# Latent Semantic Analysis (LSA) Python package
## In brief
This Python package, `LatentSemanticAnalyzer`, has different functions for computations of
Latent Semantic Analysis (LSA) workflows
(using Sparse matrix Linear Algebra.) The package mirrors
the Mathematica implementation [AAp1].
(There is also a corresponding implementation in R; see [AAp2].)
The package provides:
- Class `LatentSemanticAnalyzer`
- Functions for applying Latent Semantic Indexing (LSI) functions on matrix entries
- "Data loader" function for obtaining a `pandas` data frame ~580 abstracts of conference presentations
------
## Installation
To install from GitHub use the shell command:
```shell
python -m pip install git+https://github.com/antononcube/Python-packages.git#egg=LatentSemanticAnalyzer\&subdirectory=LatentSemanticAnalyzer
```
To install from PyPI:
```shell
python -m pip install LatentSemanticAnalyzer
```
-----
## LSA workflows
The scope of the package is to facilitate the creation and execution of the workflows encompassed in this
flow chart:
For more details see the article
["A monad for Latent Semantic Analysis workflows"](https://mathematicaforprediction.wordpress.com/2019/09/13/a-monad-for-latent-semantic-analysis-workflows/),
[AA1].
------
## Usage example
Here is an example of a LSA pipeline that:
1. Ingests a collection of texts
2. Makes the corresponding document-term matrix using stemming and removing stop words
3. Extracts 40 topics
4. Shows a table with the extracted topics
5. Shows a table with statistical thesaurus entries for selected words
```
import random
from LatentSemanticAnalyzer.LatentSemanticAnalyzer import *
from LatentSemanticAnalyzer.DataLoaders import *
import snowballstemmer
# Collection of texts
dfAbstracts = load_abstracts_data_frame()
docs = dict(zip(dfAbstracts.ID, dfAbstracts.Abstract))
print(len(docs))
# Remove non-strings
docs2 = { k:v for k, v in docs.items() if isinstance(v, str) }
print(len(docs2))
# Stemmer object (to preprocess words in the pipeline below)
stemmerObj = snowballstemmer.stemmer("english")
# Words to show statistical thesaurus entries for
words = ["notebook", "computational", "function", "neural", "talk", "programming"]
# Reproducible results
random.seed(12)
# LSA pipeline
lsaObj = (LatentSemanticAnalyzer()
.make_document_term_matrix(docs=docs2,
stop_words=True,
stemming_rules=True,
min_length=3)
.apply_term_weight_functions(global_weight_func="IDF",
local_weight_func="None",
normalizer_func="Cosine")
.extract_topics(number_of_topics=40, min_number_of_documents_per_term=10, method="NNMF")
.echo_topics_interpretation(number_of_terms=12, wide_form=True)
.echo_statistical_thesaurus(terms=stemmerObj.stemWords(words),
wide_form=True,
number_of_nearest_neighbors=12,
method="cosine",
echo_function=lambda x: print(x.to_string())))
```
------
## Related Python packages
This package is based on the Python package
["SSparseMatrix"](https://pypi.org/project/SSparseMatrix/), [AAp3]
The package
["SparseMatrixRecommender"](https://pypi.org/project/SparseMatrixRecommender/)
also uses LSI functions -- this package uses LSI methods of the class `SparseMatrixRecommender`.
------
## Related Mathematica and R packages
### Mathematica
The Python pipeline above corresponds to the following pipeline for the Mathematica package
[AAp1]:
```mathematica
lsaObj =
LSAMonUnit[aAbstracts]⟹
LSAMonMakeDocumentTermMatrix["StemmingRules" -> Automatic, "StopWords" -> Automatic]⟹
LSAMonEchoDocumentTermMatrixStatistics["LogBase" -> 10]⟹
LSAMonApplyTermWeightFunctions["IDF", "None", "Cosine"]⟹
LSAMonExtractTopics["NumberOfTopics" -> 20, Method -> "NNMF", "MaxSteps" -> 16, "MinNumberOfDocumentsPerTerm" -> 20]⟹
LSAMonEchoTopicsTable["NumberOfTerms" -> 10]⟹
LSAMonEchoStatisticalThesaurus["Words" -> Map[WordData[#, "PorterStem"]&, {"notebook", "computational", "function", "neural", "talk", "programming"}]];
```
### R
The package
[`LSAMon-R`](https://github.com/antononcube/R-packages/tree/master/LSAMon-R),
[AAp2], implements a software monad for LSA workflows.
