LatentSemanticAnalyzer 0.2.1

Latent Semantic Analysis package based on "the standard" Latent Semantic Indexing theory.

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

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Installation

To install from GitHub use the shell command:

python -m pip install git+https://github.com/antononcube/Python-packages.git#egg=LatentSemanticAnalyzer\&subdirectory=LatentSemanticAnalyzer

To install from PyPI:

python -m pip install LatentSemanticAnalyzer

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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", [AA1].

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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())))

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Related Python packages

This package is based on the Python package "SSparseMatrix", [AAp3]

The package "SparseMatrixRecommender" also uses LSI functions -- this package uses LSI methods of the class SparseMatrixRecommender.

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Related Mathematica and R packages

Mathematica

The Python pipeline above corresponds to the following pipeline for the Mathematica package [AAp1]:

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, [AAp2], implements a software monad for LSA workflows.

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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", [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.

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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:

> 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]:

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())))
*)

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References

Articles

[AA1] Anton Antonov, "A monad for Latent Semantic Analysis workflows", (2019), MathematicaForPrediction at WordPress.

[AA2] Anton Antonov, "Random mandalas deconstruction in R, Python, and Mathematica", (2022), MathematicaForPrediction at WordPress.

Mathematica and R Packages

[AAp1] Anton Antonov, Monadic Latent Semantic Analysis Mathematica package, (2017), MathematicaForPrediction at GitHub.

[AAp2] Anton Antonov, Latent Semantic Analysis Monad in R (2019), R-packages at GitHub/antononcube.

Python packages

[AAp3] Anton Antonov, SSparseMatrix Python package, (2021), PyPI.

[AAp4] Anton Antonov, SparseMatrixRecommender Python package, (2021), PyPI.

[AAp5] Anton Antonov, RandomDataGenerators Python package, (2021), PyPI.

[AAp6] Anton Antonov, RandomMandala Python package, (2021), PyPI.

[MZp1] Marinka Zitnik and Blaz Zupan, Nimfa: A Python Library for Nonnegative Matrix Factorization, (2013-2019), PyPI.

[SDp1] Snowball Developers, SnowballStemmer Python package, (2013-2021), PyPI.

Raku packages

[AAp7] Anton Antonov, DSL::English::LatentSemanticAnalysisWorkflows Raku package, (2018-2022), GitHub/antononcube. (At raku.land).

Repositories

[AAr1] Anton Antonov, "Random mandalas deconstruction with R, Python, and Mathematica" presentation project, (2022) SimplifiedMachineLearningWorkflows-book at GitHub/antononcube.

[AAr2] Anton Antonov, "Raku for Prediction" book project, (2021-2022), GitHub/antononcube.

Videos

[AAv1] Anton Antonov, "TRC 2022 Implementation of ML algorithms in Raku", (2022), Anton A. Antonov's channel at YouTube.

[AAv2] Anton Antonov, "Raku for Prediction", (2021), The Raku Conference (TRC) at YouTube.

[AAv3] Anton Antonov, "NLP Template Engine, Part 1", (2021), Anton A. Antonov's channel at YouTube.

[AAv4] Anton Antonov "Random Mandalas Deconstruction in R, Python, and Mathematica (Greater Boston useR Meetup, Feb 2022)", (2022), Anton A. Antonov's channel at YouTube.