Mercurial > repos > bgruening > sklearn_estimator_attributes
diff estimator_attributes.xml @ 2:c411ff569a26 draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
author | bgruening |
---|---|
date | Fri, 09 Aug 2019 07:24:14 -0400 |
parents | 2ad4c2798be7 |
children | 27fabe5feedc |
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--- a/estimator_attributes.xml Tue Jul 09 19:32:22 2019 -0400 +++ b/estimator_attributes.xml Fri Aug 09 07:24:14 2019 -0400 @@ -30,8 +30,7 @@ from imblearn.pipeline import Pipeline as imbPipeline from sklearn.pipeline import Pipeline -sys.path.insert(0, '$__tool_directory__') -from utils import load_model, get_search_params +from galaxy_ml.utils import load_model, get_search_params warnings.simplefilter('ignore') @@ -51,7 +50,7 @@ print(repr(res)) with open('$outfile', 'wb') as f: pickle.dump(res, f, pickle.HIGHEST_PROTOCOL) -elif attribute in ['best_estimator_', 'init_']: +elif attribute in ['best_estimator_', 'init_', 'classifier_', 'regressor_']: res = getattr(est_obj, attribute) print(repr(res)) with open('$outfile', 'wb') as f: @@ -68,8 +67,13 @@ res = pandas.DataFrame(est_obj.cv_results_) res = res[sorted(res.columns)] res.to_csv('$outfile', sep='\t', index=False) +elif attribute == 'save_weights': + est_obj.save_weights('$outfile') else: - res = getattr(est_obj, attribute) + if attribute == 'get_signature': + res = est_obj.get_signature() + else: + res = getattr(est_obj, attribute) columns = [] if res.ndim == 1 or res.shape[-1] == 1: columns = [attribute] @@ -91,6 +95,9 @@ <option value="train_score_">Fitted estimator - train_score_ </option> <option value="oob_score_">Fitted estimator - oob_score_ </option> <option value="init_">Fitted estimator - init_ </option> + <option value="classifier_">Fitted BinarizeTargetClassifier - classifier_</option> + <option value="regressor_">Fitted BinarizeTargetRegressor - regressor_</option> + <option value="get_signature">Fitted IRAPSClassifier - get_signature</option> <option value="named_steps">Pipeline - named_steps </option> <option value="final_estimator">Pipeline - final_estimator </option> <option value="cv_results_">SearchCV - cv_results_ </option> @@ -102,6 +109,7 @@ <option value="ranking_">Feature_selection - ranking_ </option> <option value="n_features_">Feature_selection - n_features_ </option> <option value="grid_scores_">Feature_selection - grid_scores_ </option> + <option value="save_weights">KerasGClassifier/KerasGRegressor - save_weights</option> </param> </inputs> <outputs> @@ -112,6 +120,9 @@ <when input="attribute_type" value="final_estimator" format="zip" /> <when input="attribute_type" value="best_estimator_" format="zip" /> <when input="attribute_type" value="init_" format="zip" /> + <when input="attribute_type" value="classifier_" format="zip" /> + <when input="attribute_type" value="regressor_" format="zip" /> + <when input="attribute_type" value="save_weights" format="h5"/> </change_format> </data> </outputs> @@ -156,6 +167,11 @@ <param name="attribute_type" value="get_params"/> <output name="outfile" value="get_params.tabular"/> </test> + <test> + <param name="infile_object" value="fitted_keras_g_regressor01.zip" ftype="zip"/> + <param name="attribute_type" value="save_weights"/> + <output name="outfile" value="keras_save_weights01.h5" compare="sim_size" delta="5"/> + </test> </tests> <help> <![CDATA[