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SparseMatrixRecommender-0.1.8
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مشاهده بیشترتوضیحات
Sparse Matrix Recommender package based on SSparseMatrix objects.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | SparseMatrixRecommender-0.1.8 |
| نام | SparseMatrixRecommender |
| نسخه کتابخانه | 0.1.8 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Anton Antonov |
| ایمیل نویسنده | antononcube@posteo.net |
| آدرس صفحه اصلی | https://github.com/antononcube/Python-packages/tree/main/SparseMatrixRecommender |
| آدرس اینترنتی | https://pypi.org/project/SparseMatrixRecommender/ |
| مجوز | - |
# Sparse Matrix Recommender (SMR) Python package
## Introduction
This Python package, `SparseMatrixRecommender`, has different functions for computations of recommendations
based on (user) profile or history using Sparse Linear Algebra (SLA). The package mirrors
the Mathematica implementation [AAp1].
(There is also a corresponding implementation in R; see [AAp2]).
The package is based on a certain "standard" Information retrieval paradigm -- it utilizes
Latent Semantic Indexing (LSI) functions like IDF, TF-IDF, etc. Hence, the package also has
document-term matrix creation functions and LSI application functions. I included them in the
package since I wanted to minimize the external package dependencies.
The package includes two data-sets `dfTitanic` and `dfMushroom` in order to make easier the
writing of introductory examples and unit tests.
For more theoretical description see the article
["Mapping Sparse Matrix Recommender to Streams Blending Recommender"](https://github.com/antononcube/MathematicaForPrediction/blob/master/Documentation/MappingSMRtoSBR/Mapping-Sparse-Matrix-Recommender-to-Streams-Blending-Recommender.pdf)
, [AA1].
For detailed examples see the files
["SMR-experiments-large-data.py"](https://github.com/antononcube/Python-packages/blob/main/SparseMatrixRecommender/examples/SMR-experiments-large-data.py)
and
["SMR-creation-from-long-form.py"](https://github.com/antononcube/Python-packages/blob/main/SparseMatrixRecommender/examples/SMR-creation-from-long-form.py).
The list of features and its implementation status is given in the [org-mode](https://orgmode.org) file
["SparseMatrixRecommender-work-plan.org"](https://github.com/antononcube/Python-packages/blob/main/org/SparseMatrixRecommender-work-plan.org).
------
## Workflows
Here is a diagram that encompasses the workflows this package supports (or will support):
Here is a diagram of typical pipeline building using a `SparseMatrixRecommender` object:
------
## Installation
To install from GitHub use the shell command:
```shell
python -m pip install git+https://github.com/antononcube/Python-packages.git#egg=SparseMatrixRecommender\&subdirectory=SparseMatrixRecommender
```
To install from [PyPI](https://pypi.org/project/SparseMatrixRecommender/):
```shell
python -m pip install SparseMatrixRecommender
```
------
## Related Python packages
This package is based on the Python package
[`SSparseMatrix`](https://github.com/antononcube/Python-packages/tree/main/SSparseMatrix),
[AAp5].
The package
[LatentSemanticAnalyzer](https://github.com/antononcube/Python-packages/tree/main/LatentSemanticAnalyzer),
[AAp6], uses the cross tabulation and LSI functions of this package.
------
## Usage example
Here is an example of an SMR pipeline for creation of a recommender
over Titanic data and recommendations for the profile "passengerSex:male" and "passengerClass:1st":
```python
from SparseMatrixRecommender.SparseMatrixRecommender import *
from SparseMatrixRecommender.DataLoaders import *
dfTitanic = load_titanic_data_frame()
smrObj = (SparseMatrixRecommender()
.create_from_wide_form(data = dfTitanic,
item_column_name="id",
columns=None,
add_tag_types_to_column_names=True,
tag_value_separator=":")
.apply_term_weight_functions(global_weight_func = "IDF",
local_weight_func = "None",
normalizer_func = "Cosine")
.recommend_by_profile(profile=["passengerSex:male", "passengerClass:1st"],
nrecs=12)
.join_across(data=dfTitanic, on="id")
.echo_value())
```
**Remark:** More examples can be found the directory
["./examples"](https://github.com/antononcube/Python-packages/tree/main/SparseMatrixRecommender/examples).
------
## Related Mathematica packages
The software monad Mathematica package
["MonadicSparseMatrixRecommender.m"](https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/MonadicSparseMatrixRecommender.m)
[AAp1], provides recommendation pipelines similar to the pipelines created with this package.
