In this MathematicaVsR at GitHub project we show how to do Progressive machine learning using two types of classifiers based on:
Progressive learning is a type of Online machine learning. For more details see [Wk1]. The Progressive learning problem is defined as follows.
Problem definition:
In view of classification, it is assumed that at a given time not all class labels are presented in the data already obtained.
Let us call this a data stream.
Make a machine learning algorithm that updates its model continuously or sequentially in time over a given data stream.
In comparison, the typical (classical) machine learning algorithms assume that representative training data is available and after training that data is no longer needed to make predictions. Progressive machine learning has more general assumptions about the data and its problem formulation is closer to how humans learn to classify objects.
Below we are shown the applications of two types of classifiers as PLA’s. One is based on Tries with Frequencies (TF), [AAp2, AAp3, AA1], the other on an Item-item Recommender (IIR) framework [AAp4, AA2].
Remark: Note that both TF and IIR come from tackling Unsupervised machine learning tasks, but here they are applied in the context of Supervised machine learning.
The Mathematica and R notebooks follow the steps in the following flow chart.
For detailed explanations see any of the notebooks.
(For details see Progressive-machine-learning-examples.md.)
Here is an example run with Tries with Frequencies, [AAp2, AA1]:
Here are the obtained ROC curves:
We can see that with the Progressive learning process does improve its success rates in time.
Here is an example run with an Item-item recommender system, [AAp4, AA2]:
Here are the obtained ROC curves:
[AAp1] Anton Antonov, Obtain and transform Mathematica machine learning data-sets, GetMachineLearningDataset.m, (2018), MathematicaVsR at GitHub.
[AAp2] Anton Antonov, Java tries with frequencies Mathematica package, JavaTriesWithFrequencies.m, (2017), MathematicaForPrediction at GitHub.
[AAp3] Anton Antonov, Tries with frequencies R package, TriesWithFrequencies.R, (2014), MathematicaForPrediction at GitHub.
[AAp4] Anton Antonov, Sparse matrix recommender framework in Mathematica, SparseMatrixRecommenderFramework.m, (2014), MathematicaForPrediction at GitHub.
[Wk1] Wikipedia entry, Online machine learning.
[AA1] Anton Antonov, "Tries with frequencies in Java", (2017), MathematicaForPrediction at WordPress.
[AA2] Anton Antonov, "A Fast and Agile Item-Item Recommender: Design and Implementation", (2011), Wolfram Technology Conference 2011.
]]>In this blog post I try to provide some further context for the panel discussion:
"Artificial Intelligence and Machine Learning application in finance and technology",
which was part of the conference "Data Science Salon Miami 2018" held in Miami on February 8 and 9.
The blog post can be read independently, but my intent is to provide a brief review of the discussion and further context to (some of) the answers. (It is probably better to see discussion’s recording first.)
Also, my comments and remarks are saturated with links for reference and further reading.
You can also read the post by panel’s discussion host, Irma Becerra.
This blog post is not written by someone particularly enamored by Artificial Intelligence (AI), Data Science (DS), or Machine Learning (ML). I do like working in those disciplines, but I do think there are more interesting and fascinating mathematical disciplines than them. Also, I think AI, DS, and ML should be seen through the lens of Operations Research, which gives the most fruitful perspectives of their utilization.
The conference "Data Science Salon Miami 2018" included approximately 300 attendants. (I was told that there were ~60% data scientist and data analysts, and ~40% managers.)
Here is a list of the people participating in the panel (Irma was the host):
Here are the main questions asked during the panel session:
What are some misconceptions about AI? What do you see as being the future of AI?
How is artificial intelligence helping us engage with customers?
What techniques can we expect to see in terms of informing our marketing strategy so we can build predictive models to support our businesses? (backup question)
What can we do make advancements in Data Science more accessible to the public?
Here are some of the questions from the audience:
What is going to happen if an adopted AI technology fails?
All of the people in the panel became data scientists later in their career. Each of us at first was studying or doing something else.
Of course, that is to be expected since the term "data science" and the related occupation came into fashion relatively recently.
(Although apparently the term itself is fairly old.)
This is of course a long and fruitful topic.
Here — and during the panel session — I want to concentrate of what kind of thinking processes generate those misconceptions. (Obviously this is a less laborious task than enumerating and discussing the different concrete misconceptions.)
I side with the so called "Weak AI". Here are some summarizing points.
All algorithms are based on 0s and 1s, so generally, I find "AI" misleading and "Strong AI" an interesting thing to think about, but far from practical use.
Classifiers are just complicated if-statements.
Also, classification problems belong to the conceptually simplest ML branch (Supervised learning.)
Generally, my personal opinions on "AI" come from living through at least one AI winter, and being a subject in the AI effect.
Study the following mind-map, "AI misconceptions reasons".
I think the major ingredients of getting the wrong AI ideas are two.
My favorite analogy to point 1 above is the following.
There are two principal ways to fish: (i) with bait and a fishing rod, and (ii) by draining the lake and picking the fish you want. Any other way of fishing is something in between. (For example, using a net or a bomb.)
So, the high performance of draining the lake can be confused with the ability of bait selection and fishing-spot picking. (And yes, not knowing how the AI algorithms work is a big misconception maker too.)
As an illustration of "draining the lake" approach to interesting puzzles humans play with, review (skim over) this article: "Solving Sudoku as an integer programming problem".
Note, that this is not even considered AI now. Granted, we do need a symbolic computations system like Mathematica, which is a very advanced system. (And yes, a good symbolic manipulation system can be seen as AI, especially 4-5 decades ago.)
I specialize in making recommenders and conversational agents.
For items that require significant investment in time (movies, books) or money (houses, automobiles, freelancers) recommenders have to make very good explanations of the recommendations. This is usually based on similarities to items consumed in the past or stated preferences.
For items that can be easily tried on and discarded (songs) we are better off making prediction algorithms that predict the "survival" of the item in customer’s mind. (How long a song is going to be in a playlist?)
The recommenders I developed in the past tend to be very agile : fast, easy to tune, with clear "recommendation proofs." Very often the agility of the recommender system is more important that giving good or precise recommendations "out of the box."
Customer engagement through conversational agents is much more about envisioning of the right work-flow and mind-flow of the customer. Although clarity and simplicity are important, I like the idea of using grammar rules and agent names that are challenging or intriguing. (Like, "Will they kill me?" or "I wanna eat!".)
In many ways the application of AI to Finance is somewhat easier, since financial data tends to be well-curated.
For example,
health insurance financial data, though, is well-curated (since people need to get payed) and fairly simple.
To clarify, if we have records with four columns:
claim ID, claim total amount, transaction amount, timestamp
then we are already in a position to do a fair amount of modeling and prediction.
My answer was two-fold:
from the other hand, some of the underlying algorithms require mastery of multiple computer science and mathematical disciplines.
In other words:
from the other hand, "art is for the educated."
I would like to point out that technology itself has already democratized and popularized the AI and ML advancements. Everyone is familiar with, say, content recommendations, search queries completion, and optical character recognition. Few decades ago these functionalities would have been considered science fiction or part of Strong AI.
Descriptions of how to analyze the pre-election tweets of Donald Trump or apply the Great Conversation idea to interesting collections of text do help the communication of Data Science ideas to the curious.
I would be much more interested in developing agriculture recommendation systems than recommenders for digital content consumption (movies, books, songs) or dating.
Also, I am a big advocate of utilizing recommenders in Operations Research frameworks.
It seems that AI came recently into fashion somewhat coinciding with the advancements in Neural Networks through the so called "deep learning" on 2011-2012.
This document shows a way to chart in Mathematica / WL the evolution of topics in collections of texts. The making of this document (and related code) is primarily motivated by the fascinating concept of the Great Conversation, [Wk1, MA1]. In brief, all western civilization books are based on great ideas; if we find the great ideas each significant book is based on we can construct a time-line (spanning centuries) of the great conversation between the authors; see [MA1, MA2, MA3].
Instead of finding the great ideas in a text collection we extract topics statistically, using dimension reduction with Non-Negative Matrix Factorization (NNMF), [AAp3, AA1, AA2].
The presented computational results are based on the text collections of State of the Union speeches of USA presidents [D2]. The code in this document can be easily configured to use the much smaller text collection [D1] available online and in Mathematica/WL. (The collection [D1] is fairly small, documents; the collection [D2] is much larger, documents.)
The procedures (and code) described in this document, of course, work on other types of text collections. For example: movie reviews, podcasts, editorial articles of a magazine, etc.
A secondary objective of this document is to illustrate the use of the monadic programming pipeline as a Software design pattern, [AA3]. In order to make the code concise in this document I wrote the package MonadicLatentSemanticAnalysis.m, [AAp5]. Compare with the code given in [AA1].
The very first version of this document was written for the 2017 summer course “Data Science for the Humanities” at the University of Oxford, UK.
The procedure described in this document has the following steps.
Number of words per document; number of documents per word.
(Some of the statistics of this step are done easier after the Linear vector space representation step.)
Optionally perform Natural Language Processing (NLP) tasks.
Remove stop words from the texts.
Apply stemming to the words in the texts.
Linear vector space representation.
Each unique word is a basis vector in that space.
For each document the corresponding point in that space is derived from the number of appearances of document’s words.
Extract topics.
In order to obtain better results with NNMF some experimentation and refinements of the topics search have to be done.
Map the documents over the extracted topics.
Alternatively,
order the topics according to those mean values.
Visualize the evolution of the documents according to their topics.
For the president speeches we can use the president names for time-line temporal axis instead of years.
Remark: Some of the functions used in this document combine several steps into one function call (with corresponding parameters.)
This loads the packages [AAp1-AAp8]:
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/MonadicProgramming/MonadicLatentSemanticAnalysis.m"];
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/MonadicProgramming/MonadicTracing.m"]
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/Misc/HeatmapPlot.m"];
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/Misc/RSparseMatrix.m"];
(Note that some of the packages that are imported automatically by [AAp5].)
The functions of the central package in this document, [AAp5], have the prefix “LSAMon”. Here is a sample of those names:
Short@Names["LSAMon*"]
(* {"LSAMon", "LSAMonAddToContext", "LSAMonApplyTermWeightFunctions", <>, "LSAMonUnit", "LSAMonUnitQ", "LSAMonWhen"} *)
In this section we load a text collection from a specified source.
The text collection from “Presidential Nomination Acceptance Speeches”, [D1], is small and can be used for multiple code verifications and re-runnings. The “State of Union addresses of USA presidents” text collection from [D2] was converted to a Mathematica/WL object by Christopher Wolfram (and sent to me in a private communication.) The text collection [D2] provides far more interesting results (and they are shown below.)
If[True,
speeches = ResourceData[ResourceObject["Presidential Nomination Acceptance Speeches"]];
names = StringSplit[Normal[speeches[[All, "Person"]]][[All, 2]], "::"][[All, 1]],
(*ELSE*)
(*State of the union addresses provided by Christopher Wolfram. *)
Get["~/MathFiles/Digital humanities/Presidential speeches/speeches.mx"];
names = Normal[speeches[[All, "Name"]]];
];
dates = Normal[speeches[[All, "Date"]]];
texts = Normal[speeches[[All, "Text"]]];
Dimensions[speeches]
(* {2453, 4} *)
Using different contingency matrices we can derive basic statistical information about the document collection. (The document-word matrix is a contingency matrix.)
