# HG changeset patch # User bgruening # Date 1533400510 14400 # Node ID 96f9b73327f25b765db9d08ca51231f1651a8672 # Parent 537c6763c0182f76473f60f143e513fe3521fae5 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 76583c1fcd9d06a4679cc46ffaee44117b9e22cd diff -r 537c6763c018 -r 96f9b73327f2 feature_selection.xml --- a/feature_selection.xml Fri Jul 13 03:55:31 2018 -0400 +++ b/feature_selection.xml Sat Aug 04 12:35:10 2018 -0400 @@ -19,19 +19,28 @@ import json import pandas import pickle +import ast import numpy as np +import xgboost import sklearn.feature_selection -from sklearn import svm, linear_model, ensemble +from sklearn import svm, linear_model, ensemble, naive_bayes, tree, neighbors @COLUMNS_FUNCTION@ - +@GET_ESTIMATOR_FUNCTION@ @FEATURE_SELECTOR_FUNCTION@ input_json_path = sys.argv[1] with open(input_json_path, "r") as param_handler: params = json.load(param_handler) -## Read features +#handle cheetah +#if $fs_algorithm_selector.selected_algorithm == "SelectFromModel"\ + and $fs_algorithm_selector.model_inputter.input_mode == "prefitted": +params['fs_algorithm_selector']['model_inputter']['fitted_estimator'] =\ + "$fs_algorithm_selector.model_inputter.fitted_estimator" +#end if + +# Read features features_has_header = params["input_options"]["header1"] input_type = params["input_options"]["selected_input"] if input_type=="tabular": @@ -53,7 +62,7 @@ else: X = mmread("$input_options.infile1") -## Read labels +# Read labels header = 'infer' if params["input_options"]["header2"] else None column_option = params["input_options"]["column_selector_options_2"]["selected_column_selector_option2"] if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]: @@ -70,21 +79,20 @@ ) y=y.ravel() -## Create feature selector -new_selector = feature_selector(params['feature_selection_algorithms']) -if params['feature_selection_algorithms']['selected_algorithm'] != 'SelectFromModel' or \ - 'extra_estimator' not in params['feature_selection_algorithms'] or \ - params['feature_selection_algorithms']['extra_estimator']['has_estimator'] != 'no_load' : +# Create feature selector +new_selector = feature_selector(params['fs_algorithm_selector']) +if params['fs_algorithm_selector']['selected_algorithm'] != 'SelectFromModel'\ + or params['fs_algorithm_selector']['model_inputter']['input_mode'] != 'prefitted' : new_selector.fit(X, y) ## Transform to select features selected_names = None -if "$select_methods.selected_method" == "fit_transform": +if "$output_method_selector.selected_method" == "fit_transform": res = new_selector.transform(X) if features_has_header: selected_names = input_df.columns[new_selector.get_support(indices=True)] else: - res = new_selector.get_support(params["select_methods"]["indices"]) + res = new_selector.get_support(params["output_method_selector"]["indices"]) res = pandas.DataFrame(res, columns = selected_names) res.to_csv(path_or_buf="$outfile", sep='\t', index=False) @@ -94,8 +102,10 @@ - - + + + + @@ -104,14 +114,16 @@ - - - - - - - - + + + + + + + + + + @@ -180,26 +192,30 @@ - - - - - - - - + + + + + + + + + + - - - - - - - - + + + + + + + + + + @@ -226,6 +242,18 @@ + + + + + + + + + + + + + +def get_search_params(params_builder): + search_params = {} + + def