| Previous changeset 28:50b3e080cef0 (2019-10-02) Next changeset 30:c72131b8fc7a (2019-11-07) |
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Commit message:
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6" |
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modified:
main_macros.xml stacking_ensembles.py |
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added:
fitted_model_eval.py simple_model_fit.py test-data/fitted_model_eval01.tabular test-data/model_fit01 test-data/model_fit02 test-data/model_fit02.h5 test-data/regression_y_split_test01.tabular test-data/train_test_split_test01.tabular test-data/train_test_split_test02.tabular test-data/train_test_split_test03.tabular test-data/train_test_split_train01.tabular test-data/train_test_split_train02.tabular test-data/train_test_split_train03.tabular train_test_split.py |
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| diff -r 50b3e080cef0 -r 66df2aa6cd6b fitted_model_eval.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/fitted_model_eval.py Fri Nov 01 17:13:42 2019 -0400 |
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| @@ -0,0 +1,160 @@ +import argparse +import json +import pandas as pd +import warnings + +from scipy.io import mmread +from sklearn.pipeline import Pipeline +from sklearn.metrics.scorer import _check_multimetric_scoring +from sklearn.model_selection._validation import _score +from galaxy_ml.utils import get_scoring, load_model, read_columns + + +def _get_X_y(params, infile1, infile2): + """ read from inputs and output X and y + + Parameters + ---------- + params : dict + Tool inputs parameter + infile1 : str + File path to dataset containing features + infile2 : str + File path to dataset containing target values + + """ + # store read dataframe object + loaded_df = {} + + input_type = params['input_options']['selected_input'] + # tabular input + if input_type == 'tabular': + 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 in ['by_index_number', 'all_but_by_index_number', + 'by_header_name', 'all_but_by_header_name']: + c = params['input_options']['column_selector_options_1']['col1'] + else: + c = None + + df_key = infile1 + repr(header) + df = pd.read_csv(infile1, sep='\t', header=header, + parse_dates=True) + loaded_df[df_key] = df + + X = read_columns(df, c=c, c_option=column_option).astype(float) + # sparse input + elif input_type == 'sparse': + X = mmread(open(infile1, 'r')) + + # Get target y + 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']: + c = params['input_options']['column_selector_options_2']['col2'] + else: + c = None + + df_key = infile2 + repr(header) + if df_key in loaded_df: + infile2 = loaded_df[df_key] + else: + infile2 = pd.read_csv(infile2, sep='\t', + header=header, parse_dates=True) + loaded_df[df_key] = infile2 + + y = read_columns( + infile2, + c=c, + c_option=column_option, + sep='\t', + header=header, + parse_dates=True) + if len(y.shape) == 2 and y.shape[1] == 1: + y = y.ravel() + + return X, y + + +def main(inputs, infile_estimator, outfile_eval, + infile_weights=None, infile1=None, + infile2=None): + """ + Parameter + --------- + inputs : str + File path to galaxy tool parameter + + infile_estimator : strgit + File path to trained estimator input + + outfile_eval : str + File path to save the evalulation results, tabular + + infile_weights : str + File path to weights input + + infile1 : str + File path to dataset containing features + + infile2 : str + File path to dataset containing target values + """ + warnings.filterwarnings('ignore') + + with open(inputs, 'r') as param_handler: + params = json.load(param_handler) + + X_test, y_test = _get_X_y(params, infile1, infile2) + + # load model + with open(infile_estimator, 'rb') as est_handler: + estimator = load_model(est_handler) + + main_est = estimator + if isinstance(estimator, Pipeline): + main_est = estimator.steps[-1][-1] + if hasattr(main_est, 'config') and hasattr(main_est, 'load_weights'): + if not infile_weights or infile_weights == 'None': + raise ValueError("The selected model skeleton asks for weights, " + "but no dataset for weights was provided!") + main_est.load_weights(infile_weights) + + # handle scorer, convert to scorer dict + scoring = params['scoring'] + scorer = get_scoring(scoring) + scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer) + + if hasattr(estimator, 'evaluate'): + scores = estimator.evaluate(X_test, y_test=y_test, + scorer=scorer, + is_multimetric=True) + else: + scores = _score(estimator, X_test, y_test, scorer, + is_multimetric=True) + + # handle output + for name, score in scores.items(): + scores[name] = [score] + df = pd.DataFrame(scores) + df = df[sorted(df.columns)] + df.to_csv(path_or_buf=outfile_eval, sep='\t', + header=True, index=False) + + +if __name__ == '__main__': + aparser = argparse.ArgumentParser() + aparser.add_argument("-i", "--inputs", dest="inputs", required=True) + aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator") + aparser.add_argument("-w", "--infile_weights", dest="infile_weights") + aparser.add_argument("-X", "--infile1", dest="infile1") + aparser.add_argument("-y", "--infile2", dest="infile2") + aparser.add_argument("-O", "--outfile_eval", dest="outfile_eval") + args = aparser.parse_args() + + main(args.inputs, args.infile_estimator, args.outfile_eval, + infile_weights=args.infile_weights, infile1=args.infile1, + infile2=args.infile2) |
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| diff -r 50b3e080cef0 -r 66df2aa6cd6b main_macros.xml --- a/main_macros.xml Wed Oct 02 03:55:06 2019 -0400 +++ b/main_macros.xml Fri Nov 01 17:13:42 2019 -0400 |
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| @@ -328,8 +328,8 @@ <!--Data interface--> - <xml name="samples_tabular" token_multiple1="false" token_multiple2="false"> - <param name="infile1" type="data" format="tabular" label="Training samples dataset:"/> + <xml name="samples_tabular" token_label1="Training samples dataset:" token_multiple1="false" token_multiple2="false"> + <param name="infile1" type="data" format="tabular" label="@LABEL1@"/> <param name="header1" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="False" label="Does the dataset contain header:" /> <conditional name="column_selector_options_1"> <expand macro="samples_column_selector_options" multiple="@MULTIPLE1@"/> |
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| diff -r 50b3e080cef0 -r 66df2aa6cd6b simple_model_fit.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/simple_model_fit.py Fri Nov 01 17:13:42 2019 -0400 |
