return (None)
elif feat_type == self.POS_FEAT:
pos_feat = h.get_postag(token)
ret_str += 'pos_feat=%s' % (pos_feat)
else:
return (None)
return (ret_str)
def extract_features(self, transition, stack, buff, labels,
previous_transitions, arcs, input_sentence):
features = defaultdict(float)
#tType = transition.transitionType
tType = -1 #Dummy value since this is not encoded in the feature for SVM
label = 'dummy_label' #Dummy label since this is not encoded in the feature for SVM
#Model7 features as described in http://stp.lingfil.uu.se/~nivre/docs/maltparser.pdf
feat21_model7 = self.add_model7_feat(features, stack, buff,
input_sentence, arcs, labels,
tType, self.POS_FEAT,
self.STACK_SOURCE, 1, 0, 0,
-1) #pos for pre-top's lmc
feat31_model7 = self.add_model7_feat(features, stack, buff,
input_sentence, arcs, labels,
tType, self.POS_FEAT,
self.STACK_SOURCE, 1, 0, 0,
1) #pos or pre-top's rmc
feat41_model7 = self.add_model7_feat(features, stack, buff,
input_sentence, arcs, labels,
tType, self.POS_FEAT,
self.STACK_SOURCE, 0, 0, 0,
-1) #pos for top's lmc
feat42_model7 = self.add_model7_feat(features, stack, buff,
input_sentence, arcs, labels,
tType, self.POS_FEAT,
self.STACK_SOURCE, 0, 0, 0,
1) #pos for top's rmc
feat5_model7 = self.add_model7_feat(features, stack, buff,
input_sentence, arcs, labels,
tType, self.LEX_FEAT,
self.STACK_SOURCE,
1) #lex for pre-top
feat6_model7 = self.add_model7_feat(features, stack, buff,
input_sentence, arcs, labels,
tType, self.LEX_FEAT,
self.STACK_SOURCE, 0) #lex for top
feat71_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.POS_FEAT, self.BUFF_SOURCE) #pos for next buffer item
feat76_model7 = self.add_model7_feat(features, stack, buff,
input_sentence, arcs, labels,
tType, self.POS_FEAT,
self.BUFF_SOURCE,
1) #pos for next-next buffer item
feat10_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.POS_FEAT, self.STACK_SOURCE, 1,
1) #pos for word after pre-top in input
feat11_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.POS_FEAT, self.STACK_SOURCE, 1,
-1) #pos for word before pre-top in input
feat12_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.DEP_FEAT, self.STACK_SOURCE, 1,
1) #dep for word after pre-top in input
feat13_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.DEP_FEAT, self.STACK_SOURCE, 1,
-1) #dep for word before pre-top in input
feat14_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.LEX_FEAT, self.STACK_SOURCE, 0,
1) #lex for word after top in input
feat16_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.POS_FEAT, self.STACK_SOURCE, 0,
1) #pos for word after top in input
feat17_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.POS_FEAT, self.STACK_SOURCE, 0,
-1) #pos for word before top in input
feat18_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.DEP_FEAT, self.STACK_SOURCE, 0,
1) #dep for word after top in input
feat19_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.DEP_FEAT, self.STACK_SOURCE, 0,
-1) #dep for word before top in input
pre_top_pos = self.get_model7_params(stack, buff, input_sentence, arcs,
labels, tType, self.POS_FEAT,
self.STACK_SOURCE, 1)
top_pos = self.get_model7_params(stack, buff, input_sentence, arcs,
labels, tType, self.POS_FEAT,
self.STACK_SOURCE, 0)
cfeat_11 = self.compose_feats(
features, [pre_top_pos, top_pos]) #pos of both pre_top and top
cfeat_12 = self.compose_feats(
features, [top_pos, feat71_model7]) #pos of top and next buff
cfeat_13 = self.compose_feats(features,
[top_pos, feat71_model7, feat76_model7
]) #pos for top next and next next
cfeat_17 = self.compose_feats(features,
[pre_top_pos, top_pos, feat41_model7
]) #pos for pre-top, top and top's lmc
cfeat_175 = self.compose_feats(features,
[pre_top_pos, top_pos, feat42_model7
]) #pos for pre-top, top and top's rmc
# Top two POS tags from the stack
for i in range(3): #was originally 2
if i >= len(stack):
break
s = stack[-(i + 1)]
pos = s[3]
features['transition=%d,s%d.pos=%s' % (tType, i, pos)] = 1
# Next 2 POS tags from the buffer
for i in range(2):
if i >= len(buff):
break
b = buff[-(i + 1)]
pos = b[3]
