Apache PredictionIO

https://predictionio.apache.org
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Apache PredictionIO is an open-source machine learning server that makes it easy to build and deploy...
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### Start Local Documentation Server

Source: https://predictionio.apache.org/community/contribute-documentation

Starts a local web server to preview the documentation site. Accessible at localhost:4567.

```bash
bundle exec middleman server

```

--------------------------------

### Install PostgreSQL with apt-get

Source: https://predictionio.apache.org/install/install-sourcecode

Use apt-get to install PostgreSQL on Ubuntu.

```bash
$ apt-get install postgresql
```

--------------------------------

### Start Elasticsearch

Source: https://predictionio.apache.org/resources/faq

Execute the binary directly from the vendor directory to start the Elasticsearch service.

```bash
$ ~/PredictionIO/vendors/elasticsearch-x.y.z/bin/elasticsearch
```

--------------------------------

### Example Successful pio status Output

Source: https://predictionio.apache.org/resources/faq

This output indicates that all components are correctly installed and configured.

```text
PredictionIO
  Installed at: /home/vagrant/PredictionIO
  Version: 0.8.6

Apache Spark
  Installed at: /home/vagrant/PredictionIO/vendors/spark-1.2.0
  Version: 1.2.0 (meets minimum requirement of 1.2.0)

Storage Backend Connections
  Verifying Meta Data Backend
  Verifying Model Data Backend
  Verifying Event Data Backend
  Test write Event Store (App Id 0)
2015-02-03 18:52:38,904 INFO  hbase.HBLEvents - The table predictionio_eventdata:events_0 doesn't exist yet. Creating now...
2015-02-03 18:52:39,868 INFO  hbase.HBLEvents - Removing table predictionio_eventdata:events_0...

(sleeping 5 seconds for all messages to show up...)
Your system is all ready to go.
```

--------------------------------

### Start PredictionIO Services

Source: https://predictionio.apache.org/templates/recommendation/quickstart

Start the Event Server, HBase, and Elasticsearch components.

```bash
$ pio-start-all
```

--------------------------------

### Start HBase

Source: https://predictionio.apache.org/resources/faq

Use the provided shell script to initialize and start the HBase service.

```bash
$ ~/PredictionIO/vendors/hbase-x.y.z/bin/start-hbase.sh
```

--------------------------------

### Install Documentation Dependencies

Source: https://predictionio.apache.org/community/contribute-documentation

Installs necessary gems and front-end packages for building the documentation locally. Requires Ruby 2.1+ and Node.js.

```bash
cd docs/manual
gem install bundler
bundle install
npm install -g bower
bower install

```

--------------------------------

### Querying the Deployed Engine

Source: https://predictionio.apache.org/templates/recommendation/customize-data-prep

Example of how to send a query to the deployed engine to get movie recommendations for a specific user.

```bash
$ curl -H "Content-Type: application/json" -d '{ "user": "1", "num": 4 }' http://localhost:8000/queries.json
```

--------------------------------

### Install PredictionIO Python SDK (easy_install)

Source: https://predictionio.apache.org/templates/ecommercerecommendation/quickstart

Install the PredictionIO Python SDK using easy_install. This is an alternative to pip for installing the required SDK.

```bash
$ easy_install predictionio
```

--------------------------------

### Install Monit on Ubuntu

Source: https://predictionio.apache.org/deploy/monitoring

Use this command to install the Monit monitoring tool on Ubuntu systems.

```bash
sudo apt-get install monit
```

--------------------------------

### Install PostgreSQL with Homebrew

Source: https://predictionio.apache.org/install/install-sourcecode

Use Homebrew to install PostgreSQL on macOS.

```bash
$ brew install postgresql
```

--------------------------------

### S3 Storage Configuration Examples

Source: https://predictionio.apache.org/system/anotherdatastore

Examples of setting S3 as a storage source and configuring its parameters like region, bucket name, and base path. Ensure AWS credentials are set via environment variables or other AWS methods.

```properties
PIO_STORAGE_SOURCES_S3_REGION=us-east-1
```

```properties
PIO_STORAGE_SOURCES_S3_BUCKET_NAME=pio_bucket
```

```properties
PIO_STORAGE_SOURCES_S3_BASE_PATH=pio_model
```

```properties
PIO_STORAGE_SOURCES_S3_DISABLE_CHUNKED_ENCODING=true
```

```properties
PIO_STORAGE_SOURCES_S3_ENDPOINT=http://localstack:4572
```

