ENM SVM workflow used for optimization

Created: 2013-08-09 16:01:50 Last updated: 2013-09-04 07:20:28

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This workflow was used for the optimization of the SVM algorithm from the openModeller toolbox (http://openmodeller.sourceforge.net). The workflow uses 10-fold cross-validation and then calculates the average AUC, which can be used as fitness value during parameter optimization.

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Descriptions (1)

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Inputs (11)

Name	Description
input_points	This input takes a text file containing species occurrence points. The file must be formatted according to the Darwin Core archive standard: each line corresponds to a different record, values are separated by comma, and the first line must be a header (also separated by comma) containing the Darwin Core terms for each column. The following terms are mandatory to run this workflow: occurrenceID, nameComplete, decimalLongitude and decimalLatitude. Only records for the first species will be used.
SvmType	Type of SVM: 0 = C-SVC, 1 = Nu-SVC, 2 = one-class SVM
KernelType	Type of kernel function: 0 = linear: u'v , 1 = polynomial: (gammau'v + coef0)^degree , 2 = radial basis function: exp(-gamma\|u-v\|^2)
Degree	Degree in kernel function (only for polynomial kernels). Domain: [0, oo)
Gamma	Gamma in kernel function (only for polynomial and radial basis kernels). When set to zero, the default value will actually be 1/k, where k is the number of layers. Domain: (oo, oo)
Coef0	Coef0 in kernel function (only for polynomial kernels). Domain: (oo, oo).
C	Cost (only for C-SVC types). Domain: (0.0, oo)
Nu	Nu (only for Nu-SVC). Domain: (0.0, 1.0]
ProbabilisticOutput	Indicates if the output should be a probability instead of a binary response (only available for C-SVC and Nu-SVC). Possible values: 0 or 1.
NumberOfPseudoAbsences	Number of pseudo absences to be generated when the SVM type is C-SVC or Nu-SVC. Zero means the same number of presence points.
mask

Processors (8)

Name	Type	Description
select_algorithm	workflow
parse_input_points	workflow
select_layers	workflow
allocate_points	workflow
create_and_test_model	workflow
extract_aucs	workflow
calculate_mean	workflow
Flatten_List	localworker	Script flatten(inputs, outputs, depth) { for (i = inputs.iterator(); i.hasNext();) { element = i.next(); if (element instanceof Collection && depth > 0) { flatten(element, outputs, depth - 1); } else { outputs.add(element); } } } outputlist = new ArrayList(); flatten(inputlist, outputlist, 1);

Beanshells (6)

Name	Inputs	Outputs
allocate_points	csv_points id_idx long_idx lat_idx folds	training_points testing_points all_points num_points
create_algorithm_xml	algorithm_id algorithm_version svm_type kernel_type degree gamma coef0 c nu probabilistic_output number_of_pseudo_absences	createmodel_algorithm
calculate_mean	auc_list	mean_auc
parse_header	csv_content	name_idx id_idx long_idx lat_idx
get_first_taxon	csv_content name_idx	taxon_name
extract_taxon_points	csv_content name_idx taxon_name	taxon_points

Outputs (1)

Name	Description
auc

Datalinks (23)

Source	Sink
SvmType	select_algorithm:SvmType
KernelType	select_algorithm:KernelType
Degree	select_algorithm:Degree
Gamma	select_algorithm:Gamma
Coef0	select_algorithm:Coef0
C	select_algorithm:C
Nu	select_algorithm:Nu
ProbabilisticOutput	select_algorithm:ProbabilisticOutput
NumberOfPseudoAbsences	select_algorithm:NumberOfPseudoAbsences
input_points	parse_input_points:csv_content
parse_input_points:id_idx	allocate_points:id_idx
parse_input_points:lat_idx	allocate_points:lat_idx
parse_input_points:long_idx	allocate_points:long_idx
parse_input_points:taxon_points	allocate_points:csv_points
select_algorithm:algorithm	create_and_test_model:algorithm_xml
allocate_points:testing_points	create_and_test_model:testing_points
select_layers:selected_layers	create_and_test_model:layers_str
allocate_points:training_points	create_and_test_model:training_points
mask	create_and_test_model:mask
create_and_test_model:test_model_statistics	extract_aucs:test_statistics_xml
Flatten_List:outputlist	calculate_mean:auc_list
extract_aucs:auc	Flatten_List:inputlist
calculate_mean:mean_auc	auc

Coordinations (1)

Controller	Target
create_and_test_model	extract_aucs

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Taverna 2

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Sonja Holl

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enm
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optimization
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svm
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workflow

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2411 viewings

1215 downloads

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ENM SVM workflow used for optimization

Created by Sonja Holl on Friday 09 August 2013 16:01:49 (UTC)

Last edited by Sonja Holl on Wednesday 04 September 2013 07:20:27 (UTC)

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This workflow was used for the optimization of the Maxent algorithm from the openModeller toolbox (http://openmodeller.sourceforge.net/). The workflow uses 10-fold cross-validation and then calculates the average AUC, which can be used as fitness value during parameter optimization.

Created: 2013-08-09 | Last updated: 2013-09-04

Credits: Sonja Holl Renato De Giovanni

ENM SVM workflow used for optimization

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