ChatterBot has a utility module that contains a collection of miscellaneous but useful functions.
"""
ChatterBot utility functions
"""
defimport_module(dotted_path):
"""
Imports the specified module based on the
dot notated import path for the module.
"""
import importlib
module_parts = dotted_path.split('.')
module_path = '.'.join(module_parts[:-1])
module = importlib.import_module(module_path)
return getattr(module, module_parts[-1])
definitialize_class(data, **kwargs):
"""
:param data: A string or dictionary containing a import_path attribute.
"""
if isinstance(data, dict):
import_path = data.get('import_path')
data.update(kwargs)
Class = import_module(import_path)
return Class(**data)
else:
Class = import_module(data)
return Class(**kwargs)
defvalidate_adapter_class(validate_class, adapter_class):
"""
Raises an exception if validate_class is not a
subclass of adapter_class.
:param validate_class: The class to be validated.
:type validate_class: class
:param adapter_class: The class type to check against.
:type adapter_class: class
:raises: Adapter.InvalidAdapterTypeException
"""
from .adapters import Adapter
# If a dictionary was passed in, check if it has an import_path attribute
if isinstance(validate_class, dict):
if 'import_path' notin validate_class:
raise Adapter.InvalidAdapterTypeException(
'The dictionary {} must contain a value for "import_path"'.format(
str(validate_class)
)
)
# Set the class to the import path for the next check
validate_class = validate_class.get('import_path')
ifnot issubclass(import_module(validate_class), adapter_class):
raise Adapter.InvalidAdapterTypeException(
'{} must be a subclass of {}'.format(
validate_class,
adapter_class.__name__
)
)
definput_function():
"""
Normalizes reading input between python 2 and 3.
The function 'raw_input' becomes 'input' in Python 3.
"""
import sys
if sys.version_info[0] < 3:
user_input = str(raw_input()) # NOQA
# Avoid problems using format strings with unicode characters
if user_input:
user_input = user_input.decode('utf-8')
else:
user_input = input() # NOQA
return user_input
defnltk_download_corpus(resource_path):
"""
Download the specified NLTK corpus file
unless it has already been downloaded.
Returns True if the corpus needed to be downloaded.
"""
from nltk.data import find
from nltk import download
from os.path import split, sep
from zipfile import BadZipfile
# Download the NLTK data only if it is not already downloaded
_, corpus_name = split(resource_path)
# From http://www.nltk.org/api/nltk.html
# When using find() to locate a directory contained in a zipfile,
# the resource name must end with the forward slash character.
# Otherwise, find() will not locate the directory.
#
# Helps when resource_path=='sentiment/vader_lexicon''
ifnot resource_path.endswith(sep):
resource_path = resource_path + sep
downloaded = Falsetry:
find(resource_path)
except LookupError:
download(corpus_name)
downloaded = Trueexcept BadZipfile:
raise BadZipfile(
'The NLTK corpus file being opened is not a zipfile, '
'or it has been corrupted and needs to be manually deleted.'
)
return downloaded
defremove_stopwords(tokens, language):
"""
Takes a language (i.e. 'english'), and a set of word tokens.
Returns the tokenized text with any stopwords removed.
Stop words are words like "is, the, a, ..."
Be sure to download the required NLTK corpus before calling this function:
- from chatterbot.utils import nltk_download_corpus
- nltk_download_corpus('corpora/stopwords')
"""
from nltk.corpus import stopwords
# Get the stopwords for the specified language
stop_words = stopwords.words(language)
# Remove the stop words from the set of word tokens
tokens = set(tokens) - set(stop_words)
return tokens
defget_response_time(chatbot):
"""
Returns the amount of time taken for a given
chat bot to return a response.
:param chatbot: A chat bot instance.
:type chatbot: ChatBot
:returns: The response time in seconds.
:rtype: float
"""
import time
start_time = time.time()
chatbot.get_response('Hello')
return time.time() - start_time
defprint_progress_bar(description, iteration_counter, total_items, progress_bar_length=20):
"""
Print progress bar
:param description: Training description
:type description: str
:param iteration_counter: Incremental counter
:type iteration_counter: int
:param total_items: total number items
:type total_items: int
:param progress_bar_length: Progress bar length
:type progress_bar_length: int
:returns: void
:rtype: void
"""
import sys
percent = float(iteration_counter) / total_items
hashes = '#' * int(round(percent * progress_bar_length))
spaces = ' ' * (progress_bar_length - len(hashes))
sys.stdout.write("\r{0}: [{1}] {2}%".format(description, hashes + spaces, int(round(percent * 100))))
sys.stdout.flush()
if total_items == iteration_counter:
print("\r")
Module imports
Method Name
Description
chatterbot.utils.import_module(dotted_path)
Imports the specified module based on the dot notated import path for the module.
Class initialization
Method Name
Description
chatterbot.utils.initialize_class(data, **kwargs)
Parameters:data – A string or dictionary containing a import_path attribute.
Terminal input
Method Name
Description
chatterbot.utils.input_function()
Normalizes reading input between python 2 and 3. The function ‘raw_input’ becomes ‘input’ in Python 3.
Takes a language (i.e. ‘english’), and a set of word tokens. Returns the tokenized text with any stopwords removed. Stop words are words like “is, the, a, …”;Be sure to download the required NLTK corpus before calling this function: – from chatterbot.utils import nltk_download_corpus – nltk_download_corpus(‘corpora/stopwords’)
ChatBot response time
Method Name
Description
chatterbot.utils.get_response_time(chatbot)
Returns the amount of time taken for a given chat bot to return a response.