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| @@ -0,0 +1,145 @@ +import argparse +import json +import pandas as pd +import pickle + +from galaxy_ml.utils import load_model, read_columns +from sklearn.pipeline import Pipeline + + +def _get_X_y(params, infile1, infile2): + """ read from inputs and output X and y + + Parameters + ---------- + params : dict + Tool inputs parameter + infile1 : str + File path to dataset containing features + infile2 : str + File path to dataset containing target values + + """ + # store read dataframe object + loaded_df = {} + + input_type = params['input_options']['selected_input'] + # tabular input + if input_type == 'tabular': + 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 in ['by_index_number', 'all_but_by_index_number', + 'by_header_name', 'all_but_by_header_name']: + c = params['input_options']['column_selector_options_1']['col1'] + else: + c = None + + df_key = infile1 + repr(header) + df = pd.read_csv(infile1, sep='\t', header=header, + parse_dates=True) + loaded_df[df_key] = df + + X = read_columns(df, c=c, c_option=column_option).astype(float) + # sparse input + elif input_type == 'sparse': + X = mmread(open(infile1, 'r')) + + # Get target y + 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']: + c = params['input_options']['column_selector_options_2']['col2'] + else: + c = None + + df_key = infile2 + repr(header) + if df_key in loaded_df: + infile2 = loaded_df[df_key] + else: + infile2 = pd.read_csv(infile2, sep='\t', + header=header, parse_dates=True) + loaded_df[df_key] = infile2 + + y = read_columns( + infile2, + c=c, + c_option=column_option, + sep='\t', + header=header, + parse_dates=True) + if len(y.shape) == 2 and y.shape[1] == 1: + y = y.ravel() + + return X, y + + +def main(inputs, infile_estimator, infile1, infile2, out_object, + out_weights=None): + """ main + + Parameters + ---------- + inputs : str + File path to galaxy tool parameter + + infile_estimator : str + File paths of input estimator + + infile1 : str + File path to dataset containing features + + infile2 : str + File path to dataset containing target labels + + out_object : str + File path for output of fitted model or skeleton + + out_weights : str + File path for output of weights + + """ + with open(inputs, 'r') as param_handler: + params = json.load(param_handler) + + # load model + with open(infile_estimator, 'rb') as est_handler: + estimator = load_model(est_handler) + + X_train, y_train = _get_X_y(params, infile1, infile2) + + estimator.fit(X_train, y_train) + + main_est = estimator + if isinstance(main_est, Pipeline): + main_est = main_est.steps[-1][-1] + if hasattr(main_est, 'model_') \ + and hasattr(main_est, 'save_weights'): + if out_weights: + main_est.save_weights(out_weights) + del main_est.model_ + del main_est.fit_params + del main_est.model_class_ + del main_est.validation_data + if getattr(main_est, 'data_generator_', None): + del main_est.data_generator_ + + with open(out_object, 'wb') as output_handler: + pickle.dump(estimator, output_handler, + pickle.HIGHEST_PROTOCOL) + + +if __name__ == '__main__': + aparser = argparse.ArgumentParser() + aparser.add_argument("-i", "--inputs", dest="inputs", required=True) + aparser.add_argument("-X", "--infile_estimator", dest="infile_estimator") + aparser.add_argument("-y", "--infile1", dest="infile1") + aparser.add_argument("-g", "--infile2", dest="infile2") + aparser.add_argument("-o", "--out_object", dest="out_object") + aparser.add_argument("-t", "--out_weights", dest="out_weights") + args = aparser.parse_args() + + main(args.inputs, args.infile_estimator, args.infile1, + args.infile2, args.out_object, args.out_weights) |
