comparison pipeline.xml @ 3:0857964e07c2 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7

2:9ddacd0b8c8b

<tool id="sklearn_build_pipeline" name="Pipeline Builder" version="@VERSION@">
    <description>constructs a list of transforms and a final estimator</description>
    <macros>
        <import>main_macros.xml</import>
    </macros>
    <expand macro="python_requirements"/>
    <expand macro="macro_stdio"/>
    <version_command>echo "@VERSION@"</version_command>
    <command>
        <![CDATA[
        python "$sklearn_pipeline_script" '$inputs'

    <configfiles>
        <inputs name="inputs" />
        <configfile name="sklearn_pipeline_script">
            <![CDATA[
import sys
import json
import pickle
import pprint
import xgboost
import ast
import sklearn.feature_selection
from sklearn import ( preprocessing, svm, linear_model, ensemble, naive_bayes,
                    tree, neighbors, decomposition, kernel_approximation, cluster)
from sklearn.pipeline import Pipeline

@GET_ESTIMATOR_FUNCTION@
@FEATURE_SELECTOR_FUNCTION@

input_json_path = sys.argv[1]
with open(input_json_path, "r") as param_handler:
    params = json.load(param_handler)

            sys.exit("The pre-processing component type can't be None when the number of components is greater than 1.")
    if input_json['component_type'] == 'pre_processor':
        preprocessor = input_json["pre_processors"]["selected_pre_processor"]
        pre_processor_options = input_json["pre_processors"]["options"]
        my_class = getattr(preprocessing, preprocessor)
        return my_class(**pre_processor_options)
    if input_json['component_type'] == 'feature_selection':
        fs_obj = feature_selector(input_json['fs_algorithm_selector'])
        return fs_obj
    if input_json['component_type'] == 'decomposition':
        algorithm = input_json['matrix_decomposition_selector']['select_algorithm']
        obj = getattr(decomposition, algorithm)()
        options = input_json['matrix_decomposition_selector']['text_params'].strip()
        if options != "":
            options = ast.literal_eval('{' + options + '}')
            obj.set_params(**options)
        return obj
    if input_json['component_type'] == 'kernel_approximation':
        algorithm = input_json['kernel_approximation_selector']['select_algorithm']
        obj = getattr(kernel_approximation, algorithm)()
        options = input_json['kernel_approximation_selector']['text_params'].strip()
        if options != "":
            options = ast.literal_eval('{' + options + '}')
            obj.set_params(**options)
        return obj
    if input_json['component_type'] == 'FeatureAgglomeration':
        algorithm = input_json['FeatureAgglomeration_selector']['select_algorithm']
        obj = getattr(cluster, algorithm)()
        options = input_json['FeatureAgglomeration_selector']['text_params'].strip()
        if options != "":
            options = ast.literal_eval('{' + options + '}')
            obj.set_params(**options)
        return obj
if len(params['pipeline_component']) == 1:
    step_obj = get_component( params['pipeline_component'][0]['component_selector'])
    if step_obj:
        pipeline_steps.append( ('preprocessing_1', step_obj) )
else:

    </configfiles>
    <inputs>
        <repeat name="pipeline_component" min="1" max="5" title="Pre-processing step">
            <conditional name="component_selector">
                <param name="component_type" type="select" label="Choose the type of transformation:">
                    <option value="none" selected="true">None</option>
                    <option value="pre_processor">Sklearn Preprocessor</option>
                    <option value="feature_selection">Feature Selection</option>
                    <option value="decomposition">Matrix Decomposition</option>
                    <option value="kernel_approximation">Kernel Approximation</option>
                    <option value="FeatureAgglomeration">Agglomerate Features</option>
                </param>
                <when value="none"/>
                <when value="pre_processor">
                    <conditional name="pre_processors">
                        <expand macro="sparse_preprocessors_ext" />
                        <expand macro="sparse_preprocessor_options_ext" />
                    </conditional>