First we convert the text data in long-form:
docWordRecords =
Join @@ MapThread[
Thread[{##}] &, {Range@Length@texts, names,
DateString[#, {"Year"}] & /@ dates,
DeleteStopwords@*TextWords /@ ToLowerCase[texts]}, 1];
Here is a sample of the rows of the long-form:
GridTableForm[RandomSample[docWordRecords, 6],
TableHeadings -> {"document index", "name", "year", "word"}]
Here is a summary:
Multicolumn[
RecordsSummary[docWordRecords, {"document index", "name", "year", "word"}, "MaxTallies" -> 8], 4, Dividers -> All, Alignment -> Top]
Using the long form we can compute the document-word matrix:
ctMat = CrossTabulate[docWordRecords[[All, {1, -1}]]];
MatrixPlot[Transpose@Sort@Map[# &, Transpose[ctMat@"XTABMatrix"]],
MaxPlotPoints -> 300, ImageSize -> 800,
AspectRatio -> 1/3]
Here is the president-word matrix:
ctMat = CrossTabulate[docWordRecords[[All, {2, -1}]]];
MatrixPlot[Transpose@Sort@Map[# &, Transpose[ctMat@"XTABMatrix"]], MaxPlotPoints -> 300, ImageSize -> 800, AspectRatio -> 1/3]
Here is an alternative way to compute text collection statistics through the document-word matrix computed within the monad LSAMon
:
LSAMonUnit[texts]⟹LSAMonEchoTextCollectionStatistics[];
Here is one way to obtain stop words:
stopWords = Complement[DictionaryLookup["*"], DeleteStopwords[DictionaryLookup["*"]]];
Length[stopWords]
RandomSample[stopWords, 12]
(* 304 *)
(* {"has", "almost", "next", "WHO", "seeming", "together", "rather", "runners-up", "there's", "across", "cannot", "me"} *)
We can complete this list with additional stop words derived from the collection itself. (Not done here.)
Remark: In the rest of the document we use “term” to mean “word” or “stemmed word”.
The following code makes a document-term matrix from the document collection, exaggerates the representations of the terms using “TF-IDF”, and then does topic extraction through dimension reduction. The dimension reduction is done with NNMF; see [AAp3, AA1, AA2].
SeedRandom[312]
mObj =
LSAMonUnit[texts]⟹
LSAMonMakeDocumentTermMatrix[{}, stopWords]⟹
LSAMonApplyTermWeightFunctions[]⟹
LSAMonTopicExtraction[Max[5, Ceiling[Length[texts]/100]], 60, 12, "MaxSteps" -> 6, "PrintProfilingInfo" -> True];
This table shows the pipeline commands above with comments:
The monad object mObj
has a context of named values that is an Association with the following keys:
Keys[mObj⟹LSAMonTakeContext]
(* {"texts", "docTermMat", "terms", "wDocTermMat", "W", "H", "topicColumnPositions", "automaticTopicNames"} *)
Let us clarify the values by briefly describing the computational steps.
This is done with LSAMonMakeDocumentTermMatrix
.
From docTermMat
is derived the (weighted) matrix wDocTermMat using “TF-IDF”.
LSAMonApplyTermWeightFunctions
.docTermMat
we find the terms that are present in sufficiently large number of documents and their column indices are assigned to topicColumnPositions.
Matrix factorization.
Compute using NNMF the factorization , where , , and is the number of topics.
The values for the keys “W, “H”, and “topicColumnPositions” are computed and assigned by LSAMonTopicExtraction
.
From the top terms of each topic are derived automatic topic names and assigned to the key automaticTopicNames
in the monad context.
LSAMonTopicExtraction
.At this point in the object mObj
we have the factors of NNMF. Using those factors we can find a statistical thesaurus for a given set of words. The following code calculates such a thesaurus, and echoes it in a tabulated form.
queryWords = {"arms", "banking", "economy", "education", "freedom",
"tariff", "welfare", "disarmament", "health", "police"};
mObj⟹
LSAMonStatisticalThesaurus[queryWords, 12]⟹
LSAMonEchoStatisticalThesaurus[];
By observing the thesaurus entries we can see that the words in each entry are semantically related.
Note, that the word “welfare” strongly associates with “[applause]”. The rest of the query words do not, which can be seen by examining larger thesaurus entries:
thRes =
mObj⟹
LSAMonStatisticalThesaurus[queryWords, 100]⟹
LSAMonTakeValue;
Cases[thRes, "[applause]", Infinity]
(* {"[applause]", "[applause]"} *)
The second “[applause]” associated word is “education”.
The statistical thesaurus is computed by using the NNMF’s right factor .
For a given term, its corresponding column in is found and the nearest neighbors of that column are found in the space using Euclidean norm.
The topics are the rows of the right factor of the factorization obtained with NNMF .
Let us tabulate the topics found above with LSAMonTopicExtraction
:
mObj⟹ LSAMonEchoTopicsTable["NumberOfTerms" -> 6, "MagnificationFactor" -> 0.8, Appearance -> "Horizontal"];
The function LSAMonTopicsRepresentation
finds the top outliers for each row of NNMF’s left factor . (The outliers are found using the package [AAp4].) The obtained list of indices gives the topic representation of the collection of texts.
Short@(mObj⟹LSAMonTopicsRepresentation[]⟹LSAMonTakeContext)["docTopicIndices"]
{{53}, {47, 53}, {25}, {46}, {44}, {15, 42}, {18}, <>, {30}, {33}, {7, 60}, {22, 25}, {12, 13, 25, 30, 49, 59}, {48, 57}, {14, 41}}
Further we can see that if the documents have tags associated with them — like author names or dates — we can make a contingency matrix of tags vs topics. (See [AAp8, AA4].) This is also done by the function LSAMonTopicsRepresentation
that takes tags as an argument. If the tags argument is Automatic
, then the tags are simply the document indices.
Here is a an example:
rsmat = mObj⟹LSAMonTopicsRepresentation[Automatic]⟹LSAMonTakeValue;
MatrixPlot[rsmat]
Here is an example of calling the function LSAMonTopicsRepresentation with arbitrary tags.
rsmat = mObj⟹LSAMonTopicsRepresentation[DateString[#, "MonthName"] & /@ dates]⟹LSAMonTakeValue;
MatrixPlot[rsmat]
Note that the matrix plots above are very close to the charting of the Great conversation that we are looking for. This can be made more obvious by observing the row names and columns names in the tabulation of the transposed matrix rsmat
:
Magnify[#, 0.6] &@MatrixForm[Transpose[rsmat]]
In this section we show several ways to chart the Great Conversation in the collection of speeches.
There are several possible ways to make the chart: using a time-line plot, using heat-map plot, and using appropriate tabulation (with MatrixForm
or Grid
).
In order to make the code in this section more concise the package RSparseMatrix.m, [AAp7, AA5], is used.
This command makes an Association between the topic names and the top topic words.
aTopicNameToTopicTable =
AssociationThread[(mObj⟹LSAMonTakeContext)["automaticTopicNames"],
mObj⟹LSAMonTopicsTable["NumberOfTerms" -> 12]⟹LSAMonTakeValue];
Here is a sample:
Magnify[#, 0.7] &@ aTopicNameToTopicTable[[1 ;; 3]]
This command makes a contingency matrix between the documents and the topics (as described above):
rsmat = ToRSparseMatrix[mObj⟹LSAMonTopicsRepresentation[Automatic]⟹LSAMonTakeValue]
This time-plot shows great conversation in the USA presidents state of union speeches:
TimelinePlot[
Association@
MapThread[
Tooltip[#2, aTopicNameToTopicTable[#2]] -> dates[[ToExpression@#1]] &,
Transpose[RSparseMatrixToTriplets[rsmat]]],
PlotTheme -> "Detailed", ImageSize -> 1000, AspectRatio -> 1/2, PlotLayout -> "Stacked"]
The plot is too cluttered, so it is a good idea to investigate other visualizations.
We can use the USA president names instead of years in the Great Conversation chart because the USA presidents terms do not overlap.
This makes a contingency matrix presidents vs topics:
rsmat2 = ToRSparseMatrix[
mObj⟹LSAMonTopicsRepresentation[
names]⟹LSAMonTakeValue];
Here we compute the chronological order of the presidents based on the dates of their speeches:
nameToMeanYearRules =
Map[#[[1, 1]] -> Mean[N@#[[All, 2]]] &,
GatherBy[MapThread[List, {names, ToExpression[DateString[#, "Year"]] & /@ dates}], First]];
ordRowInds = Ordering[RowNames[rsmat2] /. nameToMeanYearRules];
This heat-map plot uses the (experimental) package HeatmapPlot.m, [AAp6]:
Block[{m = rsmat2[[ordRowInds, All]]},
HeatmapPlot[SparseArray[m], RowNames[m],
Thread[Tooltip[ColumnNames[m], aTopicNameToTopicTable /@ ColumnNames[m]]],
DistanceFunction -> {None, Sort}, ImageSize -> 1000,
AspectRatio -> 1/2]
]
Note the value of the option DistanceFunction
: there is not re-ordering of the rows and columns are reordered by sorting. Also, the topics on the horizontal names have tool-tips.
[D1] Wolfram Data Repository, "Presidential Nomination Acceptance Speeches".
[D2] US Presidents, State of the Union Addresses, Trajectory, 2016. ISBN1681240009, 9781681240008.
[D3] Gerhard Peters, "Presidential Nomination Acceptance Speeches and Letters, 1880-2016", The American Presidency Project.
[D4] Gerhard Peters, "State of the Union Addresses and Messages", The American Presidency Project.
[AAp1] Anton Antonov, MathematicaForPrediction utilities, (2014), MathematicaForPrediction at GitHub.
[AAp2] Anton Antonov, Implementation of document-term matrix construction and re-weighting functions in Mathematica(2013), MathematicaForPrediction at GitHub.
[AAp3] Anton Antonov, Implementation of the Non-Negative Matrix Factorization algorithm in Mathematica, (2013), MathematicaForPrediction at GitHub.
[AAp4] Anton Antonov, Implementation of one dimensional outlier identifying algorithms in Mathematica, (2013), MathematicaForPrediction at GitHub.
[AAp5] Anton Antonov, Monadic latent semantic analysis Mathematica package, (2017), MathematicaForPrediction at GitHub.
[AAp6] Anton Antonov, Heatmap plot Mathematica package, (2017), MathematicaForPrediction at GitHub.
[AAp7] Anton Antonov, RSparseMatrix Mathematica package, (2015), MathematicaForPrediction at GitHub.
[AAp8] Anton Antonov, Cross tabulation implementation in Mathematica, (2017), MathematicaForPrediction at GitHub.
[AA1] Anton Antonov, "Topic and thesaurus extraction from a document collection", (2013), MathematicaForPrediction at GitHub.
[AA2] Anton Antonov, "Statistical thesaurus from NPR podcasts", (2013), MathematicaForPrediction at WordPress blog.
[AA3] Anton Antonov, "Monad code generation and extension", (2017), MathematicaForPrediction at GitHub.
[AA4] Anton Antonov, "Contingency tables creation examples", (2016), MathematicaForPrediction at WordPress blog.
[AA5] Anton Antonov, "RSparseMatrix for sparse matrices with named rows and columns", (2015), MathematicaForPrediction at WordPress blog.
[Wk1] Wikipedia entry, Great Conversation.
[MA1] Mortimer Adler, "The Great Conversation Revisited," in The Great Conversation: A Peoples Guide to Great Books of the Western World, Encyclopædia Britannica, Inc., Chicago,1990, p. 28.
[MA2] Mortimer Adler, "Great Ideas".
[MA3] Mortimer Adler, "How to Think About the Great Ideas: From the Great Books of Western Civilization", 2000, Open Court.
]]>This blog post proclaims the first committed project in the repository ConversationalAgents at GitHub. The project has designs and implementations of a phone calling conversational agent that aims at providing the following functionalities:
The design is based on a Finite State Machine (FSM) and context free grammar(s) for commands that switch between the states of the FSM. The grammar is designed as a context free grammar rules of a Domain Specific Language (DSL) in Extended Backus-Naur Form (EBNF). (For more details on DSLs design and programming see [1].)
The (current) implementation is with Wolfram Language (WL) / Mathematica using the functional parsers package [2, 3].
This movie gives an overview from an end user perspective.
The design of the Phone Conversational Agent (PhCA) is derived in a straightforward manner from the typical work flow of calling a contact (using, say, a mobile phone.)
The main goals for the conversational agent are the following:
An additional goal is to facilitate contacts retrieval by determining the most appropriate contacts in query responses. For example, while driving to work by pressing the dial button we might prefer the contacts of an up-coming meeting to be placed on top of the prompting contacts list.
In this project we assume that the voice to text conversion is done with an external (reliable) component.
