def test_pqa_english_noamb_diled_no_generalization(self):
input_parses = module_path + '/tests/data/POC-English-NoAmb/MST-fixed-manually/'
batch_dir = module_path + '/output/test_grammar_learner_' + str(
UTC())[:10]
prj_dir = batch_dir + '/noamb_pqa_diled_no_generalization/'
if check_dir(prj_dir, create=True, verbose='max'):
outpath = prj_dir
# cp,rp :: (test) corpus_path and reference_path:
cp = module_path + '/data/POC-English-NoAmb/poc_english_noamb_corpus.txt'
rp = input_parses + '/poc-english_noAmb-parses-gold.txt'
kwargs = {
'input_parses': input_parses,
'output_grammar': outpath,
'left_wall': '',
'period': False,
'context': 2,
'word_space': 'discrete',
'dim_reduction': 'none',
'clustering': 'group',
'grammar_rules': 2,
'categories_generalization': 'off',
'rules_generalization': 'off',
'tmpath': module_path + '/tmp/',
'linkage_limit': 1000,
'verbose': 'min'
}
re = learn_grammar(**kwargs)
pa, f1, precision, recall = pqa_meter(re['grammar_file'], outpath, cp,
rp, **kwargs)
self.assertTrue(pa * recall > 0.99,
str(pa) + " * " + str(recall) + " > 0.99")
def test_pqa_turtle_ddrkd_no_generalization(self):
input_parses = module_path + '/tests/data/POC-Turtle/MST-fixed-manually/'
batch_dir = module_path + '/output/test_grammar_learner_' + str(
UTC())[:10]
prj_dir = batch_dir + '/turtle_pqa_ddrkd_no_generalization/'
if check_dir(prj_dir, create=True, verbose='max'):
outpath = prj_dir
# cp,rp :: (test) corpus_path and reference_path:
cp = module_path + '/tests/data/POC-Turtle/poc-turtle-corpus.txt'
rp = input_parses + '/poc-turtle-parses-gold.txt'
kwargs = {
'input_parses': input_parses,
'output_grammar': outpath,
'left_wall': '',
'period': False,
'context': 2,
'word_space': 'vectors',
'dim_reduction': 'svd',
'clustering': ('kmeans', 'kmeans++', 18),
#-'cluster_range' : (2,50,9) ,
'cluster_range': (20, 2, 9),
'grammar_rules': 2,
'categories_generalization': 'off',
'rules_generalization': 'off',
'tmpath': module_path + '/tmp/',
'linkage_limit': 1000,
'verbose': 'min'
}
re = learn_grammar(**kwargs)
pa, f1, precision, recall = pqa_meter(re['grammar_file'], outpath, cp,
rp, **kwargs)
self.assertTrue(pa * recall > 0.99,
str(pa) + " * " + str(recall) + " > 0.99")
def test_pqa_turtle_diled_no_generalization(self):
input_parses = module_path + '/tests/data/POC-Turtle/MST-fixed-manually'
batch_dir = module_path + '/output/test_grammar_learner_' + str(UTC())[:10]
prj_dir = batch_dir + '/turtle_pqa_diled_no_generalization/'
if check_dir(prj_dir, create=True, verbose='max'): outpath = prj_dir
# cp,rp :: (test) corpus_path and reference_path:
cp = module_path + '/tests/data/POC-Turtle/poc-turtle-corpus.txt'
rp = input_parses + '/poc-turtle-parses-gold.txt'
kwargs = {
'input_parses' : input_parses,
'output_grammar': outpath,
'left_wall' : '' ,
'period' : False ,
'context' : 2 ,
'word_space' : 'discrete' ,
'dim_reduction' : 'none' ,
'clustering' : 'group' ,
'grammar_rules' : 2 ,
'categories_generalization' : 'off' ,
'rules_generalization' : 'off' ,
'tmpath' : module_path + '/tmp/',
'linkage_limit' : 1000,
'verbose' : 'min'
}
re = learn_grammar(**kwargs)
