# 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)
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+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
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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
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-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
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-57.0 51.0
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-58.0 54.0
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-59.0 55.3
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-81.0 59.2
-75.0 77.1
-76.0 77.4
-57.0 64.8
-69.0 74.2
-77.0 66.8
-55.0 49.9
-49.0 46.8
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-55.0 51.2
-56.0 55.6
-68.0 74.6
-54.0 53.4
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-49.0 49.1
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-73.0 71.0
-66.0 66.4
-69.0 66.5
-82.0 64.5
-90.0 76.7
-51.0 50.7
-77.0 57.1
-60.0 61.4
-74.0 72.8
-85.0 77.2
-68.0 62.8
-56.0 49.5
-71.0 56.2
-62.0 59.5
-83.0 77.3
-64.0 65.4
-56.0 48.4
-41.0 45.1
-65.0 66.2
-65.0 53.7
-40.0 46.0
-45.0 45.6
-52.0 48.4
-63.0 51.7
-52.0 47.6
-60.0 57.9
-81.0 75.7
-75.0 75.8
-59.0 51.4
-73.0 77.1
-75.0 77.3
-60.0 58.5
-75.0 71.3
-59.0 57.6
-53.0 49.1
-79.0 77.2
-57.0 52.1
-75.0 67.6
-71.0 69.4
-53.0 50.2
-46.0 48.8
-81.0 76.9
-49.0 48.9
-57.0 48.4
-60.0 58.8
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-61.0 64.1
-66.0 69.5
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-64.0 52.2
-71.0 65.2
-75.0 63.8
-48.0 46.4
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-49.0 47.1
-85.0 68.5
-62.0 49.4
-50.0 47.0
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-72.0 77.2
-55.0 50.7
-74.0 72.3
-85.0 77.3
-73.0 77.3
-52.0 47.4
-67.0 67.6
-45.0 45.1
-46.0 47.2
-66.0 60.6
-71.0 77.0
-70.0 69.3
-58.0 49.9
-72.0 77.1
-74.0 75.4
-65.0 64.5
-77.0 58.8
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-45.0 45.7
-53.0 50.5
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-79.0 77.3
-49.0 49.0
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-69.0 56.5
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-61.0 51.1
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-48.0 45.4
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+0 1
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+-88.5787166225 -2.5710918402200003
+-82.8452345578 -0.168636324107
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+-63.9354970901 0.9841122108220001
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+23.0341392692 1.03188231893
+67.6714937696 -0.8214378651719999
+47.39275848810001 -0.0942409319417
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 52.0 49.0 61.0
-10.0 73.0 72.0 77.0 77.0 78.0 77.0 68.0
-23.0 56.0 57.0 54.7 50.0 58.0 55.0 70.0
-24.0 45.0 40.0 45.1 44.0 47.0 46.0 39.0