                <when value="kernel_approximation">
                    <expand macro="kernel_approximation_all"/>
                </when>
                <when value="FeatureAgglomeration">
                    <expand macro="FeatureAgglomeration"/>
                </when>
            </conditional>
        </repeat>
        <section name="final_estimator" title="Final Estimator" expanded="true">
            <expand macro="estimator_selector_all" />

                    </conditional>
                </conditional>
            </repeat>
            <param name="selected_module" value="svm"/>
            <param name="selected_estimator" value="SVR"/>
            <param name="text_params" value="'kernel': 'linear'"/>
            <output name="outfile" file="pipeline01" compare="sim_size" delta="1"/>
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="pre_processor"/>

            <conditional name="component_selector">
                <param name="component_type" value="None"/>
            </conditional>
            <param name="selected_module" value="ensemble"/>
            <param name="selected_estimator" value="RandomForestRegressor"/>
            <param name="text_params" value="'n_estimators': 100, 'random_state': 42"/>
            <output name="outfile" file="pipeline05" compare="sim_size" delta="1"/>
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="decomposition"/>

        <test>
            <conditional name="component_selector">
                <param name="component_type" value="kernel_approximation"/>
                    <conditional name="kernel_approximation_selector">
                        <param name="select_algorithm" value="RBFSampler"/>
                        <param name="text_params" value="'n_components': 10, 'gamma': 2.0"/>
                    </conditional>
            </conditional>
            <param name="selected_module" value="ensemble"/>
            <param name="selected_estimator" value="AdaBoostClassifier"/>
            <output name="outfile" file="pipeline07" compare="sim_size" delta="1"/>

        <test>
            <conditional name="component_selector">
                <param name="component_type" value="FeatureAgglomeration"/>
                    <conditional name="FeatureAgglomeration_selector">
                        <param name="select_algorithm" value="FeatureAgglomeration"/>
                        <param name="text_params" value="'n_clusters': 3, 'affinity': 'euclidean'"/>
                    </conditional>
            </conditional>
            <param name="selected_module" value="ensemble"/>
            <param name="selected_estimator" value="AdaBoostClassifier"/>
            <output name="outfile" file="pipeline08" compare="sim_size" delta="1"/>
        </test>
    </tests>
    <help>
        <![CDATA[
**What it does**
Constructs a pipeline that contains a list of transfroms and a final estimator. Pipeline assembles several steps
that can be cross-validated together while setting different parameters.
please refer to `Scikit-learn pipeline Pipeline`_.

**Pre-processing components** allow None, one or a combination of up to 5 transformations from `sklearn.preprocessing`_, `feature_selection`_, `decomposition`_, `kernel_approximation`_ and/or `cluster.FeatureAgglomeration`_.

**Estimator** selector supports estimators from `xgboost`_ and many scikit-learn modules, including `svm`_, `linear_model`_, `ensemble`_, `naive_bayes`_, `tree`_ and `neighbors`_.


.. _`Scikit-learn pipeline Pipeline`: http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html

.. _`sklearn.preprocessing`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.preprocessing
.. _`feature_selection`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_selection
.. _`decomposition`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.decomposition
.. _`kernel_approximation`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.kernel_approximation
.. _`cluster.FeatureAgglomeration`: http://scikit-learn.org/stable/modules/generated/sklearn.cluster.FeatureAgglomeration.html

        ]]>
    </help>
    <expand macro="sklearn_citation"/>
</tool>

3:0857964e07c2

<tool id="sklearn_build_pipeline" name="Pipeline Builder" version="@VERSION@">
    <description>constructs a list of transforms and a final estimator</description>
    <macros>
        <import>main_macros.xml</import>
    </macros>
    <expand macro="python_requirements">
        <requirement type="package" version="0.6">skrebate</requirement>
    </expand>
    <expand macro="macro_stdio"/>
    <version_command>echo "@VERSION@"</version_command>
    <command>
        <![CDATA[
        python "$sklearn_pipeline_script" '$inputs'

