view EDeN_test.xml @ 11:bf63bd4cf462 draft default tip

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−<tool id="bg_eden_test" name="EDeN Test" version="0.1">
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−    <description></description>
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−    <macros>
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−        <import>eden_macros.xml</import>
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−    </macros>
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−    <expand macro="requirements" />
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−    <command>
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−        EDeN --action TEST
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−
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−        --input_data_file_name  $sparse_vector_infile
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−        --file_type "SPARSE_VECTOR"
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−        --binary_file_type
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−
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−        --model_file_name $model_infile
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−
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−        --minimal_output
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−
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−    </command>
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−    <inputs>
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−        <param format="eden_sparse_vector" name="sparse_vector_infile" type="data" label="Input File" help="Sparse Vector file, created with EDeN convert." />
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−        <param format="txt" name="model_infile" type="data" label="Input Model"
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−            help="Created with EDeN Train."/>
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−
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−        <expand macro="kernel_type_options" />
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−
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−        <expand macro="graph_types" />
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−
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−        <expand macro="normalization_kernel_hash_radius_dist_vertex" />
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−
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−    </inputs>
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−    <outputs>
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−        <data format="tabular" name="prediction" from_work_dir="prediction" label="EDeN on ${on_string}: Prediction"/>
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−        <data format="tabular" name="margin" from_work_dir="margin" label="EDeN on ${on_string}: Margin"/>
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−    </outputs>
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−    <tests>
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−        <test>
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−        </test>
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−    </tests>
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−    <help>
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−
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−.. class:: infomark
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−
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−**What it does** 
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−
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−The linear model is induced using the accelerated stochastic gradient descent technique by Léon Bottou and Yann LeCun.
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−When the target information is 0, a self-training algorithm is used to impute a positive or negative class to the unsupervised instances.
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−If the target information is imbalanced a minority class resampling technique is used to rebalance the training set.
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−
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−@references@
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−
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−    </help>
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−</tool>