Mercurial > repos > bgruening > sklearn_model_validation
changeset 9:c6b3efcba7bd draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 76583c1fcd9d06a4679cc46ffaee44117b9e22cd
| author | bgruening |
|---|---|
| date | Sat, 04 Aug 2018 12:35:35 -0400 |
| parents | fd7a054ffdbd |
| children | 38df89cbed19 |
| files | main_macros.xml model_validation.xml test-data/feature_selection_result01 test-data/feature_selection_result08 test-data/feature_selection_result09 test-data/feature_selection_result12 test-data/pipeline01 test-data/pipeline02 test-data/pipeline03 test-data/pipeline04 test-data/pipeline05 test-data/pipeline06 test-data/pipeline07 test-data/pipeline08 test-data/searchCV01 |
| diffstat | 15 files changed, 521 insertions(+), 1006 deletions(-) [+] |
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--- a/main_macros.xml Fri Jul 13 03:56:45 2018 -0400 +++ b/main_macros.xml Sat Aug 04 12:35:35 2018 -0400 @@ -34,24 +34,20 @@ if inputs['selected_algorithm'] == 'SelectFromModel': if not options['threshold'] or options['threshold'] == 'None': options['threshold'] = None - if 'extra_estimator' in inputs and inputs['extra_estimator']['has_estimator'] == 'no_load': - with open("inputs['extra_estimator']['fitted_estimator']", 'rb') as model_handler: - fitted_estimator = pickle.load(model_handler) - new_selector = selector(fitted_estimator, prefit=True, **options) - else: - estimator=inputs["estimator"] - if inputs["extra_estimator"]["has_estimator"]=='no': - estimator=inputs["extra_estimator"]["new_estimator"] - estimator=eval(estimator.replace('__dq__', '"').replace("__sq__","'")) - new_selector = selector(estimator, **options) + if inputs['model_inputter']['input_mode'] == 'prefitted': + model_file = inputs['model_inputter']['fitted_estimator'] + with open(model_file, 'rb') as model_handler: + fitted_estimator = pickle.load(model_handler) + new_selector = selector(fitted_estimator, prefit=True, **options) + else: + estimator_json = inputs['model_inputter']["estimator_selector"] + estimator = get_estimator(estimator_json) + new_selector = selector(estimator, **options) elif inputs['selected_algorithm'] in ['RFE', 'RFECV']: if 'scoring' in options and (not options['scoring'] or options['scoring'] == 'None'): options['scoring'] = None - estimator=inputs["estimator"] - if inputs["extra_estimator"]["has_estimator"]=='no': - estimator=inputs["extra_estimator"]["new_estimator"] - estimator=eval(estimator.replace('__dq__', '"').replace("__sq__","'")) + estimator=get_estimator(inputs["estimator_selector"]) new_selector = selector(estimator, **options) elif inputs['selected_algorithm'] == "VarianceThreshold": @@ -104,11 +100,101 @@ return X, y </token> + <token name="@GET_SEARCH_PARAMS_FUNCTION@"> +def get_search_params(params_builder): + search_params = {} + + def safe_eval(literal): + + FROM_SCIPY_STATS = [ 'bernoulli', 'binom', 'boltzmann', 'dlaplace', 'geom', 'hypergeom', + 'logser', 'nbinom', 'planck', 'poisson', 'randint', 'skellam', 'zipf' ] + + FROM_NUMPY_RANDOM = [ 'beta', 'binomial', 'bytes', 'chisquare', 'choice', 'dirichlet', 'division', + 'exponential', 'f', 'gamma', 'geometric', 'gumbel', 'hypergeometric', + 'laplace', 'logistic', 'lognormal', 'logseries', 'mtrand', 'multinomial', + 'multivariate_normal', 'negative_binomial', 'noncentral_chisquare', 'noncentral_f', + 'normal', 'pareto', 'permutation', 'poisson', 'power', 'rand', 'randint', + 'randn', 'random', 'random_integers', 'random_sample', 'ranf', 'rayleigh', + 'sample', 'seed', 'set_state', 'shuffle', 'standard_cauchy', 'standard_exponential', + 'standard_gamma', 'standard_normal', 'standard_t', 'triangular', 'uniform', + 'vonmises', 'wald', 'weibull', 'zipf' ] + + # File opening and other unneeded functions could be dropped + UNWANTED = ['open', 'type', 'dir', 'id', 'str', 'repr'] + + # Allowed symbol table. Add more if needed. + new_syms = { + 'np_arange': getattr(np, 'arange'), + 'ensemble_ExtraTreesClassifier': getattr(ensemble, 'ExtraTreesClassifier') + } + + syms = make_symbol_table(use_numpy=False, **new_syms) + + for method in FROM_SCIPY_STATS: + syms['scipy_stats_' + method] = getattr(scipy.stats, method) + + for func in FROM_NUMPY_RANDOM: + syms['np_random_' + func] = getattr(np.random, func) + + for key in UNWANTED: + syms.pop(key, None) + + aeval = Interpreter(symtable=syms, use_numpy=False, minimal=False, + no_if=True, no_for=True, no_while=True, no_try=True, + no_functiondef=True, no_ifexp=True, no_listcomp=False, + no_augassign=False, no_assert=True, no_delete=True, + no_raise=True, no_print=True) + + return aeval(literal) + + for p in params_builder['param_set']: + search_p = p['search_param_selector']['search_p'] + if search_p.strip() == '': + continue + param_type = p['search_param_selector']['selected_param_type'] + + lst = search_p.split(":") + assert (len(lst) == 2), "Error, make sure there is one and only one colon in search parameter input." + literal = lst[1].strip() + ev = safe_eval(literal) + if param_type == "final_estimator_p": + search_params["estimator__" + lst[0].strip()] = ev + else: + search_params["preprocessing_" + param_type[5:6] + "__" + lst[0].strip()] = ev + + return search_params + </token> + + <token name="@GET_ESTIMATOR_FUNCTION@"> +def get_estimator(estimator_json): + estimator_module = estimator_json['selected_module'] + estimator_cls = estimator_json['selected_estimator'] + + if estimator_module == "xgboost": + cls = getattr(xgboost, estimator_cls) + else: + module = getattr(sklearn, estimator_module) + cls = getattr(module, estimator_cls) + + estimator = cls() + + estimator_params = estimator_json['text_params'].strip() + if estimator_params != "": + try: + params = ast.literal_eval('{' + estimator_params + '}') + except ValueError: + sys.exit("Unsupported parameter input: `%s`" %estimator_params) + estimator.set_params(**params) + + return estimator + </token> + <xml name="python_requirements"> <requirements> <requirement type="package" version="2.7">python</requirement> <requirement type="package" version="0.19.1">scikit-learn</requirement> <requirement type="package" version="0.22.0">pandas</requirement> + <requirement type="package" version="0.72.1">xgboost</requirement> <yield /> </requirements> </xml> @@ -907,53 +993,54 @@ </expand> </xml> - <xml name="estimator_input_no_fit"> - <expand macro="feature_selection_estimator" /> - <conditional name="extra_estimator"> - <expand macro="feature_selection_extra_estimator" /> - <expand macro="feature_selection_estimator_choices" /> - </conditional> + <xml name="fs_selectfrommodel_prefitted"> + <param name="input_mode" type="select" label="Construct a new estimator from a selection list?" > + <option value="new" selected="true">Yes</option> + <option value="prefitted">No. Load a prefitted estimator</option> + </param> + <when value="new"> + <expand macro="estimator_selector_all"/> + </when> + <when value="prefitted"> + <param name="fitted_estimator" type="data" format='zip' label="Load a prefitted estimator" /> + </when> + </xml> + + <xml name="fs_selectfrommodel_no_prefitted"> + <param name="input_mode" type="select" label="Construct a new estimator from a selection list?" > + <option value="new" selected="true">Yes</option> + </param> + <when value="new"> + <expand macro="estimator_selector_all"/> + </when> </xml> <xml name="feature_selection_all"> - <conditional name="feature_selection_algorithms"> + <conditional name="fs_algorithm_selector"> <param name="selected_algorithm" type="select" label="Select a feature selection algorithm"> - <option value="SelectFromModel" selected="true">SelectFromModel - Meta-transformer for selecting features based on importance weights</option> - <option value="GenericUnivariateSelect" selected="true">GenericUnivariateSelect - Univariate feature selector with configurable strategy</option> + <option value="SelectKBest" selected="true">SelectKBest - Select features according to the k highest scores</option> + <option value="SelectFromModel">SelectFromModel - Meta-transformer for selecting features based on importance weights</option> + <option value="GenericUnivariateSelect">GenericUnivariateSelect - Univariate feature selector with configurable strategy</option> <option value="SelectPercentile">SelectPercentile - Select features according to a percentile of the highest scores</option> - <option value="SelectKBest">SelectKBest - Select features according to the k highest scores</option> <option value="SelectFpr">SelectFpr - Filter: Select the p-values below alpha based on a FPR test</option> <option value="SelectFdr">SelectFdr - Filter: Select the p-values for an estimated false discovery rate</option> <option value="SelectFwe">SelectFwe - Filter: Select the p-values corresponding to Family-wise error rate</option> <option value="RFE">RFE - Feature ranking with recursive feature elimination</option> <option value="RFECV">RFECV - Feature ranking with recursive feature elimination and cross-validated selection of the best number of features</option> <option value="VarianceThreshold">VarianceThreshold - Feature selector that removes all low-variance features</option> - <!--option value="chi2">Compute chi-squared stats between each non-negative feature and class</option--> - <!--option value="f_classif">Compute the ANOVA F-value for the provided sample</option--> - <!--option value="f_regression">Univariate linear regression tests</option--> - <!--option value="mutual_info_classif">Estimate mutual information for a discrete target variable</option--> - <!--option value="mutual_info_regression">Estimate mutual information for a continuous target variable</option--> </param> <when value="SelectFromModel"> - <expand macro="feature_selection_estimator" /> - <conditional name="extra_estimator"> - <expand macro="feature_selection_extra_estimator" > - <option value="no_load">No, I will load a prefitted estimator</option> - </expand> - <expand macro="feature_selection_estimator_choices" > - <when value="no_load"> - <param name="fitted_estimator" type="data" format='zip' label="Load a prefitted estimator" /> - </when> - </expand> + <conditional name="model_inputter"> + <yield/> </conditional> - <section name="options" title="Other Options" expanded="True"> + <section name="options" title="Advanced Options" expanded="False"> <param argument="threshold" type="text" value="" optional="true" label="threshold" help="The threshold value to use for feature selection. e.g. 