safe_eval(literal): + + FROM_SCIPY_STATS = [ 'bernoulli', 'binom', 'boltzmann', 'dlaplace', 'geom', 'hypergeom', + 'logser', 'nbinom', 'planck', 'poisson', 'randint', 'skellam', 'zipf' ] + + FROM_NUMPY_RANDOM = [ 'beta', 'binomial', 'bytes', 'chisquare', 'choice', 'dirichlet', 'division', + 'exponential', 'f', 'gamma', 'geometric', 'gumbel', 'hypergeometric', + 'laplace', 'logistic', 'lognormal', 'logseries', 'mtrand', 'multinomial', + 'multivariate_normal', 'negative_binomial', 'noncentral_chisquare', 'noncentral_f', + 'normal', 'pareto', 'permutation', 'poisson', 'power', 'rand', 'randint', + 'randn', 'random', 'random_integers', 'random_sample', 'ranf', 'rayleigh', + 'sample', 'seed', 'set_state', 'shuffle', 'standard_cauchy', 'standard_exponential', + 'standard_gamma', 'standard_normal', 'standard_t', 'triangular', 'uniform', + 'vonmises', 'wald', 'weibull', 'zipf' ] + + # File opening and other unneeded functions could be dropped + UNWANTED = ['open', 'type', 'dir', 'id', 'str', 'repr'] + + # Allowed symbol table. Add more if needed. + new_syms = { + 'np_arange': getattr(np, 'arange'), + 'ensemble_ExtraTreesClassifier': getattr(ensemble, 'ExtraTreesClassifier') + } + + syms = make_symbol_table(use_numpy=False, **new_syms) + + for method in FROM_SCIPY_STATS: + syms['scipy_stats_' + method] = getattr(scipy.stats, method) + + for func in FROM_NUMPY_RANDOM: + syms['np_random_' + func] = getattr(np.random, func) + + for key in UNWANTED: + syms.pop(key, None) + + aeval = Interpreter(symtable=syms, use_numpy=False, minimal=False, + no_if=True, no_for=True, no_while=True, no_try=True, + no_functiondef=True, no_ifexp=True, no_listcomp=False, + no_augassign=False, no_assert=True, no_delete=True, + no_raise=True, no_print=True) + + return aeval(literal) + + 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() + ev = safe_eval(literal) + if param_type == "final_estimator_p": + search_params["estimator__" + lst[0].strip()] = ev + else: + search_params["preprocessing_" + param_type[5:6] + "__" + lst[0].strip()] = ev + + return search_params + + + +def get_estimator(estimator_json): + estimator_module = estimator_json['selected_module'] + estimator_cls = estimator_json['selected_estimator'] + + if estimator_module == "xgboost": + cls = getattr(xgboost, estimator_cls) + else: + module = getattr(sklearn, estimator_module) + cls = getattr(module, estimator_cls) + + estimator = cls() + + estimator_params = estimator_json['text_params'].strip() + if estimator_params != "": + try: + params = ast.literal_eval('{' + estimator_params + '}') + except ValueError: + sys.exit("Unsupported parameter input: `%s`" %estimator_params) + estimator.set_params(**params) + + return estimator + + python scikit-learn pandas + xgboost @@ -907,53 +993,54 @@ - - - - - - + + + + + + + + + + + + + + + + + + + + - + - - + + + - - - - - - - - - - - - - - - - + + -
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@@ -1048,36 +1127,9 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + @@ -1101,10 +1153,312 @@ - + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -1118,7 +1472,6 @@ - diff -r 537c6763c018 -r 96f9b73327f2 test-data/feature_selection_result01 --- a/test-data/feature_selection_result01 Fri Jul 13 03:55:31 2018 -0400 +++ b/test-data/feature_selection_result01 Sat Aug 04 12:35:10 2018 -0400 @@ -1,262 +1,11 @@ -temp_1 average -69.0 69.7 -59.0 58.1 -88.0 77.3 -65.0 64.7 -50.0 47.5 -51.0 48.2 -52.0 48.6 -78.0 76.7 -35.0 45.2 -40.0 46.1 -47.0 45.3 -72.0 76.3 -76.0 74.4 -39.0 45.3 -78.0 72.2 -71.0 67.3 -48.0 47.7 -72.0 77.0 -57.0 54.7 -40.0 45.1 -54.0 47.6 -58.0 53.2 -68.0 58.6 -65.0 55.3 -47.0 48.8 -44.0 45.6 -64.0 67.1 -62.0 57.1 -66.0 65.7 -70.0 71.8 -57.0 54.2 -50.0 50.5 -55.0 51.8 -55.0 49.5 -42.0 45.2 -65.0 60.1 -63.0 65.6 -48.0 47.3 -42.0 46.3 -51.0 46.2 -64.0 68.0 -75.0 74.6 -52.0 46.7 -67.0 68.6 -68.0 68.7 -54.0 55.0 -62.0 56.8 -76.0 76.1 -73.0 73.1 -52.0 50.3 -70.0 73.9 -77.0 77.4 -60.0 56.6 -52.0 53.3 -79.0 75.0 -76.0 57.2 -66.0 66.5 -57.0 61.8 -66.0 57.4 -61.0 58.4 -55.0 53.1 -48.0 48.1 -49.0 49.2 -65.0 66.7 -60.0 62.5 -56.0 53.0 -59.0 57.4 -44.0 45.7 -82.0 63.2 -64.0 67.0 -43.0 45.5 -64.0 55.7 -63.0 52.7 -70.0 70.6 -71.0 52.4 -76.0 73.5 -68.0 62.1 -39.0 45.3 -71.0 70.7 -69.0 71.7 -74.0 71.5 -81.0 64.1 -51.0 49.3 -45.0 46.8 -87.0 76.8 -71.0 73.8 -55.0 60.3 -80.0 76.9 -67.0 69.0 -61.0 61.4 -46.0 46.6 -39.0 45.1 -67.0 68.3 -52.0 47.8 -67.0 69.8 -75.0 71.2 -68.0 73.3 -92.0 68.2 -67.0 72.8 -44.0 45.8 -61.0 61.0 -65.0 53.4 -68.0 73.0 -87.0 62.1 -117.0 54.8 -80.0 76.4 -57.0 51.0 -67.0 63.6 -58.0 54.0 -65.0 56.2 -52.0 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diff -r 537c6763c018 -r 96f9b73327f2 test-data/feature_selection_result08 --- a/test-data/feature_selection_result08 Fri Jul 13 03:55:31 2018 -0400 +++ b/test-data/feature_selection_result08 Sat Aug 04 12:35:10 2018 -0400 @@ -1,262 +1,11 @@ -day temp_2 temp_1 average forecast_noaa forecast_acc forecast_under friend -19.0 68.0 69.0 69.7 65.0 74.0 71.0 88.0 -14.0 60.0 59.0 58.1 57.0 63.0 58.0 66.0 -30.0 85.0 88.0 77.3 75.0 79.0 77.0 70.0 -15.0 82.0 65.0 64.7 63.0 69.0 64.0 58.0 -18.0 54.0 50.0 47.5 44.0 48.0 49.0 58.0 -25.0 48.0 51.0 48.2 45.0 51.0 49.0 63.0 -25.0 49.0 52.0 48.6 45.0 52.0 47.0 41.0 -20.0 73.0 78.0 76.7 75.0 78.0 77.0 66.0 -17.0 39.0 35.0 45.2 43.0 47.0 46.0 38.0 -8.0 42.0 40.0 46.1 45.0 51.0 47.0 36.0 -28.0 42.0 47.0 45.3 41.0 49.0 44.0 58.0 -17.0 76.0 72.0 76.3 76.0 78.0 77.0 88.0 -7.0 69.0 76.0 74.4 73.0 77.0 74.0 72.0 -15.0 40.0 39.0 45.3 45.0 49.0 47.0 46.0 -27.0 71.0 78.0 72.2 70.0 74.0 72.0 84.0 -31.0 64.0 71.0 67.3 63.0 72.0 68.0 85.0 -20.0 54.0 48.0 47.7 44.0 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+-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 diff -r 537c6763c018 -r 96f9b73327f2 test-data/feature_selection_result09 --- a/test-data/feature_selection_result09 Fri Jul 13 03:55:31 2018 -0400 +++ b/test-data/feature_selection_result09 Sat Aug 04 12:35:10 2018 -0400 @@ -1,262 +1,11 @@ -month day temp_2 temp_1 average forecast_noaa forecast_acc forecast_under friend week_Fri week_Mon week_Sat week_Sun week_Tues -9.0 19.0 68.0 69.0 69.7 65.0 74.0 71.0 88.0 0.0 1.0 0.0 0.0 0.0 -4.0 14.0 60.0 59.0 58.1 57.0 63.0 58.0 66.0 0.0 0.0 0.0 0.0 0.0 -7.0 30.0 85.0 88.0 77.3 75.0 79.0 77.0 70.0 0.0 0.0 1.0 0.0 0.0 -5.0 15.0 82.0 65.0 64.7 63.0 69.0 64.0 58.0 0.0 0.0 0.0 1.0 0.0 -1.0 18.0 54.0 50.0 47.5 44.0 48.0 49.0 58.0 0.0 1.0 0.0 0.0 0.0 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