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

Changeset 7:4368259ff821 (2018-12-30)
Previous changeset 6:7509d7059040 (2018-10-11) Next changeset 8:1c4a241bef5c (2019-05-14)
Commit message:
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
modified:
main_macros.xml
search_model_validation.xml
sk_whitelist.json
test-data/abc_model01
test-data/abr_model01
test-data/blobs.txt
test-data/circles.txt
test-data/classification_report.txt
test-data/feature_selection_result12
test-data/friedman1.txt
test-data/friedman2.txt
test-data/friedman3.txt
test-data/gaus.txt
test-data/gbc_model01
test-data/gbc_result01
test-data/gbr_model01
test-data/gbr_prediction_result01.tabular
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/hastie.txt
test-data/lda_model01
test-data/lda_model02
test-data/moons.txt
test-data/nn_model01
test-data/nn_model02
test-data/nn_model03
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/prp_model01
test-data/prp_model02
test-data/prp_model03
test-data/prp_model04
test-data/prp_model05
test-data/prp_model06
test-data/prp_model07
test-data/prp_model08
test-data/prp_model09
test-data/prp_result06
test-data/qda_model01
test-data/regression.txt
test-data/rfc_model01
test-data/rfc_result01
test-data/rfr_model01
test-data/rfr_result01
test-data/roc_curve.txt
test-data/scurve.txt
test-data/searchCV01
test-data/searchCV02
test-data/sparse_u.txt
test-data/svc_model01
test-data/svc_model02
test-data/svc_model03
test-data/svc_prediction_result03.tabular
test-data/swiss_r.txt
utils.py
added:
search_model_validation.py
test-data/imblearn_X.tabular
test-data/imblearn_y.tabular
test-data/pipeline11
test-data/pipeline12
removed:
test-data/cluster_result12
b
diff -r 7509d7059040 -r 4368259ff821 main_macros.xml
--- a/main_macros.xml Thu Oct 11 03:30:01 2018 -0400
+++ b/main_macros.xml Sun Dec 30 01:51:27 2018 -0500
[
b'@@ -1,13 +1,13 @@\n <macros>\n-  <token name="@VERSION@">0.9</token>\n+  <token name="@VERSION@">1.0</token>\n \n   <xml name="python_requirements">\n       <requirements>\n           <requirement type="package" version="3.6">python</requirement>\n-          <requirement type="package" version="0.19.1">scikit-learn</requirement>\n-          <requirement type="package" version="0.22.0">pandas</requirement>\n-          <requirement type="package" version="0.72.1">xgboost</requirement>\n-          <requirement type="package" version="0.9.12">asteval</requirement>\n+          <requirement type="package" version="0.20.2">scikit-learn</requirement>\n+          <requirement type="package" version="0.23.4">pandas</requirement>\n+          <requirement type="package" version="0.80">xgboost</requirement>\n+          <requirement type="package" version="0.9.13">asteval</requirement>\n           <yield />\n       </requirements>\n   </xml>\n@@ -244,7 +244,7 @@\n     <param argument="shuffle" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="@CHECKED@" label="@LABEL@" help="@HELP_TEXT@"/>\n   </xml>\n \n-  <xml name="random_state" token_default_value="" token_help_text="Integer number. The seed of the pseudo random number generator to use when shuffling the data. A fixed seed allows reproducible results.">\n+  <xml name="random_state" token_default_value="" token_help_text="Integer number. The seed of the pseudo random number generator to use when shuffling the data. A fixed seed allows reproducible results. default=None.">\n     <param argument="random_state" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Random seed number" help="@HELP_TEXT@"/>\n   </xml>\n \n@@ -346,20 +346,20 @@\n   <xml name="samples_column_selector_options" token_column_option="selected_column_selector_option" token_col_name="col1" token_multiple="False" token_infile="infile1">\n     <param name="@COLUMN_OPTION@" type="select" label="Choose how to select data by column:">\n       <option value="by_index_number" selected="true">Select columns by column index number(s)</option>\n+      <option value="all_but_by_index_number">All columns BUT by column index number(s)</option>\n       <option value="by_header_name">Select columns by column header name(s)</option>\n-      <option value="all_but_by_index_number">All columns but by column index number(s)</option>\n-      <option value="all_but_by_header_name">All columns but by column header name(s)</option>\n+      <option value="all_but_by_header_name">All columns BUT by column header name(s)</option>\n       <option value="all_columns">All columns</option>\n     </param>\n     <when value="by_index_number">\n       <param name="@COL_NAME@" multiple="@MULTIPLE@" type="data_column" data_ref="@INFILE@" label="Select target column(s):"/>\n     </when>\n+    <when value="all_but_by_index_number">\n+      <param name="@COL_NAME@" multiple="@MULTIPLE@" type="data_column" data_ref="@INFILE@" label="Select target column(s):"/>\n+    </when>\n     <when value="by_header_name">\n       <param name="@COL_NAME@" type="text" value="" label="Type header name(s):" help="Comma-separated string. For example: target1,target2"/>\n     </when>\n-    <when value="all_but_by_index_number">\n-      <param name="@COL_NAME@" multiple="@MULTIPLE@" type="data_column" data_ref="@INFILE@" label="Select target column(s):"/>\n-    </when>\n     <when value="all_but_by_header_name">\n       <param name="@COL_NAME@" type="text" value="" label="Type header name(s):" help="Comma-separated string. For example: target1,target2"/>\n     </when>\n@@ -543,9 +543,18 @@\n       <!--param argument="precompute_distances"/-->\n       <expand macro="random_state"/>\n       <param argument="copy_x" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Use a copy of data for precomputing distances" help="Mofifying the original data introduces small numerical differences caused by subtracting and then adding the data mean."/>\n+      <'..b'(=blank): sampling_strategy=\'auto\', random_state=None, n_neighbors=3, kind_sel=\'all\', allow_minority=False."/>\n+      </when>\n+      <when value="under_sampling.InstanceHardnessThreshold">\n+        <expand macro="estimator_params_text"\n+              help="Default(=blank): estimator=None, sampling_strategy=\'auto\', random_state=None, cv=5."/>\n+      </when>\n+      <when value="under_sampling.NearMiss">\n+        <expand macro="estimator_params_text"\n+              help="Default(=blank): sampling_strategy=\'auto\', random_state=None, version=1, n_neighbors=3, n_neighbors_ver3=3."/>\n+      </when>\n+      <when value="under_sampling.NeighbourhoodCleaningRule">\n+        <expand macro="estimator_params_text"\n+              help="Default(=blank): sampling_strategy=\'auto\', random_state=None, n_neighbors=3, kind_sel=\'all\', threshold_cleaning=0.5."/>\n+      </when>\n+      <when value="under_sampling.OneSidedSelection">\n+        <expand macro="estimator_params_text"\n+              help="Default(=blank): sampling_strategy=\'auto\', random_state=None, n_neighbors=None, n_seeds_S=1."/>\n+      </when>\n+      <when value="under_sampling.RandomUnderSampler">\n+        <expand macro="estimator_params_text"\n+              help="Default(=blank): sampling_strategy=\'auto\', random_state=None, replacement=False."/>\n+      </when>\n+      <when value="under_sampling.TomekLinks">\n+        <expand macro="estimator_params_text"\n+              help="Default(=blank): sampling_strategy=\'auto\', random_state=None."/>\n+      </when>\n+      <when value="over_sampling.ADASYN">\n+        <expand macro="estimator_params_text"\n+              help="Default(=blank): sampling_strategy=\'auto\', random_state=None, n_neighbors=5."/>\n+      </when>\n+      <when value="over_sampling.RandomOverSampler">\n+        <expand macro="estimator_params_text"\n+              help="Default(=blank): sampling_strategy=\'auto\', random_state=None."/>\n+      </when>\n+      <when value="over_sampling.SMOTE">\n+        <expand macro="estimator_params_text"\n+              help="Default(=blank): sampling_strategy=\'auto\', random_state=None, k_neighbors=5."/>\n+      </when>\n+      <when value="over_sampling.SVMSMOTE">\n+        <expand macro="estimator_params_text"\n+              help="Default(=blank): sampling_strategy=\'auto\', k_neighbors=5, m_neighbors=10, out_step=0.5, random_state=None, svm_estimator=None."/>\n+      </when>\n+      <when value="over_sampling.BorderlineSMOTE">\n+        <expand macro="estimator_params_text"\n+              help="Default(=blank): sampling_strategy=\'auto\', k_neighbors=5, kind=\'borderline-1\', m_neighbors=10, random_state=None."/>\n+      </when>\n+      <when value="over_sampling.SMOTENC">\n+        <expand macro="estimator_params_text"\n+              help="Default: categorical_features=[], sampling_strategy=\'auto\', random_state=None, k_neighbors=5."/>\n+      </when>\n+      <when value="combine.SMOTEENN">\n+        <expand macro="estimator_params_text"\n+              help="Default(=blank): sampling_strategy=\'auto\', random_state=None, smote=None, enn=None."/>\n+      </when>\n+      <when value="combine.SMOTETomek">\n+        <expand macro="estimator_params_text"\n+              help="Default(=blank): sampling_strategy=\'auto\', random_state=None, smote=None, tomek=None."/>\n       </when>\n     </conditional>\n   </xml>\n+\n   <!-- Outputs -->\n \n   <xml name="output">\n@@ -1498,4 +1768,19 @@\n     </citation>\n   </xml>\n \n+    <xml name="imblearn_citation">\n+    <citation type="bibtex">\n+      @article{JMLR:v18:16-365,\n+        author  = {Guillaume  Lema{{\\^i}}tre and Fernando Nogueira and Christos K. Aridas},\n+        title   = {Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning},\n+        journal = {Journal of Machine Learning Research},\n+        year    = {2017},\n+        volume  = {18},\n+        number  = {17},\n+        pages   = {1-5},\n+        url     = {http://jmlr.org/papers/v18/16-365.html}\n+      }\n+    </citation>\n+  </xml>\n+\n </macros>\n'
b
diff -r 7509d7059040 -r 4368259ff821 search_model_validation.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/search_model_validation.py Sun Dec 30 01:51:27 2018 -0500
[