'mean', 'median', '1.25*mean'." /> <param argument="norm_order" type="integer" value="1" label="norm_order" help="Order of the norm used to filter the vectors of coefficients below threshold in the case where the coef_ attribute of the estimator is of dimension 2. " /> </section> </when> <when value="GenericUnivariateSelect"> <expand macro="feature_selection_score_function" /> - <section name="options" title="Other Options" expanded="True"> + <section name="options" title="Advanced Options" expanded="False"> <param argument="mode" type="select" label="Feature selection mode"> <option value="percentile">percentile</option> <option value="k_best">k_best</option> @@ -966,53 +1053,45 @@ </when> <when value="SelectPercentile"> <expand macro="feature_selection_score_function" /> - <section name="options" title="Other Options" expanded="True"> + <section name="options" title="Advanced Options" expanded="False"> <param argument="percentile" type="integer" value="10" optional="True" label="Percent of features to keep" /> </section> </when> <when value="SelectKBest"> <expand macro="feature_selection_score_function" /> - <section name="options" title="Other Options" expanded="True"> + <section name="options" title="Advanced Options" expanded="False"> <param argument="k" type="integer" value="10" optional="True" label="Number of top features to select" help="No 'all' option is supported." /> </section> </when> <when value="SelectFpr"> <expand macro="feature_selection_score_function" /> - <section name="options" title="Other Options" expanded="True"> + <section name="options" title="Advanced Options" expanded="False"> <param argument="alpha" type="float" value="" optional="True" label="Alpha" help="The highest p-value for features to be kept."/> </section> </when> <when value="SelectFdr"> <expand macro="feature_selection_score_function" /> - <section name="options" title="Other Options" expanded="True"> + <section name="options" title="Advanced Options" expanded="False"> <param argument="alpha" type="float" value="" optional="True" label="Alpha" help="The highest uncorrected p-value for features to keep."/> </section> </when> <when value="SelectFwe"> <expand macro="feature_selection_score_function" /> - <section name="options" title="Other Options" expanded="True"> + <section name="options" title="Advanced Options" expanded="False"> <param argument="alpha" type="float" value="" optional="True" label="Alpha" help="The highest uncorrected p-value for features to keep."/> </section> </when> <when value="RFE"> - <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="True"> + <expand macro="estimator_selector_all"/> + <section name="options" title="Advanced Options" expanded="False"> <param argument="n_features_to_select" type="integer" value="" optional="true" label="n_features_to_select" help="The number of features to select. If None, half of the features are selected." /> <param argument="step" type="float" value="1" label="step" optional="true" help="Default = 1. " /> <param argument="verbose" type="integer" value="0" label="verbose" help="Controls verbosity of output." /> </section> </when> <when value="RFECV"> - <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="True"> + <expand macro="estimator_selector_all"/> + <section name="options" title="Advanced Options" expanded="False"> <param argument="step" type="float" value="1" label="step" optional="true" help="Default = 1. " /> <param argument="cv" type="integer" value="" optional="true" label="cv" help="Determines the cross-validation splitting strategy" /> <param argument="scoring" type="text" value="" optional="true" label="scoring" help="A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y)."/> @@ -1021,7 +1100,7 @@ </section> </when> <when value="VarianceThreshold"> - <section name="options" title="Options" expanded="True"> + <section name="options" title="Options" expanded="False"> <param argument="threshold" type="float" value="" optional="True" label="Threshold" help="Features with a training-set variance lower than this threshold will be removed."/> </section> </when> @@ -1048,36 +1127,9 @@ </param> </xml> - <xml name="feature_selection_estimator"> - <param argument="estimator" type="select" label="Select an estimator" help="The base estimator from which the transformer is built."> - <option value="svm.SVR(kernel="linear")">svm.SVR(kernel="linear")</option> - <option value="svm.SVC(kernel="linear")">svm.SVC(kernel="linear")</option> - <option value="svm.LinearSVC(penalty="l1", dual=False, tol=1e-3)">svm.LinearSVC(penalty="l1", dual=False, tol=1e-3)</option> - <option value="linear_model.LassoCV()">linear_model.LassoCV()</option> - <option value="ensemble.RandomForestRegressor(n_estimators = 1000, random_state = 42)">ensemble.RandomForestRegressor(n_estimators = 1000, random_state = 42)</option> - </param> - </xml> - - <xml name="feature_selection_extra_estimator"> - <param name="has_estimator" type="select" label="Does your estimator on the list above?"