# 81019 changes: # FIXME: DEL comments
# a, q, qa = pqa_meter(re['grammar_file'], outpath, cp, rp, **kwargs)
# print('parse-ability, parse-quality:', a, q)
# assert a*q > 0.99
# self.assertTrue(a*q*Decimal("100") > 0.99, str(a) + " * " + str(q) + " * 100 !> 0.99")
pa, f1, precision, recall = pqa_meter(re['grammar_file'], outpath, cp, rp, **kwargs)
# pa, f1, precision, recall: <float> 0.0 - 1.0
self.assertTrue(pa*recall > 0.99, str(pa) + " * " + str(recall) + " > 0.99")
def test_turtle_generalize_rules(self):
base = module_path + '/tests/data/POC-Turtle/' + \
'generalized_rules/dict_6C_2018-10-03_0006.4.0.dict'
input_parses = module_path + '/tests/data/POC-Turtle/MST-fixed-manually/'
batch_dir = module_path + '/output/test_grammar_learner_' + str(UTC())[:10]
prj_dir = batch_dir + '/turtle_lw_&_dot_generalized_rules/'
if check_dir(prj_dir, create=True, verbose='max'):
outpath = prj_dir
kwargs = {
'input_parses' : input_parses,
'output_grammar': outpath,
'left_wall' : 'LEFT-WALL' ,
'period' : True ,
'context' : 2 ,
'word_space' : 'discrete' ,
'dim_reduction' : 'none' ,
'clustering' : 'group' ,
'grammar_rules' : 2 ,
'categories_generalization' : 'off' ,
'rules_generalization' : 'jaccard',
'tmpath' : module_path + '/tmp/',
'verbose' : 'none'
}
response = learn_grammar(**kwargs)
with open(response['grammar_file'], 'r') as f:
rules = f.read().splitlines()
rule_list = [line for line in rules if line[0:1] in ['"', '(']]
with open(base, 'r') as f: lst = f.read().splitlines()
base_list = [line for line in lst if line[0:1] in ['"', '(']]
if len(rule_list) == len(base_list):
assert rule_list == base_list
else: assert len(rule_list) == len(base_list)
def main(argv):
""" Usage: python tstr.py config.json """
print('\nGrammar Tester v.' + __version__, 'started', UTC(),
'| Python v.' + platform.python_version(), '\n')
try:
opts, args = getopt.getopt(argv, "h", ["help"])
except getopt.GetoptError:
print('''Usage: tstr <json-config-file>''')
sys.exit()
for opt in opts:
if opt == '-h':
print('''Usage: tstr <json-config-file>''')
sys.exit()
else:
config_json = args[0]
with open(config_json) as f:
kwargs = json.load(f)
re = {}
if 'input_grammar' in kwargs: # Test .dict file # 90123
ig = module_path + kwargs['input_grammar']
og = module_path + kwargs['out_path'] # og: output grammar
rp = module_path + kwargs['reference'] # rp: reference path
if 'test_corpus' in kwargs:
cp = module_path + kwargs['test_corpus'] # cp: corpus path
else:
cp = rp # test corpus path = reference parses path
print('Input grammar:', ig, '\nOutput directory:', og)
if check_dir(og, True, 'max'):
print('Grammar test started', UTC(), '\n')
start = time.time()
a, f1, precision, q = pqa_meter(ig, og, cp, rp, **kwargs)
re.update({'grammar_test_time': sec2string(time.time() - start)})
else:
print('Output path error:', og)
else:
print('Please set "input grammar" in config.json')
sys.exit()
stats = []
if 'grammar_test_time' in re:
stats.append(['Grammar test time ', re['grammar_test_time']])
if len(stats) > 0:
list2file(stats, og + '/test_stats.txt')
copy(config_json, og)
#with open(re['project_directory'] + '/grammar_learner_log.json', 'w') as f:
# f.write(json.dumps(re))
print('\nGrammar learning and the learned grammar test ended', UTC())
#print(test_stats(re))