b'@@ -0,0 +1,234 @@\n+import imblearn\n+import json\n+import numpy as np\n+import os\n+import pandas\n+import pickle\n+import skrebate\n+import sklearn\n+import sys\n+import xgboost\n+import warnings\n+from imblearn import under_sampling, over_sampling, combine\n+from imblearn.pipeline import Pipeline as imbPipeline\n+from sklearn import (cluster, compose, decomposition, ensemble, feature_extraction,\n+                    feature_selection, gaussian_process, kernel_approximation, metrics,\n+                    model_selection, naive_bayes, neighbors, pipeline, preprocessing,\n+                    svm, linear_model, tree, discriminant_analysis)\n+from sklearn.exceptions import FitFailedWarning\n+from sklearn.externals import joblib\n+from utils import get_cv, get_scoring, get_X_y, load_model, read_columns, SafeEval\n+\n+\n+N_JOBS = int(os.environ.get(\'GALAXY_SLOTS\', 1))\n+\n+\n+def get_search_params(params_builder):\n+    search_params = {}\n+    safe_eval = SafeEval(load_scipy=True, load_numpy=True)\n+    safe_eval_es = SafeEval(load_estimators=True)\n+\n+    for p in params_builder[\'param_set\']:\n+        search_p = p[\'search_param_selector\'][\'search_p\']\n+        if search_p.strip() == \'\':\n+            continue\n+        param_type = p[\'search_param_selector\'][\'selected_param_type\']\n+\n+        lst = search_p.split(\':\')\n+        assert (len(lst) == 2), "Error, make sure there is one and only one colon in search parameter input."\n+        literal = lst[1].strip()\n+        param_name = lst[0].strip()\n+        if param_name:\n+            if param_name.lower() == \'n_jobs\':\n+                sys.exit("Parameter `%s` is invalid for search." %param_name)\n+            elif not param_name.endswith(\'-\'):\n+                ev = safe_eval(literal)\n+                if param_type == \'final_estimator_p\':\n+                    search_params[\'estimator__\' + param_name] = ev\n+                else:\n+                    search_params[\'preprocessing_\' + param_type[5:6] + \'__\' + param_name] = ev\n+            else:\n+                # only for estimator eval, add `-` to the end of param\n+                #TODO maybe add regular express check\n+                ev = safe_eval_es(literal)\n+                for obj in ev:\n+                    if \'n_jobs\' in obj.get_params():\n+                        obj.set_params( n_jobs=N_JOBS )\n+                if param_type == \'final_estimator_p\':\n+                    search_params[\'estimator__\' + param_name[:-1]] = ev\n+                else:\n+                    search_params[\'preprocessing_\' + param_type[5:6] + \'__\' + param_name[:-1]] = ev\n+        elif param_type != \'final_estimator_p\':\n+            #TODO regular express check ?\n+            ev = safe_eval_es(literal)\n+            preprocessors = [preprocessing.StandardScaler(), preprocessing.Binarizer(), preprocessing.Imputer(),\n+                            preprocessing.MaxAbsScaler(), preprocessing.Normalizer(), preprocessing.MinMaxScaler(),\n+                            preprocessing.PolynomialFeatures(),preprocessing.RobustScaler(),\n+                            feature_selection.SelectKBest(), feature_selection.GenericUnivariateSelect(),\n+                            feature_selection.SelectPercentile(), feature_selection.SelectFpr(), feature_selection.SelectFdr(),\n+                            feature_selection.SelectFwe(), feature_selection.VarianceThreshold(),\n+                            decomposition.FactorAnalysis(random_state=0), decomposition.FastICA(random_state=0), decomposition.IncrementalPCA(),\n+                            decomposition.KernelPCA(random_state=0, n_jobs=N_JOBS), decomposition.LatentDirichletAllocation(random_state=0, n_jobs=N_JOBS),\n+                            decomposition.MiniBatchDictionaryLearning(random_state=0, n_jobs=N_JOBS),\n+                            decomposition.MiniBatchSparsePCA(random_state=0, n_jobs=N_JOBS), decomposition.NMF(random_state=0),\n+                            decomposition.PCA(random_state=0), decomposition.SparsePCA(random_state=0, n_jobs=N_JOB'..b")\n+\n+    return search_params\n+\n+\n+if __name__ == '__main__':\n+\n+    warnings.simplefilter('ignore')\n+\n+    input_json_path = sys.argv[1]\n+    with open(input_json_path, 'r') as param_handler:\n+        params = json.load(param_handler)\n+\n+    infile_pipeline = sys.argv[2]\n+    infile1 = sys.argv[3]\n+    infile2 = sys.argv[4]\n+    outfile_result = sys.argv[5]\n+    if len(sys.argv) > 6:\n+        outfile_estimator = sys.argv[6]\n+    else:\n+        outfile_estimator = None\n+\n+    params_builder = params['search_schemes']['search_params_builder']\n+\n+    input_type = params['input_options']['selected_input']\n+    if input_type == 'tabular':\n+        header = 'infer' if params['input_options']['header1'] else None\n+        column_option = params['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+            c = params['input_options']['column_selector_options_1']['col1']\n+        else:\n+            c = None\n+        X = read_columns(\n+                infile1,\n+                c = c,\n+                c_option = column_option,\n+                sep='\\t',\n+                header=header,\n+                parse_dates=True\n+        )\n+    else:\n+        X = mmread(open(infile1, 'r'))\n+\n+    header = 'infer' if params['input_options']['header2'] else None\n+    column_option = params['input_options']['column_selector_options_2']['selected_column_selector_option2']\n+    if column_option in ['by_index_number', 'all_but_by_index_number', 'by_header_name', 'all_but_by_header_name']:\n+        c = params['input_options']['column_selector_options_2']['col2']\n+    else:\n+        c = None\n+    y = read_columns(\n+            infile2,\n+            c = c,\n+            c_option = column_option,\n+            sep='\\t',\n+            header=header,\n+            parse_dates=True\n+    )\n+    y = y.ravel()\n+\n+    optimizer = params['search_schemes']['selected_search_scheme']\n+    optimizer = getattr(model_selection, optimizer)\n+\n+    options = params['search_schemes']['options']\n+    splitter, groups = get_cv(options.pop('cv_selector'))\n+    if groups is None:\n+        options['cv'] = splitter\n+    elif groups == '':\n+        options['cv'] = list( splitter.split(X, y, groups=None) )\n+    else:\n+        options['cv'] = list( splitter.split(X, y, groups=groups) )\n+    options['n_jobs'] = N_JOBS\n+    primary_scoring = options['scoring']['primary_scoring']\n+    options['scoring'] = get_scoring(options['scoring'])\n+    if options['error_score']:\n+        options['error_score'] = 'raise'\n+    else:\n+        options['error_score'] = np.NaN\n+    if options['refit'] and isinstance(options['scoring'], dict):\n+        options['refit'] = 'primary'\n+    if 'pre_dispatch' in options and options['pre_dispatch'] == '':\n+        options['pre_dispatch'] = None\n+\n+    with open(infile_pipeline, 'rb') as pipeline_handler:\n+        pipeline = load_model(pipeline_handler)\n+\n+    search_params = get_search_params(params_builder)\n+    searcher = optimizer(pipeline, search_params, **options)\n+\n+    if options['error_score'] == 'raise':\n+        searcher.fit(X, y)\n+    else:\n+        warnings.simplefilter('always', FitFailedWarning)\n+        with warnings.catch_warnings(record=True) as w:\n+            try:\n+                searcher.fit(X, y)\n+            except ValueError:\n+                pass\n+            for warning in w:\n+                print(repr(warning.message))\n+\n+    cv_result = pandas.DataFrame(searcher.cv_results_)\n+    cv_result.rename(inplace=True, columns={'mean_test_primary': 'mean_test_'+primary_scoring, 'rank_test_primary': 'rank_test_'+primary_scoring})\n+    cv_result.to_csv(path_or_buf=outfile_result, sep='\\t', header=True, index=False)\n+\n+    if outfile_estimator:\n+        with open(outfile_estimator, 'wb') as output_handler:\n+            pickle.dump(searcher.best_estimator_, output_handler, pickle.HIGHEST_PROTOCOL)\n"
b
diff -r 7509d7059040 -r 4368259ff821 search_model_validation.xml
--- a/search_model_validation.xml Thu Oct 11 03:30:01 2018 -0400
+++ b/search_model_validation.xml Sun Dec 30 01:51:27 2018 -0500
[
b'@@ -5,126 +5,25 @@\n     </macros>\n     <expand macro="python_requirements">\n         <requirement type="package" version="0.6">skrebate</requirement>\n+        <requirement type="package" version="0.4.2">imbalanced-learn</requirement>\n     </expand>\n     <expand macro="macro_stdio"/>\n     <version_command>echo "@VERSION@"</version_command>\n     <command>\n         <![CDATA[\n-        python "$sklearn_search_model_validation_script" \'$inputs\'\n+        python \'$__tool_directory__/search_model_validation.py\'\n+            \'$inputs\'\n+            \'$search_schemes.infile_pipeline\'\n+            \'$input_options.infile1\'\n+            \'$input_options.infile2\'\n+            \'$outfile_result\'\n+            #if $save:\n+            \'$outfile_estimator\'\n+            #end if\n         ]]>\n     </command>\n     <configfiles>\n         <inputs name="inputs" />\n-        <configfile name="sklearn_search_model_validation_script">\n-            <![CDATA[\n-import sys\n-import os\n-import json\n-import pandas\n-import skrebate\n-from sklearn import model_selection\n-from sklearn.exceptions import FitFailedWarning\n-\n-with open("$__tool_directory__/sk_whitelist.json", "r") as f:\n-    sk_whitelist = json.load(f)\n-exec(open("$__tool_directory__/utils.py").read(), globals())\n-\n-warnings.simplefilter(\'ignore\')\n-\n-input_json_path = sys.argv[1]\n-with open(input_json_path, "r") as param_handler:\n-    params = json.load(param_handler)\n-\n-#handle cheatah\n-infile1 = "$input_options.infile1"\n-infile2 = "$input_options.infile2"\n-infile_pipeline = "$search_schemes.infile_pipeline"\n-outfile_result = "$outfile_result"\n-outfile_estimator = "$outfile_estimator"\n-\n-params_builder = params[\'search_schemes\'][\'search_params_builder\']\n-\n-input_type = params["input_options"]["selected_input"]\n-if input_type=="tabular":\n-    header = \'infer\' if params["input_options"]["header1"] else None\n-    column_option = params["input_options"]["column_selector_options_1"]["selected_column_selector_option"]\n-    if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:\n-        c = params["input_options"]["column_selector_options_1"]["col1"]\n-    else:\n-        c = None\n-    X = read_columns(\n-            infile1,\n-            c = c,\n-            c_option = column_option,\n-            sep=\'\\t\',\n-            header=header,\n-            parse_dates=True\n-    )\n-else:\n-    X = mmread(open("$input_options.infile1", \'r\'))\n-\n-header = \'infer\' if params["input_options"]["header2"] else None\n-column_option = params["input_options"]["column_selector_options_2"]["selected_column_selector_option2"]\n-if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:\n-    c = params["input_options"]["column_selector_options_2"]["col2"]\n-else:\n-    c = None\n-y = read_columns(\n-        infile2,\n-        c = c,\n-        c_option = column_option,\n-        sep=\'\\t\',\n-        header=header,\n-        parse_dates=True\n-)\n-y=y.ravel()\n-\n-optimizers = params["search_schemes"]["selected_search_scheme"]\n-optimizers = getattr(model_selection, optimizers)\n-\n-options = params["search_schemes"]["options"]\n-options[\'cv\'] = get_cv( options[\'cv\'].strip() )\n-options[\'n_jobs\'] = N_JOBS\n-primary_scoring = options[\'scoring\'][\'primary_scoring\']\n-options[\'scoring\'] = get_scoring(options[\'scoring\'])\n-if options[\'error_score\']:\n-    options[\'error_score\'] = \'raise\'\n-else:\n-    options[\'error_score\'] = 0\n-if options[\'refit\'] and isinstance(options[\'scoring\'], dict):\n-    options[\'refit\'] = \'primary\'\n-if \'pre_dispatch\' in options and options[\'pre_dispatch\'] == \'\':\n-    