sklearn_model_validation: model_validation.xml comparison

comparison model_validation.xml @ 2:dd502cb0d567 draft

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

author	bgruening
date	Wed, 30 May 2018 08:27:01 -0400
parents	333507faecab
children	424d8d21744d

comparison

equal deleted inserted replaced

-:02eadaaa4bf7
+:dd502cb0d567
 <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()
-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
+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__","'"))
-#if $model_validation_functions.selected_function == 'cross_validate':
+pipeline_steps.append( ('estimator', estimator) )
-res = validator(estimator, X, y, **options)
-rval = res["$model_validation_functions.return_type"]
+pipeline = Pipeline(pipeline_steps)
-#elif $model_validation_functions.selected_function == 'learning_curve':
+## Set up validator, run pipeline through validator and return results.
-options['train_sizes'] = eval(options['train_sizes'])
-train_sizes_abs, train_scores, test_scores = validator(estimator, X, y, **options)
+validator = params["model_validation_functions"]["selected_function"]
-rval = eval("$model_validation_functions.return_type")
+validator = getattr(sklearn.model_selection, validator)
-#elif $model_validation_functions.selected_function == 'permutation_test_score':
+selected_function = params["model_validation_functions"]["selected_function"]
-score, permutation_scores, pvalue = validator(estimator, X, y, **options)
+rval_type = params["model_validation_functions"].get("return_type", None)
-rval = eval("$model_validation_functions.return_type")
-if "$model_validation_functions.return_type" in ["score", "pvalue"]:
+if selected_function == 'cross_validate':
-rval = [rval]
+res = validator(pipeline, X, y, **options)
+rval = res[rval_type]
-#elif $model_validation_functions.selected_function == 'validation_curve':
+elif selected_function == 'learning_curve':
-options['param_range'] = eval(options['param_range'])
+options['train_sizes'] = eval(options['train_sizes'])
-train_scores, test_scores = validator(estimator, X, y, **options)
+train_sizes_abs, train_scores, test_scores = validator(pipeline, X, y, **options)
-rval = eval("$model_validation_functions.return_type")
+rval = eval(rval_type)
+elif selected_function == 'permutation_test_score':
-#else:
+score, permutation_scores, pvalue = validator(pipeline, X, y, **options)
-rval = validator(estimator, X, y, **options)
+rval = eval(rval_type)
-#end if
+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)
-rval.to_csv(path_or_buf="$outfile", sep='\t', header=False, index=False)
+if rval_type and rval_type == "cv_results_":
+rval.to_csv(path_or_buf="$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="feature_selection_estimator" />
+<expand macro="estimator_input_no_fit" />
-<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" />
+<expand macro="estimator_input_no_fit" />
-<conditional name="extra_estimator">
+<section name="options" title="Other Options" expanded="false">
-<expand macro="feature_selection_extra_estimator" />
+<!--groups-->
-<expand macro="feature_selection_estimator_choices" />
+<expand macro="model_validation_common_options" />
-</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" />
+<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="feature_selection_estimator" />
+<expand macro="estimator_input_no_fit" />
-<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" />
+<expand macro="estimator_input_no_fit" />
-<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" />
+<expand macro="estimator_input_no_fit" />
-<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" />
+<expand macro="estimator_input_no_fit" />
-<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>
+<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

Mercurial > repos > bgruening > sklearn_model_validation

comparison model_validation.xml @ 2:dd502cb0d567 draft