------
## LSA packages comparison project
The project "Random mandalas deconstruction with R, Python, and Mathematica", [AAr1, AA2],
has documents, diagrams, and (code) notebooks for comparison of LSA application to a collection of images
(in multiple programming languages.)
A big part of the motivation to make the Python package
["RandomMandala"](https://pypi.org/project/RandomMandala), [AAp6],
was to make easier the LSA package comparison.
Mathematica and R have fairly streamlined connections to Python, hence it is easier
to propagate (image) data generated in Python into those systems.
------
## Code generation with natural language commands
### Using grammar-based interpreters
The project "Raku for Prediction", [AAr2, AAv2, AAp7], has a Domain Specific Language (DSL) grammar and interpreters
that allow the generation of LSA code for corresponding Mathematica, Python, R packages.
Here is Command Line Interface (CLI) invocation example that generate code for this package:
```shell
> ToLatentSemanticAnalysisWorkflowCode Python 'create from aDocs; apply LSI functions IDF, None, Cosine; extract 20 topics; show topics table'
# LatentSemanticAnalyzer(aDocs).apply_term_weight_functions(global_weight_func = "IDF", local_weight_func = "None", normalizer_func = "Cosine").extract_topics(number_of_topics = 20).echo_topics_table( )
```
### NLP Template Engine
Here is an example using the NLP Template Engine, [AAr2, AAv3]:
```mathematica
Concretize["create from aDocs; apply LSI functions IDF, None, Cosine; extract 20 topics; show topics table",
"TargetLanguage" -> "Python"]
(*
lsaObj = (LatentSemanticAnalyzer()
.make_document_term_matrix(docs=aDocs, stop_words=None, stemming_rules=None,min_length=3)
.apply_term_weight_functions(global_weight_func='IDF', local_weight_func='None',normalizer_func='Cosine')
.extract_topics(number_of_topics=20, min_number_of_documents_per_term=20, method='SVD')
.echo_topics_interpretation(number_of_terms=10, wide_form=True)
.echo_statistical_thesaurus(terms=stemmerObj.stemWords([\"topics table\"]), wide_form=True, number_of_nearest_neighbors=12, method='cosine', echo_function=lambda x: print(x.to_string())))
*)
```
------
## References
### Articles
[AA1] Anton Antonov,
["A monad for Latent Semantic Analysis workflows"](https://mathematicaforprediction.wordpress.com/2019/09/13/a-monad-for-latent-semantic-analysis-workflows/),
(2019),
[MathematicaForPrediction at WordPress](https://mathematicaforprediction.wordpress.com).
[AA2] Anton Antonov,
["Random mandalas deconstruction in R, Python, and Mathematica"](https://mathematicaforprediction.wordpress.com/2022/03/01/random-mandala-deconstruction-in-r-python-and-mathematica/),
(2022),
[MathematicaForPrediction at WordPress](https://mathematicaforprediction.wordpress.com).
### Mathematica and R Packages
[AAp1] Anton Antonov,
[Monadic Latent Semantic Analysis Mathematica package](https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/MonadicLatentSemanticAnalysis.m),
(2017),
[MathematicaForPrediction at GitHub](https://github.com/antononcube/MathematicaForPrediction).
[AAp2] 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
[AAp3] Anton Antonov,
[SSparseMatrix Python package](https://pypi.org/project/SSparseMatrix),
(2021),
[PyPI](https://pypi.org).
[AAp4] Anton Antonov,
[SparseMatrixRecommender Python package](https://pypi.org/project/SparseMatrixRecommender),
(2021),
[PyPI](https://pypi.org).
