Mercurial > repos > bgruening > sklearn_svm_classifier
changeset 7:372582a7a34d draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d
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--- a/main_macros.xml Thu Oct 11 03:29:23 2018 -0400 +++ b/main_macros.xml Sun Dec 30 01:50:39 2018 -0500 @@ -1,13 +1,13 @@ <macros> - <token name="@VERSION@">0.9</token> + <token name="@VERSION@">1.0</token> <xml name="python_requirements"> <requirements> <requirement type="package" version="3.6">python</requirement> - <requirement type="package" version="0.19.1">scikit-learn</requirement> - <requirement type="package" version="0.22.0">pandas</requirement> - <requirement type="package" version="0.72.1">xgboost</requirement> - <requirement type="package" version="0.9.12">asteval</requirement> + <requirement type="package" version="0.20.2">scikit-learn</requirement> + <requirement type="package" version="0.23.4">pandas</requirement> + <requirement type="package" version="0.80">xgboost</requirement> + <requirement type="package" version="0.9.13">asteval</requirement> <yield /> </requirements> </xml> @@ -244,7 +244,7 @@ <param argument="shuffle" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="@CHECKED@" label="@LABEL@" help="@HELP_TEXT@"/> </xml> - <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."> + <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."> <param argument="random_state" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Random seed number" help="@HELP_TEXT@"/> </xml> @@ -346,20 +346,20 @@ <xml name="samples_column_selector_options" token_column_option="selected_column_selector_option" token_col_name="col1" token_multiple="False" token_infile="infile1"> <param name="@COLUMN_OPTION@" type="select" label="Choose how to select data by column:"> <option value="by_index_number" selected="true">Select columns by column index number(s)</option> + <option value="all_but_by_index_number">All columns BUT by column index number(s)</option> <option value="by_header_name">Select columns by column header name(s)</option> - <option value="all_but_by_index_number">All columns but by column index number(s)</option> - <option value="all_but_by_header_name">All columns but by column header name(s)</option> + <option value="all_but_by_header_name">All columns BUT by column header name(s)</option> <option value="all_columns">All columns</option> </param> <when value="by_index_number"> <param name="@COL_NAME@" multiple="@MULTIPLE@" type="data_column" data_ref="@INFILE@" label="Select target column(s):"/> </when> + <when value="all_but_by_index_number"> + <param name="@COL_NAME@" multiple="@MULTIPLE@" type="data_column" data_ref="@INFILE@" label="Select target column(s):"/> + </when> <when value="by_header_name"> <param name="@COL_NAME@" type="text" value="" label="Type header name(s):" help="Comma-separated string. For example: target1,target2"/> </when> - <when value="all_but_by_index_number"> - <param name="@COL_NAME@" multiple="@MULTIPLE@" type="data_column" data_ref="@INFILE@" label="Select target column(s):"/> - </when> <when value="all_but_by_header_name"> <param name="@COL_NAME@" type="text" value="" label="Type header name(s):" help="Comma-separated string. For example: target1,target2"/> </when> @@ -543,9 +543,18 @@ <!--param argument="precompute_distances"/--> <expand macro="random_state"/> <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."/> + <expand macro="kmeans_algorithm"/> </section> </xml> + <xml name="kmeans_algorithm"> + <param argument="algorithm" type="select" label="K-means algorithm to use:"> + <option value="auto" selected="true">auto</option> + <option value="full">full</option> + <option value="elkan">elkan</option> + </param> + </xml> + <xml name="birch_advanced_options"> <section name="options" title="Advanced Options" expanded="False"> <param argument="threshold" type="float" optional="true" value="0.5" label="Subcluster radius threshold" help="The radius of the subcluster obtained by merging a new sample; the closest subcluster should be less than the threshold to avoid a new subcluster."/> @@ -730,8 +739,8 @@ </param> <param argument="missing_values" type="text" optional="true" value="NaN" label="Placeholder for missing values" help="For missing values encoded as numpy.nan, use the string value “NaN”"/> - <param argument="axis" type="boolean" optional="true" truevalue="1" falsevalue="0" - label="Impute along axis = 1" help="If fasle, axis = 0 is selected for imputation. "/> + <!--param argument="axis" type="boolean" optional="true" truevalue="1" falsevalue="0" + label="Impute along axis = 1" help="If fasle, axis = 0 is selected for imputation. "/> --> <!--param argument="axis" type="select" optional="true" label="The axis along which to impute" help=" "> <option value="0" selected="true">Impute along columns</option> <option value="1">Impute along rows</option> @@ -802,136 +811,285 @@ </expand> </xml> - <xml name="fs_selectfrommodel_prefitted"> - <param name="input_mode" type="select" label="Construct a new estimator from a selection list?" > - <option value="new" selected="true">Yes</option> - <option value="prefitted">No. Load a prefitted estimator</option> - </param> - <when value="new"> - <expand macro="estimator_selector_all"/> - </when> - <when value="prefitted"> - <param name="fitted_estimator" type="data" format='zip' label="Load a prefitted estimator" /> - </when> + <xml name="cv_splitter"> + <option value="default" selected="true">default splitter</option> + <option value="KFold">KFold</option> + <option value="StratifiedKFold">StratifiedKFold</option> + <option value="LeaveOneOut">LeaveOneOut</option> + <option value="LeavePOut">LeavePOut</option> + <option value="RepeatedKFold">RepeatedKFold</option> + <option value="RepeatedStratifiedKFold">RepeatedStratifiedKFold</option> + <option value="ShuffleSplit">ShuffleSplit</option> + <option value="StratifiedShuffleSplit">StratifiedShuffleSplit</option> + <option value="TimeSeriesSplit">TimeSeriesSplit</option> + <option value="PredefinedSplit">PredefinedSplit</option> + <yield/> </xml> - <xml name="fs_selectfrommodel_no_prefitted"> - <param name="input_mode" type="select" label="Construct a new estimator from a selection list?" > - <option value="new" selected="true">Yes</option> - </param> - <when value="new"> - <expand macro="estimator_selector_all"/> + <xml name="cv_splitter_options"> + <when value="default"> + <expand macro="cv_n_splits"/> + </when> + <when value="KFold"> + <expand macro="cv_n_splits"/> + <expand macro="cv_shuffle"/> + <expand macro="random_state"/> + </when> + <when value="StratifiedKFold"> + <expand macro="cv_n_splits"/> + <expand macro="cv_shuffle"/> + <expand macro="random_state"/> + </when> + <when value="LeaveOneOut"> + </when> + <when value="LeavePOut"> + <param argument="p" type="integer" value="" label="p" help="Integer. Size of the test sets."