def test_for_nlpnet(self):
"""
Attempting to use nlpnet. This will cause an
error if the required dependencies are not
downloaded.
"""
try:
# Creating a new compare object
compare_nlpnet = Compare()
# Comparing using the nltk parser
compare_nlpnet.compare_strings(
text=["what time is it here?", "This is the cat's hat"],
pattern_detection=False,
parser="nlpnet")
# If that was successfuly, getting information
sentence_information = compare_nlpnet.get_pattern_information()
for sentence in sentence_information:
my_pattern = "[ Pattern ] : " + sentence.pattern
my_subject = "[ Subject ] : " + sentence.subject
my_verb = "[ Verb ] : " + sentence.verb
my_object = "[ Object ] : " + sentence.object[0]
my_preps = "[ Prep Phrases ] : " + str(
sentence.prepositional_phrases)
my_reliability_score = "[ Reliability Score ]: " + str(
sentence.reliability_score)
except:
# Getting nltk data path
running = Popen(['python -c "import nltk;print nltk.data.path"'],
stdin=PIPE,
stdout=PIPE,
stderr=PIPE,
shell=True)
stdin, stdout = running.communicate()
# Setting the path that the nlpnet dependency will be downloaded from
path = re.sub(
r"\'", "",
re.sub(r"\[", '', str(stdin.split('\n')[0].split(',')[0])))
path = path.split(r"/")
path = '/'.join(path[0:len(path) - 1]) + '/nlpnet_dependency/'
# Download the dependencies & extract
current_directory = os.getcwd()
os.mkdir(path)
os.chdir(path)
os.system(
"wget http://nilc.icmc.usp.br/nlpnet/data/dependency-en.tgz")
tar = tarfile.open(path + 'dependency-en.tgz', 'r:gz')
tar.extractall(path)
os.remove(path + 'dependency-en.tgz')
os.chdir(current_directory)
class regex4dummies:
# Setting global version variable which contains the version of this library
__version__ = '1.4.6'
def __init__(self, **kwargs):
"""
Constructor method.
"""
# Testing the system to make sure all dependencies are installed
RunDependencyTests()
# Instantiating compare object to be used
self.compare_object = Compare()
def compare_strings(self, **kwargs):
"""
Function that is integral in communicating between a compare object and the user
This function returns a 3-tuple array containing reliability score, applicability score, and pattern
"""
# Call compare_strings of compare object & return output
if kwargs.get("pattern_detection") == "literal":
return self.compare_object.compare_strings(
text=kwargs.get("text"),
pattern_detection=True,
parser=kwargs.get("parser"))
else:
return self.compare_object.compare_strings(
text=kwargs.get("text"),
pattern_detection=False,
parser=kwargs.get("parser"))
def get_pattern_information(self):
"""
This function returns the information for each sentence/pattern that was identified.
This is only useful if semantic parsing is implemented; otherwise, {} will be returned.
"""
return self.compare_object.get_pattern_information()
def get_topics(self, **kwargs):
""" Returns the list of topics that the parsers identified """
return self.compare_object.get_pattern_topics(kwargs.get("text"))
def extract_important_information(self, **kwargs):
"""
Returns the important information within the given text.
"""
return self.compare_object.extract_important_information(
kwargs.get("text"))
class regex4dummies:
# Setting global version variable which contains the version of this library
__version__ = '1.4.6'
def __init__(self, **kwargs):
"""
Constructor method.
"""
# Testing the system to make sure all dependencies are installed
RunDependencyTests()
# Instantiating compare object to be used
self.compare_object = Compare()
def compare_strings(self, **kwargs):
"""
Function that is integral in communicating between a compare object and the user
This function returns a 3-tuple array containing reliability score, applicability score, and pattern
"""
# Call compare_strings of compare object & return output
if kwargs.get("pattern_detection") == "literal":
return self.compare_object.compare_strings(text=kwargs.get("text"), pattern_detection=True, parser=kwargs.get("parser"))
else:
return self.compare_object.compare_strings(text=kwargs.get("text"), pattern_detection=False, parser=kwargs.get("parser"))
def get_pattern_information( self ):
"""
This function returns the information for each sentence/pattern that was identified.
This is only useful if semantic parsing is implemented; otherwise, {} will be returned.
"""
return self.compare_object.get_pattern_information()
def get_topics(self, **kwargs):
""" Returns the list of topics that the parsers identified """
return self.compare_object.get_pattern_topics(kwargs.get("text"))
def extract_important_information(self, **kwargs):
"""
Returns the important information within the given text.
"""
return self.compare_object.extract_important_information(kwargs.get("text"))
def test_for_nlpnet(self):
"""
Attempting to use nlpnet. This will cause an
error if the required dependencies are not
downloaded.
"""
try:
# Creating a new compare object
compare_nlpnet = Compare()
# Comparing using the nltk parser
compare_nlpnet.compare_strings(text=["what time is it here?", "This is the cat's hat"], pattern_detection=False, parser="nlpnet")
# If that was successfuly, getting information
sentence_information = compare_nlpnet.get_pattern_information()
for sentence in sentence_information:
my_pattern = "[ Pattern ] : " + sentence.pattern
my_subject = "[ Subject ] : " + sentence.subject
my_verb = "[ Verb ] : " + sentence.verb
my_object = "[ Object ] : " + sentence.object[0]
my_preps = "[ Prep Phrases ] : " + str(sentence.prepositional_phrases)
my_reliability_score = "[ Reliability Score ]: " + str(sentence.reliability_score)
except:
# Getting nltk data path
running = Popen(['python -c "import nltk;print nltk.data.path"'], stdin=PIPE, stdout=PIPE, stderr=PIPE, shell=True)
stdin, stdout = running.communicate()
# Setting the path that the nlpnet dependency will be downloaded from
path = re.sub(r"\'", "", re.sub(r"\[", '', str(stdin.split('\n')[0].split(',')[0])))
path = path.split(r"/")
path = '/'.join(path[0 : len(path) - 1]) + '/nlpnet_dependency/'
# Download the dependencies & extract
current_directory = os.getcwd()
os.mkdir(path)
os.chdir(path)
os.system("wget http://nilc.icmc.usp.br/nlpnet/data/dependency-en.tgz")
tar = tarfile.open(path + 'dependency-en.tgz', 'r:gz')
tar.extractall(path)
os.remove(path + 'dependency-en.tgz')
os.chdir(current_directory)