[AAp5] Anton Antonov,
[RandomDataGenerators Python package](https://pypi.org/project/RandomDataGenerators),
(2021),
[PyPI](https://pypi.org).
[AAp6] Anton Antonov,
[RandomMandala Python package](https://pypi.org/project/RandomMandala),
(2021),
[PyPI](https://pypi.org).
[MZp1] Marinka Zitnik and Blaz Zupan,
[Nimfa: A Python Library for Nonnegative Matrix Factorization](https://pypi.org/project/nimfa/),
(2013-2019),
[PyPI](https://pypi.org).
[SDp1] Snowball Developers,
[SnowballStemmer Python package](https://pypi.org/project/snowballstemmer/),
(2013-2021),
[PyPI](https://pypi.org).
### Raku packages
[AAp7] Anton Antonov,
[DSL::English::LatentSemanticAnalysisWorkflows Raku package](https://github.com/antononcube/Raku-DSL-English-LatentSemanticAnalysisWorkflows),
(2018-2022),
[GitHub/antononcube](https://github.com/antononcube/Raku-DSL-English-LatentSemanticAnalysisWorkflows).
([At raku.land]((https://raku.land/zef:antononcube/DSL::English::LatentSemanticAnalysisWorkflows))).
### Repositories
[AAr1] Anton Antonov,
["Random mandalas deconstruction with R, Python, and Mathematica" presentation project](https://github.com/antononcube/SimplifiedMachineLearningWorkflows-book/tree/master/Presentations/Greater-Boston-useR-Group-Meetup-2022/RandomMandalasDeconstruction),
(2022)
[SimplifiedMachineLearningWorkflows-book at GitHub/antononcube](https://github.com/antononcube/SimplifiedMachineLearningWorkflows-book).
[AAr2] Anton Antonov,
["Raku for Prediction" book project](https://github.com/antononcube/RakuForPrediction-book),
(2021-2022),
[GitHub/antononcube](https://github.com/antononcube).
### Videos
[AAv1] Anton Antonov,
["TRC 2022 Implementation of ML algorithms in Raku"](https://www.youtube.com/watch?v=efRHfjYebs4),
(2022),
[Anton A. Antonov's channel at YouTube](https://www.youtube.com/channel/UC5qMPIsJeztfARXWdIw3Xzw).
[AAv2] Anton Antonov,
["Raku for Prediction"](https://www.youtube.com/watch?v=frpCBjbQtnA),
(2021),
[The Raku Conference (TRC) at YouTube](https://www.youtube.com/channel/UCnKoF-TknjGtFIpU3Bc_jUA).
[AAv3] Anton Antonov,
["NLP Template Engine, Part 1"](https://www.youtube.com/watch?v=a6PvmZnvF9I),
(2021),
[Anton A. Antonov's channel at YouTube](https://www.youtube.com/channel/UC5qMPIsJeztfARXWdIw3Xzw).
[AAv4] Anton Antonov
["Random Mandalas Deconstruction in R, Python, and Mathematica (Greater Boston useR Meetup, Feb 2022)"](https://www.youtube.com/watch?v=nKlcts5aGwY),
(2022),
[Anton A. Antonov's channel at YouTube](https://www.youtube.com/channel/UC5qMPIsJeztfARXWdIw3Xzw).
