added API docs + cleaned up code

app/package.json

@@ -67,6 +67,8 @@
     "react-syntax-highlighter": "15.5.0",
     "react-timeago": "^7.2.0",
     "redux": "4.2.1",
+    "swagger-ui-react": "^5.9.1",
+    "swagger-ui-themes": "^3.0.1",
     "web-vitals": "3.5.0",
     "yup": "1.3.2"
   },

app/public/locales/en/common.yml

@@ -191,7 +191,7 @@ contribute:
             - "OpenCollective"
             - "GitHub Sponsors"
 about:
-    helmet: "About"
+    helmet: "About us"
     title: "About OpenML"
     core: "Authors"
     core_text: >
@@ -484,4 +484,6 @@ terms:
             do our best efforts to promptly remove such information from our records.
 
             ### Consent
-            By using our website, you hereby consent to our Privacy Policy and agree to its terms.
+            By using our website, you hereby consent to our Privacy Policy and agree to its terms.
+apis:
+    helmet: "APIs"

app/src/components/navbar/Navbar.js

@@ -80,8 +80,7 @@ const Input = styled(InputBase)`
     padding-right: ${(props) => props.theme.spacing(2.5)};
     padding-bottom: ${(props) => props.theme.spacing(2.5)};
     padding-left: ${(props) => props.theme.spacing(2.5)};
-    min-width: 260px;
-    max-width: 100%;
+    min-width: 100vw;
   }
 
   & > input::placeholder {
@@ -90,12 +89,12 @@ const Input = styled(InputBase)`
 
   ${(props) => props.theme.breakpoints.up("md")} {
     & > input {
-      min-width: 400px;
+      min-width: 440px;
     }
   }
   ${(props) => props.theme.breakpoints.up("lg")} {
     & > input {
-      min-width: 600px;
+      min-width: 740px;
     }
   }
 `;