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| diff -r 50b3e080cef0 -r 66df2aa6cd6b stacking_ensembles.py --- a/stacking_ensembles.py Wed Oct 02 03:55:06 2019 -0400 +++ b/stacking_ensembles.py Fri Nov 01 17:13:42 2019 -0400 |
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| @@ -82,7 +82,9 @@ weights = options.pop('weights', None) if weights: - options['weights'] = ast.literal_eval(weights) + weights = ast.literal_eval(weights) + if weights: + options['weights'] = weights mod_and_name = estimator_type.split('_') mod = sys.modules[mod_and_name[0]] |
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| diff -r 50b3e080cef0 -r 66df2aa6cd6b test-data/fitted_model_eval01.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/fitted_model_eval01.tabular Fri Nov 01 17:13:42 2019 -0400 |
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| @@ -0,0 +1,2 @@ +score +0.8277511130733235 |
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| diff -r 50b3e080cef0 -r 66df2aa6cd6b test-data/model_fit01 |
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| Binary file test-data/model_fit01 has changed |
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| diff -r 50b3e080cef0 -r 66df2aa6cd6b test-data/model_fit02 |
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| Binary file test-data/model_fit02 has changed |
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| diff -r 50b3e080cef0 -r 66df2aa6cd6b test-data/model_fit02.h5 |
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| Binary file test-data/model_fit02.h5 has changed |
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| diff -r 50b3e080cef0 -r 66df2aa6cd6b test-data/regression_y_split_test01.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/regression_y_split_test01.tabular Fri Nov 01 17:13:42 2019 -0400 |
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| @@ -0,0 +1,67 @@ +actual +57 +71 +75 +49 +66 +59 +68 +48 +46 +45 +67 +75 +79 +74 +60 +48 +77 +71 +85 +41 +75 +61 +76 +52 +46 +77 +88 +60 +68 +40 +89 +46 +49 +68 +57 +50 +68 +55 +64 +51 +77 +79 +42 +76 +54 +54 +59 +80 +55 +54 +54 +54 +54 +71 +56 +66 +61 +40 +71 +63 +78 +53 +75 +50 +72 +68 |
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| diff -r 50b3e080cef0 -r 66df2aa6cd6b test-data/train_test_split_test01.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/train_test_split_test01.tabular Fri Nov 01 17:13:42 2019 -0400 |
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| @@ -0,0 +1,67 @@ +year month day temp_2 temp_1 average forecast_noaa forecast_acc forecast_under friend week_Fri week_Mon week_Sat week_Sun week_Thurs week_Tues week_Wed +2016 11 2 59 57 54.2 54 58 55 70 0 0 0 0 0 0 1 +2016 11 8 61 63 52.7 49 57 52 49 0 0 0 0 0 1 0 +2016 7 13 74 77 75.6 74 78 76 56 0 0 0 0 0 0 1 +2016 3 14 52 54 53.4 49 58 55 44 0 1 0 0 0 0 0 +2016 6 13 65 70 69.3 66 72 69 79 0 1 0 0 0 0 0 +2016 5 21 63 66 65.7 62 67 65 49 0 0 1 0 0 0 0 +2016 7 4 76 71 73.8 71 76 73 86 0 1 0 0 0 0 0 +2016 1 15 55 49 47.1 46 51 46 65 1 0 0 0 0 0 0 +2016 2 1 48 47 48.8 46 49 49 51 0 1 0 0 0 0 0 +2016 1 11 50 52 46.7 42 48 48 39 0 1 0 0 0 0 0 +2016 6 8 86 85 68.5 67 70 69 81 0 0 0 0 0 0 1 +2016 7 23 81 71 77.0 75 81 76 86 0 0 1 0 0 0 0 +2016 9 14 74 75 71.2 67 75 73 77 0 0 0 0 0 0 1 +2016 9 12 77 70 71.8 67 73 73 90 0 1 0 0 0 0 0 +2016 10 17 62 60 59.1 57 63 59 62 0 1 0 0 0 0 0 +2016 1 19 50 54 47.6 47 49 48 53 0 