features['transition=%d,b%d.pos=%s' % (tType, i, pos)] = 1
# Previous transition type
if len(previous_transitions) > 0:
prev = previous_transitions[-1]
features['transition=%d,prev_transition=%d' % (tType, prev)] = 1
else:
features['transition=%d,prev_transition=None' % (tType)] = 1
if self.labeled and transition is not None: #We don't care about labelled case and transition should not be passed for SVM
# Action and label pair
features['transition=%d,label=%s' %
(transition.transitionType, transition.label)] = 1
# Label bias
features['label=%s' % (transition.label)] = 1
#Features based on http://dl.acm.org/citation.cfm?id=2002777
#Distance function
if len(stack) > 0 and len(buff) > 0:
dist = h.get_id(stack[-1]) - h.get_id(buff[-1])
if dist < 0:
features['transition=%d,neg_dist=' % (tType)] = dist
else:
features['transition=%d,pos_dist=' % (tType)] = dist
#Valency function
if (len(stack) > 1):
[left_valency,
right_valency] = self.get_valency(arcs, h.get_id(stack[-1]))
left_val_feat = 'transition=%d,head_left_valency=%d' % (
tType, left_valency)
features[left_val_feat + top_pos] = 1
features[left_val_feat] = 1
right_val_feat = 'transition=%d,head_right_valency=%d' % (
tType, right_valency)
features[right_val_feat] = 1
features[right_val_feat + top_pos] = 1
return features
def extract_features(self, transition, stack, buff, labels, previous_transitions, arcs, input_sentence):
features = defaultdict(float)
tType = transition.transitionType
label = transition.label
#Model7 features as described in http://stp.lingfil.uu.se/~nivre/docs/maltparser.pdf
feat1_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.DEP_FEAT, self.STACK_SOURCE, 1)#dep for pre-top
feat2_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.DEP_FEAT, self.STACK_SOURCE, 1, 0, 0, -1) #dep for pre-top's lmc
feat21_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.POS_FEAT, self.STACK_SOURCE, 1, 0, 0, -1) #pos for pre-top's lmc
feat3_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.DEP_FEAT, self.STACK_SOURCE, 1, 0, 0, 1)#dep or pre-top's rmc
feat31_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.POS_FEAT, self.STACK_SOURCE, 1, 0, 0, 1)#pos or pre-top's rmc
feat4_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.DEP_FEAT, self.STACK_SOURCE, 0, 0, 0, -1)#dep for top's lmc
feat41_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.POS_FEAT, self.STACK_SOURCE, 0, 0, 0, -1)#pos for top's lmc
feat5_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.LEX_FEAT, self.STACK_SOURCE, 1)#lex for pre-top
feat6_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.LEX_FEAT, self.STACK_SOURCE, 0)#lex for top
feat7_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.LEX_FEAT, self.BUFF_SOURCE)#lex for next buffer item
feat71_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.POS_FEAT, self.BUFF_SOURCE)#pos for next buffer item
feat75_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.LEX_FEAT, self.BUFF_SOURCE, 1)#lex for next-next buffer item
feat76_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.POS_FEAT, self.BUFF_SOURCE, 1)#pos for next-next buffer item
feat8_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.LEX_FEAT, self.STACK_SOURCE, 1, 1)#lex for word after pre-top in input
feat9_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.LEX_FEAT, self.STACK_SOURCE, 1, -1)#lex for word before pre-top in input
feat10_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.POS_FEAT, self.STACK_SOURCE, 1, 1)#pos for word after pre-top in input
feat11_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.POS_FEAT, self.STACK_SOURCE, 1, -1)#pos for word before pre-top in input
feat12_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.DEP_FEAT, self.STACK_SOURCE, 1, 1)#dep for word after pre-top in input
feat13_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.DEP_FEAT, self.STACK_SOURCE, 1, -1)#dep for word before pre-top in input
feat14_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.LEX_FEAT, self.STACK_SOURCE, 0, 1)#lex for word after top in input
feat15_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.LEX_FEAT, self.STACK_SOURCE, 0, -1)#lex for word before top in input
