Mercurial > repos > bgruening > sklearn_train_test_eval
view train_test_split.py @ 13:10e2498ef07b draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 8850f42c2c3763e614f7454c9c006f3d2ff572c0
author | bgruening |
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date | Fri, 27 May 2022 12:13:23 +0000 |
parents | caf7d2b71a48 |
children | 2eb5c017958d |
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import argparse import json import warnings import pandas as pd from galaxy_ml.model_validations import train_test_split from galaxy_ml.utils import get_cv, read_columns def _get_single_cv_split(params, array, infile_labels=None, infile_groups=None): """output (train, test) subset from a cv splitter Parameters ---------- params : dict Galaxy tool inputs array : pandas DataFrame object The target dataset to split infile_labels : str File path to dataset containing target values infile_groups : str File path to dataset containing group values """ y = None groups = None nth_split = params["mode_selection"]["nth_split"] # read groups if infile_groups: header = ( "infer" if (params["mode_selection"]["cv_selector"]["groups_selector"]["header_g"]) else None ) column_option = params["mode_selection"]["cv_selector"]["groups_selector"][ "column_selector_options_g" ]["selected_column_selector_option_g"] if column_option in [ "by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name", ]: c = params["mode_selection"]["cv_selector"]["groups_selector"][ "column_selector_options_g" ]["col_g"] else: c = None groups = read_columns( infile_groups, c=c, c_option=column_option, sep="\t", header=header, parse_dates=True, ) groups = groups.ravel() params["mode_selection"]["cv_selector"]["groups_selector"] = groups # read labels if infile_labels: target_input = params["mode_selection"]["cv_selector"].pop("target_input") header = "infer" if target_input["header1"] else None col_index = target_input["col"][0] - 1 df = pd.read_csv(infile_labels, sep="\t", header=header, parse_dates=True) y = df.iloc[:, col_index].values # construct the cv splitter object splitter, groups = get_cv(params["mode_selection"]["cv_selector"]) total_n_splits = splitter.get_n_splits(array.values, y=y, groups=groups) if nth_split > total_n_splits: raise ValueError( "Total number of splits is {}, but got `nth_split` " "= {}".format(total_n_splits, nth_split) ) i = 1 for train_index, test_index in splitter.split(array.values, y=y, groups=groups): # suppose nth_split >= 1 if i == nth_split: break else: i += 1 train = array.iloc[train_index, :] test = array.iloc[test_index, :] return train, test def main( inputs, infile_array, outfile_train, outfile_test, infile_labels=None, infile_groups=None, ): """ Parameter --------- inputs : str File path to galaxy tool parameter infile_array : str File paths of input arrays separated by comma infile_labels : str File path to dataset containing labels infile_groups : str File path to dataset containing groups outfile_train : str File path to dataset containing train split outfile_test : str File path to dataset containing test split """ warnings.simplefilter("ignore") with open(inputs, "r") as param_handler: params = json.load(param_handler) input_header = params["header0"] header = "infer" if input_header else None array = pd.read_csv(infile_array, sep="\t", header=header, parse_dates=True) # train test split if params["mode_selection"]["selected_mode"] == "train_test_split": options = params["mode_selection"]["options"] shuffle_selection = options.pop("shuffle_selection") options["shuffle"] = shuffle_selection["shuffle"] if infile_labels: header = "infer" if shuffle_selection["header1"] else None col_index = shuffle_selection["col"][0] - 1 df = pd.read_csv(infile_labels, sep="\t", header=header, parse_dates=True) labels = df.iloc[:, col_index].values options["labels"] = labels train, test = train_test_split(array, **options) # cv splitter else: train, test = _get_single_cv_split( params, array, infile_labels=infile_labels, infile_groups=infile_groups ) print("Input shape: %s" % repr(array.shape)) print("Train shape: %s" % repr(train.shape)) print("Test shape: %s" % repr(test.shape)) train.to_csv(outfile_train, sep="\t", header=input_header, index=False) test.to_csv(outfile_test, sep="\t", header=input_header, index=False) if __name__ == "__main__": aparser = argparse.ArgumentParser() aparser.add_argument("-i", "--inputs", dest="inputs", required=True) aparser.add_argument("-X", "--infile_array", dest="infile_array") aparser.add_argument("-y", "--infile_labels", dest="infile_labels") aparser.add_argument("-g", "--infile_groups", dest="infile_groups") aparser.add_argument("-o", "--outfile_train", dest="outfile_train") aparser.add_argument("-t", "--outfile_test", dest="outfile_test") args = aparser.parse_args() main( args.inputs, args.infile_array, args.outfile_train, args.outfile_test, args.infile_labels, args.infile_groups, )