Repository 'sklearn_searchcv'
hg clone https://toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_searchcv

Changeset 5:0987bc3904a0 (2018-09-29)
Previous changeset 4:2e6540c11251 (2018-08-23) Next changeset 6:7509d7059040 (2018-10-11)
Commit message:
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2a058459e6daf0486871f93845f00fdb4a4eaca1
modified:
main_macros.xml
search_model_validation.xml
test-data/gbc_model01
test-data/gbr_model01
test-data/glm_model01
test-data/glm_model02
test-data/glm_model03
test-data/glm_model04
test-data/glm_model05
test-data/glm_model06
test-data/glm_model07
test-data/glm_model08
test-data/lda_model01
test-data/lda_model02
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/pipeline09
test-data/pipeline10
test-data/qda_model01
test-data/qda_prediction_result01.tabular
test-data/rfc_model01
test-data/rfr_model01
test-data/searchCV01
test-data/svc_prediction_result03.tabular
utils.py
added:
sk_whitelist.json
test-data/nn_model01
test-data/nn_model02
test-data/nn_model03
test-data/pickle_blacklist
test-data/searchCV02
test-data/svc_model01
test-data/svc_model02
test-data/svc_model03
removed:
sk_whitelist.py
test-data/nn_model01.txt
test-data/nn_model02.txt
test-data/nn_model03.txt
test-data/svc_model01.txt
test-data/svc_model02.txt
test-data/svc_model03.txt
b
diff -r 2e6540c11251 -r 0987bc3904a0 main_macros.xml
--- a/main_macros.xml Thu Aug 23 16:14:26 2018 -0400
+++ b/main_macros.xml Sat Sep 29 07:26:39 2018 -0400
[
@@ -3,7 +3,7 @@
 
   <xml name="python_requirements">
       <requirements>
-          <requirement type="package" version="2.7">python</requirement>
+          <requirement type="package" version="3.6">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>
@@ -1408,7 +1408,7 @@
       <data format="tabular" name="outfile_predict">
           <filter>selected_tasks['selected_task'] == 'load'</filter>
       </data>
-      <data format="zip" name="outfile_fit">
+      <data format="zip" name="outfile_fit" label="${tool.name}.${selected_tasks.selected_algorithms.selected_algorithm}">
           <filter>selected_tasks['selected_task'] == 'train'</filter>
       </data>
     </outputs>
b
diff -r 2e6540c11251 -r 0987bc3904a0 search_model_validation.xml
--- a/search_model_validation.xml Thu Aug 23 16:14:26 2018 -0400
+++ b/search_model_validation.xml Sat Sep 29 07:26:39 2018 -0400
[
@@ -25,8 +25,9 @@
 from sklearn import model_selection
 from sklearn.exceptions import FitFailedWarning
 
-execfile("$__tool_directory__/sk_whitelist.py")
-execfile("$__tool_directory__/utils.py", globals())
+with open("$__tool_directory__/sk_whitelist.json", "r") as f:
+    sk_whitelist = json.load(f)
+exec(open("$__tool_directory__/utils.py").read(), globals())
 
 warnings.simplefilter('ignore')
 
@@ -95,19 +96,22 @@
     options['pre_dispatch'] = None
 
 with open(infile_pipeline, 'rb') as pipeline_handler:
-    pipeline = SafePickler.load(pipeline_handler)
+    pipeline = load_model(pipeline_handler)
 
 search_params = get_search_params(params_builder)
 searcher = optimizers(pipeline, search_params, **options)
 
-warnings.simplefilter('always', FitFailedWarning)
-with warnings.catch_warnings(record=True) as w:
-    try:
-        searcher.fit(X, y)
-    except ValueError:
-        pass
-    for warning in w:
-        print(repr(warning.message))
+if options['error_score'] == 'raise':
+    searcher.fit(X, y)
+else:
+    warnings.simplefilter('always', FitFailedWarning)
+    with warnings.catch_warnings(record=True) as w:
+        try:
+            searcher.fit(X, y)
+        except ValueError:
+            pass
+        for warning in w:
+            print(repr(warning.message))
 
 cv_result = pandas.DataFrame(searcher.cv_results_)
 cv_result.to_csv(path_or_buf=outfile_result, sep='\t', header=True, index=False)
@@ -169,13 +173,33 @@
             <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_result" >
+            <output name="outfile_result">
                 <assert_contents>
