Official Ruby client for the MonkeyLearn API. Build and consume machine learning models for language processing from your Ruby apps.
Install with rubygems:
gem install monkeylearn
Or add this line to your Gemfile
gem "monkeylearn"
First require and configure the lib:
require 'monkeylearn'
# Basic configuration
Monkeylearn.configure do |c|
c.token = 'INSERT_YOUR_API_TOKEN_HERE'
endClassification:
r = Monkeylearn.classifiers.classify('cl_hDDngsX8', ['Hola te va amigo?', 'How are you doing mate?'], sandbox: false)
r.result
# => [[{"probability"=>0.461, "label"=>"Spanish"}], [{"probability"=>0.996, "label"=>"English"}]]Extraction:
r = Monkeylearn.extractors.extract('ex_y7BPYzNG', ['A panel of Goldman Sachs employees spent a recent Tuesday night at the Columbia University faculty club'])
r.result
# => [[{"relevance"=>"0.962", "count"=>1, "positions_in_text"=>[80], "keyword"=>"University faculty club"}, {"relevance"=>"0.962", "count"=>1, "positions_in_text"=>[43], "keyword"=>"recent Tuesday night"}, {"relevance"=>"0.962", "count"=>1, "positions_in_text"=>[11], "keyword"=>"Goldman Sachs employees"}, {"relevance"=>"0.385", "count"=>1, "positions_in_text"=>[2], "keyword"=>"panel"}]]Pipelines:
data = {
input: [
{ text: "Friendly service, superior room! Loved the high ceiling. Housekeeping service should have been a little better. Excellent breakfast and fitness room." }
]
}
r = Monkeylearn.pipelines.run('pi_WNo4z7fJ', data, sandbox: false)
r.result
# => {"result"=>{"sentiment_labels"=>[{"sentiment"=>[{"probability"=>1.0, "label"=>"Good"}], "sentence"=>"Friendly service, superior room!"}, {"sentiment"=>[{"probability"=>1.0, "label"=>"Good"}], "sentence"=>"Loved the high ceiling."}, {"sentiment"=>[{"probability"=>0.5, "label"=>"Bad"}], "sentence"=>"Housekeeping service should have been a little better."}, {"sentiment"=>[{"probability"=>0.912, "label"=>"Good"}], "sentence"=>"Excellent breakfast and fitness room."}]}}Create a new classifier:
r = Monkeylearn.classifiers.create('Test API sentiment classifier',
description: 'This is a sentiment classifier created with the monkeylearn ruby API client',
language: 'en')
classifier_id = r.result['result']['classifier']['hashed_id']Get the details from the new classifier and the root category id:
r = Monkeylearn.classifiers.detail(classifier_id)
root_category_id = r.result['result']['sandbox_categories'][0]['id']Create two child categories:
r = Monkeylearn.classifiers.categories.create(classifier_id, 'Positive', root_category_id)
positive_category_id = r.result['result']['category']['id']
r = Monkeylearn.classifiers.categories.create(classifier_id, 'Negative', root_category_id)
negative_category_id = r.result['result']['category']['id']Upload some samples to each category:
samples = [
['Nice beatiful', positive_category_id],
['awesome excelent', positive_category_id],
['Awful bad', negative_category_id],
['sad pale', negative_category_id],
['happy sad both multilabel', [positive_category_id, negative_category_id]]
]
r = Monkeylearn.classifiers.upload_samples(classifier_id, samples)Train the classifier:
Monkeylearn.classifiers.train(classifier_id)Classify using the sandbox:
r = Monkeylearn.classifiers.classify(classifier_id, ['Awesome excelence'], sandbox: true)
r.result
# => [[{"probability"=>0.998, "label"=>"Positive"}]]Deploy a live version:
Monkeylearn.classifiers.deploy(classifier_id)Classify using the live classifier:
r = Monkeylearn.classifiers.classify(classifier_id, ['Awesome excelence'], sandbox: false)
r.result
# => [[{"probability"=>0.998, "label"=>"Positive"}]]Edit a category, rename and move the negative category:
r = Monkeylearn.classifiers.categories.edit(classifier_id, negative_category_id, 'Positive child', positive_category_id)Delete a category:
r = Monkeylearn.classifiers.categories.delete(classifier_id, negative_category_id)Delete the classifier:
r = Monkeylearn.classifiers.delete(classifier_id)