options[\'pre_dispatch\'] = None\n-\n-with open(infile_pipeline, \'rb\') as pipeline_handler:\n-    pipeline = load_model(pipeline_handler)\n-\n-search_params = get_search_params(params_builder)\n-searcher = optimizers(pipeline, search_params, **options)\n-\n-if options[\'error_score\'] == \'raise\':\n-    searcher.fit(X, y)\n-else:\n-    warnings.simplefilter(\'always\', FitFailedWarning)\n-    with wa'..b'False, gamma=None, kernel=\'linear\', kernel_params=None, max_iter=None,\n+        n_components=None, random_state=0, remove_zero_eig=False, tol=0)\n+    19  sklearn_decomposition.LatentDirichletAllocation(batch_size=128, doc_topic_prior=None, evaluate_every=-1, learning_decay=0.7,\n+        learning_method=None, learning_offset=10.0, max_doc_update_iter=100, max_iter=10, mean_change_tol=0.001, n_components=10,\n+        n_topics=None, perp_tol=0.1, random_state=0, topic_word_prior=None, total_samples=1000000.0, verbose=0)\n+    20  sklearn_decomposition.MiniBatchDictionaryLearning(alpha=1, batch_size=3, dict_init=None, fit_algorithm=\'lars\',\n+        n_components=None, n_iter=1000, random_state=0, shuffle=True, split_sign=False, transform_algorithm=\'omp\',\n+        transform_alpha=None, transform_n_nonzero_coefs=None, verbose=False)\n+    21  sklearn_decomposition.MiniBatchSparsePCA(alpha=1, batch_size=3, callback=None, method=\'lars\', n_components=None,\n+        n_iter=100, random_state=0, ridge_alpha=0.01, shuffle=True, verbose=False)\n+    22  sklearn_decomposition.NMF(alpha=0.0, beta_loss=\'frobenius\', init=None, l1_ratio=0.0, max_iter=200,\n+        n_components=None, random_state=0, shuffle=False, solver=\'cd\', tol=0.0001, verbose=0)\n+    23  sklearn_decomposition.PCA(copy=True, iterated_power=\'auto\', n_components=None, random_state=0, svd_solver=\'auto\', tol=0.0, whiten=False)\n+    24  sklearn_decomposition.SparsePCA(U_init=None, V_init=None, alpha=1, max_iter=1000, method=\'lars\',\n+        n_components=None, random_state=0, ridge_alpha=0.01, tol=1e-08, verbose=False)\n+    25  sklearn_decomposition.TruncatedSVD(algorithm=\'randomized\', n_components=2, n_iter=5, random_state=0, tol=0.0)\n+    26  sklearn_kernel_approximation.Nystroem(coef0=None, degree=None, gamma=None, kernel=\'rbf\',\n+        kernel_params=None, n_components=100, random_state=0)\n+    27  sklearn_kernel_approximation.RBFSampler(gamma=1.0, n_components=100, random_state=0)\n+    28  sklearn_kernel_approximation.AdditiveChi2Sampler(sample_interval=None, sample_steps=2)\n+    29  sklearn_kernel_approximation.SkewedChi2Sampler(n_components=100, random_state=0, skewedness=1.0)\n+    30  sklearn_cluster.FeatureAgglomeration(affinity=\'euclidean\', compute_full_tree=\'auto\', connectivity=None,\n+        linkage=\'ward\', memory=None, n_clusters=2, pooling_func=<function mean at 0x113078ae8>)\n+    31  skrebate_ReliefF(discrete_threshold=10, n_features_to_select=10, n_neighbors=100, verbose=False)\n+    32  skrebate_SURF(discrete_threshold=10, n_features_to_select=10, verbose=False)\n+    33  skrebate_SURFstar(discrete_threshold=10, n_features_to_select=10, verbose=False)\n+    34  skrebate_MultiSURF(discrete_threshold=10, n_features_to_select=10, verbose=False)\n+    35  skrebate_MultiSURFstar(discrete_threshold=10, n_features_to_select=10, verbose=False)\n+    \'sk_prep_all\':   All sklearn preprocessing estimators, i.e., 0-7\n+    \'fs_all\':        All feature_selection estimators, i.e., 8-14\n+    \'decomp_all\':    All decomposition estimators, i.e., 15-25\n+    \'k_appr_all\':    All kernel_approximation estimators, i.e., 26-29\n+    \'reb_all\':       All skrebate estimators, i.e., 31-35\n+    \'all_0\':         All except the imbalanced-learn samplers, i.e., 0-35\n+    \'imb_all\':       All imbalanced-learn sampling methods, i.e., 36-54.\n+                     **CAUTION**: Mix of imblearn and other preprocessors may not work.\n+     None:           opt out of preprocessor\n+\n+Support mix (CAUTION: Mix of imblearn and other preprocessors may not work), e.g.::\n+\n+     : [None, \'sk_prep_all\', 22, \'k_appr_all\', sklearn_feature_selection.SelectKBest(k=50)]\n \n \n .. _`Scikit-learn model_selection GridSearchCV`: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html\n@@ -578,5 +680,6 @@\n     <expand macro="sklearn_citation">\n         <expand macro="skrebate_citation"/>\n         <expand macro="xgboost_citation"/>\n+        <expand macro="imblearn_citation"/>\n     </expand>\n </tool>\n'
b
diff -r 7509d7059040 -r 4368259ff821 sk_whitelist.json
--- a/sk_whitelist.json Thu Oct 11 03:30:01 2018 -0400
+++ b/sk_whitelist.json Sun Dec 30 01:51:27 2018 -0500
[
@@ -48,7 +48,10 @@
     "sklearn.cluster.mean_shift_.get_bin_seeds", "sklearn.cluster.mean_shift_.mean_shift",
     "sklearn.cluster.spectral.SpectralClustering", "sklearn.cluster.spectral.discretize",
     "sklearn.cluster.spectral.spectral_clustering", "sklearn.cluster.spectral_clustering",
-    "sklearn.cluster.ward_tree", "sklearn.config_context",
+    "sklearn.cluster.ward_tree", "sklearn.config_context", "sklearn.compose.TransformedTargetRegressor",
+    "sklearn.compose._target.TransformedTargetRegressor", "sklearn.compose.ColumnTransformer",
+    "sklearn.compose._column_transformer.ColumnTransformer", "sklearn.compose.make_column_transformer",
+    "sklearn.compose._column_transformer.make_column_transformer",
     "sklearn.covariance.EllipticEnvelope", "sklearn.covariance.EmpiricalCovariance",
     "sklearn.covariance.GraphLasso", "sklearn.covariance.GraphLassoCV",
     "sklearn.covariance.LedoitWolf", "sklearn.covariance.MinCovDet",
@@ -749,5 +752,10 @@
     "numpy.core.multiarray._reconstruct", "numpy.ndarray",
     "numpy.dtype", "numpy.core.multiarray.scalar",
     "numpy.random.__RandomState_ctor"
+  ],
+
+  "IMBLEARN_NAMES":[
+    "imblearn.pipeline.Pipeline", "imblearn.over_sampling._random_over_sampler.RandomOverSampler",
+    "imblearn.under_sampling._prototype_selection._edited_nearest_neighbours.EditedNearestNeighbours"
   ]
 }
\ No newline at end of file
b
diff -r 7509d7059040 -r 4368259ff821 test-data/abc_model01
b
Binary file test-data/abc_model01 has changed
b
diff -r 7509d7059040 -r 4368259ff821 test-data/abr_model01
b
Binary file test-data/abr_model01 has changed
b
diff -r 7509d7059040 -r 4368259ff821 test-data/blobs.txt
--- a/test-data/blobs.txt Thu Oct 11 03:30:01 2018 -0400
+++ b/test-data/blobs.txt Sun Dec 30 01:51:27 2018 -0500
b
@@ -1,101 +1,101 @@
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--3.214299390778301 5.7592616395707115 1
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b
diff -r 7509d7059040 -r 4368259ff821 test-data/circles.txt
--- a/test-data/circles.txt Thu Oct 11 03:30:01 2018 -0400
+++ b/test-data/circles.txt Sun Dec 30 01:51:27 2018 -0500
b
@@ -1,101 +1,101 @@
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b
diff -r 7509d7059040 -r 4368259ff821 test-data/classification_report.txt
--- a/test-data/classification_report.txt Thu Oct 11 03:30:01 2018 -0400
+++ b/test-data/classification_report.txt Sun Dec 30 01:51:27 2018 -0500
b
@@ -1,9 +1,11 @@
 classification_report : 
-             precision    recall  f1-score   support
+              precision    recall  f1-score   support
 
-          0       1.00      1.00      1.00        14
-          1       1.00      0.62      0.77        16
-          2       0.60      1.00      0.75         9
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+           1       1.00      0.62      0.77        16
+           2       0.60      1.00      0.75         9
 
-avg / total       0.91      0.85      0.85        39
+   micro avg       0.85      0.85      0.85        39
+   macro avg       0.87      0.88      0.84        39
+weighted avg       0.91      0.85      0.85        39
 
b
diff -r 7509d7059040 -r 4368259ff821 test-data/cluster_result12
--- a/test-data/cluster_result12 Thu Oct 11 03:30:01 2018 -0400
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
b
@@ -1,48 +0,0 @@
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b
diff -r 7509d7059040 -r 4368259ff821 test-data/feature_selection_result12
--- a/test-data/feature_selection_result12 Thu Oct 11 03:30:01 2018 -0400
+++ b/test-data/feature_selection_result12 Sun Dec 30 01:51:27 2018 -0500
b
@@ -1,11 +1,11 @@
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b
diff -r 7509d7059040 -r 4368259ff821 test-data/friedman1.txt
--- a/test-data/friedman1.txt Thu Oct 11 03:30:01 2018 -0400
+++ b/test-data/friedman1.txt Sun Dec 30 01:51:27 2018 -0500
b
b'@@ -1,101 +1,101 @@\n 0\t1\t2\t3\t4\t5\t6\t7\t8\t9\t0\n-0.5434049417909654\t0.27836938509379616\t0.4245175907491331\t0.8447761323199037\t0.004718856190972565\t0.12156912078311422\t0.6707490847267786\t0.8258527551050476\t0.13670658968495297\t0.57509332942725\t13.160650397398078\n-0.891321954312264\t0.20920212211718958\t0.18532821955007506\t0.10837689046425514\t0.21969749262499216\t0.9786237847073697\t0.8116831490893233\t0.1719410127325942\t0.8162247487258399\t0.2740737470416992\t9.691298137658498\n-0.4317041836631217\t0.9400298196223746\t0.8176493787767274\t0.3361119501208987\t0.17541045374233666\t0.37283204628992317\t0.005688507352573424\t0.25242635344484043\t0.7956625084732873\t0.01525497124633901\t15.821619961828777\n-0.5988433769284929\t0.6038045390428536\t0.10514768541205632\t0.38194344494311006\t0.03647605659256892\t0.8904115634420757\t0.9809208570123115\t0.05994198881803725\t0.8905459447285041\t0.5769014994000329\t16.18933274618261\n-0.7424796890979773\t0.6301839364753761\t0.5818421923987779\t0.020439132026923157\t0.2100265776728606\t0.5446848781786475\t0.7691151711056516\t0.2506952291383959\t0.2858956904068647\t0.8523950878413064\t11.33767760089345\n-0.9750064936065875\t0.8848532934911055\t0.35950784393690227\t0.5988589458757472\t0.3547956116572998\t0.34019021537064575\t0.17808098950580487\t0.23769420862405044\t0.04486228246077528\t0.5054314296357892\t12.337142824178603\n-0.376252454297363\t0.5928054009758866\t0.6299418755874974\t0.14260031444628352\t0.933841299466419\t0.9463798808091013\t0.6022966577308656\t0.38776628032663074\t0.3631880041093498\t0.20434527686864423\t12.880550712301464\n-0.27676506139633517\t0.24653588120354963\t0.17360800174020508\t0.9666096944873236\t0.9570126003527981\t0.5979736843289207\t0.7313007530599226\t0.3403852228374361\t0.09205560337723862\t0.4634980189371477\t18.709003936604173\n-0.508698893238194\t0.08846017300289077\t0.5280352233180474\t0.9921580365105283\t0.3950359317582296\t0.3355964417185683\t0.8054505373292797\t0.7543489945823536\t0.3130664415885097\t0.6340366829622751\t13.321479131556272\n-0.5404045753007164\t0.2967937508800147\t0.11078790118244575\t0.3126402978757431\t0.4569791300492658\t0.6589400702261969\t0.2542575178177181\t0.6411012587007017\t0.20012360721840317\t0.6576248055289837\t13.269253863108887\n-0.7782892154498485\t0.7795983986107496\t0.6103281532093938\t0.30900034852440217\t0.697734907512956\t0.8596182957290651\t0.6253237577568076\t0.9824078296095496\t0.9765001270158553\t0.16