# HG changeset patch
# User bgruening
# Date 1528195600 14400
# Node ID 44e26f8a82c6087d88a078023d10f7ba80a12232
# Parent 3a1acc39b39bfc28672a1a26026f534d18aa6507
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 97c4f22cdcfa6cddeeffc7b102c418a7ff12a888
diff -r 3a1acc39b39b -r 44e26f8a82c6 feature_selection.xml
--- a/feature_selection.xml Wed May 30 08:25:49 2018 -0400
+++ b/feature_selection.xml Tue Jun 05 06:46:40 2018 -0400
@@ -35,10 +35,15 @@
input_type = params["input_options"]["selected_input"]
if input_type=="tabular":
header = 'infer' if features_has_header else None
- 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 == "by_index_number":
+ c = params["input_options"]["column_selector_options_1"]["col1"]
+ else:
+ c = None
X, input_df = read_columns(
"$input_options.infile1",
- "$input_options.col1",
+ c = c,
+ c_option = column_option,
return_df = True,
sep='\t',
header=header,
@@ -49,9 +54,15 @@
## 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 == "by_index_number":
+ c = params["input_options"]["column_selector_options_2"]["col2"]
+else:
+ c = None
y = read_columns(
"$input_options.infile2",
- "$input_options.col2",
+ c = c,
+ c_option = column_option,
sep='\t',
header=header,
parse_dates=True
diff -r 3a1acc39b39b -r 44e26f8a82c6 main_macros.xml
--- a/main_macros.xml Wed May 30 08:25:49 2018 -0400
+++ b/main_macros.xml Tue Jun 05 06:46:40 2018 -0400
@@ -2,12 +2,11 @@
0.9
-def read_columns(f, c, return_df=False, **args):
+def read_columns(f, c=None, c_option='by_index_number', return_df=False, **args):
data = pandas.read_csv(f, **args)
- cols = c.split (',')
- cols = map(int, cols)
- cols = list(map(lambda x: x - 1, cols))
- data = data.iloc[:,cols]
+ if c_option == 'by_index_number':
+ cols = list(map(lambda x: x - 1, c))
+ data = data.iloc[:,cols]
y = data.values
if return_df:
return y, data
@@ -17,7 +16,6 @@
## generate an instance for one of sklearn.feature_selection classes
-## must call "@COLUMNS_FUNCTION@"
def feature_selector(inputs):
selector = inputs["selected_algorithm"]
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@@ -470,10 +489,14 @@
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