【实用数据分析】Practical Data Analysis (2016, 2nd Edition)

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Practical Data Analysis - Second Edition

Hector Cuesta, Dr. Sampath Kumar



A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark

Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service.

This book explains the basic data algorithms without the theoretical jargon, and you’ll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark.

Table of Contents

1: GETTING STARTED
2: PREPROCESSING DATA
3: GETTING TO GRIPS WITH VISUALIZATION
4: TEXT CLASSIFICATION
5: SIMILARITY-BASED IMAGE RETRIEVAL
6: SIMULATION OF STOCK PRICES
7: PREDICTING GOLD PRICES
8: WORKING WITH SUPPORT VECTOR MACHINES
9: MODELING INFECTIOUS DISEASES WITH CELLULAR AUTOMATA
10: WORKING WITH SOCIAL GRAPHS
11: WORKING WITH TWITTER DATA
12: DATA PROCESSING AND AGGREGATION WITH MONGODB
13: WORKING WITH MAPREDUCE
14: ONLINE DATA ANALYSIS WITH JUPYTER AND WAKARI
15: UNDERSTANDING DATA PROCESSING USING APACHE SPARK

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1. Processing the image dataset
   
2. 
   
3. The image set used in this chapter is the Caltech-256 obtained from the Computational Vision Lab at CALTECH. We can download the collection of all 30607 images and 256 categories from the following link:
   
4. 
   
5. http://www.vision.caltech.edu/Image_Datasets/Caltech256/
   
6. 
   
7. In order to implement the DTW first, we need to extract a time series (pixel sequences) from each image. The time series will have a length of 768 values and will add the 256 values of each color in the RGB (Red, Green, and Blue) color model of each image. The following code implements the Image.open("Image.jpg") function and casts it into an array, and then simply adds the three vectors of color in the list:
   
8. 
   
9. from PIL import Image
   
10. img = Image.open("Image.jpg")
    
11. arr = array(img)
    
12. list = []
    
13. for n in arr: list.append(n[0][0]) #R
    
14. for n in arr: list.append(n[0][1]) #G
    
15. for n in arr: list.append(n[0][2]) #B

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1. Implementing DTW
   
2. 
   
3. In this example, we will look for a similarity in 684 images from eight categories. We will use four imports of PIL, numpy, mlpy, and collections:
   
4. 
   
5. from PIL import Image
   
6. from numpy import array
   
7. import mlpy
   
8. from collections import OrderedDict
   
9. Tip
   
10. First, we need to obtain the time series representation of the images and store it in a dictionary (data) with the number of the image and its time series data[fn] = list:
    
11. 
    
12. data = {}
    
13.   
    
14. for fn in range(1,685):
    
15.     img = Image.open("ImgFolder\\{0}.jpg".format(fn))
    
16.     arr = array(img)
    
17.     list = []
    
18.     for n in arr: list.append(n[0][0])
    
19.     for n in arr: list.append(n[0][1])
    
20.     for n in arr: list.append(n[0][2])
    
21.     data[fn] = list
    
22. Tip
    
23. The performance of this process will lie in the number of images processed, so beware of the use of memory with large datasets.
    
24. 
    
25. Then, we need to select an image for reference, which will be compared to all the other images in the dictionary data:
    
26. 
    
27. reference = data[31]
    
28. Now we need to apply the mlpy.dtw_std function to all the elements and store the distance in the result dictionary:
    
29. 
    
30. result ={}
    
31. for x, y in data.items():
    
32.     #print("{0} --------------- {1}".format(x,y))
    
33.     dist = mlpy.dtw_std(reference, y, dist_only=True)
    
34.     result[x] = dist
    
35. Finally, we need to sort the result in order to find the closest elements using the function OrderedDict and then we can print the ordered result:
    
36. 
    
37. sortedRes = OrderedDict(sorted(result.items(), key=lambda x: x[1]))
    
38. for a,b in sortedRes.items():
    
39.     print("{0}-{1}".format(a,b))
    
40. In the following screenshot, we can see the result and we can observe that the result is accurate with the first element (reference time series). The first result presents a distance of 0.0 because it's exactly the same as the image we used as a reference.

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