app/src/components/pages/apis/SwaggerUI.js

@@ -0,0 +1,189 @@
+import styled from "@emotion/styled";
+
+const StyledSwaggerUI = styled.div`
+  [id^="model-"][id*="_"] {
+    display: none;
+  }
+  [id^="model-"][id*="List"] {
+    display: none;
+  }
+  [id^="model-"][id*="Unprocessed"] {
+    display: none;
+  }
+  [id^="model-"][id*="Request"] {
+    display: none;
+  }
+  [id^="model-"][id*="Trace"] {
+    display: none;
+  }
+  .swagger-ui,
+  .swagger-ui *,
+  .swagger-ui .info .title,
+  .swagger-ui .info p,
+  .swagger-ui .info li,
+  .swagger-ui select,
+  .swagger-ui .opblock *,
+  .swagger-ui .opblock .opblock-section-header * {
+    color: ${(props) => props.theme.palette.text.primary};
+  }
+
+  .swagger-ui .info a {
+    color: ${(props) => props.theme.palette.text.secondary};
+  }
+  .swagger-ui select,
+  .swagger-ui .opblock,
+  .swagger-ui .opblock-tag,
+  .swagger-ui section.models .model-container,
+  .swagger-ui .opblock .opblock-section-header {
+    background-color: ${(props) => props.theme.palette.background.paper};
+  }
+  .swagger-ui .model,
+  .swagger-ui table.model tr.property-row td {
+    font-size: 10pt !important;
+  }
+  .swagger-ui .model-title {
+    font-size: 12pt !important;
+  }
+  .swagger-ui .model-toggle:after {
+    background: ${(props) =>
+      `url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' width='24' height='24' viewBox='0 0 24 24'%3E%3Cpath fill='${encodeURIComponent(
+        props.theme.name == "DARK"
+          ? props.theme.palette.primary.contrastText
+          : "#000",
+      )}' d='M10 6L8.59 7.41 13.17 12l-4.58 4.59L10 18l6-6z'/%3E%3C/svg%3E") 50% no-repeat`};
+  }
+  .swagger-ui .expand-methods svg,
+  .swagger-ui .opblock svg,
+  .swagger-ui .expand-operation svg {
+    fill: ${(props) =>
+      props.theme.name == "DARK"
+        ? props.theme.palette.primary.contrastText
+        : "#000"};
+  }
+  .swagger-ui .scheme-container {
+    background: inherit;
+    box-shadow: none;
+  }
+  .swagger-ui .wrapper:nth-of-type(2n):before {
+    content: "Actions";
+    font-size: 18pt;
+    font-weight: 600;
+  }
+  #operations-tag-data {
+    margin-top: 20px;
+  }
+  .swagger-ui .scheme-container {
+    direction: rtl;
+    display: flex;
+    justify-content: flex-end;
+    padding: 0;
+  }
+  .swagger-ui section.schemes {
+    margin-left: auto;
+  }
+  .swagger-ui .servers {
+    width: 320px;
+  }
+  .swagger-ui .servers > label {
+    margin: 0;
+  }
+  .swagger-ui section.models .model-container {
+    margin: 0;
+    border-bottom: 1px solid rgba(59, 65, 81, 0.3);
+    border-radius: 0px;
+    box-shadow:
+      0 2px 2px 0 rgba(0, 0, 0, 0.14),
+      0 3px 1px -2px rgba(0, 0, 0, 0.2),
+      0 1px 5px 0 rgba(0, 0, 0, 0.12);
+  }
+  .swagger-ui section.models .model-container:last-of-type {
+    margin: 0;
+  }
+  .swagger-ui section.models.is-open {
+    padding: 0px;
+  }
+  .swagger-ui section.models {
+    border: none;
+  }
+  .swagger-ui section.models h4 {
+    margin-bottom: 10px;
+    font-size: 18pt;
+    font-weight: 600;
+  }
+  .swagger-ui section.models.is-open h4 {
+    margin-top: 20px;
+    margin-bottom: 0px;
+    border-bottom: 0px;
+  }
+  .swagger-ui .info {
+    margin-bottom: 0px;
+  }
+  .swagger-ui .servers > label select {
+    height: 40px;
+  }
+  .swagger-ui table.model tr.property-row td {
+    padding: 0.6em;
+    font-size: 12pt;
+  }
+  .swagger-ui td .model .pointer .model-title {
+    display: none;
+  }
+  .swagger-ui .model-box {
+    padding-top: 7px;
+    padding-left: 5px;
+    padding-bottom: 7px;
+  }
+  .swagger-ui .model,
+  .model-title {
+    font-size: 12pt;
+    text-transform: lowercase;
+  }
+  .swagger-ui .model-title__text {
+    padding-left: 10px;
+  }
+  .swagger-ui td .model-toggle {
+    font-size: 20px;
+    top: 2px;
+    margin-left: 0px;
+  }
+  .swagger-ui td .brace-open {
+    font-size: 16px;
+  }
+  .renderedMarkdown {
+  }
+  .swagger-ui .opblock-tag {
+    margin: 0px;
+    box-shadow:
+      0 2px 2px 0 rgba(0, 0, 0, 0.14),
+      0 3px 1px -2px rgba(0, 0, 0, 0.2),
+      0 1px 5px 0 rgba(0, 0, 0, 0.12);
+  }
+  .no-margin {
+    display: flex;
+    flex-direction: column;
+  }
+  .swagger-ui .opblock-tag-section h4 {
+    padding-left: 20px;
+    font-size: 12pt;
+    height: 50px;
+  }
+  .swagger-ui .opblock-get {
+    padding-left: 5px;
+    margin-bottom: 1px;
+  }
+  .swagger-ui .opblock-post {
+    padding-left: 5px;
+    margin-bottom: 1px;
+    order: 2;
+  }
+  .swagger-ui .opblock-delete {
+    padding-left: 5px;
+    margin-bottom: 1px;
+    order: 3;
+  }
+  .swagger-ui .opblock-summary {
+    height: 50px;
+  }
+`;
+
+export default StyledSwaggerUI;

