# 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"] @@ -428,16 +426,37 @@ - + - - + + + + - - + + + + + + + + + + + + + + + + + @@ -470,10 +489,14 @@ - + + + - + + +