669413119885812\t16.264995170787344\n-0.02317813647840361\t0.16074454850708164\t0.9234968252590874\t0.953549849879534\t0.21097841871844636\t0.3605252508146082\t0.5493752616276721\t0.2718308491769721\t0.46060162107485014\t0.6961615648233853\t14.294427313499119\n-0.500355896674865\t0.7160709905643361\t0.525955936229779\t0.0013990231190438296\t0.3947002866898355\t0.49216696990114994\t0.40288033137914214\t0.3542983001063206\t0.5006143194429532\t0.4451766288311383\t11.026237192296225\n-0.09043278819643585\t0.27356292002744065\t0.9434770977427269\t0.026544641333941965\t0.039998689640650786\t0.28314035971981955\t0.5823441702167689\t0.9908928029248271\t0.9926422374029681\t0.9931173724810448\t5.175296804362765\n-0.11004833096656297\t0.6644814459639401\t0.5239868344883128\t0.17314990980873102\t0.9429602449150257\t0.2418600859762523\t0.998932268843212\t0.5826938151498987\t0.18327900063057578\t0.38684542191779026\t8.73494610704017\n-0.1896735289121496\t0.41077067302531\t0.5946800689017054\t0.7165860931283399\t0.4868914823691235\t0.3095898177667046\t0.5774413728278474\t0.44170781956874294\t0.35967810260053623\t0.3213319320088136\t12.202924702612343\n-0.20820724019602266\t0.45125862406183437\t0.4918429102640539\t0.8990763147937112\t0.7293604610294412\t0.7700897729196955\t0.3754392475619882\t0.34373953523538436\t0.6550352059993224\t0.7110379932104975\t15.547914734580145\n-0.11353757521867625\t0.13302868937357504\t0.45603905760612395\t0.15973623015851013\t0.9616419037746458\t0.8376157448618098\t0.5201606870379233\t0.2182722577281544\t0.13491872253239878\t0.9790703454838688\t6.918543527074358\n-0.7070434956891432\t0.8599755569456631\t0.38717262782863904\t0.2508340198317248\t0.29943801894470223\t0.8568955284050157\t0.472983990'..b'97\t0.94227191496291\t7.35500227276053\n+0.65747075744411\t0.19562874880188\t0.52567876074104\t0.31080910409256\t0.55534839433138\t0.53552980736766\t0.46511292889839\t0.76786459433331\t0.88694697168655\t0.82980936841814\t9.82967962816587\n+0.95884307895640\t0.91106399609686\t0.11967478384416\t0.11446859495951\t0.99696500632827\t0.04000832595811\t0.85956374451868\t0.46550503372369\t0.28899832738919\t0.73326395780051\t12.89083214454110\n+0.47219244963378\t0.36603378202459\t0.07374308587639\t0.82120530233350\t0.48801691478932\t0.75706206486561\t0.37107807260931\t0.26950482476264\t0.73459463542670\t0.84656452629874\t19.45300037464767\n+0.77315971269645\t0.09726311997083\t0.31288480540422\t0.05429737124805\t0.99641786449707\t0.17769873435229\t0.37123100482185\t0.3589325920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b
diff -r 7509d7059040 -r 4368259ff821 test-data/friedman2.txt
--- a/test-data/friedman2.txt Thu Oct 11 03:30:01 2018 -0400
+++ b/test-data/friedman2.txt Sun Dec 30 01:51:27 2018 -0500
b
b'@@ -1,101 +1,101 @@\n 0\t1\t2\t3\t0\n-54.340494179096545\t580.4157780449804\t0.4245175907491331\t9.447761323199037\t252.3175321312284\n-0.47188561909725646\t324.2624476565047\t0.6707490847267786\t9.258527551050477\t217.49891878908315\n-13.670658968495298\t1065.1523751493062\t0.891321954312264\t3.092021221171896\t949.4918123086619\n-18.532821955007506\t302.71124845232475\t0.21969749262499216\t10.786237847073696\t69.0385890614886\n-81.16831490893233\t406.55138981837536\t0.8162247487258399\t3.740737470416992\t341.61946168251893\n-43.17041836631217\t1661.3229093937068\t0.8176493787767274\t4.361119501208987\t1359.065329368987\n-17.541045374233665\t734.7326433201733\t0.005688507352573424\t3.524263534448404\t18.032014110410582\n-79.56625084732873\t150.58465705466725\t0.5988433769284929\t7.038045390428536\t120.2598923821887\n-10.514768541205633\t749.6172809180189\t0.03647605659256892\t9.904115634420757\t29.295002042749065\n-98.09208570123114\t223.58662823399155\t0.8905459447285041\t6.769014994000329\t221.96451266962563\n-74.24796890979773\t1155.1499432690875\t0.5818421923987779\t1.2043913202692316\t676.202880083338\n-21.00265776728606\t1015.4762722834868\t0.7691151711056516\t3.5069522913839593\t781.3002704061951\n-28.58956904068647\t1518.1603420210945\t0.9750064936065875\t9.848532934911056\t1480.4921951648091\n-35.95078439369023\t1103.9765558914746\t0.3547956116572998\t4.401902153706457\t393.33223818275417\n-17.808098950580487\t513.9676635429531\t0.04486228246077528\t6.054314296357893\t29.13372581378542\n-37.625245429736296\t1094.087314354974\t0.6299418755874974\t2.426003144462835\t690.2372882885904\n-93.3841299466419\t1671.6965482922212\t0.6022966577308656\t4.877662803266308\t1011.1784501808802\n-36.31880041093498\t459.48790885887036\t0.27676506139633517\t3.4653588120354963\t132.2541308347294\n-17.360800174020508\t1704.7445419904177\t0.9570126003527981\t6.979736843289207\t1631.554290748974\n-73.13007530599226\t681.726598181031\t0.09205560337723862\t5.634980189371477\t96.36589298307057\n-50.86988932381939\t270.1747375569969\t0.5280352233180474\t10.921580365105282\t151.45967058029146\n-39.50359317582296\t673.903510398035\t0.8054505373292797\t8.543489945823536\t544.2313687666258\n-31.306644158850972\t1161.4438984999683\t0.5404045753007164\t3.967937508800147\t628.4296697532741\n-11.078790118244575\t636.4017069153224\t0.4569791300492658\t7.589400702261969\t291.03303662060034\n-25.42575178177181\t1172.9847885050644\t0.20012360721840317\t7.576248055289837\t236.1147978147717\n-77.82892154498485\t1399.2376190930588\t0.6103281532093938\t4.090003485244022\t857.5330814879569\n-69.7734907512956\t1529.9603779755726\t0.6253237577568076\t10.824078296095497\t959.2614236165756\n-97.65001270158552\t397.9799362884419\t0.02317813647840361\t2.6074454850708166\t98.08464391759719\n-92.34968252590873\t1683.4096118144525\t0.21097841871844636\t4.605252508146082\t366.97302133435386\n-54.93752616276721\t569.734241516186\t0.46060162107485014\t7.961615648233853\t268.10919955478056\n-50.035589667486505\t1295.4574551145477\t0.525955936229779\t1.0139902311904383\t683.18750535065\n-39.470028668983545\t929.6815373737124\t0.40288033137914214\t4.542983001063206\t376.6240995010018\n-50.06143194429532\t852.9167920201943\t0.09043278819643585\t3.7356292002744063\t91.95318916050276\n-94.34770977427269\t169.0277802511735\t0.039998689640650786\t3.8314035971981957\t94.5895295076982\n-58.23441702167689\t1744.4141122286821\t0.9926422374029681\t10.931173724810447\t1732.5580336252374\n-11.004833096656297\t1211.179321268851\t0.5239868344883128\t2.73149909808731\t634.7371222778227\n-94.29602449150258\t520.7731581793785\t0.998932268843212\t6.826938151498987\t528.693948971785\n-18.327900063057577\t757.625288640914\t0.1896735289121496\t5.1077067302531\t144.86527485195924\n-59.46800689017054\t1296.2989411786257\t0.4868914823691235\t4.095898177667046\t633.9520920433132\n-57.744137282784735\t847.2500474585627\t0.35967810260053623\t4.213319320088136\t310.15968510981907\n-20.820724019602267\t862.8525108188742\t0.4918429102640539\t9.990763147937113\t424.8982058212394\n-72.93604610294412\t1383.7040602123484\t0.3754392475619882\t4.437395352353843\t524.5916835666012\n-65.50352059993224\t1287.2354088081224\t0.'..b'5.36613567278891\t0.68462427169271\t5.88293166805099\t1156.58767097944997\n+48.54143101843673\t1704.88050248556237\t0.21134788749712\t5.11648138177833\t363.57774253644357\n+98.96655767792834\t172.07811591269444\t0.70132651409352\t1.25171563884812\t156.06931868323713\n+32.08817260865362\t245.77958638999525\t0.06088456434664\t2.11406316704053\t35.40508437542623\n+16.92689081454309\t1151.06970045219464\t0.43839309463984\t9.30903764603975\t504.90473090436518\n+23.97921895644722\t436.13916546124790\t0.71189965858292\t9.58294925326778\t311.41167624355961\n+55.90558855960195\t1276.42473559746963\t0.60511203551818\t6.59217283268040\t774.40045791345551\n+86.03941909075867\t1628.20197943455605\t0.84960732575898\t3.54466535494455\t1386.00528001290149\n+87.75555422867708\t836.5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b
diff -r 7509d7059040 -r 4368259ff821 test-data/friedman3.txt
--- a/test-data/friedman3.txt Thu Oct 11 03:30:01 2018 -0400
+++ b/test-data/friedman3.txt Sun Dec 30 01:51:27 2018 -0500
b
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b
diff -r 7509d7059040 -r 4368259ff821 test-data/gaus.txt
--- a/test-data/gaus.txt Thu Oct 11 03:30:01 2018 -0400
+++ b/test-data/gaus.txt Sun Dec 30 01:51:27 2018 -0500
b
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b
diff -r 7509d7059040 -r 4368259ff821 test-data/gbc_model01
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Binary file test-data/gbc_model01 has changed
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diff -r 7509d7059040 -r 4368259ff821 test-data/gbc_result01
--- a/test-data/gbc_result01 Thu Oct 11 03:30:01 2018 -0400
+++ b/test-data/gbc_result01 Sun Dec 30 01:51:27 2018 -0500
b
@@ -1,6 +1,6 @@
 0 1 2 3 predicted
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+0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1
b
diff -r 7509d7059040 -r 4368259ff821 test-data/gbr_model01
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Binary file test-data/gbr_model01 has changed
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diff -r 7509d7059040 -r 4368259ff821 test-data/gbr_prediction_result01.tabular
--- a/test-data/gbr_prediction_result01.tabular Thu Oct 11 03:30:01 2018 -0400
+++ b/test-data/gbr_prediction_result01.tabular Sun Dec 30 01:51:27 2018 -0500
b
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diff -r 7509d7059040 -r 4368259ff821 test-data/glm_model06
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diff -r 7509d7059040 -r 4368259ff821 test-data/glm_model07
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diff -r 7509d7059040 -r 4368259ff821 test-data/glm_model08
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diff -r 7509d7059040 -r 4368259ff821 test-data/hastie.txt
--- a/test-data/hastie.txt Thu Oct 11 03:30:01 2018 -0400
+++ b/test-data/hastie.txt Sun Dec 30 01:51:27 2018 -0500
b