It is assumed that an user of PhCA can react to both visual and spoken query results.
The main algorithm is the following.
1) Parse and interpret a natural language command.
2) If the command is a contacts query that returns a single contact then call that contact.
3) If the command is a contacts query that returns multiple contacts then :
3.1) use natural language commands to refine and filter the query results,
3.2) until a single contact is obtained. Call that single contact.
4) If other type of command is given act accordingly.
PhCA has commands for system usage help and for canceling the current contact search and starting over.
The following FSM diagram gives the basic structure of PhCA:
This movie demonstrates how different natural language commands switch the FSM states.
The derived grammar describes sentences that: 1. fit end user expectations, and 2. are used to switch between the FSM states.
Because of the simplicity of the FSM and the natural language commands only few iterations were done with the Parser-generation-by-grammars work flow.
The base grammar is given in the file "./Mathematica/PhoneCallingDialogsGrammarRules.m" in EBNF used by [2].
Here are parsing results of a set of test natural language commands:
using the WL command:
ParsingTestTable[ParseJust[pCALLCONTACT\[CirclePlus]pCALLFILTER], ToLowerCase /@ queries]
(Note that according to PhCA’s FSM diagram the parsing of pCALLCONTACT
is separated from pCALLFILTER
, hence the need to combine the two parsers in the code line above.)
PhCA’s FSM implementation provides interpretation and context of the functional programming expressions obtained by the parser.
In the running script "./Mathematica/PhoneDialingAgentRunScript.m" the grammar parsers are modified to do successful parsing using data elements of the provided fake address book.
The base grammar can be extended with the "Time specifications grammar" in order to include queries based on temporal commands.
In order to experiment with the agent just run in Mathematica the command:
Import["https://raw.githubusercontent.com/antononcube/ConversationalAgents/master/Projects/PhoneDialingDialogsAgent/Mathematica/PhoneDialingAgentRunScript.m"]
The imported Wolfram Language file, "./Mathematica/PhoneDialingAgentRunScript.m", uses a fake address book based on movie creators metadata. The code structure of "./Mathematica/PhoneDialingAgentRunScript.m" allows easy experimentation and modification of the running steps.
Here are several screen-shots illustrating a particular usage path (scan left-to-right):
See this movie demonstrating a PhCA run.
[1] Anton Antonov, "Creating and programming domain specific languages", (2016), MathematicaForPrediction at WordPress blog.
[2] Anton Antonov, Functional parsers, Mathematica package, MathematicaForPrediction at GitHub, 2014.
[3] Anton Antonov, "Natural language processing with functional parsers", (2014), MathematicaForPrediction at WordPress blog.
]]>Anton Antonov
MathematicaForPrediction at GitHub
June 2017
This document aims to introduce monadic programming in Mathematica / Wolfram Language (WL) in a concise and code-direct manner. The core of the monad codes discussed is simple, derived from the fundamental principles of Mathematica / WL.
The usefulness of the monadic programming approach manifests in multiple ways. Here are a few we are interested in:
Speaking informally,
The theoretical background provided in this document is given in the Wikipedia article on Monadic programming, [Wk1], and the article “The essence of functional programming” by Philip Wadler, [H3]. The code in this document is based on the primary monad definition given in [Wk1,H3]. (Based on the “Kleisli triple” and used in Haskell.)
The general monad structure can be seen as:
In this document we treat the monad structure as a design pattern, [Wk3]. (After reading [H3] point 2 becomes more obvious. A similar in spirit, minimalistic approach to Object-oriented Design Patterns is given in [AA1].)
We do not deal with types for monads explicitly, we generate code for monads instead. One reason for this is the “monad design pattern” perspective; another one is that in Mathematica / WL the notion of algebraic data type is not needed — pattern matching comes from the core “book of replacement rules” principle.
The rest of the document is organized as follows.
1. Fundamental sections The section “What is a monad?” gives the necessary definitions. The section “The basic Maybe monad” shows how to program a monad from scratch in Mathematica / WL. The section “Extensions with polymorphic behavior” shows how extensions of the basic monad functions can be made. (These three sections form a complete read on monadic programming, the rest of the document can be skipped.)
2. Monadic programming in practice The section “Monad code generation” describes packages for generating monad code. The section “Flow control in monads” describes additional, control flow functionalities. The section “General work-flow of monad code generation utilization” gives a general perspective on the use of monad code generation. The section “Software design with monadic programming” discusses (small scale) software design with monadic programming.
3. Case study sections The case study sections “Contextual monad classification” and “Tracing monad pipelines” hopefully have interesting and engaging examples of monad code generation, extension, and utilization.
In this document a monad is any set of a symbol m and two operators unit and bind that adhere to the monad laws. (See the next sub-section.) The definition is taken from [Wk1] and [H3] and phrased in Mathematica / WL terms in this section. In order to be brief, we deliberately do not consider the equivalent monad definition based on unit, join, and map (also given in [H3].)
Here are operators for a monad associated with a certain symbol M
:
Unit[x_] := M[x]
;Bind[M[x_], f_] := f[x] with MatchQ[f[x],M[_]]
giving True
.Note that:
Bind
unwraps the content of M[_]
and gives it to the function f
;M
.Here is an illustration formula showing a monad pipeline:
From the definition and formula it should be clear that if for the result of Bind[_M,f[x]]
the test MatchQ[f[x],_M]
is True
then the result is ready to be fed to the next binding operation in monad’s pipeline. Also, it is clear that it is easy to program the pipeline functionality with Fold
:
Fold[Bind, M[x], {f1, f2, f3}]
(* Bind[Bind[Bind[M[x], f1], f2], f3] *)
The monad laws definitions are taken from [H1] and [H3].In the monad laws given below the symbol “⟹” is for monad’s binding operation and ↦ is for a function in anonymous form.
Here is a table with the laws:
Remark: The monad laws are satisfied for every symbol in Mathematica / WL with List being the unit operation and Apply being the binding operation.
Looking at formula (1) — and having certain programming experiences — we can expect the following features when using monadic programming.
Fold
, but with suitable definitions of infix operators like DoubleLongRightArrow
(⟹) we can produce code that resembles the pipeline in formula (1).Bind
.)These points are clarified below. For more complete discussions see [Wk1] or [H3].
It is fairly easy to program the basic monad Maybe discussed in [Wk1].
The goal of the Maybe monad is to provide easy exception handling in a sequence of chained computational steps. If one of the computation steps fails then the whole pipeline returns a designated failure symbol, say None
otherwise the result after the last step is wrapped in another designated symbol, say Maybe.
Here is the special version of the generic pipeline formula (1) for the Maybe monad:
Here is the minimal code to get a functional Maybe monad (for a more detailed exposition of code and explanations see [AA7]):
MaybeUnitQ[x_] := MatchQ[x, None] || MatchQ[x, Maybe[___]];
MaybeUnit[None] := None;
MaybeUnit[x_] := Maybe[x];
MaybeBind[None, f_] := None;
MaybeBind[Maybe[x_], f_] :=
Block[{res = f[x]}, If[FreeQ[res, None], res, None]];
MaybeEcho[x_] := Maybe@Echo[x];
MaybeEchoFunction[f___][x_] := Maybe@EchoFunction[f][x];
MaybeOption[f_][xs_] :=
Block[{res = f[xs]}, If[FreeQ[res, None], res, Maybe@xs]];
In order to make the pipeline form of the code we write let us give definitions to a suitable infix operator (like “⟹”) to use MaybeBind:
DoubleLongRightArrow[x_?MaybeUnitQ, f_] := MaybeBind[x, f];
DoubleLongRightArrow[x_, y_, z__] :=
DoubleLongRightArrow[DoubleLongRightArrow[x, y], z];
Here is an example of a Maybe monad pipeline using the definitions so far:
data = {0.61, 0.48, 0.92, 0.90, 0.32, 0.11};
MaybeUnit[data]⟹(* lift data into the monad *)
(Maybe@ Join[#, RandomInteger[8, 3]] &)⟹(* add more values *)
MaybeEcho⟹(* display current value *)
(Maybe @ Map[If[# < 0.4, None, #] &, #] &)(* map values that are too small to None *)
(* {0.61,0.48,0.92,0.9,0.32,0.11,4,4,0}
None *)
The result is None
because:
MaybeBind
stops the pipeline aggressively using a FreeQ[_,None]
test.Let us convince ourselves that the current definition of MaybeBind
gives a monad.
The verification is straightforward to program and shows that the implemented Maybe monad adheres to the monad laws.
We can see from formulas (1) and (2) that the monad codes can be easily extended through overloading the pipeline functions.
For example the extension of the Maybe monad to handle of Dataset
objects is fairly easy and straightforward.
Here is the formula of the Maybe monad pipeline extended with Dataset
objects:
Here is an example of a polymorphic function definition for the Maybe monad:
MaybeFilter[filterFunc_][xs_] := Maybe@Select[xs, filterFunc[#] &];
MaybeFilter[critFunc_][xs_Dataset] := Maybe@xs[Select[critFunc]];
See [AA7] for more detailed examples of polymorphism in monadic programming with Mathematica / WL.
A complete discussion can be found in [H3]. (The main message of [H3] is the poly-functional and polymorphic properties of monad implementations.)
The R package dplyr, [R1], has implementations centered around monadic polymorphic behavior. The command pipelines based on dplyrcan work on R data frames, SQL tables, and Spark data frames without changes.
Here is a diagram of a typical work-flow with dplyr:
The diagram shows how a pipeline made with dplyr can be re-run (or reused) for data stored in different data structures.
We can see monad code definitions like the ones for Maybe as some sort of initial templates for monads that can be extended in specific ways depending on their applications. Mathematica / WL can easily provide code generation for such templates; (see [WL1]). As it was mentioned in the introduction, we do not deal with types for monads explicitly, we generate code for monads instead.
In this section are given examples with packages that generate monad codes. The case study sections have examples of packages that utilize generated monad codes.
The package [AA2] provides a Maybe code generator that takes as an argument a prefix for the generated functions. (Monad code generation is discussed further in the section “General work-flow of monad code generation utilization”.)
Here is an example:
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/MonadicProgramming/MaybeMonadCodeGenerator.m"]
GenerateMaybeMonadCode["AnotherMaybe"]
data = {0.61, 0.48, 0.92, 0.90, 0.32, 0.11};
AnotherMaybeUnit[data]⟹(* lift data into the monad *)
(AnotherMaybe@Join[#, RandomInteger[8, 3]] &)⟹(* add more values *)
AnotherMaybeEcho⟹(* display current value *)
(AnotherMaybe @ Map[If[# < 0.4, None, #] &, #] &)(* map values that are too small to None *)
(* {0.61,0.48,0.92,0.9,0.32,0.11,8,7,6}
AnotherMaybeBind: Failure when applying: Function[AnotherMaybe[Map[Function[If[Less[Slot[1], 0.4], None, Slot[1]]], Slot[1]]]]
None *)
We see that we get the same result as above (None
) and a message prompting failure.
The State monad is also basic and its programming in Mathematica / WL is not that difficult. (See [AA3].)
Here is the special version of the generic pipeline formula (1) for the State monad:
Note that since the State monad pipeline caries both a value and a state, it is a good idea to have functions that manipulate them separately. For example, we can have functions for context modification and context retrieval. (These are done in [AA3].)
This loads the package [AA3]:
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/MonadicProgramming/StateMonadCodeGenerator.m"]
This generates the State monad for the prefix “StMon”:
GenerateStateMonadCode["StMon"]
The following StMon pipeline code starts with a random matrix and then replaces numbers in the current pipeline value according to a threshold parameter kept in the context. Several times are invoked functions for context deposit and retrieval.
