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231,400 | litaotao/IPython-Dashboard | dashboard/server/utils.py | build_response | def build_response(content, code=200):
"""Build response, add headers"""
response = make_response( jsonify(content), content['code'] )
response.headers['Access-Control-Allow-Origin'] = '*'
response.headers['Access-Control-Allow-Headers'] = \
'Origin, X-Requested-With, Content-Type, Accept, A... | python | def build_response(content, code=200):
"""Build response, add headers"""
response = make_response( jsonify(content), content['code'] )
response.headers['Access-Control-Allow-Origin'] = '*'
response.headers['Access-Control-Allow-Headers'] = \
'Origin, X-Requested-With, Content-Type, Accept, A... | [
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231,401 | litaotao/IPython-Dashboard | dashboard/server/resources/sql.py | SqlData.post | def post(self):
'''return executed sql result to client.
post data format:
{"options": ['all', 'last', 'first', 'format'], "sql_raw": "raw sql ..."}
Returns:
sql result.
'''
## format sql
data = request.get_json()
options, sql_raw = dat... | python | def post(self):
'''return executed sql result to client.
post data format:
{"options": ['all', 'last', 'first', 'format'], "sql_raw": "raw sql ..."}
Returns:
sql result.
'''
## format sql
data = request.get_json()
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231,402 | litaotao/IPython-Dashboard | dashboard/server/resources/home.py | DashListData.get | def get(self, page=0, size=10):
"""Get dashboard meta info from in page `page` and page size is `size`.
Args:
page: page number.
size: size number.
Returns:
list of dict containing the dash_id and accordingly meta info.
maybe empty list [] when p... | python | def get(self, page=0, size=10):
"""Get dashboard meta info from in page `page` and page size is `size`.
Args:
page: page number.
size: size number.
Returns:
list of dict containing the dash_id and accordingly meta info.
maybe empty list [] when p... | [
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231,403 | litaotao/IPython-Dashboard | dashboard/server/resources/storage.py | KeyList.get | def get(self):
"""Get key list in storage.
"""
keys = r_kv.keys()
keys.sort()
return build_response(dict(data=keys, code=200)) | python | def get(self):
"""Get key list in storage.
"""
keys = r_kv.keys()
keys.sort()
return build_response(dict(data=keys, code=200)) | [
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231,404 | litaotao/IPython-Dashboard | dashboard/server/resources/storage.py | Key.get | def get(self, key):
"""Get a key-value from storage according to the key name.
"""
data = r_kv.get(key)
# data = json.dumps(data) if isinstance(data, str) else data
# data = json.loads(data) if data else {}
return build_response(dict(data=data, code=200)) | python | def get(self, key):
"""Get a key-value from storage according to the key name.
"""
data = r_kv.get(key)
# data = json.dumps(data) if isinstance(data, str) else data
# data = json.loads(data) if data else {}
return build_response(dict(data=data, code=200)) | [
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231,405 | litaotao/IPython-Dashboard | dashboard/server/resources/dash.py | Dash.get | def get(self, dash_id):
"""Just return the dashboard id in the rendering html.
JS will do other work [ajax and rendering] according to the dash_id.
Args:
dash_id: dashboard id.
Returns:
rendered html.
"""
return make_response(render_template('da... | python | def get(self, dash_id):
"""Just return the dashboard id in the rendering html.
JS will do other work [ajax and rendering] according to the dash_id.
Args:
dash_id: dashboard id.
Returns:
rendered html.
"""
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231,406 | litaotao/IPython-Dashboard | dashboard/server/resources/dash.py | DashData.get | def get(self, dash_id):
"""Read dashboard content.
Args:
dash_id: dashboard id.
Returns:
A dict containing the content of that dashboard, not include the meta info.
"""
data = json.loads(r_db.hmget(config.DASH_CONTENT_KEY, dash_id)[0])
return bui... | python | def get(self, dash_id):
"""Read dashboard content.
Args:
dash_id: dashboard id.
Returns:
A dict containing the content of that dashboard, not include the meta info.
"""
data = json.loads(r_db.hmget(config.DASH_CONTENT_KEY, dash_id)[0])
return bui... | [
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231,407 | litaotao/IPython-Dashboard | dashboard/server/resources/dash.py | DashData.put | def put(self, dash_id=0):
"""Update a dash meta and content, return updated dash content.
Args:
dash_id: dashboard id.
Returns:
A dict containing the updated content of that dashboard, not include the meta info.
"""
data = request.get_json()
upda... | python | def put(self, dash_id=0):
"""Update a dash meta and content, return updated dash content.
Args:
dash_id: dashboard id.
Returns:
A dict containing the updated content of that dashboard, not include the meta info.
"""
data = request.get_json()
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231,408 | litaotao/IPython-Dashboard | dashboard/server/resources/dash.py | DashData.delete | def delete(self, dash_id):
"""Delete a dash meta and content, return updated dash content.
Actually, just remove it to a specfied place in database.
Args:
dash_id: dashboard id.
Returns:
Redirect to home page.
"""
removed_info = dict(
... | python | def delete(self, dash_id):
"""Delete a dash meta and content, return updated dash content.
Actually, just remove it to a specfied place in database.
Args:
dash_id: dashboard id.
Returns:
Redirect to home page.
"""
removed_info = dict(
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231,409 | totalgood/nlpia | src/nlpia/translate.py | main | def main(
lang='deu', n=900, epochs=50, batch_size=64, num_neurons=256,
encoder_input_data=None,
decoder_input_data=None,
decoder_target_data=None,
checkpoint_dir=os.path.join(BIGDATA_PATH, 'checkpoints'),
):
""" Train an LSTM encoder-decoder squence-to-sequence model... | python | def main(
lang='deu', n=900, epochs=50, batch_size=64, num_neurons=256,
encoder_input_data=None,
decoder_input_data=None,
decoder_target_data=None,
checkpoint_dir=os.path.join(BIGDATA_PATH, 'checkpoints'),
):
""" Train an LSTM encoder-decoder squence-to-sequence model... | [
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Train on 360 samples, validate on 40 samples
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# 64 common characters... | [
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231,410 | totalgood/nlpia | src/nlpia/book/forum/boltz.py | BoltzmanMachine.energy | def energy(self, v, h=None):
"""Compute the global energy for the current joint state of all nodes
>>> q11_4 = BoltzmanMachine(bv=[0., 0.], bh=[-2.], Whh=np.zeros((1, 1)), Wvv=np.zeros((2, 2)), Wvh=[[3.], [-1.]])
>>> q11_4.configurations()
>>> v1v2h = product([0, 1], [0, 1], [0, 1])
... | python | def energy(self, v, h=None):
"""Compute the global energy for the current joint state of all nodes
>>> q11_4 = BoltzmanMachine(bv=[0., 0.], bh=[-2.], Whh=np.zeros((1, 1)), Wvv=np.zeros((2, 2)), Wvh=[[3.], [-1.]])
>>> q11_4.configurations()
>>> v1v2h = product([0, 1], [0, 1], [0, 1])
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231,411 | totalgood/nlpia | src/nlpia/book/forum/boltz.py | Hopfield.energy | def energy(self):
r""" Compute the global energy for the current joint state of all nodes
- sum(s[i] * b[i]) - sum([s[i]*s[j]*W[i,j] for (i, j) in product(range(N), range(N)) if i<j)])
E = − ∑ s i b i − ∑
i i< j
s i s j w ij
"""
s, b, W, N = self.state, self.b, ... | python | def energy(self):
r""" Compute the global energy for the current joint state of all nodes
- sum(s[i] * b[i]) - sum([s[i]*s[j]*W[i,j] for (i, j) in product(range(N), range(N)) if i<j)])
E = − ∑ s i b i − ∑
i i< j
s i s j w ij
"""
s, b, W, N = self.state, self.b, ... | [
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231,412 | totalgood/nlpia | src/nlpia/translators.py | HyperlinkStyleCorrector.translate | def translate(self, text, to_template='{name} ({url})', from_template=None, name_matcher=None, url_matcher=None):
""" Translate hyperinks into printable book style for Manning Publishing
>>> translator = HyperlinkStyleCorrector()
>>> adoc = 'See http://totalgood.com[Total Good] about that.'
... | python | def translate(self, text, to_template='{name} ({url})', from_template=None, name_matcher=None, url_matcher=None):
""" Translate hyperinks into printable book style for Manning Publishing
>>> translator = HyperlinkStyleCorrector()
>>> adoc = 'See http://totalgood.com[Total Good] about that.'
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231,413 | totalgood/nlpia | src/nlpia/scripts/cleandialog.py | main | def main(dialogpath=None):
""" Parse the state transition graph for a set of dialog-definition tables to find an fix deadends """
if dialogpath is None:
args = parse_args()
dialogpath = os.path.abspath(os.path.expanduser(args.dialogpath))
else:
dialogpath = os.path.abspath(os.path.ex... | python | def main(dialogpath=None):
""" Parse the state transition graph for a set of dialog-definition tables to find an fix deadends """
if dialogpath is None:
args = parse_args()
dialogpath = os.path.abspath(os.path.expanduser(args.dialogpath))
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dialogpath = os.path.abspath(os.path.ex... | [
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] | efa01126275e9cd3c3a5151a644f1c798a9ec53f | https://github.com/totalgood/nlpia/blob/efa01126275e9cd3c3a5151a644f1c798a9ec53f/src/nlpia/scripts/cleandialog.py#L24-L31 |
231,414 | totalgood/nlpia | src/nlpia/book/scripts/create_raw_ubuntu_dataset.py | prepare_data_maybe_download | def prepare_data_maybe_download(directory):
"""
Download and unpack dialogs if necessary.
