def _get_gen(self, us_instances, m, systemname, print_ont, **kw):
patterns = Constructor(self.nlp, us_instances, m)
out = patterns.make(systemname, self.threshold, self.link)
# Print out the ontology in the terminal, if argument '-o'/'--print_ont' is chosen
if print_ont:
Printer._print_head("MANCHESTER OWL")
print(out[0])
return out
def gen_concept_model(self):
nlp = self.nlp
miner = StoryMiner()
counter = Counter()
# Keep tracking the number of successes and fails
success = 0
fail = 0
# Keeps tracking of all success and failed User Stories
user_stories_lst = []
failed_stories_lst = []
success_stories_lst = []
us_id = 1
# Parse every user story (remove punctuation and mine)
for story_line in self.messages:
try:
user_story = UserStory.parse(story_line, us_id,
self.system_name, nlp, miner)
user_story = counter.count(user_story)
success = success + 1
user_stories_lst.append(user_story)
success_stories_lst.append(story_line)
except ValueError as err:
failed_stories_lst.append([us_id, story_line, err.args])
fail = fail + 1
us_id = us_id + 1
# Generate the term-by-user story matrix (m), and additional data in two other matrices
matrix = Matrix(self.base_weight, self.weights)
matrices = matrix.generate(
user_stories_lst, ' '.join([u.sentence for u in user_stories_lst]),
nlp)
m, count_matrix, stories_list, rme = matrices
# Generate the ontology
patterns = Constructor(nlp, user_stories_lst, m)
out = patterns.make(self.system_name, self.threshold, self.link)
output_ontology, output_prolog, output_ontobj, output_prologobj, onto_per_role = out
# Return objects so that they can be used as input for other tools
return {
'stories': user_stories_lst,
'ontology': output_ontology,
'prolog': output_prolog,
'matrix': m
}
def main(filename, systemname, print_us, print_ont, statistics, link, prolog,
json, per_role, threshold, base, weights, spacy_nlp):
"""General class to run the entire program
"""
start_nlp_time = timeit.default_timer()
nlp = spacy_nlp
nlp_time = timeit.default_timer() - start_nlp_time
start_parse_time = timeit.default_timer()
miner = StoryMiner()
# Read the input file
set = Reader.parse(filename)
us_id = 1
# Keep track of all errors
success = 0
fail = 0
list_of_fails = []
errors = ""
c = Counter()
# Keeps track of all succesfully created User Stories objects
us_instances = []
failed_stories = []
success_stories = []
# Parse every user story (remove punctuation and mine)
for s in set:
try:
user_story = parse(s, us_id, systemname, nlp, miner)
user_story = c.count(user_story)
success = success + 1
us_instances.append(user_story)
success_stories.append(s)
except ValueError as err:
failed_stories.append([us_id, s, err.args])
errors += "\n[User Story " + str(us_id) + " ERROR] " + str(
err.args[0]) + "! (\"" + " ".join(str.split(s)) + "\")"
fail = fail + 1
us_id = us_id + 1
# Print errors (if found)
if errors:
Printer.print_head("PARSING ERRORS")
print(errors)
parse_time = timeit.default_timer() - start_parse_time
# Generate the term-by-user story matrix (m), and additional data in two other matrices
start_matr_time = timeit.default_timer()
matrix = Matrix(base, weights)
matrices = matrix.generate(us_instances,
' '.join([u.sentence for u in us_instances]),
nlp)
m, count_matrix, stories_list, rme = matrices
matr_time = timeit.default_timer() - start_matr_time
# Print details per user story, if argument '-u'/'--print_us' is chosen
if print_us:
print("Details:\n")
for us in us_instances:
Printer.print_us_data(us)
# Generate the ontology
start_gen_time = timeit.default_timer()
patterns = Constructor(nlp, us_instances, m)
out = patterns.make(systemname, threshold, link)