SeedRandom[34]
StMonUnit[RandomReal[{0, 1}, {3, 2}], <|"mark" -> "TooSmall", "threshold" -> 0.5|>]⟹
StMonEchoValue⟹
StMonEchoContext⟹
StMonAddToContext["data"]⟹
StMonEchoContext⟹
(StMon[#1 /. (x_ /; x < #2["threshold"] :> #2["mark"]), #2] &)⟹
StMonEchoValue⟹
StMonRetrieveFromContext["data"]⟹
StMonEchoValue⟹
StMonRetrieveFromContext["mark"]⟹
StMonEchoValue;
(* value: {{0.789884,0.831468},{0.421298,0.50537},{0.0375957,0.289442}}
context: <|mark->TooSmall,threshold->0.5|>
context: <|mark->TooSmall,threshold->0.5,data->{{0.789884,0.831468},{0.421298,0.50537},{0.0375957,0.289442}}|>
value: {{0.789884,0.831468},{TooSmall,0.50537},{TooSmall,TooSmall}}
value: {{0.789884,0.831468},{0.421298,0.50537},{0.0375957,0.289442}}
value: TooSmall *)
We can implement dedicated functions for governing the pipeline flow in a monad.
Let us look at a breakdown of these kind of functions using the State monad StMon generated above.
A basic and simple pipeline control function is for optional acceptance of result — if failure is obtained applying f then we ignore its result (and keep the current pipeline value.)
Here is an example with StMonOption
:
SeedRandom[34]
StMonUnit[RandomReal[{0, 1}, 5]]⟹
StMonEchoValue⟹
StMonOption[If[# < 0.3, None] & /@ # &]⟹
StMonEchoValue
(* value: {0.789884,0.831468,0.421298,0.50537,0.0375957}
value: {0.789884,0.831468,0.421298,0.50537,0.0375957}
StMon[{0.789884, 0.831468, 0.421298, 0.50537, 0.0375957}, <||>] *)
Without StMonOption
we get failure:
SeedRandom[34]
StMonUnit[RandomReal[{0, 1}, 5]]⟹
StMonEchoValue⟹
(If[# < 0.3, None] & /@ # &)⟹
StMonEchoValue
(* value: {0.789884,0.831468,0.421298,0.50537,0.0375957}
StMonBind: Failure when applying: Function[Map[Function[If[Less[Slot[1], 0.3], None]], Slot[1]]]
None *)
It is natural to want to have the ability to chose a pipeline function application based on a condition.
This can be done with the functions StMonIfElse
and StMonWhen
.
SeedRandom[34]
StMonUnit[RandomReal[{0, 1}, 5]]⟹
StMonEchoValue⟹
StMonIfElse[
Or @@ (# < 0.4 & /@ #) &,
(Echo["A too small value is present.", "warning:"];
StMon[Style[#1, Red], #2]) &,
StMon[Style[#1, Blue], #2] &]⟹
StMonEchoValue
(* value: {0.789884,0.831468,0.421298,0.50537,0.0375957}
warning: A too small value is present.
value: {0.789884,0.831468,0.421298,0.50537,0.0375957}
StMon[{0.789884,0.831468,0.421298,0.50537,0.0375957},<||>] *)
Remark: Using flow control functions like StMonIfElse
and StMonWhen
with appropriate messages is a better way of handling computations that might fail. The silent failures handling of the basic Maybe monad is convenient only in a small number of use cases.
The last group of pipeline flow control functions we consider comprises iterative functions that provide the functionalities of Nest
, NestWhile
, FoldList
, etc.
In [AA3] these functionalities are provided through the function StMonIterate.
Here is a basic example using Nest
that corresponds to Nest[#+1&,1,3]
:
StMonUnit[1]⟹StMonIterate[Nest, (StMon[#1 + 1, #2]) &, 3]
(* StMon[4, <||>] *)
Consider this command that uses the full signature of NestWhileList
:
NestWhileList[# + 1 &, 1, # < 10 &, 1, 4]
(* {1, 2, 3, 4, 5} *)
Here is the corresponding StMon iteration code:
StMonUnit[1]⟹StMonIterate[NestWhileList, (StMon[#1 + 1, #2]) &, (#[[1]] < 10) &, 1, 4]
(* StMon[{1, 2, 3, 4, 5}, <||>] *)
Here is another results accumulation example with FixedPointList
:
StMonUnit[1.]⟹
StMonIterate[FixedPointList, (StMon[(#1 + 2/#1)/2, #2]) &]
(* StMon[{1., 1.5, 1.41667, 1.41422, 1.41421, 1.41421, 1.41421}, <||>] *)
When the functions NestList
, NestWhileList
, FixedPointList
are used with StMonIterate
their results can be stored in the context. Here is an example:
StMonUnit[1.]⟹
StMonIterate[FixedPointList, (StMon[(#1 + 2/#1)/2, #2]) &, "fpData"]
(* StMon[{1., 1.5, 1.41667, 1.41422, 1.41421, 1.41421, 1.41421}, <|"fpData" -> {StMon[1., <||>],
StMon[1.5, <||>], StMon[1.41667, <||>], StMon[1.41422, <||>], StMon[1.41421, <||>],
StMon[1.41421, <||>], StMon[1.41421, <||>]} |>] *)
More elaborate tests can be found in [AA8].
Because of the associativity law we can design pipeline flows based on functions made of “sub-pipelines.”
fEcho = Function[{x, ct}, StMonUnit[x, ct]⟹StMonEchoValue⟹StMonEchoContext];
fDIter = Function[{x, ct},
StMonUnit[y^x, ct]⟹StMonIterate[FixedPointList, StMonUnit@D[#, y] &, 20]];
StMonUnit[7]⟹fEcho⟹fDIter⟹fEcho;
(*
value: 7
context: <||>
value: {y^7,7 y^6,42 y^5,210 y^4,840 y^3,2520 y^2,5040 y,5040,0,0}
context: <||> *)
With the abilities to generate and utilize monad codes it is natural to consider the following work flow. (Also shown in the diagram below.)
The template nature of the general monads can be exemplified with the group of functions in the package StateMonadCodeGenerator.m, [4].
They are in five groups:
We can say that all monad implementations will have their own versions of these groups of functions. The more specialized monads will have functions specific to their intended use. Such special monads are discussed in the case study sections.
The application of monadic programming to a particular problem domain is very similar to designing a software framework or designing and implementing a Domain Specific Language (DSL).
The answers of the question “When to use monadic programming?” can form a large list. This section provides only a couple of general, personal viewpoints on monadic programming in software design and architecture. The principles of monadic programming can be used to build systems from scratch (like Haskell and Scala.) Here we discuss making specialized software with or within already existing systems.
Software framework design is about architectural solutions that capture the commonality and variability in a problem domain in such a way that: 1) significant speed-up can be achieved when making new applications, and 2) a set of policies can be imposed on the new applications.
The rigidness of the framework provides and supports its flexibility — the framework has a backbone of rigid parts and a set of “hot spots” where new functionalities are plugged-in.
Usually Object-Oriented Programming (OOP) frameworks provide inversion of control — the general work-flow is already established, only parts of it are changed. (This is characterized with “leave the driving to us” and “don’t call us we will call you.”)
The point of utilizing monadic programming is to be able to easily create different new work-flows that share certain features. (The end user is the driver, on certain rail paths.)
In my opinion making a software framework of small to moderate size with monadic programming principles would produce a library of functions each with polymorphic behaviour that can be easily sequenced in monadic pipelines. This can be contrasted with OOP framework design in which we are more likely to end up with backbone structures that (i) are static and tree-like, and (ii) are extended or specialized by plugging-in relevant objects. (Those plugged-in objects themselves can be trees, but hopefully short ones.)
Given a problem domain the general monad structure can be used to shape and guide the development of DSLs for that problem domain.
Generally, in order to make a DSL we have to choose the language syntax and grammar. Using monadic programming the syntax and grammar commands are clear. (The monad pipelines are the commands.) What is left is “just” the choice of particular functions and their implementations.
Another way to develop such a DSL is through a grammar of natural language commands. Generally speaking, just designing the grammar — without developing the corresponding interpreters — would be very helpful in figuring out the components at play. Monadic programming meshes very well with this approach and applying the two approaches together can be very fruitful.
In this section we show an extension of the State monad into a monad aimed at machine learning classification work-flows.
We want to provide a DSL for doing machine learning classification tasks that allows us:
Also, we want the DSL design to provide clear directions how to add (hook-up or plug-in) new functionalities.
The package [AA4] discussed below provides such a DSL through monadic programming.
This loads the package [AA4]:
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/MonadicProgramming/MonadicContextualClassification.m"]
This gets some test data (the Titanic dataset):
dataName = "Titanic";
ds = Dataset[Flatten@*List @@@ ExampleData[{"MachineLearning", dataName}, "Data"]];
varNames = Flatten[List @@ ExampleData[{"MachineLearning", dataName}, "VariableDescriptions"]];
varNames = StringReplace[varNames, "passenger" ~~ (WhitespaceCharacter ..) -> ""];
If[dataName == "FisherIris", varNames = Most[varNames]];
ds = ds[All, AssociationThread[varNames -> #] &];
The package [AA4] provides functions for the monad ClCon — the functions implemented in [AA4] have the prefix “ClCon”.
The classifier contexts are Association objects. The pipeline values can have the form:
ClCon[ val, context:(_String|_Association) ]
The ClCon specific monad functions deposit or retrieve values from the context with the keys: “trainData”, “testData”, “classifier”. The general idea is that if the current value of the pipeline cannot provide all arguments for a ClCon function, then the needed arguments are taken from the context. If that fails, then an message is issued. This is illustrated with the following pipeline with comments example.
The pipeline and results above demonstrate polymorphic behaviour over the classifier variable in the context: different functions are used if that variable is a ClassifierFunction object or an association of named ClassifierFunction
objects.
Note the demonstrated granularity and sequentiality of the operations coming from using a monad structure. With those kind of operations it would be easy to make interpreters for natural language DSLs.
This monadic pipeline in this example goes through several stages: data summary, classifier training, evaluation, acceptance test, and if the results are rejected a new classifier is made with a different algorithm using the same data splitting. The context keeps track of the data and its splitting. That allows the conditional classifier switch to be concisely specified.
First let us define a function that takes a Classify
method as an argument and makes a classifier and calculates performance measures.
ClSubPipe[method_String] :=
Function[{x, ct},
ClConUnit[x, ct]⟹
ClConMakeClassifier[method]⟹
ClConEchoFunctionContext["classifier:",
ClassifierInformation[#["classifier"], Method] &]⟹
ClConEchoFunctionContext["training time:", ClassifierInformation[#["classifier"], "TrainingTime"] &]⟹
ClConClassifierMeasurements[{"Accuracy", "Precision", "Recall"}]⟹
ClConEchoValue⟹
ClConEchoFunctionContext[
ClassifierMeasurements[#["classifier"],
ClConToNormalClassifierData[#["testData"]], "ROCCurve"] &]
];
Using the sub-pipeline function ClSubPipe
we make the outlined pipeline.
SeedRandom[12]
res =
ClConUnit[ds]⟹
ClConSplitData[0.7]⟹
ClConEchoFunctionValue["summaries:", ColumnForm[Normal[RecordsSummary /@ #]] &]⟹
ClConEchoFunctionValue["xtabs:",
MatrixForm[CrossTensorate[Count == varNames[[1]] + varNames[[-1]], #]] & /@ # &]⟹
ClSubPipe["LogisticRegression"]⟹
(If[#1["Accuracy"] > 0.8,
Echo["Good accuracy!", "Success:"]; ClConFail,
Echo["Make a new classifier", "Inaccurate:"];
ClConUnit[#1, #2]] &)⟹
ClSubPipe["RandomForest"];
The monadic implementations in the package MonadicTracing.m, [AA5] allow tracking of the pipeline execution of functions within other monads.
The primary reason for developing the package was the desire to have the ability to print a tabulated trace of code and comments using the usual monad pipeline notation. (I.e. without conversion to strings etc.)
It turned out that by programming MonadicTracing.m I came up with a monad transformer; see [Wk2], [H2].