Here is a Mathematica monadic pipeline that corresponds to the Python pipeline above:
```mathematica
smrObj =
SMRMonUnit[]⟹
SMRMonCreate[dfTitanic, "id",
"AddTagTypesToColumnNames" -> True,
"TagValueSeparator" -> ":"]⟹
SMRMonApplyTermWeightFunctions["IDF", "None", "Cosine"]⟹
SMRMonRecommendByProfile[{"passengerSex:male", "passengerClass:1st"}, 12]⟹
SMRMonJoinAcross[dfTitanic, "id"]⟹
SMRMonEchoValue[];
```
*(Compare the pipeline diagram above with the
[corresponding diagram using Mathematica notation](https://github.com/antononcube/SimplifiedMachineLearningWorkflows-book/raw/master/Part-2-Monadic-Workflows/Diagrams/A-monad-for-Recommender-workflows/SMRMon-pipeline.jpeg)
.)*
------
## Related R packages
The package
[`SMRMon-R`](https://github.com/antononcube/R-packages/tree/master/SMRMon-R),
[AAp2], implements a software monad for SMR workflows.
Most of `SMRMon-R` functions delegate to `SparseMatrixRecommender`.
The package
[`SparseMatrixRecommenderInterfaces`](https://github.com/antononcube/R-packages/tree/master/SparseMatrixRecommenderInterfaces),
[AAp3], provides functions for interactive
[Shiny](https://shiny.rstudio.com)
interfaces for the recommenders made with `SparseMatrixRecommender` and/or `SMRMon-R`.
The package
[`LSAMon-R`](https://github.com/antononcube/R-packages/tree/master/LSAMon-R),
[AAp4], can be used to make matrices for `SparseMatrixRecommender` and/or `SMRMon-R`.
Here is the `SMRMon-R` pipeline that corresponds to the Python pipeline above:
```r
smrObj <-
SMRMonCreate( data = dfTitanic,
itemColumnName = "id",
addTagTypesToColumnNamesQ = TRUE,
sep = ":") %>%
SMRMonApplyTermWeightFunctions(globalWeightFunction = "IDF",
localWeightFunction = "None",
normalizerFunction = "Cosine") %>%
SMRMonRecommendByProfile( profile = c("passengerSex:male", "passengerClass:1st"),
nrecs = 12) %>%
SMRMonJoinAcross( data = dfTitanic, by = "id") %>%
SMRMonEchoValue
```
------
## References
### Articles
[AA1] Anton Antonov,
["Mapping Sparse Matrix Recommender to Streams Blending Recommender"](https://github.com/antononcube/MathematicaForPrediction/blob/master/Documentation/MappingSMRtoSBR/Mapping-Sparse-Matrix-Recommender-to-Streams-Blending-Recommender.pdf)
(2017),
[MathematicaForPrediction at GitHub](https://github.com/antononcube/MathematicaForPrediction).
### Mathematica and R Packages
[AAp1] Anton Antonov,
[Monadic Sparse Matrix Recommender Mathematica package](https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/MonadicSparseMatrixRecommender.m),
(2018),
[MathematicaForPrediction at GitHub](https://github.com/antononcube/MathematicaForPrediction).
[AAp2] Anton Antonov,
[Sparse Matrix Recommender Monad in R](https://github.com/antononcube/R-packages/tree/master/SMRMon-R)
(2019),
[R-packages at GitHub/antononcube](https://github.com/antononcube/R-packages).
[AAp3] Anton Antonov,
[Sparse Matrix Recommender framework interface functions](https://github.com/antononcube/R-packages/tree/master/SparseMatrixRecommenderInterfaces)
(2019),
[R-packages at GitHub/antononcube](https://github.com/antononcube/R-packages).
[AAp4] Anton Antonov,
[Latent Semantic Analysis Monad in R](https://github.com/antononcube/R-packages/tree/master/LSAMon-R)
(2019),
[R-packages at GitHub/antononcube](https://github.com/antononcube/R-packages).
### Python packages
[AAp5] Anton Antonov,
[SSparseMatrix package in Python](https://github.com/antononcube/Python-packages/tree/master/SSparseMatrix)
(2021),
[Python-packages at GitHub/antononcube](https://github.com/antononcube/Python-packages).
[AAp6] Anton Antonov,
[LatentSemanticAnalyzer package in Python](https://github.com/antononcube/Python-packages/tree/main/LatentSemanticAnalyzer)
(2021),
[Python-packages at GitHub/antononcube](https://github.com/antononcube/Python-packages).
نیازمندی
| مقدار | نام |
|---|---|
| - | numpy |
| - | scipy |
| - | SSparseMatrix |
| - | pandas |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl SparseMatrixRecommender-0.1.8:
pip install SparseMatrixRecommender-0.1.8.whl
نصب پکیج tar.gz SparseMatrixRecommender-0.1.8:
pip install SparseMatrixRecommender-0.1.8.tar.gz