> - <option value="yes">Yes, my estimator is on the list</option> - <option value="no">No, I need make a new estimator</option> - <yield/> - </param> - </xml> - - <xml name="feature_selection_estimator_choices"> - <when value="yes"> - </when> - <when value="no"> - <param name="new_estimator" type="text" value="" label="Make a new estimator" /> - </when> - <yield/> - </xml> - - <xml name="feature_selection_methods"> - <conditional name="select_methods"> - <param name="selected_method" type="select" label="Select an operation"> + <xml name="feature_selection_output_mothods"> + <conditional name="output_method_selector"> + <param name="selected_method" type="select" label="Select an output method:"> <option value="fit_transform">fit_transform - Fit to data, then transform it</option> <option value="get_support">get_support - Get a mask, or integer index, of the features selected</option> </param> @@ -1101,10 +1153,312 @@ <param argument="scoring" type="text" value="" optional="true" label="scoring" help="A metric used to evaluate the estimator"/> </xml> - <xml name="pre_dispatch" token_type="text" token_default_value="all" token_help="Number of predispatched jobs for parallel execution"> + <xml name="pre_dispatch" token_type="hidden" token_default_value="all" token_help="Number of predispatched jobs for parallel execution"> <param argument="pre_dispatch" type="@TYPE@" value="@DEFAULT_VALUE@" optional="true" label="pre_dispatch" help="@HELP@"/> </xml> + <xml name="search_cv_estimator"> + <param name="infile_pipeline" type="data" format="zip" label="Choose the dataset containing pipeline object:"/> + <section name="search_params_builder" title="Search parameters Builder" expanded="true"> + <repeat name="param_set" min="1" max="20" title="Parameter setting for search:"> + <conditional name="search_param_selector"> + <param name="selected_param_type" type="select" label="Choose the transformation the parameter belongs to"> + <option value="final_estimator_p" selected="true">Final estimator</option> + <option value="prep_1_p">Pre-processing step #1</option> + <option value="prep_2_p">Pre-processing step #2</option> + <option value="prep_3_p">Pre-processing step #3</option> + <option value="prep_4_p">Pre-processing step #4</option> + <option value="prep_5_p">Pre-processing step #5</option> + </param> + <when value="final_estimator_p"> + <expand macro="search_param_input" /> + </when> + <when value="prep_1_p"> + <expand macro="search_param_input" label="Pre_processing component #1 parameter:" help="One parameter per box. For example: with_centering: [True, False]."/> + </when> + <when value="prep_2_p"> + <expand macro="search_param_input" label="Pre_processing component #2 parameter:" help="One parameter per box. For example: k: [3, 5, 7, 9]. See bottom for more examples"/> + </when> + <when value="prep_3_p"> + <expand macro="search_param_input" label="Pre_processing component #3 parameter:" help="One parameter per box. For example: n_components: [1, 10, 100, 1000]. See bottom for more examples"/> + </when> + <when value="prep_4_p"> + <expand macro="search_param_input" label="Pre_processing component #4 parameter:" help="One parameter per box. For example: n_components: [1, 10, 100, 1000]. See bottom for more examples"/> + </when> + <when value="prep_5_p"> + <expand macro="search_param_input" label="Pre_processing component #5 parameter:" help="One parameter per box. For example: affinity: ['euclidean', 'l1', 'l2', 'manhattan']. See bottom for more examples"/> + </when> + </conditional> + </repeat> + </section> + </xml> + + <xml name="search_param_input" token_label="Estimator parameter:" token_help="One parameter per box. For example: C: [1, 10, 100, 1000]. See bottom for more examples"> + <param name="search_p" type="text" value="" size="100" optional="true" label="@LABEL@" help="@HELP@"> + <sanitizer> + <valid initial="default"> + <add value="'"/> + <add value="""/> + <add value="["/> + <add value="]"/> + </valid> + </sanitizer> + </param> + </xml> + + <xml name="search_cv_options"> + <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="If True, data is identically distributed across the folds"/> + <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=""/> + </xml> + + <xml name="estimator_selector_all"> + <conditional name="estimator_selector"> + <param name="selected_module" type="select" label="Choose the module that contains target estimator:" > + <option value="svm" selected="true">sklearn.svm</option> + <option value="linear_model">sklearn.linear_model</option> + <option value="ensemble">sklearn.ensemble</option> + <option value="naive_bayes">sklearn.naive_bayes</option> + <option value="tree">sklearn.tree</option> + <option value="neighbors">sklearn.neighbors</option> + <option value="xgboost">xgboost</option> + <!--more--> + </param> + <when value="svm"> + <param name="selected_estimator" type="select" label="Choose estimator class:"> + <option value="LinearSVC" selected="true">LinearSVC</option> + <option value="LinearSVR">LinearSVR</option> + <option value="NuSVC">NuSVC</option> + <option value="NuSVR">NuSVR</option> + <option value="OneClassSVM">OneClassSVM</option> + <option value="SVC">SVC</option> + <option value="SVR">SVR</option> + </param> + <expand macro="estimator_params_text"/> + </when> + <when value="linear_model"> + <param name="selected_estimator" type="select" label="Choose estimator class:"> + <option value="ARDRegression" selected="true">ARDRegression</option> + <option value="BayesianRidge">BayesianRidge</option> + <option value="ElasticNet">ElasticNet</option> + <option value="ElasticNetCV">ElasticNetCV</option> + <option value="HuberRegressor">HuberRegressor</option> + <option