"""
filename = 'ubuntu_dialogs.tgz'
url = 'http://cs.mcgill.ca/~jpineau/datasets/ubuntu-corpus-1.0/ubuntu_dialogs.tgz'
dialogs_path = os.path.join(directory, 'dialogs')
# test it there are some dialogs... | python | def prepare_data_maybe_download(directory):
"""
Download and unpack dialogs if necessary.
"""
filename = 'ubuntu_dialogs.tgz'
url = 'http://cs.mcgill.ca/~jpineau/datasets/ubuntu-corpus-1.0/ubuntu_dialogs.tgz'
dialogs_path = os.path.join(directory, 'dialogs')
# test it there are some dialogs... | [
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231,415 | totalgood/nlpia | src/nlpia/skeleton.py | fib | def fib(n):
"""Fibonacci example function
Args:
n (int): integer
Returns:
int: n-th Fibonacci number
"""
assert n > 0
a, b = 1, 1
for i in range(n - 1):
a, b = b, a + b
return a | python | def fib(n):
"""Fibonacci example function
Args:
n (int): integer
Returns:
int: n-th Fibonacci number
"""
assert n > 0
a, b = 1, 1
for i in range(n - 1):
a, b = b, a + b
return a | [
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231,416 | totalgood/nlpia | src/nlpia/skeleton.py | main | def main(args):
"""Main entry point allowing external calls
Args:
args ([str]): command line parameter list
"""
args = parse_args(args)
setup_logging(args.loglevel)
_logger.debug("Starting crazy calculations...")
print("The {}-th Fibonacci number is {}".format(args.n, fib(args.n)))
... | python | def main(args):
"""Main entry point allowing external calls
Args:
args ([str]): command line parameter list
"""
args = parse_args(args)
setup_logging(args.loglevel)
_logger.debug("Starting crazy calculations...")
print("The {}-th Fibonacci number is {}".format(args.n, fib(args.n)))
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231,417 | totalgood/nlpia | src/nlpia/features.py | optimize_feature_power | def optimize_feature_power(df, output_column_name=None, exponents=[2., 1., .8, .5, .25, .1, .01]):
""" Plot the correlation coefficient for various exponential scalings of input features
>>> np.random.seed(314159)
>>> df = pd.DataFrame()
>>> df['output'] = np.random.randn(1000)
>>> df['x10'] = df.o... | python | def optimize_feature_power(df, output_column_name=None, exponents=[2., 1., .8, .5, .25, .1, .01]):
""" Plot the correlation coefficient for various exponential scalings of input features
>>> np.random.seed(314159)
>>> df = pd.DataFrame()
>>> df['output'] = np.random.randn(1000)
>>> df['x10'] = df.o... | [
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>>> df['x10'] = df.output * 10
>>> df['sq'] = df.output ** 2
>>> df['sqrt'] = df.output ** .5
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231,418 | totalgood/nlpia | src/nlpia/highd.py | representative_sample | def representative_sample(X, num_samples, save=False):
"""Sample vectors in X, preferring edge cases and vectors farthest from other vectors in sample set
"""
X = X.values if hasattr(X, 'values') else np.array(X)
N, M = X.shape
rownums = np.arange(N)
np.random.shuffle(rownums)
idx = Annoy... | python | def representative_sample(X, num_samples, save=False):
"""Sample vectors in X, preferring edge cases and vectors farthest from other vectors in sample set
"""
X = X.values if hasattr(X, 'values') else np.array(X)
N, M = X.shape
rownums = np.arange(N)
np.random.shuffle(rownums)
idx = Annoy... | [
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231,419 | totalgood/nlpia | src/nlpia/book/examples/ch03-2.py | cosine_sim | def cosine_sim(vec1, vec2):
"""
Since our vectors are dictionaries, lets convert them to lists for easier mathing.
"""
vec1 = [val for val in vec1.values()]
vec2 = [val for val in vec2.values()]
dot_prod = 0
for i, v in enumerate(vec1):
dot_prod += v * vec2[i]
mag_1... | python | def cosine_sim(vec1, vec2):
"""
Since our vectors are dictionaries, lets convert them to lists for easier mathing.
"""
vec1 = [val for val in vec1.values()]
vec2 = [val for val in vec2.values()]
dot_prod = 0
for i, v in enumerate(vec1):
dot_prod += v * vec2[i]
mag_1... | [
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231,420 | totalgood/nlpia | src/nlpia/models.py | LinearRegressor.fit | def fit(self, X, y):
""" Compute average slope and intercept for all X, y pairs
Arguments:
X (np.array): model input (independent variable)
y (np.array): model output (dependent variable)
Returns:
Linear Regression instance with `slope` and `intercept` attributes
... | python | def fit(self, X, y):
""" Compute average slope and intercept for all X, y pairs
Arguments:
X (np.array): model input (independent variable)
y (np.array): model output (dependent variable)
Returns:
Linear Regression instance with `slope` and `intercept` attributes
... | [
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231,421 | totalgood/nlpia | src/nlpia/web.py | looks_like_url | def looks_like_url(url):
""" Simplified check to see if the text appears to be a URL.
Similar to `urlparse` but much more basic.
Returns:
True if the url str appears to be valid.
False otherwise.
>>> url = looks_like_url("totalgood.org")
>>> bool(url)
True
"""
if not isins... | python | def looks_like_url(url):
""" Simplified check to see if the text appears to be a URL.
Similar to `urlparse` but much more basic.
Returns:
True if the url str appears to be valid.
False otherwise.
>>> url = looks_like_url("totalgood.org")
>>> bool(url)
True
"""
if not isins... | [
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Similar to `urlparse` but much more basic.
Returns:
True if the url str appears to be valid.
False otherwise.
>>> url = looks_like_url("totalgood.org")
>>> bool(url)
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231,422 | totalgood/nlpia | src/nlpia/web.py | try_parse_url | def try_parse_url(url):
""" User urlparse to try to parse URL returning None on exception """
if len(url.strip()) < 4:
logger.info('URL too short: {}'.format(url))
return None
try:
parsed_url = urlparse(url)
except ValueError:
logger.info('Parse URL ValueError: {}'.format... | python | def try_parse_url(url):
""" User urlparse to try to parse URL returning None on exception """
if len(url.strip()) < 4:
logger.info('URL too short: {}'.format(url))
return None
try:
parsed_url = urlparse(url)
except ValueError:
logger.info('Parse URL ValueError: {}'.format... | [
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231,423 | totalgood/nlpia | src/nlpia/web.py | get_url_filemeta | def get_url_filemeta(url):
""" Request HTML for the page at the URL indicated and return the url, filename, and remote size
TODO: just add remote_size and basename and filename attributes to the urlparse object
instead of returning a dict
>>> sorted(get_url_filemeta('mozilla.com').items())
[... | python | def get_url_filemeta(url):
""" Request HTML for the page at the URL indicated and return the url, filename, and remote size
TODO: just add remote_size and basename and filename attributes to the urlparse object
instead of returning a dict
>>> sorted(get_url_filemeta('mozilla.com').items())
[... | [
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>>> sorted(get_url_filemeta('mozilla.com').items())
[('filename', ''),
('hostname',... | [
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231,424 | totalgood/nlpia | src/nlpia/web.py | save_response_content | def save_response_content(response, filename='data.csv', destination=os.path.curdir, chunksize=32768):
""" For streaming response from requests, download the content one CHUNK at a time """
chunksize = chunksize or 32768
if os.path.sep in filename:
full_destination_path = filename
else:
... | python | def save_response_content(response, filename='data.csv', destination=os.path.curdir, chunksize=32768):
""" For streaming response from requests, download the content one CHUNK at a time """
chunksize = chunksize or 32768
if os.path.sep in filename:
full_destination_path = filename
else:
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231,425 | totalgood/nlpia | src/nlpia/web.py | download_file_from_google_drive | def download_file_from_google_drive(driveid, filename=None, destination=os.path.curdir):
""" Download script for google drive shared links
Thank you @turdus-merula and Andrew Hundt!
https://stackoverflow.com/a/39225039/623735
"""
if '&id=' in driveid:
# https://drive.google.com/uc?export=... | python | def download_file_from_google_drive(driveid, filename=None, destination=os.path.curdir):
""" Download script for google drive shared links
Thank you @turdus-merula and Andrew Hundt!
https://stackoverflow.com/a/39225039/623735
"""
if '&id=' in driveid:
# https://drive.google.com/uc?export=... | [
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231,426 | totalgood/nlpia | src/nlpia/book/examples/ch11_greetings.py | find_greeting | def find_greeting(s):
""" Return the the greeting string Hi, Hello, or Yo if it occurs at the beginning of a string
>>> find_greeting('Hi Mr. Turing!')
'Hi'
>>> find_greeting('Hello, Rosa.')
'Hello'
>>> find_greeting("Yo, what's up?")
'Yo'
>>> find_greeting("Hello")
'Hello'
>>> ... | python | def find_greeting(s):
""" Return the the greeting string Hi, Hello, or Yo if it occurs at the beginning of a string
>>> find_greeting('Hi Mr. Turing!')
'Hi'
>>> find_greeting('Hello, Rosa.')
'Hello'
>>> find_greeting("Yo, what's up?")
'Yo'
>>> find_greeting("Hello")
'Hello'
>>> ... | [
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>>> find_greeting('Hi Mr. Turing!')
'Hi'
>>> find_greeting('Hello, Rosa.')
'Hello'
>>> find_greeting("Yo, what's up?")
