# HG changeset patch
# User bgruening
# Date 1523387931 14400
# Node ID f1761288587e27d67cc9c97bd7d2648e4b69f544
# Parent 0431274c367dd1a85d7f733741b6c4e17894a249
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
diff -r 0431274c367d -r f1761288587e ensemble.xml
--- a/ensemble.xml Thu Mar 22 13:46:46 2018 -0400
+++ b/ensemble.xml Tue Apr 10 15:18:51 2018 -0400
@@ -31,6 +31,21 @@
algorithm = params["selected_tasks"]["selected_algorithms"]["selected_algorithm"]
options = params["selected_tasks"]["selected_algorithms"]["options"]
+if "select_max_features" in options:
+ if options["select_max_features"]["max_features"] == "number_input":
+ options["select_max_features"]["max_features"] = options["select_max_features"]["num_max_features"]
+ options["select_max_features"].pop("num_max_features")
+ options["max_features"] = options["select_max_features"]["max_features"]
+ options.pop("select_max_features")
+if "presort" in options:
+ if options["presort"] == "true":
+ options["presort"] = True
+ if options["presort"] == "false":
+ options["presort"] = False
+if "min_samples_leaf" in options and options["min_samples_leaf"] == 1.0:
+ options["min_samples_leaf"] = 1
+if "min_samples_split" in options and options["min_samples_split"] > 1.0:
+ options["min_samples_split"] = int(options["min_samples_split"])
input_type = params["selected_tasks"]["selected_algorithms"]["input_options"]["selected_input"]
if input_type=="tabular":
header = 'infer' if params["selected_tasks"]["selected_algorithms"]["input_options"]["header1"] else None
@@ -52,6 +67,7 @@
header=header,
parse_dates=True
)
+y=y.ravel()
my_class = getattr(sklearn.ensemble, algorithm)
estimator = my_class(**options)
@@ -60,7 +76,8 @@
#else:
classifier_object = pickle.load(open("$selected_tasks.infile_model", 'r'))
-data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=0, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False)
+header = 'infer' if params["selected_tasks"]["header"] else None
+data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=header, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False)
prediction = classifier_object.predict(data)
prediction_df = pandas.DataFrame(prediction)
res = pandas.concat([data, prediction_df], axis=1)
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diff -r 0431274c367d -r f1761288587e test-data/gbc_model01
Binary file test-data/gbc_model01 has changed
diff -r 0431274c367d -r f1761288587e test-data/gbc_result01
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/gbc_result01 Tue Apr 10 15:18:51 2018 -0400
@@ -0,0 +1,6 @@
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+0.76831404394 1.38267855169 -0.989045048734 0.649504257894 1
diff -r 0431274c367d -r f1761288587e test-data/gbr_model01
Binary file test-data/gbr_model01 has changed
diff -r 0431274c367d -r f1761288587e test-data/gbr_prediction_result01.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/gbr_prediction_result01.tabular Tue Apr 10 15:18:51 2018 -0400
@@ -0,0 +1,88 @@
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diff -r 0431274c367d -r f1761288587e test-data/regression_X.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regression_X.tabular Tue Apr 10 15:18:51 2018 -0400
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regression_test_X.tabular Tue Apr 10 15:18:51 2018 -0400
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