--------------------------------

### Train and Deploy the Engine

Source: https://predictionio.apache.org/machinelearning/dimensionalityreduction

Standard commands to train the model and start the deployment server.

```bash
pio train
pio deploy
```

--------------------------------

### Install PredictionIO Python SDK (pip)

Source: https://predictionio.apache.org/templates/ecommercerecommendation/quickstart

Install the PredictionIO Python SDK using pip. This is required for running the sample data import script.

```bash
$ pip install predictionio
```

--------------------------------

### Start All PredictionIO Components

Source: https://predictionio.apache.org/templates/classification/quickstart

Start all necessary PredictionIO components, including the Event Server, HBase, and Elasticsearch. This command is used when running with HBase and Elasticsearch.

```bash
pio-start-all
```

--------------------------------

### Request Event Server Plugins - curl example

Source: https://predictionio.apache.org/datacollection/plugin

Example using curl to request a JSON response listing all enabled plugins on the event server.

```bash
curl -XGET http://localhost:7070/plugins.json?accessKey=$ACCESS_KEY
```

--------------------------------

### Engine Client Query Example

Source: https://predictionio.apache.org/community/contribute-sdk

Example JSON object for querying recommendations or predictions from an Engine Client. The format is defined by the specific Engine.

```json
{
    "user": 1,
    "num": 4
}
```

--------------------------------

### Start PostgreSQL Server Manually

Source: https://predictionio.apache.org/install/install-sourcecode

Manually start the PostgreSQL server if it's not running. Ensure the paths to the data directory and log file are correct for your system.

```bash
$ pg_ctl -D /usr/local/var/postgres -l /usr/local/var/postgres/server.log start
```

--------------------------------

### Build Success Output

Source: https://predictionio.apache.org/install/install-sourcecode

Example output indicating a successful build of the binary distribution.

```text
...
PredictionIO-0.14.0/sbt/sbt
PredictionIO-0.14.0/conf/
PredictionIO-0.14.0/conf/pio-env.sh
PredictionIO binary distribution created at PredictionIO-0.14.0.tar.gz
```

--------------------------------

### Install PredictionIO Python SDK

Source: https://predictionio.apache.org/templates/classification/quickstart

Install the PredictionIO Python SDK using pip or easy_install. You may need sudo privileges. This is required to run the sample data import script.

```bash
$ pip install predictionio
```

```bash
$ easy_install predictionio
```

--------------------------------

### App List Output

Source: https://predictionio.apache.org/templates/recommendation/quickstart

Example console output for the app list command.

```text
[INFO] [App$]                 Name |   ID |                                                       Access Key | Allowed Event(s)
[INFO] [App$]               MyApp1 |    1 | 3mZWDzci2D5YsqAnqNnXH9SB6Rg3dsTBs8iHkK6X2i54IQsIZI1eEeQQyMfs7b3F | (all)
[INFO] [App$]               MyApp2 |    2 | io5lz6Eg4m3Xe4JZTBFE13GMAf1dhFl6ZteuJfrO84XpdOz9wRCrDU44EUaYuXq5 | (all)
[INFO] [App$] Finished listing 2 app(s).
```

--------------------------------

### Configure engine.json for Algorithms

Source: https://predictionio.apache.org/templates/classification/dase

Example configuration for algorithm parameters in engine.json.

```json
{
  ...
  "algorithms": [
    {
      "name": "naive",
      "params": {
        "lambda": 1.0
      }
    }
  ]
  ...
}
```

--------------------------------

### Install Apache Spark

Source: https://predictionio.apache.org/install/install-sourcecode

Download and extract Apache Spark into the vendors directory.

```bash
$ wget https://archive.apache.org/dist/spark/spark-2.4.0/spark-2.4.0-bin-hadoop2.7.tgz
$ tar zxvfC spark-2.4.0-bin-hadoop2.7.tgz PredictionIO-0.14.0/vendors
```