feat16_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.POS_FEAT, self.STACK_SOURCE, 0, 1)#pos for word after top in input
feat17_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.POS_FEAT, self.STACK_SOURCE, 0, -1)#pos for word before top in input
feat18_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.DEP_FEAT, self.STACK_SOURCE, 0, 1)#dep for word after top in input
feat19_model7 = self.add_model7_feat(features, stack, buff, input_sentence, arcs, labels, tType,self.DEP_FEAT, self.STACK_SOURCE, 0, -1)#dep for word before top in input
pre_top_pos = self.get_model7_params(stack, buff, input_sentence, arcs, labels, tType, self.POS_FEAT, self.STACK_SOURCE, 1)
top_pos = self.get_model7_params(stack, buff, input_sentence, arcs, labels, tType, self.POS_FEAT, self.STACK_SOURCE, 0)
cfeat_1 = self.compose_feats(features, [feat5_model7, pre_top_pos])#lex_pos of pre-top
cfeat_2 = self.compose_feats(features, [feat6_model7, top_pos])#lex_pos for top
cfeat_3 = self.compose_feats(features, [feat7_model7, feat71_model7])#lex_pos for next buffer item
cfeat_4 = self.compose_feats(features, [feat75_model7,feat76_model7])#lex_pos for next-next buffer item
cfeat_5 = self.compose_feats(features, [cfeat_1, cfeat_2])#lex_pos for both pre-top and top
cfeat_6 = self.compose_feats(features, [cfeat_1, feat6_model7])#lex_pos of pre-top with lex of top
cfeat_7 = self.compose_feats(features, [feat5_model7, cfeat_2])#lex of pre-top with lex_pos of top
cfeat_8 = self.compose_feats(features, [cfeat_1, top_pos])#lex_pos of pre-top with pos of top
cfeat_9 = self.compose_feats(features, [pre_top_pos, cfeat_2]) #pos of pre-top with lex_pos of top
cfeat_10 = self.compose_feats(features, [feat5_model7, feat6_model7])#lex of both pre_top and top
cfeat_11 = self.compose_feats(features, [pre_top_pos, top_pos])#pos of both pre_top and top
cfeat_12 = self.compose_feats(features, [top_pos, feat71_model7])#pos of top and next buff
cfeat_13 = self.compose_feats(features, [top_pos, feat71_model7, feat76_model7])#pos for top next and next next
cfeat_14 = self.compose_feats(features, [pre_top_pos, top_pos, feat71_model7])#pos for pre-top, top and next
cfeat_15 = self.compose_feats(features, [pre_top_pos, feat21_model7, top_pos])#pos for pre-top pre top lmc and top
cfeat_16 = self.compose_feats(features, [pre_top_pos, feat31_model7, top_pos])#pos for pre-top, pre-top rmc and top
cfeat_17 = self.compose_feats(features, [pre_top_pos, top_pos, feat41_model7])#pos for pre-top, top and top's lmc
# Top two POS tags from the stack
for i in range(3):#was originally 2
if i >= len(stack):
break
s = stack[-(i+1)]
pos = s[3]
features['transition=%d,s%d.pos=%s' % (tType, i, pos)] = 1
# Next four POS tags from the buffer
for i in range(3):
if i >= len(buff):
break
b = buff[-(i+1)]
pos = b[3]
features['transition=%d,b%d.pos=%s' % (tType, i, pos)] = 1
# Previous transition type
if len(previous_transitions) > 0:
prev = previous_transitions[-1]
features['transition=%d,prev_transition=%d' % (tType, prev)] = 1
else:
features['transition=%d,prev_transition=None' % (tType)] = 1
# Bias feature
features['transition=%d' % (transition.transitionType)] = 1
if self.labeled:
# Action and label pair
features['transition=%d,label=%s' % (transition.transitionType, transition.label)] = 1
# Label bias
features['label=%s' % (transition.label)] = 1
#Features based on http://dl.acm.org/citation.cfm?id=2002777
#Distance function
if len(stack) > 0 and len(buff) > 0:
dist = h.get_id(stack[-1]) - h.get_id(buff[-1])
if dist < 0:
features['transition=%d,neg_dist=' % (tType)] = dist
else:
features['transition=%d,pos_dist=' % (tType)] = dist
#Valency function
if len(stack) > 1:
if tType == Transition.LeftArc: # Left Arc
[left_valency, right_valency] = self.get_valency(arcs, h.get_id(stack[-1]))
features['transition=%d,head_left_valency=%d' % (tType, left_valency)] = 1
features['transition=%d,head_right_valency=%d' % (tType, right_valency)] = 1
elif tType == Transition.RightArc:#should probably check for right arc here!