This loads the package [AA5]:
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/MonadicProgramming/MonadicTracing.m"]
This generates a Maybe monad to be used in the example (for the prefix “Perhaps”):
GenerateMaybeMonadCode["Perhaps"]
GenerateMaybeMonadSpecialCode["Perhaps"]
In following example we can see that pipeline functions of the Perhaps monad are interleaved with comment strings. Producing the grid of functions and comments happens “naturally” with the monad function TraceMonadEchoGrid
.
data = RandomInteger[10, 15];
TraceMonadUnit[PerhapsUnit[data]]⟹"lift to monad"⟹
TraceMonadEchoContext⟹
PerhapsFilter[# > 3 &]⟹"filter current value"⟹
PerhapsEcho⟹"display current value"⟹
PerhapsWhen[#[[3]] > 3 &,
PerhapsEchoFunction[Style[#, Red] &]]⟹
(Perhaps[#/4] &)⟹
PerhapsEcho⟹"display current value again"⟹
TraceMonadEchoGrid[Grid[#, Alignment -> Left] &];
Note that :
TraceMonadUnit
;Another example is the ClCon pipeline in the sub-section “Monad design” in the previous section.
This document presents a style of using monadic programming in Wolfram Language (Mathematica). The style has some shortcomings, but it definitely provides convenient features for day-to-day programming and in coming up with architectural designs.
The style is based on WL’s basic language features. As a consequence it is fairly concise and produces light overhead.
Ideally, the packages for the code generation of the basic Maybe and State monads would serve as starting points for other more general or more specialized monadic programs.
[Wk1] Wikipedia entry: Monad (functional programming), URL: https://en.wikipedia.org/wiki/Monad_(functional_programming) .
[Wk2] Wikipedia entry: Monad transformer, URL: https://en.wikipedia.org/wiki/Monad_transformer .
[Wk3] Wikipedia entry: Software Design Pattern, URL: https://en.wikipedia.org/wiki/Software_design_pattern .
[H1] Haskell.org article: Monad laws, URL: https://wiki.haskell.org/Monad_laws.
[H2] Sheng Liang, Paul Hudak, Mark Jones, “Monad transformers and modular interpreters”, (1995), Proceedings of the 22nd ACM SIGPLAN-SIGACT symposium on Principles of programming languages. New York, NY: ACM. pp. 333[Dash]343. doi:10.1145/199448.199528.
[H3] Philip Wadler, “The essence of functional programming”, (1992), 19’th Annual Symposium on Principles of Programming Languages, Albuquerque, New Mexico, January 1992.
[R1] Hadley Wickham et al., dplyr: A Grammar of Data Manipulation, (2014), tidyverse at GitHub, URL: https://github.com/tidyverse/dplyr . (See also, http://dplyr.tidyverse.org .)
[WL1] Leonid Shifrin, “Metaprogramming in Wolfram Language”, (2012), Mathematica StackExchange. (Also posted at Wolfram Community in 2017.) URL of the Mathematica StackExchange answer: https://mathematica.stackexchange.com/a/2352/34008 . URL of the Wolfram Community post: http://community.wolfram.com/groups/-/m/t/1121273 .
[AA1] Anton Antonov, “Implementation of Object-Oriented Programming Design Patterns in Mathematica”, (2016) MathematicaForPrediction at GitHub, https://github.com/antononcube/MathematicaForPrediction.
[AA2] Anton Antonov, Maybe monad code generator Mathematica package, (2017), MathematicaForPrediction at GitHub. URL: https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/MaybeMonadCodeGenerator.m .
[AA3] Anton Antonov, State monad code generator Mathematica package, (2017), MathematicaForPrediction at GitHub. URL: https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/StateMonadCodeGenerator.m .
[AA4] Anton Antonov, Monadic contextual classification Mathematica package, (2017), MathematicaForPrediction at GitHub. URL: https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/MonadicContextualClassification.m .
[AA5] Anton Antonov, Monadic tracing Mathematica package, (2017), MathematicaForPrediction at GitHub. URL: https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/MonadicTracing.m .
[AA6] Anton Antonov, MathematicaForPrediction utilities, (2014), MathematicaForPrediction at GitHub. URL: https://github.com/antononcube/MathematicaForPrediction/blob/master/MathematicaForPredictionUtilities.m .
[AA7] Anton Antonov, Simple monadic programming, (2017), MathematicaForPrediction at GitHub. (Preliminary version, 40% done.) URL: https://github.com/antononcube/MathematicaForPrediction/blob/master/Documentation/Simple-monadic-programming.pdf .
[AA8] Anton Antonov, Generated State Monad Mathematica unit tests, (2017), MathematicaForPrediction at GitHub. URL: https://github.com/antononcube/MathematicaForPrediction/blob/master/UnitTests/GeneratedStateMonadTests.m .
[AA9] Anton Antonov, Classifier ensembles functions Mathematica package, (2016), MathematicaForPrediction at GitHub. URL: https://github.com/antononcube/MathematicaForPrediction/blob/master/ClassifierEnsembles.m .
[AA10] Anton Antonov, “ROC for classifier ensembles, bootstrapping, damaging, and interpolation”, (2016), MathematicaForPrediction at WordPress. URL: https://mathematicaforprediction.wordpress.com/2016/10/15/roc-for-classifier-ensembles-bootstrapping-damaging-and-interpolation/ .
]]>This document discusses concrete algorithms for two different approaches of generation of mandala images, [1]: direct construction with graphics primitives, and use of machine learning algorithms.
In the experiments described in this document better results were obtained with the direct algorithms. The direct algorithms were made for the Mathematica StackExchange question "Code that generates a mandala", [3].
The main goals of this document are:
to provide an illustrative example of comparing dimension reduction methods,
to give a set-up for further discussions and investigations on mandala creation with machine learning algorithms.
Two direct construction algorithms are given: one uses "seed" segment rotations, the other superimposing of layers of different types. The following plots show the order in which different mandala parts are created with each of the algorithms.
In this document we use several algorithms for dimension reduction applied to collections of images following the procedure described in [4,5]. We are going to show that with Non-Negative Matrix Factorization (NNMF) we can use mandalas made with the seed segment rotation algorithm to extract layer types and superimpose them to make colored mandalas. Using the same approach with Singular Value Decomposition (SVD) or Independent Component Analysis (ICA) does not produce good layers and the superimposition produces more "watered-down", less diverse mandalas.
From a more general perspective this document compares the statistical approach of "trying to see without looking" with the "direct simulation" approach. Another perspective is the creation of "design spaces"; see [6].
The idea of using machine learning algorithms is appealing because there is no need to make the mental effort of understanding, discerning, approximating, and programming the principles of mandala creation. We can "just" use a large collection of mandala images and generate new ones using the "internal knowledge" data of machine learning algorithms. For example, a Neural network system like Deep Dream, [2], might be made to dream of mandalas.
In this section we present two different algorithms for generating mandalas. The first sees a mandala as being generated by rotation of a "seed" segment. The second sees a mandala as being generated by different component layers. For other approaches see [3].
The request of [3] is for generation of mandalas for coloring by hand. That is why the mandala generation algorithms are in the grayscale space. Coloring the generated mandala images is a secondary task.
One way to come up with mandalas is to generate a segment and then by appropriate number of rotations to produce a mandala.
Here is a function and an example of random segment (seed) generation:
Clear[MakeSeedSegment]
MakeSeedSegment[radius_, angle_, n_Integer: 10,
connectingFunc_: Polygon, keepGridPoints_: False] :=
Block[{t},
t = Table[
Line[{radius*r*{Cos[angle], Sin[angle]}, {radius*r, 0}}], {r, 0, 1, 1/n}];
Join[If[TrueQ[keepGridPoints], t, {}], {GrayLevel[0.25],
connectingFunc@RandomSample[Flatten[t /. Line[{x_, y_}] :> {x, y}, 1]]}]
];
seed = MakeSeedSegment[10, Pi/12, 10];
Graphics[seed, Frame -> True]
This function can make a seed segment symmetric:
Clear[MakeSymmetric]
MakeSymmetric[seed_] := {seed,
GeometricTransformation[seed, ReflectionTransform[{0, 1}]]};
seed = MakeSymmetric[seed];
Graphics[seed, Frame -> True]
Using a seed we can generate mandalas with different specification signatures:
Clear[MakeMandala]
MakeMandala[opts : OptionsPattern[]] :=
MakeMandala[
MakeSymmetric[
MakeSeedSegment[20, Pi/12, 12,
RandomChoice[{Line, Polygon, BezierCurve,
FilledCurve[BezierCurve[#]] &}], False]], Pi/6, opts];
MakeMandala[seed_, angle_?NumericQ, opts : OptionsPattern[]] :=
Graphics[GeometricTransformation[seed,
Table[RotationMatrix[a], {a, 0, 2 Pi - angle, angle}]], opts];
This code randomly selects symmetricity and seed generation parameters (number of concentric circles, angles):
SeedRandom[6567]
n = 12;
Multicolumn@
MapThread[
Image@If[#1,
MakeMandala[MakeSeedSegment[10, #2, #3], #2],
MakeMandala[
MakeSymmetric[MakeSeedSegment[10, #2, #3, #4, False]], 2 #2]
] &, {RandomChoice[{False, True}, n],
RandomChoice[{Pi/7, Pi/8, Pi/6}, n],
RandomInteger[{8, 14}, n],
RandomChoice[{Line, Polygon, BezierCurve,
FilledCurve[BezierCurve[#]] &}, n]}]
Here is a more concise way to generate symmetric segment mandalas:
Multicolumn[Table[Image@MakeMandala[], {12}], 5]
Note that with this approach the programming of the mandala coloring is not that trivial — weighted blending of colorized mandalas is the easiest thing to do. (Shown below.)
This approach was given by Simon Woods in [3].
"For this one I’ve defined three types of layer, a flower, a simple circle and a ring of small circles. You could add more for greater variety."
The coloring approach with image blending given below did not work well for this algorithm, so I modified the original code in order to produce colored mandalas.
ClearAll[LayerFlower, LayerDisk, LayerSpots, MandalaByLayers]
LayerFlower[n_, a_, r_, colorSchemeInd_Integer] :=
Module[{b = RandomChoice[{-1/(2 n), 0}]}, {If[
colorSchemeInd == 0, White,
RandomChoice[ColorData[colorSchemeInd, "ColorList"]]],
Cases[ParametricPlot[
r (a + Cos[n t])/(a + 1) {Cos[t + b Sin[2 n t]], Sin[t + b Sin[2 n t]]}, {t, 0, 2 Pi}],
l_Line :> FilledCurve[l], -1]}];
LayerDisk[_, _, r_, colorSchemeInd_Integer] := {If[colorSchemeInd == 0, White,
RandomChoice[ColorData[colorSchemeInd, "ColorList"]]],
Disk[{0, 0}, r]};
LayerSpots[n_, a_, r_, colorSchemeInd_Integer] := {If[colorSchemeInd == 0, White,
RandomChoice[ColorData[colorSchemeInd, "ColorList"]]],
Translate[Disk[{0, 0}, r a/(4 n)], r CirclePoints[n]]};
MandalaByLayers[n_, m_, coloring : (False | True) : False, opts : OptionsPattern[]] :=
Graphics[{EdgeForm[Black], White,
Table[RandomChoice[{3, 2, 1} -> {LayerFlower, LayerDisk, LayerSpots}][n, RandomReal[{3, 5}], i,
If[coloring, RandomInteger[{1, 17}], 0]]~Rotate~(Pi i/n), {i, m, 1, -1}]}, opts];
Here are generated black-and-white mandalas.
SeedRandom[6567]
ImageCollage[Table[Image@MandalaByLayers[16, 20], {12}], Background -> White, ImagePadding -> 3, ImageSize -> 1200]
Here are some colored mandalas. (Which make me think more of Viking and Native American art than mandalas.)
ImageCollage[Table[Image@MandalaByLayers[16, 20, True], {12}], Background -> White, ImagePadding -> 3, ImageSize -> 1200]
iSize = 400;
SeedRandom[6567]
AbsoluteTiming[
mandalaImages =
Table[Image[
MakeMandala[
MakeSymmetric@
MakeSeedSegment[10, Pi/12, 12, RandomChoice[{Polygon, FilledCurve[BezierCurve[#]] &}]], Pi/6],
ImageSize -> {iSize, iSize}, ColorSpace -> "Grayscale"], {300}];
]
(* {39.31, Null} *)
ImageCollage[ColorNegate /@ RandomSample[mandalaImages, 12], Background -> White, ImagePadding -> 3, ImageSize -> 400]
See the section "Using World Wide Web images".
