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# Quick Start The Owner API uses the JSON format, and must be accessed over a [secure connection](https://en.wikipedia.org/wiki/HTTPS). Let’s assume that the access token provided by your account manager is “TOKEN”. Here’s how to get the list of ids of all your invoices from the first week of August with a shell script: ```bash query="end_date=2018-08-08T00%3A00%3A00%2B00%3A00&start_date=2018-08-01T00%3A00%3A00%2B00%3A00" curl -i "https://api-eu.getaround.com/owner/v1/invoices?${query}" \ -H "Authorization: Bearer TOKEN" \ -H "Accept:application/json" \ -H "Content-Type:application/json" ``` And here’s how to get the invoice with the id 12345: ```bash curl -i "https://api-eu.getaround.com/owner/v1/invoices/12345" \ -H "Authorization: Bearer TOKEN" \ -H "Accept: application/json" \ -H "Content-Type: application/json"" ``` See the [endpoints section](#tag/Invoices) of this guide for details about the response format. Dates in request params should follow the ISO 8601 standard. # Authentication All requests must be authenticated with a [bearer token header](https://tools.ietf.org/html/rfc6750#section-2.1). You token will be sent to you by your account manager. Unauthenticated requests will return a 401 status. # Pagination The page number and the number of items per page can be set with the “page” and “per_page” params. For example, this request will return the second page of invoices, and 50 invoices per page: `https://api-eu.getaround.com/owner/v1/invoices?page=2&per_page=50` Both of these params are optional. The default page size is 30 items. The Getaround Owner API follows the [RFC 8288 convention](https://datatracker.ietf.org/doc/html/rfc8288) of using the `Link` header to provide the `next` page URL. Please don't build the pagination URLs yourself. The `next` page will be missing when you are requesting the last available page. Here's an example response header from requesting the second page of invoices `https://api-eu.getaround.com/owner/v1/invoices?page=2&per_page=50` ``` Link: <https://api-eu.getaround.com/owner/v1/invoices?page=3&per_page=50>; rel="next" ``` # Throttling policy and Date range limitation We have throttling policy that prevents you to perform more than 100 requests per min from the same IP. Also, there is a limitation on the size of the range of dates given in params in some requests. All requests that need start_date and end_date, do not accept a range bigger than 30 days. # Webhooks Getaround can send webhook events that notify your application when certain events happen on your account. This is especially useful to follow the lifecycle of rentals, tracking for example bookings or cancellations. ### Setup To set up an endpoint, you need to define a route on your server for receiving events, and then <a href="mailto:owner-api@getaround.com">ask Getaround</a> to add this URL to your account. To acknowledge receipt of a event, your endpoint must: - Return a `2xx` HTTP status code. - Be a secure `https` endpoint with a valid SSL certificate. ### Testing Once Getaround has set up the endpoint, and it is properly configured as described above, a test `ping` event can be sent by clicking the button below: <form action="/docs/api/owner/fire_ping_webhook" method="post"><input type="submit" value="Send Ping Event"></form> You should receive the following JSON payload: ```json { "data": { "ping": "pong" }, "type": "ping", "occurred_at": "2019-04-18T08:30:05Z" } ``` ### Retries Webhook deliveries will be attempted for up to three days with an exponential back off. After that point the delivery will be abandoned. ### Verifying Signatures Getaround will also provide you with a secret token, which is used to create a hash signature with each payload. This hash signature is passed along with each request in the headers as `X-Drivy-Signature`. Suppose you have a basic server listening to webhooks that looks like this: ```ruby require 'sinatra' require 'json' post '/payload' do push = JSON.parse(params[:payload]) "I got some JSON: #{push.inspect}" end ``` The goal is to compute a hash using your secret token, and ensure that the hash from Getaround matches. Getaround uses an HMAC hexdigest to compute the hash, so you could change your server to look a little like this: ```ruby post '/payload' do request.body.rewind payload_body = request.body.read verify_signature(payload_body) push = JSON.parse(params[:payload]) "I got some JSON: #{push.inspect}" end def verify_signature(payload_body) signature = 'sha1=' + OpenSSL::HMAC.hexdigest(OpenSSL::Digest.new('sha1'), ENV['SECRET_TOKEN'], payload_body) return halt 500, "Signatures didn't match!" unless Rack::Utils.secure_compare(signature, request.env['HTTP_X_DRIVY_SIGNATURE']) end ``` Obviously, your language and server implementations may differ from this code. There are a couple of important things to point out, however: No matter which implementation you use, the hash signature starts with `sha1=`, using the key of your secret token and your payload body. Using a plain `==` operator is not advised. A method like secure_compare performs a "constant time" string comparison, which renders it safe from certain timing attacks against regular equality operators. ### Best Practices - **Acknowledge events immediately**. If your webhook script performs complex logic, or makes network calls, it’s possible that the script would time out before Getaround sees its complete execution. Ideally, your webhook handler code (acknowledging receipt of an event by returning a `2xx` status code) is separate of any other logic you do for that event. - **Handle duplicate events**. Webhook endpoints might occasionally receive the same event more than once. We advise you to guard against duplicated event receipts by making your event processing idempotent. One way of doing this is logging the events you’ve processed, and then not processing already-logged events. - **Do not expect events in order**. Getaround does not guarantee delivery of events in the order in which they are generated. Your endpoint should therefore handle this accordingly. We do provide an `occurred_at` timestamp for each event, though, to help reconcile ordering.

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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