comparison model_validation.xml @ 2:eb4a0fccbb3f draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 4ed8c4f6ef9ece81797a398b17a99bbaf49a6978

1:33171e815126

        <configfile name="sklearn_model_validation_script">
            <![CDATA[
import sys
import json
import pandas
import pickle
import numpy as np
import sklearn.model_selection
from sklearn import svm, linear_model, ensemble

@COLUMNS_FUNCTION@

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

input_type = params["input_options"]["selected_input"]

        header=header,
        parse_dates=True
)
y=y.ravel()

validator = params["model_validation_functions"]["selected_function"]
validator = getattr(sklearn.model_selection, validator)
options = params["model_validation_functions"]["options"]
if 'scoring' in options and options['scoring'] == '':
    options['scoring'] = None

estimator=params["model_validation_functions"]["estimator"]
if params["model_validation_functions"]["extra_estimator"]["has_estimator"] == 'no':
    estimator = params["model_validation_functions"]["extra_estimator"]["new_estimator"]
estimator = eval(estimator.replace('__dq__', '"').replace("__sq__","'"))

#if $model_validation_functions.selected_function == 'cross_validate':
res = validator(estimator, X, y, **options)
rval = res["$model_validation_functions.return_type"]

#elif $model_validation_functions.selected_function == 'learning_curve':
options['train_sizes'] = eval(options['train_sizes'])
train_sizes_abs, train_scores, test_scores = validator(estimator, X, y, **options)
rval = eval("$model_validation_functions.return_type")

#elif $model_validation_functions.selected_function == 'permutation_test_score':
score, permutation_scores, pvalue = validator(estimator, X, y, **options)
rval = eval("$model_validation_functions.return_type")
if "$model_validation_functions.return_type" in ["score", "pvalue"]:
    rval = [rval]

#elif $model_validation_functions.selected_function == 'validation_curve':
options['param_range'] = eval(options['param_range'])
train_scores, test_scores = validator(estimator, X, y, **options)
rval = eval("$model_validation_functions.return_type")

#else:
rval = validator(estimator, X, y, **options)
#end if

rval = pandas.DataFrame(rval)
rval.to_csv(path_or_buf="$outfile", sep='\t', header=False, index=False)

            ]]>
        </configfile>
    </configfiles>
    <inputs>
        <conditional name="model_validation_functions">
            <param name="selected_function" type="select" label="Select a model validation function">
                <option value="cross_validate">cross_validate - Evaluate metric(s) by cross-validation and also record fit/score times</option>
                <option value="cross_val_predict">cross_val_predict - Generate cross-validated estimates for each input data point</option>
                <option value="cross_val_score">cross_val_score - Evaluate a score by cross-validation</option>
                <option value="learning_curve">learning_curve - Learning curve</option>
                <option value="permutation_test_score">permutation_test_score - Evaluate the significance of a cross-validated score with permutations</option>
                <option value="validation_curve">validation_curve - Validation curve</option>
            </param>
            <when value="cross_validate">
                <expand macro="feature_selection_estimator" />
                <conditional name="extra_estimator">
                    <expand macro="feature_selection_extra_estimator" />
                    <expand macro="feature_selection_estimator_choices" />
                </conditional>
                <section name="options" title="Other Options" expanded="false">
                    <!--groups-->
                    <expand macro="model_validation_common_options"/>
                    <expand macro="scoring"/>
                    <!--fit_params-->