    <configfiles>
        <inputs name="inputs" />
        <configfile name="sklearn_pipeline_script">
            <![CDATA[
import sys
import os
import json
import pickle
import pprint
import skrebate
from sklearn import (preprocessing, svm, linear_model, ensemble, naive_bayes,
                    tree, neighbors, decomposition, kernel_approximation, cluster)
from sklearn.pipeline import Pipeline

execfile("$__tool_directory__/utils.py")

safe_eval = SafeEval()

input_json_path = sys.argv[1]
with open(input_json_path, "r") as param_handler:
    params = json.load(param_handler)

            sys.exit("The pre-processing component type can't be None when the number of components is greater than 1.")
    if input_json['component_type'] == 'pre_processor':
        preprocessor = input_json["pre_processors"]["selected_pre_processor"]
        pre_processor_options = input_json["pre_processors"]["options"]
        my_class = getattr(preprocessing, preprocessor)
        obj = my_class(**pre_processor_options)
    elif input_json['component_type'] == 'feature_selection':
        obj = feature_selector(input_json['fs_algorithm_selector'])
    elif input_json['component_type'] == 'decomposition':
        algorithm = input_json['matrix_decomposition_selector']['select_algorithm']
        obj = getattr(decomposition, algorithm)()
        options = input_json['matrix_decomposition_selector']['text_params'].strip()
        if options != "":
            options = safe_eval('dict(' + options + ')')
            obj.set_params(**options)
    elif input_json['component_type'] == 'kernel_approximation':
        algorithm = input_json['kernel_approximation_selector']['select_algorithm']
        obj = getattr(kernel_approximation, algorithm)()
        options = input_json['kernel_approximation_selector']['text_params'].strip()
        if options != "":
            options = safe_eval('dict(' + options + ')')
            obj.set_params(**options)
    elif input_json['component_type'] == 'FeatureAgglomeration':
        algorithm = input_json['FeatureAgglomeration_selector']['select_algorithm']
        obj = getattr(cluster, algorithm)()
        options = input_json['FeatureAgglomeration_selector']['text_params'].strip()
        if options != "":
            options = safe_eval('dict(' + options + ')')
            obj.set_params(**options)
    elif input_json['component_type'] == 'skrebate':
        algorithm = input_json['skrebate_selector']['select_algorithm']
        if algorithm == 'TuRF':
            obj = getattr(skrebate, algorithm)(core_algorithm='ReliefF')
        else:
            obj = getattr(skrebate, algorithm)()
        options = input_json['skrebate_selector']['text_params'].strip()
        if options != "":
            options = safe_eval('dict(' + options + ')')
            obj.set_params(**options)
    if 'n_jobs' in obj.get_params():
        obj.set_params( n_jobs=N_JOBS )
    return obj

if len(params['pipeline_component']) == 1:
    step_obj = get_component( params['pipeline_component'][0]['component_selector'])
    if step_obj:
        pipeline_steps.append( ('preprocessing_1', step_obj) )
else:

    </configfiles>
    <inputs>
        <repeat name="pipeline_component" min="1" max="5" title="Pre-processing step">
            <conditional name="component_selector">
                <param name="component_type" type="select" label="Choose the type of transformation:">
                    <option value="None" selected="true">None</option>
                    <option value="pre_processor">Sklearn Preprocessor</option>
                    <option value="feature_selection">Feature Selection</option>
                    <option value="decomposition">Matrix Decomposition</option>
                    <option value="kernel_approximation">Kernel Approximation</option>
                    <option value="FeatureAgglomeration">Agglomerate Features</option>
                    <option value="skrebate">Skrebate algorithm</option>
                </param>
                <when value="None"/>
                <when value="pre_processor">
                    <conditional name="pre_processors">
                        <expand macro="sparse_preprocessors_ext" />
                        <expand macro="sparse_preprocessor_options_ext" />
                    </conditional>

                <when value="kernel_approximation">
                    <expand macro="kernel_approximation_all"/>
                </when>
                <when value="FeatureAgglomeration">
                    <expand macro="FeatureAgglomeration"/>
                </when>
                <when value="skrebate">
                    <expand macro="skrebate"/>
                </when>
            </conditional>
        </repeat>
        <section name="final_estimator" title="Final Estimator" expanded="true">
            <expand macro="estimator_selector_all" />