-                    <has_text_matching expression="[^/d]+0.7938837807353147[^/d]+{u'estimator__C': 1, u'preprocessing_2__k': 9}[^/d]+1" />
-                    <has_text text="0.0"/>
+                    <has_n_columns n="13"/>
+                    <has_text text="0.7938837807353147"/>
+                    <has_text text="{'estimator__C': 1, 'preprocessing_2__k': 9}"/>
                 </assert_contents>
             </output>
         </test>
+        <test expect_failure="true">
+            <param name="selected_search_scheme" value="GridSearchCV"/>
+            <param name="infile_pipeline" value="pipeline01" ftype="zip"/>
+            <conditional name="search_param_selector">
+                <param name="search_p" value="C: [1, 10, 100, 1000]"/>
+                <param name="selected_param_type" value="final_estimator_p"/>
+            </conditional>
+            <conditional name="search_param_selector">
+                <param name="search_p" value="k: [-1, 3, 5, 7, 9]"/>
+                <param name="selected_param_type" value="prep_2_p"/>
+            </conditional>
+            <param name="error_score" value="true"/>
+            <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"/>
+        </test>
         <test>
             <param name="selected_search_scheme" value="RandomizedSearchCV"/>
             <param name="infile_pipeline" value="pipeline01" ftype="zip"/>
@@ -372,7 +396,7 @@
             <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_estimator" file="searchCV01" compare="sim_size" delta="1"/>
+            <output name="outfile_estimator" file="searchCV02" compare="sim_size" delta="1"/>
         </test>
         <test>
             <param name="selected_search_scheme" value="GridSearchCV"/>
@@ -396,7 +420,7 @@
             <output name="outfile_result" >
                 <assert_contents>
                     <has_n_columns n="13" />
-                    <has_text text="0.05366527890058046"/>
+                    <has_text text="0.09003449195911103"/>
                 </assert_contents>
             </output>
         </test>
b
diff -r 2e6540c11251 -r 0987bc3904a0 sk_whitelist.json
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/sk_whitelist.json Sat Sep 29 07:26:39 2018 -0400
[
b'@@ -0,0 +1,753 @@\n+{ "SK_NAMES": [\n+    "sklearn._ASSUME_FINITE", "sklearn._isotonic._inplace_contiguous_isotonic_regression",\n+    "sklearn._isotonic._make_unique", "sklearn.base.BaseEstimator",\n+    "sklearn.base.BiclusterMixin", "sklearn.base.ClassifierMixin",\n+    "sklearn.base.ClusterMixin", "sklearn.base.DensityMixin",\n+    "sklearn.base.MetaEstimatorMixin", "sklearn.base.RegressorMixin",\n+    "sklearn.base.TransformerMixin", "sklearn.base._first_and_last_element",\n+    "sklearn.base._pprint", "sklearn.base.clone",\n+    "sklearn.base.is_classifier", "sklearn.base.is_regressor",\n+    "sklearn.clone", "sklearn.cluster.AffinityPropagation",\n+    "sklearn.cluster.AgglomerativeClustering", "sklearn.cluster.Birch",\n+    "sklearn.cluster.DBSCAN", "sklearn.cluster.FeatureAgglomeration",\n+    "sklearn.cluster.KMeans", "sklearn.cluster.MeanShift",\n+    "sklearn.cluster.MiniBatchKMeans", "sklearn.cluster.SpectralBiclustering",\n+    "sklearn.cluster.SpectralClustering", "sklearn.cluster.SpectralCoclustering",\n+    "sklearn.cluster._dbscan_inner.dbscan_inner", "sklearn.cluster._feature_agglomeration.AgglomerationTransform",\n+    "sklearn.cluster._hierarchical.WeightedEdge", "sklearn.cluster._hierarchical._get_parents",\n+    "sklearn.cluster._hierarchical._hc_get_descendent", "sklearn.cluster._hierarchical.average_merge",\n+    "sklearn.cluster._hierarchical.compute_ward_dist", "sklearn.cluster._hierarchical.hc_get_heads",\n+    "sklearn.cluster._hierarchical.max_merge", "sklearn.cluster._k_means._assign_labels_array",\n+    "sklearn.cluster._k_means._assign_labels_csr", "sklearn.cluster._k_means._centers_dense",\n+    "sklearn.cluster._k_means._centers_sparse", "sklearn.cluster._k_means._mini_batch_update_csr",\n+    "sklearn.cluster._k_means_elkan.k_means_elkan", "sklearn.cluster.affinity_propagation",\n+    "sklearn.cluster.affinity_propagation_.AffinityPropagation", "sklearn.cluster.affinity_propagation_.affinity_propagation",\n+    "sklearn.cluster.bicluster.BaseSpectral", "sklearn.cluster.bicluster.SpectralBiclustering",\n+    "sklearn.cluster.bicluster.SpectralCoclustering", "sklearn.cluster.bicluster._bistochastic_normalize",\n+    "sklearn.cluster.bicluster._log_normalize", "sklearn.cluster.bicluster._scale_normalize",\n+    "sklearn.cluster.birch.Birch", "sklearn.cluster.birch._CFNode",\n+    "sklearn.cluster.birch._CFSubcluster", "sklearn.cluster.birch._iterate_sparse_X",\n+    "sklearn.cluster.birch._split_node", "sklearn.cluster.dbscan",\n+    "sklearn.cluster.dbscan_.DBSCAN", "sklearn.cluster.dbscan_.dbscan",\n+    "sklearn.cluster.estimate_bandwidth", "sklearn.cluster.get_bin_seeds",\n+    "sklearn.cluster.hierarchical.AgglomerativeClustering", "sklearn.cluster.hierarchical.FeatureAgglomeration",\n+    "sklearn.cluster.hierarchical._TREE_BUILDERS", "sklearn.cluster.hierarchical._average_linkage",\n+    "sklearn.cluster.hierarchical._complete_linkage", "sklearn.cluster.hierarchical._fix_connectivity",\n+    "sklearn.cluster.hierarchical._hc_cut", "sklearn.cluster.hierarchical.linkage_tree",\n+    "sklearn.cluster.hierarchical.ward_tree", "sklearn.cluster.k_means",\n+    "sklearn.cluster.k_means_.FLOAT_DTYPES", "sklearn.cluster.k_means_.KMeans",\n+    "sklearn.cluster.k_means_.MiniBatchKMeans", "sklearn.cluster.k_means_._init_centroids",\n+    "sklearn.cluster.k_means_._k_init", "sklearn.cluster.k_means_._kmeans_single_elkan",\n+    "sklearn.cluster.k_means_._kmeans_single_lloyd", "sklearn.cluster.k_means_._labels_inertia",\n+    "sklearn.cluster.k_means_._labels_inertia_precompute_dense", "sklearn.cluster.k_means_._mini_batch_convergence",\n+    "sklearn.cluster.k_means_._mini_batch_step", "sklearn.cluster.k_means_._tolerance",\n+    "sklearn.cluster.k_means_._validate_center_shape", "sklearn.cluster.k_means_.k_means",\n+    "sklearn.cluster.k_means_.string_types", "sklearn.cluster.linkage_tree",\n+    "sklearn.cluster.mean_shift", "sklearn.cluster.mean_shift_.MeanShift",\n+    "sklearn.cluster.mean_shift_._mean_shift_single_seed", "sklearn.cluster'..b'.validation.check_X_y",\n+    "sklearn.utils.validation.check_array", "sklearn.utils.validation.check_consistent_length",\n+    "sklearn.utils.validation.check_is_fitted", "sklearn.utils.validation.check_memory",\n+    "sklearn.utils.validation.check_non_negative", "sklearn.utils.validation.check_random_state",\n+    "sklearn.utils.validation.check_symmetric", "sklearn.utils.validation.column_or_1d",\n+    "sklearn.utils.validation.has_fit_parameter", "sklearn.utils.validation.indexable",\n+    "sklearn.utils.weight_vector.WeightVector"\n+],\n+\n+  "SKR_NAMES": [\n+    "skrebate.MultiSURF", "skrebate.MultiSURFstar",\n+    "skrebate.ReliefF", "skrebate.SURF",\n+    "skrebate.SURFstar", "skrebate.TuRF",\n+    "skrebate.multisurf.MultiSURF", "skrebate.multisurfstar.MultiSURFstar",\n+    "skrebate.relieff.ReliefF", "skrebate.scoring_utils.MultiSURF_compute_scores",\n+    "skrebate.scoring_utils.MultiSURFstar_compute_scores", "skrebate.scoring_utils.ReliefF_compute_scores",\n+    "skrebate.scoring_utils.SURF_compute_scores", "skrebate.scoring_utils.SURFstar_compute_scores",\n+    "skrebate.scoring_utils.compute_score", "skrebate.scoring_utils.get_row_missing",\n+    "skrebate.scoring_utils.ramp_function", "skrebate.surf.SURF",\n+    "skrebate.surfstar.SURFstar", "skrebate.turf.TuRF"\n+  ],\n+\n+  "XGB_NAMES": [\n+    "xgboost.Booster", "xgboost.DMatrix",\n+    "xgboost.VERSION_FILE", "xgboost.XGBClassifier",\n+    "xgboost.XGBModel", "xgboost.XGBRegressor",\n+    "xgboost.callback._fmt_metric", "xgboost.callback._get_callback_context",\n+    "xgboost.callback.early_stop", "xgboost.callback.print_evaluation",\n+    "xgboost.callback.record_evaluation", "xgboost.callback.reset_learning_rate",\n+    "xgboost.compat.PANDAS_INSTALLED", "xgboost.compat.PY3",\n+    "xgboost.compat.SKLEARN_INSTALLED", "xgboost.compat.STRING_TYPES",\n+    "xgboost.compat.py_str", "xgboost.core.Booster",\n+    "xgboost.core.CallbackEnv", "xgboost.core.DMatrix",\n+    "xgboost.core.EarlyStopException", "xgboost.core.PANDAS_DTYPE_MAPPER",\n+    "xgboost.core.PANDAS_INSTALLED", "xgboost.core.PY3",\n+    "xgboost.core.STRING_TYPES", "xgboost.core.XGBoostError",\n+    "xgboost.core._check_call", "xgboost.core._load_lib",\n+    "xgboost.core._maybe_pandas_data", "xgboost.core._maybe_pandas_label",\n+    "xgboost.core.c_array", "xgboost.core.c_str",\n+    "xgboost.core.ctypes2buffer", "xgboost.core.ctypes2numpy",\n+    "xgboost.core.from_cstr_to_pystr", "xgboost.core.from_pystr_to_cstr",\n+    "xgboost.cv", "xgboost.f",\n+    "xgboost.libpath.XGBoostLibraryNotFound", "xgboost.libpath.find_lib_path",\n+    "xgboost.plot_importance", "xgboost.plot_tree",\n+    "xgboost.plotting._EDGEPAT", "xgboost.plotting._EDGEPAT2",\n+    "xgboost.plotting._LEAFPAT", "xgboost.plotting._NODEPAT",\n+    "xgboost.plotting._parse_edge", "xgboost.plotting._parse_node",\n+    "xgboost.plotting.plot_importance", "xgboost.plotting.plot_tree",\n+    "xgboost.plotting.to_graphviz", "xgboost.rabit.DTYPE_ENUM__",\n+    "xgboost.rabit.STRING_TYPES", "xgboost.rabit._init_rabit",\n+    "xgboost.rabit.allreduce", "xgboost.rabit.broadcast",\n+    "xgboost.rabit.finalize", "xgboost.rabit.get_processor_name",\n+    "xgboost.rabit.get_rank", "xgboost.rabit.get_world_size",\n+    "xgboost.rabit.init", "xgboost.rabit.tracker_print",\n+    "xgboost.rabit.version_number", "xgboost.sklearn.SKLEARN_INSTALLED",\n+    "xgboost.sklearn.XGBClassifier", "xgboost.sklearn.XGBModel",\n+    "xgboost.sklearn.XGBRegressor", "xgboost.sklearn._objective_decorator",\n+    "xgboost.to_graphviz", "xgboost.train",\n+    "xgboost.training.CVPack", "xgboost.training.SKLEARN_INSTALLED",\n+    "xgboost.training.STRING_TYPES", "xgboost.training._train_internal",\n+    "xgboost.training.aggcv", "xgboost.training.cv",\n+    "xgboost.training.mknfold", "xgboost.training.train"\n+  ],\n+\n+\n+  "NUMPY_NAMES": [\n+    "numpy.core.multiarray._reconstruct", "numpy.ndarray",\n+    "numpy.dtype", "numpy.core.multiarray.scalar",\n+    "numpy.random.__RandomState_ctor"\n+  ]\n+}\n\\ No newline at end of file\n'
b
diff -r 2e6540c11251 -r 0987bc3904a0 sk_whitelist.py
--- a/sk_whitelist.py Thu Aug 23 16:14:26 2018 -0400
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
b
b"@@ -1,757 +0,0 @@\n-# class or function names from scikit-learn\n-SK_NAMES = (\n-    'sklearn._ASSUME_FINITE', 'sklearn._isotonic._inplace_contiguous_isotonic_regression',\n-    'sklearn._isotonic._make_unique', 'sklearn.base.BaseEstimator',\n-    'sklearn.base.BiclusterMixin', 'sklearn.base.ClassifierMixin',\n-    'sklearn.base.ClusterMixin', 'sklearn.base.DensityMixin',\n-    'sklearn.base.MetaEstimatorMixin', 'sklearn.base.RegressorMixin',\n-    'sklearn.base.TransformerMixin', 'sklearn.base._first_and_last_element',\n-    'sklearn.base._pprint', 'sklearn.base.clone',\n-    'sklearn.base.is_classifier', 'sklearn.base.is_regressor',\n-    'sklearn.clone', 'sklearn.cluster.AffinityPropagation',\n-    'sklearn.cluster.AgglomerativeClustering', 'sklearn.cluster.Birch',\n-    'sklearn.cluster.DBSCAN', 'sklearn.cluster.FeatureAgglomeration',\n-    'sklearn.cluster.KMeans', 'sklearn.cluster.MeanShift',\n-    'sklearn.cluster.MiniBatchKMeans', 'sklearn.cluster.SpectralBiclustering',\n-    'sklearn.cluster.SpectralClustering', 'sklearn.cluster.SpectralCoclustering',\n-    'sklearn.cluster._dbscan_inner.dbscan_inner', 'sklearn.cluster._feature_agglomeration.AgglomerationTransform',\n-    'sklearn.cluster._hierarchical.WeightedEdge', 'sklearn.cluster._hierarchical._get_parents',\n-    'sklearn.cluster._hierarchical._hc_get_descendent', 