changeset 29:95d0f81e46e8 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
author bgruening
date Fri, 01 Nov 2019 17:14:31 -0400
parents 7696d389675c
children 18b39ada6f35
files fitted_model_eval.py main_macros.xml simple_model_fit.py stacking_ensembles.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
diffstat 16 files changed, 2060 insertions(+), 3 deletions(-) [+]
line wrap: on
line diff
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/fitted_model_eval.py	Fri Nov 01 17:14:31 2019 -0400
@@ -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)
--- a/main_macros.xml	Wed Oct 02 03:42:25 2019 -0400
+++ b/main_macros.xml	Fri Nov 01 17:14:31 2019 -0400
@@ -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@"/>
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/simple_model_fit.py	Fri Nov 01 17:14:31 2019 -0400
@@ -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)
--- a/stacking_ensembles.py	Wed Oct 02 03:42:25 2019 -0400
+++ b/stacking_ensembles.py	Fri Nov 01 17:14:31 2019 -0400
@@ -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]]
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/fitted_model_eval01.tabular	Fri Nov 01 17:14:31 2019 -0400
@@ -0,0 +1,2 @@
+score
+0.8277511130733235
Binary file test-data/model_fit01 has changed
Binary file test-data/model_fit02 has changed
Binary file test-data/model_fit02.h5 has changed
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regression_y_split_test01.tabular	Fri Nov 01 17:14:31 2019 -0400
@@ -0,0 +1,67 @@
+actual
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/train_test_split_test01.tabular	Fri Nov 01 17:14:31 2019 -0400
@@ -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
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+2016	10	29	60	65	55.3	55	59	55	65	0	0	1	0	0	0	0
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/train_test_split_test02.tabular	Fri Nov 01 17:14:31 2019 -0400
@@ -0,0 +1,201 @@
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+++ b/test-data/train_test_split_test03.tabular	Fri Nov 01 17:14:31 2019 -0400
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+++ b/test-data/train_test_split_train01.tabular	Fri Nov 01 17:14:31 2019 -0400
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/train_test_split_train03.tabular	Fri Nov 01 17:14:31 2019 -0400
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/train_test_split.py	Fri Nov 01 17:14:31 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)