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The prediction service represents a web interface which allows to request, insert and modify prediction data from the database. Operations are performed by sending URL requests, whereas each operation is well defined as a so called route. The property service comes along with a graphical user interface at http://localhost:8004/ui/ which provides visual access to all available routes. Hereby, all routes involving the insertion or modification of data are enlisted under the Edit section.
For this tutorial we are using only two routes. One to add a new machine learning configuration and one to add the consequential prediction.
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Each route can hold up to three different parameter types which are described in details by the following article: "Ingest property data in database (REST API)".
Implementation
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Ingest
In our example we run a machine learning algorithm which produces a flare prediction to store within our database. Hereby, the algorithm consists of a training phase and a test or prediction phase. Within the training phase the algorithm learns and tunes its parameters which then can be stored within the database as a configuration for later use. Afterwards, within the test phase, we use this configuration to compute flare predictions which are also stored within the database. The following code shows the two corresponding functions.
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