| Previous changeset 15:931b7b90377b (2018-10-11) Next changeset 17:40ee30b5e456 (2019-05-15) |
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Commit message:
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d |
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modified:
main_macros.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 |
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added:
search_model_validation.py test-data/imblearn_X.tabular test-data/imblearn_y.tabular test-data/pipeline11 test-data/pipeline12 |
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removed:
test-data/cluster_result12 |
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| diff -r 931b7b90377b -r 268ecaf96b68 main_macros.xml --- a/main_macros.xml Thu Oct 11 03:36:25 2018 -0400 +++ b/main_macros.xml Sun Dec 30 01:59:44 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' |
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| diff -r 931b7b90377b -r 268ecaf96b68 search_model_validation.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/search_model_validation.py Sun Dec 30 01:59:44 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" |
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| diff -r 931b7b90377b -r 268ecaf96b68 sk_whitelist.json --- a/sk_whitelist.json Thu Oct 11 03:36:25 2018 -0400 +++ b/sk_whitelist.json Sun Dec 30 01:59:44 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 |
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| diff -r 931b7b90377b -r 268ecaf96b68 test-data/abc_model01 |
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| Binary file test-data/abc_model01 has changed |
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| diff -r 931b7b90377b -r 268ecaf96b68 test-data/abr_model01 |
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| Binary file test-data/abr_model01 has changed |
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| diff -r 931b7b90377b -r 268ecaf96b68 test-data/blobs.txt --- a/test-data/blobs.txt Thu Oct 11 03:36:25 2018 -0400 +++ b/test-data/blobs.txt Sun Dec 30 01:59:44 2018 -0500 |
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| @@ -1,101 +1,101 @@ 0 1 0 -0.3368184589673989 -3.402879612990731 0 +0.33681845896740 -3.40287961299073 0 -9.48324265575857 -8.66266051536995 2 --1.9333632849607592 5.709539081468901 1 --10.031824059894126 -5.578343934583625 2 -0.5418607766170062 -4.3769362832693 0 --8.129629290673781 -7.05554320549807 2 --0.7308257856942714 7.323755516994815 1 --1.8414253200701474 6.206304668308321 1 -0.4100718503166844 -3.997448810001185 0 --8.735095893232405 -5.490905352087513 2 -1.8494196227705402 -3.918393456729695 0 --9.152560688488569 -9.17805648051067 2 --3.214299390778301 5.7592616395707115 1 -0.2845037854966439 -3.615765226438298 0 --0.9290748492230643 5.79099955373578 1 -0.366925241944827 6.518619296229099 1 -1.5990991796541174 -3.0710561729787464 0 --9.71270568435724 -7.917076514990086 2 --10.080404430632045 -6.551353241086546 2 -1.1059434577429306 -4.419063749495465 0 -2.4870804964945683 -2.8910071236106702 0 -0.00587148930882897 -3.1831425553970982 0 -1.6185435973534872 -4.888559225592079 0 --9.1585672210814 -7.138941148475106 2 --3.0763357145957295 7.800496767864756 1 -0.11174653022487324 -3.6161582871047915 0 --9.439323507823358 -7.298630345706627 2 --1.6946622959144526 4.408371111175304 1 -1.0526175263832516 -3.495530097015125 0 --10.505605921029415 -5.99245086001851 2 -1.5408196415289697 -4.537023441514705 0 -0.32228789680819747 6.898540080429289 1 -0.6162196966060958 -5.275048036375375 0 --10.225453923298636 -8.716359184214301 2 --10.610041075915571 -8.159992705422887 2 --0.7454796670028672 -2.9618984315119485 0 -0.7884875899019093 -5.3223437793891115 0 --10.420052767549333 -7.784677704340977 2 --2.9066475299706225 5.798350661758252 1 --10.321439212021199 -8.927120521097516 2 --0.21338559861828132 7.847798272479965 1 --0.07194732572545948 -5.260544662489948 0 --7.606968935466872 -7.733827136978448 2 --1.3772203838685648 6.917736574437465 1 --3.215600190755509 7.26468660350508 1 --10.361544895394568 -6.919444657083034 2 --9.604573412392483 -9.253517546022897 2 --2.7269023156583536 6.738257479022937 1 --2.8060399921674897 6.990662089963528 1 --0.8195267147926311 7.582412712536479 1 --2.088474009808327 5.696071447204143 1 --0.3199187614984107 -4.982358491659567 0 --11.320665797033074 -8.209377507348288 2 --7.962360612746552 -9.0160536966573 2 -2.1678469105746174 -6.165707921777358 0 -1.8950202752190992 -5.864802909183004 0 --8.668714990990322 -7.798902262764823 2 -2.057721103848434 -6.123229124507681 0 --9.31359960682017 -8.005681999989289 2 --0.7674305635615133 -5.476822175833394 0 --3.467729419225205 6.760721334408079 1 -1.0904984443746135 -5.875829293349414 0 --0.11521126331032128 -4.075104544956712 0 -1.0892785050407092 -5.50265562869237 0 --0.6150504792573254 7.65521576624828 1 -0.42996321311489133 -5.550930544379513 0 --0.7591948546904975 5.588530307317255 1 --9.125996572516852 -8.00673850068656 2 --9.775374420827845 -6.619256719676729 2 --3.017233345281725 7.003406777204688 1 --0.9730894643674084 -4.066519071956773 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| b |
| diff -r 931b7b90377b -r 268ecaf96b68 test-data/circles.txt --- a/test-data/circles.txt Thu Oct 11 03:36:25 2018 -0400 +++ b/test-data/circles.txt Sun Dec 30 01:59:44 2018 -0500 |
| b |
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0 +0.53582679497900 0.84432792550202 0 |
| b |
| diff -r 931b7b90377b -r 268ecaf96b68 test-data/classification_report.txt --- a/test-data/classification_report.txt Thu Oct 11 03:36:25 2018 -0400 +++ b/test-data/classification_report.txt Sun Dec 30 01:59:44 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 + 0 1.00 1.00 1.00 14 + 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 931b7b90377b -r 268ecaf96b68 test-data/cluster_result12 --- a/test-data/cluster_result12 Thu Oct 11 03:36:25 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
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| b |
| diff -r 931b7b90377b -r 268ecaf96b68 test-data/feature_selection_result12 --- a/test-data/feature_selection_result12 Thu Oct 11 03:36:25 2018 -0400 +++ b/test-data/feature_selection_result12 Sun Dec 30 01:59:44 2018 -0500 |
| b |
| @@ -1,11 +1,11 @@ 0 1 -143.762620712 -1.1796457192799998 --88.5787166225 -2.5710918402200003 --82.8452345578 -0.168636324107 -72.4951388149 0.991068834926 -11.805182128 -0.7096855607860001 --63.9354970901 0.9841122108220001 -126.32584079600001 0.35353444883900004 -23.0341392692 1.03188231893 -67.6714937696 -0.8214378651719999 -47.39275848810001 -0.0942409319417 +143.762620712 -0.330941870584 +-88.5787166225 1.08055532812 +-82.8452345578 0.272541389247 +72.4951388149 -0.26868660527800003 +11.805182128 1.0360467096600001 +-63.9354970901 -0.101485840571 +126.32584079600001 -0.35999834017899995 +23.0341392692 0.5185404651359999 +67.6714937696 -0.115688051547 +47.39275848810001 -0.7850965413680001 |
| b |
| diff -r 931b7b90377b -r 268ecaf96b68 test-data/friedman1.txt --- a/test-data/friedman1.txt Thu Oct 11 03:36:25 2018 -0400 +++ b/test-data/friedman1.txt Sun Dec 30 01:59:44 2018 -0500 |
| b |
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|
| b |