'Yo'
>>> find_greeting("Hello")
'Hello'
>>> print(find_greeting("hello"))
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231,427 | totalgood/nlpia | src/nlpia/scripts/hunspell_to_json.py | file_to_list | def file_to_list(in_file):
''' Reads file into list '''
lines = []
for line in in_file:
# Strip new line
line = line.strip('\n')
# Ignore empty lines
if line != '':
# Ignore comments
if line[0] != '#':
lines.append(line)
return li... | python | def file_to_list(in_file):
''' Reads file into list '''
lines = []
for line in in_file:
# Strip new line
line = line.strip('\n')
# Ignore empty lines
if line != '':
# Ignore comments
if line[0] != '#':
lines.append(line)
return li... | [
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231,428 | totalgood/nlpia | src/nlpia/scripts/hunspell_to_json.py | CompoundRule.add_flag_values | def add_flag_values(self, entry, flag):
''' Adds flag value to applicable compounds '''
if flag in self.flags:
self.flags[flag].append(entry) | python | def add_flag_values(self, entry, flag):
''' Adds flag value to applicable compounds '''
if flag in self.flags:
self.flags[flag].append(entry) | [
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231,429 | totalgood/nlpia | src/nlpia/scripts/hunspell_to_json.py | CompoundRule.get_regex | def get_regex(self):
''' Generates and returns compound regular expression '''
regex = ''
for flag in self.compound:
if flag == '?' or flag == '*':
regex += flag
else:
regex += '(' + '|'.join(self.flags[flag]) + ')'
return regex | python | def get_regex(self):
''' Generates and returns compound regular expression '''
regex = ''
for flag in self.compound:
if flag == '?' or flag == '*':
regex += flag
else:
regex += '(' + '|'.join(self.flags[flag]) + ')'
return regex | [
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231,430 | totalgood/nlpia | src/nlpia/scripts/hunspell_to_json.py | DICT.__parse_dict | def __parse_dict(self):
''' Parses dictionary with according rules '''
i = 0
lines = self.lines
for line in lines:
line = line.split('/')
word = line[0]
flags = line[1] if len(line) > 1 else None
# Base Word
self.num_words += ... | python | def __parse_dict(self):
''' Parses dictionary with according rules '''
i = 0
lines = self.lines
for line in lines:
line = line.split('/')
word = line[0]
flags = line[1] if len(line) > 1 else None
# Base Word
self.num_words += ... | [
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231,431 | totalgood/nlpia | src/nlpia/loaders.py | load_imdb_df | def load_imdb_df(dirpath=os.path.join(BIGDATA_PATH, 'aclImdb'), subdirectories=(('train', 'test'), ('pos', 'neg', 'unsup'))):
""" Walk directory tree starting at `path` to compile a DataFrame of movie review text labeled with their 1-10 star ratings
Returns:
DataFrame: columns=['url', 'rating', 'text'], ... | python | def load_imdb_df(dirpath=os.path.join(BIGDATA_PATH, 'aclImdb'), subdirectories=(('train', 'test'), ('pos', 'neg', 'unsup'))):
""" Walk directory tree starting at `path` to compile a DataFrame of movie review text labeled with their 1-10 star ratings
Returns:
DataFrame: columns=['url', 'rating', 'text'], ... | [
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TODO:
Make this more robust/general by allowing the subdirectories ... | [
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231,432 | totalgood/nlpia | src/nlpia/loaders.py | load_glove | def load_glove(filepath, batch_size=1000, limit=None, verbose=True):
r""" Load a pretrained GloVE word vector model
First header line of GloVE text file should look like:
400000 50\n
First vector of GloVE text file should look like:
the .12 .22 .32 .42 ... .42
>>> wv = load_glove(os.pa... | python | def load_glove(filepath, batch_size=1000, limit=None, verbose=True):
r""" Load a pretrained GloVE word vector model
First header line of GloVE text file should look like:
400000 50\n
First vector of GloVE text file should look like:
the .12 .22 .32 .42 ... .42
>>> wv = load_glove(os.pa... | [
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400000 50\n
First vector of GloVE text file should look like:
the .12 .22 .32 .42 ... .42
>>> wv = load_glove(os.path.join(BIGDATA_PATH, 'glove_test.txt'))
>>> wv.most_similar('and')[:... | [
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231,433 | totalgood/nlpia | src/nlpia/loaders.py | load_glove_df | def load_glove_df(filepath, **kwargs):
""" Load a GloVE-format text file into a dataframe
>>> df = load_glove_df(os.path.join(BIGDATA_PATH, 'glove_test.txt'))
>>> df.index[:3]
Index(['the', ',', '.'], dtype='object', name=0)
>>> df.iloc[0][:3]
1 0.41800
2 0.24968
3 -0.41242
... | python | def load_glove_df(filepath, **kwargs):
""" Load a GloVE-format text file into a dataframe
>>> df = load_glove_df(os.path.join(BIGDATA_PATH, 'glove_test.txt'))
>>> df.index[:3]
Index(['the', ',', '.'], dtype='object', name=0)
>>> df.iloc[0][:3]
1 0.41800
2 0.24968
3 -0.41242
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>>> df.index[:3]
Index(['the', ',', '.'], dtype='object', name=0)
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231,434 | totalgood/nlpia | src/nlpia/loaders.py | get_en2fr | def get_en2fr(url='http://www.manythings.org/anki/fra-eng.zip'):
""" Download and parse English->French translation dataset used in Keras seq2seq example """
download_unzip(url)
return pd.read_table(url, compression='zip', header=None, skip_blank_lines=True, sep='\t', skiprows=0, names='en fr'.split()) | python | def get_en2fr(url='http://www.manythings.org/anki/fra-eng.zip'):
""" Download and parse English->French translation dataset used in Keras seq2seq example """
download_unzip(url)
return pd.read_table(url, compression='zip', header=None, skip_blank_lines=True, sep='\t', skiprows=0, names='en fr'.split()) | [
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231,435 | totalgood/nlpia | src/nlpia/loaders.py | load_anki_df | def load_anki_df(language='deu'):
""" Load into a DataFrame statements in one language along with their translation into English
>>> get_data('zsm').head(1)
eng zsm
0 Are you new? Awak baru?
"""
if os.path.isfile(langua... | python | def load_anki_df(language='deu'):
""" Load into a DataFrame statements in one language along with their translation into English
>>> get_data('zsm').head(1)
eng zsm
0 Are you new? Awak baru?
"""
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231,436 | totalgood/nlpia | src/nlpia/loaders.py | generate_big_urls_glove | def generate_big_urls_glove(bigurls=None):
""" Generate a dictionary of URLs for various combinations of GloVe training set sizes and dimensionality """
bigurls = bigurls or {}
for num_dim in (50, 100, 200, 300):
# not all of these dimensionality, and training set size combinations were trained by S... | python | def generate_big_urls_glove(bigurls=None):
""" Generate a dictionary of URLs for various combinations of GloVe training set sizes and dimensionality """
bigurls = bigurls or {}
for num_dim in (50, 100, 200, 300):
# not all of these dimensionality, and training set size combinations were trained by S... | [
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231,437 | totalgood/nlpia | src/nlpia/loaders.py | normalize_ext_rename | def normalize_ext_rename(filepath):
""" normalize file ext like '.tgz' -> '.tar.gz' and '300d.txt' -> '300d.glove.txt' and rename the file
>>> pth = os.path.join(DATA_PATH, 'sms_slang_dict.txt')
>>> pth == normalize_ext_rename(pth)
True
"""
logger.debug('normalize_ext.filepath=' + str(filepath)... | python | def normalize_ext_rename(filepath):
""" normalize file ext like '.tgz' -> '.tar.gz' and '300d.txt' -> '300d.glove.txt' and rename the file
>>> pth = os.path.join(DATA_PATH, 'sms_slang_dict.txt')
>>> pth == normalize_ext_rename(pth)
True
"""
logger.debug('normalize_ext.filepath=' + str(filepath)... | [
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231,438 | totalgood/nlpia | src/nlpia/loaders.py | untar | def untar(fname, verbose=True):
""" Uunzip and untar a tar.gz file into a subdir of the BIGDATA_PATH directory """
if fname.lower().endswith(".tar.gz"):
dirpath = os.path.join(BIGDATA_PATH, os.path.basename(fname)[:-7])
if os.path.isdir(dirpath):
return dirpath
with tarfile.o... | python | def untar(fname, verbose=True):
""" Uunzip and untar a tar.gz file into a subdir of the BIGDATA_PATH directory """
if fname.lower().endswith(".tar.gz"):
dirpath = os.path.join(BIGDATA_PATH, os.path.basename(fname)[:-7])
if os.path.isdir(dirpath):
return dirpath
with tarfile.o... | [
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231,439 | totalgood/nlpia | src/nlpia/loaders.py | endswith_strip | def endswith_strip(s, endswith='.txt', ignorecase=True):
""" Strip a suffix from the end of a string
>>> endswith_strip('http://TotalGood.com', '.COM')
'http://TotalGood'
>>> endswith_strip('http://TotalGood.com', endswith='.COM', ignorecase=False)
'http://TotalGood.com'
"""
if ignorecase:
... | python | def endswith_strip(s, endswith='.txt', ignorecase=True):
""" Strip a suffix from the end of a string
>>> endswith_strip('http://TotalGood.com', '.COM')
'http://TotalGood'
>>> endswith_strip('http://TotalGood.com', endswith='.COM', ignorecase=False)
'http://TotalGood.com'
"""
if ignorecase:
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>>> endswith_strip('http://TotalGood.com', '.COM')
'http://TotalGood'
>>> endswith_strip('http://TotalGood.com', endswith='.COM', ignorecase=False)
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231,440 | totalgood/nlpia | src/nlpia/loaders.py | startswith_strip | def startswith_strip(s, startswith='http://', ignorecase=True):
""" Strip a prefix from the beginning of a string
>>> startswith_strip('HTtp://TotalGood.com', 'HTTP://')
'TotalGood.com'
>>> startswith_strip('HTtp://TotalGood.com', startswith='HTTP://', ignorecase=False)
'HTtp://TotalGood.com'
"... | python | def startswith_strip(s, startswith='http://', ignorecase=True):
""" Strip a prefix from the beginning of a string
>>> startswith_strip('HTtp://TotalGood.com', 'HTTP://')
'TotalGood.com'
>>> startswith_strip('HTtp://TotalGood.com', startswith='HTTP://', ignorecase=False)
'HTtp://TotalGood.com'
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] | efa01126275e9cd3c3a5151a644f1c798a9ec53f | https://github.com/totalgood/nlpia/blob/efa01126275e9cd3c3a5151a644f1c798a9ec53f/src/nlpia/loaders.py#L587-L601 |
231,441 | totalgood/nlpia | src/nlpia/loaders.py | get_longest_table | def get_longest_table(url='https://www.openoffice.org/dev_docs/source/file_extensions.html', header=0):
""" Retrieve the HTML tables from a URL and return the longest DataFrame found
>>> get_longest_table('https://en.wikipedia.org/wiki/List_of_sovereign_states').columns
Index(['Common and formal names', 'M... | python | def get_longest_table(url='https://www.openoffice.org/dev_docs/source/file_extensions.html', header=0):
""" Retrieve the HTML tables from a URL and return the longest DataFrame found
>>> get_longest_table('https://en.wikipedia.org/wiki/List_of_sovereign_states').columns
Index(['Common and formal names', 'M... | [
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>>> get_longest_table('https://en.wikipedia.org/wiki/List_of_sovereign_states').columns
Index(['Common and formal names', 'Membership within the UN System[a]',
'Sovereignty dispute[b]',
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231,442 | totalgood/nlpia | src/nlpia/loaders.py | get_filename_extensions | def get_filename_extensions(url='https://www.webopedia.com/quick_ref/fileextensionsfull.asp'):
""" Load a DataFrame of filename extensions from the indicated url
>>> df = get_filename_extensions('https://www.openoffice.org/dev_docs/source/file_extensions.html')
>>> df.head(2)
ext ... | python | def get_filename_extensions(url='https://www.webopedia.com/quick_ref/fileextensionsfull.asp'):
""" Load a DataFrame of filename extensions from the indicated url
>>> df = get_filename_extensions('https://www.openoffice.org/dev_docs/source/file_extensions.html')
>>> df.head(2)
ext ... | [
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>>> df = get_filename_extensions('https://www.openoffice.org/dev_docs/source/file_extensions.html')
>>> df.head(2)
ext description
0 .a UNIX static library file.