/> + </when> + <when value="RepeatedKFold"> + <expand macro="cv_n_splits" value="5"/> + <param argument="n_repeats" type="integer" value="10" label="n_repeats" help="Number of times cross-validator needs to be repeated." /> + <expand macro="random_state" /> </when> + <when value="RepeatedStratifiedKFold"> + <expand macro="cv_n_splits" value="5"/> + <param argument="n_repeats" type="integer" value="10" label="n_repeats" help="Number of times cross-validator needs to be repeated." /> + <expand macro="random_state" /> + </when> + <when value="ShuffleSplit"> + <expand macro="cv_n_splits" value="10" help="Number of re-shuffling and splitting iterations."/> + <expand macro="cv_test_size" value="0.1" /> + <expand macro="random_state"/> + </when> + <when value="StratifiedShuffleSplit"> + <expand macro="cv_n_splits" value="10" help="Number of re-shuffling and splitting iterations."/> + <expand macro="cv_test_size" value="0.1" /> + <expand macro="random_state"/> + </when> + <when value="TimeSeriesSplit"> + <expand macro="cv_n_splits"/> + <param argument="max_train_size" type="integer" value="" optional="true" label="Maximum size of the training set" help="Maximum size for a single training set." /> + </when> + <when value="PredefinedSplit"> + <param argument="test_fold" type="text" value="" area="true" label="test_fold" help="List, e.g., [0, 1, -1, 1], represents two test sets, [X[0]] and [X[1], X[3]], X[2] is excluded from any test set due to '-1'."/> + </when> + <yield/> </xml> <xml name="cv"> - <param argument="cv" type="text" value="" optional="true" label="cv" help="Optional. Integer or evalable splitter object, e.g., StratifiedKFold(n_splits=3, shuffle=True, random_state=10). Leave blank for default." > - <sanitizer> - <valid initial="default"> - <add value="'"/> - </valid> - </sanitizer> - </param> + <conditional name="cv_selector"> + <param name="selected_cv" type="select" label="Select the cv splitter:"> + <expand macro="cv_splitter"> + <option value="GroupKFold">GroupKFold</option> + <option value="GroupShuffleSplit">GroupShuffleSplit</option> + <option value="LeaveOneGroupOut">LeaveOneGroupOut</option> + <option value="LeavePGroupsOut">LeavePGroupsOut</option> + </expand> + </param> + <expand macro="cv_splitter_options"> + <when value="GroupKFold"> + <expand macro="cv_n_splits"/> + <expand macro="cv_groups" /> + </when> + <when value="GroupShuffleSplit"> + <expand macro="cv_n_splits" value="5"/> + <expand macro="cv_test_size"/> + <expand macro="random_state"/> + <expand macro="cv_groups"/> + </when> + <when value="LeaveOneGroupOut"> + <expand macro="cv_groups"/> + </when> + <when value="LeavePGroupsOut"> + <param argument="n_groups" type="integer" value="" label="n_groups" help="Number of groups (p) to leave out in the test split." /> + <expand macro="cv_groups"/> + </when> + </expand> + </conditional> + </xml> + + <xml name="cv_reduced"> + <conditional name="cv_selector"> + <param name="selected_cv" type="select" label="Select the cv splitter:"> + <expand macro="cv_splitter"/> + </param> + <expand macro="cv_splitter_options"/> + </conditional> + </xml> + + <xml name="cv_n_splits" token_value="3" token_help="Number of folds. Must be at least 2."> + <param argument="n_splits" type="integer" value="@VALUE@" min="2" label="n_splits" help="@HELP@"/> + </xml> + + <xml name="cv_shuffle"> + <param argument="shuffle" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Whether to shuffle data before splitting" /> + </xml> + + <xml name="cv_test_size" token_value="0.2"> + <param argument="test_size" type="float" value="@VALUE@" min="0.0" label="Portion or number of the test set" help="0.0-1.0, proportion of the dataset to include in the test split; >1, integer only, the absolute number of test samples "/> + </xml> + + <xml name="cv_groups" > + <param argument="groups" type="text" value="" area="true" label="Groups" help="Group lables in a list. e.g., [1, 1, 2, 2, 3, 3, 3]"/> + </xml> + + <xml name="feature_selection_algorithms"> + <option value="SelectKBest" selected="true">SelectKBest - Select features according to the k highest scores</option> + <option value="GenericUnivariateSelect">GenericUnivariateSelect - Univariate feature selector with configurable strategy</option> + <option value="SelectPercentile">SelectPercentile - Select features according to a percentile of the highest scores</option> + <option value="SelectFpr">SelectFpr - Filter: Select the p-values below alpha based on a FPR test</option> + <option value="SelectFdr">SelectFdr - Filter: Select the p-values for an estimated false discovery rate</option> + <option value="SelectFwe">SelectFwe - Filter: Select the p-values corresponding to Family-wise error rate</option> + <option value="VarianceThreshold">VarianceThreshold - Feature selector that removes all low-variance features</option> + <option value="SelectFromModel">SelectFromModel - Meta-transformer for selecting features based on importance weights</option> + <option value="RFE">RFE - Feature ranking with recursive feature elimination</option> + <option value="RFECV">RFECV - Feature ranking with recursive feature elimination and cross-validated