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Description:

clayrs 0.5.1

ClayRS




ClayRS is a python framework for (mainly) content-based recommender systems which allows you to perform several operations, starting from a raw representation of users and items to building and evaluating a recommender system. It also supports graph-based recommendation with feature selection algorithms and graph manipulation methods.
The framework has three main modules, which you can also use individually:

Given a raw source, the Content Analyzer:

Creates and serializes contents,
Using the chosen configuration

The RecSys module allows to:

Instantiate a recommender system

Using items and users serialized by the Content Analyzer

Make score prediction or recommend items for the active user(s)

The EvalModel has the task of evaluating a recommender system, using several state-of-the-art metrics
Code examples for all three modules will follow in the Usage section
Installation
ClayRS requires Python 3.7 or later, while package dependencies are in requirements.txt and are all installable
via pip, as ClayRS itself.
To install it execute the following command:
pip install clayrs
Usage
Content Analyzer
The first thing to do is to import the Content Analyzer module

We will access its methods and classes via dot notation

import clayrs.content_analyzer as ca

Then, let's point to the source containing raw information to process
raw_source = ca.JSONFile('items_info.json')

We can now start building the configuration for the items

Note that same operations that can be specified for items, could be also specified for users, via the
ca.UserAnalyzerConfig class

# Configuration of item representation
movies_ca_config = ca.ItemAnalyzerConfig(
source=raw_source,
id='movielens_id', # id which uniquely identifies each item
output_directory='movies_codified/' # where items complexly represented will be stored
)

Let's represent the plot field of each content with a TfIdf representation

Since the preprocessing parameter has been specified, then each field is first preprocessed with the specified
operations

movies_ca_config.add_single_config(
'plot',
ca.FieldConfig(ca.SkLearnTfIdf(),
preprocessing=ca.NLTK(stopwords_removal=True,
lemmatization=True),
id='tfidf') # Custom id
)

To finalize the Content Analyzer part, let's instantiate the ContentAnalyzer class by passing the built configuration
and by calling its fit() method

The items will be created with the specified representations and serialized

ca.ContentAnalyzer(movies_ca_config).fit()

RecSys
Similarly above, we must first import the RecSys module
import clayrs.recsys as rs

Then we load the rating frame from a TSV file

In this case in our file the first three columns are user_id, item_id, score in this order

If your file has a different structure you must specify how to map the column via parameters, check documentation
for more

ratings = ca.Ratings(ca.CSVFile('ratings.tsv', separator='\t'))

Let's split with the KFold technique the loaded rating frame into train set and test set

since n_splits=2, train_list will contain two train_sets and test_list will contain two test_sets

train_list, test_list = rs.KFoldPartitioning(n_splits=2).split_all(ratings)

In order to recommend items to users, we must choose an algorithm to use

In this case we are using the CentroidVector algorithm which will work by using the first representation
specified for the plot field
You can freely choose which representation to use among all representation codified for the fields in the Content
Analyzer phase

centroid_vec = rs.CentroidVector(
{'plot': 'tfidf'},

similarity=rs.CosineSimilarity()
)

Let's now compute the top-10 ranking for each user of the train set

By default the candidate items are those in the test set of the user, but you can change this behaviour with the
methodology parameter

Since we used the kfold technique, we iterate over the train sets and test sets
result_list = []

for train_set, test_set in zip(train_list, test_list):

cbrs = rs.ContentBasedRS(centroid_vec, train_set, 'movies_codified/')
rank = cbrs.fit_rank(test_set, n_recs=10)

result_list.append(rank)

EvalModel
Similarly to the Content Analyzer and RecSys module, we must first import the evaluation module
import clayrs.evaluation as eva

The Evaluation module needs the following parameters:

A list of computed rank/predictions (in case multiple splits must be evaluated)
A list of truths (in case multiple splits must be evaluated)
List of metrics to compute

Obviously the list of computed rank/predictions and list of truths must have the same length,
and the rank/prediction in position will be compared
with the truth at position
em = eva.EvalModel(
pred_list=result_list,
truth_list=test_list,
metric_list=[
eva.NDCG(),
eva.Precision(),
eva.RecallAtK(k=5)
]
)

Then simply call the fit() method of the instantiated object

It will return two pandas DataFrame: the first one contains the metrics aggregated for the system,
while the second contains the metrics computed for each user (where possible)

sys_result, users_result = em.fit()

Note that the EvalModel is able to compute evaluation of recommendations generated by other tools/frameworks, check
documentation for more