b'@@ -1,12001 +1,12001 @@\n 0\t1\t2\t3\t4\t5\t6\t7\t8\t9\t0\n--1.7497654730546974\t0.34268040332750216\t1.153035802563644\t-0.25243603652138985\t0.9813207869512316\t0.5142188413943821\t0.22117966922140045\t-1.0700433305682933\t-0.18949583082317534\t0.25500144427338167\t-1.0\n--0.4580269855026243\t0.43516348812289213\t-0.5835950503226648\t0.816847071685779\t0.672720805709661\t-0.10441114339062771\t-0.5312803768519098\t1.0297326851333461\t-0.43813562270441736\t-1.1183182462554362\t-1.0\n-1.6189816606752596\t1.5416051745134067\t-0.251879139213213\t-0.8424357382512976\t0.18451869056394285\t0.9370822011089522\t0.7310003438348087\t1.361556125145331\t-0.32623805920230253\t0.055676014854776905\t-1.0\n-0.22239960855530486\t-1.4432169952253369\t-0.7563523055944354\t0.8164540110192858\t0.7504447615341785\t-0.4559469274680022\t1.1896222680291255\t-1.690616826383604\t-1.3563990488613127\t-1.2324345139149262\t1.0\n--0.5444391616724643\t-0.6681717368134277\t0.007314563228903049\t-0.6129387354781626\t1.299748074755309\t-1.733095623653281\t-0.98331009912963\t0.3575077531673654\t-1.6135785028221759\t1.4707138666121289\t1.0\n--1.1880175973177194\t-0.5497461935354897\t-0.9400461615447682\t-0.8279323643658708\t0.1088634678336795\t0.50780959049232\t-0.8622273465104797\t1.249469742726979\t-0.07961124591739943\t-0.8897314812650339\t-1.0\n--0.8817983894830175\t0.01863894948806016\t0.2378446219236218\t0.013548548628612411\t-1.635529399380822\t-1.044209877709317\t0.6130388816875463\t0.7362052133238238\t1.0269214393997905\t-1.4321906110589266\t-1.0\n--1.841188300186717\t0.36609322616730366\t-0.3317771350528086\t-0.6892179780897532\t2.0346075615049335\t-0.5507144119145928\t0.7504533303268411\t-1.3069923390808191\t0.5805733357942744\t-1.1045230926622938\t1.0\n-0.6901214702247076\t0.6868900661384048\t-1.566687529578391\t0.9049741214666812\t0.7788223993230673\t0.4282328705967407\t0.10887198989791452\t0.028283634823073247\t-0.5788258247909884\t-1.1994511991939312\t-1.0\n--1.7059520057381703\t0.3691639571070058\t1.8765734269621657\t-0.37690335016897475\t1.8319360818255361\t0.003017434031214063\t-0.07602346572462335\t0.003957593987599105\t-0.18501411089711395\t-2.4871515352227695\t1.0\n--1.7046512057609624\t-1.1362610068273629\t-2.9733154740508856\t0.03331727813886288\t-0.24888866705810792\t-0.45017643501165083\t0.13242780114877378\t0.022213928039390988\t0.31736797594106797\t-0.7524141777250374\t1.0\n--1.2963918071501508\t0.095139443565453\t-0.42371509994342044\t-1.185983564929173\t-0.365461992676627\t-1.271023040846661\t1.5861709384232352\t0.6933906585165882\t-1.9580812342078666\t-0.13480131198999493\t1.0\n--1.5406160245526137\t2.0467139684821385\t-1.3969993449532836\t-1.0971719846398227\t-0.23871286931467955\t-1.429066898448291\t0.9490047765052598\t-0.019397585962465276\t0.8945977057600133\t0.7596931198502055\t1.0\n--1.4977203810831696\t-1.193885976791938\t1.2962625863990573\t0.9522756260818906\t-1.2172541306410147\t-0.15726516737513793\t-1.5075851602643862\t0.10788413080661359\t0.7470556550991475\t0.4296764358626096\t1.0\n--1.415042920852526\t-0.6407599230105716\t0.7796263036636958\t-0.43812091634884287\t2.0747931679465657\t-0.34329768218246975\t-0.6166293716831945\t0.7631836460599923\t0.19291719182330652\t-0.3484589306523706\t-1.0\n-2.2986539407136757\t-0.16520955264073184\t0.46629936835718944\t0.26998723863108903\t-0.3198310471180883\t-1.1477415998765863\t1.703623988120705\t-0.7221507700557533\t1.0936866496587212\t-0.2295177532399562\t1.0\n--0.008898663292110321\t-0.5431980084071721\t0.7530621876919789\t-1.6094388961729544\t1.943262263433996\t-1.4474361123195851\t0.13024845535270219\t0.9493608646609868\t-2.0151887171225265\t-0.07954058693411101\t1.0\n-0.30104946378806546\t-1.6848999616851803\t0.22239080944544862\t-0.6849217352472302\t-0.12620118371357705\t1.9902736497540905\t0.5229978045207515\t-0.016345402757487165\t-0.41581633584065\t-1.3585029367597998\t1.0\n--0.5144298913687853\t-0.21606012000326133\t0.4223802204219781\t-1.094042931032235\t1.2369078851902253\t-0.23028467842710793\t-0.7044181997350232\t-0.591375121085172\t0.7369951690182122\t0.4358672525149101\t-1.0\n-1.7759935855067666\t0.5130743788396454\t1.1705269829481435\t2.0777122322502035\t-0.45592201921402026\t0.64917'..b'06309931633\t-1.06717262694293\t0.50073241156502\t0.18992453098454\t2.04628516955088\t1.82528927949279\t0.42917283635627\t1.00000000000000\n+-1.22259082208966\t1.80486825966875\t0.25472873542702\t-1.14612326011794\t-0.65895878644957\t-0.50665881367303\t-0.58717488257737\t1.98654951853110\t-0.92459516782334\t0.30357698596096\t1.00000000000000\n+-0.45373427820446\t-0.61483801155467\t-0.47897312964695\t-0.04537445187094\t1.32531372085786\t0.33328592586201\t-0.71798479536006\t-0.10644860260678\t-1.33607751334297\t-1.07453058288167\t-1.00000000000000\n+0.27622491542758\t-0.42838847957279\t-2.04367124772039\t-1.90685851796119\t0.96798821663439\t2.17219080431942\t0.10964573562466\t-1.27426723194757\t1.23222183027782\t-0.21419343967053\t1.00000000000000\n+1.25575137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b
diff -r 7509d7059040 -r 4368259ff821 test-data/imblearn_X.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/imblearn_X.tabular Sun Dec 30 01:51:27 2018 -0500
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b
diff -r 7509d7059040 -r 4368259ff821 test-data/imblearn_y.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/imblearn_y.tabular Sun Dec 30 01:51:27 2018 -0500
b
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diff -r 7509d7059040 -r 4368259ff821 test-data/moons.txt
--- a/test-data/moons.txt Thu Oct 11 03:30:01 2018 -0400
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diff -r 7509d7059040 -r 4368259ff821 test-data/prp_result06
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diff -r 7509d7059040 -r 4368259ff821 test-data/regression.txt
--- a/test-data/regression.txt Thu Oct 11 03:30:01 2018 -0400
+++ b/test-data/regression.txt Sun Dec 30 01:51:27 2018 -0500
b
b'@@ -1,101 +1,101 @@\n 0\t1\t2\t3\t4\t5\t6\t7\t8\t9\t10\t11\t12\t13\t14\t15\t16\t17\t18\t19\t20\t21\t22\t23\t24\t25\t26\t27\t28\t29\t30\t31\t32\t33\t34\t35\t36\t37\t38\t39\t40\t41\t42\t43\t44\t45\t46\t47\t48\t49\t50\t51\t52\t53\t54\t55\t56\t57\t58\t59\t60\t61\t62\t63\t64\t65\t66\t67\t68\t69\t70\t71\t72\t73\t74\t75\t76\t77\t78\t79\t80\t81\t82\t83\t84\t85\t86\t87\t88\t89\t90\t91\t92\t93\t94\t95\t96\t97\t98\t99\t0\n-0.04046644371002013\t-0.3222186916867068\t-1.9432702884775699\t0.14089076168330988\t0.5079729819698269\t-1.18817519027507\t-0.5583786466981011\t-1.4762039928244617\t-0.2406790907279704\t-0.0635802910775822\t0.8092951093257438\t0.3575461139841666\t-0.3546352850532605\t-0.013800738789073546\t-0.08714823571982741\t-0.794536549893067\t0.04252844430408998\t1.1848057906070117\t-0.34427022696107923\t1.8129164839023801\t1.9372513425840665\t-0.23132382095439452\t0.47212052875794286\t-1.0860498262479517\t-0.22514835388270557\t-0.2688128201531403\t-2.6129045486033537\t-1.40689902631265\t-0.6852241305315773\t0.30338447890178527\t0.23198841547816262\t1.0774387686891118\t0.16518761240475535\t-1.0047595220548933\t-0.449001129008708\t0.06253225964952577\t0.5096561871734692\t0.3419804166006587\t0.2006377249932491\t-0.8895878021839609\t0.103279908087676\t-0.40064406812252806\t0.9972796871650385\t0.30650582998705583\t1.8256815454177233\t-0.4173700312044624\t0.4402675253740327\t-0.2804669140583019\t0.29390729185835485\t-0.25861302105802453\t1.6135408335741044\t-0.21018097777041758\t0.7327269904660167\t0.33128542535217953\t0.2840068142931275\t-1.0878133919784179\t-1.5651521450771562\t0.11108254244933571\t-1.058341107083241\t2.715905661175428\t-0.46709306413711094\t-0.348900726843459\t-1.2551363680401528\t-0.387061571599947\t-2.0898306570129823\t-0.05398563142234402\t-1.695827187014135\t0.11871279626399817\t0.11536367037547492\t0.14893574125345024\t-0.7502531502828071\t2.9675579781460506\t0.14570961888674075\t-1.166292932911318\t-1.1132677395256523\t0.31364953071626966\t0.1097126825912871\t-1.0826121288108281\t1.516741546949991\t1.7411107338741112\t1.6514358866722028\t0.5929466112193225\t1.8306710073227783\t-1.1760025974824964\t0.5001066856635897\t-0.6570293996628584\t-9.313425995401874e-05\t0.06338028324230935\t2.216791384854668\t-0.0456555745358126\t0.9572926200414316\t1.629934925613996\t0.37185201934035095\t-0.2522202464496184\t0.23738912982157237\t0.8154637324443926\t-0.852861753338149\t0.9603719837230026\t-0.3090723967625725\t-0.56066669259864\t-6.008321346938075\n--0.7142440680403472\t0.28458137387342325\t-0.3062544898706742\t-0.17448841397392895\t1.125783615924232\t-1.8058408019479073\t-1.0678527622740026\t0.8139881757232938\t0.7366186955374812\t-1.4962992713815841\t-1.567173603615006\t-1.1201845678212112\t0.10177233182573647\t-2.4550466142310423\t-0.4055420715795304\t-1.0596390892575\t-0.3721075714225364\t0.40749776579756786\t0.6855364862553506\t-0.3988459000205521\t-0.9445982960407311\t-2.1011971049841307\t0.3867916033916995\t-1.4181696233434609\t-0.5192383517609591\t0.9789842607419063\t0.40508582774258556\t-0.7765281127198043\t1.1017576064074774\t-0.7922803547040447\t0.6100116286031322\t-0.3306263767151245\t-0.32042678881723646\t-0.7267249384746718\t-0.18435061102908676\t-2.646293296293553\t0.23852982942962941\t-1.2688817610198302\t1.452287979486792\t-1.446316001204991\t-0.039442465091911745\t0.8366452783370826\t-0.32080614910166755\t1.7326524695395533\t-0.43674374808586897\t0.0006425951063431532\t-0.5490831404626035\t0.4413887293067582\t-0.4330613886387762\t-0.5078891532344042\t-0.7156252974248424\t0.24730882154569564\t2.34264998959277\t1.3973125791335586\t0.1845204000538479\t1.2853732452582287\t1.976677027556592\t-1.0069633092095855\t1.1158189890375427\t-0.4935143775781184\t1.0352039943343663\t2.010059685469909\t-0.07145737231420544\t-0.0582415057117433\t-0.05500426148539334\t-0.1849607732851202\t0.6382535885768728\t-0.7976219502118782\t1.9065964797307375\t-2.1823257723449716\t-1.222003337203902\t0.5995685095364299\t0.5307364716391962\t0.9231332509108808\t0.5463410857422811\t1.2375305062148136\t-0.8053140950405908\t1.400537549212823\t-0.828851647296513\t-1.412316839028218\t-0.7750661967244074\t0.7871040721446222\t-0.11413232110538522\t-1.589665213428925\t1.8207914876493103\t0.8874790066565631\t-1.14489712094298'..b'317523741\t0.24107892253465\t0.24350121915887\t-0.27274824801804\t0.54381104462477\t0.51820246808255\t-0.86009434942919\t-1.21423044904284\t0.36792051934856\t1.94082103916430\t2.11497218011132\t-0.43432765000649\t0.84999093815312\t1.83997148290589\t-2.18970055774918\t0.83007009623657\t-0.36828110384226\t0.32488842221473\t-0.40800572079705\t1.32393049920405\t0.32899969445643\t-0.28744255330986\t-0.60348124002864\t-2.04249975403047\t-0.12214358859249\t-1.61254032348568\t334.59256835042021\n+0.59156531213735\t-0.11033751258127\t-0.51236076329509\t-0.02454130061915\t0.27734607491290\t0.58725946809788\t-0.70971120747053\t-0.60061928108883\t-0.45186439475169\t0.36767347630475\t-0.28352190645161\t2.22550996123369\t-1.19436828147964\t1.89224039000434\t1.01762849591326\t1.00772158295772\t1.64615065381323\t-1.52145268077060\t-0.03805465490844\t0.64006527712100\t1.11018655266838\t1.72123119765181\t-0.96688703561691\t0.50951459531196\t-0.62580619481323\t1.65406935887170\t-0.27590718483285\t-0.59168211787820\t1.04792553943564\t-1.44913283833913\t-1.71554047709632\t0.92937971799566\t0.45187539696618\t1.56973338225729\t0.09924302883050\t-1.43045835132095\t-1.77900521980859\t0.97878159675935\t0.45962084160257\t0.00203998931280\t0.67057138105928\t0.13284630812698\t0.47422403034583\t-0.35687701161565\t0.90670081209711\t-1.35109741007581\t1.35258835724488\t0.72577510861552\t-0.09572608603917\t1.02184266769816\t-0.88361389539080\t-0.94127337729540\t1.59477698865504\t1.02092398022724\t0.09230308711426\t-0.04862094515233\t0.21076381406530\t1.64185343672488\t-0.24434612500460\t0.35034932531788\t0.75172685734187\t0.41889930681095\t-1.19270821234849\t0.56363994003780\t1.05623566225127\t1.09453422287246\t-1.03407151900200\t-0.04100745586156\t-1.49164829714866\t-0.58664552154587\t0.42604107028870\t-1.86180235120416\t-0.49850298818036\t0.14073031758719\t-1.18336380112792\t-0.71721011226228\t-0.01462447874399\t-2.21756850024936\t0.68942651065361\