app/src/components/pages/apis/javaCodeExamples.js

@@ -0,0 +1,34 @@
+// Installation
+export const InstallationExample = `// Download the jar file, or install via Maven. See the full docs.`;
+
+// Query and download data
+export const DataExample = `// Create a client. Your API key can be found in your account.
+OpenmlConnector openml = new OpenmlConnector("api_key");
+
+// List all datasets and their properties
+DataSet[] datasets = openml.dataList();
+
+// Get dataset by ID
+DataSetDescription data = openml.dataGet(40996);
+String file_url = data.getUrl();`;
+
+// Run and publish models
+export const RunExample = `// Build any model you like
+Classifier model = new RandomForest();
+
+// Download any OpenML task
+Task t = openml.taskGet(3954);
+
+// Load the data as WEKA Instances (optional)
+Instances d = InstancesHelper.getDatasetFromTask(openml, t);
+
+// Run and evaluate your model on the task, and upload to OpenML
+Pair<Integer, Run> result = RunOpenmlJob.executeTask(openml, new WekaConfig(), 3954, model);`;
+
+// Using Benchmarks
+export const BenchmarkExample = `// List all tasks in a benchmark
+Study benchmark = openml.studyGet("OpenML-CC18", "tasks");
+Task[] tasks = benchmark.getTasks()
+
+// Return benchmark results
+EvaluationList el = openml.evaluationList(tasks, null, "area_under_roc_curve", 1000);`;

app/src/components/pages/apis/juliaCodeExamples.js

@@ -0,0 +1,17 @@
+// Installation
+export const InstallationExample = `using Pkg
+Pkg.add("OpenML")`;
+
+// Query and download data
+export const DataExample = `using OpenML
+using DataFrames
+
+# List all datasets and their properties
+ds = OpenML.list_datasets(output_format = DataFrame)
+
+# Get dataset by ID
+OpenML.describe_dataset(40996)
+
+# Get the data itself as a dataframe (or otherwise)
+table = OpenML.load(40996)
+df = DataFrame(table)`;

app/src/components/pages/apis/pythonCodeExamples.js

@@ -0,0 +1,46 @@
+// Installation
+export const InstallationExample = `pip install openml`;
+
+// Query and download data
+export const DataExample = `import openml
+
+# List all datasets and their properties
+openml.datasets.list_datasets(output_format="dataframe")
+
+# Get dataset by ID
+dataset = openml.datasets.get_dataset(61)
+
+# Get dataset by name
+dataset = openml.datasets.get_dataset('Fashion-MNIST')
+
+# Get the data itself as a dataframe (or otherwise)
+X, y, _, _ = dataset.get_data(dataset_format="dataframe")`;
+
+// Run and publish models
+export const RunExample = `from sklearn import ensemble
+from openml import tasks, runs
+
+# Build any model you like
+clf = ensemble.RandomForestClassifier()
+
+# Download any OpenML task
+task = tasks.get_task(3954)
+
+# Run and evaluate your model on the task
+run = runs.run_model_on_task(clf, task)
+
+# Share the results on OpenML. Your API key can be found in your account.
+# openml.config.apikey = 'YOUR_KEY'
+run.publish()`;
+
+// Using Benchmarks
+export const BenchmarkExample = `# List all tasks in a benchmark
+benchmark = openml.study.get_suite('OpenML-CC18')
+tasks.list_tasks(output_format="dataframe", task_id=benchmark.tasks)
+
+# Return benchmark results
+openml.evaluations.list_evaluations(
+    function="area_under_roc_curve",
+    tasks=benchmark.tasks,
+    output_format="dataframe"
+)`;

app/src/components/pages/apis/rCodeExamples.js

@@ -0,0 +1,44 @@
+// Installation
+export const InstallationExample = `# Note: The old OpenML package is superseded by the mlr3oml package.
+# The documentation of the OpenML package can be found here: http://openml.github.io/openml-r/.
+install.packages("mlr3oml")`;
+
+// Query and download data
+export const DataExample = `library(mlr3oml)
+library(mlr3)
+
+# Search for specific datasets
+odatasets = list_oml_data(
+  number_features = c(10, 20),
+  number_instances = c(45000, 50000),
+  number_classes = 2
+)
+
+# Get dataset
+odata = odt(id = 1590)
+# Access the actual data
+odata$data
+
+# Convert to an mlr3::Task
+tsk_adult = as_task(odata, target = "class")`;
+
+// Run and publish models
+export const RunExample = `# create an mlr3 Learner and Resampling and run a resample experiment
+
+rr = resample(
+  task = tsk_adult,
+  learner = lrn("classif.rpart"),
+  resampling = rsmp("cv", folds = 10)
+)`;
+
+// Using Benchmarks
+export const BenchmarkExample = `# Access a Benchmark Suite
+ocollection = ocl(353)
+
+# The IDs
+ocollection$task_ids
+id = ocollection$task_ids[1L]
+
+# Create mlr3 Task and Resampling from the OpenML Task
+task = tsk("oml", task_id = id)
+resampling = rsmp("oml", task_id = id)`;