README.md

In rustyoldrake/WatsonR: R Interface to IBM Watson Developer Cloud Services

WatsonR

A package for R to engage Watson Developer Cloud Services Demo Video - https://www.youtube.com/watch?v=leKCMu9TrEI

Status - Feb 15, 2017

added new credentials method for alchemy only.  This new method is described under 'Secrets Management' below.  Soon all the API will be calllable using this method.

Status - Feb 12, 2017

Joe and Ryan had a short talk to synch up. (thanks Joe!)
TO DO BEFORE GOOD ENOUGH FOR CRAN  (Soft target March 2017)
1) Still working on better authentication
2) 4 Warnings and 3 notes when building - so need to get rid of those

Some Tidy up Merged on Feb 12.

Status - Aug 23rd

STATUS: STILL IN DEVELOPMENT - CAN PULL CODE - MUCH WILL WORK - BUT NOT INSTALLING CLEAN AS OF AUG 23
DONE:   Alchemy, Tone, Personality Insights, Speech to Text, and Text to Speech (basics);
        Natural Language Classifier (NLC); 
SOON:   DEMO At Aggregate; and being less ham fisted with loading libraries & credentials; VIsion
Q4:     Once functionality is established across features, will code review with an R Guru and tighten up

Objective

To create a WatsonR package that is available for the R Programming language that can be used by R programmers to access IBM Watson Developer Cloud (WDC) Services. https://www.ibm.com/watson/developercloud/services-catalog.html

Secret Management

(JD Feb 15 2017) I am moving WatsonR away from using the watson.keys method described below. The prefered method will be to create a folder in your R project to hold files for your secrets. Each file should contain the json document from bluemix that contains your credentials for the service you want.

For example, To use the alchemy API, you first need to create an alchemy instance on bluemix. Next go to the credentials tab and copy the full json document containing your credentials.

Next, in RStudio or your favorite text editor, open a text file and paste the json into the file. save the file with a '.json' extension.

When you call the api, one of the parameters of the call is the name of the credentials file to use to access the service. For example, to call the combined alchemy api, follow the steps above to create a credentials file and then call the method as follows:

WatsonR::watson.alchemy.combined("Credentials/alchemy1.json", "I shot an elephant in my pajamas")

watson.keys doesn't work correctly in the package setting so we will be deprecating that method.

watson.keys

enter in keys / authenticate API keys are needed for EACH service - so building out a few ways to check and enter KEYS Free Trial here: https://console.ng.bluemix.net/ Register, Login and Step-By-Step instructions here https://dreamtolearn.com/ryan/r_journey_to_watson/43 List of Services here: https://www.ibm.com/watson/developercloud/services-catalog.html (these are just the WDC services, there are heaps of other IBM Watson technologies around)

watson.keys.load() [1] "This Function will Load your API keys from KEYS.R file if it is in working directory, then [1] "This Function Displays your Existing API Keys for the IBM Watson Services" watson.keys.enter() [1] "This Funtcion Helps you enter API Keys Manually for the IBM Watson Services" watson.keys.display() [1] "This Function Displays your Existing API Keys for the IBM Watson Services"

watson.demo

including NLC/Harry potter (needs keys, alerts if no keys in) MACRO Demo - There are a bunch of other little demos, so probably just calls them

watson.demo() [1] "FULL Demo that will do all available services - and also train NLC with Ground truth "

watson.alchemy

https://www.ibm.com/watson/developercloud/alchemy-language.html AlchemyLanguage is a collection of APIs that offer text analysis through natural language processing. The AlchemyLanguage APIs can analyze text and help you to understand its sentiment, keywords, entities, high-level concepts and more.

watson.alchemy.test() [1] "Short Test of Alchemy - Hitting Endpoint; watson.alchemy.combined("I like beer in Calgary") [1] "Alchemy combined call- running multiple Alchemy Language calls" $docSentiment$type [1] "positive" $entities[[1]]$text [1] "Calgary" $taxonomy[[1]]$label [1] "/food and drink/beverages/alcoholic beverages/cocktails and beer" - etc...

watson.tone

https://www.ibm.com/watson/developercloud/tone-analyzer.html Discover, understand, and revise the language tones in text. one Analyzer Service uses linguistic analysis to detect three types of tones from text: emotion, social tendencies, and language style. Emotions identified include things like anger, fear, joy, sadness, and disgust. Identified social tendencies include things from the Big Five personality traits used by some psychologists. These include openness, conscientiousness, extroversion, agreeableness, and emotional range. Identified language styles include confident, analytical, and tentative.

