type_conversion¶

Module: `missions.nodes.type_manipulation.type_conversion`¶

Convert feature to prediction vectors and TimeSeries and vice versa

Known issues: No unit tests!

Inheritance diagram for pySPACE.missions.nodes.type_manipulation.type_conversion:

Inheritance diagram of pySPACE.missions.nodes.type_manipulation.type_conversion

Class Summary¶

`Prediction2FeaturesNode`([name])	Use the prediction values as features
`Features2PredictionNode`(class_labels, \\kwargs)	Use the feature vectors as prediction values
`FeatureVector2TimeSeriesNode`([reshape])	Convert feature vector to time series
`Feature2MonoTimeSeriesNode`([store, retrain, ...])	Convert feature vector to time series with only one time stamp
`MonoTimeSeries2FeatureNode`([store, retrain, ...])	Convert time series with only one time stamp to feature vector
`CastDatatypeNode`([datatype, selected_channels])	Changes the datatype of the data

Function Summary¶

uniquify_list(seq) Uniquify a list by preserving its original order

Classes¶

`Prediction2FeaturesNode`¶

class pySPACE.missions.nodes.type_manipulation.type_conversion.Prediction2FeaturesNode(name='', **kwargs)[source]¶

Bases: pySPACE.missions.nodes.base_node.BaseNode

Use the prediction values as features

This node converts the type PredictionVector to the type FeatureVector. This is needed, whenever one want to feed classification predictions into a node that expects feature vectors (e.g. gating functions).

Parameters

name:
String. A prefix of the new feature.

(optional, default: ‘’)

Exemplary Call
-
    node : Prediction2Features
    parameters :
        name : "SVM_"

POSSIBLE NODE NAMES:
Author:	Mario Krell (Mario.Krell@dfki.de)
Created:	2010/08/06
	Prediction2Features Prediction2FeaturesNode
POSSIBLE INPUT TYPES:
	PredictionVector

Class Components Summary

`_execute`(data)	Extract the prediction features from the given data
`get_output_type`(input_type[, as_string])
`input_types`

input_types = ['PredictionVector']¶

__init__(name='', **kwargs)[source]¶

_execute(data)[source]¶: Extract the prediction features from the given data

get_output_type(input_type, as_string=True)[source]¶

`Features2PredictionNode`¶

class pySPACE.missions.nodes.type_manipulation.type_conversion.Features2PredictionNode(class_labels, **kwargs)[source]¶

Bases: pySPACE.missions.nodes.base_node.BaseNode

Use the feature vectors as prediction values

This node converts the type PredictionVector to the type FeatureVector. The feature values are used as individual predictions and the labels are created based on the passed parameter “class_labels”.

Parameters

class_labels:
List of length two of class_labels

If a feature’s values is larger than 0, the second class label is used as the prediction vector’s label otherwise the first.

Exemplary Call
-
    node : Features2Prediction
    parameters :
        class_labels : ['Standard', 'Target']

POSSIBLE NODE NAMES:
Author:	Jan Hendrik Metzen (jhm@informatik.uni-bremen.de)
Created:	2010/089/24
	Features2PredictionNode Features2Prediction
POSSIBLE INPUT TYPES:
	FeatureVector

Class Components Summary

`_execute`(data)	Extract the prediction features from the given data
`get_output_type`(input_type[, as_string])
`input_types`

input_types = ['FeatureVector']¶

__init__(class_labels, **kwargs)[source]¶

_execute(data)[source]¶: Extract the prediction features from the given data

get_output_type(input_type, as_string=True)[source]¶

`FeatureVector2TimeSeriesNode`¶

class pySPACE.missions.nodes.type_manipulation.type_conversion.FeatureVector2TimeSeriesNode(reshape=False, **kwargs)[source]¶

Bases: pySPACE.missions.nodes.base_node.BaseNode

Convert feature vector to time series

This node converts the type PredictionVector to TimeSeries. The feature values are extracted and put into their respective place of sensor name and time. The sampling_frequency is also calculated.

Parameters

reshape:
Assuming, that the data is in a simple structure (the features are sorted first by sensors and second by time), a simple reshape is required and no complex iteration over all entries. This speeds up the transformation and is turned on by this parameter.

If you are unsure, just leave the parameter as it is. With the first incoming sample, the structure will be checked and if possible the parameter changed.

If the structure of your data changes, you should reset this node.

(optional, default: False)

Exemplary Call

-
    node : FeatureVector2TimeSeries

POSSIBLE NODE NAMES:
Author:	Mario Michael Krell
Created:	2011/09/23
Refactored:	2013/04/24
	FeatureVector2TimeSeries Feature2TimeSeries LabeledFeature2TimeSeries FeatureVector2TimeSeriesNode
POSSIBLE INPUT TYPES:
	FeatureVector

Class Components Summary

`_execute`(data)	Extract feature values from and match it to their respective sensor name and time
`get_output_type`(input_type[, as_string])
`input_types`

input_types = ['FeatureVector']¶

__init__(reshape=False, **kwargs)[source]¶

_execute(data)[source]¶: Extract feature values from and match it to their respective sensor name and time

get_output_type(input_type, as_string=True)[source]¶

`Feature2MonoTimeSeriesNode`¶

class pySPACE.missions.nodes.type_manipulation.type_conversion.Feature2MonoTimeSeriesNode(store=False, retrain=False, input_dim=None, output_dim=None, dtype=None, kwargs_warning=True, **kwargs)[source]¶

Bases: pySPACE.missions.nodes.base_node.BaseNode

Convert feature vector to time series with only one time stamp

This node converts the type FeatureVector to TimeSeries. No real mapping of the features to the corresponding times series place is done. Instead every feature is identified with a channel.