--------------------------------

### Configure engine.json for DataSource

Source: https://predictionio.apache.org/templates/classification/dase

Example configuration for the datasource parameters in engine.json.

```json
{
  ...
  "datasource": {
    "params": {
      "appName": "MyApp1"
    }
  },
  ...
}
```

--------------------------------

### Start Zeppelin Daemon

Source: https://predictionio.apache.org/datacollection/analytics-zeppelin

Initializes the Zeppelin server process.

```bash
$ bin/zeppelin-daemon.sh start
```

--------------------------------

### Create Vendors Directory

Source: https://predictionio.apache.org/install/install-sourcecode

Create a directory to store dependencies within the installation path.

```bash
$ mkdir PredictionIO-0.14.0/vendors
```

--------------------------------

### Aggregate properties output

Source: https://predictionio.apache.org/datacollection/eventmodel

Example output showing aggregated user properties.

```text
res0: Array[(String, org.apache.predictionio.data.storage.PropertyMap)] =
Array((2,PropertyMap(Map(), 2014-09-09T16:17:42.937-08:00, 2014-09-13T16:17:42.143-08:00)))
```

--------------------------------

### Query with blacklist

Source: https://predictionio.apache.org/templates/ecommercerecommendation/quickstart

Example of requesting recommendations while excluding specific items using a blacklist.

```bash
$ curl -H "Content-Type: application/json" \
-d '{
  "user": "u1",
  "num": 4,
  "blackList": ["i21", "i26", "i40"]
}' \
http://localhost:8000/queries.json

{"itemScores":[{"item":"i33","score":0.005999267822052019},{"item":"i3","score":0.0030070150265619003},{"item":"i2","score":0.0028489173099429527},{"item":"i5","score":0.0028489173099429527}]}
```

--------------------------------

### Assign Source to Repository

Source: https://predictionio.apache.org/system/anotherdatastore

Example of mapping the PGSQL source to the metadata repository.

```bash
PIO_STORAGE_REPOSITORIES_METADATA_NAME=predictionio_metadata
PIO_STORAGE_REPOSITORIES_METADATA_SOURCE=PGSQL
```

--------------------------------

### GET /evaluation-instances/completed

Source: https://predictionio.apache.org/api/current/org/apache/predictionio/data/storage/EvaluationInstances.html

Retrieves completed EvaluationInstances sorted by start time.

```APIDOC
## GET /evaluation-instances/completed

### Description
Get instances that are produced by evaluation and have run to completion, reverse sorted by the start time.

### Method
GET

### Endpoint
/evaluation-instances/completed
```

--------------------------------

### Import Sample Data (Bash)

Source: https://predictionio.apache.org/templates/ecommercerecommendation/quickstart

Execute this bash command to import sample data into the Event Server. Ensure you are in the correct directory and have set your ACCESS_KEY environment variable.

```bash
cd MyECommerceRecommendation
python data/import_eventserver.py --access_key $ACCESS_KEY
```

--------------------------------

### Create app for experimental upgrade

Source: https://predictionio.apache.org/resources/upgrade

Initializes a new app to store data during the experimental upgrade process.

```bash
$ bin/pio app new <my app>
```

--------------------------------

### Configure Repository Backends

Source: https://predictionio.apache.org/system/anotherdatastore

Example configuration for metadata, event data, and model data repositories in conf/pio-env.sh.

```bash
PIO_STORAGE_REPOSITORIES_METADATA_NAME=predictionio_metadata
PIO_STORAGE_REPOSITORIES_METADATA_SOURCE=ELASTICSEARCH

PIO_STORAGE_REPOSITORIES_EVENTDATA_NAME=predictionio_eventdata
PIO_STORAGE_REPOSITORIES_EVENTDATA_SOURCE=HBASE

PIO_STORAGE_REPOSITORIES_MODELDATA_NAME=pio_
PIO_STORAGE_REPOSITORIES_MODELDATA_SOURCE=LOCALFS
```