1 .asm Non-UNIX assembler source file... | [
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231,443 | totalgood/nlpia | src/nlpia/loaders.py | create_big_url | def create_big_url(name):
""" If name looks like a url, with an http, add an entry for it in BIG_URLS """
# BIG side effect
global BIG_URLS
filemeta = get_url_filemeta(name)
if not filemeta:
return None
filename = filemeta['filename']
remote_size = filemeta['remote_size']
url = f... | python | def create_big_url(name):
""" If name looks like a url, with an http, add an entry for it in BIG_URLS """
# BIG side effect
global BIG_URLS
filemeta = get_url_filemeta(name)
if not filemeta:
return None
filename = filemeta['filename']
remote_size = filemeta['remote_size']
url = f... | [
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231,444 | totalgood/nlpia | src/nlpia/loaders.py | get_data | def get_data(name='sms-spam', nrows=None, limit=None):
""" Load data from a json, csv, or txt file if it exists in the data dir.
References:
[cities_air_pollution_index](https://www.numbeo.com/pollution/rankings.jsp)
[cities](http://download.geonames.org/export/dump/cities.zip)
[cities_us](ht... | python | def get_data(name='sms-spam', nrows=None, limit=None):
""" Load data from a json, csv, or txt file if it exists in the data dir.
References:
[cities_air_pollution_index](https://www.numbeo.com/pollution/rankings.jsp)
[cities](http://download.geonames.org/export/dump/cities.zip)
[cities_us](ht... | [
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References:
[cities_air_pollution_index](https://www.numbeo.com/pollution/rankings.jsp)
[cities](http://download.geonames.org/export/dump/cities.zip)
[cities_us](http://download.geonames.org/export/dump/cities_us.zip)
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231,445 | totalgood/nlpia | src/nlpia/loaders.py | get_wikidata_qnum | def get_wikidata_qnum(wikiarticle, wikisite):
"""Retrieve the Query number for a wikidata database of metadata about a particular article
>>> print(get_wikidata_qnum(wikiarticle="Andromeda Galaxy", wikisite="enwiki"))
Q2469
"""
resp = requests.get('https://www.wikidata.org/w/api.php', timeout=5, pa... | python | def get_wikidata_qnum(wikiarticle, wikisite):
"""Retrieve the Query number for a wikidata database of metadata about a particular article
>>> print(get_wikidata_qnum(wikiarticle="Andromeda Galaxy", wikisite="enwiki"))
Q2469
"""
resp = requests.get('https://www.wikidata.org/w/api.php', timeout=5, pa... | [
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>>> print(get_wikidata_qnum(wikiarticle="Andromeda Galaxy", wikisite="enwiki"))
Q2469 | [
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231,446 | totalgood/nlpia | src/nlpia/loaders.py | normalize_column_names | def normalize_column_names(df):
r""" Clean up whitespace in column names. See better version at `pugnlp.clean_columns`
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['Hello World', 'not here'])
>>> normalize_column_names(df)
['hello_world', 'not_here']
"""
columns = df.columns if hasattr(df, ... | python | def normalize_column_names(df):
r""" Clean up whitespace in column names. See better version at `pugnlp.clean_columns`
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['Hello World', 'not here'])
>>> normalize_column_names(df)
['hello_world', 'not_here']
"""
columns = df.columns if hasattr(df, ... | [
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>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['Hello World', 'not here'])
>>> normalize_column_names(df)
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231,447 | totalgood/nlpia | src/nlpia/loaders.py | clean_column_values | def clean_column_values(df, inplace=True):
r""" Convert dollar value strings, numbers with commas, and percents into floating point values
>>> df = get_data('us_gov_deficits_raw')
>>> df2 = clean_column_values(df, inplace=False)
>>> df2.iloc[0]
Fiscal year ... | python | def clean_column_values(df, inplace=True):
r""" Convert dollar value strings, numbers with commas, and percents into floating point values
>>> df = get_data('us_gov_deficits_raw')
>>> df2 = clean_column_values(df, inplace=False)
>>> df2.iloc[0]
Fiscal year ... | [
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>>> df = get_data('us_gov_deficits_raw')
>>> df2 = clean_column_values(df, inplace=False)
>>> df2.iloc[0]
Fiscal year 10/2017-3/2018
Presiden... | [
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231,448 | totalgood/nlpia | src/nlpia/loaders.py | isglove | def isglove(filepath):
""" Get the first word vector in a GloVE file and return its dimensionality or False if not a vector
>>> isglove(os.path.join(DATA_PATH, 'cats_and_dogs.txt'))
False
"""
with ensure_open(filepath, 'r') as f:
header_line = f.readline()
vector_line = f.readline(... | python | def isglove(filepath):
""" Get the first word vector in a GloVE file and return its dimensionality or False if not a vector
>>> isglove(os.path.join(DATA_PATH, 'cats_and_dogs.txt'))
False
"""
with ensure_open(filepath, 'r') as f:
header_line = f.readline()
vector_line = f.readline(... | [
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>>> isglove(os.path.join(DATA_PATH, 'cats_and_dogs.txt'))
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231,449 | totalgood/nlpia | src/nlpia/loaders.py | nlp | def nlp(texts, lang='en', linesep=None, verbose=True):
r""" Use the SpaCy parser to parse and tag natural language strings.
Load the SpaCy parser language model lazily and share it among all nlpia modules.
Probably unnecessary, since SpaCy probably takes care of this with `spacy.load()`
>>> _parse is ... | python | def nlp(texts, lang='en', linesep=None, verbose=True):
r""" Use the SpaCy parser to parse and tag natural language strings.
Load the SpaCy parser language model lazily and share it among all nlpia modules.
Probably unnecessary, since SpaCy probably takes care of this with `spacy.load()`
>>> _parse is ... | [
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Load the SpaCy parser language model lazily and share it among all nlpia modules.
Probably unnecessary, since SpaCy probably takes care of this with `spacy.load()`
>>> _parse is None
True
>>> doc = nlp("Domo arigatto Mr. Roboto."... | [
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231,450 | totalgood/nlpia | src/nlpia/talk.py | get_decoder | def get_decoder(libdir=None, modeldir=None, lang='en-us'):
""" Create a decoder with the requested language model """
modeldir = modeldir or (os.path.join(libdir, 'model') if libdir else MODELDIR)
libdir = os.path.dirname(modeldir)
config = ps.Decoder.default_config()
config.set_string('-hmm', os.pa... | python | def get_decoder(libdir=None, modeldir=None, lang='en-us'):
""" Create a decoder with the requested language model """
modeldir = modeldir or (os.path.join(libdir, 'model') if libdir else MODELDIR)
libdir = os.path.dirname(modeldir)
config = ps.Decoder.default_config()
config.set_string('-hmm', os.pa... | [
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231,451 | totalgood/nlpia | src/nlpia/talk.py | transcribe | def transcribe(decoder, audio_file, libdir=None):
""" Decode streaming audio data from raw binary file on disk. """
decoder = get_decoder()
decoder.start_utt()
stream = open(audio_file, 'rb')
while True:
buf = stream.read(1024)
if buf:
decoder.process_raw(buf, False, Fal... | python | def transcribe(decoder, audio_file, libdir=None):
""" Decode streaming audio data from raw binary file on disk. """
decoder = get_decoder()
decoder.start_utt()
stream = open(audio_file, 'rb')
while True:
buf = stream.read(1024)
if buf:
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231,452 | totalgood/nlpia | src/nlpia/book/examples/ch09.py | pre_process_data | def pre_process_data(filepath):
"""
This is dependent on your training data source but we will try to generalize it as best as possible.
