Results for common-readme

A simple Ruby library to check if a given string is present in a blacklist of 95,000 common passwords. This gem has been DEPRECATED; please switch to password_blocklist for continued support. See migration details in the README.

Add in ActiveRelation scopes that are common across all models. You can also add in a prefix to those scopes so you can have multiple per model. See README for more info.

Datastruct is made to be a more feature rich alternative to Ruby's Struct class. It defines several methods common for data structures, like hash lookup and serialization to JSON and YAML. See the README file for examples.

Web Security Scanner scans your Compute and App Engine apps for common web vulnerabilities. Note that google-cloud-web_security_scanner-v1 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-web_security_scanner instead. See the readme for more details.

Web Security Scanner scans your Compute and App Engine apps for common web vulnerabilities. Note that google-cloud-web_security_scanner-v1beta is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-web_security_scanner instead. See the readme for more details.

Cloud Optimization API provides a portfolio of solvers to address common optimization use cases starting with optimal route planning for vehicle fleets. Note that google-cloud-optimization-v1 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-optimization instead. See the readme for more details.

Google Cloud provides container resources such as organizations and projects that allow you to group and hierarchically organize other Google Cloud resources. This hierarchical organization helps you manage common aspects of your resources, such as access control and configuration settings. The Resource Manager API enables you to programmatically manage these container resources. Note that google-cloud-resource_manager-v3 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-resource_manager instead. See the readme for more details.

README Builder creates standardized, comprehensive README.md files for your GitHub repositories. It analyzes your project structure, dependencies, and configuration files to generate relevant sections including installation instructions, usage examples, contributing guidelines, and license information. The gem supports customizable templates and can automatically detect common project patterns to create appropriate documentation.

== Ruby Data Objects If you're building something in Ruby that needs access to a database, you may opt to use an ORM like ActiveRecord, DataMapper or Sequel. But if your needs don't fit well with an ORM—maybe you're even writing an ORM—then you'll need some other way of talking to your database. RDO provides a common interface to a number of RDBMS backends, using a clean Ruby syntax, while supporting all the functionality you'd expect from a robust database connection library: * Consistent API to connect to various DBMS's * Type casting to Ruby types * Time zone handling (via the DBMS, not via some crazy time logic in Ruby) * Native bind values parameterization of queries, where supported by the DBMS * Retrieve query info from executed commands (e.g. affected rows) * Access RETURNING values just like any read query * Native prepared statements where supported, emulated where not * Results given using simple core Ruby data types == RDBMS Support Support for each RDBMS is provided in separate gems, so as to minimize the installation requirements and to facilitate the maintenace of each driver. Many gems are maintained by separate users who work more closely with those RDBMS's. Due to the nature of this gem, most of the nitty-gritty code is actually written in C. See the official README for full details.

<!-- TABLE OF CONTENTS --> <details open="open"> <summary>Table of Contents</summary> <ol> <li> <a href="#about-the-project">About The Project</a> <ul> <li><a href="#built-with">Built With</a></li> </ul> </li> <li> <a href="#getting-started">Getting Started</a> <ul> <li><a href="#prerequisites">Prerequisites</a></li> <li><a href="#installation">Installation</a></li> </ul> </li> <li><a href="#usage">Usage</a></li> <li><a href="#roadmap">Roadmap</a></li> <li><a href="#contributing">Contributing</a></li> <li><a href="#license">License</a></li> <li><a href="#contact">Contact</a></li> <li><a href="#acknowledgements">Acknowledgements</a></li> </ol> </details> <!-- ABOUT THE PROJECT --> ## About The Project [![Product Name Screen Shot][Screenshot of gameplay and test list]](https://www.dropbox.com/s/mu1rrbx2mqowjkn/studio-game.png?dl=0) This game is a project built following the [Pragmatic Studio Ruby Course](https://online.pragmaticstudio.com/courses/ruby/). I absolutely adored going through this course, because it was unlike other courses in that the main focus wasn't syntax, but how to build a principle-driven, object-oriented program that contains many of the skills we'd need to build real-world projects. The instructors purposefully created exercises to let us build a program using the skills they demonstrated by building a different program. This wasn't a copy and paste kind of course. This game was actually my second run-through, where I test-drove everything from the start based on the objectives only. Skills I valued developing further with this project: - Test-driven development (50+ tests). - Using inheritance to model "is-a" relationships. For example, a clumsy player *is a* kind of player. - Using mixins (modules) to reuse behaviours that are common between classes, but should not be modeled with an inheritance relationship. A good tip was to look for 'able' behaviors in a class to extract, like 'playable', 'printable', 'taxable' etc. - Using a file block which lets you add in class usage examples that are only run when you run the class file specifically. - Overriding default methods (like sort, and renaming things so that they keep a specific format) Things I struggled with: - Testing behaviour that uses blocks. I had a lightbulb moment when I realised I should test the behaviour performed inside the block on a single item. Testing the output of an entire block is like testing Ruby syntax works. Alternatively, test the before and after state of something that changes as a result of using a block. Cooool. - Puts. It felt wrong to use puts to show the output in the console. I'd like to learn how to seperate the view logic for a command-line project later. Things I did to make it my own: - Wrote a lot more tests for my second run-through. - Noticed and extracted further 'able' behaviours into modules (like printing stats, formatting output and handling csv files). ### Built With * [Ruby (language)](https://www.ruby-lang.org/en/) * [RSpec (framework)](https://rspec.info/) * [Vim (text-editor)](https://www.vim.org/) <!-- GETTING STARTED --> ## Getting Started To get a local copy up and running follow these steps: ### Prerequisites This is an example of how to list things you need to use the software and how to install them. * gem ```sh npm install npm@latest -g ``` ### Installation 1. Install the gem ```sh gem install studio_game_2021 ``` <!-- USAGE EXAMPLES --> ## Usage To play a game from the command-line, open a new command project and run the command-line script like so: ```sh studio_game ``` Or, if you'd like to use the game as a library, here's an example of how to use it in `irb`. You can also check the bottom of each class or module file for further usage instructions ``` >> require 'studio_game/game' => true >> game = StudioGame::Game.new("Knuckleheads") => #<StudioGame::Game:0x007fdea10252d8 @title="Knuckleheads", @players=[]> >> player = StudioGame::Player.new("Moe", 90) => I'm Moe with health = 90, points = 0, and score = 90. >> game.add_player(player) => [I'm Moe with health = 90, points = 0, and score = 90.] >> game.play(1) ``` <!-- ROADMAP --> ## Roadmap I plan to customize this game further now that I have a solid foundation to explore from. It'll be fun to let the players interact with each other more, like swapping treasures, and maybe add some kind of board game with it's own features. That's my next focus. ## Contributing Feel free to fork this project and play around with it. Open to feedback-related pr requests. <!-- LICENSE --> ## License Distributed under the MIT License. See `LICENSE` for more information. <!-- CONTACT --> ## Contact Becca - [@becca9941](https://twitter.com/Becca9941) - becca@essentialistdev.com Project Link: [https://gitlab.com/EssentialistDev/studio-game](https://gitlab.com/EssentialistDev/studio-game) <!-- ACKNOWLEDGEMENTS --> ## Acknowledgements - [Pragmatic Studio](https://online.pragmaticstudio.com/courses/ruby/) for empowering me with awesome new development skills. - [Best-README-Template](https://github.com/Becca9941/Best-README-Template) for helping me write a README for this project.