t1.51410447510684\t-1.25650641629701\t0.67624979937259\t1.64272679099395\t-0.59274406249763\t2.66282719183090\t-1.58227678600233\t-0.05242307406752\t0.96243709558340\t-0.33997777557902\t0.57406693541952\t-1.36830195639258\t0.11957236699383\t0.90590645568978\t1.21752159269684\t1.02860799311460\t0.89057952995516\t0.02075411792998\t1.76027271828188\t0.98122091065469\t0.03053194235190\t-58.56926535911965\n+0.11442909702019\t-0.60013542716991\t-1.17498506051978\t0.37789812105231\t0.80426397769103\t-0.25412543454522\t0.19100846967688\t0.05793637913866\t-0.57265676903169\t0.67137704493643\t1.00486767335942\t1.08035585383206\t-0.58595322636766\t0.31773863810775\t-1.48177365409568\t0.86636864807494\t-0.68610025158762\t0.98118972532235\t0.01499274526023\t-0.96048079613058\t-1.42376242684708\t-1.41447921097575\t-1.07641241372230\t-0.53471921590327\t0.63968104716280\t0.00003821688661\t-1.64789645160895\t-0.47946793783441\t-0.58027590555339\t-0.35626565190656\t-0.35395058607792\t1.22971874563225\t-1.11134507414587\t-1.94996110855379\t-0.18462590495313\t-1.08253549941625\t0.28175986241297\t1.43139435246322\t-0.21681273301629\t2.03318107641510\t-0.85554039248279\t0.80865738804815\t-0.81274796855477\t0.96225703674330\t0.83971775809643\t0.16367264651409\t0.37612382180038\t-1.20540534803405\t-0.39646871176150\t0.50440678609316\t-2.12269357911018\t-1.73337919698402\t0.66146222578848\t-1.25318872693810\t-1.73345228013854\t-0.63842960648510\t-0.52108483212612\t-1.07578377352847\t-0.17170592241337\t-1.58621109170536\t0.90224730254889\t0.10062624722110\t0.73537091959573\t-0.47506469682613\t-0.64652941101725\t0.95479548044025\t-2.06126245571583\t-0.89892744374809\t-0.64543661765593\t1.56589257557317\t0.05620965608259\t0.18979697580970\t0.21927974168967\t1.08315275138023\t1.43153004847297\t-0.27009563032084\t-1.13690656369851\t1.80239042546146\t-0.76721517469843\t-1.13280035299273\t0.20345737220567\t1.40956378493415\t-0.31306670625260\t0.01704629098668\t1.72791506643712\t-1.12339337581082\t-2.57092069804582\t-0.82500883083613\t1.87542985267408\t0.31904409765191\t-0.74511306613439\t0.73266290512627\t1.27807444106703\t0.97324132419371\t0.97757166179023\t0.33609045136699\t-0.42708309660138\t0.82163782590776\t-0.64757790004240\t0.73025258429468\t-40.12207652891863\n'
b
diff -r 7509d7059040 -r 4368259ff821 test-data/rfc_model01
b
Binary file test-data/rfc_model01 has changed
b
diff -r 7509d7059040 -r 4368259ff821 test-data/rfc_result01
--- a/test-data/rfc_result01 Thu Oct 11 03:30:01 2018 -0400
+++ b/test-data/rfc_result01 Sun Dec 30 01:51:27 2018 -0500
b
@@ -1,4 +1,5 @@
-3.68258022948 2.82110345641 -3.990140724 -1.9523364774 0
+0 1 2 3 predicted
+3.68258022948 2.82110345641 -3.9901407239999998 -1.9523364774 1
 0.015942057224 -0.7119585943469999 0.125502976978 -0.972218263337 0
 2.0869076882499997 0.929399321468 -2.1292408448400004 -1.9971402218799998 1
 1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 1
b
diff -r 7509d7059040 -r 4368259ff821 test-data/rfr_model01
b
Binary file test-data/rfr_model01 has changed
b
diff -r 7509d7059040 -r 4368259ff821 test-data/rfr_result01
--- a/test-data/rfr_result01 Thu Oct 11 03:30:01 2018 -0400
+++ b/test-data/rfr_result01 Sun Dec 30 01:51:27 2018 -0500
b
@@ -1,5 +1,6 @@
-86.9702122735 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 predicted
-91.2021798817 -0.6215229712070001 1.11914889596 0.390012184498 1.28956938152 0.8511213285107001
--47.4101632272 -0.638416457964 -0.7327774684530001 -0.8640261049779999 -1.06109770116 0.05344095304070007
-61.712804630200004 -1.0999480057700002 -0.739679672932 0.585657963012 1.4890682753600002 1.1892759745694002
--206.998295124 0.130238853011 0.70574123041 1.3320656526399999 -1.3322092373799999 -0.35023626536690006
+0 1 2 3 4 predicted
+86.97021227350001 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 0.6686209127804698
+91.2021798817 -0.6215229712070001 1.11914889596 0.390012184498 1.28956938152 1.0374491367850487
+-47.4101632272 -0.638416457964 -0.7327774684530001 -0.8640261049779999 -1.06109770116 -0.16198314840411981
+61.712804630200004 -1.0999480057700002 -0.739679672932 0.585657963012 1.4890682753600002 1.1603837128651284
+-206.998295124 0.130238853011 0.70574123041 1.3320656526399999 -1.3322092373799999 -0.6710618307873705
b
diff -r 7509d7059040 -r 4368259ff821 test-data/roc_curve.txt
--- a/test-data/roc_curve.txt Thu Oct 11 03:30:01 2018 -0400
+++ b/test-data/roc_curve.txt Sun Dec 30 01:51:27 2018 -0500
[
@@ -1,2 +1,2 @@
 roc_curve : 
-(array([0., 1.]), array([1., 1.]), array([1, 0]))
+(array([0., 0., 1.]), array([0., 1., 1.]), array([2, 1, 0]))
b
diff -r 7509d7059040 -r 4368259ff821 test-data/scurve.txt
--- a/test-data/scurve.txt Thu Oct 11 03:30:01 2018 -0400
+++ b/test-data/scurve.txt Sun Dec 30 01:51:27 2018 -0500
b
b'@@ -1,101 +1,101 @@\n 0\t1\t2\t0\n-0.3977671870599717\t1.556578430899697\t-0.08251361596021634\t0.40908193877996885\n--0.8687998111588782\t1.5591967972214993\t1.4951634963628657\t-2.0888193347997555\n--0.6528985856951757\t1.2206563064187876\t0.2425546641524292\t-0.7114049471333452\n--0.10763690701724954\t0.6180006970488043\t-1.994190271652142\t3.2494384932879354\n-0.9990111870215757\t1.395469815025912\t1.0444595119827287\t-4.667914808555971\n-0.4123520816278936\t1.7192365914581302\t1.911024566505834\t-3.5666270101178843\n-0.9992598944951743\t1.2506475155136152\t-1.038466391220041\t1.6092722105544868\n-0.07044439596581016\t1.9648156592190993\t-1.9975157076843513\t3.071089864770035\n-0.2786297608351926\t1.9530002540317106\t1.9603985924484288\t-3.4239597268300024\n-0.6501166570301194\t0.33338826239771624\t-0.24016558892612527\t0.7077379561867405\n--0.5197257228645411\t0.04635627295680722\t-1.8543331744658698\t3.688122530567428\n--0.3902353755064801\t0.3214890970141633\t1.9207151305932344\t-2.740705430508417\n--0.17497560644179438\t1.8469936505181748\t1.9845727688446022\t-2.9657116612605137\n-0.5221484980242218\t1.907099699759068\t1.8528545866740995\t-3.690960851680461\n--0.4792533123380228\t0.4219568374368927\t1.8776766275873098\t-2.6417888938563703\n--0.9797742589851274\t0.7210505016292164\t-1.2001059754983456\t4.510922897610046\n-0.20263517733108996\t1.0987505232553443\t-1.9792543004286465\t2.9375444742802506\n--0.049689070724760004\t0.5436616983539442\t1.998764735185674\t-3.0918831130301685\n-0.16054679698370528\t0.9212032421497003\t-1.9870282295751591\t2.9803480424411313\n--0.848046462831257\t1.3923231296467706\t1.5299218780909065\t-2.1293047696406013\n--0.6001374278102054\t1.00071179334973\t0.20010308930378384\t-0.6436729046245641\n--0.8444784826965399\t1.4321419811286722\t-1.5355894810977433\t4.147183346058282\n-0.14728007148811859\t1.051911872459558\t-1.9890948288927885\t2.993774864346985\n--0.9996571179505837\t0.002798046238087659\t0.9738151469216869\t-1.544608480534027\n--0.08231500933732551\t0.789400573379671\t1.9966063612268368\t-3.0591844018653584\n--0.93150531410199\t0.9843339398022999\t0.6362722861813346\t-1.1985297274428728\n-0.9985631716839931\t0.8057606627582843\t1.0535872387458443\t-4.6587760616584815\n--0.7230904998637341\t0.7085966002126411\t1.6907533054620987\t-2.333326647720376\n-0.34762708311665286\t1.0012286388859064\t-1.937632876494744\t2.7865534936848286\n-0.9896822071936343\t0.8903532576622766\t1.1432798965812592\t-4.568614263590023\n-0.802561656573729\t0.1808655763928717\t-0.40343081926935587\t0.9315768804436803\n-0.8295686274528585\t0.5471258400548813\t-0.44159522535531603\t0.9783347317989111\n-0.5478589244187156\t1.8869541954854538\t1.8365707375558675\t-3.721395392287229\n--0.8968769078869208\t0.05308928266788393\t0.5577197584119364\t-1.112656818224533\n-0.9414877787137416\t0.07999737928130157\t1.3370471221248814\t-4.36861024611527\n--0.5123763050446365\t0.5662807194396391\t-1.8587610389560099\t3.6795422987583914\n--0.9838764948236677\t1.1646883404335377\t-1.1788492184313182\t4.5325722940439555\n-0.8446207585774639\t1.9817856058496541\t1.5353650849467395\t-4.147449045247767\n--0.5134635265087703\t1.9852844748059362\t-1.8581114187243855\t3.6808088125450618\n-0.6629704248661121\t1.9862347449620896\t-0.2513544124534983\t0.7247795566955495\n-0.7554072589100717\t0.22009666193312594\t-1.6552555785233514\t2.285317229744828\n-0.9414665775358844\t1.3289628919278802\t-0.6628936616097694\t1.2269546953393253\n-0.6970998337140184\t1.0479736689766257\t-0.2830259266640892\t0.771344491181049\n-0.9815033210055982\t0.34629981961746203\t1.1914451118597227\t-4.519754699320089\n--0.39737776530431507\t1.8859204898300514\t1.9176551158478596\t-2.732935119957695\n-0.4088057226043997\t0.4837201719525046\t-0.0873785663453358\t0.42114505503778177\n-0.5689610393575872\t1.997864537686424\t-1.8223644786182913\t2.536350733545226\n--0.7117248012522958\t1.1653876302997974\t1.7024584025281355\t-2.3496421099311062\n--0.9017093613161719\t0.36655800126115157\t1.4323427202056043\t-2.0178855783591256\n--0.1786879758312358\t0.7736908438355805\t-1.9839057918791492\t3.3212454573701335\n--0.9723842056197265\t0.3793470578242992\t-1.23338585360148'..b'9981961746\t1.19144511185972\t-4.51975469932009\n+-0.39737776530432\t1.88592048983005\t1.91765511584786\t-2.73293511995769\n+0.40880572260440\t0.48372017195250\t-0.08737856634534\t0.42114505503778\n+0.56896103935759\t1.99786453768642\t-1.82236447861829\t2.53635073354523\n+-0.71172480125230\t1.16538763029980\t1.70245840252814\t-2.34964210993111\n+-0.90170936131617\t0.36655800126115\t1.43234272020560\t-2.01788557835913\n+-0.17868797583124\t0.77369084383558\t-1.98390579187915\t3.32124545737013\n+-0.97238420561973\t0.37934705782430\t-1.23338585360148\t4.47683073216571\n+-0.46670740559088\t0.82154134605062\t-1.88441178054492\t3.62715683862448\n+-0.96972627581563\t1.18936013780341\t0.75580550785746\t-1.32410737612446\n+0.80264918483869\t1.43317218625668\t-0.40354858866980\t0.93172361431463\n+-0.97961162574302\t0.97378296473825\t0.79909937105844\t-1.36851911905928\n+-0.99791255777848\t0.61917963553341\t0.93542038225568\t-1.50617173609004\n+-0.10737008100010\t1.15488274565569\t1.99421912358697\t-3.03401519525838\n+-0.62053033447500\t0.88341563913749\t1.78418244305541\t-2.47217384154222\n+0.91193663680462\t0.71935620520107\t1.41033104982866\t-4.28957192937856\n+0.05116766471514\t0.64266386401763\t-0.00130992290521\t0.05119001832686\n+-0.91929803801872\t0.41641448039205\t0.60643791176621\t-1.16629314143751\n+0.76733212242118\t0.90251724812367\t-0.35875011586699\t0.87467029775790\n+0.94069506387414\t0.98368582052811\t-0.66074670701256\t1.22467332521808\n+0.22488014225528\t1.79815262958742\t1.97438643341297\t-3.36841267959257\n+-0.81182171546475\t1.45872092205888\t-1.58390538814081\t4.08885791768265\n+-0.87500135199695\t1.54017954583939\t-1.48412047467908\t4.20703126278048\n+0.82154938436974\t0.75087849512398\t-0.42986264019825\t0.96412328524223\n+-0.87126700078957\t0.68747907047077\t0.50919065480052\t-1.05777788763274\n+-0.96067491767947\t1.31007041199864\t0.72232446535292\t-1.28942268363907\n+-0.34769588110474\t1.42207598642100\t1.93760736679209\t-2.78648011856577\n+-0.86121371561088\t0.22707515043735\