[left_valency, right_valency] = self.get_valency(arcs, h.get_id(stack[-2]))
features['transition=%d,head_left_valency=%d' % (tType, left_valency)] = 1
features['transition=%d,head_right_valency=%d' % (tType, right_valency)] = 1
return features
def test_helper():
df = pd.DataFrame([[1, 2, 3], [2, 2, 3], [3, 3, 3]])
help = Helper()
assert type(help) == pd.Dataframe
def is_removable(self, word, arcs, leftmost_child, rightmost_child):
if ( (leftmost_child != p.INFINITY and not arcs.get(leftmost_child, False)) or (rightmost_child != -1 and not arcs.get(rightmost_child, False)) ):#CHeck if thie value is correct
return(False)
if ( p.is_root(word) ):
return(False)
return True
def extract_features(self, transition, stack, buff, labels,
previous_transitions, arcs, input_sentence):
features = defaultdict(float)
tType = transition.transitionType
label = transition.label
#Model7 features as described in http://stp.lingfil.uu.se/~nivre/docs/maltparser.pdf
feat1_model7 = self.add_model7_feat(features, stack, buff,
input_sentence, arcs, labels,
tType, self.DEP_FEAT,
self.STACK_SOURCE,
1) #dep for pre-top
feat2_model7 = self.add_model7_feat(features, stack, buff,
input_sentence, arcs, labels,
tType, self.DEP_FEAT,
self.STACK_SOURCE, 1, 0, 0,
-1) #dep for pre-top's lmc
feat21_model7 = self.add_model7_feat(features, stack, buff,
input_sentence, arcs, labels,
tType, self.POS_FEAT,
self.STACK_SOURCE, 1, 0, 0,
-1) #pos for pre-top's lmc
feat3_model7 = self.add_model7_feat(features, stack, buff,
input_sentence, arcs, labels,
tType, self.DEP_FEAT,
self.STACK_SOURCE, 1, 0, 0,
1) #dep or pre-top's rmc
feat31_model7 = self.add_model7_feat(features, stack, buff,
input_sentence, arcs, labels,
tType, self.POS_FEAT,
self.STACK_SOURCE, 1, 0, 0,
1) #pos or pre-top's rmc
feat4_model7 = self.add_model7_feat(features, stack, buff,
input_sentence, arcs, labels,
tType, self.DEP_FEAT,
self.STACK_SOURCE, 0, 0, 0,
-1) #dep for top's lmc
feat41_model7 = self.add_model7_feat(features, stack, buff,
input_sentence, arcs, labels,
tType, self.POS_FEAT,
self.STACK_SOURCE, 0, 0, 0,
-1) #pos for top's lmc
feat5_model7 = self.add_model7_feat(features, stack, buff,
input_sentence, arcs, labels,
tType, self.LEX_FEAT,
self.STACK_SOURCE,
1) #lex for pre-top
feat6_model7 = self.add_model7_feat(features, stack, buff,
input_sentence, arcs, labels,
tType, self.LEX_FEAT,
self.STACK_SOURCE, 0) #lex for top
feat7_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.LEX_FEAT, self.BUFF_SOURCE) #lex for next buffer item
feat71_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.POS_FEAT, self.BUFF_SOURCE) #pos for next buffer item
feat75_model7 = self.add_model7_feat(features, stack, buff,
input_sentence, arcs, labels,
tType, self.LEX_FEAT,
self.BUFF_SOURCE,
1) #lex for next-next buffer item
feat76_model7 = self.add_model7_feat(features, stack, buff,
input_sentence, arcs, labels,
tType, self.POS_FEAT,
self.BUFF_SOURCE,
1) #pos for next-next buffer item
feat8_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.LEX_FEAT, self.STACK_SOURCE, 1,
1) #lex for word after pre-top in input
feat9_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.LEX_FEAT, self.STACK_SOURCE, 1,
-1) #lex for word before pre-top in input
feat10_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.POS_FEAT, self.STACK_SOURCE, 1,
1) #pos for word after pre-top in input
feat11_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.POS_FEAT, self.STACK_SOURCE, 1,
-1) #pos for word before pre-top in input
feat12_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.DEP_FEAT, self.STACK_SOURCE, 1,
1) #dep for word after pre-top in input
feat13_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.DEP_FEAT, self.STACK_SOURCE, 1,
-1) #dep for word before pre-top in input
feat14_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.LEX_FEAT, self.STACK_SOURCE, 0,
1) #lex for word after top in input
feat15_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.LEX_FEAT, self.STACK_SOURCE, 0,
-1) #lex for word before top in input
feat16_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.POS_FEAT, self.STACK_SOURCE, 0,
1) #pos for word after top in input
