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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 CHAPTER 4
4.2 Algorithms
Step1:  Imports, logging, and data
On top of doing the things we already know, we now additionally import also the CollaborativeFiltering algorithm, which is, as should be obvious by now, accessible through the bestPy.algorithms subpackage.
Step2:  Creating a new CollaborativeFiltering object with data
Again, this is as straightforward as you would expect. This time, we will attach the data to the algorithm right away.
Step3:  Parameters of the collaborative filtering algorithm
Inspecting the new recommendation object with Tab completion again reveals binarize as a first attribute.
Step4:  It has the same meaning as in the baseline recommendation
Step5:  Indeed, collaborative filtering cannot necessarily provide recommendations for all customers. Specifically, it fails to do so if the customer in question only bought articles that no other customer has bought. For these cases, we need a fallback solution, which is provided by the algorithm specified through the baseline attribute. As you can see, that algorithm is currently a Baseline instance. We could, of course, also provide the baseline algorithm manually.
Step6:  More about that later. There is one more paramter to be explored first.
Step7:  In short, collaborative filtering (as it is implemented in bestPy) works by recommending articles that are most similar to the articles the target customer has already bought. What exactly similar means, however, is not set in stone and quite a few similarity measures are available.
+ Dice (dice)
+ Jaccard (jaccard)
+ Kulsinksi (kulsinski)
+ Sokal-Sneath (sokalsneath)
+ Russell-Rao (russellrao)
+ cosine (cosine)
+ binary cosine (cosine_binary)
In the last option, we recognize again our concept of binarize where, to compute the cosine similarity between two articles, we do not count how often they have been bought by any particular user but only if they have been bought.
It is not obvious which similarity measure is best in which case, so some experimentation is required. If we want to set the similarity to something other than the default choice of kulsinski, we have to import what we need from the logically located subsubpackage.
Step8:  And that's it for the parameters of the collaborative filtering algorithm.
Making a recommendation for a target customer
Now that everything is set up and we have data attached to the algorithm, its for_one() method is available and can be called with the internal integer index of the target customer as argument.
Step9:  And, voilà, your recommendation. Again, a higher number means that the article with the same index as that number is more highly recommended for the target customer.
To appreciate the necessity for this fallback solution, we try to get a recommendation for the customer with ID '4' next.
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Python Code:
import sys
sys.path.append('../..')
Explanation: CHAPTER 4
4.2 Algorithms: Collaborative filtering
Having understood the basics of how an algorithm is configured, married with data, and deployed in bestPy, we are now ready to move from a baseline recommendation to something more inolved. In particular, we are going to discuss the implementation and use of collaborative filtering without, however, going too deep into the technical details of how the algorithm works.
Preliminaries
We only need this because the examples folder is a subdirectory of the bestPy package.
End of explanation
from bestPy import write_log_to
from bestPy.datastructures import Transactions
from bestPy.algorithms import Baseline, CollaborativeFiltering  # Additionally import CollaborativeFiltering
logfile = 'logfile.txt'
write_log_to(logfile, 20)
file = 'examples_data.csv'
data = Transactions.from_csv(file)
Explanation: Imports, logging, and data
On top of doing the things we already know, we now additionally import also the CollaborativeFiltering algorithm, which is, as should be obvious by now, accessible through the bestPy.algorithms subpackage.
End of explanation
recommendation = CollaborativeFiltering().operating_on(data)
recommendation.has_data
Explanation: Creating a new CollaborativeFiltering object with data
Again, this is as straightforward as you would expect. This time, we will attach the data to the algorithm right away.
End of explanation
recommendation.binarize
Explanation: Parameters of the collaborative filtering algorithm
Inspecting the new recommendation object with Tab completion again reveals binarize as a first attribute.
End of explanation
recommendation.baseline
Explanation: It has the same meaning as in the baseline recommendation: True means we only care whether or not a customer bought an article and False means we also take into account how often a customer bought an article.
Speaking about baseline, you will notice that the recommendation object we just created actually has an attribute baseline.
End of explanation
recommendation.baseline = Baseline()
recommendation.baseline
Explanation: Indeed, collaborative filtering cannot necessarily provide recommendations for all customers. Specifically, it fails to do so if the customer in question only bought articles that no other customer has bought. For these cases, we need a fallback solution, which is provided by the algorithm specified through the baseline attribute. As you can see, that algorithm is currently a Baseline instance. We could, of course, also provide the baseline algorithm manually.
End of explanation
recommendation.similarity
Explanation: More about that later. There is one more paramter to be explored first.
End of explanation
from bestPy.algorithms.similarities import dice, jaccard, sokalsneath, russellrao, cosine, cosine_binary
recommendation.similarity = dice
recommendation.similarity
Explanation: In short, collaborative filtering (as it is implemented in bestPy) works by recommending articles that are most similar to the articles the target customer has already bought. What exactly similar means, however, is not set in stone and quite a few similarity measures are available.
+ Dice (dice)
+ Jaccard (jaccard)
+ Kulsinksi (kulsinski)
+ Sokal-Sneath (sokalsneath)
+ Russell-Rao (russellrao)
+ cosine (cosine)
+ binary cosine (cosine_binary)
In the last option, we recognize again our concept of binarize where, to compute the cosine similarity between two articles, we do not count how often they have been bought by any particular user but only if they have been bought.
It is not obvious which similarity measure is best in which case, so some experimentation is required. If we want to set the similarity to something other than the default choice of kulsinski, we have to import what we need from the logically located subsubpackage.
End of explanation
customer = data.user.index_of['5']
recommendation.for_one(customer)
Explanation: And that's it for the parameters of the collaborative filtering algorithm.
Making a recommendation for a target customer
Now that everything is set up and we have data attached to the algorithm, its for_one() method is available and can be called with the internal integer index of the target customer as argument.
End of explanation
customer = data.user.index_of['4']
recommendation.for_one(customer)
Explanation: And, voilà, your recommendation. Again, a higher number means that the article with the same index as that number is more highly recommended for the target customer.
To appreciate the necessity for this fallback solution, we try to get a recommendation for the customer with ID '4' next.
End of explanation
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