'sklearn.cluster._hierarchical.average_merge',\n-    'sklearn.cluster._hierarchical.compute_ward_dist', 'sklearn.cluster._hierarchical.hc_get_heads',\n-    'sklearn.cluster._hierarchical.max_merge', 'sklearn.cluster._k_means._assign_labels_array',\n-    'sklearn.cluster._k_means._assign_labels_csr', 'sklearn.cluster._k_means._centers_dense',\n-    'sklearn.cluster._k_means._centers_sparse', 'sklearn.cluster._k_means._mini_batch_update_csr',\n-    'sklearn.cluster._k_means_elkan.k_means_elkan', 'sklearn.cluster.affinity_propagation',\n-    'sklearn.cluster.affinity_propagation_.AffinityPropagation', 'sklearn.cluster.affinity_propagation_.affinity_propagation',\n-    'sklearn.cluster.bicluster.BaseSpectral', 'sklearn.cluster.bicluster.SpectralBiclustering',\n-    'sklearn.cluster.bicluster.SpectralCoclustering', 'sklearn.cluster.bicluster._bistochastic_normalize',\n-    'sklearn.cluster.bicluster._log_normalize', 'sklearn.cluster.bicluster._scale_normalize',\n-    'sklearn.cluster.birch.Birch', 'sklearn.cluster.birch._CFNode',\n-    'sklearn.cluster.birch._CFSubcluster', 'sklearn.cluster.birch._iterate_sparse_X',\n-    'sklearn.cluster.birch._split_node', 'sklearn.cluster.dbscan',\n-    'sklearn.cluster.dbscan_.DBSCAN', 'sklearn.cluster.dbscan_.dbscan',\n-    'sklearn.cluster.estimate_bandwidth', 'sklearn.cluster.get_bin_seeds',\n-    'sklearn.cluster.hierarchical.AgglomerativeClustering', 'sklearn.cluster.hierarchical.FeatureAgglomeration',\n-    'sklearn.cluster.hierarchical._TREE_BUILDERS', 'sklearn.cluster.hierarchical._average_linkage',\n-    'sklearn.cluster.hierarchical._complete_linkage', 'sklearn.cluster.hierarchical._fix_connectivity',\n-    'sklearn.cluster.hierarchical._hc_cut', 'sklearn.cluster.hierarchical.linkage_tree',\n-    'sklearn.cluster.hierarchical.ward_tree', 'sklearn.cluster.k_means',\n-    'sklearn.cluster.k_means_.FLOAT_DTYPES', 'sklearn.cluster.k_means_.KMeans',\n-    'sklearn.cluster.k_means_.MiniBatchKMeans', 'sklearn.cluster.k_means_._init_centroids',\n-    'sklearn.cluster.k_means_._k_init', 'sklearn.cluster.k_means_._kmeans_single_elkan',\n-    'sklearn.cluster.k_means_._kmeans_single_lloyd', 'sklearn.cluster.k_means_._labels_inertia',\n-    'sklearn.cluster.k_means_._labels_inertia_precompute_dense', 'sklearn.cluster.k_means_._mini_batch_convergence',\n-    'sklearn.cluster.k_means_._mini_batch_step', 'sklearn.cluster.k_means_._tolerance',\n-    'sklearn.cluster.k_means_._validate_center_shape', 'sklearn.cluster.k_means_.k_means',\n-    'sklearn.cluster.k_means_.string_types', 'sklearn.cluster.linkage_tree',\n-    'sklearn.cluster.mean_shift', 'sklearn.cluster.mean_shift_.MeanShift',\n-    'sklearn.cluster.mean_shift_."..b"n.utils.validation.check_array', 'sklearn.utils.validation.check_consistent_length',\n-    'sklearn.utils.validation.check_is_fitted', 'sklearn.utils.validation.check_memory',\n-    'sklearn.utils.validation.check_non_negative', 'sklearn.utils.validation.check_random_state',\n-    'sklearn.utils.validation.check_symmetric', 'sklearn.utils.validation.column_or_1d',\n-    'sklearn.utils.validation.has_fit_parameter', 'sklearn.utils.validation.indexable',\n-    'sklearn.utils.weight_vector.WeightVector'\n-)\n-\n-\n-# class or function names from skrebate\n-SKR_NAMES = (\n-    'skrebate.MultiSURF', 'skrebate.MultiSURFstar',\n-    'skrebate.ReliefF', 'skrebate.SURF',\n-    'skrebate.SURFstar', 'skrebate.TuRF',\n-    'skrebate.multisurf.MultiSURF', 'skrebate.multisurfstar.MultiSURFstar',\n-    'skrebate.relieff.ReliefF', 'skrebate.scoring_utils.MultiSURF_compute_scores',\n-    'skrebate.scoring_utils.MultiSURFstar_compute_scores', 'skrebate.scoring_utils.ReliefF_compute_scores',\n-    'skrebate.scoring_utils.SURF_compute_scores', 'skrebate.scoring_utils.SURFstar_compute_scores',\n-    'skrebate.scoring_utils.compute_score', 'skrebate.scoring_utils.get_row_missing',\n-    'skrebate.scoring_utils.ramp_function', 'skrebate.surf.SURF',\n-    'skrebate.surfstar.SURFstar', 