feat17_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.POS_FEAT, self.STACK_SOURCE, 0,
-1) #pos for word before top in input
feat18_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.DEP_FEAT, self.STACK_SOURCE, 0,
1) #dep for word after top in input
feat19_model7 = self.add_model7_feat(
features, stack, buff, input_sentence, arcs, labels, tType,
self.DEP_FEAT, self.STACK_SOURCE, 0,
-1) #dep for word before top in input
pre_top_pos = self.get_model7_params(stack, buff, input_sentence, arcs,
labels, tType, self.POS_FEAT,
self.STACK_SOURCE, 1)
top_pos = self.get_model7_params(stack, buff, input_sentence, arcs,
labels, tType, self.POS_FEAT,
self.STACK_SOURCE, 0)
cfeat_1 = self.compose_feats(
features, [feat5_model7, pre_top_pos]) #lex_pos of pre-top
cfeat_2 = self.compose_feats(features,
[feat6_model7, top_pos]) #lex_pos for top
cfeat_3 = self.compose_feats(
features,
[feat7_model7, feat71_model7]) #lex_pos for next buffer item
cfeat_4 = self.compose_feats(
features,
[feat75_model7, feat76_model7]) #lex_pos for next-next buffer item
cfeat_5 = self.compose_feats(
features, [cfeat_1, cfeat_2]) #lex_pos for both pre-top and top
cfeat_6 = self.compose_feats(
features,
[cfeat_1, feat6_model7]) #lex_pos of pre-top with lex of top
cfeat_7 = self.compose_feats(
features,
[feat5_model7, cfeat_2]) #lex of pre-top with lex_pos of top
cfeat_8 = self.compose_feats(
features, [cfeat_1, top_pos]) #lex_pos of pre-top with pos of top
cfeat_9 = self.compose_feats(
features,
[pre_top_pos, cfeat_2]) #pos of pre-top with lex_pos of top
cfeat_10 = self.compose_feats(
features,
[feat5_model7, feat6_model7]) #lex of both pre_top and top
cfeat_11 = self.compose_feats(
features, [pre_top_pos, top_pos]) #pos of both pre_top and top
cfeat_12 = self.compose_feats(
features, [top_pos, feat71_model7]) #pos of top and next buff
cfeat_13 = self.compose_feats(features,
[top_pos, feat71_model7, feat76_model7
]) #pos for top next and next next
cfeat_14 = self.compose_feats(features,
[pre_top_pos, top_pos, feat71_model7
]) #pos for pre-top, top and next
cfeat_15 = self.compose_feats(features,
[pre_top_pos, feat21_model7, top_pos
]) #pos for pre-top pre top lmc and top
cfeat_16 = self.compose_feats(
features, [pre_top_pos, feat31_model7, top_pos
]) #pos for pre-top, pre-top rmc and top
cfeat_17 = self.compose_feats(features,
[pre_top_pos, top_pos, feat41_model7
]) #pos for pre-top, top and top's lmc
# Top two POS tags from the stack
for i in range(3): #was originally 2
if i >= len(stack):
break
s = stack[-(i + 1)]
pos = s[3]
features['transition=%d,s%d.pos=%s' % (tType, i, pos)] = 1
# Next four POS tags from the buffer
for i in range(3):
if i >= len(buff):
break
b = buff[-(i + 1)]
pos = b[3]
features['transition=%d,b%d.pos=%s' % (tType, i, pos)] = 1
# Previous transition type
if len(previous_transitions) > 0:
prev = previous_transitions[-1]
features['transition=%d,prev_transition=%d' % (tType, prev)] = 1
else:
features['transition=%d,prev_transition=None' % (tType)] = 1
# Bias feature
features['transition=%d' % (transition.transitionType)] = 1
if self.labeled:
# Action and label pair
features['transition=%d,label=%s' %
(transition.transitionType, transition.label)] = 1
# Label bias
features['label=%s' % (transition.label)] = 1
#Features based on http://dl.acm.org/citation.cfm?id=2002777
#Distance function
if len(stack) > 0 and len(buff) > 0:
dist = h.get_id(stack[-1]) - h.get_id(buff[-1])
if dist < 0:
features['transition=%d,neg_dist=' % (tType)] = dist
else:
features['transition=%d,pos_dist=' % (tType)] = dist
#Valency function
if len(stack) > 1:
if tType == Transition.LeftArc: # Left Arc
[left_valency,
right_valency] = self.get_valency(arcs, h.get_id(stack[-1]))
features['transition=%d,head_left_valency=%d' %
(tType, left_valency)] = 1
features['transition=%d,head_right_valency=%d' %
(tType, right_valency)] = 1
elif tType == Transition.RightArc: #should probably check for right arc here!
[left_valency,
right_valency] = self.get_valency(arcs, h.get_id(stack[-2]))
features['transition=%d,head_left_valency=%d' %
(tType, left_valency)] = 1
features['transition=%d,head_right_valency=%d' %
(tType, right_valency)] = 1
return features