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"description": "# Latent Semantic Analysis (LSA) Python package \n\n## In brief\n\nThis Python package, `LatentSemanticAnalyzer`, has different functions for computations of \nLatent Semantic Analysis (LSA) workflows\n(using Sparse matrix Linear Algebra.) The package mirrors\nthe Mathematica implementation [AAp1]. \n(There is also a corresponding implementation in R; see [AAp2].) \n\nThe package provides: \n- Class `LatentSemanticAnalyzer`\n- Functions for applying Latent Semantic Indexing (LSI) functions on matrix entries\n- \"Data loader\" function for obtaining a `pandas` data frame ~580 abstracts of conference presentations\n\n------\n\n## Installation\n\nTo install from GitHub use the shell command:\n\n```shell\npython -m pip install git+https://github.com/antononcube/Python-packages.git#egg=LatentSemanticAnalyzer\\&subdirectory=LatentSemanticAnalyzer\n```\n\nTo install from PyPI:\n\n```shell\npython -m pip install LatentSemanticAnalyzer\n```\n\n----- \n\n## LSA workflows\n\nThe scope of the package is to facilitate the creation and execution of the workflows encompassed in this\nflow chart:\n\n\n\nFor more details see the article \n[\"A monad for Latent Semantic Analysis workflows\"](https://mathematicaforprediction.wordpress.com/2019/09/13/a-monad-for-latent-semantic-analysis-workflows/),\n[AA1].\n\n------\n\n## Usage example\n\nHere is an example of a LSA pipeline that:\n1. Ingests a collection of texts\n2. Makes the corresponding document-term matrix using stemming and removing stop words\n3. Extracts 40 topics\n4. Shows a table with the extracted topics\n5. Shows a table with statistical thesaurus entries for selected words \n\n```\nimport random\nfrom LatentSemanticAnalyzer.LatentSemanticAnalyzer import *\nfrom LatentSemanticAnalyzer.DataLoaders import *\nimport snowballstemmer\n\n# Collection of texts\ndfAbstracts = load_abstracts_data_frame()\ndocs = dict(zip(dfAbstracts.ID, dfAbstracts.Abstract))\nprint(len(docs))\n\n# Remove non-strings\ndocs2 = { k:v for k, v in docs.items() if isinstance(v, str) }\nprint(len(docs2))\n\n# Stemmer object (to preprocess words in the pipeline below)\nstemmerObj = snowballstemmer.stemmer(\"english\")\n\n# Words to show statistical thesaurus entries for\nwords = [\"notebook\", \"computational\", \"function\", \"neural\", \"talk\", \"programming\"]\n\n# Reproducible results\nrandom.seed(12)\n\n# LSA pipeline\nlsaObj = (LatentSemanticAnalyzer()\n .make_document_term_matrix(docs=docs2,\n stop_words=True,\n stemming_rules=True,\n min_length=3)\n .apply_term_weight_functions(global_weight_func=\"IDF\",\n local_weight_func=\"None\",\n normalizer_func=\"Cosine\")\n .extract_topics(number_of_topics=40, min_number_of_documents_per_term=10, method=\"NNMF\")\n .echo_topics_interpretation(number_of_terms=12, wide_form=True)\n .echo_statistical_thesaurus(terms=stemmerObj.stemWords(words),\n wide_form=True,\n number_of_nearest_neighbors=12,\n method=\"cosine\",\n echo_function=lambda x: print(x.to_string())))\n```\n\n------\n\n## Related Python packages\n\nThis package is based on the Python package \n[\"SSparseMatrix\"](https://pypi.org/project/SSparseMatrix/), [AAp3]\n\nThe package \n[\"SparseMatrixRecommender\"](https://pypi.org/project/SparseMatrixRecommender/)\nalso uses LSI functions -- this package uses LSI methods of the class `SparseMatrixRecommender`.