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retrevalv0.1.2

README ====== This is a simple API to evaluate information retrieval results. It allows you to load ranked and unranked query results and calculate various evaluation metrics (precision, recall, MAP, kappa) against a previously loaded gold standard. Start this program from the command line with: retreval -l <gold-standard-file> -q <query-results> -f <format> -o <output-prefix> The options are outlined when you pass no arguments and just call retreval You will find further information in the RDOC documentation and the HOWTO section below. If you want to see an example, use this command: retreval -l example/gold_standard.yml -q example/query_results.yml -f yaml -v INSTALLATION ============ If you have RubyGems, just run gem install retreval You can manually download the sources and build the Gem from there by `cd`ing to the folder where this README is saved and calling gem build retreval.gemspec This will create a gem file called which you just have to install with `gem install <file>` and you're done. HOWTO ===== This API supports the following evaluation tasks: - Loading a Gold Standard that takes a set of documents, queries and corresponding judgements of relevancy (i.e. "Is this document relevant for this query?") - Calculation of the _kappa measure_ for the given gold standard - Loading ranked or unranked query results for a certain query - Calculation of _precision_ and _recall_ for each result - Calculation of the _F-measure_ for weighing precision and recall - Calculation of _mean average precision_ for multiple query results - Calculation of the _11-point precision_ and _average precision_ for ranked query results - Printing of summary tables and results Typically, you will want to use this Gem either standalone or within another application's context. Standalone Usage ================ Call parameters --------------- After installing the Gem (see INSTALLATION), you can always call `retreval` from the commandline. The typical call is: retreval -l <gold-standard-file> -q <query-results> -f <format> -o <output-prefix> Where you have to define the following options: - `gold-standard-file` is a file in a specified format that includes all the judgements - `query-results` is a file in a specified format that includes all the query results in a single file - `format` is the format that the files will use (either "yaml" or "plain") - `output-prefix` is the prefix of output files that will be created Formats ------- Right now, we focus on the formats you can use to load data into the API. Currently, we support YAML files that must adhere to a special syntax. So, in order to load a gold standard, we need a file in the following format: * "query" denotes the query * "documents" these are the documents judged for this query * "id" the ID of the document (e.g. its filename, etc.) * "judgements" an array of judgements, each one with: * "relevant" a boolean value of the judgment (relevant or not) * "user" an optional identifier of the user Example file, with one query, two documents, and one judgement: - query: 12th air force germany 1957 documents: - id: g5701s.ict21311 judgements: [] - id: g5701s.ict21313 judgements: - relevant: false user: 2 So, when calling the program, specify the format as `yaml`. For the query results, a similar format is used. Note that it is necessary to specify whether the result sets are ranked or not, as this will heavily influence the calculations. You can specify the score for a document. By "score" we mean the score that your retrieval algorithm has given the document. But this is not necessary. The documents will always be ranked in the order of their appearance, regardless of their score. Thus in the following example, the document with "07" at the end is the first and "25" is the last, regardless of the score. --- query: 12th air force germany 1957 ranked: true documents: - score: 0.44034874 document: g5701s.ict21307 - score: 0.44034874 document: g5701s.ict21309 - score: 0.44034874 document: g5701s.ict21311 - score: 0.44034874 document: g5701s.ict21313 - score: 0.44034874 document: g5701s.ict21315 - score: 0.44034874 document: g5701s.ict21317 - score: 0.44034874 document: g5701s.ict21319 - score: 0.44034874 document: g5701s.ict21321 - score: 0.44034874 document: g5701s.ict21323 - score: 0.44034874 document: g5701s.ict21325 --- query: 1612 ranked: true documents: - score: 1.0174774 document: g3290.np000144 - score: 0.763108 document: g3201b.ct000726 - score: 0.763108 document: g3400.ct000886 - score: 0.6359234 document: g3201s.ct000130 --- **Note**: You can also use the `plain` format, which will load the gold standard in a different way (but not the results): my_query my_document_1 false my_query my_document_2 