The most interesting results are obtained with the image blending procedure coded below over mandala images generated with the seed segment rotation algorithm.
SeedRandom[3488]
directBlendingImages = Table[
RemoveBackground@
ImageAdjust[
Blend[Colorize[#,
ColorFunction ->
RandomChoice[{"IslandColors", "FruitPunchColors",
"AvocadoColors", "Rainbow"}]] & /@
RandomChoice[mandalaImages, 4], RandomReal[1, 4]]], {36}];
ImageCollage[directBlendingImages, Background -> White, ImagePadding -> 3, ImageSize -> 1200]
In this section we are going to apply the dimension reduction algorithms Singular Value Decomposition (SVD), Independent Component Analysis (ICA), and Non-Negative Matrix Factorization (NNMF) to a linear vector space representation (a matrix) of an image dataset. In the next section we use the bases generated by those algorithms to make mandala images.
We are going to use the packages [7,8] for ICA and NNMF respectively.
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/IndependentComponentAnalysis.m"]
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/NonNegativeMatrixFactorization.m"]
The linear vector space representation of the images is simple — each image is flattened to a vector (row-wise), and the image vectors are put into a matrix.
mandalaMat = Flatten@*ImageData@*ColorNegate /@ mandalaImages;
Dimensions[mandalaMat]
(* {300, 160000} *)
The following code re-factors the images matrix with SVD, ICA, and NNMF and extracts the basis images.
AbsoluteTiming[
svdRes = SingularValueDecomposition[mandalaMat, 20];
]
(* {5.1123, Null} *)
svdBasisImages = Map[ImageAdjust@Image@Partition[#, iSize] &, Transpose@svdRes[[3]]];
AbsoluteTiming[
icaRes =
IndependentComponentAnalysis[Transpose[mandalaMat], 20,
PrecisionGoal -> 4, "MaxSteps" -> 100];
]
(* {23.41, Null} *)
icaBasisImages = Map[ImageAdjust@Image@Partition[#, iSize] &, Transpose[icaRes[[1]]]];
SeedRandom[452992]
AbsoluteTiming[
nnmfRes =
GDCLS[mandalaMat, 20, PrecisionGoal -> 4,
"MaxSteps" -> 20, "RegularizationParameter" -> 0.1];
]
(* {233.209, Null} *)
nnmfBasisImages = Map[ImageAdjust@Image@Partition[#, iSize] &, nnmfRes[[2]]];
Let us visualize the bases derived with the matrix factorization methods.
Grid[{{"SVD", "ICA", "NNMF"},
Map[ImageCollage[#, Automatic, {400, 500},
Background -> LightBlue, ImagePadding -> 5, ImageSize -> 350] &,
{svdBasisImages, icaBasisImages, nnmfBasisImages}]
}, Dividers -> All]
Here are some observations for the bases.
The SVD and ICA bases are structured similarly. That is because ICA and SVD are both based on orthogonality — ICA factorization uses an orthogonality criteria based on Gaussian noise properties (which is more relaxed than SVD’s standard orthogonality criteria.)
As expected, the NNMF basis images have black background because of the enforced non-negativity. (Black corresponds to 0, white to 1.)
Compared to the SVD and ICA bases the images of the NNMF basis are structured in a radial manner. This can be demonstrated using image binarization.
Grid[{{"SVD", "ICA", "NNMF"}, Map[ImageCollage[Binarize[#, 0.5] & /@ #, Automatic, {400, 500}, Background -> LightBlue, ImagePadding -> 5, ImageSize -> 350] &, {svdBasisImages, icaBasisImages, nnmfBasisImages}] }, Dividers -> All]
We can see that binarizing of the NNMF basis images shows them as mandala layers. In other words, using NNMF we can convert the mandalas of the seed segment rotation algorithm into mandalas generated by an algorithm that superimposes layers of different types.
In this section we just show different blending images using the SVD, ICA, and NNMF bases.
ClearAll[MandalaImageBlending]
Options[MandalaImageBlending] = {"BaseImage" -> {}, "BaseImageWeight" -> Automatic, "PostBlendingFunction" -> (RemoveBackground@*ImageAdjust)};
MandalaImageBlending[basisImages_, nSample_Integer: 4, n_Integer: 12, opts : OptionsPattern[]] :=
Block[{baseImage, baseImageWeight, postBlendingFunc, sImgs, sImgWeights},
baseImage = OptionValue["BaseImage"];
baseImageWeight = OptionValue["BaseImageWeight"];
postBlendingFunc = OptionValue["PostBlendingFunction"];
Table[(
sImgs =
Flatten@Join[{baseImage}, RandomSample[basisImages, nSample]];
If[NumericQ[baseImageWeight] && ImageQ[baseImage],
sImgWeights =
Join[{baseImageWeight}, RandomReal[1, Length[sImgs] - 1]],
sImgWeights = RandomReal[1, Length[sImgs]]
];
postBlendingFunc@
Blend[Colorize[#,
DeleteCases[{opts}, ("BaseImage" -> _) | ("BaseImageWeight" -> _) | ("PostBlendingFunction" -> _)],
ColorFunction ->
RandomChoice[{"IslandColors", "FruitPunchColors",
"AvocadoColors", "Rainbow"}]] & /@ sImgs,
sImgWeights]), {n}]
];
SeedRandom[17643]
svdBlendedImages = MandalaImageBlending[Rest@svdBasisImages, 4, 24];
ImageCollage[svdBlendedImages, Background -> White, ImagePadding -> 3, ImageSize -> 1200]
SeedRandom[17643]
svdBlendedImages = MandalaImageBlending[Rest@svdBasisImages, 4, 24, "BaseImage" -> First[svdBasisImages], "BaseImageWeight" -> 0.5];
ImageCollage[svdBlendedImages, Background -> White, ImagePadding -> 3, ImageSize -> 1200]
SeedRandom[17643]
icaBlendedImages = MandalaImageBlending[Rest[icaBasisImages], 4, 36, "BaseImage" -> First[icaBasisImages], "BaseImageWeight" -> Automatic];
ImageCollage[icaBlendedImages, Background -> White, ImagePadding -> 3, ImageSize -> 1200]
SeedRandom[17643]
nnmfBlendedImages = MandalaImageBlending[nnmfBasisImages, 4, 36];
ImageCollage[nnmfBlendedImages, Background -> White, ImagePadding -> 3, ImageSize -> 1200]
A natural question to ask is:
What would be the outcomes of the above procedures to mandala images found in the World Wide Web (WWW) ?
Those WWW images are most likely man made or curated.
The short answer is that the results are not that good. Better results might be obtained using a larger set of WWW images (than just 100 in the experiment results shown below.)
Here is a sample from the WWW mandala images:
Here are the results obtained with NNMF basis:
My other motivation for writing this document is to set up a basis for further investigations and discussions on the following topics.
Utilization of Neural Network algorithms to mandala creation.
Utilization of Cellular Automata to mandala generation.
Investigate mandala morphing and animations.
Making a domain specific language of specifications for mandala creation and modification.
The idea of using machine learning algorithms for mandala image generation was further supported by an image classifier that recognizes fairly well (suitably normalized) mandala images obtained in different ways:
[1] Wikipedia entry: Mandala, https://en.wikipedia.org/wiki/Mandala .
[2] Wikipedia entry: DeepDream, https://en.wikipedia.org/wiki/DeepDream .
[3] "Code that generates a mandala", Mathematica StackExchange, http://mathematica.stackexchange.com/q/136974 .
[4] Anton Antonov, "Comparison of PCA and NNMF over image de-noising", (2016), MathematicaForPrediction at WordPress blog. URL: https://mathematicaforprediction.wordpress.com/2016/05/07/comparison-of-pca-and-nnmf-over-image-de-noising/ .
[5] Anton Antonov, "Handwritten digits recognition by matrix factorization", (2016), MathematicaForPrediction at WordPress blog. URL: https://mathematicaforprediction.wordpress.com/2016/11/12/handwritten-digits-recognition-by-matrix-factorization/ .
[6] Chris Carlson, "Social Exploration of Design Spaces: A Proposal", (2016), Wolfram Technology Conference 2016. URL: http://wac .36f4.edgecastcdn.net/0036F4/pub/www.wolfram.com/technology-conference/2016/SocialExplorationOfDesignSpaces.nb , YouTube: https://www.youtube.com/watch?v=YK2523nfcms .
[7] Anton Antonov, Independent Component Analysis Mathematica package, (2016), source code at MathematicaForPrediction at GitHub, package IndependentComponentAnalysis.m .
[8] Anton Antonov, Implementation of the Non-Negative Matrix Factorization algorithm in Mathematica, (2013), source code at MathematicaForPrediction at GitHub, package NonNegativeMatrixFactorization.m.
]]>This blog post describes the installation and use in Mathematica of Tries with frequencies [1] implemented in Java [2] through a corresponding Mathematica package [3].
Prefix tree or Trie, [6], is a tree data structure that stores a set of "words" that consist of "characters" — each element can be seen as a key to itself. The article [1] and packages [2,3,4] extend that data structure to have additional data (frequencies) associated with each key.
The packages [2,3] work with lists of strings only. The package [4] can work with more general data but it is much slower.
The main motivation to create the package [3] was to bring the fast Trie functions implementations of [2] into Mathematica in order to prototype, implement, and experiment with different text processing algorithms. (Like, inductive grammar parsers generation and entity name recognition.) The speed of combining [2] and [3] is evaluated in the section "Performance tests" below.
This following directory path has to have the jar file "TriesWithFrequencies.jar".
$JavaTriesWithFrequenciesPath =
"/Users/antonov/MathFiles/MathematicaForPrediction/Java/TriesWithFrequencies";
FileExistsQ[
FileNameJoin[{$JavaTriesWithFrequenciesPath, "TriesWithFrequencies.jar"}]]
(* True *)
For more details see the explanations in the README file in the GitHub directory of [2].
The following directory is expected to have the Mathematica package [3].
dirName = "/Users/antonov/MathFiles/MathematicaForPrediction";
FileExistsQ[FileNameJoin[{dirName, "JavaTriesWithFrequencies.m"}]]
(* True *)
AppendTo[$Path, dirName];
Needs["JavaTriesWithFrequencies`"]
This commands installs Java (via JLink`) and loads the necessary Java libraries.
JavaTrieInstall[$JavaTriesWithFrequenciesPath]
For brevity the basic examples are not included in this blog post. Here is album of images that shows the "JavaTrie.*"
commands with their effects:
More detailed explanations can be found in the Markdown document, [7]:
Next, we are going to look into performance evaluation examples (also given in [7].)
Assume we want find the words of "Hamlet" that are not in the book "Origin of Species". This section shows that the Java trie creation and query times for this task are quite small.