\n\n------\n\n## Related Mathematica and R packages\n\n### Mathematica\n\nThe Python pipeline above corresponds to the following pipeline for the Mathematica package\n[AAp1]:\n\n```mathematica\nlsaObj =\n LSAMonUnit[aAbstracts]\u27f9\n LSAMonMakeDocumentTermMatrix[\"StemmingRules\" -> Automatic, \"StopWords\" -> Automatic]\u27f9\n LSAMonEchoDocumentTermMatrixStatistics[\"LogBase\" -> 10]\u27f9\n LSAMonApplyTermWeightFunctions[\"IDF\", \"None\", \"Cosine\"]\u27f9\n LSAMonExtractTopics[\"NumberOfTopics\" -> 20, Method -> \"NNMF\", \"MaxSteps\" -> 16, \"MinNumberOfDocumentsPerTerm\" -> 20]\u27f9\n LSAMonEchoTopicsTable[\"NumberOfTerms\" -> 10]\u27f9\n LSAMonEchoStatisticalThesaurus[\"Words\" -> Map[WordData[#, \"PorterStem\"]&, {\"notebook\", \"computational\", \"function\", \"neural\", \"talk\", \"programming\"}]];\n```\n\n### R \n\nThe package \n[`LSAMon-R`](https://github.com/antononcube/R-packages/tree/master/LSAMon-R), \n[AAp2], implements a software monad for LSA workflows. \n\n------\n\n## LSA packages comparison project\n\nThe project \"Random mandalas deconstruction with R, Python, and Mathematica\", [AAr1, AA2],\nhas documents, diagrams, and (code) notebooks for comparison of LSA application to a collection of images\n(in multiple programming languages.)\n\nA big part of the motivation to make the Python package \n[\"RandomMandala\"](https://pypi.org/project/RandomMandala), [AAp6], \nwas to make easier the LSA package comparison. \nMathematica and R have fairly streamlined connections to Python, hence it is easier\nto propagate (image) data generated in Python into those systems. \n\n------\n\n## Code generation with natural language commands\n\n### Using grammar-based interpreters\n\nThe project \"Raku for Prediction\", [AAr2, AAv2, AAp7], has a Domain Specific Language (DSL) grammar and interpreters \nthat allow the generation of LSA code for corresponding Mathematica, Python, R packages. \n\nHere is Command Line Interface (CLI) invocation example that generate code for this package:\n\n```shell\n> ToLatentSemanticAnalysisWorkflowCode Python 'create from aDocs; apply LSI functions IDF, None, Cosine; extract 20 topics; show topics table'\n# LatentSemanticAnalyzer(aDocs).apply_term_weight_functions(global_weight_func = \"IDF\", local_weight_func = \"None\", normalizer_func = \"Cosine\").extract_topics(number_of_topics = 20).echo_topics_table( )\n```\n\n### NLP Template Engine\n\nHere is an example using the NLP Template Engine, [AAr2, AAv3]:\n\n```mathematica\nConcretize[\"create from aDocs; apply LSI functions IDF, None, Cosine; extract 20 topics; show topics table\", \n \"TargetLanguage\" -> \"Python\"]\n(* \nlsaObj = (LatentSemanticAnalyzer()\n .make_document_term_matrix(docs=aDocs, stop_words=None, stemming_rules=None,min_length=3)\n .apply_term_weight_functions(global_weight_func='IDF', local_weight_func='None',normalizer_func='Cosine')\n .extract_topics(number_of_topics=20, min_number_of_documents_per_term=20, method='SVD')\n .echo_topics_interpretation(number_of_terms=10, wide_form=True)\n .echo_statistical_thesaurus(terms=stemmerObj.stemWords([\\\"topics table\\\"]), wide_form=True, number_of_nearest_neighbors=12, method='cosine', echo_function=lambda x: print(x.to_string())))\n*)\n\n```\n\n------\n\n## References\n\n### Articles\n\n[AA1] Anton Antonov,\n[\"A monad for Latent Semantic Analysis workflows\"](https://mathematicaforprediction.wordpress.com/2019/09/13/a-monad-for-latent-semantic-analysis-workflows/),\n(2019),\n[MathematicaForPrediction at WordPress](https://mathematicaforprediction.wordpress.com).\n\n[AA2] Anton Antonov,\n[\"Random mandalas deconstruction in R, Python, and Mathematica\"](https://mathematicaforprediction.wordpress.com/2022/03/01/random-mandala-deconstruction-in-r-python-and-mathematica/),\n(2022),\n[MathematicaForPrediction at WordPress](https://mathematicaforprediction.wordpress.com).