--------------------------------

### Shell Output Example

Source: https://predictionio.apache.org/customize/troubleshooting

Sample output returned after executing a collection command in the pio-shell.

```text
...
15/05/18 14:24:42 INFO DAGScheduler: Job 0 finished: collect at <console>:24, took 1.850779 s
c: Array[org.apache.predictionio.data.storage.Event] = Array(Event(id=Some(AaQUUBsFZxteRpDV_7fDGQAAAU1ZfRW1tX9LSWdZSb0),event=$set,eType=item,eId=i42,tType=None,tId=None,p=DataMap(Map(categories -> JArray(List(JString(c2), JString(c1), JString(c6), JString(c3))))),t=2015-05-15T21:31:19.349Z,tags=List(),pKey=None,ct=2015-05-15T21:31:19.354Z), Event(id=Some(DjvP3Dnci9F4CWmiqoLabQAAAU1ZfROaqdRYO-pZ_no),event=$set,eType=user,eId=u9,tType=None,tId=None,p=DataMap(Map()),t=2015-05-15T21:31:18.810Z,tags=List(),pKey=None,ct=2015-05-15T21:31:18.817Z), Event(id=Some(DjvP3Dnci9F4CWmiqoLabQAAAU1ZfRq7tsanlemwmZQ),event=view,eType=user,eId=u9,tType=Some(item),tId=Some(i25),p=DataMap(Map()),t=2015-05-15T21:31:20.635Z,tags=List(),pKey=None,ct=2015-05-15T21:31:20.639Z), Event(id=Some(DjvP3Dnci9F4CWmiqoLabQAAAU1ZfR...
```

--------------------------------

### Import Sample Data with Python Script

Source: https://predictionio.apache.org/templates/classification/quickstart

Navigate to your engine's directory and execute this script to import sample data into the Event Server using the Python SDK. Ensure your ACCESS_KEY environment variable is set.

```bash
cd MyClassification
python data/import_eventserver.py --access_key $ACCESS_KEY
```

--------------------------------

### Start PredictionIO and Dependent Services

Source: https://predictionio.apache.org/install/install-sourcecode

Use this command to start all PredictionIO services, including Elasticsearch and HBase. If using PostgreSQL or MySQL, start the event server separately.

```bash
PredictionIO-0.14.0/bin/pio-start-all
```

--------------------------------

### Initialize Notebook Environment

Source: https://predictionio.apache.org/datacollection/analytics-ipynb

Sets up the SQL context and registers the Parquet data as a temporary table for SQL queries.

```python
import pandas as pd
def rows_to_df(rows):
    return pd.DataFrame(map(lambda e: e.asDict(), rows))
from pyspark.sql import SQLContext
sqlc = SQLContext(sc)
rdd = sqlc.parquetFile("/tmp/movies")
rdd.registerTempTable("events")
```

--------------------------------

### Import Sample Data

Source: https://predictionio.apache.org/templates/recommendation/quickstart

Download sample movie data and run the import script within the engine directory.

```bash
$ cd MyRecommendation
$ curl https://raw.githubusercontent.com/apache/spark/master/data/mllib/sample_movielens_data.txt --create-dirs -o data/sample_movielens_data.txt
$ python data/import_eventserver.py --access_key $ACCESS_KEY
```

--------------------------------

### App Creation Output

Source: https://predictionio.apache.org/templates/recommendation/quickstart

Example console output showing the generated App ID and Access Key.

```text
...
[INFO] [App$] Initialized Event Store for this app ID: 1.
[INFO] [App$] Created new app:
[INFO] [App$]       Name: MyApp1
[INFO] [App$]         ID: 1
[INFO] [App$] Access Key: 3mZWDzci2D5YsqAnqNnXH9SB6Rg3dsTBs8iHkK6X2i54IQsIZI1eEeQQyMfs7b3F
```

--------------------------------

### Import Tutorial Data

Source: https://predictionio.apache.org/demo/textclassification

Use the pio import command to load JSON data files into the application.

```bash
$ pio import --appid *** --input data/stopwords.json

$ pio import --appid *** --input data/emails.json
```

--------------------------------

### Build, Train, and Deploy Engine

Source: https://predictionio.apache.org/templates/classification/add-algorithm

Execute these commands in the terminal to apply configuration changes and start the engine service.

```bash
$ pio build
$ pio train
$ pio deploy
```