value="Lars">Lars</option> + <option value="LarsCV">LarsCV</option> + <option value="Lasso">Lasso</option> + <option value="LassoCV">LassoCV</option> + <option value="LassoLars">LassoLars</option> + <option value="LassoLarsCV">LassoLarsCV</option> + <option value="LassoLarsIC">LassoLarsIC</option> + <option value="LinearRegression">LinearRegression</option> + <option value="LogisticRegression">LogisticRegression</option> + <option value="LogisticRegressionCV">LogisticRegressionCV</option> + <option value="MultiTaskLasso">MultiTaskLasso</option> + <option value="MultiTaskElasticNet">MultiTaskElasticNet</option> + <option value="MultiTaskLassoCV">MultiTaskLassoCV</option> + <option value="MultiTaskElasticNetCV">MultiTaskElasticNetCV</option> + <option value="OrthogonalMatchingPursuit">OrthogonalMatchingPursuit</option> + <option value="OrthogonalMatchingPursuitCV">OrthogonalMatchingPursuitCV</option> + <option value="PassiveAggressiveClassifier">PassiveAggressiveClassifier</option> + <option value="PassiveAggressiveRegressor">PassiveAggressiveRegressor</option> + <option value="Perceptron">Perceptron</option> + <option value="RANSACRegressor">RANSACRegressor</option> + <option value="Ridge">Ridge</option> + <option value="RidgeClassifier">RidgeClassifier</option> + <option value="RidgeClassifierCV">RidgeClassifierCV</option> + <option value="RidgeCV">RidgeCV</option> + <option value="SGDClassifier">SGDClassifier</option> + <option value="SGDRegressor">SGDRegressor</option> + <option value="TheilSenRegressor">TheilSenRegressor</option> + </param> + <expand macro="estimator_params_text"/> + </when> + <when value="ensemble"> + <param name="selected_estimator" type="select" label="Choose estimator class:"> + <option value="AdaBoostClassifier" selected="true">AdaBoostClassifier</option> + <option value="AdaBoostRegressor">AdaBoostRegressor</option> + <option value="BaggingClassifier">BaggingClassifier</option> + <option value="BaggingRegressor">BaggingRegressor</option> + <option value="ExtraTreesClassifier">ExtraTreesClassifier</option> + <option value="ExtraTreesRegressor">ExtraTreesRegressor</option> + <option value="GradientBoostingClassifier">GradientBoostingClassifier</option> + <option value="GradientBoostingRegressor">GradientBoostingRegressor</option> + <option value="IsolationForest">IsolationForest</option> + <option value="RandomForestClassifier">RandomForestClassifier</option> + <option value="RandomForestRegressor">RandomForestRegressor</option> + <option value="RandomTreesEmbedding">RandomTreesEmbedding</option> + <option value="VotingClassifier">VotingClassifier</option> + </param> + <expand macro="estimator_params_text"/> + </when> + <when value="naive_bayes"> + <param name="selected_estimator" type="select" label="Choose estimator class:"> + <option value="BernoulliNB" selected="true">BernoulliNB</option> + <option value="GaussianNB">GaussianNB</option> + <option value="MultinomialNB">MultinomialNB</option> + </param> + <expand macro="estimator_params_text"/> + </when> + <when value="tree"> + <param name="selected_estimator" type="select" label="Choose estimator class:"> + <option value="DecisionTreeClassifier" selected="true">DecisionTreeClassifier</option> + <option value="DecisionTreeRegressor">DecisionTreeRegressor</option> + <option value="ExtraTreeClassifier">ExtraTreeClassifier</option> + <option value="ExtraTreeRegressor">ExtraTreeRegressor</option> + </param> + <expand macro="estimator_params_text"/> + </when> + <when value="neighbors"> + <param name="selected_estimator" type="select" label="Choose estimator class:"> + <option value="BallTree" selected="true">BallTree</option> + <option value="DistanceMetric">DistanceMetric</option> + <option value="KDTree">KDTree</option> + <option value="KernelDensity">KernelDensity</option> + <option value="KNeighborsClassifier">KNeighborsClassifier</option> + <option value="KNeighborsRegressor">KNeighborsRegressor</option> + <option value="LocalOutlierFactor">LocalOutlierFactor</option> + <option value="RadiusNeighborsClassifier">RadiusNeighborsClassifier</option> + <option value="RadiusNeighborsRegressor">RadiusNeighborsRegressor</option> + <option value="NearestCentroid">NearestCentroid</option> + <option value="NearestNeighbors">NearestNeighbors</option> + </param> + <expand macro="estimator_params_text"/> + </when> + <when value="xgboost"> + <param name="selected_estimator" type="select" label="Choose estimator class:"> + <option value="XGBRegressor" selected="true">XGBRegressor</option> + <option value="XGBClassifier">XGBClassifier</option> + </param> + <expand macro="estimator_params_text"/> + </when> + </conditional> + </xml> + + <xml name="estimator_params_text" token_label="Type in estimator parameters:" + token_help="Parameters in dictionary without braces ('{}'), e.g., 'C': 1, 'kernel': 'linear'. No double quotes. Leave this box blank for default estimator."