t1.50824298917320\t-2.10393972942553\n+-0.68364804192970\t0.26605737874715\t1.72981186258219\t-2.38884304067862\n+-0.06537388681621\t0.91207811521225\t1.99786083945736\t-3.07617211177039\n+-0.95089045248252\t0.31947246031702\t-1.30952761973303\t4.39769276488546\n+-0.91904458599080\t1.92328380754929\t-1.39415358549809\t4.30724228359896\n+0.79764487680537\t1.67523148972362\t-0.39687260839343\t0.92338022079859\n+0.82012784273065\t1.04032137407585\t-1.57218032260641\t2.17995823974852\n+-0.99779218141586\t0.43654451545631\t0.93358642679862\t-1.50433383401485\n+0.64665631513320\t0.26983744506480\t1.76278149564495\t-3.84478535850956\n+-0.33727714800813\t1.95814069096774\t0.05859460091228\t-0.34402306664559\n+0.08189332342924\t1.41408699137829\t-0.00335889931344\t0.08198513727442\n+0.67212785139840\t1.71995111389133\t1.74043510949615\t-3.87867149146120\n+0.26116197153766\t0.77434525565728\t-0.03470500642417\t0.26422575485318\n+-0.99726999031677\t0.50166803966345\t-1.07384149520151\t4.63848021571996\n+-0.83562138750324\t0.59887603788940\t0.45069416829314\t-0.98926303703773\n+-0.99977253923164\t1.71379105681003\t0.97867232318507\t-1.54946703276290\n+0.25977496592172\t0.94596798113644\t-1.96566918097264\t2.87880349232616\n+0.67753788404984\t1.32655409403226\t-1.73548787595532\t2.39718279848362\n+-0.98181280555323\t1.61145721487357\t1.18985156004547\t-1.76180728144481\n+0.95308405257346\t0.50596100929945\t-0.69729422085108\t1.26326597551748\n+0.37166713582053\t0.15914687794065\t-0.07163394065112\t0.38080415080844\n+-0.94128581409549\t1.46552121003143\t1.33761074654488\t-1.91517377819665\n+0.50263574695009\t1.92279495500721\t1.86449829721517\t-3.66823761100048\n+-0.98104238623297\t1.90760946833533\t1.19379328268628\t-1.76582359131701\n+-0.39444347866163\t0.98099810376780\t0.08107979555278\t-0.40546214696481\n+0.99734968930361\t1.26438412886551\t-0.92724288869125\t1.49797487095100\n+-0.73490346461986\t1.46599003967598\t1.67817173170940\t-2.31606833009633\n+0.97111212627401\t1.80481900649593\t-0.76137636704726\t1.32984803323919\n+-0.31012473730097\t0.32449383749640\t1.95069587530083\t-2.82626841764302\n+0.99637118283100\t0.81176264473512\t-0.91488557100025\t1.48557879322013\n'
b
diff -r 7509d7059040 -r 4368259ff821 test-data/searchCV01
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Binary file test-data/searchCV01 has changed
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diff -r 7509d7059040 -r 4368259ff821 test-data/searchCV02
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Binary file test-data/searchCV02 has changed
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diff -r 7509d7059040 -r 4368259ff821 test-data/sparse_u.txt
--- a/test-data/sparse_u.txt Thu Oct 11 03:30:01 2018 -0400
+++ b/test-data/sparse_u.txt Sun Dec 30 01:51:27 2018 -0500
b
b'@@ -1,101 +1,101 @@\n 0\t1\t2\t3\t4\t5\t6\t7\t8\t9\t0\n--1.7497654730546974\t0.34268040332750216\t1.153035802563644\t-0.25243603652138985\t0.9813207869512316\t0.5142188413943821\t0.22117966922140045\t-1.0700433305682933\t-0.18949583082317534\t0.25500144427338167\t-3.2026895707176237\n--0.4580269855026243\t0.43516348812289213\t-0.5835950503226648\t0.816847071685779\t0.672720805709661\t-0.10441114339062771\t-0.5312803768519098\t1.0297326851333461\t-0.43813562270441736\t-1.1183182462554362\t-0.3226956819689937\n-1.6189816606752596\t1.5416051745134067\t-0.251879139213213\t-0.8424357382512976\t0.18451869056394285\t0.9370822011089522\t0.7310003438348087\t1.361556125145331\t-0.32623805920230253\t0.055676014854776905\t6.6047216717528965\n-0.22239960855530486\t-1.4432169952253369\t-0.7563523055944354\t0.8164540110192858\t0.7504447615341785\t-0.4559469274680022\t1.1896222680291255\t-1.690616826383604\t-1.3563990488613127\t-1.2324345139149262\t-2.5402638213726094\n--0.5444391616724643\t-0.6681717368134277\t0.007314563228903049\t-0.6129387354781626\t1.299748074755309\t-1.733095623653281\t-0.98331009912963\t0.3575077531673654\t-1.6135785028221759\t1.4707138666121289\t0.12371686928073311\n--1.1880175973177194\t-0.5497461935354897\t-0.9400461615447682\t-0.8279323643658708\t0.1088634678336795\t0.50780959049232\t-0.8622273465104797\t1.249469742726979\t-0.07961124591739943\t-0.8897314812650339\t0.24874422979446853\n--0.8817983894830175\t0.01863894948806016\t0.2378446219236218\t0.013548548628612411\t-1.635529399380822\t-1.044209877709317\t0.6130388816875463\t0.7362052133238238\t1.0269214393997905\t-1.4321906110589266\t-0.3078788026415402\n--1.841188300186717\t0.36609322616730366\t-0.3317771350528086\t-0.6892179780897532\t2.0346075615049335\t-0.5507144119145928\t0.7504533303268411\t-1.3069923390808191\t0.5805733357942744\t-1.1045230926622938\t-0.19844513286689391\n-0.6901214702247076\t0.6868900661384048\t-1.566687529578391\t0.9049741214666812\t0.7788223993230673\t0.4282328705967407\t0.10887198989791452\t0.028283634823073247\t-0.5788258247909884\t-1.1994511991939312\t5.457557440881324\n--1.7059520057381703\t0.3691639571070058\t1.8765734269621657\t-0.37690335016897475\t1.8319360818255361\t0.003017434031214063\t-0.07602346572462335\t0.003957593987599105\t-0.18501411089711395\t-2.4871515352227695\t-2.388554168522426\n--1.7046512057609624\t-1.1362610068273629\t-2.9733154740508856\t0.03331727813886288\t-0.24888866705810792\t-0.45017643501165083\t0.13242780114877378\t0.022213928039390988\t0.31736797594106797\t-0.7524141777250374\t1.946196089279402\n--1.2963918071501508\t0.095139443565453\t-0.42371509994342044\t-1.185983564929173\t-0.365461992676627\t-1.271023040846661\t1.5861709384232352\t0.6933906585165882\t-1.9580812342078666\t-0.13480131198999493\t0.8264174251866306\n--1.5406160245526137\t2.0467139684821385\t-1.3969993449532836\t-1.0971719846398227\t-0.23871286931467955\t-1.429066898448291\t0.9490047765052598\t-0.019397585962465276\t0.8945977057600133\t0.7596931198502055\t8.96789675053551\n--1.4977203810831696\t-1.193885976791938\t1.2962625863990573\t0.9522756260818906\t-1.2172541306410147\t-0.15726516737513793\t-1.5075851602643862\t0.10788413080661359\t0.7470556550991475\t0.4296764358626096\t-9.126817938253476\n--1.415042920852526\t-0.6407599230105716\t0.7796263036636958\t-0.43812091634884287\t2.0747931679465657\t-0.34329768218246975\t-0.6166293716831945\t0.7631836460599923\t0.19291719182330652\t-0.3484589306523706\t-4.730132384531053\n-2.2986539407136757\t-0.16520955264073184\t0.46629936835718944\t0.26998723863108903\t-0.3198310471180883\t-1.1477415998765863\t1.703623988120705\t-0.7221507700557533\t1.0936866496587212\t-0.2295177532399562\t3.157726161553892\n--0.008898663292110321\t-0.5431980084071721\t0.7530621876919789\t-1.6094388961729544\t1.943262263433996\t-1.4474361123195851\t0.13024845535270219\t0.9493608646609868\t-2.0151887171225265\t-0.07954058693411101\t-0.9240755528835013\n-0.30104946378806546\t-1.6848999616851803\t0.22239080944544862\t-0.6849217352472302\t-0.12620118371357705\t1.9902736497540905\t0.5229978045207515\t-0.016345402757487165\t-0.41581633584065\t-1.3585029367597998\t-2.2362357945556193\n--0.5144298913687853\t-0.21606012000326133\t0'..b'194420\t-1.08125857121519\t-0.06307879670507\t-0.50356048600791\t-2.05090576304937\t0.08725798075221\t-1.32944561779624\t-1.65101496770809\n+0.75637688792742\t0.82428920150463\t0.37967322200031\t0.52422365195372\t-0.45271329511708\t0.68759278675132\t0.91674695152792\t1.11971610167859\t1.26354483633054\t-1.45610559752933\t0.32205421816296\n+0.32128693891874\t-2.43702669941400\t0.97337371093006\t-0.64248112674987\t0.29283256357178\t-0.46398126201592\t0.38673364020325\t0.67249644253334\t-1.09097595301491\t-0.52700342019866\t-6.40574884617228\n+-0.30440284592937\t0.77081843337639\t-0.23575096986828\t-0.17778292517384\t2.28863529133324\t-2.52894751088469\t0.56775355409626\t0.07355255089438\t0.74832418672378\t0.91465664311128\t2.18526983290342\n+1.25223156262730\t-0.88472860123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b
diff -r 7509d7059040 -r 4368259ff821 test-data/svc_model01
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Binary file test-data/svc_model01 has changed
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diff -r 7509d7059040 -r 4368259ff821 test-data/svc_model02
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Binary file test-data/svc_model02 has changed
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diff -r 7509d7059040 -r 4368259ff821 test-data/svc_model03
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Binary file test-data/svc_model03 has changed
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diff -r 7509d7059040 -r 4368259ff821 test-data/svc_prediction_result03.tabular
--- a/test-data/svc_prediction_result03.tabular Thu Oct 11 03:30:01 2018 -0400
+++ b/test-data/svc_prediction_result03.tabular Sun Dec 30 01:51:27 2018 -0500
b
@@ -25,7 +25,7 @@
 1 -50 97 45 2
 1 -61 111 45 2
 2 -109 23 -92 2
-2 -94 20 -96 3
+2 -94 20 -96 1
 2 -85 26 -88 2
 2 -90 33 -114 0
 2 -63 9 -106 0
@@ -33,7 +33,7 @@
 2 -99 26 -108 1
 2 -81 19 -110 0
 2 -108 21 -108 1
-2 -92 27 -106 0
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 2 -88 2 -106 3
 2 -88 15 -103 3
 3 54 -74 4 3
b
diff -r 7509d7059040 -r 4368259ff821 test-data/swiss_r.txt
--- a/test-data/swiss_r.txt Thu Oct 11 03:30:01 2018 -0400
+++ b/test-data/swiss_r.txt Sun Dec 30 01:51:27 2018 -0500
b
b'@@ -1,101 +1,101 @@\n 0\t1\t2\t0\n--9.022432560391366\t16.34407352444682\t-3.911586790185589\t9.833859899549347\n-3.6324989224084425\t16.371566370825743\t6.373479468911753\t7.335958625969624\n--6.5999037486787\t12.81689121739727\t5.688948917237484\t8.713373013636033\n-12.600582699437291\t6.489007319012446\t1.3642134579818455\t12.674216454057314\n-0.21148781431603128\t14.652433057772075\t-4.752159504191911\t4.756863152213408\n-5.336919430343019\t18.05198421031037\t-2.4156207389915676\t5.8581509506514955\n-0.4244400906317155\t13.13179891289296\t-11.025883810051546\t11.034050171323868\n-12.464824437123061\t20.630564421800543\t-0.8802638610387414\t12.495867825539413\n-5.763177385414243\t20.50650266733296\t-1.6720065493379934\t6.000818233939377\n--7.699034264457019\t3.5005767551760205\t-6.587317375235983\t10.13251591695612\n-11.202785903218851\t0.4867408660464758\t6.815111686710793\t13.112900491336806\n-6.154126712593873\t3.3756355186487146\t2.6083615537589355\t6.684072530260963\n-6.3594207906583025\t19.393433330440836\t1.1301790428043181\t6.459066299508866\n-4.890112220636909\t20.024546847470212\t-2.9939039914563663\t5.7338171090889185\n-5.953270969209806\t4.430546793087373\t3.2507699778706525\t6.782989066913009\n-2.7886170145191778\t7.571030267106772\t13.653840981957098\t13.935700858379423\n-12.105857407807882\t11.536880494181116\t-2.5050413988504086\t12.362322435049629\n-6.325072045560973\t5.708447832716414\t0.31467565998177954\t6.332894847739211\n-12.244209556605643\t9.672634042571852\t-1.9916032459947095\t12.405126003210512\n-3.8660308550048237\t14.619392861291091\t6.186900234417021\t7.295473191128778\n--7.0239788069091595\t10.507473830172165\t5.269869801725942\t8.781105056144815\n-7.26899971380247\t15.037490801851058\t11.461229291605976\t13.