true See that every query/document/relevancy pair is separated by a tabulator? You can also add the user's ID in the fourth column if necessary. Running the evaluation ----------------------- After you have specified the input files and the format, you can run the program. If needed, the `-v` switch will turn on verbose messages, such as information on how many judgements, documents and users there are, but this shouldn't be necessary. The program will first load the gold standard and then calculate the statistics for each result set. The output files are automatically created and contain a YAML representation of the results. Calculations may take a while depending on the amount of judgements and documents. If there are a thousand judgements, always consider a few seconds for each result set. Interpreting the output files ------------------------------ Two output files will be created: - `output_avg_precision.yml` - `output_statistics.yml` The first lists the average precision for each query in the query result file. The second file lists all supported statistics for each query in the query results file. For example, for a ranked evaluation, the first two entries of such a query result statistic look like this: --- 12th air force germany 1957: - :precision: 0.0 :recall: 0.0 :false_negatives: 1 :false_positives: 1 :true_negatives: 2516 :true_positives: 0 :document: g5701s.ict21313 :relevant: false - :precision: 0.0 :recall: 0.0 :false_negatives: 1 :false_positives: 2 :true_negatives: 2515 :true_positives: 0 :document: g5701s.ict21317 :relevant: false You can see the precision and recall for that specific point and also the number of documents for the contingency table (true/false positives/negatives). Also, the document identifier is given. API Usage ========= Using this API in another ruby application is probably the more common use case. All you have to do is include the Gem in your Ruby or Ruby on Rails application. For details about available methods, please refer to the API documentation generated by RDoc. **Important**: For this implementation, we use the document ID, the query and the user ID as the primary keys for matching objects. This means that your documents and queries are identified by a string and thus the strings should be sanitized first. Loading the Gold Standard ------------------------- Once you have loaded the Gem, you will probably start by creating a new gold standard. gold_standard = GoldStandard.new Then, you can load judgements into this standard, either from a file, or manually: gold_standard.load_from_yaml_file "my-file.yml" gold_standard.add_judgement :document => doc_id, :query => query_string, :relevant => boolean, :user => John There is a nice shortcut for the `add_judgement` method. Both lines are essentially the same: gold_standard.add_judgement :document => doc_id, :query => query_string, :relevant => boolean, :user => John gold_standard << :document => doc_id, :query => query_string, :relevant => boolean, :user => John Note the usage of typical Rails hashes for better readability (also, this Gem was developed to be used in a Rails webapp). Now that you have loaded the gold standard, you can do things like: gold_standard.contains_judgement? :document => "a document", :query => "the query" gold_standard.relevant? :document => "a document", :query => "the query" Loading the Query Results ------------------------- Now we want to create a new `QueryResultSet`. A query result set can contain more than one result, which is what we normally want. It is important that you specify the gold standard it belongs to. query_result_set = QueryResultSet.new :gold_standard => gold_standard Just like the Gold Standard, you can read a query result set from a file: query_result_set.load_from_yaml_file "my-results-file.yml" Alternatively, you can load the query results one by one. To do this, you have to create the results (either ranked or unranked) and then add documents: my_result = RankedQueryResult.new :query => "the query" my_result.add_document :document => "test_document 1", :score => 13 my_result.add_document :document => "test_document 2", :score => 11 my_result.add_document :document => "test_document 3", :score => 3 This result would be ranked, obviously, and contain three documents. Documents can have a score, but this is optional. You can also create an Array of documents first and add them altogether: documents = Array.new documents << ResultDocument.new :id => "test_document 1", :score => 20 documents << ResultDocument.new :id => "test_document 2", :score => 21 my_result = RankedQueryResult.new :query => "the query", :documents => documents The same applies to `UnrankedQueryResult`s, obviously. The order of ranked documents is the same as the order in which they were added to the