'skrebate.turf.TuRF'\n-)\n-\n-\n-# class or function names from xgboost\n-XGB_NAMES = (\n-    'xgboost.Booster', 'xgboost.DMatrix',\n-    'xgboost.VERSION_FILE', 'xgboost.XGBClassifier',\n-    'xgboost.XGBModel', 'xgboost.XGBRegressor',\n-    'xgboost.callback._fmt_metric', 'xgboost.callback._get_callback_context',\n-    'xgboost.callback.early_stop', 'xgboost.callback.print_evaluation',\n-    'xgboost.callback.record_evaluation', 'xgboost.callback.reset_learning_rate',\n-    'xgboost.compat.PANDAS_INSTALLED', 'xgboost.compat.PY3',\n-    'xgboost.compat.SKLEARN_INSTALLED', 'xgboost.compat.STRING_TYPES',\n-    'xgboost.compat.py_str', 'xgboost.core.Booster',\n-    'xgboost.core.CallbackEnv', 'xgboost.core.DMatrix',\n-    'xgboost.core.EarlyStopException', 'xgboost.core.PANDAS_DTYPE_MAPPER',\n-    'xgboost.core.PANDAS_INSTALLED', 'xgboost.core.PY3',\n-    'xgboost.core.STRING_TYPES', 'xgboost.core.XGBoostError',\n-    'xgboost.core._check_call', 'xgboost.core._load_lib',\n-    'xgboost.core._maybe_pandas_data', 'xgboost.core._maybe_pandas_label',\n-    'xgboost.core.c_array', 'xgboost.core.c_str',\n-    'xgboost.core.ctypes2buffer', 'xgboost.core.ctypes2numpy',\n-    'xgboost.core.from_cstr_to_pystr', 'xgboost.core.from_pystr_to_cstr',\n-    'xgboost.cv', 'xgboost.f',\n-    'xgboost.libpath.XGBoostLibraryNotFound', 'xgboost.libpath.find_lib_path',\n-    'xgboost.plot_importance', 'xgboost.plot_tree',\n-    'xgboost.plotting._EDGEPAT', 'xgboost.plotting._EDGEPAT2',\n-    'xgboost.plotting._LEAFPAT', 'xgboost.plotting._NODEPAT',\n-    'xgboost.plotting._parse_edge', 'xgboost.plotting._parse_node',\n-    'xgboost.plotting.plot_importance', 'xgboost.plotting.plot_tree',\n-    'xgboost.plotting.to_graphviz', 'xgboost.rabit.DTYPE_ENUM__',\n-    'xgboost.rabit.STRING_TYPES', 'xgboost.rabit._init_rabit',\n-    'xgboost.rabit.allreduce', 'xgboost.rabit.broadcast',\n-    'xgboost.rabit.finalize', 'xgboost.rabit.get_processor_name',\n-    'xgboost.rabit.get_rank', 'xgboost.rabit.get_world_size',\n-    'xgboost.rabit.init', 'xgboost.rabit.tracker_print',\n-    'xgboost.rabit.version_number', 'xgboost.sklearn.SKLEARN_INSTALLED',\n-    'xgboost.sklearn.XGBClassifier', 'xgboost.sklearn.XGBModel',\n-    'xgboost.sklearn.XGBRegressor', 'xgboost.sklearn._objective_decorator',\n-    'xgboost.to_graphviz', 'xgboost.train',\n-    'xgboost.training.CVPack', 'xgboost.training.SKLEARN_INSTALLED',\n-    'xgboost.training.STRING_TYPES', 'xgboost.training._train_internal',\n-    'xgboost.training.aggcv', 'xgboost.training.cv',\n-    'xgboost.training.mknfold', 'xgboost.training.train'\n-)\n-\n-\n-NUMPY_NAMES = (\n-    'numpy.core.multiarray._reconstruct', 'numpy.ndarray',\n-    'numpy.dtype', 'numpy.core.multiarray.scalar',\n-    'numpy.random.__RandomState_ctor'\n-)\n"
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--- a/test-data/nn_model01.txt Thu Aug 23 16:14:26 2018 -0400
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
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b
diff -r 2e6540c11251 -r 0987bc3904a0 test-data/nn_model02
b
Binary file test-data/nn_model02 has changed
b
diff -r 2e6540c11251 -r 0987bc3904a0 test-data/nn_model02.txt
--- a/test-data/nn_model02.txt Thu Aug 23 16:14:26 2018 -0400
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
[
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diff -r 2e6540c11251 -r 0987bc3904a0 test-data/nn_model03
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diff -r 2e6540c11251 -r 0987bc3904a0 test-data/nn_model03.txt
--- a/test-data/nn_model03.txt Thu Aug 23 16:14:26 2018 -0400
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
b
@@ -1,71 +0,0 @@
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\ No newline at end of file
b
diff -r 2e6540c11251 -r 0987bc3904a0 test-data/pickle_blacklist
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/pickle_blacklist Sat Sep 29 07:26:39 2018 -0400
b
@@ -0,0 +1,4 @@
+cos
+system
+(S'ls ~'
+tR.