watson.tone.demo() [1] "EXAMPLE: Angry and Confident person:" [1] "I am so angry that I got a speeding ticket. I'm certain it's the last time!" watson.tone.analyze("I hate parking tickets") [1] "Tone Analyzer - Analyzing..." trait signal anger 0.88

watson.pi

https://www.ibm.com/watson/developercloud/personality-insights.html Uncover a deeper understanding of people's personality characteristics, needs, and values to drive personalization. Personality Insights extracts and analyzes a spectrum of personality attributes to help discover actionable insights about people and entities, and in turn guides end users to highly personalized interactions. The service outputs personality characteristics that are divided into three dimensions: the Big 5, Values, and Needs

watson.pi.demo() [1] "Loading Text for Ronald Reagan Speech 1986 - Space Shuttle Challenger" [1] "Personality Insights" trait category percentage error row Openness personality 0.9900 0.0529 1 watson.pi.analyze("at least a hundred words here")

watson.nlc

https://www.ibm.com/watson/developercloud/nl-classifier.html Interpret and classify natural language with confidence. The service enables developers without a background in machine learning or statistical algorithms to create natural language interfaces for their applications. The service interprets the intent behind text and returns a corresponding classification with associated confidence levels. The return value can then be used to trigger a corresponding action, such as redirecting the request or answering a question. THIS IS A REALLY POWERFUL SERVICE. (My fave)

watson.nlc.listallclassifiers() classifier name date_created 1: 340008x87-nlc-967 name sortinghat3 created 2016-08-17T080131.570Z watson.nlc.checkclassifierstatus("7ace87x92-nlc-1514") [1] "{\n \"classifier_id\" : \"7ace87x92-nlc-1514\",\n \"name\" : \"sorting_hat_834\",\n \"language\" \"status_description\" : \"The classifier instance is in its training phase, not yet ready watson.nlc.processtext("340008x87-nlc-967","bravery and zeal") class confidence 1: Gryffindor 0.9897621641325142 2: Hufflepuff 0.0035370741165151928 3: Slytherin 0.003411116817893181 4: Ravenclaw 0.003289644933077479 watson.nlc.createnewclassifier(file,classifiername) watson.nlc.deleteclassifier(kill_classifier_id)

watson.stt

https://www.ibm.com/watson/developercloud/speech-to-text.html The Speech to Text service converts the human voice into the written word. The Speech to Text service can be used anywhere voice-interactivity is needed. The service is great for mobile experiences, transcribing media files, call center transcriptions, voice control of embedded systems, or converting sound to text to then make data searchable. Supported languages include US English, UK English, Japanese, Spanish, Brazilian Portuguese, Modern Standard Arabic, and Mandarin. The Speech to Text service now provides the ability to detect the presence of specific keywords or key phrases in the input stream.

watson.stt.test() [1] "Speech to text Test using the speech_test.WAV Test file in the media dir" [1] " the quick brown fox jumped over the lazy red dog " watson.stt.process(filename)

watson.tts

https://www.ibm.com/watson/developercloud/text-to-speech.html Designed for streaming low-latency synthesis of audio from written text. The service synthesizes natural-sounding speech from input text in a variety of languages and voices that speak with appropriate cadence and intonation. Brazilian Portuguese speech (1 female voice) US English speech (choose between 3 voices: 2 female, 1 male) UK English speech (1 female voice) French speech (1 female voice) German speech (choose between 2 voices: 1 female, 1 male) Japanese speech (1 female voice) Italian speech (1 female voice) Castilian Spanish speech (choose between 2 voices: 1 female, 1 male) North American Spanish speech (1 female voice)

watson.tts.demovoices() [1] "en-GB_KateVoice - UK" [1] "Text to Speech - Sending Transcript to TTS service...." [1] "now playing the WAV file on your computer... open filename.WAV.... watson.tts.listvoices() data.V1_04 1 en-GB_KateVoice 2 ja-JP_EmiVoice . etc.. watson.tts.process(transcript,voice_number)

R Consortium

Early June 2016, IBM announced it joined the R Consortium – an open-source foundation launched by the Linux Foundation in 2015 to support the R programming language and its user community. IBM joined Microsoft and R-Studio as platinum members. R is a free programming language and software environment for statistical computing and graphics - widely used among statisticians and data miners for developing statistical software and data analysis. R provides an interactive environment for data analysis, modeling and visualization.

Other Notes

1) API KEYS ARE REQUIRED TO ACCESS EACH SERVICE- FREE 30 DAY TRIAL here: https://console.ng.bluemix.net/ 2) These are the services in this version AlchemyLanguage Natural Language Classifier Personality Insights Tone Analyzer Speech to Text Text to Speech 3) Code Was largely derived from https://github.com/rustyoldrake/R_Scripts_for_Watson and https://dreamtolearn.com/ryan/r_journey_to_watson/ projects

rustyoldrake/WatsonR documentation built on May 28, 2019, 10:41 a.m.