result. The `QueryResultSet` will now contain all the results. They are stored in an array called `query_results`, which you can access. So, to iterate over each result, you might want to use the following code: query_result_set.query_results.each_with_index do |result, index| # ... end Or, more simply: for result in query_result_set.query_results # ... end Calculating statistics ---------------------- Now to the interesting part: Calculating statistics. As mentioned before, there is a conceptual difference between ranked and unranked results. Unranked results are much easier to calculate and thus take less CPU time. No matter if unranked or ranked, you can get the most important statistics by just calling the `statistics` method. statistics = my_result.statistics In the simple case of an unranked result, you will receive a hash with the following information: * `precision` - the precision of the results * `recall` - the recall of the results * `false_negatives` - number of not retrieved but relevant items * `false_positives` - number of retrieved but nonrelevant * `true_negatives` - number of not retrieved and nonrelevantv items * `true_positives` - number of retrieved and relevant items In case of a ranked result, you will receive an Array that consists of _n_ such Hashes, depending on the number of documents. Each Hash will give you the information at a certain rank, e.g. the following to lines return the recall at the fourth rank. statistics = my_ranked_result.statistics statistics[3][:recall] In addition to the information mentioned above, you can also get for each rank: * `document` - the ID of the document that was returned at this rank * `relevant` - whether the document was relevant or not Calculating statistics with missing judgements ---------------------------------------------- Sometimes, you don't have judgements for all document/query pairs in the gold standard. If this happens, the results will be cleaned up first. This means that every document in the results that doesn't appear to have a judgement will be removed temporarily. As an example, take the following results: * A * B * C * D Our gold standard only contains judgements for A and C. The results will be cleaned up first, thus leading to: * A * C With this approach, we can still provide meaningful results (for precision and recall). Other statistics ---------------- There are several other statistics that can be calculated, for example the **F measure**. The F measure weighs precision and recall and has one parameter, either "alpha" or "beta". Get the F measure like so: my_result.f_measure :beta => 1 If you don't specify either alpha or beta, we will assume that beta = 1. Another interesting measure is **Cohen's Kappa**, which tells us about the inter-agreement of assessors. Get the kappa statistic like this: gold_standard.kappa This will calculate the average kappa for each pairwise combination of users in the gold standard. For ranked results one might also want to calculate an **11-point precision**. Just call the following: my_ranked_result.eleven_point_precision This will return a Hash that has indices at the 11 recall levels from 0 to 1 (with steps of 0.1) and the corresponding precision at that recall level.

Abandoned. Last published 14 years ago.

rooibosv0.8.0

RubyGems

== Confidently Build Terminal Apps Rooibos[https://rooibos.run] helps you build interactive terminal applications. Keep your code understandable and testable as it scales. Rooibos handles keyboard, mouse, and async work so you can focus on behavior and user experience. gem install rooibos Currently in beta. APIs may change before 1.0. === Get Started in Seconds rooibos new my_app cd my_app rooibos run That's it. You have a working app with keyboard navigation, mouse support, and clickable buttons. Open <tt>lib/my_app.rb</tt> to make it your own. --- === The Pattern \Rooibos uses Model-View-Update, the architecture behind Elm[https://guide.elm-lang.org/architecture/], Redux[https://redux.js.org/], and {Bubble Tea}[https://github.com/charmbracelet/bubbletea]. State lives in one place. Updates flow in one direction. The runtime handles rendering and runs background work for you. --- === Hello, MVU The simplest \Rooibos app. Press any key to increment the counter. Press <tt>Ctrl</tt>+<tt>C</tt> to quit. require "rooibos" module Counter # Init: How do you create the initial model? Init = -> { 0 } # View: What does the user see? View = -> (model, tui) { tui.paragraph(text: <<~END) } Current count: #{model}. Press any key to increment. Press Ctrl+C to quit. END # Update: What happens when things change? Update = -> (message, model) { if message.ctrl_c? Rooibos::Command.exit elsif message.key? model + 1 end } end Rooibos.run(Counter) That's the whole pattern: Model