571961306827662\n-12.283126381654526\t11.045074660825358\t-1.8290053478621142\t12.418552825116365\n--0.20634108007216687\t0.02937948549992042\t7.877467511574221\t7.880169480235352\n-6.343991033788326\t8.288706020486545\t0.5239838932388041\t6.365593558904021\n--2.992114463212683\t10.335506367924149\t7.662793944465749\t8.226248233326507\n-0.2553968816308009\t8.460486958961985\t-4.759153972628113\t4.766001899110898\n-4.898443435012139\t7.440264302232732\t5.1277610747119375\t7.091451313049004\n-11.449745837470637\t10.512900708302018\t-4.244989534482564\t12.211331454454207\n-0.6957906323135219\t9.348709205453904\t-4.806058806318065\t4.8561636971793565\n--6.178282122978317\t1.899088552125153\t-8.311613297429309\t10.35635484121306\n--5.809147798696848\t5.7448213205762535\t-8.630095917611289\t10.40311269256829\n-4.771282961878387\t19.813019052597266\t-3.1246490395170863\t5.70338256848215\n--3.676286947033984\t0.5574374680127813\t7.454949508307122\t8.312121142544846\n-1.7041667772049045\t0.8399724824536665\t-4.760320110473833\t5.0561677146541095\n-11.253479680884357\t5.94594755411621\t6.714343194698412\t13.104320259527771\n-2.4962611844455296\t12.229227574552146\t13.73230884573197\t13.957350254813335\n-2.82529764315011\t20.808748861421368\t-4.45734155189065\t5.277328915521612\n-11.246053659264389\t20.84548698546233\t6.7292408015927405\t13.105586773314442\n--7.598421450999555\t20.85546482210194\t-6.728856459556753\t10.149557517464928\n-7.673105198623886\t2.3110149502978223\t-8.845890909442362\t11.710095190514206\n--3.590766593212459\t13.954110365242743\t-10.028250288573874\t10.651732656108704\n--7.310355446606637\t11.00372352425457\t-7.107715265782408\t10.196122451950428\n-0.9390427269626991\t3.6361481059833514\t-4.81429662072219\t4.905023261449291\n-6.140803817320717\t19.80216514321554\t2.6591895538494246\t6.691842840811685\n--8.985600378339388\t5.079061805501299\t-4.025069673184332\t9.845923015807161\n-9.836407362386318\t20.977577645707452\t-6.805416213807093\t11.961128694314606\n-4.969988627449396\t12.236570118147872\t5.0355496572708605\t7.075135850838273\n-3.20231600088142\t3.8488590132420915\t6.678864199480768\t7.406892382410254\n-12.54088626453474\t8.123753860273595\t2.277561124484857\t12.746023418139512\n-3.2444388112344487\t3.983144107155142\t13.517704725715975\t13.901608692935088\n-11.5432848954881\t8.62618413353151\t6.0914346281664\t13.05193479939386\n--1.978139139431402\t12.488281446935813\t7.85543'..b'72219\t4.90502326144929\n+6.14080381732072\t19.80216514321554\t2.65918955384942\t6.69184284081168\n+-8.98560037833939\t5.07906180550130\t-4.02506967318433\t9.84592301580716\n+9.83640736238632\t20.97757764570745\t-6.80541621380709\t11.96112869431461\n+4.96998862744940\t12.23657011814787\t5.03554965727086\t7.07513585083827\n+3.20231600088142\t3.84885901324209\t6.67886419948077\t7.40689238241025\n+12.54088626453474\t8.12375386027360\t2.27756112448486\t12.74602341813951\n+3.24443881123445\t3.98314410715514\t13.51770472571597\t13.90160869293509\n+11.54328489548810\t8.62618413353151\t6.09143462816640\t13.05193479939386\n+-1.97813913943140\t12.48828144693581\t7.85543311765692\t8.10067058464492\n+-6.17714998090234\t15.04830795569514\t-8.31263754702176\t10.35650157508401\n+-1.6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b
diff -r 7509d7059040 -r 4368259ff821 utils.py
--- a/utils.py Thu Oct 11 03:30:01 2018 -0400
+++ b/utils.py Sun Dec 30 01:51:27 2018 -0500
[
b'@@ -1,21 +1,34 @@\n-import sys\n+import json\n+import numpy as np\n import os\n import pandas\n+import pickle\n import re\n-import pickle\n-import warnings\n-import numpy as np\n-import xgboost\n import scipy\n import sklearn\n+import sys\n+import warnings\n+import xgboost\n+\n from asteval import Interpreter, make_symbol_table\n-from sklearn import (cluster, decomposition, ensemble, feature_extraction, feature_selection,\n-                    gaussian_process, kernel_approximation, metrics,\n+from sklearn import (cluster, compose, decomposition, ensemble, feature_extraction,\n+                    feature_selection, gaussian_process, kernel_approximation, metrics,\n                     model_selection, naive_bayes, neighbors, pipeline, preprocessing,\n                     svm, linear_model, tree, discriminant_analysis)\n \n+try:\n+    import skrebate\n+except ModuleNotFoundError:\n+    pass\n+\n+\n N_JOBS = int(os.environ.get(\'GALAXY_SLOTS\', 1))\n \n+try:\n+    sk_whitelist\n+except NameError:\n+    sk_whitelist = None\n+\n \n class SafePickler(pickle.Unpickler):\n     """\n@@ -25,6 +38,13 @@\n     """\n     def find_class(self, module, name):\n \n+        # sk_whitelist could be read from tool\n+        global sk_whitelist\n+        if not sk_whitelist:\n+            whitelist_file = os.path.join(os.path.dirname(__file__), \'sk_whitelist.json\')\n+            with open(whitelist_file, \'r\') as f:\n+                sk_whitelist = json.load(f)\n+\n         bad_names = (\'and\', \'as\', \'assert\', \'break\', \'class\', \'continue\',\n                     \'def\', \'del\', \'elif\', \'else\', \'except\', \'exec\',\n                     \'finally\', \'for\', \'from\', \'global\', \'if\', \'import\',\n@@ -46,13 +66,14 @@\n                 or  (   (   module.startswith(\'sklearn.\')\n                             or module.startswith(\'xgboost.\')\n                             or module.startswith(\'skrebate.\')\n+                            or module.startswith(\'imblearn\')\n                             or module.startswith(\'numpy.\')\n                             or module == \'numpy\'\n                         )\n                         and (name not in bad_names)\n                     ):\n                 # TODO: replace with a whitelist checker\n-                if fullname not in sk_whitelist[\'SK_NAMES\'] + sk_whitelist[\'SKR_NAMES\'] + sk_whitelist[\'XGB_NAMES\'] + sk_whitelist[\'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\'] + sk_whitelist[\'IMBLEARN_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@@ -83,44 +104,56 @@\n         return y, data\n     else:\n         return y\n-    return y\n \n \n ## generate an instance for one of sklearn.feature_selection classes\n def feature_selector(inputs):\n-    selector = inputs["selected_algorithm"]\n+    selector = inputs[\'selected_algorithm\']\n     selector = getattr(sklearn.feature_selection, selector)\n-    options = inputs["options"]\n+    options = inputs[\'options\']\n \n     if inputs[\'selected_algorithm\'] == \'SelectFromModel\':\n         if not options[\'threshold\'] or options[\'threshold\'] == \'None\':\n             options[\'threshold\'] = None\n+        else:\n+            try:\n+                options[\'threshold\'] = float(options[\'threshold\'])\n+            except ValueError:\n+                pass\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 = load_model(model_handler)\n             new_selector = selector(fitted_estimator, prefit=True, **options)\n         else:\n-            estimator_json = inputs[\'model_inputter\']["estimator_selector"]\n+            estimator_json = inputs[\'model_in'..b'or_p":\n-            search_params["estimator__" + lst[0].strip()] = ev\n-        else:\n-            search_params["preprocessing_" + param_type[5:6] + "__" + lst[0].strip()] = ev\n-\n-    return search_params\n-\n \n def get_estimator(estimator_json):\n+\n     estimator_module = estimator_json[\'selected_module\']\n+\n+    if estimator_module == \'customer_estimator\':\n+        c_estimator = estimator_json[\'c_estimator\']\n+        with open(c_estimator, \'rb\') as model_handler:\n+            new_model = load_model(model_handler)\n+        return new_model\n+\n     estimator_cls = estimator_json[\'selected_estimator\']\n \n-    if estimator_module == "xgboost":\n+    if estimator_module == \'xgboost\':\n         cls = getattr(xgboost, estimator_cls)\n     else:\n         module = getattr(sklearn, estimator_module)\n@@ -244,7 +288,7 @@\n     estimator = cls()\n \n     estimator_params = estimator_json[\'text_params\'].strip()\n-    if estimator_params != "":\n+    if estimator_params != \'\':\n         try:\n             params = safe_eval(\'dict(\' + estimator_params + \')\')\n         except ValueError:\n@@ -256,32 +300,68 @@\n     return estimator\n \n \n-def get_cv(literal):\n-    safe_eval = SafeEval()\n-    if literal == "":\n-        return None\n-    if literal.isdigit():\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+def get_cv(cv_json):\n+    """\n+    cv_json:\n+            e.g.:\n+            {\n+                \'selected_cv\': \'StratifiedKFold\',\n+                \'n_splits\': 3,\n+                \'shuffle\': True,\n+                \'random_state\': 0\n+            }\n+    """\n+    cv = cv_json.pop(\'selected_cv\')\n+    if cv == \'default\':\n+        return cv_json[\'n_splits\'], None\n+\n+    groups = cv_json.pop(\'groups\', None)\n+    if groups:\n+        groups = groups.strip()\n+        if groups != \'\':\n+            if groups.startswith(\'__ob__\'):\n+                groups = groups[6:]\n+            if groups.endswith(\'__cb__\'):\n+                groups = groups[:-6]\n+            groups = [int(x.strip()) for x in groups.split(\',\')]\n+\n+    for k, v in cv_json.items():\n+        if v == \'\':\n+            cv_json[k] = None\n+\n+    test_fold = cv_json.get(\'test_fold\', None)\n+    if test_fold:\n+        if test_fold.startswith(\'__ob__\'):\n+            test_fold = test_fold[6:]\n+        if test_fold.endswith(\'__cb__\'):\n+            test_fold = test_fold[:-6]\n+        cv_json[\'test_fold\'] = [int(x.strip()) for x in test_fold.split(\',\')]\n+\n+    test_size = cv_json.get(\'test_size\', None)\n+    if test_size and test_size > 1.0:\n+        cv_json[\'test_size\'] = int(test_size)\n+\n+    cv_class = getattr(model_selection, cv)\n+    splitter = cv_class(**cv_json)\n+\n+    return splitter, groups\n+\n+\n+# needed when sklearn < v0.20\n+def balanced_accuracy_score(y_true, y_pred):\n+    C = metrics.confusion_matrix(y_true, y_pred)\n+    with np.errstate(divide=\'ignore\', invalid=\'ignore\'):\n+        per_class = np.diag(C) / C.sum(axis=1)\n+    if np.any(np.isnan(per_class)):\n+        warnings.warn(\'y_pred contains classes not in y_true\')\n+        per_class = per_class[~np.isnan(per_class)]\n+    score = np.mean(per_class)\n+    return score\n \n \n def get_scoring(scoring_json):\n-    def balanced_accuracy_score(y_true, y_pred):\n-        C = metrics.confusion_matrix(y_true, y_pred)\n-        with np.errstate(divide=\'ignore\', invalid=\'ignore\'):\n-            per_class = np.diag(C) / C.sum(axis=1)\n-        if np.any(np.isnan(per_class)):\n-            warnings.warn(\'y_pred contains classes not in y_true\')\n-            per_class = per_class[~np.isnan(per_class)]\n-        score = np.mean(per_class)\n-        return score\n \n-    if scoring_json[\'primary_scoring\'] == "default":\n+    if scoring_json[\'primary_scoring\'] == \'default\':\n         return None\n \n     my_scorers = metrics.SCORERS\n'