> + <param name="text_params" type="text" value="" size="50" optional="true" label="@LABEL@" help="@HELP@"> + <sanitizer> + <valid initial="default"> + <add value="'"/> + </valid> + </sanitizer> + </param> + </xml> + + <xml name="kernel_approximation_all"> + <conditional name="kernel_approximation_selector"> + <param name="select_algorithm" type="select" label="Choose a kernel approximation algorithm:"> + <option value="Nystroem" selected="true">Nystroem</option> + <option value="RBFSampler">RBFSampler</option> + <option value="AdditiveChi2Sampler">AdditiveChi2Sampler</option> + <option value="SkewedChi2Sampler">SkewedChi2Sampler</option> + </param> + <when value="Nystroem"> + <expand macro="estimator_params_text" label="Type in kernel approximater parameters:" + help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'kernel': 'rbf'. No double quotes. Leave this box blank for class default."/> + </when> + <when value="RBFSampler"> + <expand macro="estimator_params_text" label="Type in kernel approximater parameters:" + help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'gamma': 1.0. No double quotes. Leave this box blank for class default."/> + </when> + <when value="AdditiveChi2Sampler"> + <expand macro="estimator_params_text" label="Type in kernel approximater parameters:" + help="Parameters in dictionary without braces ('{}'), e.g., 'sample_steps': 2, 'sample_interval': None. No double quotes. Leave this box blank for class default."/> + </when> + <when value="SkewedChi2Sampler"> + <expand macro="estimator_params_text" label="Type in kernel approximater parameters:" + help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'skewedness': 1.0. No double quotes. Leave this box blank for class default."/> + </when> + </conditional> + </xml> + + <xml name="matrix_decomposition_all"> + <conditional name="matrix_decomposition_selector"> + <param name="select_algorithm" type="select" label="Choose a matrix decomposition algorithm:"> + <option value="DictionaryLearning" selected="true">DictionaryLearning</option> + <option value="FactorAnalysis">FactorAnalysis</option> + <option value="FastICA">FastICA</option> + <option value="IncrementalPCA">IncrementalPCA</option> + <option value="KernelPCA">KernelPCA</option> + <option value="LatentDirichletAllocation">LatentDirichletAllocation</option> + <option value="MiniBatchDictionaryLearning">MiniBatchDictionaryLearning</option> + <option value="MiniBatchSparsePCA">MiniBatchSparsePCA</option> + <option value="NMF">NMF</option> + <option value="PCA">PCA</option> + <option value="SparsePCA">SparsePCA</option> + <option value="SparseCoder">SparseCoder</option> + <option value="TruncatedSVD">TruncatedSVD</option> + </param> + <when value="DictionaryLearning"> + <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:" + help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': None, 'alpha': 1.0. No double quotes. Leave this box blank for class default."/> + </when> + <when value="FactorAnalysis"> + <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:" + help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'random_state': 42. No double quotes. Leave this box blank for class default."/> + </when> + <when value="FastICA"> + <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:" + help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'random_state': 42. No double quotes. Leave this box blank for class default."/> + </when> + <when value="IncrementalPCA"> + <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:" + help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'whiten': False. No double quotes. Leave this box blank for class default."/> + </when> + <when value="KernelPCA"> + <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:" + help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'random_state': 42. No double quotes. Leave this box blank for class default."/> + </when> + <when value="LatentDirichletAllocation"> + <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:" + help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'random_state': 42. No double quotes. Leave this box blank for class default."/> + </when> + <when value="MiniBatchDictionaryLearning"> + <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:" + help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'random_state': 42. No double quotes. Leave this box blank for class default."/> + </when> + <when value="MiniBatchSparsePCA"> + <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:" + help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'random_state': 42. No double quotes. Leave this box blank for class default."/> + </when> + <when value="NMF"> + <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:" + help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'init': 'random'. No double quotes. Leave this box blank for class default."/> + </when> + <when value="PCA"> + <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:" + help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'random_state': 42. No double quotes. Leave this box blank for class default."/> + </when> + <when value="SparsePCA"> + <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:" + help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 100, 'random_state': 42. No double quotes. Leave this box blank for class default."/> + </when> + <when value="SparseCoder"> + <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:" + help="Parameters in dictionary without braces ('{}'), e.g., 'transform_algorithm': 'omp', 'transform_alpha': 1.0. No double quotes. Leave this box blank for class default."