comparison numeric_clustering.xml @ 23:9d234733ccfd draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d

22:f1ef5f9320d7

my_class = getattr(sklearn.cluster, selected_algorithm)
cluster_object = my_class()
options = params["input_types"]["algorithm_options"]["options"]

cluster_object.set_params(**options)

#if $input_types.selected_input_type == "sparse":
data_matrix = mmread("$infile")
#else:
data = pandas.read_csv("$infile", sep='\t', header=0, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False )

                            <!--param argument="compute_full_tree"-->
                            <param argument="linkage" type="select" optional="true" label="Linkage" help="">
                                <option value="ward" selected="true">ward</option>
                                <option value="complete">complete</option>
                                <option value="average">average</option>
                            </param>
                            <!--param argument="pooling_func"-->
                        </section>
                    </when>
                </conditional>

            <param name="eigen_solver" value="arpack"/>
            <param name="n_neighbors" value="12"/>
            <param name="n_clusters" value="4"/>
            <param name="assign_labels" value="discretize"/>
            <param name="random_state" value="100"/>
            <output name="outfile" file="cluster_result12" compare="sim_size" />
        </test>
        <test>
            <param name="infile" value="numeric_values.tabular" ftype="tabular"/>
            <param name="selected_algorithm" value="SpectralClustering"/>
            <param name="selected_input_type" value="tabular"/>

23:9d234733ccfd

my_class = getattr(sklearn.cluster, selected_algorithm)
cluster_object = my_class()
options = params["input_types"]["algorithm_options"]["options"]

cluster_object.set_params(**options)
if 'n_jobs' in cluster_object.get_params():
    cluster_object.set_params( n_jobs=N_JOBS )

#if $input_types.selected_input_type == "sparse":
data_matrix = mmread("$infile")
#else:
data = pandas.read_csv("$infile", sep='\t', header=0, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False )

                            <!--param argument="compute_full_tree"-->
                            <param argument="linkage" type="select" optional="true" label="Linkage" help="">
                                <option value="ward" selected="true">ward</option>
                                <option value="complete">complete</option>
                                <option value="average">average</option>
                                <option value="single">single</option>
                            </param>
                            <!--param argument="pooling_func"-->
                        </section>
                    </when>
                </conditional>

            <param name="eigen_solver" value="arpack"/>
            <param name="n_neighbors" value="12"/>
            <param name="n_clusters" value="4"/>
            <param name="assign_labels" value="discretize"/>
            <param name="random_state" value="100"/>
            <output name="outfile" file="cluster_result12.txt" compare="sim_size" />
        </test>
        <test>
            <param name="infile" value="numeric_values.tabular" ftype="tabular"/>
            <param name="selected_algorithm" value="SpectralClustering"/>
            <param name="selected_input_type" value="tabular"/>