Mercurial > repos > bgruening > sklearn_model_validation
diff model_validation.xml @ 12:2c1851992069 draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d00173591e4a783a4c1cb2664e4bb192ab5414f7
author | bgruening |
---|---|
date | Fri, 17 Aug 2018 12:30:18 -0400 |
parents | e4ab6b0bdf37 |
children | badd86b9ce24 |
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--- a/model_validation.xml Tue Aug 07 05:48:38 2018 -0400 +++ b/model_validation.xml Fri Aug 17 12:30:18 2018 -0400 @@ -16,22 +16,16 @@ <configfile name="sklearn_model_validation_script"> <![CDATA[ import sys +import os import json import pandas -import re -import ast -import pickle import numpy as np -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@ -@SAFE_EVAL_FUNCTION@ -@GET_CV_FUNCTION@ +execfile("$__tool_directory__/utils.py") +safe_eval = SafeEval() input_json_path = sys.argv[1] with open(input_json_path, "r") as param_handler: @@ -74,8 +68,9 @@ options = params["model_validation_functions"]["options"] options['cv'] = get_cv( options['cv'] ) -if 'scoring' in options and options['scoring'] == '': - options['scoring'] = None +options['n_jobs'] = N_JOBS +if 'scoring' in options: + options['scoring'] = get_scoring(options['scoring']) if 'pre_dispatch' in options and options['pre_dispatch'] == '': options['pre_dispatch'] = None @@ -175,7 +170,7 @@ <section name="options" title="Other Options" expanded="false"> <!--groups--> <expand macro="model_validation_common_options"/> - <expand macro="scoring"/> + <expand macro="scoring_selection"/> <!--fit_params--> <expand macro="pre_dispatch"/> </section> @@ -204,7 +199,7 @@ <section name="options" title="Other Options" expanded="false"> <!--groups--> <expand macro="model_validation_common_options"/> - <expand macro="scoring"/> + <expand macro="scoring_selection"/> <!--fit_params--> <expand macro="pre_dispatch"/> </section> @@ -215,7 +210,7 @@ <!--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"/> + <expand macro="scoring_selection"/> <param argument="exploit_incremental_learning" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="exploit_incremental_learning" help="Whether to apply incremental learning to speed up fitting of the estimator if supported"/> <expand macro="pre_dispatch"/> <expand macro="shuffle" checked="false" label="shuffle" help="Whether to shuffle training data before taking prefixes"/> @@ -232,7 +227,7 @@ <section name="options" title="Other Options" expanded="false"> <!--groups--> <expand macro="model_validation_common_options"/> - <expand macro="scoring"/> + <expand macro="scoring_selection"/> <param name="n_permutations" type="integer" value="100" optional="true" label="n_permutations" help="Number of times to permute y"/> <expand macro="random_state"/> </section> @@ -249,7 +244,7 @@ <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"/> - <expand macro="scoring"/> + <expand macro="scoring_selection"/> <expand macro="pre_dispatch"/> </section> <param name="return_type" type="select" label="Select a return type"> @@ -320,7 +315,7 @@ <param name="selected_function" value="validation_curve"/> <param name="selected_module" value="svm"/> <param name="selected_estimator" value="SVC"/> - <param name="text_params" value="'kernel': 'linear'"/> + <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"/>