rapidminer 9.6.0.0

Tools for running RapidMiner processes and managing RapidMiner repositories.

RapidMiner Python package

This Python package allows you to interact with RapidMiner Studio and Server. You can collaborate using the RapidMiner repository and leverage the scalable Server infrastructure to run processes. This document shows examples on how to use the package. Additional notebook files provide more advanced examples. There is an API document for each classes: Studio, Server, Scoring.

Table of contents

• Requirements
• Known current limitations
• Overview
• Installation
• Studio
• Server
• Scoring

Requirements

• RapidMiner Studio 9.5.0 or later for Studio class
• RapidMiner Server 9.5.0 or later for Server class
• Python Scripting extension 9.6.0 or later installed for both Studio and Server, download it from the Marketplace

Known current limitations

• Python version:
  • Extensive tests were only carried out using Python 3.7, but earlier versions are expected to work as well.
  • Python 2 is not supported.
• Studio and Server processes guarantee reproducibility. That means you should always get the same result after a version update. The same feature cannot be guaranteed when using this Python library (the library depends on other libraries that our not in our control).
• Server with SAML authentication is not yet supported.

Overview

Both Studio and Server classes provide a read and a write method for reading / writing data and other objects, and a run method to run processes. The method signatures are the same, with somewhat different extra parameters.

Studio class requires a local Studio installation and is suitable in the following cases:

• Implementing certain data science steps in Python using your favorite IDE or notebook implementation. You may even use the resulting code afterwards in a RapidMiner process within an Execute Python operator.
• You are using coding primarily, but you want to incorporate methods that are impemented in a RapidMiner process.
• Creating batch tasks that also interact with the repository and / or run processes.

Server class connects directly to a Server instance without the need of a Studio installation. It is suitable in the following cases:

• Collaborating with RapidMiner users, sharing data easily.
• Calling, running, scheduling processes on the RapidMiner Server platform from a local script.

Installation

The library can be installed easily:

• install in one step:

    $ pip install rapidminer
  

• or clone the repository and install:

    $ git clone https://github.com/rapidminer/python-rapidminer.git
    $ cd python-rapidminer
    $ python setup.py install
  

Studio

You need to have a locally installed RapidMiner Studio instance to use this class. The only thing you need to provide is your installation path. Once that is specified, you can read from and write data or other objects to any configured repository. You can also run processes from files or from the repository. In this section, we show you some examples on how to read and write repository data and run processes. For more advanced scenarios see the included IPython notebook and the documentation of the Studio class.

Note that each Studio method starts a Studio instance in the background and stops it when it is done. It is not recommended to run multiple instances in parallel, e.g. on different Notebook tabs. If you have several RapidMiner extensions installed, all of them will be loaded each time, that may lead to longer runtime. Provide multiple parameters to a read or write call, if possible, to avoid the startup overhead.

First you need a Connector object to interact with Studio. Once you have that, you can read and write data or run a process with a single line. To create a Studio Connector object, run the following code:

import rapidminer
connector = rapidminer.Studio("/path/to/you/studio/installation")

where you replace "/path/to/you/studio/installation" with the location of your Studio installation. In case of Windows, a typical path is "C:/Program Files/RapidMiner/RapidMiner Studio" - note that you should either use forward "/" as separators or put an r before the first quote character to indicate raw string . In case of Mac, the path is usually "/Applications/RapidMiner Studio.app/Contents/Resources/RapidMiner-Studio". Alternatively you can define this location via the RAPIDMINER_HOME environment variable.

Reading ExampleSet

Once you have a connector instance, you can read a RapidMiner ExampleSet in Python by running the following line:

df = connector.read_resource("//Samples/data/Iris")

The resulting df is a pandas DataFrame object, which you can use in the conventional way.

Writing ExampleSet

You can save any pandas DataFrame object to a RapidMiner repository (or file) with the following command:

connector.write_resource(df, "//Local Repository/data/mydata")

where df is the DataFrame object you want to write to the repository, and "//Local Repository/data/mydata" is the location where you want to store it.

Running a process

To run a process execute the following line:

df = connector.run_process("//Samples/processes/02_Preprocessing/01_Normalization")

You will get the results as pandas DataFrames. You can also define inputs, and many more. For more examples, see the examples notebook

Server

With Server class, you can directly connect to a local or remote Server instance without the need for any local RapidMiner (Studio) installation. You can read data from and write data to the Server repository and you can execute processes using the scalable Job Agent architecture. In this section, we show you some examples on how to read and write repository data and run processes. For more advanced scenarios see the included IPython notebook and the documentation of the Server class.

Installation of Server API

The Server class requires a web service backend to be installed on RapidMiner Server. This is done automatically on the first instantiation of the Server class. The repository folder /shared is used by default to store the backend process. This folder exists and is accessible by anyone starting from RapidMiner Server 9.6.0.

Server class instantiation can be fully automated (thus, no need for user input), if you specify url, username and password parameters.

On the RapidMiner Server web UI you can see the installed web service backend (Processes->Web Services). It has the name Repository Service by default, but you can change that with the optional parameter of Server class named webservice. You can change the process path location by setting 'processpath', but you need to make sure that it will be executable by all users of the Server API. If the web service is deleted, the next Server instantiation will re-create it.

Usage of Server API

To create a Server Connector object, run the following code:

import rapidminer
connector = rapidminer.Server("https://myserver.mycompany.com:8080", username="myrmuser")

where you replace "https://myserver.mycompany.com:8080" with the url of your Server instance and "myrmuser" with your username.

Reading ExampleSet

Once you have a connector instance, you can read a RapidMiner ExampleSet in Python by running the following line:

df = connector.read_resource("/home/myrmuser/data/mydata")

The resulting df is a pandas DataFrame object, which you can use in the conventional way.

Writing ExampleSet

You can save any pandas DataFrame object to the Server repository with the following command:

connector.write_resource(df, "/home/myrmuser/data/myresult")

where df is the DataFrame object you want to write to the repository, and "/home/myrmuser/data/myresult" is the location where you want to store it.

Running a process

To run a process execute the following line:

df = connector.run_process("/home/myrmsuer/process/transform_data", inputs=df)

You will get the results as pandas DataFrames. You can also define multiple inputs, and other parameters. For more examples, see the examples notebook

Scoring

This class allows you to easily use a deployed Real-Time Scoring service. You only need to provide the Server url and the particular scoring service endpoint to create a class instance. After that, you can use the predict method to do scoring on a Pandas DataFrame and get the result in a Pandas DataFrame as well. For instructions on how to deploy Real-Time Scoring on Server, please refer to its documentation.

sc = rapidminer.Scoring("http://myserver.mycompany.com:8090", "score-sales/score1")
prediction = sc.predict(df)

where the scoring endpoint is at "score-sales/score1" that can be applied to the dataset df, and the resulting prediction is a pandas DataFrame object. You can find the Scoring class documentation here.