#print('Output directory:', re['project_directory'], '\n')
print(f'PA = {int(round(a*100,0))}%, PQ = {int(round(q*100,0))}%, '
f'F1 = {round(f1,2)}')
def main(argv):
""" Usage: python ppln.py config.json """
print('\nGrammar Learner + Tester ppln v.' + __version__, 'started', UTC(),
'| Python v.' + platform.python_version(), '\n')
try:
opts, args = getopt.getopt(argv, "h", ["help"])
except getopt.GetoptError:
print('''Usage: ppln <json-config-file>''')
sys.exit()
for opt in opts:
if opt == '-h':
print('''Usage: ppln <json-config-file>''')
sys.exit()
else:
config_json = args[0]
with open(config_json) as f:
kwargs = json.load(f)
corpus = kwargs['corpus']
del kwargs['corpus']
dataset = kwargs['dataset']
del kwargs['dataset']
if 'input_parses' not in kwargs:
kwargs['input_parses'] = '/data/' + corpus + '/' + dataset
line = [[0, corpus, dataset, 0, 0, kwargs['rules_generalization']]]
out_path = module_path + kwargs['out_path']
rp = module_path + kwargs['reference']
if 'test_corpus' in kwargs:
cp = module_path + kwargs['test_corpus']
else:
cp = rp # test corpus path = reference parses path
if 'tmpath' not in kwargs:
kwargs['tmp_dir'] = ''
else:
if len(kwargs['tmpath']) == 0:
kwargs['tmp_dir'] = ''
else:
if 'home' in kwargs['tmpath']:
tmpath = kwargs['tmpath']
else:
tmpath = module_path + kwargs['tmpath']
if check_dir(tmpath, True, 'none'):
kwargs['tmp_dir'] = tmpath
else:
kwargs['tmp_dir'] = ''
a, _, hdr, log, rules = wide_rows(line, out_path, cp, rp, (1, 1), **kwargs)
copy(config_json, log['project_directory'])
print('\nGrammar learning and the learned grammar test ended', UTC())
print(test_stats(log))
print('Output directory:', log['project_directory'], '\n')
def test_pqa_english_noamb_ddrkd_no_generalization(self):
input_parses = module_path + '/tests/data/POC-English-NoAmb/MST-fixed-manually/'
batch_dir = module_path + '/output/test_grammar_learner_' + str(
UTC())[:10]
prj_dir = batch_dir + '/noamb_pqa_ddrkd_no_generalization/'
if check_dir(prj_dir, create=True, verbose='max'):
outpath = prj_dir
# cp,rp :: (test) corpus_path and reference_path:
cp = module_path + '/data/POC-English-NoAmb/poc_english_noamb_corpus.txt'
rp = input_parses + '/poc-english_noAmb-parses-gold.txt'
kwargs = {
'input_parses': input_parses,
'output_grammar': outpath,
'left_wall': '',
'period': False,
'context': 2,
'word_space': 'vectors',
'dim_reduction': 'svd',
'clustering': ('kmeans', 'kmeans++', 18),
'cluster_range': (12, 12, 5),
'grammar_rules': 2,
'categories_generalization': 'off',
'rules_generalization': 'off',
'tmpath': module_path + '/tmp/',
'linkage_limit': 1000,
'verbose': 'min'
}
# Sometimes pqa_meter(with test_grammar updated 2018-10-19) returns pa,recall = 0,0
# FIXME: check with further test_grammar updates and delete.
x = 0.
n = 0
while x < 0.1:
re = learn_grammar(**kwargs)
pa, f1, precision, recall = pqa_meter(re['grammar_file'], outpath,
cp, rp, **kwargs)
print(
f'\nnoAmb dDRKd: pa {round(pa,3)}, f1 {round(f1,3)}, precision {round(precision,3)}, recall {round(recall,3)} \n'
)
x = pa * recall
n += 1
if n > 24: break
self.assertTrue(pa * recall > 0.99,
str(pa) + " * " + str(recall) + " > 0.99")
module_path = os.path.abspath(os.path.join('.'))