selection of the best number of features</option> </xml> - <xml name="feature_selection_all"> + <xml name="feature_selection_algorithm_details"> + <when value="GenericUnivariateSelect"> + <expand macro="feature_selection_score_function" /> + <section name="options" title="Advanced Options" expanded="False"> + <param argument="mode" type="select" label="Feature selection mode"> + <option value="percentile">percentile</option> + <option value="k_best">k_best</option> + <option value="fpr">fpr</option> + <option value="fdr">fdr</option> + <option value="fwe">fwe</option> + </param> + <param argument="param" type="float" value="" optional="true" label="Parameter of the corresponding mode" help="float or int depending on the feature selection mode" /> + </section> + </when> + <when value="SelectPercentile"> + <expand macro="feature_selection_score_function" /> + <section name="options" title="Advanced Options" expanded="False"> + <param argument="percentile" type="integer" value="10" optional="True" label="Percent of features to keep" /> + </section> + </when> + <when value="SelectKBest"> + <expand macro="feature_selection_score_function" /> + <section name="options" title="Advanced Options" expanded="False"> + <param argument="k" type="integer" value="10" optional="True" label="Number of top features to select" help="No 'all' option is supported." /> + </section> + </when> + <when value="SelectFpr"> + <expand macro="feature_selection_score_function" /> + <section name="options" title="Advanced Options" expanded="False"> + <param argument="alpha" type="float" value="" optional="True" label="Alpha" help="The highest p-value for features to be kept."/> + </section> + </when> + <when value="SelectFdr"> + <expand macro="feature_selection_score_function" /> + <section name="options" title="Advanced Options" expanded="False"> + <param argument="alpha" type="float" value="" optional="True" label="Alpha" help="The highest uncorrected p-value for features to keep."/> + </section> + </when> + <when value="SelectFwe"> + <expand macro="feature_selection_score_function" /> + <section name="options" title="Advanced Options" expanded="False"> + <param argument="alpha" type="float" value="" optional="True" label="Alpha" help="The highest uncorrected p-value for features to keep."/> + </section> + </when> + <when value="VarianceThreshold"> + <section name="options" title="Options" expanded="False"> + <param argument="threshold" type="float" value="" optional="True" label="Threshold" help="Features with a training-set variance lower than this threshold will be removed."/> + </section> + </when> + </xml> + + <xml name="feature_selection_SelectFromModel"> + <when value="SelectFromModel"> + <conditional name="model_inputter"> + <param name="input_mode" type="select" label="Construct a new estimator from a selection list?" > + <option value="new" selected="true">Yes</option> + <option value="prefitted">No. Load a prefitted estimator</option> + </param> + <when value="new"> + <expand macro="estimator_selector_fs"/> + </when> + <when value="prefitted"> + <param name="fitted_estimator" type="data" format='zip' label="Load a prefitted estimator" /> + </when> + </conditional> + <expand macro="feature_selection_SelectFromModel_options"/> + </when> + </xml> + + <xml name="feature_selection_SelectFromModel_no_prefitted"> + <when value="SelectFromModel"> + <conditional name="model_inputter"> + <param name="input_mode" type="select" label="Construct a new estimator from a selection list?" > + <option value="new" selected="true">Yes</option> + </param> + <when value="new"> + <expand macro="estimator_selector_all"/> + </when> + </conditional> + <expand macro="feature_selection_SelectFromModel_options"/> + </when> + </xml> + + <xml name="feature_selection_SelectFromModel_options"> + <section name="options" title="Advanced Options" expanded="False"> + <param argument="threshold" type="text" value="" optional="true" label="threshold" help="The threshold value to use for feature selection. e.g. 'mean', 'median', '1.25*mean'." /> + <param argument="norm_order" type="integer" value="1" label="norm_order" help="Order of the norm used to filter the vectors of coefficients below threshold in the case where the coef_ attribute of the estimator is of dimension 2. " /> + <param argument="max_features" type="integer" value="" optional="true" label="The maximum number of features selected scoring above threshold" help="To disable threshold and only select based on max_features, set threshold=-np.inf."/> + </section> + </xml> + + <xml name="feature_selection_RFE"> + <when value="RFE"> + <yield/> + <section name="options" title="Advanced Options" expanded="False"> + <param argument="n_features_to_select" type="integer" value="" optional="true" label="n_features_to_select" help="The number of features to select. If None, half of the features are selected." /> + <param argument="step" type="float" value="1" label="step" optional="true" help="Default = 1. " /> + <param argument="verbose" type="integer" value="0" label="verbose" help="Controls verbosity of output." /> + </section> + </when> + </xml> + + <xml name="feature_selection_RFECV"> + <when value="RFECV"> + <yield/> + <section name="options" title="Advanced Options" expanded="False"> + <param argument="step" type="float" value="1" label="step" optional="true" help="Default = 1. " /> + <param argument="min_features_to_select" type="integer" value="1" optional="true" label="The minimum number of features to be selected"/> + <expand macro="cv_reduced"/> + <expand macro="scoring_selection"/> + <param argument="verbose" type="integer" value="0" label="verbose" help="Controls verbosity of output." /> + </section> + </when> + </xml> + + <xml name="feature_selection_pipeline"> + <!