Lookout-Rack Lookout-Rack provides easy interaction with Rack¹ from Lookout². It provides you with a session connected to your Rack application through which you can make requests, check responses, follow redirects and set, inspect, and clear cookies. ¹ See http://rack.rubyforge.org/ ² See http://disu.se/software/lookout/ § Installation Install Lookout-Rack with % gem install lookout-rack § Usage Include the following code in your ‹Rakefile› (provided that you’re using Lookout-Rake¹): require 'lookout-rack-3.0' Lookout::Rake::Tasks::Test.new do |t| t.requires << 'lookout-rack-3.0' end ¹ See http://disu.se/software/lookout-rake/ Then set up a ‹fixtures/config.ru› file that Lookout-Rack will use for loading your Rack app. load 'path/to/app.rb' use Rack::Lint run Path::To::App This file, if it exists, will be loaded during the first call to #session. If it doesn’t exist, ‹config.ru› will be used instead. You can now test your app: Expectations do expect 200 do session.get('/').response.status end end The #session method returns an object that lets you #get, #post, #put, and #delete resources from the Rack app. You call these method with a URI¹ that you want to access/modify together with any parameters that you want to pass and any Rack environment that you want to use (which isn’t very common). For example, let’s get ‹/pizzas/› with olives on them: expect 200 do session.get('/pizzas/', 'olives' => '1').response.status end ¹ Abbreviation for Uniform Resource Identifier The #response method on #session returns a mock Rack response object that can be queried for results. Similarly, there’s a #request method that lets you inspect the request that was made. Lookout-Rack also deals with cookies. Assuming that ‹/cookies/set/› will set any cookies that we pass it and that ‹/cookies/show/› will simply do nothing relevant, the following expectation will pass: expect 'value' => '1' do session. get('/cookies/set/', 'value' => '1'). get('/cookies/show/').request.cookies end Sometimes you may want to set cookies yourself before making a request. You then use the #cookie method, which takes a String of ‹KEY=VALUE› pairs separated by newlines, commas, and/or semicolons and sets those cookies in the session: expect 'value' => '1', 'other' => '2' do session. cookie("value=1\n\nother=2"). get('/cookies/show/').request.cookies end You may also want to clear all cookies in your session using #clear: expect({}) do session. get('/cookies/set', 'value' => '1'). clear. get('/cookies/show').request.cookies end Finally, to test redirects, call the #redirect! method on the session object, assuming that ‹/redirected/› redirects to another location: expect result.redirect? do session.get('/redirected/').response end expect result.not.redirect? do session.get('/redirected/').redirect!.response end That’s basically all there’s to it. You can check the {API documentation}¹ for more information. ¹ See http://disu.se/software/lookout-rack/api/Lookout/Rack/ § Financing Currently, most of my time is spent at my day job and in my rather busy private life. Please motivate me to spend time on this piece of software by donating some of your money to this project. Yeah, I realize that requesting money to develop software is a bit, well, capitalistic of me. But please realize that I live in a capitalistic society and I need money to have other people give me the things that I need to continue living under the rules of said society. So, if you feel that this piece of software has helped you out enough to warrant a reward, please PayPal a donation to now@disu.se¹. Thanks! Your support won’t go unnoticed! ¹ Send a donation: https://www.paypal.com/cgi-bin/webscr?cmd=_donations&business=now@disu.se&item_name=Lookout-Rack § Reporting Bugs Please report any bugs that you encounter to the {issue tracker}¹. ¹ See https://github.com/now/lookout-rack/issues § Authors Nikolai Weibull wrote the code, the tests, the documentation, and this README. § Licensing Lookout-Rack is free software: you may redistribute it and/or modify it under the terms of the {GNU Lesser General Public License, version 3}¹ or later², as published by the {Free Software Foundation}³. ¹ See http://disu.se/licenses/lgpl-3.0/ ² See http://gnu.org/licenses/ ³ See http://fsf.org/

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.

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