-19.0 50.0 54.0 47.6 47.0 49.0 48.0 53.0
-6.0 65.0 58.0 53.2 52.0 57.0 55.0 71.0
-17.0 60.0 68.0 58.6 58.0 62.0 59.0 54.0
-29.0 60.0 65.0 55.3 55.0 59.0 55.0 65.0
-1.0 48.0 47.0 48.8 46.0 49.0 49.0 51.0
-12.0 44.0 44.0 45.6 43.0 50.0 45.0 42.0
-30.0 64.0 64.0 67.1 64.0 70.0 66.0 69.0
-23.0 59.0 62.0 57.1 57.0 58.0 59.0 67.0
-30.0 68.0 66.0 65.7 64.0 67.0 65.0 74.0
-12.0 77.0 70.0 71.8 67.0 73.0 73.0 90.0
-2.0 59.0 57.0 54.2 54.0 58.0 55.0 70.0
-17.0 55.0 50.0 50.5 46.0 51.0 50.0 57.0
-3.0 58.0 55.0 51.8 49.0 54.0 50.0 71.0
-21.0 57.0 55.0 49.5 46.0 51.0 49.0 67.0
-27.0 42.0 42.0 45.2 41.0 50.0 47.0 47.0
-24.0 64.0 65.0 60.1 57.0 61.0 60.0 41.0
-20.0 64.0 63.0 65.6 63.0 70.0 64.0 73.0
-16.0 49.0 48.0 47.3 45.0 52.0 46.0 28.0
-7.0 40.0 42.0 46.3 44.0 51.0 46.0 62.0
-7.0 44.0 51.0 46.2 45.0 49.0 46.0 38.0
-24.0 67.0 64.0 68.0 65.0 71.0 66.0 64.0
-30.0 79.0 75.0 74.6 74.0 76.0 75.0 63.0
-11.0 50.0 52.0 46.7 42.0 48.0 48.0 39.0
-9.0 85.0 67.0 68.6 66.0 73.0 69.0 80.0
-22.0 67.0 68.0 68.7 65.0 70.0 69.0 56.0
-25.0 53.0 54.0 55.0 53.0 57.0 57.0 42.0
-24.0 62.0 62.0 56.8 52.0 61.0 57.0 70.0
-16.0 77.0 76.0 76.1 76.0 78.0 75.0 61.0
-1.0 74.0 73.0 73.1 71.0 75.0 72.0 93.0
-18.0 50.0 52.0 50.3 50.0 53.0 50.0 35.0
-3.0 75.0 70.0 73.9 71.0 75.0 73.0 68.0
-2.0 73.0 77.0 77.4 75.0 80.0 79.0 62.0
-5.0 69.0 60.0 56.6 52.0 58.0 56.0 72.0
-13.0 55.0 52.0 53.3 50.0 55.0 53.0 54.0
-28.0 81.0 79.0 75.0 71.0 77.0 76.0 85.0
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+0 1
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+-88.5787166225 1.08055532812
+-82.8452345578 0.272541389247
+72.4951388149 -0.26868660527800003
+11.805182128 1.0360467096600001
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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
-1.0 25.0 48.0 51.0 48.2 45.0 51.0 49.0 63.0 0.0 1.0 0.0 0.0 0.0
-11.0 25.0 49.0 52.0 48.6 45.0 52.0 47.0 41.0 1.0 0.0 0.0 0.0 0.0
-7.0 20.0 73.0 78.0 76.7 75.0 78.0 77.0 66.0 0.0 0.0 0.0 0.0 0.0
-12.0 17.0 39.0 35.0 45.2 43.0 47.0 46.0 38.0 0.0 0.0 1.0 0.0 0.0
-12.0 8.0 42.0 40.0 46.1 45.0 51.0 47.0 36.0 0.0 0.0 0.0 0.0 0.0
-12.0 28.0 42.0 47.0 45.3 41.0 49.0 44.0 58.0 0.0 0.0 0.0 0.0 0.0
-7.0 17.0 76.0 72.0 76.3 76.0 78.0 77.0 88.0 0.0 0.0 0.0 1.0 0.0
-7.0 7.0 69.0 76.0 74.4 73.0 77.0 74.0 72.0 0.0 0.0 0.0 0.0 0.0
-12.0 15.0 40.0 39.0 45.3 45.0 49.0 47.0 46.0 0.0 0.0 0.0 0.0 0.0
-6.0 27.0 71.0 78.0 72.2 70.0 74.0 72.0 84.0 0.0 1.0 0.0 0.0 0.0
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-9.0 24.0 67.0 64.0 68.0 65.0 71.0 66.0 64.0 0.0 0.0 1.0 0.0 0.0
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-9.0 22.0 67.0 68.0 68.7 65.0 70.0 69.0 56.0 0.0 0.0 0.0 0.0 0.0
-3.0 25.0 53.0 54.0 55.0 53.0 57.0 57.0 42.0 1.0 0.0 0.0 0.0 0.0
-10.0 24.0 62.0 62.0 56.8 52.0 61.0 57.0 70.0 0.0 1.0 0.0 0.0 0.0
-7.0 16.0 77.0 76.0 76.1 76.0 78.0 75.0 61.0 0.0 0.0 1.0 0.0 0.0
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