--compare to `feature_selection_fs`, no fitted estimator for SelectFromModel and no customer estimator for RFE and RFECV--> <conditional name="fs_algorithm_selector"> <param name="selected_algorithm" type="select" label="Select a feature selection algorithm"> - <option value="SelectKBest" selected="true">SelectKBest - Select features according to the k highest scores</option> - <option value="SelectFromModel">SelectFromModel - Meta-transformer for selecting features based on importance weights</option> - <option value="GenericUnivariateSelect">GenericUnivariateSelect - Univariate feature selector with configurable strategy</option> - <option value="SelectPercentile">SelectPercentile - Select features according to a percentile of the highest scores</option> - <option value="SelectFpr">SelectFpr - Filter: Select the p-values below alpha based on a FPR test</option> - <option value="SelectFdr">SelectFdr - Filter: Select the p-values for an estimated false discovery rate</option> - <option value="SelectFwe">SelectFwe - Filter: Select the p-values corresponding to Family-wise error rate</option> - <option value="RFE">RFE - Feature ranking with recursive feature elimination</option> - <option value="RFECV">RFECV - Feature ranking with recursive feature elimination and cross-validated selection of the best number of features</option> - <option value="VarianceThreshold">VarianceThreshold - Feature selector that removes all low-variance features</option> + <expand macro="feature_selection_algorithms"/> </param> - <when value="SelectFromModel"> - <conditional name="model_inputter"> - <yield/> - </conditional> - <section name="options" title="Advanced Options" expanded="False"> - <param argument="threshold" type="text" value="" optional="true" label="threshold" help="The threshold value to use for feature selection. e.g. 'mean', 'median', '1.25*mean'." /> - <param argument="norm_order" type="integer" value="1" label="norm_order" help="Order of the norm used to filter the vectors of coefficients below threshold in the case where the coef_ attribute of the estimator is of dimension 2. " /> - </section> - </when> - <when value="GenericUnivariateSelect"> - <expand macro="feature_selection_score_function" /> - <section name="options" title="Advanced Options" expanded="False"> - <param argument="mode" type="select" label="Feature selection mode"> - <option value="percentile">percentile</option> - <option value="k_best">k_best</option> - <option value="fpr">fpr</option> - <option value="fdr">fdr</option> - <option value="fwe">fwe</option> - </param> - <param argument="param" type="float" value="" optional="true" label="Parameter of the corresponding mode" help="float or int depending on the feature selection mode" /> - </section> - </when> - <when value="SelectPercentile"> - <expand macro="feature_selection_score_function" /> - <section name="options" title="Advanced Options" expanded="False"> - <param argument="percentile" type="integer" value="10" optional="True" label="Percent of features to keep" /> - </section> - </when> - <when value="SelectKBest"> - <expand macro="feature_selection_score_function" /> - <section name="options" title="Advanced Options" expanded="False"> - <param argument="k" type="integer" value="10" optional="True" label="Number of top features to select" help="No 'all' option is supported." /> - </section> - </when> - <when value="SelectFpr"> - <expand macro="feature_selection_score_function" /> - <section name="options" title="Advanced Options" expanded="False"> - <param argument="alpha" type="float" value="" optional="True" label="Alpha" help="The highest p-value for features to be kept."/> - </section> - </when> - <when value="SelectFdr"> - <expand macro="feature_selection_score_function" /> - <section name="options" title="Advanced Options" expanded="False"> - <param argument="alpha" type="float" value="" optional="True" label="Alpha" help="The highest uncorrected p-value for features to keep."/> - </section> - </when> - <when value="SelectFwe"> - <expand macro="feature_selection_score_function" /> - <section name="options" title="Advanced Options" expanded="False"> - <param argument="alpha" type="float" value="" optional="True" label="Alpha" help="The highest uncorrected p-value for features to keep."/> - </section> - </when> - <when value="RFE"> + <expand macro="feature_selection_algorithm_details"/> + <expand macro="feature_selection_SelectFromModel_no_prefitted"/> + <expand macro="feature_selection_RFE"> + <expand macro="estimator_selector_all"/> + </expand> + <expand macro="feature_selection_RFECV"> <expand macro="estimator_selector_all"/> - <section name="options" title="Advanced Options" expanded="False"> - <param argument="n_features_to_select" type="integer" value="" optional="true" label="n_features_to_select" help="The number of features to select. If None, half of the features are selected." /> - <param argument="step" type="float" value="1" label="step" optional="true" help="Default = 1. " /> - <param argument="verbose" type="integer" value="0" label="verbose" help="Controls verbosity of output." /> - </section> - </when> - <when value="RFECV"> - <expand macro="estimator_selector_all"/> - <section name="options" title="Advanced Options" expanded="False"> - <param argument="step" type="float" value="1" label="step" optional="true" help="Default = 1. " /> - <expand macro="cv"/> - <expand macro="scoring_selection"/> - <param argument="verbose" type="integer" value="0" label="verbose" help="Controls verbosity of output." /> - </section> - </when> - <when value="VarianceThreshold"> - <section name="options" title="Options" expanded="False"> - <param argument="threshold" type="float" value="" optional="True" label="Threshold" help="Features with a training-set variance lower than this threshold will be removed."/> - </section> - </when> - <!