--------------------------------

### Create a New App

Source: https://predictionio.apache.org/templates/recommendation/quickstart

Initialize a new application in PredictionIO to store data.

```bash
$ pio app new MyApp1
```

--------------------------------

### Event Server Plugin Status Response - JSON Example

Source: https://predictionio.apache.org/datacollection/plugin

Example JSON response from the event server detailing the enabled input blockers and sniffers.

```json
{
  "plugins": {
    "inputblockers": {
      "my-eventserver-plugin": {
        "name": "my-eventserver-plugin",
        "description": "an example of event server plug-in",
        "class": "com.example.MyEventServerPlugin"
      }
    },
    "inputsniffers": {}
  }
}
```

--------------------------------

### Custom Backend Storage Configuration

Source: https://predictionio.apache.org/system/anotherdatastore

Configuration example for using a custom storage backend. Specify the complete package name for your custom StorageClient implementation and assign it to the desired repositories.

```properties
PIO_STORAGE_SOURCES_MYJDBC_TYPE=org.mystorage.jdbc
```

```properties
PIO_STORAGE_REPOSITORIES_METADATA_SOURCE=MYJDBC
```

--------------------------------

### Start PredictionIO Event Server

Source: https://predictionio.apache.org/templates/classification/quickstart

Start the PredictionIO Event Server, which is necessary for collecting and managing event data. This command is used when running with PostgreSQL or MySQL.

```bash
pio eventserver &
```

--------------------------------

### Build Documentation Site

Source: https://predictionio.apache.org/community/contribute-documentation

Builds the static HTML site for the documentation. The output is typically placed in a 'build' directory.

```bash
bundle exec middleman build

```

--------------------------------

### Engine Client Response Example

Source: https://predictionio.apache.org/community/contribute-sdk

Example JSON object for a successful response from an Engine Client, containing item scores. The structure is defined by the specific Engine.

```json
{
    "itemScores": [
        {
            "item": 39,
            "score": "6.177719297832409"
        },
        {
            "item": 79,
            "score": "5.931687319083594"
        },
        ...
    ]
}
```

--------------------------------

### Clone Similar Product Engine Template

Source: https://predictionio.apache.org/templates/similarproduct/quickstart

Download the Similar Product Engine Template from the official GitHub repository to start building your recommendation engine. Navigate into the created directory afterwards.

```bash
git clone https://github.com/apache/predictionio-template-similar-product.git MySimilarProduct
cd MySimilarProduct
```

--------------------------------

### Start PredictionIO Services with PostgreSQL

Source: https://predictionio.apache.org/install/install-docker

Use docker-compose to pull and start PredictionIO services along with PostgreSQL for event, metadata, and model storage. This command configures PostgreSQL as the backend for all storage types.

```bash
docker-compose -f docker-compose.yml \
    -f pgsql/docker-compose.base.yml \
    -f pgsql/docker-compose.meta.yml \
    -f pgsql/docker-compose.event.yml \
    -f pgsql/docker-compose.model.yml \
    up
```

--------------------------------

### Verify Source Release

Source: https://predictionio.apache.org/install/install-sourcecode

Import the project keys and verify the source distribution archive using GPG.

```bash
$ gpg --import KEYS
$ gpg --verify apache-predictionio-0.14.0.tar.gz.asc apache-predictionio-0.14.0.tar.gz
```

--------------------------------

### Implement Event Server Plugin - Scala Example

Source: https://predictionio.apache.org/datacollection/plugin

An example of an Event Server Plugin extending `EventServerPlugin`. It defines plugin properties and implements `process` and `handleREST` methods for event handling and REST requests.

```scala
package com.example

import org.apache.predictionio.data.api.*

class MyEventServerPlugin extends EventServerPlugin {
  val pluginName = "my-eventserver-plugin"
  val pluginDescription = "an example of event server plug-in"

  // inputBlocker or inputSniffer
  val pluginType = EventServerPlugin.inputBlocker   

  // Plug-in can handle input data in this method.
  // If plug-in found invalid data, it's possible to block them 
  // by throwing an exception in this method.
  override def process(
      eventInfo: EventInfo, 
      context: EventServerPluginContext): Unit = {
    println(eventInfo)
  }

  // Plug-in can handle requests to /plugins/<pluginType>/<pluginName>/* 
  // on the event server in this method.
  override def handleREST(
      appId: Int, 
      channelId: Option[Int], 
      arguments: Seq[String]): String = {
    """{"pluginName": "my-eventserver-plugin"}"""
  }
}
```