/> + </when> + <when value="TruncatedSVD"> + <expand macro="estimator_params_text" label="Type in maxtrix decomposition parameters:" + help="Parameters in dictionary without braces ('{}'), e.g., 'n_components': 2, 'algorithm': 'randomized'. No double quotes. Leave this box blank for default estimator."/> + </when> + </conditional> + </xml> + + <xml name="FeatureAgglomeration"> + <conditional name="FeatureAgglomeration_selector"> + <param name="select_algorithm" type="select" label="Choose the algorithm:"> + <option value="FeatureAgglomeration" selected="true">FeatureAgglomeration</option> + </param> + <when value="FeatureAgglomeration"> + <expand macro="estimator_params_text" label="Type in parameters:" + help="Parameters in dictionary without braces ('{}'), e.g., 'n_clusters': 2, 'affinity': 'euclidean'. No double quotes. Leave this box blank for class default."/> + </when> + </conditional> + </xml> <!-- Outputs --> <xml name="output"> @@ -1118,7 +1472,6 @@ </outputs> </xml> - <!--Citations--> <xml name="eden_citation"> <citations>
--- a/model_validation.xml Fri Jul 13 03:56:45 2018 -0400 +++ b/model_validation.xml Sat Aug 04 12:35:35 2018 -0400 @@ -21,13 +21,14 @@ import ast import pickle import numpy as np -import sklearn.model_selection -from sklearn import svm, linear_model, ensemble, preprocessing +import sklearn.feature_selection +from sklearn import preprocessing, model_selection, svm, linear_model, ensemble, naive_bayes, tree, neighbors from sklearn.pipeline import Pipeline @COLUMNS_FUNCTION@ +@GET_ESTIMATOR_FUNCTION@ +@FEATURE_SELECTOR_FUNCTION@ -@FEATURE_SELECTOR_FUNCTION@ input_json_path = sys.argv[1] with open(input_json_path, "r") as param_handler: @@ -85,14 +86,12 @@ ## 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']) + feature_selector = feature_selector(params['feature_selection']['fs_algorithm_selector']) 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__","'")) +estimator_json = params["model_validation_functions"]['estimator_selector'] +estimator = get_estimator(estimator_json) pipeline_steps.append( ('estimator', estimator) ) @@ -101,7 +100,7 @@ ## Set up validator, run pipeline through validator and return results. validator = params["model_validation_functions"]["selected_function"] -validator = getattr(sklearn.model_selection, validator) +validator = getattr(model_selection, validator) selected_function = params["model_validation_functions"]["selected_function"] rval_type = params["model_validation_functions"].get("return_type", None) @@ -123,24 +122,11 @@ 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) +rval.to_csv(path_or_buf="$outfile", sep='\t', header=False, index=False) ]]> </configfile> @@ -166,12 +152,13 @@ </param> <when value="No"/> <when value="Yes"> - <expand macro="feature_selection_all"/> + <expand macro="feature_selection_all"> + <expand macro="fs_selectfrommodel_no_prefitted"/> + </expand> </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> @@ -179,28 +166,8 @@ <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" /> + <expand macro="estimator_selector_all" /> <section name="options" title="Other Options" expanded="false"> <!--groups--> <expand macro="model_validation_common_options"/> @@ -216,7 +183,7 @@ </param> </when> <when value="cross_val_predict"> - <expand macro="estimator_input_no_fit" /> + <expand macro="estimator_selector_all" /> <section name="options" title="Other Options" expanded="false"> <!--groups--> <expand macro="model_validation_common_options" /> @@ -229,7 +196,7 @@ </section> </when> <when value="cross_val_score"> - <expand macro="estimator_input_no_fit" /> + <expand macro="estimator_selector_all" /> <section name="options" title="Other Options" expanded="false"> <!--groups--> <expand macro="model_validation_common_options"/> @@ -239,7 +206,7 @@ </section> </when> <when value="learning_curve"> - <expand macro="estimator_input_no_fit" /> + <expand macro="estimator_selector_all" /> <section name="options" title="Other Options" expanded="false"> <!--groups--> <expand macro="model_validation_common_options"/> @@ -257,7 +224,7 @@ </param> </when> <when value="permutation_test_score"> - <expand macro="estimator_input_no_fit" /> + <expand macro="estimator_selector_all" /> <section name="options" title="Other Options" expanded="false"> <!--groups--> <expand macro="model_validation_common_options"/> @@ -272,7 +239,7 @@ </param> </when> <when value="validation_curve"> - <expand macro="estimator_input_no_fit" /> + <expand macro="estimator_selector_all" /> <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."