| diff -r 931b7b90377b -r 268ecaf96b68 test-data/friedman2.txt --- a/test-data/friedman2.txt Thu Oct 11 03:36:25 2018 -0400 +++ b/test-data/friedman2.txt Sun Dec 30 01:59:44 2018 -0500 |
| b |
| b'@@ -1,101 +1,101 @@\n 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|
| b |
| diff -r 931b7b90377b -r 268ecaf96b68 test-data/friedman3.txt --- a/test-data/friedman3.txt Thu Oct 11 03:36:25 2018 -0400 +++ b/test-data/friedman3.txt Sun Dec 30 01:59:44 2018 -0500 |
| b |
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|
| b |
| diff -r 931b7b90377b -r 268ecaf96b68 test-data/gaus.txt --- a/test-data/gaus.txt Thu Oct 11 03:36:25 2018 -0400 +++ b/test-data/gaus.txt Sun Dec 30 01:59:44 2018 -0500 |
| b |
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|
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| diff -r 931b7b90377b -r 268ecaf96b68 test-data/gbc_model01 |
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| Binary file test-data/gbc_model01 has changed |
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| diff -r 931b7b90377b -r 268ecaf96b68 test-data/gbc_result01 --- a/test-data/gbc_result01 Thu Oct 11 03:36:25 2018 -0400 +++ b/test-data/gbc_result01 Sun Dec 30 01:59:44 2018 -0500 |
| b |
| @@ -1,6 +1,6 @@ 0 1 2 3 predicted -3.68258022948 2.82110345641 -3.990140724 -1.9523364774 1 -0.015942057224 -0.711958594347 0.125502976978 -0.972218263337 0 -2.08690768825 0.929399321468 -2.12924084484 -1.99714022188 1 -1.41321052084 0.523750660422 -1.4210539291 -1.49298569451 1 -0.76831404394 1.38267855169 -0.989045048734 0.649504257894 1 +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 +0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1 |
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| diff -r 931b7b90377b -r 268ecaf96b68 test-data/gbr_model01 |
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| Binary file test-data/gbr_model01 has changed |
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| diff -r 931b7b90377b -r 268ecaf96b68 test-data/gbr_prediction_result01.tabular --- a/test-data/gbr_prediction_result01.tabular Thu Oct 11 03:36:25 2018 -0400 +++ b/test-data/gbr_prediction_result01.tabular Sun Dec 30 01:59:44 2018 -0500 |
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| diff -r 931b7b90377b -r 268ecaf96b68 test-data/hastie.txt --- a/test-data/hastie.txt Thu Oct 11 03:36:25 2018 -0400 +++ b/test-data/hastie.txt Sun Dec 30 01:59:44 2018 -0500 |
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| b'@@ -1,12001 +1,12001 @@\n 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| diff -r 931b7b90377b -r 268ecaf96b68 test-data/imblearn_X.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/imblearn_X.tabular Sun Dec 30 01:59:44 2018 -0500 |
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|
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| diff -r 931b7b90377b -r 268ecaf96b68 test-data/imblearn_y.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/imblearn_y.tabular Sun Dec 30 01:59:44 2018 -0500 |
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| @@ -0,0 +1,1001 @@ +0 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +0 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +0 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +0 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +0 +1 +0 +1 +0 +1 +1 +1 +1 +1 +0 +0 +1 +1 +0 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +0 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +0 +0 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +0 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +0 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +0 +1 +0 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +0 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +0 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +0 +1 +0 +1 +0 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +0 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +0 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +0 +0 +1 +1 +1 +0 +1 +1 +1 +1 +0 +0 +1 +1 +0 +1 +1 +1 +1 |