\n\n### Mathematica and R Packages \n\n[AAp1] Anton Antonov, \n[Monadic Latent Semantic Analysis Mathematica package](https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/MonadicLatentSemanticAnalysis.m),\n(2017),\n[MathematicaForPrediction at GitHub](https://github.com/antononcube/MathematicaForPrediction).\n\n[AAp2] Anton Antonov,\n[Latent Semantic Analysis Monad in R](https://github.com/antononcube/R-packages/tree/master/LSAMon-R)\n(2019),\n[R-packages at GitHub/antononcube](https://github.com/antononcube/R-packages).\n\n### Python packages\n\n[AAp3] Anton Antonov,\n[SSparseMatrix Python package](https://pypi.org/project/SSparseMatrix),\n(2021),\n[PyPI](https://pypi.org).\n\n[AAp4] Anton Antonov,\n[SparseMatrixRecommender Python package](https://pypi.org/project/SparseMatrixRecommender),\n(2021),\n[PyPI](https://pypi.org).\n\n[AAp5] Anton Antonov,\n[RandomDataGenerators Python package](https://pypi.org/project/RandomDataGenerators),\n(2021),\n[PyPI](https://pypi.org).\n\n[AAp6] Anton Antonov,\n[RandomMandala Python package](https://pypi.org/project/RandomMandala),\n(2021),\n[PyPI](https://pypi.org).\n\n[MZp1] Marinka Zitnik and Blaz Zupan,\n[Nimfa: A Python Library for Nonnegative Matrix Factorization](https://pypi.org/project/nimfa/),\n(2013-2019),\n[PyPI](https://pypi.org).\n\n[SDp1] Snowball Developers,\n[SnowballStemmer Python package](https://pypi.org/project/snowballstemmer/),\n(2013-2021),\n[PyPI](https://pypi.org).\n\n### Raku packages\n\n[AAp7] Anton Antonov,\n[DSL::English::LatentSemanticAnalysisWorkflows Raku package](https://github.com/antononcube/Raku-DSL-English-LatentSemanticAnalysisWorkflows),\n(2018-2022),\n[GitHub/antononcube](https://github.com/antononcube/Raku-DSL-English-LatentSemanticAnalysisWorkflows).\n([At raku.land]((https://raku.land/zef:antononcube/DSL::English::LatentSemanticAnalysisWorkflows))).\n\n### Repositories\n\n[AAr1] Anton Antonov,\n[\"Random mandalas deconstruction with R, Python, and Mathematica\" presentation project](https://github.com/antononcube/SimplifiedMachineLearningWorkflows-book/tree/master/Presentations/Greater-Boston-useR-Group-Meetup-2022/RandomMandalasDeconstruction),\n(2022)\n[SimplifiedMachineLearningWorkflows-book at GitHub/antononcube](https://github.com/antononcube/SimplifiedMachineLearningWorkflows-book).\n\n[AAr2] Anton Antonov,\n[\"Raku for Prediction\" book project](https://github.com/antononcube/RakuForPrediction-book),\n(2021-2022),\n[GitHub/antononcube](https://github.com/antononcube).\n\n\n### Videos\n\n[AAv1] Anton Antonov,\n[\"TRC 2022 Implementation of ML algorithms in Raku\"](https://www.youtube.com/watch?v=efRHfjYebs4),\n(2022),\n[Anton A. Antonov's channel at YouTube](https://www.youtube.com/channel/UC5qMPIsJeztfARXWdIw3Xzw).\n\n[AAv2] Anton Antonov,\n[\"Raku for Prediction\"](https://www.youtube.com/watch?v=frpCBjbQtnA),\n(2021),\n[The Raku Conference (TRC) at YouTube](https://www.youtube.com/channel/UCnKoF-TknjGtFIpU3Bc_jUA).\n\n[AAv3] Anton Antonov,\n[\"NLP Template Engine, Part 1\"](https://www.youtube.com/watch?v=a6PvmZnvF9I),\n(2021),\n[Anton A. Antonov's channel at YouTube](https://www.youtube.com/channel/UC5qMPIsJeztfARXWdIw3Xzw).\n\n[AAv4] Anton Antonov\n[\"Random Mandalas Deconstruction in R, Python, and Mathematica (Greater Boston useR Meetup, Feb 2022)\"](https://www.youtube.com/watch?v=nKlcts5aGwY),\n(2022),\n[Anton A. Antonov's channel at YouTube](https://www.youtube.com/channel/UC5qMPIsJeztfARXWdIw3Xzw).\n",
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