\ No newline at end of file
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diff -r 2e6540c11251 -r 0987bc3904a0 test-data/qda_model01
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diff -r 2e6540c11251 -r 0987bc3904a0 test-data/qda_prediction_result01.tabular
--- a/test-data/qda_prediction_result01.tabular Thu Aug 23 16:14:26 2018 -0400
+++ b/test-data/qda_prediction_result01.tabular Sat Sep 29 07:26:39 2018 -0400
b
@@ -2,4 +2,4 @@
 0.015942057224 -0.7119585943469999 0.125502976978 -0.972218263337 0
 2.0869076882499997 0.929399321468 -2.1292408448400004 -1.9971402218799998 0
 1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 0
-0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1
+0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 0
b
diff -r 2e6540c11251 -r 0987bc3904a0 test-data/rfc_model01
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diff -r 2e6540c11251 -r 0987bc3904a0 test-data/rfr_model01
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diff -r 2e6540c11251 -r 0987bc3904a0 test-data/searchCV01
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diff -r 2e6540c11251 -r 0987bc3904a0 test-data/svc_model01
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diff -r 2e6540c11251 -r 0987bc3904a0 test-data/svc_model01.txt
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diff -r 2e6540c11251 -r 0987bc3904a0 test-data/svc_model02
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diff -r 2e6540c11251 -r 0987bc3904a0 test-data/svc_model02.txt
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diff -r 2e6540c11251 -r 0987bc3904a0 test-data/svc_model03
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diff -r 2e6540c11251 -r 0987bc3904a0 test-data/svc_model03.txt
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diff -r 2e6540c11251 -r 0987bc3904a0 test-data/svc_prediction_result03.tabular
--- a/test-data/svc_prediction_result03.tabular Thu Aug 23 16:14:26 2018 -0400
+++ b/test-data/svc_prediction_result03.tabular Sat Sep 29 07:26:39 2018 -0400
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diff -r 2e6540c11251 -r 0987bc3904a0 utils.py
--- a/utils.py Thu Aug 23 16:14:26 2018 -0400
+++ b/utils.py Sat Sep 29 07:26:39 2018 -0400
[
b'@@ -2,28 +2,27 @@\n import os\n import pandas\n import re\n-import cPickle as pickle\n+import pickle\n import warnings\n import numpy as np\n import xgboost\n import scipy\n import sklearn\n-import ast\n from asteval import Interpreter, make_symbol_table\n from sklearn import (cluster, decomposition, ensemble, feature_extraction, feature_selection,\n-                    gaussian_process, kernel_approximation, linear_model, metrics,\n+                    gaussian_process, kernel_approximation, metrics,\n                     model_selection, naive_bayes, neighbors, pipeline, preprocessing,\n                     svm, linear_model, tree, discriminant_analysis)\n \n-N_JOBS = int( os.environ.get(\'GALAXY_SLOTS\', 1) )\n+N_JOBS = int(os.environ.get(\'GALAXY_SLOTS\', 1))\n \n-class SafePickler(object):\n+\n+class SafePickler(pickle.Unpickler):\n     """\n     Used to safely deserialize scikit-learn model objects serialized by cPickle.dump\n     Usage:\n         eg.: SafePickler.load(pickled_file_object)\n     """\n-    @classmethod\n     def find_class(self, module, name):\n \n         bad_names = (\'and\', \'as\', \'assert\', \'break\', \'class\', \'continue\',\n@@ -39,11 +38,11 @@\n                     \'__init__\', \'func_globals\', \'func_code\', \'func_closure\',\n                     \'im_class\', \'im_func\', \'im_self\', \'gi_code\', \'gi_frame\',\n                     \'__asteval__\', \'f_locals\', \'__mro__\')\n-        good_names = (\'copy_reg._reconstructor\', \'__builtin__.object\')\n+        good_names = [\'copy_reg._reconstructor\', \'__builtin__.object\']\n \n         if re.match(r\'^[a-zA-Z_][a-zA-Z0-9_]*$\', name):\n             fullname = module + \'.\' + name\n-            if  (fullname in good_names)\\\n+            if (fullname in good_names)\\\n                 or  (   (   module.startswith(\'sklearn.\')\n                             or module.startswith(\'xgboost.\')\n                             or module.startswith(\'skrebate.\')\n@@ -51,26 +50,25 @@\n                             or module == \'numpy\'\n                         )\n                         and (name not in bad_names)\n-                    ) :\n+                    ):\n                 # TODO: replace with a whitelist checker\n-                if fullname not in SK_NAMES + SKR_NAMES + XGB_NAMES + NUMPY_NAMES + good_names:\n+                if fullname not in sk_whitelist[\'SK_NAMES\'] + sk_whitelist[\'SKR_NAMES\'] + sk_whitelist[\'XGB_NAMES\'] + sk_whitelist[\'NUMPY_NAMES\'] + good_names:\n                     print("Warning: global %s is not in pickler whitelist yet and will loss support soon. Contact tool author or leave a message at github.com" % fullname)\n                 mod = sys.modules[module]\n                 return getattr(mod, name)\n \n         raise pickle.UnpicklingError("global \'%s\' is forbidden" % fullname)\n \n-    @classmethod\n-    def load(self, file):\n-        obj = pickle.Unpickler(file)\n-        obj.find_global = self.find_class\n-        return obj.load()\n+\n+def