"""
positive_path = os.path.join(filepath, 'pos')
negative_path = os.path.join(filepath, 'neg')
pos_label = 1
neg_label = 0
dataset = []
for fil... | python | def pre_process_data(filepath):
"""
This is dependent on your training data source but we will try to generalize it as best as possible.
"""
positive_path = os.path.join(filepath, 'pos')
negative_path = os.path.join(filepath, 'neg')
pos_label = 1
neg_label = 0
dataset = []
for fil... | [
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231,453 | totalgood/nlpia | src/nlpia/book/examples/ch09.py | pad_trunc | def pad_trunc(data, maxlen):
""" For a given dataset pad with zero vectors or truncate to maxlen """
new_data = []
# Create a vector of 0's the length of our word vectors
zero_vector = []
for _ in range(len(data[0][0])):
zero_vector.append(0.0)
for sample in data:
if len(sampl... | python | def pad_trunc(data, maxlen):
""" For a given dataset pad with zero vectors or truncate to maxlen """
new_data = []
# Create a vector of 0's the length of our word vectors
zero_vector = []
for _ in range(len(data[0][0])):
zero_vector.append(0.0)
for sample in data:
if len(sampl... | [
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231,454 | totalgood/nlpia | src/nlpia/book/examples/ch09.py | clean_data | def clean_data(data):
""" Shift to lower case, replace unknowns with UNK, and listify """
new_data = []
VALID = 'abcdefghijklmnopqrstuvwxyz123456789"\'?!.,:; '
for sample in data:
new_sample = []
for char in sample[1].lower(): # Just grab the string, not the label
if char in... | python | def clean_data(data):
""" Shift to lower case, replace unknowns with UNK, and listify """
new_data = []
VALID = 'abcdefghijklmnopqrstuvwxyz123456789"\'?!.,:; '
for sample in data:
new_sample = []
for char in sample[1].lower(): # Just grab the string, not the label
if char in... | [
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231,455 | totalgood/nlpia | src/nlpia/book/examples/ch09.py | char_pad_trunc | def char_pad_trunc(data, maxlen):
""" We truncate to maxlen or add in PAD tokens """
new_dataset = []
for sample in data:
if len(sample) > maxlen:
new_data = sample[:maxlen]
elif len(sample) < maxlen:
pads = maxlen - len(sample)
new_data = sample + ['PAD']... | python | def char_pad_trunc(data, maxlen):
""" We truncate to maxlen or add in PAD tokens """
new_dataset = []
for sample in data:
if len(sample) > maxlen:
new_data = sample[:maxlen]
elif len(sample) < maxlen:
pads = maxlen - len(sample)
new_data = sample + ['PAD']... | [
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231,456 | totalgood/nlpia | src/nlpia/book/examples/ch09.py | create_dicts | def create_dicts(data):
""" Modified from Keras LSTM example"""
chars = set()
for sample in data:
chars.update(set(sample))
char_indices = dict((c, i) for i, c in enumerate(chars))
indices_char = dict((i, c) for i, c in enumerate(chars))
return char_indices, indices_char | python | def create_dicts(data):
""" Modified from Keras LSTM example"""
chars = set()
for sample in data:
chars.update(set(sample))
char_indices = dict((c, i) for i, c in enumerate(chars))
indices_char = dict((i, c) for i, c in enumerate(chars))
return char_indices, indices_char | [
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231,457 | totalgood/nlpia | src/nlpia/book/examples/ch09.py | onehot_encode | def onehot_encode(dataset, char_indices, maxlen):
"""
One hot encode the tokens
Args:
dataset list of lists of tokens
char_indices dictionary of {key=character, value=index to use encoding vector}
maxlen int Length of each sample
Return:
np array of shape (samples, ... | python | def onehot_encode(dataset, char_indices, maxlen):
"""
One hot encode the tokens
Args:
dataset list of lists of tokens
char_indices dictionary of {key=character, value=index to use encoding vector}
maxlen int Length of each sample
Return:
np array of shape (samples, ... | [
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231,458 | totalgood/nlpia | src/nlpia/book/examples/ch04_sklearn_pca_source.py | _fit_full | def _fit_full(self=self, X=X, n_components=6):
"""Fit the model by computing full SVD on X"""
n_samples, n_features = X.shape
# Center data
self.mean_ = np.mean(X, axis=0)
print(self.mean_)
X -= self.mean_
print(X.round(2))
U, S, V = linalg.svd(X, full_matrices=False)
print(V.round... | python | def _fit_full(self=self, X=X, n_components=6):
"""Fit the model by computing full SVD on X"""
n_samples, n_features = X.shape
# Center data
self.mean_ = np.mean(X, axis=0)
print(self.mean_)
X -= self.mean_
print(X.round(2))
U, S, V = linalg.svd(X, full_matrices=False)
print(V.round... | [
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231,459 | totalgood/nlpia | src/nlpia/clean_alice.py | extract_aiml | def extract_aiml(path='aiml-en-us-foundation-alice.v1-9'):
""" Extract an aiml.zip file if it hasn't been already and return a list of aiml file paths """
path = find_data_path(path) or path
if os.path.isdir(path):
paths = os.listdir(path)
paths = [os.path.join(path, p) for p in paths]
e... | python | def extract_aiml(path='aiml-en-us-foundation-alice.v1-9'):
""" Extract an aiml.zip file if it hasn't been already and return a list of aiml file paths """
path = find_data_path(path) or path
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paths = os.listdir(path)
paths = [os.path.join(path, p) for p in paths]
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231,460 | totalgood/nlpia | src/nlpia/clean_alice.py | create_brain | def create_brain(path='aiml-en-us-foundation-alice.v1-9.zip'):
""" Create an aiml_bot.Bot brain from an AIML zip file or directory of AIML files """
path = find_data_path(path) or path
bot = Bot()
num_templates = bot._brain.template_count
paths = extract_aiml(path=path)
for path in paths:
... | python | def create_brain(path='aiml-en-us-foundation-alice.v1-9.zip'):
""" Create an aiml_bot.Bot brain from an AIML zip file or directory of AIML files """
path = find_data_path(path) or path
bot = Bot()
num_templates = bot._brain.template_count
paths = extract_aiml(path=path)
for path in paths:
... | [
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231,461 | totalgood/nlpia | src/nlpia/transcoders.py | minify_urls | def minify_urls(filepath, ext='asc', url_regex=None, output_ext='.urls_minified', access_token=None):
""" Use bitly or similar minifier to shrink all URLs in text files within a folder structure.
Used for the NLPIA manuscript directory for Manning Publishing
bitly API: https://dev.bitly.com/links.html
... | python | def minify_urls(filepath, ext='asc', url_regex=None, output_ext='.urls_minified', access_token=None):
""" Use bitly or similar minifier to shrink all URLs in text files within a folder structure.
Used for the NLPIA manuscript directory for Manning Publishing
bitly API: https://dev.bitly.com/links.html
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Used for the NLPIA manuscript directory for Manning Publishing
bitly API: https://dev.bitly.com/links.html
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path (str): Directory or file path
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231,462 | totalgood/nlpia | src/nlpia/transcoders.py | delimit_slug | def delimit_slug(slug, sep=' '):
""" Return a str of separated tokens found within a slugLike_This => 'slug Like This'
>>> delimit_slug("slugLike_ThisW/aTLA's")
'slug Like This W a TLA s'
>>> delimit_slug('slugLike_ThisW/aTLA', '|')
'slug|Like|This|W|a|TLA'
"""
hyphenated_slug = re.sub(CRE_... | python | def delimit_slug(slug, sep=' '):
""" Return a str of separated tokens found within a slugLike_This => 'slug Like This'
>>> delimit_slug("slugLike_ThisW/aTLA's")
'slug Like This W a TLA s'
>>> delimit_slug('slugLike_ThisW/aTLA', '|')
'slug|Like|This|W|a|TLA'
"""
hyphenated_slug = re.sub(CRE_... | [
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'slug Like This W a TLA s'
>>> delimit_slug('slugLike_ThisW/aTLA', '|')
'slug|Like|This|W|a|TLA' | [
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231,463 | totalgood/nlpia | src/nlpia/transcoders.py | clean_asciidoc | def clean_asciidoc(text):
r""" Transform asciidoc text into ASCII text that NL parsers can handle
TODO:
Tag lines and words with meta data like italics, underlined, bold, title, heading 1, etc
>>> clean_asciidoc('**Hello** _world_!')
'"Hello" "world"!'
"""
text = re.sub(r'(\b|^)[\[_*]{1,... | python | def clean_asciidoc(text):
r""" Transform asciidoc text into ASCII text that NL parsers can handle
TODO:
Tag lines and words with meta data like italics, underlined, bold, title, heading 1, etc
>>> clean_asciidoc('**Hello** _world_!')
'"Hello" "world"!'
"""
text = re.sub(r'(\b|^)[\[_*]{1,... | [
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>>> clean_asciidoc('**Hello** _world_!')
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231,464 | totalgood/nlpia | src/nlpia/transcoders.py | split_sentences_regex | def split_sentences_regex(text):
""" Use dead-simple regex to split text into sentences. Very poor accuracy.
>>> split_sentences_regex("Hello World. I'm I.B.M.'s Watson. --Watson")
['Hello World.', "I'm I.B.M.'s Watson.", '--Watson']
"""
parts = regex.split(r'([a-zA-Z0-9][.?!])[\s$]', text)
sen... | python | def split_sentences_regex(text):
""" Use dead-simple regex to split text into sentences. Very poor accuracy.
