feature_vector_source

Module: missions.nodes.source.feature_vector_source

Source for FeatureVector elements e.g. from arff or csv 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.feature_vector_source:

Inheritance diagram of pySPACE.missions.nodes.source.feature_vector_source

FeatureVectorSourceNode

class pySPACE.missions.nodes.source.feature_vector_source.FeatureVectorSourceNode(**kwargs)[source]

Bases: pySPACE.missions.nodes.base_node.BaseNode

Source for samples of type FeatureVector

This node reads FeatureVector elements accumulated in a feature_vector and passes them into the node_chain. As described in feature_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.

Parameters

Exemplary Call

- 
    node : Feature_Vector_Source
Author:

Jan Hendrik Metzen (jhm@informatik.uni-bremen.de)
Created:

2008/11/25
POSSIBLE NODE NAMES:
 
  • FeatureVectorSource
  • FeatureVectorSourceNode
  • Feature_Vector_Source
  • Labeled_Feature_Vector_Source
POSSIBLE INPUT TYPES:
 
  • FeatureVector

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 = ['FeatureVector']
__init__(**kwargs)[source]
set_input_dataset(dataset)[source]

Sets the dataset from which this node reads the data

register_input_node(node)[source]

Register the given node as input

use_next_split()[source]

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)[source]

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

request_data_for_training(use_test_data)[source]

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

request_data_for_testing()[source]

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

get_metadata(key)[source]

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