if module_path not in sys.path: sys.path.append(module_path)
from src.grammar_learner.utl import UTC
from src.grammar_learner.read_files import check_dir
from src.grammar_learner.learner import learn_grammar
from src.grammar_learner.pqa_table import pqa_meter
# from ull.grammartest.optconst import *
#base = module_path + '/tests/data/POC-Turtle/' + \
# 'generalized_rules/dict_6C_2018-10-03_0006.4.0.dict'
input_parses = module_path + '/tests/data/dataSymbols/dynsym/'
batch_dir = module_path + '/output/test_dynsym_' + str(UTC())[:10]
prj_dir = batch_dir + '/dynsym_rules_b/'
if check_dir(prj_dir, create=True, verbose='max'):
outpath = prj_dir
kwargs = {'input_parses': input_parses, 'output_grammar': outpath}
response = learn_grammar(**kwargs)
with open(response['grammar_file'], 'r') as f:
rules = f.read().splitlines()
rule_list = [line for line in rules if line[0:1] in ['"', '(']]
#with open(base, 'r') as f: lst = f.read().splitlines()
#base_list = [line for line in lst if line[0:1] in ['"', '(']]
#if len(rule_list) == len(base_list):
# assert rule_list == base_list
#else:
# assert len(rule_list) == len(base_list), f"\nlen(rule_list)={len(rule_list)}" \
# f"\nlen(base_list)={len(base_list)}" \
# f"\nrule_list:\n{rule_list}" \
# f"\nbase_list:\n{base_list}"
def main(argv):
""" Usage: python ppln.py config.json """
print('\nGrammar Learner + Tester ppln v.' + __version__, 'started', UTC(),
'| Python v.' + platform.python_version(), '\n')
try:
opts, args = getopt.getopt(argv, "h", ["help"])
except getopt.GetoptError:
print('''Usage: ppln <json-config-file>''')
sys.exit()
for opt in opts:
if opt == '-h':
print('''Usage: ppln <json-config-file>''')
sys.exit()
else:
config_json = args[0]
with open(config_json) as f:
kwargs = json.load(f)
corpus = kwargs['corpus']
del kwargs['corpus']
dataset = kwargs['dataset']
del kwargs['dataset']
if 'input_parses' not in kwargs:
kwargs[
'input_parses'] = module_path + '/data/' + corpus + '/' + dataset
else:
if '/home/' in kwargs['input_parses']:
kwargs['input_parses'] = kwargs['input_parses']
else:
kwargs['input_parses'] = module_path + kwargs['input_parses']
if 'output_grammar' not in kwargs:
if 'out_path' in kwargs:
if '/home/' in kwargs['out_path']:
kwargs['output_grammar'] = kwargs['out_path']
else:
kwargs['output_grammar'] = module_path + kwargs['out_path']
else:
print('Please set "output_grammar" or "out_path" in config.json')
sys.exit()
if 'tmpath' not in kwargs:
kwargs['tmp_dir'] = ''
else:
if len(kwargs['tmpath']) == 0:
kwargs['tmp_dir'] = ''
else:
if 'home' in kwargs['tmpath']:
tmpath = kwargs['tmpath']
else:
tmpath = module_path + kwargs['tmpath']
if check_dir(tmpath, True, 'none'):
kwargs['tmp_dir'] = tmpath
else:
kwargs['tmp_dir'] = ''
rules, re = learn(**kwargs)
if 'error' in re:
print('Grammar Learner error log:\n', re)
sys.exit()
if kwargs['linkage_limit'] > 0:
og = module_path + kwargs['out_path']
rp = module_path + kwargs['reference']
if 'test_corpus' in kwargs:
cp = module_path + kwargs['test_corpus']
else:
cp = rp # test corpus path = reference parses path
start = time.time()
a, f1, precision, q = pqa_meter(re['grammar_file'], og, cp, rp,
**kwargs)
re.update({'grammar_test_time': sec2string(time.time() - start)})
stats = []
if 'grammar_learn_time' in re:
stats.append(['Grammar learn time', re['grammar_learn_time']])
if 'grammar_test_time' in re:
stats.append(['Grammar test time ', re['grammar_test_time']])
if len(stats) > 0:
x = re['corpus_stats_file']
list2file(stats, x[:x.rfind('/')] + '/learn_&_test_stats.txt')
copy(config_json, re['project_directory'])
with open(re['project_directory'] + '/grammar_learner_log.json', 'w') as f:
f.write(json.dumps(re))
print('\nGrammar learning and the learned grammar test ended', UTC())
print(test_stats(re))
print('Output directory:', re['project_directory'], '\n')