>>> split_sentences_regex("Hello World. I'm I.B.M.'s Watson. --Watson")
['Hello World.', "I'm I.B.M.'s Watson.", '--Watson']
"""
parts = regex.split(r'([a-zA-Z0-9][.?!])[\s$]', text)
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>>> split_sentences_regex("Hello World. I'm I.B.M.'s Watson. --Watson")
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231,465 | totalgood/nlpia | src/nlpia/transcoders.py | split_sentences_spacy | def split_sentences_spacy(text, language_model='en'):
r""" You must download a spacy language model with python -m download 'en'
The default English language model for spacy tends to be a lot more agressive than NLTK's punkt:
>>> split_sentences_nltk("Hi Ms. Lovelace.\nI'm a wanna-\nbe human @ I.B.M. ;) -... | python | def split_sentences_spacy(text, language_model='en'):
r""" You must download a spacy language model with python -m download 'en'
The default English language model for spacy tends to be a lot more agressive than NLTK's punkt:
>>> split_sentences_nltk("Hi Ms. Lovelace.\nI'm a wanna-\nbe human @ I.B.M. ;) -... | [
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231,466 | totalgood/nlpia | src/nlpia/transcoders.py | segment_sentences | def segment_sentences(path=os.path.join(DATA_PATH, 'book'), splitter=split_sentences_nltk, **find_files_kwargs):
""" Return a list of all sentences and empty lines.
TODO:
1. process each line with an aggressive sentence segmenter, like DetectorMorse
2. process our manuscript to create a complet... | python | def segment_sentences(path=os.path.join(DATA_PATH, 'book'), splitter=split_sentences_nltk, **find_files_kwargs):
""" Return a list of all sentences and empty lines.
TODO:
1. process each line with an aggressive sentence segmenter, like DetectorMorse
2. process our manuscript to create a complet... | [
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231,467 | totalgood/nlpia | src/nlpia/transcoders.py | fix_hunspell_json | def fix_hunspell_json(badjson_path='en_us.json', goodjson_path='en_us_fixed.json'):
"""Fix the invalid hunspellToJSON.py json format by inserting double-quotes in list of affix strings
Args:
badjson_path (str): path to input json file that doesn't properly quote
goodjson_path (str): path to output ... | python | def fix_hunspell_json(badjson_path='en_us.json', goodjson_path='en_us_fixed.json'):
"""Fix the invalid hunspellToJSON.py json format by inserting double-quotes in list of affix strings
Args:
badjson_path (str): path to input json file that doesn't properly quote
goodjson_path (str): path to output ... | [
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goodjson_path (str): path to output json file with properly quoted strings in list of affixes
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231,468 | totalgood/nlpia | src/nlpia/book/examples/ch12_retrieval.py | format_ubuntu_dialog | def format_ubuntu_dialog(df):
""" Print statements paired with replies, formatted for easy review """
s = ''
for i, record in df.iterrows():
statement = list(split_turns(record.Context))[-1] # <1>
reply = list(split_turns(record.Utterance))[-1] # <2>
s += 'Statement: {}\n'.format(s... | python | def format_ubuntu_dialog(df):
""" Print statements paired with replies, formatted for easy review """
s = ''
for i, record in df.iterrows():
statement = list(split_turns(record.Context))[-1] # <1>
reply = list(split_turns(record.Utterance))[-1] # <2>
s += 'Statement: {}\n'.format(s... | [
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231,469 | totalgood/nlpia | src/nlpia/regexes.py | splitext | def splitext(filepath):
""" Like os.path.splitext except splits compound extensions as one long one
>>> splitext('~/.bashrc.asciidoc.ext.ps4.42')
('~/.bashrc', '.asciidoc.ext.ps4.42')
>>> splitext('~/.bash_profile')
('~/.bash_profile', '')
"""
exts = getattr(CRE_FILENAME_EXT.search(filepath... | python | def splitext(filepath):
""" Like os.path.splitext except splits compound extensions as one long one
>>> splitext('~/.bashrc.asciidoc.ext.ps4.42')
('~/.bashrc', '.asciidoc.ext.ps4.42')
>>> splitext('~/.bash_profile')
('~/.bash_profile', '')
"""
exts = getattr(CRE_FILENAME_EXT.search(filepath... | [
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"... | Like os.path.splitext except splits compound extensions as one long one
>>> splitext('~/.bashrc.asciidoc.ext.ps4.42')
('~/.bashrc', '.asciidoc.ext.ps4.42')
>>> splitext('~/.bash_profile')
('~/.bash_profile', '') | [
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231,470 | totalgood/nlpia | src/nlpia/plots.py | offline_plotly_scatter3d | def offline_plotly_scatter3d(df, x=0, y=1, z=-1):
""" Plot an offline scatter plot colored according to the categories in the 'name' column.
>> df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/iris.csv')
>> offline_plotly(df)
"""
data = []
# clusters = []
colors = ... | python | def offline_plotly_scatter3d(df, x=0, y=1, z=-1):
""" Plot an offline scatter plot colored according to the categories in the 'name' column.
>> df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/iris.csv')
>> offline_plotly(df)
"""
data = []
# clusters = []
colors = ... | [
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>> df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/iris.csv')
>> offline_plotly(df) | [
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231,471 | totalgood/nlpia | src/nlpia/plots.py | offline_plotly_data | def offline_plotly_data(data, filename=None, config=None, validate=True,
default_width='100%', default_height=525, global_requirejs=False):
r""" Write a plotly scatter plot to HTML file that doesn't require server
>>> from nlpia.loaders import get_data
>>> df = get_data('etpinard') ... | python | def offline_plotly_data(data, filename=None, config=None, validate=True,
default_width='100%', default_height=525, global_requirejs=False):
r""" Write a plotly scatter plot to HTML file that doesn't require server
>>> from nlpia.loaders import get_data
>>> df = get_data('etpinard') ... | [
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>>> from nlpia.loaders import get_data
>>> df = get_data('etpinard') # pd.read_csv('https://plot.ly/~etpinard/191.csv')
>>> df.columns = [eval(c) if c[0] in '"\'' else str(c) for c in df.columns]
>>> data = {'data': [
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231,472 | totalgood/nlpia | src/nlpia/plots.py | normalize_etpinard_df | def normalize_etpinard_df(df='https://plot.ly/~etpinard/191.csv', columns='x y size text'.split(),
category_col='category', possible_categories=['Africa', 'Americas', 'Asia', 'Europe', 'Oceania']):
"""Reformat a dataframe in etpinard's format for use in plot functions and sklearn models"""... | python | def normalize_etpinard_df(df='https://plot.ly/~etpinard/191.csv', columns='x y size text'.split(),
category_col='category', possible_categories=['Africa', 'Americas', 'Asia', 'Europe', 'Oceania']):
"""Reformat a dataframe in etpinard's format for use in plot functions and sklearn models"""... | [
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231,473 | totalgood/nlpia | src/nlpia/plots.py | offline_plotly_scatter_bubble | def offline_plotly_scatter_bubble(df, x='x', y='y', size_col='size', text_col='text',
category_col='category', possible_categories=None,
filename=None,
config={'displaylogo': False},
x... | python | def offline_plotly_scatter_bubble(df, x='x', y='y', size_col='size', text_col='text',
category_col='category', possible_categories=None,
filename=None,
config={'displaylogo': False},
x... | [
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config keys:
fillFrame setBackground displaylogo sendData showLink linkText staticPlot scrollZoom plot3dPixelRatio displayModeBar
showTips workspace doubleClick autosizable editable
layout keys:
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231,474 | totalgood/nlpia | src/nlpia/data_utils.py | format_hex | def format_hex(i, num_bytes=4, prefix='0x'):
""" Format hexidecimal string from decimal integer value
>>> format_hex(42, num_bytes=8, prefix=None)
'0000002a'
>>> format_hex(23)
'0x0017'
"""
prefix = str(prefix or '')
i = int(i or 0)
return prefix + '{0:0{1}x}'.format(i, num_bytes) | python | def format_hex(i, num_bytes=4, prefix='0x'):
""" Format hexidecimal string from decimal integer value
>>> format_hex(42, num_bytes=8, prefix=None)
'0000002a'
>>> format_hex(23)
'0x0017'
"""
prefix = str(prefix or '')
i = int(i or 0)
return prefix + '{0:0{1}x}'.format(i, num_bytes) | [
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'0000002a'
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'0x0017' | [
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231,475 | totalgood/nlpia | src/nlpia/data_utils.py | is_up_url | def is_up_url(url, allow_redirects=False, timeout=5):
r""" Check URL to see if it is a valid web page, return the redirected location if it is
Returns:
None if ConnectionError
False if url is invalid (any HTTP error code)
cleaned up URL (following redirects and possibly adding HTTP schema "ht... | python | def is_up_url(url, allow_redirects=False, timeout=5):
r""" Check URL to see if it is a valid web page, return the redirected location if it is
Returns:
None if ConnectionError
False if url is invalid (any HTTP error code)
cleaned up URL (following redirects and possibly adding HTTP schema "ht... | [
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Returns:
None if ConnectionError
False if url is invalid (any HTTP error code)
cleaned up URL (following redirects and possibly adding HTTP schema "http://")
>>> is_up_url("duckduckgo.com") # a more pri... | [
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231,476 | totalgood/nlpia | src/nlpia/data_utils.py | get_markdown_levels | def get_markdown_levels(lines, levels=set((0, 1, 2, 3, 4, 5, 6))):
r""" Return a list of 2-tuples with a level integer for the heading levels
>>> get_markdown_levels('paragraph \n##bad\n# hello\n ### world\n')
[(0, 'paragraph '), (2, 'bad'), (0, '# hello'), (3, 'world')]
>>> get_markdown_levels('- bul... | python | def get_markdown_levels(lines, levels=set((0, 1, 2, 3, 4, 5, 6))):
r""" Return a list of 2-tuples with a level integer for the heading levels
>>> get_markdown_levels('paragraph \n##bad\n# hello\n ### world\n')
[(0, 'paragraph '), (2, 'bad'), (0, '# hello'), (3, 'world')]
>>> get_markdown_levels('- bul... | [
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[(0, 'paragraph '), (2, 'bad'), (0, '# hello'), (3, 'world')]
>>> get_markdown_levels('- bullet \n##bad\n# hello\n ### world\n')
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231,477 | totalgood/nlpia | src/nlpia/data_utils.py | iter_lines | def iter_lines(url_or_text, ext=None, mode='rt'):
r""" Return an iterator over the lines of a file or URI response.
