def test_init(self):
client = Client("http://example.com:9004")
self.assertEqual(client._endpoint, "http://example.com:9004")
client = Client("http://example.com/", 10.0)
self.assertEqual(client._endpoint, "http://example.com/")
client = Client(endpoint="https://example.com/", query_timeout=-10.0)
self.assertEqual(client._endpoint, "https://example.com/")
self.assertEqual(client.query_timeout, 0.0)
# valid name tests
with self.assertRaises(ValueError):
Client('')
with self.assertRaises(TypeError):
Client(1.0)
with self.assertRaises(ValueError):
Client("*#")
with self.assertRaises(TypeError):
Client()
with self.assertRaises(ValueError):
Client("http:/www.example.com/")
with self.assertRaises(ValueError):
Client("httpx://www.example.com:9004")
def main():
client = ipp.Client(profile="default")
dview = client[:]
dview.execute("import bodo")
dview.execute("import numpy as np")
dview.execute("import pandas as pd")
dview.execute("import os")
dview.execute(inspect.getsource(lr_from_sql))
tp_client = Client('http://localhost:8080/')
tp_client.deploy('lr_snowflake',
lr_snowflake,
'Logistic regression from Snowflake table',
override=True)
def deploy_model(funcName, func, funcDescription):
# running from deploy_models.py
config_file_path = sys.argv[1] if len(
sys.argv) > 1 else get_default_config_file_path()
port, auth_on, prefix = parse_config(config_file_path)
connection = Client(f"{prefix}://localhost:{port}/")
if auth_on:
# credentials are passed in from setup.py
user, passwd = sys.argv[2], sys.argv[3] if len(
sys.argv) == 4 else get_creds()
connection.set_credentials(user, passwd)
connection.deploy(funcName, func, funcDescription, override=True)
print(f"Successfully deployed {funcName}")
def clean_text(input_df):
'''
This function create preprocessed PAR and output the new dataframe.
Called in Tableau Prep
Args:
------
whole dataframe from Tableau
Returns:
--------
Returns processed pandas dataframe
'''
client = Client("http://10.155.94.140:9004/")
processed = client.query('clean_text',
input_df['X_PAR_COMMENTS'].tolist())['response']
input_df['PROCESSED_PAR'] = processed
output_df = input_df
# return the entire df
return output_df
def deploy_model(funcName, func, funcDescription):
# running from deploy_models.py
if len(sys.argv) > 1:
config_file_path = sys.argv[1]
else:
config_file_path = get_default_config_file_path()
port, auth_on, prefix = parse_config(config_file_path)
connection = Client(f'{prefix}://localhost:{port}/')
if auth_on:
# credentials are passed in from setup.py
if len(sys.argv) == 4:
user, passwd = sys.argv[2], sys.argv[3]
# running Sentiment Analysis independently
else:
user, passwd = get_creds()
connection.set_credentials(user, passwd)
connection.deploy(funcName, func, funcDescription, override=True)
print(f'Successfully deployed {funcName}')
from tabpy.tabpy_tools.client import Client
client = Client('http://localhost:9004/')
client.set_credentials('kikin', 'karate10')
# Deploying a Function
# * Add Function
def add(x, y):
import numpy as np
return np.add(x, y).tolist()
client.deploy('add', add, 'Adds two numbers x and y', override=True)
from tabpy.tabpy_tools.client import Client
client = Client('http://localhost:8080/')
def clustering(x, y):
import numpy as np
from sklearn.cluster import DBSCAN
from sklearn.preprocessing import StandardScaler
X = np.column_stack([x, y])
X = StandardScaler().fit_transform(X)
db = DBSCAN(eps=1, min_samples=3).fit(X)
return db.labels_.tolist()
if __name__ == "__main__":
client.deploy(
'clustering',
clustering,
'Returns cluster Ids for each data point specified by the '
'pairs in x and y'
)
#!/usr/bin/env python
# coding: utf-8
import pandas as pd
from scipy.spatial import KDTree
from tabpy.tabpy_tools.client import Client
connection = Client('http://localhost:9004/')
def rec(arg1, arg2, arg3, arg4, arg5, arg6, arg7, arg8, arg9, arg10, arg11,
arg12, arg13, arg14, arg15, arg16):
_arg1 = arg1[0]
_arg2 = arg2[0]
_arg3 = arg3[0]
_arg4 = arg4[0]
_arg5 = arg5[0]
_arg6 = arg6[0]
_arg7 = arg7[0]
_arg8 = arg8[0]
_arg9 = arg9[0]
_arg10 = arg10[0]
_arg11 = arg11[0]
_arg12 = arg12[0]
_arg13 = arg13[0]
_arg14 = arg14[0]
_arg15 = arg15[0]
_arg16 = arg16[0]
data = pd.read_csv('data_cleaned_and_pruned.csv', delimiter="|")
def setUp(self):
self.client = Client("http://example.com/")
self.client._service = Mock() # TODO: should spec this
# Import packages
from tabpy.tabpy_tools.client import Client
client = Client("http://10.155.94.140:9004/")
def clean_text(text_list):
"""
Clean text with spacy library
"""
import pandas as pd
import numpy as np
import spacy
# configure stopword path
stopword_path = "/home/nusintern/project/nus/scripts/stopwords.txt"
# Import language model
nlp = spacy.load('en_core_web_sm', disable=['tagger','parser', 'ner'])
#Load custom stopwords
custom_stopwords = []
with open(stopword_path) as f:
custom_stopwords = f.read().splitlines()
custom_stopwords = set(custom_stopwords)
return [process_text(x, nlp, custom_stopwords) for x in text_list]
def process_text(text, nlp, stopwords):
'''
This function performs text data preprocessing, including tokenizing the text, converting text to lower case, removing
punctuation, removing digits, removing stop words, stemming the tokens, then converting the tokens back to strings.
