def get_factor_part(self, matrix, story, part):
for token in eval('story.' + str(part) + '.text'):
if NLPUtility.case(token) in matrix.index.values:
matrix = self.add(matrix, NLPUtility.case(token),
story.txtnr(), self.score(token, story))
return matrix
def count_occurence(self, cm, sl, stories):
for story in stories:
for token in story.data:
c = NLPUtility.case(token)
if c in cm.index.values:
for s in sl:
if s[0] == c:
s[1].append(story.number)
if self.is_phrasal('role.functional_role', token,
story) == 1:
cm = self.add(cm, c, 'Functional Role')
elif self.is_phrasal('role.functional_role', token,
story) == 2:
cm = self.add(cm, c, 'Functional Role Compound')
if self.is_phrasal('means.main_object', token, story) == 1:
cm = self.add(cm, c, 'Main Object')
elif self.is_phrasal('means.main_object', token,
story) == 2:
cm = self.add(cm, c, 'Main Object Compound')
if self.is_freeform('means', token, story) == 1:
cm = self.add(cm, c, 'Means Free Form Noun')
if story.ends.free_form:
if self.is_phrasal('ends.main_object', token,
story) > 0 or self.is_freeform(
'ends', token, story) == 1:
cm = self.add(cm, c, 'Ends Free Form Noun')
return cm, sl
def get_namedict(self, tokens):
namedict = {}
for token in tokens:
namedict[token.lemma] = NLPUtility.case(token)
return namedict
def get_role_means_ends(self, matrix, stories): cases = matrix.index.values for case in cases: for story in stories: if story.role.indicator: if case in [NLPUtility.case(token) for token in story.role.text]: matrix.set_value(case, (story.txtnr(), 'Role'), 1) if story.means.indicator: if case in [NLPUtility.case(token) for token in story.means.text]: matrix.set_value(case, (story.txtnr(), 'Means'), 1) if story.ends.indicator: if case in [NLPUtility.case(token) for token in story.ends.text]: matrix.set_value(case, (story.txtnr(), 'Ends'), 1) return matrix
def count_occurence(self, cm, sl, stories):
for story in stories:
for token in story.data:
c = NLPUtility.case(token)
if c in cm.index.values:
for s in sl:
if s[0] == c:
s[1].append(story.number)
if self.is_phrasal('role.functional_role', token, story) == 1:
cm = self.add(cm, c, 'Functional Role')
elif self.is_phrasal('role.functional_role', token, story) == 2:
cm = self.add(cm, c, 'Functional Role Compound')
if self.is_phrasal('means.main_object', token, story) == 1:
cm = self.add(cm, c, 'Main Object')
elif self.is_phrasal('means.main_object', token, story) == 2:
cm = self.add(cm, c, 'Main Object Compound')
if self.is_freeform('means', token, story) == 1:
cm = self.add(cm, c, 'Means Free Form Noun')
if story.ends.free_form:
if self.is_phrasal('ends.main_object', token, story) > 0 or self.is_freeform('ends', token, story) == 1:
cm = self.add(cm, c, 'Ends Free Form Noun')
return cm, sl
def get_namedict(self, tokens):
namedict = {}
for token in tokens:
namedict[token.lemma] = NLPUtility.case(token)
return namedict
def make(stories, weights):
weighted_tokens = []
indices = [weight[0] for weight in weights]
w = 0.0
c = ""
for story in stories:
if story.has_ends:
parts = ['role', 'means', 'ends']
else:
parts = ['role', 'means']
for part in parts:
for token in eval('story.' + str(part) + '.text'):
c = NLPUtility.case(token)
if c in indices:
for weight in weights:
if weight[0] == c:
w = weight[1]
break
else:
w = 0.0
weighted_tokens.append(WeightedToken(token, w))
return weighted_tokens
def make(stories, weights):
weighted_tokens = []
indices = [weight[0] for weight in weights]
w = 0.0
c = ""
for story in stories:
if story.has_ends:
parts = ['role', 'means', 'ends']
else:
parts = ['role', 'means']
for part in parts:
for token in eval('story.' + str(part) + '.text'):
c = NLPUtility.case(token)
if c in indices:
for weight in weights:
if weight[0] == c:
w = weight[1]
break
else:
w = 0.0
weighted_tokens.append(WeightedToken(token, w))
return weighted_tokens
def find_story(self, w_token, stories):
nrs = []
for story in stories:
if w_token.case in [
NLPUtility.case(t) for t in story.data if t.pos_ == 'NOUN'
]:
nrs.append(story.number)
return nrs
def remove_indicators(self, matrix, stories, nlp):
indicators = []
for story in stories:
ind = story.role.indicator + " " + story.means.indicator
if story.has_ends:
ind += " " + story.ends.indicator
[indicators.append(NLPUtility.case(t)) for t in nlp(ind)]
def make_patterns(self, user_stories, threshold):
pi = PatternIdentifier(self.weighted_tokens)
self.sysname = str.lower(NLPUtility.case(user_stories[0].system.main))
for story in user_stories:
pi.identify(story)
relationships = self.apply_threshold(pi.relationships, threshold)
self.create(relationships, user_stories, threshold, pi.roles)
return self.onto
def get_role_means_ends(self, matrix, stories):
cases = matrix.index.values
for case in cases:
for story in stories:
if story.role.indicator:
if case in [
NLPUtility.case(token) for token in story.role.text
]:
matrix.set_value(case, (story.txtnr(), 'Role'), 1)
if story.means.indicator:
if case in [
NLPUtility.case(token)
for token in story.means.text
]:
matrix.set_value(case, (story.txtnr(), 'Means'), 1)
if story.ends.indicator:
if case in [
NLPUtility.case(token) for token in story.ends.text
]:
matrix.set_value(case, (story.txtnr(), 'Ends'), 1)
def make_patterns(self, user_stories, threshold): pi = PatternIdentifier(self.weighted_tokens) self.sysname = str.lower(NLPUtility.case(user_stories[0].system.main)) for story in user_stories: pi.identify(story) relationships = self.apply_threshold(pi.relationships, threshold) self.create(relationships, user_stories, threshold, pi.roles) return self.onto
def remove_verbs(self, matrix, stories):
verbs = []
cases = matrix.index.values.tolist()
for case in cases:
pos = []
for story in stories:
for token in story.data:
if NLPUtility.case(token) == case:
pos.append(token.pos_)
if len(set(pos)) == 1 and pos[0] == 'VERB':
verbs.append(case)
return matrix[(-matrix.index.isin(verbs))]
def remove_indicators(self, matrix, stories, nlp): indicators = [] for story in stories: ind = story.role.indicator + " " + story.means.indicator if story.has_ends: ind += " " + story.ends.indicator [indicators.append(NLPUtility.case(t)) for t in nlp(ind)] [indicators.append(i) for i in story.indicators] for indicator in indicators: if matrix.loc[indicator, 'sum'] > 0: indicators.remove(indicator) return matrix[(-matrix.index.isin(indicators))]
def get_parts(self, class_name, story):
case = class_name.split()
means_compounds = []
means_compounds.append(story.means.main_object.compound)
ends_compounds = story.ends.compounds
if story.means.free_form:
if len(story.means.compounds) > 0:
if type(story.means.compounds[0]) is list:
mc = [item for item in sublist for sublist in story.means.compounds]
else:
mc = story.means.compounds
means_compounds.extend(mc)
if len(ends_compounds) > 0:
if type(ends_compounds[0]) is list:
ends_compounds = [item for item in sublist for sublist in story.ends.compounds]
role = []
means = []
ends = []
rme = []
for token in story.data:
if token in story.role.text:
if len(case) != 1:
role.append(NLPUtility.case(token))
elif token not in story.role.functional_role.compound:
role.append(NLPUtility.case(token))
if token in story.means.text:
if len(case) != 1:
means.append(NLPUtility.case(token))
elif token not in means_compounds:
means.append(NLPUtility.case(token))
if story.has_ends:
if token in story.ends.text:
if len(case) != 1:
ends.append(NLPUtility.case(token))
elif token not in ends_compounds:
ends.append(NLPUtility.case(token))
if Utility.is_sublist(case, role):
rme.append('Role')
if Utility.is_sublist(case, means):
rme.append('Means')
if Utility.is_sublist(case, ends):
rme.append('Ends')
return rme
def remove_verbs(self, matrix, stories):
verbs = []
cases = matrix.index.values.tolist()
for case in cases:
pos = []
for story in stories:
for token in story.data:
if NLPUtility.case(token) == case:
pos.append(token)
if len(set(pos)) == 1 and NLPUtility.is_verb(pos[0]):
verbs.append(case)
for verb in verbs:
if matrix.loc[verb, 'sum'] > 0:
verbs.remove(verb)
return matrix[(-matrix.index.isin(verbs))]
def remove_verbs(self, matrix, stories): verbs = [] cases = matrix.index.values.tolist() for case in cases: pos = [] for story in stories: for token in story.data: if NLPUtility.case(token) == case: pos.append(token) if len(set(pos)) == 1 and NLPUtility.is_verb(pos[0]): verbs.append(case) for verb in verbs: if matrix.loc[verb, 'sum'] > 0: verbs.remove(verb) return matrix[(-matrix.index.isin(verbs))]
def __init__(self, token, weight): self.token = token self.case = NLPUtility.case(token) self.weight = weight
def find_story(self, w_token, stories): nrs = [] for story in stories: if w_token.case in [NLPUtility.case(t) for t in story.data]: nrs.append(story.number) return nrs
def get_factor_part(self, matrix, story, part):
for token in eval('story.' + str(part) + '.text'):
if NLPUtility.case(token) in matrix.index.values:
matrix = self.add(matrix, NLPUtility.case(token), story.txtnr(), eval('self.score_' + str(part) + '(token, story)'))
return matrix
for token in story.data:
if token in story.role.text:
if len(case) != 1:
role.append(NLPUtility.case(token))
elif token not in story.role.functional_role.compound:
role.append(NLPUtility.case(token))
if token in story.means.text:
if len(case) != 1:
means.append(NLPUtility.case(token))
elif token not in means_compounds:
means.append(NLPUtility.case(token))
if story.has_ends:
if token in story.ends.text:
if len(case) != 1:
ends.append(NLPUtility.case(token))
elif token not in ends_compounds:
ends.append(NLPUtility.case(token))
if Utility.is_sublist(case, role):
rme.append('Role')
if Utility.is_sublist(case, means):
rme.append('Means')
if Utility.is_sublist(case, ends):
rme.append('Ends')
return rme
class Constructor:
def __init__(self, nlp, user_stories, matrix):
self.nlp = nlp
self.user_stories = user_stories
self.weights = matrix['sum'].reset_index().values.tolist()
def make(self, ontname, threshold, link):
weighted_tokens = WeightAttacher.make(self.user_stories, self.weights)
self.onto = Ontology(ontname, self.user_stories)
self.prolog = Ontology(ontname, self.user_stories)
pf = PatternFactory(self.onto, self.prolog, weighted_tokens)
self.onto = pf.make_patterns(self.user_stories, threshold)
self.prolog = pf.prolog
if link:
self.link_to_story(self.onto.classes, self.user_stories)
g = Generator(self.onto.classes, self.onto.relationships)
g_prolog = Generator(self.prolog.classes, self.prolog.relationships,
False)
per_role_out = []
per_role_onto = self.get_per_role(self.user_stories, link)
for p in per_role_onto:
per_role_out.append([p[0].replace('/', '_'), p[1].prt(self.onto)])
return g.prt(self.onto), g_prolog.prt(
self.prolog), self.onto, self.prolog, per_role_out
def link_to_story(self, classes, stories):
used_stories = []
for cl in classes:
for story in cl.stories:
if story >= 0:
s = self.get_story(int(story), stories)
parts = self.get_parts(cl.name, s)
#for part in part_name:
# n = s.txtnr() + part
# self.onto.get_class_by_name(-1, n, s.txtnr())
# self.onto.new_relationship(-1, cl.name, cl.name + 'OccursIn' + n, n)
self.onto.new_relationship(
-1, cl.name, cl.name + 'OccursIn' + s.txtnr(),
s.txtnr())
for part in parts:
self.prolog.new_relationship(-1, cl.name, part,
s.txtnr())
used_stories.append(s.txtnr())
for story in used_stories:
self.onto.get_class_by_name(-1, story, 'UserStory')
def get_per_role(self, stories, link):
roles_link = []
roles = []
stories_per_role = []
per_role_ontos = []
# Get a list of roles and a list where the stories are linked to their roles
for story in self.user_stories:
roles_link.append([story.role.t, story.number])
if str.lower(story.role.t) not in [str.lower(s) for s in roles]:
roles.append(story.role.t)
# Get a list of stories per role and get the generator object for these stories
for role in roles:
stories_per_role = []
for link in roles_link:
if str.lower(role) == str.lower(link[0]):
stories_per_role.append(link[1])
per_role_ontos.append(
[role, self.get_generator(role, stories_per_role, link)])
return per_role_ontos
def get_generator(self, role, spr, link):
role_classes = []
role_relationships = []
cl_names = []
# Get classes
for cl in self.onto.classes:
for story in cl.stories:
if story >= 0 and story in spr and cl.name not in cl_names:
role_classes.append(cl)
cl_names.append(cl.name)
if cl.parent != '':
for cp in self.onto.classes:
if cp.name == cl.parent:
role_classes.append(cp)
# Get the general classes
if cl.stories[0] == -1:
if cl.name == 'FunctionalRole' or cl.name == 'Person':
role_classes.append(cl)
story_classes = []
# Get all relationships belonging to these classes
for rel in self.onto.relationships:
for story in rel.stories:
if rel.domain in cl_names and rel.range in cl_names and story in spr:
role_relationships.append(rel)
# If 'link' add these classes too
if link:
for story in spr:
if rel.domain in cl_names and rel.range == 'US' + str(
story):
role_relationships.append(rel)
story_classes.append(rel.range)
# Retrieve all classes for the relationships created in link
if link:
for cl in self.onto.classes:
for c in story_classes:
if cl.name == c:
role_classes.append(cl)
if cl.name == 'UserStory':
role_classes.append(cl)
return Generator(role_classes, role_relationships)
def get_story(self, nr, stories):
for story in stories:
if nr == story.number:
return story
return False
def get_parts(self, class_name, story):
case = class_name.split()
means_compounds = []
means_compounds.append(story.means.main_object.compound)
ends_compounds = story.ends.compounds
if story.means.free_form:
if len(story.means.compounds) > 0:
if type(story.means.compounds[0]) is list:
mc = [
item for item in sublist
for sublist in story.means.compounds
]
else:
mc = story.means.compounds
means_compounds.extend(mc)
if len(ends_compounds) > 0:
if type(ends_compounds[0]) is list:
ends_compounds = [
item for item in sublist
for sublist in story.ends.compounds
]
role = []
means = []
ends = []
rme = []
for token in story.data:
if token in story.role.text:
if len(case) != 1:
role.append(NLPUtility.case(token))
elif token not in story.role.functional_role.compound:
role.append(NLPUtility.case(token))
if token in story.means.text:
if len(case) != 1:
means.append(NLPUtility.case(token))
elif token not in means_compounds:
means.append(NLPUtility.case(token))
def __init__(self, token, weight):
self.token = token
self.case = NLPUtility.case(token)
self.weight = weight