--when value="chi2"> - </when> - <when value="f_classif"> - </when> - <when value="f_regression"> - </when> - <when value="mutual_info_classif"> - </when> - <when value="mutual_info_regression"> - </when--> + </expand> + </conditional> + </xml> + + <xml name="feature_selection_fs"> + <conditional name="fs_algorithm_selector"> + <param name="selected_algorithm" type="select" label="Select a feature selection algorithm"> + <expand macro="feature_selection_algorithms"/> + </param> + <expand macro="feature_selection_algorithm_details"/> + <expand macro="feature_selection_SelectFromModel"/> + <expand macro="feature_selection_RFE"> + <expand macro="estimator_selector_fs"/> + </expand> + <expand macro="feature_selection_RFECV"> + <expand macro="estimator_selector_fs"/> + </expand> </conditional> </xml> @@ -945,21 +1103,6 @@ </param> </xml> - <xml name="feature_selection_output_mothods"> - <conditional name="output_method_selector"> - <param name="selected_method" type="select" label="Select an output method:"> - <option value="fit_transform">fit_transform - Fit to data, then transform it</option> - <option value="get_support">get_support - Get a mask, or integer index, of the features selected</option> - </param> - <when value="fit_transform"> - <!--**fit_params--> - </when> - <when value="get_support"> - <param name="indices" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Indices" help="If True, the return value will be an array of integers, rather than a boolean mask."/> - </when> - </conditional> - </xml> - <xml name="model_validation_common_options"> <expand macro="cv"/> <expand macro="verbose"/> @@ -1122,22 +1265,22 @@ <expand macro="pre_dispatch" value="2*n_jobs" help="Controls the number of jobs that get dispatched during parallel execution"/> <param argument="iid" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="iid" help="If True, data is identically distributed across the folds"/> <param argument="refit" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="refit" help="Refit an estimator using the best found parameters on the whole dataset."/> - <param argument="error_score" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Raise fit error:" help="If false, the metric score is assigned to 0 if an error occurs in estimator fitting and FitFailedWarning is raised."/> + <param argument="error_score" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Raise fit error:" help="If false, the metric score is assigned to NaN if an error occurs in estimator fitting and FitFailedWarning is raised."/> <param argument="return_train_score" type="boolean" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="return_train_score" help=""/> </xml> - <xml name="estimator_selector_all"> - <conditional name="estimator_selector"> - <param name="selected_module" type="select" label="Choose the module that contains target estimator:" > - <option value="svm" selected="true">sklearn.svm</option> - <option value="linear_model">sklearn.linear_model</option> - <option value="ensemble">sklearn.ensemble</option> - <option value="naive_bayes">sklearn.naive_bayes</option> - <option value="tree">sklearn.tree</option> - <option value="neighbors">sklearn.neighbors</option> - <option value="xgboost">xgboost</option> - <!--more--> - </param> + <xml name="estimator_module_options"> + <option value="svm" selected="true">sklearn.svm</option> + <option value="linear_model">sklearn.linear_model</option> + <option value="ensemble">sklearn.ensemble</option> + <option value="naive_bayes">sklearn.naive_bayes</option> + <option value="tree">sklearn.tree</option> + <option value="neighbors">sklearn.neighbors</option> + <option value="xgboost">xgboost</option> + <yield/> + </xml> + + <xml name="estimator_suboptions"> <when value="svm"> <param name="selected_estimator" type="select" label="Choose estimator class:"> <option value="LinearSVC" selected="true">LinearSVC</option> @@ -1244,6 +1387,30 @@ </param> <expand macro="estimator_params_text"/> </when> + <yield/> + </xml> + + <xml name="estimator_selector_all"> + <conditional name="estimator_selector"> + <param name="selected_module" type="select" label="Choose the module that contains target estimator:" > + <expand macro="estimator_module_options"/> + </param> + <expand macro="estimator_suboptions"/> + </conditional> + </xml> + + <xml name="estimator_selector_fs"> + <conditional name="estimator_selector"> + <param name="selected_module" type="select" label="Choose the module that contains target estimator:" > + <expand macro="estimator_module_options"> + <option value="customer_estimator">Load a customer estimator</option> + </expand> + </param> + <expand macro="estimator_suboptions"> + <when value="customer_estimator"> + <param name="c_estimator" type="data" format="zip" label="Choose the dataset containing the customer estimator or pipeline:"/> + </when> + </expand> </conditional> </xml> @@ -1373,7 +1540,7 @@ <option value="SURFstar">SURFstar</option> <option value="MultiSURF">MultiSURF</option> <option value="MultiSURFstar">MultiSURFstar</option> - <option value="TuRF">TuRF</option> + <!--option value="TuRF">TuRF</option> --> </param> <when value="ReliefF"> <expand macro="estimator_params_text" @@ -1395,12 +1562,115 @@ <expand macro="estimator_params_text" help="Default(=blank): discrete_threshold=10, n_features_to_select=10, verbose=False."/> </when> - <when value="TuRF"> + <!--when value="TuRF"> <expand macro="estimator_params_text" help="Default(=blank): core_algorithm='ReliefF', discrete_threshold=10, n_features_to_select=10, n_neighbors=100, pct=0.5, verbose=False."