The following code reads the words in the texts. We get 33000 words from "Hamlet" and 151000 words from "Origin of Species".
hWords =
Block[{words},
words =
StringSplit[
ExampleData[{"Text", "Hamlet"}], {Whitespace,
PunctuationCharacter}];
words = Select[ToLowerCase[words], StringLength[#] > 0 &]
];
Length[hWords]
(* 32832 *)
osWords =
Block[{words},
words =
StringSplit[
ExampleData[{"Text", "OriginOfSpecies"}], {Whitespace,
PunctuationCharacter}];
words = Select[ToLowerCase[words], StringLength[#] > 0 &]
];
Length[osWords]
(* 151205 *)
First we create trie with "Origin of species" words:
AbsoluteTiming[
jOStr = JavaTrieCreateBySplit[osWords];
]
(* {0.682531, Null} *)
Sanity check — the "Origin of species" words are in the trie:
AbsoluteTiming[
And @@ JavaObjectToExpression[
JavaTrieContains[jOStr, Characters /@ osWords]]
]
(* {1.32224, True} *)
Membership of "Hamlet" words into "Origin of Species":
AbsoluteTiming[
res = JavaObjectToExpression[
JavaTrieContains[jOStr, Characters /@ hWords]];
]
(* {0.265307, Null} *)
Tallies of belonging:
Tally[res]
(* {{True, 24924}, {False, 7908}} *)
Sample of words from "Hamlet" that do not belong to "Origin of Species":
RandomSample[Pick[hWords, Not /@ res], 30]
(* {"rosencrantz", "your", "mar", "airy", "rub", "honesty", \
"ambassadors", "oph", "returns", "pale", "virtue", "laertes", \
"villain", "ham", "earnest", "trail", "unhand", "quit", "your", \
"your", "fishmonger", "groaning", "your", "wake", "thou", "liest", \
"polonius", "upshot", "drowned", "grosser"} *)
Common words sample:
RandomSample[Pick[hWords, res], 30]
(* {"as", "indeed", "it", "with", "wild", "will", "to", "good", "so", \
"dirt", "the", "come", "not", "or", "but", "the", "why", "my", "to", \
"he", "and", "you", "it", "to", "potent", "said", "the", "are", \
"question", "soft"} *)
The node counts statistics calculation is fast:
AbsoluteTiming[
JavaTrieNodeCounts[jOStr]
]
(* {0.002344, <|"total" -> 20723, "internal" -> 15484, "leaves" -> 5239|>} *)
The node counts statistics computation after shrinking is comparably fast :
AbsoluteTiming[
JavaTrieNodeCounts[JavaTrieShrink[jOStr]]
]
(* {0.00539, <|"total" -> 8918, "internal" -> 3679, "leaves" -> 5239|>} *)
The conversion of a large trie to JSON and computing statistics over the obtained tree is reasonably fast:
AbsoluteTiming[
res = JavaTrieToJSON[jOStr];
]
(* {0.557221, Null} *)
AbsoluteTiming[
Quantile[
Cases[res, ("value" -> v_) :> v, \[Infinity]],
Range[0, 1, 0.1]]
]
(* {0.019644, {1., 1., 1., 1., 2., 3., 5., 9., 17., 42., 151205.}} *)
Get all words from a dictionary:
allWords = DictionaryLookup["*"];
allWords // Length
(* 92518 *)
Trie creation and shrinking:
AbsoluteTiming[
jDTrie = JavaTrieCreateBySplit[allWords];
jDShTrie = JavaTrieShrink[jDTrie];
]
(* {0.30508, Null} *)
JSON form extraction:
AbsoluteTiming[
jsonRes = JavaTrieToJSON[jDShTrie];
]
(* {3.85955, Null} *)
Here are the node statistics of the original and shrunk tries:
Find the infixes that have more than three characters and appear more than 10 times:
Multicolumn[#, 4] &@
Select[SortBy[
Tally[Cases[
jsonRes, ("key" -> v_) :> v, Infinity]], -#[[-1]] &], StringLength[#[[1]]] > 3 && #[[2]] > 10 &]
Many of example shown in this document have corresponding tests in the file JavaTriesWithFrequencies-Unit-Tests.wlt hosted at GitHub.
tr = TestReport[
dirName <> "/UnitTests/JavaTriesWithFrequencies-Unit-Tests.wlt"]
[1] Anton Antonov, "Tries with frequencies for dataÂ mining", (2013), MathematicaForPrediction at WordPress blog. URL: https://mathematicaforprediction.wordpress.com/2013/12/06/tries-with-frequencies-for-data-mining/ .
[2] Anton Antonov, Tries with frequencies in Java, (2017), source code at MathematicaForPrediction at GitHub, project Java/TriesWithFrequencies.
[3] Anton Antonov, Java tries with frequencies Mathematica package, (2017), source code at MathematicaForPrediction at GitHub, package JavaTriesWithFrequencies.m .
[4] Anton Antonov, Tries with frequencies Mathematica package, (2013), source code at MathematicaForPrediction at GitHub, package TriesWithFrequencies.m .
[5] Anton Antonov, Java tries with frequencies Mathematica unit tests, (2017), source code at MathematicaForPrediction at GitHub, unit tests file JavaTriesWithFrequencies-Unit-Tests.wlt .
[6] Wikipedia, Trie, https://en.wikipedia.org/wiki/Trie .
[7] Anton Antonov, "Tries with frequencies in Java", (2017), MathematicaForPrediction at GitHub.
]]>This post is to proclaim the MathematicaVsR at GitHub project “Text analysis of Trump tweets” in which we compare Mathematica and R over text analyses of Twitter messages made by Donald Trump (and his staff) before the USA president elections in 2016.
The project follows and extends the exposition and analysis of the R-based blog post "Text analysis of Trump’s tweets confirms he writes only the (angrier) Android half" by David Robinson at VarianceExplained.org; see [1].
The blog post [1] links to several sources that claim that during the election campaign Donald Trump tweeted from his Android phone and his campaign staff tweeted from an iPhone. The blog post [1] examines this hypothesis in a quantitative way (using various R packages.)
The hypothesis in question is well summarized with the tweet:
Every non-hyperbolic tweet is from iPhone (his staff).
Every hyperbolic tweet is from Android (from him). pic.twitter.com/GWr6D8h5ed
— Todd Vaziri (@tvaziri) August 6, 2016
This conjecture is fairly well supported by the following mosaic plots, [2]:
We can see the that Twitter messages from iPhone are much more likely to be neutral, and the ones from Android are much more polarized. As Christian Rudder (one of the founders of OkCupid, a dating website) explains in the chapter "Death by a Thousand Mehs" of the book "Dataclysm", [3], having a polarizing image (online persona) is as a very good strategy to engage online audience:
[…] And the effect isn’t small-being highly polarizing will in fact get you about 70 percent more messages. That means variance allows you to effectively jump several "leagues" up in the dating pecking order – […]
(The mosaic plots above were made for the Mathematica-part of this project. Mosaic plots and weekday tags are not used in [1].)
The R part consists of :
the blog post [1], and
The Mathematica-part of this project does not follow closely the blog post [1]. After the ingestion of the data provided in [1], the Mathematica-part applies alternative algorithms to support and extend the analysis in [1].
The sections in the R-part notebook correspond to some — not all — of the sections in the Mathematica-part.
The following list of steps is for the Mathematica-part.
That can be done in Mathematica too using the built-in function ServiceConnect
, but that is not necessary since [1] provides a link to the ingested data used [1]:
load(url("http://varianceexplained.org/files/trump_tweets_df.rda"))
Which leads to the ingesting of an R data frame in the Mathematica-part using RLink.
Adding tags
Using the message time-stamps each message is associated with time tags corresponding to the creation time month, hour, weekday, etc.
Here is summary of the data at this stage:
Time series and time related distributions
Here is a Mathematica made plot for the same statistic computed in [1] that shows differences in tweet posting behavior:
Classification into sentiments and Facebook topics
In [1] the results of this step are derived in several stages.
Here is a mosaic plot for conditional probabilities of devices, topics, and sentiments:
Device-word association rules
In the Mathematica-part these associations rules are not needed for the sentiment analysis (because of the built-in classifiers.)
The association rule mining is done mostly to support and extend the text analysis in [1] and, of course, for comparison purposes.
Here is an example of derived association rules together with their most important measures:
In [1] the sentiments are derived from computed device-word associations, so in [1] the order of steps is 1-2-3-5-4. In Mathematica we do not need the steps 3 and 5 in order to get the sentiments in the 4th step.
Using Mathematica for sentiment analysis is much more direct because of the built-in classifiers.
The R-based blog post [1] uses heavily the "pipeline" operator %>%
which is kind of a recent addition to R (and it is both fashionable and convenient to use it.) In Mathematica the related operators are Postfix
(//
), Prefix
(@
), Infix
(~~
), Composition
(@*
), and RightComposition
(/*
).
Making the time series plots with the R package "ggplot2" requires making special data frames. I am inclined to think that the Mathematica plotting of time series is more direct, but for this task the data wrangling codes in Mathematica and R are fairly comparable.
Generally speaking, the R package "arules" — used in this project for Associations rule learning — is somewhat awkward to use:
requires the use of factors.
The Apriori implementation in “arules” is much faster than the one in “AprioriAlgorithm.m” — “arules” uses a more efficient algorithm implemented in C.
[1] David Robinson, "Text analysis of Trump’s tweets confirms he writes only the (angrier) Android half", (2016), VarianceExplained.org.
[2] Anton Antonov, "Mosaic plots for data visualization", (2014), MathematicaForPrediction at WordPress.
[3] Christian Rudder, Dataclysm, Crown, 2014. ASIN: B00J1IQUX8 .
]]>The Pareto principle is an interesting law that manifests in many contexts. It is also known as "Pareto law", "the law of significant few", "the 80-20 rule".
For example:
"10% of all lakes contain 90% of all lake water."
For extensive discussion and studied examples see the Wikipedia entry "Pareto principle", [4].
It is a good idea to see for which parts of the analyzed data the Pareto principle manifests. Testing for the Pareto principle is usually simple. For example, assume that we have the GDP of all countries:
countries = CountryData["Countries"];
gdps = {CountryData[#, "Name"], CountryData[#, "GDP"]} & /@ countries;
gdps = DeleteCases[gdps, {_, _Missing}] /. Quantity[x_, _] :> x;
Grid[{RecordsSummary[gdps, {"country", "GDP"}]}, Alignment -> Top, Dividers -> All]
In order to test for the manifestation of the Pareto principle we have to (i) sort the GDP values in descending order, (ii) find the cumulative sums, (iii) normalize the obtained vector by the sum of all values, and (iv) plot the result. These steps are done with the following two commands:
t = Reverse@Sort@gdps[[All, 2]];
ListPlot[Accumulate[t]/Total[t], PlotRange -> All, GridLines -> {{0.2} Length[t], {0.8}}, Frame -> True]
In this document we are going to use the special function ParetoLawPlot
defined in the next section and the package [1]. Most of the examples use data that is internally accessible within Mathematica. Several external data examples are considered.
See the package [1] for the function RecordsSummary
. See the source file [2] for R functions that facilitate the plotting of Pareto principle graphs. See the package [3] for the outlier detection functions used below.
This simple function makes a list plot that would help assessing the manifestation of the Pareto principle. It takes the same options as ListPlot
.
Clear[ParetoLawPlot]
Options[ParetoLawPlot] = Options[ListPlot];
ParetoLawPlot[dataVec : {_?NumberQ ..}, opts : OptionsPattern[]] := ParetoLawPlot[{Tooltip[dataVec, 1]}, opts];
ParetoLawPlot[dataVecs : {{_?NumberQ ..} ..}, opts : OptionsPattern[]] := ParetoLawPlot[MapThread[Tooltip, {dataVecs, Range[Length[dataVecs]]}], opts];
ParetoLawPlot[dataVecs : {Tooltip[{_?NumberQ ..}, _] ..}, opts : OptionsPattern[]] :=
Block[{t, mc = 0.5},
t = Map[Tooltip[(Accumulate[#]/Total[#] &)[SortBy[#[[1]], -# &]], #[[2]]] &, dataVecs];
ListPlot[t, opts, PlotRange -> All, GridLines -> {Length[t[[1, 1]]] Range[0.1, mc, 0.1], {0.8}}, Frame -> True, FrameTicks -> {{Automatic, Automatic}, {Automatic, Table[{Length[t[[1, 1]]] c, ToString[Round[100 c]] <> "%"}, {c, Range[0.1, mc, 0.1]}]}}]
];
This function is useful for coloring the outliers in the list plots.
ClearAll[ColorPlotOutliers]
ColorPlotOutliers[] := # /. {Point[ps_] :> {Point[ps], Red, Point[ps[[OutlierPosition[ps[[All, 2]]]]]]}} &;
ColorPlotOutliers[oid_] := # /. {Point[ps_] :> {Point[ps], Red, Point[ps[[OutlierPosition[ps[[All, 2]], oid]]]]}} &;
These definitions can be also obtained by loading the packages MathematicaForPredictionUtilities.m and OutlierIdentifiers.m; see [1,3].