holds state, Init creates it, View renders it, and Update changes it. The runtime handles everything else. --- === Your First Real Application A file browser in sixty lines. It opens files, navigates directories, handles errors, styles directories and hidden files differently, and supports vim-style keyboard shortcuts. If you can do this much with this little code, imagine how easy _your_ app will be to build. require "rooibos" module FileBrowser # Model: What state does your app need? Model = Data.define(:path, :entries, :selected, :error) Init = -> { path = Dir.pwd entries = Entries[path] Ractor.make_shareable( # Ensures thread safety Model.new(path:, entries:, selected: entries.first, error: nil)) } View = -> (model, tui) { tui.block( titles: [model.error || model.path, { content: KEYS, position: :bottom, alignment: :right}], borders: [:all], border_style: if model.error then tui.style(fg: :red) else nil end, children: [tui.list(items: model.entries.map(&ListItem[model, tui]), selected_index: model.entries.index(model.selected), highlight_symbol: "", highlight_style: tui.style(modifiers: [:reversed]))] ) } Update = -> (message, model) { return model.with(error: ERROR) if message.error? model = model.with(error: nil) if model.error && message.key? if message.ctrl_c? || message.q? then Rooibos::Command.exit elsif message.home? || message.g? then model.with(selected: model.entries.first) elsif message.end? || message.G? then model.with(selected: model.entries.last) elsif message.up_arrow? || message.k? then Select[:-, model] elsif message.down_arrow? || message.j? then Select[:+, model] elsif message.enter? then Open[model] elsif message.escape? then Navigate[File.dirname(model.path), model] end } private # Lines below this are implementation details KEYS = "↑/↓/Home/End: Select | Enter: Open | Esc: Navigate Up | q: Quit" ERROR = "Sorry, opening the selected file failed." ListItem = -> (model, tui) { -> (name) { modifiers = name.start_with?(".") ? [:dim] : [] fg = :blue if name.end_with?("/") tui.list_item(content: name, style: tui.style(fg:, modifiers:)) } } Select = -> (operator, model) { new_index = model.entries.index(model.selected).public_send(operator, 1) model.with(selected: model.entries[new_index.clamp(0, model.entries.length - 1)]) } Open = -> (model) { full = File.join(model.path, model.selected.delete_suffix("/")) model.selected.end_with?("/") ? Navigate[full, model] : Rooibos::Command.open(full) } Navigate = -> (path, model) { entries = Entries[path] model.with(path:, entries:, selected: entries.first, error: nil) } Entries = -> (path) { Dir.children(path).map { |name| File.directory?(File.join(path, name)) ? "#{name}/" : name }.sort_by { |name| [name.end_with?("/") ? 0 : 1, name.downcase] } } end Rooibos.run(FileBrowser) --- === Batteries Included ==== Commands Applications fetch data, run shell commands, and set timers. \Rooibos Commands run off the main thread and send results back as messages. HTTP requests: Update = -> (message, model) { case message in :fetch_users [model.with(loading: true), Rooibos::Command.http(:get, "/api/users", :got_users)] in { type: :http, envelope: :got_users, status: 200, body: } model.with(loading: false, users: JSON.parse(body)) in { type: :http, envelope: :got_users, status: } model.with(error: "HTTP #{status}") end } Shell commands: Update = -> (message, model) { case message in :list_files Rooibos::Command.system("ls -la", :listed_files) in { type: :system, envelope: :listed_files, stdout:, status: 0 } model.with(files: stdout.lines.map(&:chomp)) in { type: :system, envelope: :listed_files, stderr:, status: } model.with(error: stderr) end } Timers: Update = -> (message, model) { case message in { type: :timer, envelope: :tick, elapsed: } [model.with(frame: model.frame + 1), Rooibos::Command.wait(1.0 / 24, :tick)] end } And more! \Rooibos includes <tt>all</tt>, <tt>batch</tt>, <tt>bubble</tt>, <tt>cancel</tt>, <tt>custom</tt>, <tt>deliver</tt>, <tt>exit</tt>, <tt>http</tt>, <tt>map</tt>, <tt>open</tt>, <tt>system</tt>, <tt>tick</tt>, and <tt>wait</tt> commands. You can also define your own custom commands for complex orchestration. Every command produces a message, and Update handles it the same way. ==== Testing \Rooibos makes TUIs so easy to test, you'll save more time by writing tests than by not testing. Unit test Update, View, and Init. No terminal needed. Test helpers included. def test_moves_selection_down_with_j model = Ractor.make_shareable(FileBrowser::Model.new( path: "/", entries: %w[bin exe lib], selected: "bin", error: nil)) message = RatatuiRuby::Event::Key.new(code: "j") result = FileBrowser::Update.call(message, model) assert_equal "exe", result.selected end Style assertions. Draw to a headless terminal, verify colors and modifiers. def