output_ontology, output_prolog, output_ontobj, output_prologobj, onto_per_role = out
# Print out the ontology in the terminal, if argument '-o'/'--print_ont' is chosen
if print_ont:
Printer.print_head("MANCHESTER OWL")
print(output_ontology)
gen_time = timeit.default_timer() - start_gen_time
# Gather statistics and print the results
stats_time = 0
if statistics:
start_stats_time = timeit.default_timer()
statsarr = Statistics.to_stats_array(us_instances)
Printer.print_head("USER STORY STATISTICS")
Printer.print_stats(statsarr[0], True)
Printer.print_stats(statsarr[1], True)
Printer.print_subhead(
"Term - by - User Story Matrix ( Terms w/ total weight 0 hidden )")
hide_zero = m[(m['sum'] > 0)]
print(hide_zero)
stats_time = timeit.default_timer() - start_stats_time
# Write output files
w = Writer()
folder = "output/" + str(systemname)
reports_folder = folder + "/reports"
stats_folder = reports_folder + "/stats"
outputfile = w.make_file(folder + "/ontology", str(systemname), "omn",
output_ontology)
files = [["Manchester Ontology", outputfile]]
outputcsv = ""
sent_outputcsv = ""
matrixcsv = ""
if statistics:
files.append([
"General statistics",
w.make_file(stats_folder, str(systemname), "csv", statsarr[0])
])
files.append([
"Term-by-User Story matrix",
w.make_file(stats_folder,
str(systemname) + "-term_by_US_matrix", "csv", m)
])
files.append([
"Sentence statistics",
w.make_file(stats_folder,
str(systemname) + "-sentences", "csv", statsarr[1])
])
if prolog:
files.append([
"Prolog",
w.make_file(folder + "/prolog", str(systemname), "pl",
output_prolog)
])
if json:
output_json_li = [str(us.toJSON()) for us in us_instances]
output_json = "\n".join(output_json_li)
files.append([
"JSON",
w.make_file(folder + "/json",
str(systemname) + "-user_stories", "json", output_json)
])
if per_role:
for o in onto_per_role:
files.append([
"Individual Ontology for '" + str(o[0]) + "'",
w.make_file(folder + "/ontology",
str(systemname) + "-" + str(o[0]), "omn", o[1])
])
# Print the used ontology generation settings
Printer.print_gen_settings(matrix, base, threshold)
# Print details of the generation
Printer.print_details(fail, success, nlp_time, parse_time, matr_time,
gen_time, stats_time)
report_dict = {
"stories":
us_instances,
"failed_stories":
failed_stories,
"systemname":
systemname,
"us_success":
success,
"us_fail":
fail,
"times": [[
"Initializing Natural Language Processor (<em>spaCy</em> v" +
pkg_resources.get_distribution("spacy").version + ")", nlp_time
], ["Mining User Stories", parse_time],
["Creating Factor Matrix", matr_time],
["Generating Manchester Ontology", gen_time],
["Gathering statistics", stats_time]],
"dir":
os.path.dirname(os.path.realpath(__file__)),
"inputfile":
filename,
"inputfile_lines":
len(set),
"outputfiles":
files,
"threshold":
threshold,
"base":
base,
"matrix":
matrix,
"weights":
m['sum'].copy().reset_index().sort_values(
['sum'], ascending=False).values.tolist(),
"counts":
count_matrix.reset_index().values.tolist(),
"classes":
output_ontobj.classes,
"relationships":
output_prologobj.relationships,
"types":
list(count_matrix.columns.values),
"ontology":
multiline(output_ontology)
}
# Finally, generate a report
report = w.make_file(reports_folder,
str(systemname) + "_REPORT", "html",
generate_report(report_dict))
files.append(["Report", report])
# Print the location and name of all output files
for file in files:
if str(file[1]) != "":
print(
str(file[0]) + " file succesfully created at: \"" +
str(file[1]) + "\"")
# Return objects so that they can be used as input for other tools
return {
'us_instances': us_instances,
'output_ontobj': output_ontobj,
'output_prologobj': output_prologobj,