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/MathematicaForPredictionUtilities.m"]
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/OutlierIdentifiers.m"]
Below we are going to use the metric system of units. (If preferred we can easily switch to the imperial system.)
$UnitSystem = "Metric";(*"Imperial"*)
We are going to consider a typical Pareto principle example — weatlh of income distribution.
This code find the Gross Domestic Product (GDP) of different countries:
gdps = {CountryData[#, "Name"], CountryData[#, "GDP"]} & /@CountryData["Countries"];
gdps = DeleteCases[gdps, {_, _Missing}] /. Quantity[x_, _] :> x;
The corresponding Pareto plot (note the default grid lines) shows that 10% of countries have 90% of the wealth:
ParetoLawPlot[gdps[[All, 2]], ImageSize -> 400]
Here is the log histogram of the GDP values.
Histogram[Log10@gdps[[All, 2]], 20, PlotRange -> All]
The following code shows the log plot of countries GDP values and the found outliers.
Manipulate[
DynamicModule[{data = Transpose[{Range[Length[gdps]], Sort[gdps[[All, 2]]]}], pos},
pos = OutlierPosition[modFunc@data[[All, 2]], tb@*opar];
If[Length[pos] > 0,
ListLogPlot[{data, data[[pos]]}, PlotRange -> All, PlotTheme -> "Detailed", FrameLabel -> {"Index", "GDP"}, PlotLegends -> SwatchLegend[{"All", "Outliers"}]],
ListLogPlot[{data}, PlotRange -> All, PlotTheme -> "Detailed", FrameLabel -> {"Index", "GDP"}, PlotLegends -> SwatchLegend[{"All", "Outliers"}]]
]
],
{{opar, SPLUSQuartileIdentifierParameters, "outliers detector"}, {HampelIdentifierParameters, SPLUSQuartileIdentifierParameters}},
{{tb, TopOutliers, "bottom|top"}, {BottomOutliers, TopOutliers}},
{{modFunc, Identity, "data modifier function"}, {Identity, Log}}
]
This table gives the values for countries with highest GDP.
Block[{data = gdps[[OutlierPosition[gdps[[All, 2]], TopOutliers@*SPLUSQuartileIdentifierParameters]]]},
Row[Riffle[#, " "]] &@Map[Grid[#, Dividers -> All, Alignment -> {Left, "."}] &, Partition[SortBy[data, -#[[-1]] &], Floor[Length[data]/3]]]
]
Similar data retrieval and plots can be made for countries populations.
pops = {CountryData[#, "Name"], CountryData[#, "Population"]} & /@CountryData["Countries"];
unit = QuantityUnit[pops[[All, 2]]][[1]];
pops = DeleteCases[pops, {_, _Missing}] /. Quantity[x_, _] :> x;
In the following Pareto plot we can see that 15% of countries have 80% of the total population:
ParetoLawPlot[pops[[All, 2]], PlotLabel -> Row[{"Population", ", ", unit}]]
Here are the countries with most people:
Block[{data = pops[[OutlierPosition[pops[[All, 2]], TopOutliers@*SPLUSQuartileIdentifierParameters]]]},
Row[Riffle[#, " "]] &@Map[Grid[#, Dividers -> All, Alignment -> {Left, "."}] &, Partition[SortBy[data, -#[[-1]] &], Floor[Length[data]/3]]]
]
We can also see that the Pareto principle holds for the countries areas:
areas = {CountryData[#, "Name"], CountryData[#, "Area"]} & /@CountryData["Countries"];
areas = DeleteCases[areas, {_, _Missing}] /. Quantity[x_, _] :> x;
ParetoLawPlot[areas[[All, 2]]]
Block[{data = areas[[OutlierPosition[areas[[All, 2]], TopOutliers@*SPLUSQuartileIdentifierParameters]]]},
Row[Riffle[#, " "]] &@Map[Grid[#, Dividers -> All, Alignment -> {Left, "."}] &, Partition[SortBy[data, -#[[-1]] &], Floor[Length[data]/3]]]
]
An interesting diagram is to plot together the curves of GDP changes for different countries. We can see China and Poland have had rapid growth.
res = Table[
(t = CountryData[countryName, {{"GDP"}, {1970, 2015}}];
t = Reverse@Sort[t["Path"][[All, 2]] /. Quantity[x_, _] :> x];
Tooltip[t, countryName])
, {countryName, {"USA", "China", "Poland", "Germany", "France", "Denmark"}}];
ParetoLawPlot[res, PlotRange -> All, Joined -> True, PlotLegends -> res[[All, 2]]]
This dynamic interface can be used for a given country to compare (i) the GDP evolution in time and (ii) the corresponding Pareto plot.
Manipulate[
DynamicModule[{ts, t},
ts = CountryData[countryName, {{"GDP"}, {1970, 2015}}];
t = Reverse@Sort[ts["Path"][[All, 2]] /. Quantity[x_, _] :> x];
Grid[{{"Date list plot of GDP values", "GDP Pareto plot"}, {DateListPlot[ts, ImageSize -> Medium],
ParetoLawPlot[t, ImageSize -> Medium]}}]
], {countryName, {"USA", "China", "Poland", "Germany", "France", "Denmark"}}]
The following code demonstrates that the colors of the pixels in country flags also adhere to the Pareto principle.
flags = CountryData[#, "Flag"] & /@ CountryData["Countries"];
flags[[1 ;; 12]]
ids = ImageData /@ flags;
pixels = Apply[Join, Flatten[ids, 1]];
Clear[ToBinFunc]
ToBinFunc[x_] := Evaluate[Piecewise[MapIndexed[{#2[[1]], #1[[1]] < x <= #1[[2]]} &, Partition[Range[0, 1, 0.1], 2, 1]]]];
pixelsInt = Transpose@Table[Map[ToBinFunc, pixels[[All, i]]], {i, 1, 3}];
pixelsIntTally = SortBy[Tally[pixelsInt], -#[[-1]] &];
ParetoLawPlot[pixelsIntTally[[All, 2]]]
Loking at lengths in the tunnel data we can see the manifestation of an exaggerated Pareto principle.
tunnelLengths = TunnelData[All, {"Name", "Length"}];
tunnelLengths // Length
(* 1552 *)
t = Reverse[Sort[DeleteMissing[tunnelLengths[[All, -1]]] /. Quantity[x_, _] :> x]];
ParetoLawPlot[t]
Here is the logarithmic histogram of the lengths:
Histogram[Log10@t, PlotRange -> All, PlotTheme -> "Detailed"]
The following code gathers the data and make the Pareto plots surface areas, volumes, and fish catch values for lakes. We can that the lakes volumes show exaggerated Pareto principle.
lakeAreas = LakeData[All, "SurfaceArea"];
lakeVolumes = LakeData[All, "Volume"];
lakeFishCatch = LakeData[All, "CommercialFishCatch"];
data = {lakeAreas, lakeVolumes, lakeFishCatch};
t = N@Map[DeleteMissing, data] /. Quantity[x_, _] :> x;
opts = {PlotRange -> All, ImageSize -> Medium}; MapThread[ParetoLawPlot[#1, PlotLabel -> Row[{#2, ", ", #3}], opts] &, {t, {"Lake area", "Lake volume", "Commercial fish catch"}, DeleteMissing[#][[1, 2]] & /@ data}]
One of the examples given in [5] is that the city areas obey the Power Law. Since the Pareto principle is a kind of Power Law we can confirm that observation using Pareto principle plots.
The following grid of Pareto principle plots is for areas and population sizes of cities in selected states of USA.
res = Table[
(cities = CityData[{All, stateName, "USA"}];
t = Transpose@Outer[CityData, cities, {"Area", "Population"}];
t = Map[DeleteMissing[#] /. Quantity[x_, _] :> x &, t, {1}];
ParetoLawPlot[MapThread[Tooltip, {t, {"Area", "Population"}}], PlotLabel -> stateName, ImageSize -> 250])
, {stateName, {"Alabama", "California", "Florida", "Georgia", "Illinois", "Iowa", "Kentucky", "Ohio", "Tennessee"}}];
Legended[Grid[ArrayReshape[res, {3, 3}]], SwatchLegend[Cases[res[[1]], _RGBColor, Infinity], {"Area", "Population"}]]
Looking into the MovieLens 20M dataset, [6], we can see that the Pareto princple holds for (1) most rated movies and (2) most active users. We can also see the manifestation of an exaggerated Pareto law — 90% of all ratings are for 10% of the movies.
The following plot taken from the blog post "PIN analysis", [7], shows that the four digit passwords people use adhere to the Pareto principle: the first 20% of (the unique) most frequently used passwords correspond to the 70% of all passwords use.
ColorNegate[Import["http://www.datagenetics.com/blog/september32012/c.png"]]
[1] Anton Antonov, "MathematicaForPrediction utilities", (2014), source code MathematicaForPrediction at GitHub, https://github.com/antononcube/MathematicaForPrediction, package MathematicaForPredictionUtilities.m.
[2] Anton Antonov, Pareto principle functions in R, source code MathematicaForPrediction at GitHub, https://github.com/antononcube/MathematicaForPrediction, source code file ParetoLawFunctions.R .
[3] Anton Antonov, Implementation of one dimensional outlier identifying algorithms in Mathematica, (2013), MathematicaForPrediction at GitHub, URL: https://github.com/antononcube/MathematicaForPrediction/blob/master/OutlierIdentifiers.m .
[4] Wikipedia entry, "Pareto principle", URL: https://en.wikipedia.org/wiki/Pareto_principle .
[5] Wikipedia entry, "Power law", URL: https://en.wikipedia.org/wiki/Power_law .
[6] GroupLens Research, MovieLens 20M Dataset, (2015).
[7] "PIN analysis", (2012), DataGenetics.
]]>This MathematicaVsR at GitHub project is for comparing Mathematica and R for the tasks of classifier creation, execution, and evaluation using the MNIST database of images of handwritten digits.
Here are the bases built with two different classifiers:
Here are the confusion matrices of the two classifiers:
The blog post "Classification of handwritten digits" (published 2013) has a related more elaborated discussion over a much smaller database of handwritten digits.
The concrete steps taken in scripts and documents of this project follow.
For each digit find the corresponding representation matrix and factorize it.
Store the matrix factorization results in a suitable data structure. (These results comprise the classifier training.)
For a given test image (and its linear vector space representation) find the basis that approximates it best. The corresponding digit is the classifier prediction for the given test image.
Evaluate the classifier(s) over all test images and compute accuracy, F-Scores, and other measures.
There are scripts going through the steps listed above:
R : "./R/HandwrittenDigitsClassificationByMatrixFactorization.Rmd".
The following documents give expositions that are suitable for reading and following of steps and corresponding results.
R : "./R/HandwrittenDigitsClassificationByMatrixFactorization.pdf", "./R/HandwrittenDigitsClassificationByMatrixFactorization.html".
I figured out first in R how to ingest the data in the binary files of the MNIST database. There were at least several online resources (blog posts, GitHub repositories) that discuss the MNIST binary files ingestion.
After that making the corresponding code in Mathematica was easy.
Same in Mathematica and R for for SVD and NNMF. (As expected.)
NNMF classifiers use the MathematicaForPrediction at GitHub implementations: NonNegativeMatrixFactorization.m and NonNegativeMatrixFactorization.R.
Both Mathematica and R have relatively simple set-up of parallel computations.
It was not very straightforward to come up in R with visualizations for MNIST images. The Mathematica visualization is much more flexible when it comes to plot labeling.
Using Mathematica’s built-in classifiers it was easy to compare the SVD and NNMF classifiers with neural network ones and others. (The SVD and NNMF are much faster to built and they bring comparable precision.)
It would be nice to repeat that in R using one or several of the neural network classifiers provided by Google, Microsoft, H2O, Baidu, etc.
Another possible extension is to use classifier ensembles and Receiver Operation Characteristic (ROC) to create better classifiers. (Both in Mathematica and R.)
Using classifier agnostic importance of variables procedure we can figure out :
which NNMF basis vectors (images) are most important for the classification precision,
which image rows or columns are most important for each digit, or similarly
which image squares of a, say, 4×4 image grid are most important.