                    <option value="fit_time">fit_time</option>
                    <option value="score_time">score_time</option>
                </param>
            </when>
            <when value="cross_val_predict">
                <expand macro="feature_selection_estimator" />
                <conditional name="extra_estimator">
                    <expand macro="feature_selection_extra_estimator" />
                    <expand macro="feature_selection_estimator_choices" />
                </conditional>
                <section name="options" title="Other Options" expanded="false">
                    <!--groups-->
                    <param argument="cv" type="integer" value="" optional="true" label="cv" help="The number of folds in a (Stratified)KFold" />
                    <expand macro="n_jobs"/>
                    <expand macro="verbose"/>
                    <!--fit_params-->
                    <param argument="pre_dispatch" type="integer" value="" optional="true" label="pre_dispatch" help="Controls the number of jobs that get dispatched during parallel execution" />
                    <param argument="method" type="select" label="Invokes the passed method name of the passed estimator">
                        <option value="predict" selected="true">predict</option>
                        <option value="predict_proba">predict_proba</option>
                    </param>
                </section>
            </when>
            <when value="cross_val_score">
                <expand macro="feature_selection_estimator" />
                <conditional name="extra_estimator">
                    <expand macro="feature_selection_extra_estimator" />
                    <expand macro="feature_selection_estimator_choices" />
                </conditional>
                <section name="options" title="Other Options" expanded="false">
                    <!--groups-->
                    <expand macro="model_validation_common_options"/>
                    <expand macro="scoring"/>
                    <!--fit_params-->
                    <expand macro="pre_dispatch"/>
                </section>
            </when>
            <when value="learning_curve">
                <expand macro="feature_selection_estimator" />
                <conditional name="extra_estimator">
                    <expand macro="feature_selection_extra_estimator" />
                    <expand macro="feature_selection_estimator_choices" />
                </conditional>
                <section name="options" title="Other Options" expanded="false">
                    <!--groups-->
                    <expand macro="model_validation_common_options"/>
                    <param argument="train_sizes" type="text" value="np.linspace(0.1, 1.0, 5)" label="train_sizes" help="Relative or absolute numbers of training examples that will be used to generate the learning curve"/>
                    <expand macro="scoring"/>

                    <option value="train_scores">train_scores</option>
                    <option value="test_scores">test_scores</option>
                </param>
            </when>
            <when value="permutation_test_score">
                <expand macro="feature_selection_estimator" />
                <conditional name="extra_estimator">
                    <expand macro="feature_selection_extra_estimator" />
                    <expand macro="feature_selection_estimator_choices" />
                </conditional>
                <section name="options" title="Other Options" expanded="false">
                    <!--groups-->
                    <expand macro="model_validation_common_options"/>
                    <expand macro="scoring"/>
                    <param name="n_permutations" type="integer" value="100" optional="true" label="n_permutations" help="Number of times to permute y"/>

                    <option value="permutation_scores">permutation_scores</option>
                    <option value="pvalue">pvalue</option>
                </param>
            </when>
            <when value="validation_curve">
                <expand macro="feature_selection_estimator" />
                <conditional name="extra_estimator">
                    <expand macro="feature_selection_extra_estimator" />
                    <expand macro="feature_selection_estimator_choices" />
                </conditional>
                <section name="options" title="Other Options" expanded="false">
                    <param name="param_name" type="text" value="gamma" label="param_name" help="Name of the parameter that will be varied"/>
                    <param name="param_range" type="text" value="np.logspace(-6, -1, 5)" label="param_range" help="The values of the parameter that will be evaluated."/>
                    <!--groups-->                    
                    <expand macro="model_validation_common_options"/>

            <param name="header2" value="true" />
            <param name="col2" value="1"/>
            <param name="return_type" value="test_scores"/>
            <output name="outfile" file="mv_result06.tabular"/>
        </test>
    </tests>
    <help>
        <![CDATA[
**What it does**
This tool includes model validation functions to evaluate estimator performance in the cross-validation approach. This tool is based on

2:eb4a0fccbb3f

        <configfile name="sklearn_model_validation_script">
            <![CDATA[
import sys
import json
import pandas
import ast
import pickle
import numpy as np
import sklearn.model_selection
from sklearn import svm, linear_model, ensemble
from sklearn.pipeline import Pipeline

@COLUMNS_FUNCTION@

@FEATURE_SELECTOR_FUNCTION@

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

input_type = params["input_options"]["selected_input"]

        header=header,
        parse_dates=True
)
y=y.ravel()

options = params["model_validation_functions"]["options"]
if 'scoring' in options and options['scoring'] == '':
    options['scoring'] = None
if 'pre_dispatch' in options and options['pre_dispatch'] == '':
    options['pre_dispatch'] = None

pipeline_steps = []

## Set up feature selector and add to pipeline steps.
if params['feature_selection']['do_feature_selection'] == 'Yes':
    feature_selector = feature_selector(params['feature_selection']['feature_selection_algorithms'])
    pipeline_steps.append( ('feature_selector', feature_selector))

## Set up estimator and add to pipeline.
estimator=params["model_validation_functions"]["estimator"]
if params["model_validation_functions"]["extra_estimator"]["has_estimator"] == 'no':
    estimator = params["model_validation_functions"]["extra_estimator"]["new_estimator"]
estimator = eval(estimator.replace('__dq__', '"').replace("__sq__","'"))

pipeline_steps.append( ('estimator', estimator) )

pipeline = Pipeline(pipeline_steps)