'matrix': m
}
def main(filename, systemname, print_us, print_ont, statistics, link, prolog,
json, per_role, threshold, base, weights, spacy_nlp):
"""General class to run the entire program
"""
start_nlp_time = timeit.default_timer()
nlp = spacy_nlp
nlp_time = timeit.default_timer() - start_nlp_time
start_parse_time = timeit.default_timer()
miner = StoryMiner()
# Read the input file
set = Reader.parse(filename)
us_id = 1
# Keep track of all errors
success = 0
fail = 0
list_of_fails = []
errors = ""
c = Counter()
# Keeps track of all succesfully created User Stories objects
us_instances = []
failed_stories = []
success_stories = []
# Parse every user story (remove punctuation and mine)
for s in set:
try:
user_story = parse(s, us_id, systemname, nlp, miner)
user_story = c.count(user_story)
success = success + 1
us_instances.append(user_story)
success_stories.append(s)
except ValueError as err:
failed_stories.append([us_id, s, err.args])
errors += "\n[User Story " + str(us_id) + " ERROR] " + str(
err.args[0]) + "! (\"" + " ".join(str.split(s)) + "\")"
fail = fail + 1
us_id = us_id + 1
# Print errors (if found)
if errors:
Printer.print_head("PARSING ERRORS")
print(errors)
parse_time = timeit.default_timer() - start_parse_time
# Generate the term-by-user story matrix (m), and additional data in two other matrices
start_matr_time = timeit.default_timer()
matrix = Matrix(base, weights)
matrices = matrix.generate(us_instances,
' '.join([u.sentence for u in us_instances]),
nlp)
m, count_matrix, stories_list, rme = matrices
matr_time = timeit.default_timer() - start_matr_time
# Generate the ontology
start_gen_time = timeit.default_timer()
patterns = Constructor(nlp, us_instances, m)
out = patterns.make(systemname, threshold, link)
output_ontology, output_prolog, output_ontobj, output_prologobj, onto_per_role = out
all_classes_list = []
i = 0
for class_vn in output_ontobj.classes:
one_concept = {
'id': i,
'class_name': class_vn.name,
'parent_name': class_vn.parent,
'occurs_in': occurence_list(class_vn.stories),
'weight': '0',
'group': class_vn.is_role
}
all_classes_list.append(one_concept)
i += 1
nodes = [{
"id": cl["id"],
"label": cl["class_name"],
"weight": cl["weight"]
} for cl in all_classes_list]
relationships_query = output_prologobj.relationships
all_relationships_list = []
for relationship in relationships_query:
one_concept = {
'relationship_domain': relationship.domain,
'relationship_name': relationship.name,
'relationship_range': relationship.range
}
all_relationships_list.append(one_concept)
edges_id_list = []
concepts_query = []
concepts_dict = {}
concepts_dict_list = []
relationshipslist = []
i = 0
for class_vn in all_classes_list:
one_concept = {
'class_id': i,
'class_name': class_vn['class_name'],
'parent_name': class_vn['parent_name'],
'weight': '0',
'group': class_vn['group']
}
concepts_query.append(one_concept)
i += 1
for concept in concepts_query:
# print(concept)
concepts_dict[concept['class_id']] = concept['class_name']
concepts_dict_list.append([concept['class_id'], concept['class_name']])
i = 0
for rel in all_relationships_list: #app.py 868
# print(rel)
relationshipslist.append([
rel['relationship_domain'], rel['relationship_range'],
rel['relationship_name']
])
for concept in concepts_dict_list:
if rel['relationship_domain'] == concept[1]:
x = concept[0]
for concept in concepts_dict_list:
if rel['relationship_range'] == concept[1]:
y = concept[0]
if rel['relationship_name'] == 'isa':
edges_id_dict = {
'from': x,
'to': y,
'label': rel['relationship_name'],
'dashes': "true"
}
else:
edges_id_dict = {
'from': x,
'to': y,
'label': rel['relationship_name']