>>> len(list(iter_lines('cats_and_dogs.txt')))
263
>>> len(list(iter_lines(list('abcdefgh'))))
8
>>> len(list(iter_lines('abc\n def\n gh\n')))
3
>>> len(list(iter_lines('a... | python | def iter_lines(url_or_text, ext=None, mode='rt'):
r""" Return an iterator over the lines of a file or URI response.
>>> len(list(iter_lines('cats_and_dogs.txt')))
263
>>> len(list(iter_lines(list('abcdefgh'))))
8
>>> len(list(iter_lines('abc\n def\n gh\n')))
3
>>> len(list(iter_lines('a... | [
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>>> 20000 > len(list(iter_... | [
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231,478 | totalgood/nlpia | src/nlpia/data_utils.py | parse_utf_html | def parse_utf_html(url=os.path.join(DATA_PATH, 'utf8_table.html')):
""" Parse HTML table UTF8 char descriptions returning DataFrame with `ascii` and `mutliascii` """
utf = pd.read_html(url)
utf = [df for df in utf if len(df) > 1023 and len(df.columns) > 2][0]
utf = utf.iloc[:1024] if len(utf) == 1025 el... | python | def parse_utf_html(url=os.path.join(DATA_PATH, 'utf8_table.html')):
""" Parse HTML table UTF8 char descriptions returning DataFrame with `ascii` and `mutliascii` """
utf = pd.read_html(url)
utf = [df for df in utf if len(df) > 1023 and len(df.columns) > 2][0]
utf = utf.iloc[:1024] if len(utf) == 1025 el... | [
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231,479 | totalgood/nlpia | src/nlpia/data_utils.py | clean_csvs | def clean_csvs(dialogpath=None):
""" Translate non-ASCII characters to spaces or equivalent ASCII characters """
dialogdir = os.dirname(dialogpath) if os.path.isfile(dialogpath) else dialogpath
filenames = [dialogpath.split(os.path.sep)[-1]] if os.path.isfile(dialogpath) else os.listdir(dialogpath)
for ... | python | def clean_csvs(dialogpath=None):
""" Translate non-ASCII characters to spaces or equivalent ASCII characters """
dialogdir = os.dirname(dialogpath) if os.path.isfile(dialogpath) else dialogpath
filenames = [dialogpath.split(os.path.sep)[-1]] if os.path.isfile(dialogpath) else os.listdir(dialogpath)
for ... | [
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] | efa01126275e9cd3c3a5151a644f1c798a9ec53f | https://github.com/totalgood/nlpia/blob/efa01126275e9cd3c3a5151a644f1c798a9ec53f/src/nlpia/data_utils.py#L294-L302 |
231,480 | totalgood/nlpia | src/nlpia/data_utils.py | unicode2ascii | def unicode2ascii(text, expand=True):
r""" Translate UTF8 characters to ASCII
>> unicode2ascii("żółw")
zozw
utf8_letters = 'ą ę ć ź ż ó ł ń ś “ ” ’'.split()
ascii_letters = 'a e c z z o l n s " " \''
"""
translate = UTF8_TO_ASCII if not expand else UTF8_TO_MULTIASCII
output = ''
f... | python | def unicode2ascii(text, expand=True):
r""" Translate UTF8 characters to ASCII
>> unicode2ascii("żółw")
zozw
utf8_letters = 'ą ę ć ź ż ó ł ń ś “ ” ’'.split()
ascii_letters = 'a e c z z o l n s " " \''
"""
translate = UTF8_TO_ASCII if not expand else UTF8_TO_MULTIASCII
output = ''
f... | [
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>> unicode2ascii("żółw")
zozw
utf8_letters = 'ą ę ć ź ż ó ł ń ś “ ” ’'.split()
ascii_letters = 'a e c z z o l n s " " \'' | [
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231,481 | totalgood/nlpia | src/nlpia/data_utils.py | clean_df | def clean_df(df, header=None, **read_csv_kwargs):
""" Convert UTF8 characters in a CSV file or dataframe into ASCII
Args:
df (DataFrame or str): DataFrame or path or url to CSV
"""
df = read_csv(df, header=header, **read_csv_kwargs)
df = df.fillna(' ')
for col in df.columns:
df[co... | python | def clean_df(df, header=None, **read_csv_kwargs):
""" Convert UTF8 characters in a CSV file or dataframe into ASCII
Args:
df (DataFrame or str): DataFrame or path or url to CSV
"""
df = read_csv(df, header=header, **read_csv_kwargs)
df = df.fillna(' ')
for col in df.columns:
df[co... | [
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231,482 | totalgood/nlpia | src/nlpia/book_parser.py | get_acronyms | def get_acronyms(manuscript=os.path.expanduser('~/code/nlpia/lane/manuscript')):
""" Find all the 2 and 3-letter acronyms in the manuscript and return as a sorted list of tuples """
acronyms = []
for f, lines in get_lines(manuscript):
for line in lines:
matches = CRE_ACRONYM.finditer(lin... | python | def get_acronyms(manuscript=os.path.expanduser('~/code/nlpia/lane/manuscript')):
""" Find all the 2 and 3-letter acronyms in the manuscript and return as a sorted list of tuples """
acronyms = []
for f, lines in get_lines(manuscript):
for line in lines:
matches = CRE_ACRONYM.finditer(lin... | [
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231,483 | totalgood/nlpia | src/nlpia/book_parser.py | write_glossary | def write_glossary(manuscript=os.path.expanduser('~/code/nlpia/lane/manuscript'), linesep=None):
""" Compose an asciidoc string with acronyms culled from the manuscript """
linesep = linesep or os.linesep
lines = ['[acronyms]', '== Acronyms', '', '[acronyms,template="glossary",id="terms"]']
acronyms = g... | python | def write_glossary(manuscript=os.path.expanduser('~/code/nlpia/lane/manuscript'), linesep=None):
""" Compose an asciidoc string with acronyms culled from the manuscript """
linesep = linesep or os.linesep
lines = ['[acronyms]', '== Acronyms', '', '[acronyms,template="glossary",id="terms"]']
acronyms = g... | [
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231,484 | totalgood/nlpia | src/nlpia/book_parser.py | infer_url_title | def infer_url_title(url):
""" Guess what the page title is going to be from the path and FQDN in the URL
>>> infer_url_title('https://ai.googleblog.com/2018/09/the-what-if-tool-code-free-probing-of.html')
'the what if tool code free probing of'
"""
meta = get_url_filemeta(url)
title = ''
if... | python | def infer_url_title(url):
""" Guess what the page title is going to be from the path and FQDN in the URL
>>> infer_url_title('https://ai.googleblog.com/2018/09/the-what-if-tool-code-free-probing-of.html')
'the what if tool code free probing of'
"""
meta = get_url_filemeta(url)
title = ''
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'the what if tool code free probing of' | [
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231,485 | totalgood/nlpia | src/nlpia/book_parser.py | translate_book | def translate_book(translators=(HyperlinkStyleCorrector().translate, translate_line_footnotes),
book_dir=BOOK_PATH, dest=None, include_tags=None,
ext='.nlpiabak', skip_untitled=True):
""" Fix any style corrections listed in `translate` list of translation functions
>>> len... | python | def translate_book(translators=(HyperlinkStyleCorrector().translate, translate_line_footnotes),
book_dir=BOOK_PATH, dest=None, include_tags=None,
ext='.nlpiabak', skip_untitled=True):
""" Fix any style corrections listed in `translate` list of translation functions
>>> len... | [
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>>> len(translate_book(book_dir=BOOK_PATH, dest='cleaned_hyperlinks'))
3
>>> rm_rf(os.path.join(BOOK_PATH, 'cleaned_hyperlinks')) | [
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231,486 | totalgood/nlpia | src/nlpia/book_parser.py | filter_lines | def filter_lines(input_file, output_file, translate=lambda line: line):
""" Translate all the lines of a single file """
filepath, lines = get_lines([input_file])[0]
return filepath, [(tag, translate(line=line, tag=tag)) for (tag, line) in lines] | python | def filter_lines(input_file, output_file, translate=lambda line: line):
""" Translate all the lines of a single file """
filepath, lines = get_lines([input_file])[0]
return filepath, [(tag, translate(line=line, tag=tag)) for (tag, line) in lines] | [
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231,487 | totalgood/nlpia | src/nlpia/book_parser.py | filter_tagged_lines | def filter_tagged_lines(tagged_lines, include_tags=None, exclude_tags=None):
r""" Return iterable of tagged lines where the tags all start with one of the include_tags prefixes
>>> filter_tagged_lines([('natural', "Hello."), ('code', '[source,python]'), ('code', '>>> hello()')])
<generator object filter_ta... | python | def filter_tagged_lines(tagged_lines, include_tags=None, exclude_tags=None):
r""" Return iterable of tagged lines where the tags all start with one of the include_tags prefixes
>>> filter_tagged_lines([('natural', "Hello."), ('code', '[source,python]'), ('code', '>>> hello()')])
<generator object filter_ta... | [
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231,488 | totalgood/nlpia | src/nlpia/book/examples/ch04_catdog_lsa_sorted.py | accuracy_study | def accuracy_study(tdm=None, u=None, s=None, vt=None, verbosity=0, **kwargs):
""" Reconstruct the term-document matrix and measure error as SVD terms are truncated
"""
smat = np.zeros((len(u), len(vt)))
np.fill_diagonal(smat, s)
smat = pd.DataFrame(smat, columns=vt.index, index=u.index)
if verbo... | python | def accuracy_study(tdm=None, u=None, s=None, vt=None, verbosity=0, **kwargs):
""" Reconstruct the term-document matrix and measure error as SVD terms are truncated
"""
smat = np.zeros((len(u), len(vt)))
np.fill_diagonal(smat, s)
smat = pd.DataFrame(smat, columns=vt.index, index=u.index)
if verbo... | [
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231,489 | totalgood/nlpia | src/nlpia/anki.py | get_anki_phrases | def get_anki_phrases(lang='english', limit=None):
""" Retrieve as many anki paired-statement corpora as you can for the requested language
If `ankis` (requested languages) is more than one, then get the english texts associated with those languages.