## Set up validator, run pipeline through validator and return results.

validator = params["model_validation_functions"]["selected_function"]
validator = getattr(sklearn.model_selection, validator)

selected_function = params["model_validation_functions"]["selected_function"]
rval_type = params["model_validation_functions"].get("return_type", None)

if selected_function == 'cross_validate':
    res = validator(pipeline, X, y, **options)
    rval = res[rval_type]
elif selected_function == 'learning_curve':
    options['train_sizes'] = eval(options['train_sizes'])
    train_sizes_abs, train_scores, test_scores = validator(pipeline, X, y, **options)
    rval = eval(rval_type)
elif selected_function == 'permutation_test_score':
    score, permutation_scores, pvalue = validator(pipeline, X, y, **options)
    rval = eval(rval_type)
    if rval_type in ["score", "pvalue"]:
        rval = [rval]
elif selected_function == 'validation_curve':
    options['param_name'] = 'estimator__' + options['param_name']
    options['param_range'] = eval(options['param_range'])
    train_scores, test_scores = validator(pipeline, X, y, **options)
    rval = eval(rval_type)
elif selected_function == 'GridSearchCV':
    param_grid = params["model_validation_functions"]["param_grid"].replace("__sq__","'")\
        .replace('__dq__','"').replace("__oc__", "{").replace("__cc__", "}")\
        .replace("__ob__", "[").replace("__cb__", "]")
    param_grid = ast.literal_eval(param_grid)
    grid = validator(pipeline, param_grid, **options)
    grid.fit(X, y)
    rval = getattr(grid, rval_type)
    if rval_type in ["best_estimator_", "best_score_", "best_index_"]:
        rval = [rval]     
else:
    rval = validator(pipeline, X, y, **options)

rval = pandas.DataFrame(rval)
if rval_type and rval_type == "cv_results_":
    rval.to_csv(path_or_buf="$outfile", sep='\t', header=True, index=False)
else:
    rval.to_csv(path_or_buf="$outfile", sep='\t', header=False, index=False)

            ]]>
        </configfile>
    </configfiles>
    <inputs>
        <conditional name="feature_selection">
            <param name="do_feature_selection" type="select" label="Do feature selection?">
                <option value="No" selected="true"/>
                <option value="Yes"/>
            </param>
            <when value="No"/>
            <when value="Yes">
                <expand macro="feature_selection_all"/>
            </when>
        </conditional>
        <conditional name="model_validation_functions">
            <param name="selected_function" type="select" label="Select a model validation function">
                <option value="GridSearchCV">GridSearchCV - Exhaustive search over specified parameter values for an estimator </option>
                <option value="cross_validate">cross_validate - Evaluate metric(s) by cross-validation and also record fit/score times</option>
                <option value="cross_val_predict">cross_val_predict - Generate cross-validated estimates for each input data point</option>
                <option value="cross_val_score">cross_val_score - Evaluate a score by cross-validation</option>
                <option value="learning_curve">learning_curve - Learning curve</option>
                <option value="permutation_test_score">permutation_test_score - Evaluate the significance of a cross-validated score with permutations</option>
                <option value="validation_curve">validation_curve - Validation curve</option>
            </param>
            <when value="GridSearchCV">
                <expand macro="estimator_input_no_fit" />
                <param argument="param_grid" type="text" value="[{'feature_selector__k': [3, 5, 7, 9], 'estimator__C': [1, 10, 100, 1000]}]" label="param_grid" help="Dictionary with parameters names (string) as keys and lists of parameter settings to try as values, or a list of such dictionaries, in which case the grids spanned by each dictionary in the list are explored"/>
                <section name="options" title="Other Options" expanded="false">
                    <expand macro="scoring"/>
                    <expand macro="model_validation_common_options"/>
                    <expand macro="pre_dispatch" value="2*n_jobs" help="Controls the number of jobs that get dispatched during parallel execution"/>
                    <param argument="iid" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="iid" help="Data is identically distributed?"/>
                    <param argument="refit" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="refit" help="Refit an estimator using the best found parameters on the whole dataset."/>
                    <!--error_score-->
                    <param argument="return_train_score" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="return_train_score" help=""/>
                </section>
                <param name="return_type" type="select" label="Select a return type">
                    <option value="cv_results_" selected="true">cv_results_</option>
                    <option value="best_estimator_">best_estimator_</option>
                    <option value="best_score_">best_score_</option>
                    <option value="best_params_">best_params_</option>
                    <option value="best_index_">best_index_</option>
                </param>
            </when>
            <when value="cross_validate">
                <expand macro="estimator_input_no_fit" />
                <section name="options" title="Other Options" expanded="false">
                    <!--groups-->
                    <expand macro="model_validation_common_options"/>
                    <expand macro="scoring"/>
                    <!--fit_params-->