}
i += 1
# ELSE??
edges_id_list.append(edges_id_dict)
print({'nodes': nodes, 'edges': edges_id_list})
return ({'nodes': nodes, 'edges': edges_id_list})
def main(filename, systemname, print_us, print_ont, statistics, link, prolog,
json, per_role, threshold, base, weights, spacy_nlp):
"""General class to run the entire program
"""
start_nlp_time = timeit.default_timer()
nlp = spacy_nlp
nlp_time = timeit.default_timer() - start_nlp_time
start_parse_time = timeit.default_timer()
miner = StoryMiner()
# Read the input file
set = Reader.parse(filename)
us_id = 1
# Keep track of all errors
success = 0
fail = 0
list_of_fails = []
errors = ""
c = Counter()
# Keeps track of all succesfully created User Stories objects
us_instances = []
failed_stories = []
success_stories = []
# Parse every user story (remove punctuation and mine)
for s in set:
try:
user_story = parse(s, us_id, systemname, nlp, miner)
user_story = c.count(user_story)
success = success + 1
us_instances.append(user_story)
success_stories.append(s)
except ValueError as err:
failed_stories.append([us_id, s, err.args])
errors += "\n[User Story " + str(us_id) + " ERROR] " + str(
err.args[0]) + "! (\"" + " ".join(str.split(s)) + "\")"
fail = fail + 1
us_id = us_id + 1
# Print errors (if found)
if errors:
Printer.print_head("PARSING ERRORS")
print(errors)
parse_time = timeit.default_timer() - start_parse_time
# Generate the term-by-user story matrix (m), and additional data in two other matrices
start_matr_time = timeit.default_timer()
matrix = Matrix(base, weights)
matrices = matrix.generate(us_instances,
' '.join([u.sentence for u in us_instances]),
nlp)
m, count_matrix, stories_list, rme = matrices
matr_time = timeit.default_timer() - start_matr_time
# Print details per user story, if argument '-u'/'--print_us' is chosen
if print_us:
print("Details:\n")
for us in us_instances:
Printer.print_us_data(us)
# Generate the ontology
start_gen_time = timeit.default_timer()
patterns = Constructor(nlp, us_instances, m)
out = patterns.make(systemname, threshold, link)
output_ontology, output_prolog, output_ontobj, output_prologobj, onto_per_role = out
print("HEY THIS IS THE OUTPUT_ONTOBJ WITH THE CLASSES APPARENTLY???")
print(output_ontobj.classes)
all_classes_list = []
for class_vn in output_ontobj.classes:
one_concept = {
'class_name': class_vn.name,
'parent_name': class_vn.parent,
'occurs_in': occurence_list(class_vn.stories),
'weight': '0',
'group': class_vn.is_role
}
all_classes_list.append(one_concept)
print(all_classes_list)
# nodes = []
# for cl in all_classes_list:
# print(cl)
# nodes.append({"label": cl['class_name']})
# taking out class_id from the nodes. idk if this will bite me later.
nodes = [{
"label": cl["class_name"],
"weight": cl["weight"]
} for cl in all_classes_list]
# print(nodes)
print('IDK WHAT THIS IS BUT IMMA PRINT IT OUT TOO')
relationships_query = output_prologobj.relationships
all_relationships_list = []
for relationship in relationships_query:
one_concept = {
'relationship_domain': relationship.domain,
'relationship_name': relationship.name,
'relationship_range': relationship.range
}
all_relationships_list.append(one_concept)
print(all_relationships_list)
edges_id_list = []
concepts_query = []
concepts_dict = {}
concepts_dict_list = []
relationshipslist = []
i = 0
for class_vn in all_classes_list:
one_concept = {
'class_id': i,
'class_name': class_vn['class_name'],
'parent_name': class_vn['parent_name'],
'weight': '0',
'group': class_vn['group']
}
concepts_query.append(one_concept)
i += 1
# print(concepts_query)
for concept in concepts_query:
print(concept)
concepts_dict[concept['class_id']] = concept['class_name']
concepts_dict_list.append([concept['class_id'], concept['class_name']])
print('THIS IS WHAT UR CURRENTLY LOOKING AT')
print(concepts_dict_list)
i = 0
for rel in all_relationships_list: #app.py 868
# print(rel)
relationshipslist.append([
rel['relationship_domain'], rel['relationship_range'],
rel['relationship_name']
])
for concept in concepts_dict_list:
if rel['relationship_domain'] == concept[1]:
x = concept[0]
for concept in concepts_dict_list:
if rel['relationship_range'] == concept[1]:
y = concept[0]
if rel['relationship_name'] == 'isa':
edges_id_dict = {
'id': i,
'from': x,
'to': y,
'label': rel['relationship_name'],
'dashes': "true"
}
else:
edges_id_dict = {
'id': i,
'from': x,
'to': y,
'label': rel['relationship_name']