0 0 0 0 1 0 +2016 9 26 67 76 67.2 64 69 69 74 0 1 0 0 0 0 0 +2016 9 15 75 79 71.0 66 76 69 64 0 0 0 0 1 0 0 +2016 7 28 79 83 77.3 76 80 78 76 0 0 0 0 1 0 0 +2016 12 24 45 40 45.1 44 47 46 39 0 0 1 0 0 0 0 +2016 6 1 71 79 67.4 65 69 66 58 0 0 0 0 0 0 1 +2016 10 3 63 65 64.5 63 68 65 49 0 1 0 0 0 0 0 +2016 4 8 68 77 57.1 57 61 57 41 1 0 0 0 0 0 0 +2016 11 17 55 50 50.5 46 51 50 57 0 0 0 0 1 0 0 +2016 12 4 50 49 46.8 45 47 47 53 0 0 0 1 0 0 0 +2016 9 10 72 74 72.3 70 77 74 91 0 0 1 0 0 0 0 +2016 7 29 83 85 77.3 77 80 79 77 1 0 0 0 0 0 0 +2016 10 14 66 60 60.2 56 64 60 78 1 0 0 0 0 0 0 +2016 3 30 56 64 55.7 51 57 56 57 0 0 0 0 0 0 1 +2016 12 5 49 46 46.6 43 50 45 65 0 1 0 0 0 0 0 +2016 4 18 68 77 58.8 55 59 57 39 0 1 0 0 0 0 0 +2016 12 19 35 39 45.1 42 46 45 51 0 1 0 0 0 0 0 +2016 2 4 51 49 49.0 44 54 51 44 0 0 0 0 1 0 0 +2016 4 30 64 61 61.4 60 65 62 78 0 0 1 0 0 0 0 +2016 4 5 69 60 56.6 52 58 56 72 0 0 0 0 0 1 0 +2016 11 16 57 55 50.7 50 51 49 34 0 0 0 0 0 0 1 +2016 9 28 77 69 66.5 66 68 66 62 0 0 0 0 0 0 1 +2016 1 13 45 49 46.9 45 51 46 33 0 0 0 0 0 0 1 +2016 3 5 59 57 52.1 49 53 51 46 0 0 1 0 0 0 0 +2016 1 24 57 48 48.1 46 50 48 54 0 0 0 1 0 0 0 +2016 7 14 77 75 75.8 74 76 77 77 0 0 0 0 1 0 0 +2016 8 23 84 81 75.7 73 78 77 89 0 0 0 0 0 1 0 +2016 12 25 40 41 45.1 42 49 44 31 0 0 0 1 0 0 0 +2016 9 25 64 67 67.6 64 72 67 62 0 0 0 1 0 0 0 +2016 11 21 57 55 49.5 46 51 49 67 0 1 0 0 0 0 0 +2016 1 16 49 48 47.3 45 52 46 28 0 0 1 0 0 0 0 +2016 2 24 51 60 50.8 47 53 50 46 0 0 0 0 0 0 1 +2016 8 4 73 75 77.3 73 79 78 66 0 0 0 0 1 0 0 +2016 3 2 54 58 51.6 47 54 52 37 0 0 0 0 0 0 1 +2016 1 25 48 51 48.2 45 51 49 63 0 1 0 0 0 0 0 +2016 1 18 54 50 47.5 44 48 49 58 0 1 0 0 0 0 0 +2016 11 22 55 54 49.3 46 54 49 58 0 0 0 0 0 1 0 +2016 3 13 55 52 53.3 50 55 53 54 0 0 0 1 0 0 0 +2016 5 17 57 60 65.0 62 65 65 55 0 0 0 0 0 1 0 +2016 1 28 56 57 48.4 44 52 48 34 0 0 0 0 1 0 0 +2016 5 24 66 65 66.2 66 71 66 67 0 0 0 0 0 1 0 +2016 11 6 65 58 53.2 52 57 55 71 0 0 0 1 0 0 0 +2016 12 23 49 45 45.1 45 49 44 35 1 0 0 0 0 0 0 +2016 6 25 68 69 71.7 68 73 73 89 0 0 1 0 0 0 0 +2016 4 2 73 71 56.2 55 58 58 45 0 0 1 0 0 0 0 +2016 6 26 69 71 71.9 67 74 72 70 0 0 0 1 0 0 0 +2016 11 26 52 52 48.4 48 50 47 58 0 0 1 0 0 0 0 +2016 9 13 70 74 71.5 71 75 70 82 0 0 0 0 0 1 0 +2016 12 2 52 46 47.2 46 51 49 41 1 0 0 0 0 0 0 +2016 8 6 80 79 77.2 76 81 79 60 0 0 1 0 0 0 0 +2016 10 29 60 65 55.3 55 59 55 65 0 0 1 0 0 0 0 |
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| diff -r 50b3e080cef0 -r 66df2aa6cd6b test-data/train_test_split_test02.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/train_test_split_test02.tabular Fri Nov 01 17:13:42 2019 -0400 |
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| b'@@ -0,0 +1,201 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|
| b |
| diff -r 50b3e080cef0 -r 66df2aa6cd6b test-data/train_test_split_test03.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/train_test_split_test03.tabular Fri Nov 01 17:13:42 2019 -0400 |
| b |