The purpose of this node is to enable the user to use time series nodes on feature vectors, especially on feature vectors without any time structure.

Exemplary Call

-
    node : Feature2MonoTimeSeries

POSSIBLE NODE NAMES:
Author:	Mario Krell (mario.krell@dfki.de)
Created:	2012/08/31
	Feature2MonoTimeSeriesNode Feature2MonoTimeSeries
POSSIBLE INPUT TYPES:
	FeatureVector

Class Components Summary

`_execute`(data)	Identify feature names with channel names
`_invert`(data)	The invert function is needed for the inverse node
`get_output_type`(input_type[, as_string])
`input_types`
`is_invertable`()	Inversion is only a mapping of names

input_types = ['FeatureVector']¶

_execute(data)[source]¶: Identify feature names with channel names

is_invertable()[source]¶: Inversion is only a mapping of names

_invert(data)[source]¶: The invert function is needed for the inverse node

get_output_type(input_type, as_string=True)[source]¶

`MonoTimeSeries2FeatureNode`¶

class pySPACE.missions.nodes.type_manipulation.type_conversion.MonoTimeSeries2FeatureNode(store=False, retrain=False, input_dim=None, output_dim=None, dtype=None, kwargs_warning=True, **kwargs)[source]¶

Bases: pySPACE.missions.nodes.type_manipulation.type_conversion.Feature2MonoTimeSeriesNode

Convert time series with only one time stamp to feature vector

This node converts the type TimeSeries to FeatureVector. Each channel is mapped to one feature.

The purpose of this node is to enable the user to use time series nodes on feature vectors. Especially on feature vectors without any time structure. Therefore this node is the back transformation from the pySPACE.missions.nodes.type_manipulation.type_conversion.Feature2MonoTimeSeriesNode

Exemplary Call

-
    node : MonoTimeSeries2Feature

POSSIBLE NODE NAMES:
Author:	Mario Krell (mario.krell@dfki.de)
Created:	2012/08/31
	MonoTimeSeries2Feature MonoTimeSeries2FeatureNode
POSSIBLE INPUT TYPES:
	TimeSeries

Class Components Summary

`_execute`(data)	Identify channel names with feature names
`_invert`(data)	Irrelevant inversion introduced just for completeness
`get_output_type`(input_type[, as_string])
`input_types`

input_types = ['TimeSeries']¶

_execute(data)[source]¶: Identify channel names with feature names

_invert(data)[source]¶: Irrelevant inversion introduced just for completeness

get_output_type(input_type, as_string=True)[source]¶

`CastDatatypeNode`¶

class pySPACE.missions.nodes.type_manipulation.type_conversion.CastDatatypeNode(datatype=<type 'numpy.int16'>, selected_channels=None, **kwargs)[source]¶

Bases: pySPACE.missions.nodes.base_node.BaseNode

Changes the datatype of the data

Parameters

datatype:

Type to cast to.

(optional, default: “eval(numpy.float64)”)

Exemplary Call

-
    node : CastDatatype

POSSIBLE NODE NAMES:
Authors:	Hendrik Woehrle (hendrik.woehrle@dfki.de)
Created:	2012/03/29
	CastDatatypeNode CastDatatype
POSSIBLE INPUT TYPES:
	TimeSeries

Class Components Summary

`_execute`(data)	Apply the cast
`get_output_type`(input_type[, as_string])
`input_types`

input_types = ['TimeSeries']¶

__init__(datatype=<type 'numpy.int16'>, selected_channels=None, **kwargs)[source]¶

_execute(data)[source]¶: Apply the cast

get_output_type(input_type, as_string=True)[source]¶

Function¶

`uniquify_list()`¶

pySPACE.missions.nodes.type_manipulation.type_conversion.uniquify_list(seq)[source]¶: Uniquify a list by preserving its original order

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type_conversion¶

Module: `missions.nodes.type_manipulation.type_conversion`¶

Class Summary¶

Function Summary¶

Classes¶

`Prediction2FeaturesNode`¶

`Features2PredictionNode`¶

`FeatureVector2TimeSeriesNode`¶

`Feature2MonoTimeSeriesNode`¶

`MonoTimeSeries2FeatureNode`¶

`CastDatatypeNode`¶

Function¶

`uniquify_list()`¶

type_conversion¶

Module: missions.nodes.type_manipulation.type_conversion¶

Class Summary¶

Function Summary¶

Classes¶

Function¶

Module: `missions.nodes.type_manipulation.type_conversion`¶