}
i += 1
# ELSE??
edges_id_list.append(edges_id_dict)
print(edges_id_list)
# Print out the ontology in the terminal, if argument '-o'/'--print_ont' is chosen
if print_ont:
Printer.print_head("MANCHESTER OWL")
print(output_ontology)
gen_time = timeit.default_timer() - start_gen_time
# Gather statistics and print the results
stats_time = 0
if statistics:
start_stats_time = timeit.default_timer()
statsarr = Statistics.to_stats_array(us_instances)
Printer.print_head("USER STORY STATISTICS")
Printer.print_stats(statsarr[0], True)
Printer.print_stats(statsarr[1], True)
Printer.print_subhead(
"Term - by - User Story Matrix ( Terms w/ total weight 0 hidden )")
hide_zero = m[(m['sum'] > 0)]
print(hide_zero)
stats_time = timeit.default_timer() - start_stats_time
# Write output files
w = Writer()
folder = "output/" + str(systemname)
reports_folder = folder + "/reports"
stats_folder = reports_folder + "/stats"
outputfile = w.make_file(folder + "/ontology", str(systemname), "omn",
output_ontology)
files = [["Manchester Ontology", outputfile]]
outputcsv = ""
sent_outputcsv = ""
matrixcsv = ""
if statistics:
files.append([
"General statistics",
w.make_file(stats_folder, str(systemname), "csv", statsarr[0])
])
files.append([
"Term-by-User Story matrix",
w.make_file(stats_folder,
str(systemname) + "-term_by_US_matrix", "csv", m)
])
files.append([
"Sentence statistics",
w.make_file(stats_folder,
str(systemname) + "-sentences", "csv", statsarr[1])
])
if prolog:
files.append([
"Prolog",
w.make_file(folder + "/prolog", str(systemname), "pl",
output_prolog)
])
if json:
output_json_li = [str(us.toJSON()) for us in us_instances]
output_json = "\n".join(output_json_li)
files.append([
"JSON",
w.make_file(folder + "/json",
str(systemname) + "-user_stories", "json", output_json)
])
if per_role:
for o in onto_per_role:
files.append([
"Individual Ontology for '" + str(o[0]) + "'",
w.make_file(folder + "/ontology",
str(systemname) + "-" + str(o[0]), "omn", o[1])
])
# Print the used ontology generation settings
Printer.print_gen_settings(matrix, base, threshold)
# Print details of the generation
Printer.print_details(fail, success, nlp_time, parse_time, matr_time,
gen_time, stats_time)
report_dict = {
"stories":
us_instances,
"failed_stories":
failed_stories,
"systemname":
systemname,
"us_success":
success,
"us_fail":
fail,
"times": [[
"Initializing Natural Language Processor (<em>spaCy</em> v" +
pkg_resources.get_distribution("spacy").version + ")", nlp_time
], ["Mining User Stories", parse_time],
["Creating Factor Matrix", matr_time],
["Generating Manchester Ontology", gen_time],
["Gathering statistics", stats_time]],
"dir":
os.path.dirname(os.path.realpath(__file__)),
"inputfile":
filename,
"inputfile_lines":
len(set),
"outputfiles":
files,
"threshold":
threshold,
"base":
base,
"matrix":
matrix,
"weights":
m['sum'].copy().reset_index().sort_values(
['sum'], ascending=False).values.tolist(),
"counts":
count_matrix.reset_index().values.tolist(),
"classes":
output_ontobj.classes,
"relationships":
output_prologobj.relationships,
"types":
list(count_matrix.columns.values),
"ontology":
multiline(output_ontology)
}
# Finally, generate a report
report = w.make_file(reports_folder,
str(systemname) + "_REPORT", "html",
generate_report(report_dict))
files.append(["Report", report])
# Print the location and name of all output files
for file in files:
if str(file[1]) != "":
print(
str(file[0]) + " file succesfully created at: \"" +
str(file[1]) + "\"")
# Return objects so that they can be used as input for other tools
return {
'us_instances': us_instances,
'output_ontobj': output_ontobj,
'output_prologobj': output_prologobj,
'matrix': m
}