[GitHub]Large Scale Machine Learning with Python

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Large Scale Machine Learning with Python

This is the code repository for Large Scale Machine Learning with Python, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

Instructions

The execution of the code examples provided in this book requires an installation of Python 2.7 or higher versions on macOS, Linux, or Microsoft Windows. The examples throughout the book will make frequent use of Python's essential libraries, such as SciPy, NumPy, Scikit-learn, and StatsModels, and to a minor extent, matplotlib and pandas, for scientific and statistical computing. We will also make use of an out-of-core cloud computing application called H2O. This book is highly dependent on Jupyter and its Notebooks powered by the Python kernel. We will use its most recent version, 4.1, for this book. The first chapter will provide you with all the step-by-step instructions and some useful tips to set up your Python environment, these core libraries, and all the necessary tools.

Related books

• R Machine Learning By Example
• R Machine Learning Essentials
• Machine Learning with R
  

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https://github.com/PacktPublishing/Large-Scale-Machine-Learning-With-Python

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关键词：Learning machine earning GitHub python supporting Microsoft necessary essential execution

1. Non-linear & faster with Vowpal Wabbit
   
2. Useful functions
   
3. In [1]:
   
4. import numpy as np
   
5. 
   
6. def sigmoid(x):
   
7.     return 1. / (1. + np.exp(-x))
   
8. 
   
9. def apply_log(x):
   
10.     return np.log(x + 1.0)
    
11. 
    
12. def apply_exp(x):
    
13.     return np.exp(x) - 1.0
    
14. Useful dataset examples
    
15. In [37]:
    
16. with open('house_dataset','wb') as W:
    
17.     W.write("0 | price:.23 sqft:.25 age:.05 2006\n")
    
18.     W.write("1 2 'second_house | price:.18 sqft:.15 age:.35 1976\n")
    
19.     W.write("0 1 0.5 'third_house | price:.53 sqft:.32 age:.87 1924\n")
    
20. 
    
21. with open('house_dataset','rb') as R:
    
22.     for line in R:
    
23.         print line.strip()
    
24. 0 | price:.23 sqft:.25 age:.05 2006
    
25. 1 2 'second_house | price:.18 sqft:.15 age:.35 1976
    
26. 0 1 0.5 'third_house | price:.53 sqft:.32 age:.87 1924
    
27. A way to call VW from Python
    
28. In [2]:
    
29. import subprocess
    
30. 
    
31. def execute_vw(parameters):
    
32.     execution = subprocess.Popen('vw '+parameters, shell=True, stderr=subprocess.PIPE)
    
33.     line = ""
    
34.     history = ""
    
35.     while True:
    
36.         out = execution.stderr.read(1)
    
37.         history += out
    
38.         if out == '' and execution.poll() != None:
    
39.             print '------------ COMPLETED ------------\n'
    
40.             break
    
41.         if out != '':
    
42.             line += out
    
43.             if '\n' in line[-2:]:
    
44.                 print line[:-2]
    
45.                 line = ''
    
46.     return history.split('\r\n')
    
47. 
    
48. 
    
49. params = "house_dataset"
    
50. results = execute_vw(params)

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Large Scale Machine Learning with Python

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