/> + </when> --> + </conditional> + </xml> + + <xml name="imbalanced_learn_sampling"> + <conditional name="imblearn_selector"> + <param name="select_algorithm" type="select" label="Choose the algorithm:"> + <option value="under_sampling.ClusterCentroids" selected="true">under_sampling.ClusterCentroids</option> + <option value="under_sampling.CondensedNearestNeighbour">under_sampling.CondensedNearestNeighbour</option> + <option value="under_sampling.EditedNearestNeighbours">under_sampling.EditedNearestNeighbours</option> + <option value="under_sampling.RepeatedEditedNearestNeighbours">under_sampling.RepeatedEditedNearestNeighbours</option> + <option value="under_sampling.AllKNN">under_sampling.AllKNN</option> + <option value="under_sampling.InstanceHardnessThreshold">under_sampling.InstanceHardnessThreshold</option> + <option value="under_sampling.NearMiss">under_sampling.NearMiss</option> + <option value="under_sampling.NeighbourhoodCleaningRule">under_sampling.NeighbourhoodCleaningRule</option> + <option value="under_sampling.OneSidedSelection">under_sampling.OneSidedSelection</option> + <option value="under_sampling.RandomUnderSampler">under_sampling.RandomUnderSampler</option> + <option value="under_sampling.TomekLinks">under_sampling.TomekLinks</option> + <option value="over_sampling.ADASYN">over_sampling.ADASYN</option> + <option value="over_sampling.RandomOverSampler">over_sampling.RandomOverSampler</option> + <option value="over_sampling.SMOTE">over_sampling.SMOTE</option> + <option value="over_sampling.SVMSMOTE">over_sampling.SVMSMOTE</option> + <option value="over_sampling.BorderlineSMOTE">over_sampling.BorderlineSMOTE</option> + <option value="over_sampling.SMOTENC">over_sampling.SMOTENC</option> + <option value="combine.SMOTEENN">combine.SMOTEENN</option> + <option value="combine.SMOTETomek">combine.SMOTETomek</option> + </param> + <when value="under_sampling.ClusterCentroids"> + <expand macro="estimator_params_text" + help="Default(=blank): sampling_strategy='auto', random_state=None, estimator=None, voting='auto'."/> + </when> + <when value="under_sampling.CondensedNearestNeighbour"> + <expand macro="estimator_params_text" + help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=None, n_seeds_S=1."/> + </when> + <when value="under_sampling.EditedNearestNeighbours"> + <expand macro="estimator_params_text" + help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=3, max_iter=100, kind_sel='all'."/> + </when> + <when value="under_sampling.RepeatedEditedNearestNeighbours"> + <expand macro="estimator_params_text" + help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=3, max_iter=100, kind_sel='all'."/> + </when> + <when value="under_sampling.AllKNN"> + <expand macro="estimator_params_text" + help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=3, kind_sel='all', allow_minority=False."/> + </when> + <when value="under_sampling.InstanceHardnessThreshold"> + <expand macro="estimator_params_text" + help="Default(=blank): estimator=None, sampling_strategy='auto', random_state=None, cv=5."/> + </when> + <when value="under_sampling.NearMiss"> + <expand macro="estimator_params_text" + help="Default(=blank): sampling_strategy='auto', random_state=None, version=1, n_neighbors=3, n_neighbors_ver3=3."/> + </when> + <when value="under_sampling.NeighbourhoodCleaningRule"> + <expand macro="estimator_params_text" + help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=3, kind_sel='all', threshold_cleaning=0.5."/> + </when> + <when value="under_sampling.OneSidedSelection"> + <expand macro="estimator_params_text" + help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=None, n_seeds_S=1."/> + </when> + <when value="under_sampling.RandomUnderSampler"> + <expand macro="estimator_params_text" + help="Default(=blank): sampling_strategy='auto', random_state=None, replacement=False."/> + </when> + <when value="under_sampling.TomekLinks"> + <expand macro="estimator_params_text" + help="Default(=blank): sampling_strategy='auto', random_state=None."/> + </when> + <when value="over_sampling.ADASYN"> + <expand macro="estimator_params_text" + help="Default(=blank): sampling_strategy='auto', random_state=None, n_neighbors=5."/> + </when> + <when value="over_sampling.RandomOverSampler"> + <expand macro="estimator_params_text" + help="Default(=blank): sampling_strategy='auto', random_state=None."/> + </when> + <when value="over_sampling.SMOTE"> + <expand macro="estimator_params_text" + help="Default(=blank): sampling_strategy='auto', random_state=None, k_neighbors=5."/> + </when> + <when value="over_sampling.SVMSMOTE"> + <expand macro="estimator_params_text" + help="Default(=blank): sampling_strategy='auto', k_neighbors=5, m_neighbors=10, out_step=0.5, random_state=None, svm_estimator=None."/> + </when> + <when value="over_sampling.BorderlineSMOTE"> + <expand macro="estimator_params_text" + help="Default(=blank): sampling_strategy='auto', k_neighbors=5, kind='borderline-1', m_neighbors=10, random_state=None."/> + </when> + <when value="over_sampling.SMOTENC"> + <expand macro="estimator_params_text" + help="Default: categorical_features=[], sampling_strategy='auto', random_state=None, k_neighbors=5."/> + </when> + <when value="combine.SMOTEENN"> + <expand macro="estimator_params_text" + help="Default(=blank): sampling_strategy='auto', random_state=None, smote=None, enn=None."/> + </when> + <when value="combine.SMOTETomek"> + <expand macro="estimator_params_text" + help="Default(=blank): sampling_strategy='auto', random_state=None, smote=None, tomek=None."/> </when> </conditional> </xml> + <!-- Outputs --> <xml name="output"> @@ -1498,4 +1768,19 @@ </citation> </xml> + <xml name="imblearn_citation"> + <citation type="bibtex"> + @article{JMLR:v18:16-365, + author = {Guillaume Lema{{\^i}}tre and Fernando Nogueira and Christos K. Aridas}, + title = {Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning}, + journal = {Journal of Machine Learning Research}, + year = {2017}, + volume = {18}, + number = {17}, + pages = {1-5}, + url = {http://jmlr.org/papers/v18/16-365.html} + } + </citation> + </xml> + </macros>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/search_model_validation.py Sun Dec 30 01:50:39 2018 -0500 @@ -0,0 +1,234 @@ +import imblearn +import json +import numpy as np +import os +import pandas +import pickle +import skrebate +import sklearn +import sys +import xgboost +import warnings +from imblearn import under_sampling, over_sampling, combine +from imblearn.pipeline import Pipeline as imbPipeline +from sklearn import (cluster, compose, decomposition, ensemble, feature_extraction, + feature_selection, gaussian_process, kernel_approximation, metrics, + model_selection, naive_bayes, neighbors, pipeline, preprocessing, + svm, linear_model, tree, discriminant_analysis) +from sklearn.exceptions import FitFailedWarning +from sklearn.externals import joblib +from utils import get_cv, get_scoring, get_X_y, load_model, read_columns, SafeEval + + +N_JOBS = int(os.environ.get('GALAXY_SLOTS', 1)) + + +def get_search_params(params_builder): + search_params = {} + safe_eval = SafeEval(load_scipy=True, load_numpy=True) + safe_eval_es = SafeEval(load_estimators=True) + + for p in params_builder['param_set']: + search_p = p['search_param_selector']['search_p'] + if search_p.strip() == '': + continue + param_type = p['search_param_selector']['selected_param_type'] + + lst = search_p.split(':') + assert (len(lst) == 2), "Error, make sure there is one and only one colon in search parameter input." + literal = lst[1].strip() + param_name = lst[0].strip() + if param_name: + if param_name.lower() == 'n_jobs': + sys.exit("Parameter `%s` is invalid for search." %param_name) + elif not param_name.endswith('-'): + ev = safe_eval(literal) + if param_type == 'final_estimator_p': + search_params['estimator__' + param_name] = ev + else: + search_params['preprocessing_' + param_type[5:6] + '__' + param_name] = ev + else: + # only for estimator eval, add `-` to the end of param + #TODO maybe add regular express check + ev = safe_eval_es(literal) + for obj in ev: + if 'n_jobs' in obj.get_params(): + obj.set_params( n_jobs=N_JOBS ) + if param_type == 'final_estimator_p': + search_params['estimator__' + param_name[:-1]] = ev + else: + search_params['preprocessing_' + param_type[5:6] + '__' + param_name[:-1]] = ev + elif param_type != 'final_estimator_p': + #TODO regular express check ? + ev = safe_eval_es(literal) + preprocessors = [preprocessing.StandardScaler(), preprocessing.Binarizer(), preprocessing.Imputer(), + preprocessing.MaxAbsScaler(), preprocessing.Normalizer(), preprocessing.MinMaxScaler(), + preprocessing.PolynomialFeatures(),preprocessing.RobustScaler(), + feature_selection.SelectKBest(), feature_selection.GenericUnivariateSelect(), + feature_selection.SelectPercentile(), feature_selection.SelectFpr(), feature_selection.SelectFdr(), + feature_selection.SelectFwe(), feature_selection.VarianceThreshold(), + decomposition.FactorAnalysis(random_state=0), decomposition.FastICA(random_state=0), decomposition.IncrementalPCA(), + decomposition.KernelPCA(random_state=0, n_jobs=N_JOBS), decomposition.LatentDirichletAllocation(random_state=0, n_jobs=N_JOBS), + decomposition.MiniBatchDictionaryLearning(random_state=0, n_jobs=N_JOBS), + decomposition.MiniBatchSparsePCA(random_state=0, n_jobs=N_JOBS), decomposition.NMF(random_state=0), + decomposition.PCA(random_state=0), decomposition.SparsePCA(random_state=0, n_jobs=N_JOBS), + decomposition.TruncatedSVD(random_state=0), + kernel_approximation.Nystroem(random_state=0), kernel_approximation.RBFSampler(random_state=0), + kernel_approximation.AdditiveChi2Sampler(), kernel_approximation.SkewedChi2Sampler(random_state=0), + cluster.FeatureAgglomeration(), + skrebate.ReliefF(n_jobs=N_JOBS), skrebate.SURF(n_jobs=N_JOBS), skrebate.SURFstar(n_jobs=N_JOBS), + skrebate.MultiSURF(n_jobs=N_JOBS), skrebate.MultiSURFstar(n_jobs=N_JOBS), + imblearn.under_sampling.ClusterCentroids(random_state=0, n_jobs=N_JOBS), + imblearn.under_sampling.CondensedNearestNeighbour(random_state=0, n_jobs=N_JOBS), + imblearn.under_sampling.EditedNearestNeighbours(random_state=0, n_jobs=N_JOBS), + imblearn.under_sampling.RepeatedEditedNearestNeighbours(random_state=0, n_jobs=N_JOBS), + imblearn.under_sampling.AllKNN(random_state=0, n_jobs=N_JOBS), + imblearn.under_sampling.InstanceHardnessThreshold(random_state=0, n_jobs=N_JOBS), + imblearn.under_sampling.NearMiss(random_state=0, n_jobs=N_JOBS), + imblearn.under_sampling.NeighbourhoodCleaningRule(random_state=0, n_jobs=N_JOBS), + imblearn.under_sampling.OneSidedSelection(random_state=0, n_jobs=N_JOBS), + imblearn.under_sampling.RandomUnderSampler(random_state=0), + imblearn.under_sampling.TomekLinks(random_state=0, n_jobs=N_JOBS), + imblearn.over_sampling.ADASYN(random_state=0, n_jobs=N_JOBS), + imblearn.over_sampling.RandomOverSampler(random_state=0), + imblearn.over_sampling.SMOTE(random_state=0, n_jobs=N_JOBS), + imblearn.over_sampling.SVMSMOTE(random_state=0, n_jobs=N_JOBS), + imblearn.over_sampling.BorderlineSMOTE(random_state=0, n_jobs=N_JOBS), + imblearn.over_sampling.SMOTENC(categorical_features=[], random_state=0, n_jobs=N_JOBS), + imblearn.combine.SMOTEENN(random_state=0), imblearn.combine.SMOTETomek(random_state=0)] + newlist = [] + for obj in ev: + if obj is None: + newlist.append(None) + elif obj == 'all_0': + newlist.extend(preprocessors[0:36]) + elif obj == 'sk_prep_all': # no KernalCenter() + newlist.extend(preprocessors[0:8]) + elif obj == 'fs_all': + newlist.extend(preprocessors[8:15]) + elif obj == 'decomp_all': + newlist.extend(preprocessors[15:26]) + elif obj == 'k_appr_all': + newlist.extend(preprocessors[26:30]) + elif obj == 'reb_all': + newlist.extend(preprocessors[31:36]) + elif obj == 'imb_all': + newlist.extend(preprocessors[36:55]) + elif type(obj) is int and -1 < obj < len(preprocessors): + newlist.append(preprocessors[obj]) + elif hasattr(obj, 'get_params'): # user object + if 'n_jobs' in obj.get_params(): + newlist.append( obj.set_params(n_jobs=N_JOBS) ) + else: + newlist.append(obj) + else: + sys.exit("Unsupported preprocessor type: %r" %(obj)) + search_params['preprocessing_' + param_type[5:6]] = newlist + else: + sys.exit("Parameter name of the final estimator can't be skipped!") + + return search_params + + +if __name__ == '__main__': + + warnings.simplefilter('ignore') + + input_json_path = sys.argv[1] + with open(input_json_path, 'r') as param_handler: + params = json.load(param_handler) + + infile_pipeline = sys.argv[2] + infile1 = sys.argv[3] + infile2 = sys.argv[4] + outfile_result = sys.argv[5] + if len(sys.argv) > 6: + outfile_estimator = sys.argv[6] + else: + outfile_estimator = None + + params_builder = params['search_schemes']['search_params_builder'] + + input_type = params['input_options']['selected_input'] + if input_type == 'tabular': + header = 'infer' if params['input_options']['header1'] else None + column_option = params['input_options']['column_selector_options_1']['selected_column_selector_option'] + if column_option in ['by_index_number', 'all_but_by_index_number', 'by_header_name', 'all_but_by_header_name']: + c = params['input_options']['column_selector_options_1']['col1'] + else: + c = None + X = read_columns( + infile1, + c = c, + c_option = column_option, + sep='\t', + header=header, + parse_dates=True + ) + else: + X = mmread(open(infile1, 'r')) + + header = 'infer' if params['input_options']['header2'] else None + column_option = params['input_options']['column_selector_options_2']['selected_column_selector_option2'] + if column_option in ['by_index_number', 'all_but_by_index_number', 'by_header_name', 'all_but_by_header_name']: + c = params['input_options']['column_selector_options_2']['col2'] + else: + c = None + y = read_columns( + infile2, + c = c, + c_option = column_option, + sep='\t', + header=header, + parse_dates=True + ) + y = y.ravel() + + optimizer = params['search_schemes']['selected_search_scheme'] + optimizer = getattr(model_selection, optimizer) + + options = params['search_schemes']['options'] + splitter, groups = get_cv(options.pop('cv_selector')) + if groups is None: + options['cv'] = splitter + elif groups == '': + options['cv'] = list( splitter.split(X, y, groups=None) ) + else: + options['cv'] = list( splitter.split(X, y, groups=groups) ) + options['n_jobs'] = N_JOBS + primary_scoring = options['scoring']['primary_scoring'] + options['scoring'] = get_scoring(options['scoring']) + if options['error_score']: + options['error_score'] = 'raise' + else: + options['error_score'] = np.NaN + if options['refit'] and isinstance(options['scoring'], dict): + options['refit'] = 'primary' + if 'pre_dispatch' in options and options['pre_dispatch'] == '': + options['pre_dispatch'] = None + + with open(infile_pipeline, 'rb') as pipeline_handler: + pipeline = load_model(pipeline_handler) + + search_params = get_search_params(params_builder) + searcher = optimizer(pipeline, search_params, **options) + + if options['error_score'] == 'raise': + searcher.fit(X, y) + else: + warnings.simplefilter('always', FitFailedWarning) + with warnings.catch_warnings(record=True) as w: + try: + searcher.fit(X, y) + except ValueError: + pass + for warning in w: + print(repr(warning.message)) + + cv_result = pandas.DataFrame(searcher.cv_results_) + cv_result.rename(inplace=True, columns={'mean_test_primary': 'mean_test_'+primary_scoring, 'rank_test_primary': 'rank_test_'+primary_scoring}) + cv_result.to_csv(path_or_buf=outfile_result, sep='\t', header=True, index=False) + + if outfile_estimator: + with open(outfile_estimator, 'wb') as output_handler: + pickle.dump(searcher.best_estimator_, output_handler, pickle.HIGHEST_PROTOCOL)
--- a/sk_whitelist.json Thu Oct 11 03:29:23 2018 -0400 +++ b/sk_whitelist.json Sun Dec 30 01:50:39 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
--- a/test-data/blobs.txt Thu Oct 11 03:29:23 2018 -0400 +++ b/test-data/blobs.txt Sun Dec 30 01:50:39 2018 -0500 @@ -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 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--- a/test-data/circles.txt Thu Oct 11 03:29:23 2018 -0400 +++ b/test-data/circles.txt Sun Dec 30 01:50:39 2018 -0500 @@ -1,101 +1,101 @@ 0 1 0 --0.06279051952931321 -0.9980267284282716 0 -0.05023241562345065 0.7984213827426173 1 --0.9921147013144779 -0.12533323356430429 0 -0.4257792915650726 -0.9048270524660196 0 --0.30901699437494756 -0.9510565162951535 0 --1.0 -3.216245299353273e-16 0 --0.18738131458572463 -0.9822872507286887 0 --0.5358267949789963 -0.8443279255020153 0 --0.7748665289029049 -0.19895190973188404 1 --0.8763066800438636 0.4817536741017152 0 --0.24721359549995806 -0.7608452130361228 1 -0.8 0.0 1 -0.42866143598319745 -0.675462340401612 1 --0.5831749019371294 0.5476376847429508 1 -0.7010453440350909 -0.38540293928137226 1 --0.743821188710601 -0.29449964214774266 1 --0.7438211887106012 0.2944996421477422 1 -0.8090169943749475 0.5877852522924731 0 -0.30901699437494723 -0.9510565162951536 0 -0.18738131458572452 0.9822872507286887 0 --0.8763066800438635 -0.4817536741017154 0 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--- a/test-data/classification_report.txt Thu Oct 11 03:29:23 2018 -0400 +++ b/test-data/classification_report.txt Sun Dec 30 01:50:39 2018 -0500 @@ -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
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--- a/test-data/feature_selection_result12 Thu Oct 11 03:29:23 2018 -0400 +++ b/test-data/feature_selection_result12 Sun Dec 30 01:50:39 2018 -0500 @@ -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
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--- a/test-data/gbc_result01 Thu Oct 11 03:29:23 2018 -0400 +++ b/test-data/gbc_result01 Sun Dec 30 01:50:39 2018 -0500 @@ -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
--- a/test-data/gbr_prediction_result01.tabular Thu Oct 11 03:29:23 2018 -0400 +++ b/test-data/gbr_prediction_result01.tabular Sun Dec 30 01:50:39 2018 -0500 @@ -1,88 +1,88 @@ -year month day temp_2 temp_1 average forecast_noaa forecast_acc forecast_under friend week_Fri week_Mon week_Sat week_Sun week_Thurs week_Tues week_Wed 0 -2016 9 29 69 68 66.1 63 71 68 57 0 0 0 0 1 0 0 69.8047715468 -2016 4 27 59 60 60.7 59 65 60 50 0 0 0 0 0 0 1 62.3940847433 -2016 11 28 53 48 48.0 46 48 49 44 0 1 0 0 0 0 0 51.1656331745 -2016 10 12 60 62 61.0 60 63 63 52 0 0 0 0 0 0 1 60.7602326565 -2016 6 19 67 65 70.4 69 73 70 58 0 0 0 1 0 0 0 66.2416657667 -2016 5 7 68 77 63.0 61 65 63 83 0 0 1 0 0 0 0 71.7162060939 -2016 7 25 75 80 77.1 75 82 76 81 0 1 0 0 0 0 0 78.6168727393 -2016 8 15 90 83 76.6 76 79 75 70 0 1 0 0 0 0 0 77.9015583717 -2016 10 28 58 60 55.6 52 56 55 52 1 0 0 0 0 0 0 61.4191796096 -2016 6 5 80 81 68.0 64 70 66 54 0 0 0 1 0 0 0 74.4136969328 -2016 3 19 58 63 54.2 54 59 54 62 0 0 1 0 0 0 0 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