                    <option value="fit_time">fit_time</option>
                    <option value="score_time">score_time</option>
                </param>
            </when>
            <when value="cross_val_predict">
                <expand macro="estimator_input_no_fit" />
                <section name="options" title="Other Options" expanded="false">
                    <!--groups-->
                    <expand macro="model_validation_common_options" />
                    <!--fit_params-->
                    <expand macro="pre_dispatch" value="2*n_jobs’" help="Controls the number of jobs that get dispatched during parallel execution"/>
                    <param argument="method" type="select" label="Invokes the passed method name of the passed estimator">
                        <option value="predict" selected="true">predict</option>
                        <option value="predict_proba">predict_proba</option>
                    </param>
                </section>
            </when>
            <when value="cross_val_score">
                <expand macro="estimator_input_no_fit" />
                <section name="options" title="Other Options" expanded="false">
                    <!--groups-->
                    <expand macro="model_validation_common_options"/>
                    <expand macro="scoring"/>
                    <!--fit_params-->
                    <expand macro="pre_dispatch"/>
                </section>
            </when>
            <when value="learning_curve">
                <expand macro="estimator_input_no_fit" />
                <section name="options" title="Other Options" expanded="false">
                    <!--groups-->
                    <expand macro="model_validation_common_options"/>
                    <param argument="train_sizes" type="text" value="np.linspace(0.1, 1.0, 5)" label="train_sizes" help="Relative or absolute numbers of training examples that will be used to generate the learning curve"/>
                    <expand macro="scoring"/>

                    <option value="train_scores">train_scores</option>
                    <option value="test_scores">test_scores</option>
                </param>
            </when>
            <when value="permutation_test_score">
                <expand macro="estimator_input_no_fit" />
                <section name="options" title="Other Options" expanded="false">
                    <!--groups-->
                    <expand macro="model_validation_common_options"/>
                    <expand macro="scoring"/>
                    <param name="n_permutations" type="integer" value="100" optional="true" label="n_permutations" help="Number of times to permute y"/>

                    <option value="permutation_scores">permutation_scores</option>
                    <option value="pvalue">pvalue</option>
                </param>
            </when>
            <when value="validation_curve">
                <expand macro="estimator_input_no_fit" />
                <section name="options" title="Other Options" expanded="false">
                    <param name="param_name" type="text" value="gamma" label="param_name" help="Name of the parameter that will be varied"/>
                    <param name="param_range" type="text" value="np.logspace(-6, -1, 5)" label="param_range" help="The values of the parameter that will be evaluated."/>
                    <!--groups-->                    
                    <expand macro="model_validation_common_options"/>

            <param name="header2" value="true" />
            <param name="col2" value="1"/>
            <param name="return_type" value="test_scores"/>
            <output name="outfile" file="mv_result06.tabular"/>
        </test>
        <test>
            <param name="do_feature_selection" value="Yes"/>
            <param name="selected_algorithm" value="SelectKBest"/>
            <param name="score_func" value="chi2"/>
            <param name="selected_function" value="GridSearchCV"/>
            <param name="estimator" value="svm.SVR(kernel=&quot;linear&quot;)"/>
            <param name="has_estimator" value="yes"/>
            <param name="param_grid" value="[{'feature_selector__k': [3, 7], 'estimator__C': [1, 100]}]"/>
            <param name="return_type" value="best_score_"/>
            <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
            <param name="header1" value="true" />
            <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
            <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
            <param name="header2" value="true" />
            <param name="col2" value="1"/>
            <output name="outfile" file="mv_result07.tabular"/>
        </test>
    </tests>
    <help>
        <![CDATA[
**What it does**
This tool includes model validation functions to evaluate estimator performance in the cross-validation approach. This tool is based on