load_model(file):\n+    return SafePickler(file).load()\n+\n \n def read_columns(f, c=None, c_option=\'by_index_number\', return_df=False, **args):\n     data = pandas.read_csv(f, **args)\n     if c_option == \'by_index_number\':\n         cols = list(map(lambda x: x - 1, c))\n-        data = data.iloc[:,cols]\n+        data = data.iloc[:, cols]\n     if c_option == \'all_but_by_index_number\':\n         cols = list(map(lambda x: x - 1, c))\n         data.drop(data.columns[cols], axis=1, inplace=True)\n@@ -100,7 +98,7 @@\n         if inputs[\'model_inputter\'][\'input_mode\'] == \'prefitted\':\n             model_file = inputs[\'model_inputter\'][\'fitted_estimator\']\n             with open(model_file, \'rb\') as model_handler:\n-                fitted_estimator = SafePickler.load(model_handler)\n+                fitted_estimator = load_model(model_handler)\n             new_selector = selector(fitted_estimator, prefit=True, **options)\n         else:\n             estimator_json = inputs[\'model_inputter\']["estimator_selector"]\n@@ -108,14 +106,14 @@\n             new_selector = selector(estimator, **options)\n \n     elif inputs[\'se'..b't_type = params["selected_tasks"]["selected_algorithms"]["input_options"]["selected_input"]\n-    if input_type=="tabular":\n+    if input_type == "tabular":\n         header = \'infer\' if params["selected_tasks"]["selected_algorithms"]["input_options"]["header1"] else None\n         column_option = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_1"]["selected_column_selector_option"]\n         if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:\n@@ -140,8 +138,8 @@\n             c = None\n         X = read_columns(\n             file1,\n-            c = c,\n-            c_option = column_option,\n+            c=c,\n+            c_option=column_option,\n             sep=\'\\t\',\n             header=header,\n             parse_dates=True\n@@ -157,13 +155,13 @@\n         c = None\n     y = read_columns(\n         file2,\n-        c = c,\n-        c_option = column_option,\n+        c=c,\n+        c_option=column_option,\n         sep=\'\\t\',\n         header=header,\n         parse_dates=True\n     )\n-    y=y.ravel()\n+    y = y.ravel()\n     return X, y\n \n \n@@ -197,14 +195,14 @@\n                                 \'randn\', \'random\', \'random_integers\', \'random_sample\', \'ranf\', \'rayleigh\',\n                                 \'sample\', \'seed\', \'set_state\', \'shuffle\', \'standard_cauchy\', \'standard_exponential\',\n                                 \'standard_gamma\', \'standard_normal\', \'standard_t\', \'triangular\', \'uniform\',\n-                                \'vonmises\', \'wald\', \'weibull\', \'zipf\' ]\n+                                \'vonmises\', \'wald\', \'weibull\', \'zipf\']\n             for f in from_numpy_random:\n                 syms[\'np_random_\' + f] = getattr(np.random, f)\n \n         for key in unwanted:\n             syms.pop(key, None)\n \n-        super(SafeEval, self).__init__( symtable=syms, use_numpy=False, minimal=False,\n+        super(SafeEval, self).__init__(symtable=syms, use_numpy=False, minimal=False,\n                                         no_if=True, no_for=True, no_while=True, no_try=True,\n                                         no_functiondef=True, no_ifexp=True, no_listcomp=False,\n                                         no_augassign=False, no_assert=True, no_delete=True,\n@@ -250,10 +248,10 @@\n         try:\n             params = safe_eval(\'dict(\' + estimator_params + \')\')\n         except ValueError:\n-            sys.exit("Unsupported parameter input: `%s`" %estimator_params)\n+            sys.exit("Unsupported parameter input: `%s`" % estimator_params)\n         estimator.set_params(**params)\n     if \'n_jobs\' in estimator.get_params():\n-        estimator.set_params( n_jobs=N_JOBS )\n+        estimator.set_params(n_jobs=N_JOBS)\n \n     return estimator\n \n@@ -266,10 +264,10 @@\n         return int(literal)\n     m = re.match(r\'^(?P<method>\\w+)\\((?P<args>.*)\\)$\', literal)\n     if m:\n-        my_class = getattr( model_selection, m.group(\'method\') )\n-        args = safe_eval( \'dict(\'+ m.group(\'args\') + \')\' )\n-        return my_class( **args )\n-    sys.exit("Unsupported CV input: %s" %literal)\n+        my_class = getattr(model_selection, m.group(\'method\'))\n+        args = safe_eval(\'dict(\'+ m.group(\'args\') + \')\')\n+        return my_class(**args)\n+    sys.exit("Unsupported CV input: %s" % literal)\n \n \n def get_scoring(scoring_json):\n@@ -293,11 +291,10 @@\n     if scoring_json[\'secondary_scoring\'] != \'None\'\\\n             and scoring_json[\'secondary_scoring\'] != scoring_json[\'primary_scoring\']:\n         scoring = {}\n-        scoring[\'primary\'] = my_scorers[ scoring_json[\'primary_scoring\'] ]\n+        scoring[\'primary\'] = my_scorers[scoring_json[\'primary_scoring\']]\n         for scorer in scoring_json[\'secondary_scoring\'].split(\',\'):\n             if scorer != scoring_json[\'primary_scoring\']:\n                 scoring[scorer] = my_scorers[scorer]\n         return scoring\n \n-    return my_scorers[ scoring_json[\'primary_scoring\'] ]\n-\n+    return my_scorers[scoring_json[\'primary_scoring\']]\n'