| @@ -0,0 +1,54 @@ +year month day temp_2 temp_1 average forecast_noaa forecast_acc forecast_under friend week_Fri week_Mon week_Sat week_Sun week_Thurs week_Tues week_Wed +2016 9 19 68 69 69.7 65 74 71 88 0 1 0 0 0 0 0 +2016 1 25 48 51 48.2 45 51 49 63 0 1 0 0 0 0 0 +2016 12 17 39 35 45.2 43 47 46 38 0 0 1 0 0 0 0 +2016 7 17 76 72 76.3 76 78 77 88 0 0 0 1 0 0 0 +2016 6 27 71 78 72.2 70 74 72 84 0 1 0 0 0 0 0 +2016 4 17 60 68 58.6 58 62 59 54 0 0 0 1 0 0 0 +2016 11 2 59 57 54.2 54 58 55 70 0 0 0 0 0 0 1 +2016 12 27 42 42 45.2 41 50 47 47 0 0 0 0 0 1 0 +2016 1 16 49 48 47.3 45 52 46 28 0 0 1 0 0 0 0 +2016 12 7 40 42 46.3 44 51 46 62 0 0 0 0 0 0 1 +2016 8 28 81 79 75.0 71 77 76 85 0 0 0 1 0 0 0 +2016 10 19 60 61 58.4 58 60 57 41 0 0 0 0 0 0 1 +2016 5 5 74 60 62.5 58 66 62 56 0 0 0 0 1 0 0 +2016 12 11 36 44 45.7 41 46 47 35 0 0 0 1 0 0 0 +2016 3 30 56 64 55.7 51 57 56 57 0 0 0 0 0 0 1 +2016 10 9 64 68 62.1 58 65 63 55 0 0 0 1 0 0 0 +2016 1 12 52 45 46.8 44 50 45 61 0 0 0 0 0 1 0 +2016 8 13 80 87 76.8 73 79 78 73 0 0 1 0 0 0 0 +2016 9 23 68 67 68.3 67 69 67 61 1 0 0 0 0 0 0 +2016 6 16 60 67 69.8 68 72 71 87 0 0 0 0 1 0 0 +2016 9 8 68 67 72.8 69 77 73 56 0 0 0 0 1 0 0 +2016 12 4 50 49 46.8 45 47 47 53 0 0 0 1 0 0 0 +2016 1 13 45 49 46.9 45 51 46 33 0 0 0 0 0 0 1 +2016 2 5 49 49 49.1 47 50 49 45 1 0 0 0 0 0 0 +2016 6 22 76 73 71.0 66 71 72 78 0 0 0 0 0 0 1 +2016 5 25 65 66 66.4 65 67 66 60 0 0 0 0 0 0 1 +2016 4 8 68 77 57.1 57 61 57 41 1 0 0 0 0 0 0 +2016 10 11 57 60 61.4 58 66 61 58 0 0 0 0 0 1 0 +2016 11 4 57 65 53.7 49 55 54 38 1 0 0 0 0 0 0 +2016 11 30 52 52 47.6 47 52 49 44 0 0 0 0 0 0 1 +2016 8 4 73 75 77.3 73 79 78 66 0 0 0 0 1 0 0 +2016 9 20 69 71 69.4 67 73 69 81 0 0 0 0 0 1 0 +2016 2 19 57 53 50.2 50 52 51 42 1 0 0 0 0 0 0 +2016 9 4 70 67 73.7 72 77 75 64 0 0 0 1 0 0 0 +2016 10 4 65 61 64.1 62 69 65 60 0 0 0 0 0 1 0 +2016 5 21 63 66 65.7 62 67 65 49 0 0 1 0 0 0 0 +2016 1 9 45 48 46.4 46 50 45 47 0 0 1 0 0 0 0 +2016 8 3 77 73 77.3 77 81 77 93 0 0 0 0 0 0 1 +2016 10 7 66 63 62.9 62 67 64 78 1 0 0 0 0 0 0 +2016 10 17 62 60 59.1 57 63 59 62 0 1 0 0 0 0 0 +2016 6 18 71 67 70.2 67 75 69 77 0 0 1 0 0 0 0 +2016 12 26 41 42 45.2 45 48 46 58 0 1 0 0 0 0 0 +2016 11 20 55 57 49.8 47 54 48 30 0 0 0 1 0 0 0 +2016 2 22 53 51 50.6 46 51 50 59 0 1 0 0 0 0 0 +2016 6 26 69 71 71.9 67 74 72 70 0 0 0 1 0 0 0 +2016 7 11 71 74 75.3 74 79 75 71 0 1 0 0 0 0 0 +2016 6 21 70 76 70.8 68 75 71 57 0 0 0 0 0 1 0 +2016 3 2 54 58 51.6 47 54 52 37 0 0 0 0 0 0 1 +2016 6 12 67 65 69.1 65 73 70 83 0 0 0 1 0 0 0 +2016 5 13 81 77 64.3 63 67 66 67 1 0 0 0 0 0 0 +2016 4 12 59 58 57.7 54 59 57 61 0 0 0 0 0 1 0 +2016 10 14 66 60 60.2 56 64 60 78 1 0 0 0 0 0 0 +2016 4 15 59 59 58.3 58 61 60 40 1 0 0 0 0 0 0 |
| b |
| diff -r 50b3e080cef0 -r 66df2aa6cd6b test-data/train_test_split_train01.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/train_test_split_train01.tabular Fri Nov 01 17:13:42 2019 -0400 |
| b |
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|
| b |
| diff -r 50b3e080cef0 -r 66df2aa6cd6b test-data/train_test_split_train02.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/train_test_split_train02.tabular Fri Nov 01 17:13:42 2019 -0400 |
| b |
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|
| b |
| diff -r 50b3e080cef0 -r 66df2aa6cd6b test-data/train_test_split_train03.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/train_test_split_train03.tabular Fri Nov 01 17:13:42 2019 -0400 |
| b |
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| b |
| diff -r 50b3e080cef0 -r 66df2aa6cd6b train_test_split.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/train_test_split.py Fri Nov 01 17:13:42 2019 -0400 |
| [ |
| @@ -0,0 +1,154 @@ +import argparse +import json +import pandas as pd +import warnings + +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) |