TODO: improve modularity: def function that takes a sing... | python | def get_anki_phrases(lang='english', limit=None):
""" Retrieve as many anki paired-statement corpora as you can for the requested language
If `ankis` (requested languages) is more than one, then get the english texts associated with those languages.
TODO: improve modularity: def function that takes a sing... | [
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If `ankis` (requested languages) is more than one, then get the english texts associated with those languages.
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>>> g... | [
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231,490 | totalgood/nlpia | src/nlpia/anki.py | get_anki_phrases_english | def get_anki_phrases_english(limit=None):
""" Return all the English phrases in the Anki translation flashcards
>>> len(get_anki_phrases_english(limit=100)) > 700
True
"""
texts = set()
for lang in ANKI_LANGUAGES:
df = get_data(lang)
phrases = df.eng.str.strip().values
... | python | def get_anki_phrases_english(limit=None):
""" Return all the English phrases in the Anki translation flashcards
>>> len(get_anki_phrases_english(limit=100)) > 700
True
"""
texts = set()
for lang in ANKI_LANGUAGES:
df = get_data(lang)
phrases = df.eng.str.strip().values
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231,491 | totalgood/nlpia | src/nlpia/anki.py | get_vocab | def get_vocab(docs):
""" Build a DataFrame containing all the words in the docs provided along with their POS tags etc
>>> doc = nlp("Hey Mr. Tangerine Man!")
<BLANKLINE>
...
>>> get_vocab([doc])
word pos tag dep ent_type ent_iob sentiment
0 ! PUNCT . pun... | python | def get_vocab(docs):
""" Build a DataFrame containing all the words in the docs provided along with their POS tags etc
>>> doc = nlp("Hey Mr. Tangerine Man!")
<BLANKLINE>
...
>>> get_vocab([doc])
word pos tag dep ent_type ent_iob sentiment
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>>> get_vocab([doc])
word pos tag dep ent_type ent_iob sentiment
0 ! PUNCT . punct O 0.... | [
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231,492 | totalgood/nlpia | src/nlpia/anki.py | get_word_vectors | def get_word_vectors(vocab):
""" Create a word2vec embedding matrix for all the words in the vocab """
wv = get_data('word2vec')
vectors = np.array(len(vocab), len(wv['the']))
for i, tok in enumerate(vocab):
word = tok[0]
variations = (word, word.lower(), word.lower()[:-1])
for w... | python | def get_word_vectors(vocab):
""" Create a word2vec embedding matrix for all the words in the vocab """
wv = get_data('word2vec')
vectors = np.array(len(vocab), len(wv['the']))
for i, tok in enumerate(vocab):
word = tok[0]
variations = (word, word.lower(), word.lower()[:-1])
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231,493 | totalgood/nlpia | src/nlpia/anki.py | get_anki_vocab | def get_anki_vocab(lang=['eng'], limit=None, filename='anki_en_vocabulary.csv'):
""" Get all the vocab words+tags+wordvectors for the tokens in the Anki translation corpus
Returns a DataFrame of with columns = word, pos, tag, dep, ent, ent_iob, sentiment, vectors
"""
texts = get_anki_phrases(lang=lang,... | python | def get_anki_vocab(lang=['eng'], limit=None, filename='anki_en_vocabulary.csv'):
""" Get all the vocab words+tags+wordvectors for the tokens in the Anki translation corpus
Returns a DataFrame of with columns = word, pos, tag, dep, ent, ent_iob, sentiment, vectors
"""
texts = get_anki_phrases(lang=lang,... | [
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231,494 | totalgood/nlpia | src/nlpia/scripts/lsa_tweets.py | lsa_twitter | def lsa_twitter(cased_tokens):
""" Latent Sentiment Analyis on random sampling of twitter search results for words listed in cased_tokens """
# Only 5 of these tokens are saved for a no_below=2 filter:
# PyCons NLPS #PyCon2016 #NaturalLanguageProcessing #naturallanguageprocessing
if cased_tokens is N... | python | def lsa_twitter(cased_tokens):
""" Latent Sentiment Analyis on random sampling of twitter search results for words listed in cased_tokens """
# Only 5 of these tokens are saved for a no_below=2 filter:
# PyCons NLPS #PyCon2016 #NaturalLanguageProcessing #naturallanguageprocessing
if cased_tokens is N... | [
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231,495 | totalgood/nlpia | src/nlpia/futil.py | wc | def wc(f, verbose=False, nrows=None):
r""" Count lines in a text file
References:
https://stackoverflow.com/q/845058/623735
>>> with open(os.path.join(DATA_PATH, 'dictionary_fda_drug_names.txt')) as fin:
... print(wc(fin) == wc(fin) == 7037 == wc(fin.name))
True
>>> wc(fin.name)
... | python | def wc(f, verbose=False, nrows=None):
r""" Count lines in a text file
References:
https://stackoverflow.com/q/845058/623735
>>> with open(os.path.join(DATA_PATH, 'dictionary_fda_drug_names.txt')) as fin:
... print(wc(fin) == wc(fin) == 7037 == wc(fin.name))
True
>>> wc(fin.name)
... | [
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References:
https://stackoverflow.com/q/845058/623735
>>> with open(os.path.join(DATA_PATH, 'dictionary_fda_drug_names.txt')) as fin:
... print(wc(fin) == wc(fin) == 7037 == wc(fin.name))
True
>>> wc(fin.name)
7037 | [
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] | efa01126275e9cd3c3a5151a644f1c798a9ec53f | https://github.com/totalgood/nlpia/blob/efa01126275e9cd3c3a5151a644f1c798a9ec53f/src/nlpia/futil.py#L36-L54 |
231,496 | totalgood/nlpia | src/nlpia/futil.py | normalize_filepath | def normalize_filepath(filepath):
r""" Lowercase the filename and ext, expanding extensions like .tgz to .tar.gz.
>>> normalize_filepath('/Hello_World.txt\n')
'hello_world.txt'
>>> normalize_filepath('NLPIA/src/nlpia/bigdata/Goog New 300Dneg\f.bIn\n.GZ')
'NLPIA/src/nlpia/bigdata/goog new 300dneg.bi... | python | def normalize_filepath(filepath):
r""" Lowercase the filename and ext, expanding extensions like .tgz to .tar.gz.
>>> normalize_filepath('/Hello_World.txt\n')
'hello_world.txt'
>>> normalize_filepath('NLPIA/src/nlpia/bigdata/Goog New 300Dneg\f.bIn\n.GZ')
'NLPIA/src/nlpia/bigdata/goog new 300dneg.bi... | [
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>>> normalize_filepath('/Hello_World.txt\n')
'hello_world.txt'
>>> normalize_filepath('NLPIA/src/nlpia/bigdata/Goog New 300Dneg\f.bIn\n.GZ')
'NLPIA/src/nlpia/bigdata/goog new 300dneg.bin.gz' | [
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231,497 | totalgood/nlpia | src/nlpia/futil.py | find_filepath | def find_filepath(
filename,
basepaths=(os.path.curdir, DATA_PATH, BIGDATA_PATH, BASE_DIR, '~', '~/Downloads', os.path.join('/', 'tmp'), '..')):
""" Given a filename or path see if it exists in any of the common places datafiles might be
>>> p = find_filepath('iq_test.csv')
>>> p == expand_... | python | def find_filepath(
filename,
basepaths=(os.path.curdir, DATA_PATH, BIGDATA_PATH, BASE_DIR, '~', '~/Downloads', os.path.join('/', 'tmp'), '..')):
""" Given a filename or path see if it exists in any of the common places datafiles might be
>>> p = find_filepath('iq_test.csv')
>>> p == expand_... | [
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>>> p == expand_filepath(os.path.join(DATA_PATH, 'iq_test.csv'))
True
>>> p[-len('iq_test.csv'):]
'iq_test.csv'
>>> find_filepath('exponentially-crazy-filename-2.7182818... | [
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231,498 | neo4j/neo4j-python-driver | neo4j/__init__.py | Driver.close | def close(self):
""" Shut down, closing any open connections in the pool.
"""
if not self._closed:
self._closed = True
if self._pool is not None:
self._pool.close()
self._pool = None | python | def close(self):
""" Shut down, closing any open connections in the pool.
"""
if not self._closed:
self._closed = True
if self._pool is not None:
self._pool.close()
self._pool = None | [
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231,499 | neo4j/neo4j-python-driver | neo4j/types/spatial.py | hydrate_point | def hydrate_point(srid, *coordinates):
""" Create a new instance of a Point subclass from a raw
set of fields. The subclass chosen is determined by the
given SRID; a ValueError will be raised if no such
subclass can be found.
"""
try:
point_class, dim = __srid_table[srid]
except KeyE... | python | def hydrate_point(srid, *coordinates):
""" Create a new instance of a Point subclass from a raw
set of fields. The subclass chosen is determined by the
given SRID; a ValueError will be raised if no such
subclass can be found.
"""
try:
point_class, dim = __srid_table[srid]
except KeyE... | [
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