【GitHub】Data Science Algorithms in a Week

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Data Science Algorithms in a Week

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https://github.com/PacktPublishing/Data-Science-Algorithms-in-a-Week

This is the code repository for Data Science Algorithms in a Week, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

About the Book

This book will address the problems related to accurate and efficient data classification and prediction. Over the course of 7 days, you will be introduced to seven algorithms, along with exercises that will help you learn different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. You will then find out how to predict data based on the existing trends in your datasets.

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关键词：Data Science Algorithms Algorithm Science Data

1. # A program that reads the CSV file with the data and returns
   
2. # the Bayesian probability for the unknown value denoted by ? to
   
3. # belong to a certain class.
   
4. # An input CSV file should be of the following format:
   
5. # 1. items in a row should be separated by a comma ','
   
6. # 2. the first row should be a heading - should contain a name for each
   
7. # column of the data.
   
8. # 3. the remaining rows should contain the data itself - rows with
   
9. # complete and rows with the incomplete data.
   
10. # A row with complete data is the row that has a non-empty and
    
11. # non-question mark value for each column. A row with incomplete data is
    
12. # the row that has the last column with the value of a question mark ?.
    
13. # Please, run this file on the example chess.csv to understand this help
    
14. # better:
    
15. # $ python naive_bayes.py chess.csv
    
16. 
    
17. import imp
    
18. import sys
    
19. sys.path.append('../common')
    
20. import common  # noqa
    
21. 
    
22. # Calculates the Baysian probability for the rows of incomplete data and
    
23. # returns them completed by the Bayesian probabilities. complete_data
    
24. # are the rows with the data that is complete and are used to calculate
    
25. # the conditional probabilities to complete the incomplete data.
    
26. 
    
27. 
    
28. def bayes_probability(heading, complete_data, incomplete_data,
    
29.                       enquired_column):
    
30.     conditional_counts = {}
    
31.     enquired_column_classes = {}
    
32.     for data_item in complete_data:
    
33.         common.dic_inc(enquired_column_classes,
    
34.                        data_item[enquired_column])
    
35.         for i in range(0, len(heading)):
    
36.             if i != enquired_column:
    
37.                 common.dic_inc(
    
38.                     conditional_counts, (
    
39.                         heading[i], data_item[i],
    
40.                         data_item[enquired_column]))
    
41. 
    
42.     completed_items = []
    
43.     for incomplete_item in incomplete_data:
    
44.         partial_probs = {}
    
45.         complete_probs = {}
    
46.         probs_sum = 0
    
47.         for enquired_group in enquired_column_classes.items():
    
48.             # For each class in the of the enquired variable A calculate
    
49.             # the probability P(A)*P(B1|A)*P(B2|A)*...*P(Bn|A) where
    
50.             # B1,...,Bn are the remaining variables.
    
51.             probability = float(common.dic_key_count(
    
52.                 enquired_column_classes,
    
53.                 enquired_group[0])) / len(complete_data)
    
54.             for i in range(0, len(heading)):
    
55.                 if i != enquired_column:
    
56.                     probability = probability * (float(
    
57.                         common.dic_key_count(
    
58.                             conditional_counts, (
    
59.                                 heading[i], incomplete_item[i],
    
60.                                 enquired_group[0]))) / (
    
61.                         common.dic_key_count(enquired_column_classes,
    
62.                                              enquired_group[0])))
    
63.             partial_probs[enquired_group[0]] = probability
    
64.             probs_sum += probability
    
65. 
    
66.         for enquired_group in enquired_column_classes.items():
    
67.             complete_probs[enquired_group[0]
    
68.                            ] = partial_probs[enquired_group[0]
    
69.                                              ] / probs_sum
    
70.         incomplete_item[enquired_column] = complete_probs
    
71.         completed_items.append(incomplete_item)
    
72.     return completed_items
    
73. 
    
74. # Program start
    
75. if len(sys.argv) < 2:
    
76.     sys.exit('Please, input as an argument the name of the CSV file.')
    
77. 
    
78. (heading, complete_data, incomplete_data,
    
79. enquired_column) = common.csv_file_to_ordered_data(sys.argv[1])
    
80. 
    
81. # Calculate the Bayesian probability for the incomplete data
    
82. # and output it.
    
83. completed_data = bayes_probability(
    
84.     heading, complete_data, incomplete_data, enquired_column)
    
85. print completed_data

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谢谢楼主分享！

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