--------------------------------

### Create a new PredictionIO app

Source: https://predictionio.apache.org/resources/upgrade

Command to initialize a new application instance.

```bash
$ pio app new <my app name>
```

--------------------------------

### GET /engine-instances

Source: https://predictionio.apache.org/api/current/org/apache/predictionio/data/storage/EngineInstances.html

Retrieves all EngineInstances.

```APIDOC
## GET /engine-instances

### Description
Get all EngineInstances.

### Method
GET

### Endpoint
/engine-instances
```

--------------------------------

### Start PredictionIO Dashboard

Source: https://predictionio.apache.org/evaluation/evaluationdashboard

Use this command to launch the PredictionIO dashboard. Note that the dashboard is an experimental tool and should not be run in production environments.

```bash
$ pio dashboard
```

--------------------------------

### Example Output Part 1 for Batch Prediction

Source: https://predictionio.apache.org/batchpredict

An example of the output generated by batch prediction. Results are written in a multi-object JSON format, with each object containing the original query and its corresponding prediction. Output is partitioned.

```json
{"query":{"user":"1"},"prediction":{"itemScores":[{"item":"1","score":33},{"item":"2","score":32}]}}
{"query":{"user":"3"},"prediction":{"itemScores":[{"item":"2","score":16},{"item":"3","score":12}]}}
{"query":{"user":"4"},"prediction":{"itemScores":[{"item":"3","score":19},{"item":"1","score":18}]}}
```

--------------------------------

### Verify project directory files

Source: https://predictionio.apache.org/resources/upgrade

List the files in the project directory after adding the plugin.

```bash
your_template_directory$ ls project/
assembly.sbt  pio-build.sbt
```

--------------------------------

### GET /evaluation-instances

Source: https://predictionio.apache.org/api/current/org/apache/predictionio/data/storage/EvaluationInstances.html

Retrieves a list of all EvaluationInstances.

```APIDOC
## GET /evaluation-instances

### Description
Retrieves all EvaluationInstances.

### Method
GET

### Endpoint
/evaluation-instances
```

--------------------------------

### Build the engine

Source: https://predictionio.apache.org/templates/recommendation/batch-evaluator

Execute this command to compile the engine project.

```bash
$ pio build
```

--------------------------------

### pioEnvVars

Source: https://predictionio.apache.org/api/current/org/apache/predictionio/workflow/WorkflowUtils%24.html

Grab environmental variables that starts with 'PIO_'.

```APIDOC
## pioEnvVars

### Description
Grab environmental variables that starts with 'PIO_'.

### Response
#### Success Response (200)
- **Map[String, String]** - A map of environment variables starting with PIO_.
```

--------------------------------

### Launch IPython Notebook with PySpark

Source: https://predictionio.apache.org/datacollection/analytics-ipynb

Commands to start the IPython Notebook environment with PySpark integration.

```bash
$ PYSPARK_DRIVER_PYTHON=ipython PYSPARK_DRIVER_PYTHON_OPTS="notebook --pylab inline" $SPARK_HOME/bin/pyspark
```

```bash
PYSPARK_DRIVER_PYTHON=ipython PYSPARK_DRIVER_PYTHON_OPTS="notebook --`%pylab inline`" $SPARK_HOME/bin/pyspark
```

--------------------------------

### GET /engineParamsList

Source: https://predictionio.apache.org/api/current/org/apache/predictionio/workflow/FakeRun.html

Retrieves the list of engine parameters.

```APIDOC
## GET /engineParamsList

### Description
Returns the list of EngineParams for this EngineParamsGenerator.

### Method
GET

### Response
#### Success Response (200)
- **engineParamsList** (Seq[EngineParams]) - A sequence of engine parameters.
```

