prediction_vector_source

Module: missions.nodes.source.prediction_vector_source

Source for PredictionVector data from *.csv and *.pickle files

Note

Nearly a total copy of the time_series_source. The important part of the code can be found in the corresponding metadata.yaml .

Inheritance diagram for pySPACE.missions.nodes.source.prediction_vector_source:

PredictionVectorSourceNode

class pySPACE.missions.nodes.source.prediction_vector_source.PredictionVectorSourceNode(**kwargs)

Bases: pySPACE.missions.nodes.base_node.BaseNode

Source for samples of type PredictionVector

This node reads PredictionVector elements accumulated in a prediction_vector and passes them into the node_chain. As described in prediction_vector it is important, that the storage format is correct specified in the metadata.yaml. If the dataset has been constructed by pySPACE, this is done automatically.

Note

This node is still in an experimental phase as of 17 Dec 2014. Further testing and development are required before releasing the node.

Parameters

Exemplary Call

-
    node : PredictionVectorSource

Author:

Andrei Ignat (andrei_cristian.ignat@dfki.de)

Created:

2014/17/12

POSSIBLE NODE NAMES:  

• PredictionVectorSource
• PredictionVectorSourceNode

POSSIBLE INPUT TYPES:  

• PredictionVector

Class Components Summary

get_metadata(key)	Return the value corresponding to the given key from the dataset meta data of this source node.
input_types
register_input_node(node)	Register the given node as input
request_data_for_testing()	Returns the data that can be used for testing of subsequent nodes
request_data_for_training(use_test_data)	Returns the time windows that can be used for training of subsequent nodes
set_input_dataset(dataset)	Sets the dataset from which this node reads the data
train_sweep(use_test_data)	Performs the actual training of the node.
use_next_split()	Use the next split of the data into training and test data.

input_types = ['PredictionVector']

__init__(**kwargs)

set_input_dataset(dataset)

Sets the dataset from which this node reads the data

register_input_node(node)

Register the given node as input

use_next_split()

Use the next split of the data into training and test data. Returns True if more splits are available, otherwise False.

This method is useful for benchmarking

train_sweep(use_test_data)

Performs the actual training of the node. .. note:: Source nodes cannot be trained

request_data_for_training(use_test_data)

Returns the time windows that can be used for training of subsequent nodes

request_data_for_testing()

Returns the data that can be used for testing of subsequent nodes

get_metadata(key)

Return the value corresponding to the given key from the dataset meta data of this source node.