/> @@ -295,8 +262,8 @@ <tests> <test> <param name="selected_function" value="cross_validate"/> - <param name="estimator" value="linear_model.LassoCV()"/> - <param name="has_estimator" value="yes"/> + <param name="selected_module" value="linear_model"/> + <param name="selected_estimator" value="LassoCV"/> <param name="infile1" value="regression_train.tabular" ftype="tabular"/> <param name="col1" value="1,2,3,4,5"/> <param name="infile2" value="regression_train.tabular" ftype="tabular"/> @@ -305,8 +272,8 @@ </test> <test> <param name="selected_function" value="cross_val_predict"/> - <param name="estimator" value="linear_model.LassoCV()"/> - <param name="has_estimator" value="yes"/> + <param name="selected_module" value="linear_model"/> + <param name="selected_estimator" value="LassoCV"/> <param name="infile1" value="regression_train.tabular" ftype="tabular"/> <param name="col1" value="1,2,3,4,5"/> <param name="infile2" value="regression_train.tabular" ftype="tabular"/> @@ -315,8 +282,8 @@ </test> <test> <param name="selected_function" value="cross_val_score"/> - <param name="estimator" value="linear_model.LassoCV()"/> - <param name="has_estimator" value="yes"/> + <param name="selected_module" value="linear_model"/> + <param name="selected_estimator" value="LassoCV"/> <param name="infile1" value="regression_train.tabular" ftype="tabular"/> <param name="col1" value="1,2,3,4,5"/> <param name="infile2" value="regression_train.tabular" ftype="tabular"/> @@ -325,8 +292,8 @@ </test> <test> <param name="selected_function" value="learning_curve"/> - <param name="estimator" value="linear_model.LassoCV()"/> - <param name="has_estimator" value="yes"/> + <param name="selected_module" value="linear_model"/> + <param name="selected_estimator" value="LassoCV"/> <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"/> @@ -337,8 +304,8 @@ </test> <test> <param name="selected_function" value="permutation_test_score"/> - <param name="estimator" value="linear_model.LassoCV()"/> - <param name="has_estimator" value="yes"/> + <param name="selected_module" value="linear_model"/> + <param name="selected_estimator" value="LassoCV"/> <param name="infile1" value="regression_train.tabular" ftype="tabular"/> <param name="col1" value="1,2,3,4,5"/> <param name="infile2" value="regression_train.tabular" ftype="tabular"/> @@ -347,8 +314,9 @@ </test> <test> <param name="selected_function" value="validation_curve"/> - <param name="estimator" value="svm.SVC(kernel="linear")"/> - <param name="has_estimator" value="yes"/> + <param name="selected_module" value="svm"/> + <param name="selected_estimator" value="SVC"/> + <param name="text_params" value="'kernel': 'linear'"/> <param name="infile1" value="regression_X.tabular" ftype="tabular"/> <param name="header1" value="true" /> <param name="selected_column_selector_option" value="all_columns"/> @@ -358,79 +326,15 @@ <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="linear")"/> - <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="selected_column_selector_option" value="all_columns"/> - <param name="infile2" value="regression_y.tabular" ftype="tabular"/> - <param name="header2" value="true" /> - <param name="selected_column_selector_option2" value="all_columns"/> - <output name="outfile" > - <assert_contents> - <has_line line="0.7824428015300172" /> - </assert_contents> - </output> - </test> - <test> - <param name="do_pre_processing" value="Yes"/> - <param name="selected_pre_processor" value="RobustScaler"/> - <param name="do_feature_selection" value="Yes"/> - <param name="selected_algorithm" value="SelectKBest"/> - <param name="score_func" value="f_classif"/> - <param name="selected_function" value="GridSearchCV"/> - <param name="estimator" value="svm.SVR(kernel="linear")"/> - <param name="has_estimator" value="yes"/> - <param name="param_grid" value="[{'feature_selector__k': [3, 5, 7, 9], 'estimator__C': [1, 10, 100, 1000]}]"/> - <param name="return_type" value="best_score_"/> - <param name="infile1" value="regression_X.tabular" ftype="tabular"/> - <param name="header1" value="true" /> - <param name="selected_column_selector_option" value="all_columns"/> - <param name="infile2" value="regression_y.tabular" ftype="tabular"/> - <param name="header2" value="true" /> - <param name="selected_column_selector_option2" value="all_columns"/> - <output name="outfile" > - <assert_contents> - <has_line line="0.7938837807353147" /> - </assert_contents> - </output> - </test> - <test> - <param name="do_pre_processing" value="Yes"/> - <param name="selected_pre_processor" value="RobustScaler"/> - <param name="selected_function" value="GridSearchCV"/> - <param name="estimator" value="svm.SVR(kernel="linear")"/> - <param name="has_estimator" value="yes"/> - <param name="param_grid" value="[{'estimator__C': [1, 10, 100, 1000]}]"/> - <param name="return_type" value="best_score_"/> - <param name="infile1" value="regression_X.tabular" ftype="tabular"/> - <param name="header1" value="true" /> - <param name="selected_column_selector_option" value="all_columns"/> - <param name="infile2" value="regression_y.tabular" ftype="tabular"/> - <param name="header2" value="true" /> - <param name="selected_column_selector_option2" value="all_columns"/> - <output name="outfile" > - <assert_contents> - <has_line line="0.7904476204861263" /> - </assert_contents> - </output> - </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 sklearn.model_selection package. -For information about classification metric functions and their parameter settings please refer to `Scikit-learn classification metrics`_. +For information about model validation functions and their parameter settings please refer to `Scikit-learn model_selection`_. -.. _`Scikit-learn classification metrics`: http://scikit-learn.org/stable/modules/model_evaluation.html#classification-metrics +.. _`Scikit-learn model_selection`: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection ]]> </help> <expand macro="sklearn_citation"/>
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