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| diff -r 931b7b90377b -r 268ecaf96b68 test-data/lda_model01 |
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| Binary file test-data/lda_model01 has changed |
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| diff -r 931b7b90377b -r 268ecaf96b68 test-data/lda_model02 |
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| Binary file test-data/lda_model02 has changed |
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| diff -r 931b7b90377b -r 268ecaf96b68 test-data/moons.txt --- a/test-data/moons.txt Thu Oct 11 03:36:25 2018 -0400 +++ b/test-data/moons.txt Sun Dec 30 01:59:44 2018 -0500 |
| b |
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| b'@@ -1,101 +1,101 @@\n 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|
| b |
| diff -r 931b7b90377b -r 268ecaf96b68 test-data/rfc_model01 |
| b |
| Binary file test-data/rfc_model01 has changed |
| b |
| diff -r 931b7b90377b -r 268ecaf96b68 test-data/rfc_result01 --- a/test-data/rfc_result01 Thu Oct 11 03:36:25 2018 -0400 +++ b/test-data/rfc_result01 Sun Dec 30 01:59:44 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 931b7b90377b -r 268ecaf96b68 test-data/rfr_model01 |
| b |
| Binary file test-data/rfr_model01 has changed |
| b |
| diff -r 931b7b90377b -r 268ecaf96b68 test-data/rfr_result01 --- a/test-data/rfr_result01 Thu Oct 11 03:36:25 2018 -0400 +++ b/test-data/rfr_result01 Sun Dec 30 01:59:44 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 931b7b90377b -r 268ecaf96b68 test-data/roc_curve.txt --- a/test-data/roc_curve.txt Thu Oct 11 03:36:25 2018 -0400 +++ b/test-data/roc_curve.txt Sun Dec 30 01:59:44 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 931b7b90377b -r 268ecaf96b68 test-data/scurve.txt --- a/test-data/scurve.txt Thu Oct 11 03:36:25 2018 -0400 +++ b/test-data/scurve.txt Sun Dec 30 01:59:44 2018 -0500 |
| b |
| b'@@ -1,101 +1,101 @@\n 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|
| b |
| diff -r 931b7b90377b -r 268ecaf96b68 test-data/searchCV01 |
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| Binary file test-data/searchCV01 has changed |
| b |
| diff -r 931b7b90377b -r 268ecaf96b68 test-data/searchCV02 |
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| Binary file test-data/searchCV02 has changed |
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| diff -r 931b7b90377b -r 268ecaf96b68 test-data/sparse_u.txt --- a/test-data/sparse_u.txt Thu Oct 11 03:36:25 2018 -0400 +++ b/test-data/sparse_u.txt Sun Dec 30 01:59:44 2018 -0500 |
| b |
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|
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| diff -r 931b7b90377b -r 268ecaf96b68 test-data/svc_prediction_result03.tabular --- a/test-data/svc_prediction_result03.tabular Thu Oct 11 03:36:25 2018 -0400 +++ b/test-data/svc_prediction_result03.tabular Sun Dec 30 01:59:44 2018 -0500 |
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| @@ -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 +2 -92 27 -106 1 2 -88 2 -106 3 2 -88 15 -103 3 3 54 -74 4 3 |
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| diff -r 931b7b90377b -r 268ecaf96b68 test-data/swiss_r.txt --- a/test-data/swiss_r.txt Thu Oct 11 03:36:25 2018 -0400 +++ b/test-data/swiss_r.txt Sun Dec 30 01:59:44 2018 -0500 |
| b |