--------------------------------

### Verify Binary Release

Source: https://predictionio.apache.org/install/install-sourcecode

Import the project keys and verify the binary distribution archive using GPG.

```bash
$ gpg --import KEYS
$ gpg --verify apache-predictionio-0.14.0-bin.tar.gz.asc apache-predictionio-0.14.0-bin.tar.gz
```

--------------------------------

### GET /events

Source: https://predictionio.apache.org/api/current/org/apache/predictionio/data/storage/LEvents.html

Retrieves an event in a non-blocking fashion.

```APIDOC
## GET /events

### Description
Get an Event in a non-blocking fashion.

### Method
GET

### Parameters
#### Path Parameters
- **eventId** (String) - Required - ID of the Event
- **appId** (Int) - Required - ID of the app that contains the Event
```

--------------------------------

### GET /engine-instances/latest-completed

Source: https://predictionio.apache.org/api/current/org/apache/predictionio/data/storage/EngineInstances.html

Retrieves the latest completed EngineInstance.

```APIDOC
## GET /engine-instances/latest-completed

### Description
Get an instance that has started training the latest and has trained to completion.

### Method
GET

### Endpoint
/engine-instances/latest-completed

### Parameters
#### Query Parameters
- **engineId** (String) - Required - The engine ID.
- **engineVersion** (String) - Required - The engine version.
- **engineVariant** (String) - Required - The engine variant.
```

--------------------------------

### GET /engine-instances/completed

Source: https://predictionio.apache.org/api/current/org/apache/predictionio/data/storage/EngineInstances.html

Retrieves all EngineInstances that have completed training.

```APIDOC
## GET /engine-instances/completed

### Description
Get all instances that have trained to completion.

### Method
GET

### Endpoint
/engine-instances/completed

### Parameters
#### Query Parameters
- **engineId** (String) - Required - The engine ID.
- **engineVersion** (String) - Required - The engine version.
- **engineVariant** (String) - Required - The engine variant.
```

--------------------------------

### GET /apps/name/{name}

Source: https://predictionio.apache.org/api/current/org/apache/predictionio/data/storage/Apps.html

Retrieves an App by its name.

```APIDOC
## GET /apps/name/{name}

### Description
Get an App by app name.

### Method
GET

### Endpoint
/apps/name/{name}

### Parameters
#### Path Parameters
- **name** (String) - Required - The name of the App.
```

--------------------------------

### Import Sample Data

Source: https://predictionio.apache.org/templates/similarproduct/quickstart

Execute the provided Python script to import batch events into the event server.

```bash
$ cd MySimilarProduct
$ python data/import_eventserver.py --access_key $ACCESS_KEY
```

--------------------------------

### GET /apps

Source: https://predictionio.apache.org/api/current/org/apache/predictionio/data/storage/Apps.html

Retrieves a list of all available Apps.

```APIDOC
## GET /apps

### Description
Get all Apps.

### Method
GET

### Endpoint
/apps
```

--------------------------------

### Query Output

Source: https://predictionio.apache.org/demo/textclassification

Example JSON responses returned by the engine.

```json
{"category":"not spam","confidence":0.852619510921587}
```

```json
{"category":"spam","confidence":0.5268770133242983}
```

--------------------------------

### Import Sample Data with Events Server

Source: https://predictionio.apache.org/templates/similarproduct/multi-events-multi-algos

Execute this Python script to import sample 'like' and 'dislike' events. Ensure you replace 'YOUR_ACCESS_KEY' with your actual PredictionIO Access Key. Update the Python SDK if you encounter a 'TypeError: init() got an unexpected keyword argument 'access_key'' error.

```bash
python data/import_eventserver.py --access_key YOUR_ACCESS_KEY
```

--------------------------------

### GET /

Source: https://predictionio.apache.org/sdk/php/api/source-class-predictionio.BaseClient.html

Retrieves the status of the Event Server or Engine Instance.

```APIDOC
## GET /

### Description
Get the status of the Event Server or Engine Instance.

### Method
GET

### Endpoint
/

### Response
#### Success Response (200)
- **status** (string) - The status message returned by the server.
```
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