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def add(a, b): | |
return a + b |
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def add(a: int, b: int) -> int: | |
return a + b |
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... | |
wait_time = 2 | |
number_of_ip_rotations = 3 | |
tor_handler = TorHandler() | |
ip = tor_handler.open_url('http://icanhazip.com/') | |
print('My first IP: {}'.format(ip)) | |
# Cycle through the specified number of IP addresses via TOR |
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from urllib.request import ProxyHandler, build_opener, install_opener, Request, urlopen | |
from stem import Signal | |
from stem.control import Controller | |
class TorHandler: | |
def __init__(self): | |
self.headers = { | |
'User-Agent': 'Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.9.0.7) Gecko/2009021910 Firefox/3.0.7'} |
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from adder import add | |
x = add(4, 6) |
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from adder import add | |
z = add(4.0, 6.0) |
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from pandas import read_csv | |
from sklearn.model_selection import train_test_split | |
url = "https://raw.githubusercontent.com/baatout/ml-in-prod/master/pima-indians-diabetes.csv" | |
features = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age'] | |
label = 'label' | |
dataframe = read_csv(url, names=features + [label]) | |
X = dataframe[features] | |
Y = dataframe[label] | |
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=42) |
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<?xml version="1.0" encoding="UTF-8" standalone="yes"?> | |
<PMML xmlns="http://www.dmg.org/PMML-4_3" xmlns:data="http://jpmml.org/jpmml-model/InlineTable" version="4.3"> | |
<Header> | |
<Application name="JPMML-SkLearn" version="1.5.6"/> | |
<Timestamp>2018-09-08T11:13:03Z</Timestamp> | |
</Header> | |
<MiningBuildTask> | |
<Extension>PMMLPipeline(steps=[('mapper', DataFrameMapper(default=False, df_out=False, | |
features=[(['mass'], FunctionTransformer(accept_sparse=False, func=<ufunc 'log1p'>, | |
inv_kw_args=None, inverse_func=None, kw_args=None, |
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# with X_train, X_test, Y_train, Y_test | |
import numpy as np | |
from sklearn_pandas import DataFrameMapper | |
from sklearn2pmml import PMMLPipeline, sklearn2pmml | |
from sklearn.linear_model import LogisticRegression | |
from sklearn.preprocessing import FunctionTransformer | |
clf = PMMLPipeline([ | |
("mapper", DataFrameMapper([ | |
(['mass'], FunctionTransformer(np.log1p)), |
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# with X_train, X_test, Y_train, Y_test | |
from sklearn.linear_model import LogisticRegression | |
clf = LogisticRegression() | |
clf.fit(X_train, Y_train) | |
print(clf.score(X_test, Y_test)) | |
import json | |
with open('logreg_coefs', 'w') as f: | |
json.dump(clf.coef_.tolist(), f) |
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