def testConstructor(self):
"""
A new Matrix instance should be a 3 x 3 by default,
unless a different number of rows and columns are specified.
The matrix should be the zero matrix.
"""
with self.assertRaises(TypeError):
badArguments = Matrix(2.5, 'foo')
matrix1 = Matrix()
self.assertEqual(matrix1.identity(), self.identity)
matrix2 = Matrix(4,2)
self.assertEqual(matrix2.rows(), 4)
self.assertEqual(matrix2.columns(), 2)
def main(filename, systemname, print_us, print_ont, statistics, link, prolog,
per_role, threshold, base, weights):
"""General class to run the entire program
"""
# Initialize spaCy just once (this takes most of the time...)
print("Initializing Natural Language Processor . . .")
start_nlp_time = timeit.default_timer()
nlp = English()
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(success_stories), nlp)
m = matrices[0]
count_matrix = matrices[1]
stories_list = matrices[2]
rme = matrices[3]
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 = out[0]
output_prolog = out[1]
output_ontobj = out[2]
output_prologobj = out[3]
onto_per_role = out[4]
# 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:
outputcsv = w.make_file(stats_folder, str(systemname), "csv",
statsarr[0])
matrixcsv = w.make_file(stats_folder,
str(systemname) + "-term_by_US_matrix", "csv",
m)
sent_outputcsv = w.make_file(stats_folder,
str(systemname) + "-sentences", "csv",
statsarr[1])
files.append(["General statistics", outputcsv])
files.append(["Term-by-User Story matrix", matrixcsv])
files.append(["Sentence statistics", sent_outputcsv])
if prolog:
outputpl = w.make_file(folder + "/prolog", str(systemname), "pl",
output_prolog)
files.append(["Prolog", outputpl])
if per_role:
for o in onto_per_role:
name = str(systemname) + "-" + str(o[0])
pont = w.make_file(folder + "/ontology", name, "omn", o[1])
files.append(["Individual Ontology for '" + str(o[0]) + "'", pont])
# 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":
Utility.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]) + "\"")
def setUp(self):
self.matrix = Matrix()
def buildIdentityMatrix(model):
request.identity = Matrix(model["rows"], model["columns"]).identity()