test_directories_are_blue with_test_terminal(60, 10) do model = Ractor.make_shareable(FileBrowser::Model.new( path: "/", entries: %w[file.txt subdir/], selected: "file.txt", error: nil)) widget = FileBrowser::View.call(model, RatatuiRuby::TUI.new) RatatuiRuby.draw { |frame| frame.render_widget(widget, frame.area) } assert_blue(1, 2) # "subdir/" at column 1, row 2 end end System tests. Inject events, run the full app, snapshot the result. def test_selection_moves_down with_test_terminal(120, 30) do Dir.mktmpdir do |dir| FileUtils.touch(File.join(dir, "a")) FileUtils.touch(File.join(dir, "b")) FileUtils.touch(File.join(dir, "c")) inject_key(:down) inject_key(:ctrl_c) # Tests use explicit params to inject deterministic initial state. Rooibos.run( model: Ractor.make_shareable(FileBrowser::Model.new( path: dir, entries: %w[a b c], selected: "a", error: nil)), view: FileBrowser::View, update: FileBrowser::Update ) assert_snapshots("selection_moved_down") do |lines| title = "┌/tmp/test#{'─' * 107}┐" lines.map do |l| l.gsub(/┌#{Regexp.escape(dir)}[^┐]*┐/, title) end end end end end Snapshots record both plain text and ANSI colors. Normalization blocks mask dynamic content (timestamps, temp paths) for cross-platform reproducibility. Run <tt>UPDATE_SNAPSHOTS=1 rake test</tt> to regenerate baselines. ==== Scale Up Large applications decompose into fragments. Each fragment has its own Model, View, Update, and Init. Parents compose children. The pattern scales. The Router DSL eliminates boilerplate: module Dashboard include Rooibos::Router route :stats, to: StatsPanel route :network, to: NetworkPanel receive_events :ctrl_c, -> { Rooibos::Command.exit } only when: -> (_message, model) { !model.modal_open } do receive_events :q, -> { Rooibos::Command.exit } forward_events :s, to: :stats, as: :fetch forward_events :p, to: :network, as: :ping end Update = from_router # ... Model, Init, View below end Declare routes and event handlers. The router generates Update for you. Use guards to ignore messages when needed. ==== CLI The <tt>rooibos</tt> command scaffolds projects and runs applications. rooibos new my_app # Generate project structure rooibos run # Run the app in current directory Generated apps include tests, type signatures, and a working welcome screen with keyboard and mouse support. --- === The Ecosystem \Rooibos builds on RatatuiRuby[https://www.ratatui-ruby.dev], a Rubygem built on Ratatui[https://ratatui.rs]. You get native performance with the joy of Ruby. \Rooibos is one way to manage state and composition. Kit is another. ==== Rooibos[https://www.rooibos.run] Model-View-Update architecture. Inspired by Elm, Bubble Tea, and React + Redux. Your UI is a pure function of state. - Functional programming with MVU - Commands work off the main thread - Messages, not callbacks, drive updates ==== {Kit}[https://sr.ht/~kerrick/ratatui_ruby/#chapter-3-the-object-path--kit] (Coming Soon) Component-based architecture. Encapsulate state, input handling, and rendering in reusable pieces. - OOP with stateful components - Separate UI state from domain logic - Built-in focus management & click handling Both use the same widget library and rendering engine. Pick the paradigm that fits your brain. --- === Links [Get Started] {Getting Started}[https://www.rooibos.run/docs/trunk/doc/getting_started/index_md.html], {Tutorial}[https://www.rooibos.run/docs/trunk/doc/tutorial/index_md.html], {Examples}[https://www.rooibos.run/docs/trunk/examples/app_fractal_dashboard/README_md.html] [Coming From...] {React/Redux}[https://www.rooibos.run/docs/trunk/doc/getting_started/for_react_developers_md.html], {BubbleTea}[https://www.rooibos.run/docs/trunk/doc/getting_started/for_go_developers_md.html], {Textual}[https://www.rooibos.run/docs/trunk/doc/getting_started/for_python_developers_md.html] [Learn More] {Essentials}[https://www.rooibos.run/docs/trunk/doc/essentials/index_md.html], {Scaling Up}[https://www.rooibos.run/docs/trunk/doc/scaling_up/index_md.html], {Best Practices}[https://www.rooibos.run/docs/trunk/doc/best_practices/index_md.html], {Troubleshooting}[https://www.rooibos.run/docs/trunk/doc/troubleshooting/index_md.html] [Community] {Forum}[https://forum.setdef.com/c/rooibos], {Announcements}[https://forum.setdef.com/tags/c/rooibos/announcement], {Bug Tracker}[https://forum.setdef.com/tags/c/rooibos/bug], {Contribution Guide}[https://github.com/setdef/Rooibos/blob/trunk/CONTRIBUTING.md], {Code of Conduct}[https://github.com/setdef/Rooibos/blob/trunk/CODE_OF_CONDUCT.md] --- [Website] https://rooibos.run [Source] https://github.com/setdef/Rooibos [RubyGems] https://rubygems.org/gems/rooibos © 2026 Kerrick Long · Library: LGPL-3.0-or-later · Website: CC-BY-NC-ND-4.0 · Snippets: MIT-0

Maintained. Niche but maintained, actively maintained.