                    </conditional>
                </conditional>
            </repeat>
            <param name="selected_module" value="svm"/>
            <param name="selected_estimator" value="SVR"/>
            <param name="text_params" value="kernel='linear'"/>
            <output name="outfile" file="pipeline01" compare="sim_size" delta="1"/>
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="pre_processor"/>

            <conditional name="component_selector">
                <param name="component_type" value="None"/>
            </conditional>
            <param name="selected_module" value="ensemble"/>
            <param name="selected_estimator" value="RandomForestRegressor"/>
            <param name="text_params" value="n_estimators=100, random_state=42"/>
            <output name="outfile" file="pipeline05" compare="sim_size" delta="1"/>
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="decomposition"/>

        <test>
            <conditional name="component_selector">
                <param name="component_type" value="kernel_approximation"/>
                    <conditional name="kernel_approximation_selector">
                        <param name="select_algorithm" value="RBFSampler"/>
                        <param name="text_params" value="n_components=10, gamma=2.0"/>
                    </conditional>
            </conditional>
            <param name="selected_module" value="ensemble"/>
            <param name="selected_estimator" value="AdaBoostClassifier"/>
            <output name="outfile" file="pipeline07" compare="sim_size" delta="1"/>

        <test>
            <conditional name="component_selector">
                <param name="component_type" value="FeatureAgglomeration"/>
                    <conditional name="FeatureAgglomeration_selector">
                        <param name="select_algorithm" value="FeatureAgglomeration"/>
                        <param name="text_params" value="n_clusters=3, affinity='euclidean'"/>
                    </conditional>
            </conditional>
            <param name="selected_module" value="ensemble"/>
            <param name="selected_estimator" value="AdaBoostClassifier"/>
            <output name="outfile" file="pipeline08" compare="sim_size" delta="1"/>
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="skrebate"/>
                    <conditional name="skrebate_selector">
                        <param name="select_algorithm" value="ReliefF"/>
                        <param name="text_params" value="n_features_to_select=3, n_neighbors=100"/>
                    </conditional>
            </conditional>
            <param name="selected_module" value="ensemble"/>
            <param name="selected_estimator" value="RandomForestRegressor"/>
            <output name="outfile" file="pipeline09" compare="sim_size" delta="1"/>
        </test>
        <test>
            <conditional name="component_selector">
                <param name="component_type" value="skrebate"/>
                    <conditional name="skrebate_selector">
                        <param name="select_algorithm" value="TuRF"/>
                        <param name="text_params" value=""/>
                    </conditional>
            </conditional>
            <param name="selected_module" value="ensemble"/>
            <param name="selected_estimator" value="RandomForestRegressor"/>
            <output name="outfile" file="pipeline10" compare="sim_size" delta="1"/>
        </test>
    </tests>
    <help>
        <![CDATA[
**What it does**
Constructs a pipeline that contains a list of transfroms and a final estimator. Pipeline assembles several steps
that can be cross-validated together while setting different parameters.
please refer to `Scikit-learn pipeline Pipeline`_.

**Pre-processing components** allow None, one or a combination of up to 5 transformations from `sklearn.preprocessing`_, `feature_selection`_, `decomposition`_, `kernel_approximation`_, `cluster.FeatureAgglomeration`_ and/or `skrebate`_.

**Estimator** selector supports estimators from `xgboost`_ and many scikit-learn modules, including `svm`_, `linear_model`_, `ensemble`_, `naive_bayes`_, `tree`_ and `neighbors`_.


.. _`Scikit-learn pipeline Pipeline`: http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html

.. _`sklearn.preprocessing`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.preprocessing
.. _`feature_selection`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_selection
.. _`decomposition`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.decomposition
.. _`kernel_approximation`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.kernel_approximation
.. _`cluster.FeatureAgglomeration`: http://scikit-learn.org/stable/modules/generated/sklearn.cluster.FeatureAgglomeration.html
.. _`skrebate`: https://epistasislab.github.io/scikit-rebate/using/

        ]]>
    </help>
    <expand macro="sklearn_citation"/>
</tool>