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clayrs-0.4.0
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مشاهده بیشترتوضیحات
Complexly represent contents, build recommender systems, evaluate them. All in one place!
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | clayrs-0.4.0 |
| نام | clayrs |
| نسخه کتابخانه | 0.4.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Antonio Silletti, Elio Musacchio, Roberta Sallustio |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/swapUniba/ClayRS |
| آدرس اینترنتی | https://pypi.org/project/clayrs/ |
| مجوز | GPL-3.0 |
<p align="center">
<img src="https://user-images.githubusercontent.com/26851363/172485577-be6993ef-47c3-4b3c-9187-4988f6c44d94.svg" alt="ClayRS logo" style="width:75%;"/>
</p>
# ClayRS
[](https://github.com/swapUniba/ClayRS/actions/workflows/testing_pipeline.yml)
[](https://swapuniba.github.io/ClayRS/)
[](https://codecov.io/gh/swapUniba/ClayRS)
[](https://www.python.org/downloads/)
***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:
<p align="center">
<img src="https://user-images.githubusercontent.com/26851363/164490523-00d60efd-7b17-4d20-872a-28eaf2323b03.png" alt="ClayRS" style="width:75%;"/>
</p>
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
```python
import clayrs.content_analyzer as ca
```
Then, let's point to the source containing raw information to process
```python
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
```python
# 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
```python
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
```python
ca.ContentAnalyzer(movies_ca_config).fit()
```
### RecSys
Similarly above, we must first import the RecSys module
```python
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
```python
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*
```python
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
*
```python
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
```python
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
```python
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 <img src="https://render.githubusercontent.com/render/math?math=i"> will be compared
with the truth at position <img src="https://render.githubusercontent.com/render/math?math=i">
```python
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)
```python
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
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl clayrs-0.4.0:
pip install clayrs-0.4.0.whl
نصب پکیج tar.gz clayrs-0.4.0:
pip install clayrs-0.4.0.tar.gz