| b'@@ -1,101 +1,101 @@\n 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12975159\t7.89200482133423\t8.05625884171010\n+-0.51138056305762\t6.50138617310080\t7.90207659171039\t7.91860622467934\n+6.35381855577856\t12.12626882938480\t0.68617671578531\t6.39076276551100\n+5.45211008381264\t9.27586421094360\t4.31430175957628\t6.95260411922716\n+2.10713448194703\t7.55324015461126\t-4.68298251756532\t5.13520603139082\n+-9.46355519159139\t6.74797057218509\t-0.48486315240582\t9.47596797909624\n+-3.25022653114329\t4.37235204411648\t7.59200889141913\t8.25848481933186\n+-6.60452000241460\t9.47643110529852\t-7.90309749198288\t10.29944825852728\n+-3.61286141728060\t10.32870111554513\t-10.01788625769654\t10.64945128598746\n+5.90124016577200\t18.88060261066794\t-1.36195628598096\t6.05636528117681\n+7.89068480280119\t15.31656968161827\t10.97066306101087\t13.51363587845203\n+6.59943795203969\t16.17188523131361\t11.92785150077068\t13.63180922354986\n+-5.92310072764215\t7.88422419880175\t-8.53499542293883\t10.38890124601161\n+-4.10660182718020\t7.21853023994307\t7.28989105932790\t8.36700007313664\n+-2.25898912612109\t13.75573932598577\t7.81543176115041\t8.13535527713031\n+6.22411695981015\t14.93179785742045\t2.30810881728065\t6.63829784220360\n+3.72076470595161\t2.38428907959220\t6.30480629460181\t7.32083823134385\n+5.13490876903855\t2.79360247684508\t4.81010313126482\t7.03593492009076\n+6.33502516186602\t9.57682020972860\t0.41503304021319\t6.34860584899899\n+4.27843646254146\t3.35446083332871\t13.14365544274426\t13.82247072565484\n+5.41252501545014\t20.19447997926756\t12.62033886030277\t13.73202024436834\n+-6.24125765198132\t17.58993064209800\t-8.25415535789923\t10.34815818156797\n+6.64000170297454\t10.92337442779639\t-9.51736726558904\t11.60473620051790\n+-0.52602499579966\t4.58371741229124\t7.90295722301686\t7.92044412675453\n+4.25631510283951\t2.83329317318037\t-3.60833745464785\t5.57999260225982\n+-8.54867168512033\t20.56047725516125\t3.06273111245095\t9.08075489412379\n+-9.47483083800073\t14.84791340947201\t-0.77854042515321\t9.50676309804379\n+4.10653195087950\t18.05948669585893\t-3.72769262484286\t5.54610646930818\n+-9.35274677942807\t8.13062518440142\t-2.53039931260771\t9.68900371562256\n+1.03845201115681\t5.26751441646622\t14.02486534548972\t14.06325817648934\n+-4.63367754105607\t6.28819839783875\t7.04889668487289\t8.43551492373165\n+-0.16796208628964\t17.99480609650533\t7.87351960373171\t7.87531092800648\n+11.88118942484094\t9.93266380193264\t-3.19616245269299\t12.30358145309554\n+8.69490880845012\t13.92881798733869\t-8.00982627814450\t11.82196075925300\n+1.45482693805244\t16.92030075617249\t7.52360274153979\t7.66297067932457\n+-3.23533266731161\t5.31259059764421\t-10.18660422887945\t10.68804393628686\n+-9.10316962454735\t1.67104221837682\t-3.64441261846314\t9.80558211157782\n+2.53532307433492\t15.38797270533002\t7.06868388652787\t7.50960418257273\n+4.97651933022564\t20.18934702757571\t-2.89344295855440\t5.75654034976890\n+1.48425390920063\t20.02989941752092\t7.51375887065696\t7.65895436945237\n+-8.28803153159531\t10.30048008956189\t3.55761030474492\t9.01931581380457\n+-0.79470794357540\t13.27603335308786\t-10.89380414305644\t10.92275283172038\n+4.82092592045283\t15.39289541659784\t5.22421533655818\t7.10870963067305\n+-2.56630792573832\t18.95059956820729\t-10.44394771632344\t10.75462599400857\n+6.27317580578337\t3.40718529371220\t2.04636103864001\t6.59850954312636\n+-0.92862878529940\t8.52350776971879\t-10.87076506408071\t10.91035675398950\n' |
| b |
| diff -r 931b7b90377b -r 268ecaf96b68 utils.py --- a/utils.py Thu Oct 11 03:36:25 2018 -0400 +++ b/utils.py Sun Dec 30 01:59:44 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' |