Mercurial > repos > bgruening > sklearn_svm_classifier

annotate model_validations.py @ 8:1a9d5a8fff12 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
author bgruening
date Tue, 14 May 2019 18:03:50 -0400
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8
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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1 """
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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2 class
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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3 -----
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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4 OrderedKFold
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5 RepeatedOrderedKold
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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6
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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7
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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8 function
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9 --------
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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10 train_test_split
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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11 """
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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12
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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13 import numpy as np
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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14 import warnings
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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15
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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16 from itertools import chain
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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17 from math import ceil, floor
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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18 from sklearn.model_selection import (GroupShuffleSplit, ShuffleSplit,
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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19 StratifiedShuffleSplit)
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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20 from sklearn.model_selection._split import _BaseKFold, _RepeatedSplits
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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21 from sklearn.utils import check_random_state, indexable, safe_indexing
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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22 from sklearn.utils.validation import _num_samples, check_array
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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23
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24
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25 def _validate_shuffle_split(n_samples, test_size, train_size,
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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26 default_test_size=None):
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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27 """
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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28 Validation helper to check if the test/test sizes are meaningful wrt to the
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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29 size of the data (n_samples)
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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30 """
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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31 if test_size is None and train_size is None:
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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32 test_size = default_test_size
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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33
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34 test_size_type = np.asarray(test_size).dtype.kind
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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35 train_size_type = np.asarray(train_size).dtype.kind
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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36
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37 if (test_size_type == 'i' and (test_size >= n_samples or test_size <= 0)
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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38 or test_size_type == 'f' and (test_size <= 0 or test_size >= 1)):
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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39 raise ValueError('test_size={0} should be either positive and smaller'
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
bgruening
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40 ' than the number of samples {1} or a float in the '
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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41 '(0, 1) range'.format(test_size, n_samples))
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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42
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43 if (train_size_type == 'i' and (train_size >= n_samples or train_size <= 0)
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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44 or train_size_type == 'f' and (train_size <= 0 or train_size >= 1)):
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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45 raise ValueError('train_size={0} should be either positive and smaller'
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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46 ' than the number of samples {1} or a float in the '
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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47 '(0, 1) range'.format(train_size, n_samples))
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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48
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49 if train_size is not None and train_size_type not in ('i', 'f'):
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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50 raise ValueError("Invalid value for train_size: {}".format(train_size))
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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51 if test_size is not None and test_size_type not in ('i', 'f'):
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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52 raise ValueError("Invalid value for test_size: {}".format(test_size))
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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53
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54 if (train_size_type == 'f' and test_size_type == 'f' and
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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55 train_size + test_size > 1):
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56 raise ValueError(
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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57 'The sum of test_size and train_size = {}, should be in the (0, 1)'
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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58 ' range. Reduce test_size and/or train_size.'
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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59 .format(train_size + test_size))
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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60
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61 if test_size_type == 'f':
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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62 n_test = ceil(test_size * n_samples)
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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63 elif test_size_type == 'i':
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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64 n_test = float(test_size)
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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65
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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66 if train_size_type == 'f':
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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67 n_train = floor(train_size * n_samples)
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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68 elif train_size_type == 'i':
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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69 n_train = float(train_size)
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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70
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71 if train_size is None:
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72 n_train = n_samples - n_test
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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73 elif test_size is None:
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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74 n_test = n_samples - n_train
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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75
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76 if n_train + n_test > n_samples:
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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77 raise ValueError('The sum of train_size and test_size = %d, '
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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78 'should be smaller than the number of '
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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79 'samples %d. Reduce test_size and/or '
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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80 'train_size.' % (n_train + n_test, n_samples))
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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81
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82 n_train, n_test = int(n_train), int(n_test)
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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83
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84 if n_train == 0:
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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85 raise ValueError(
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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86 'With n_samples={}, test_size={} and train_size={}, the '
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
bgruening
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87 'resulting train set will be empty. Adjust any of the '
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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88 'aforementioned parameters.'.format(n_samples, test_size,
1a9d5a8fff12 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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89 train_size)
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90 )
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91
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92 return n_train, n_test
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93
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94
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95 def train_test_split(*arrays, **options):
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96 """Extend sklearn.model_selection.train_test_slit to have group split.
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97
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98 Parameters
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99 ----------
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100 *arrays : sequence of indexables with same length / shape[0]
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101 Allowed inputs are lists, numpy arrays, scipy-sparse
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102 matrices or pandas dataframes.
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103
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104 test_size : float, int or None, optional (default=None)
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105 If float, should be between 0.0 and 1.0 and represent the proportion
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106 of the dataset to include in the test split. If int, represents the
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107 absolute number of test samples. If None, the value is set to the
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108 complement of the train size. If ``train_size`` is also None, it will
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109 be set to 0.25.
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110
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111 train_size : float, int, or None, (default=None)
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112 If float, should be between 0.0 and 1.0 and represent the
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113 proportion of the dataset to include in the train split. If
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114 int, represents the absolute number of train samples. If None,
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115 the value is automatically set to the complement of the test size.
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116
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117 random_state : int, RandomState instance or None, optional (default=None)
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118 If int, random_state is the seed used by the random number generator;
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119 If RandomState instance, random_state is the random number generator;
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120 If None, the random number generator is the RandomState instance used
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121 by `np.random`.
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122
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123 shuffle : None or str (default='simple')
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124 How to shuffle the data before splitting.
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125 None, no shuffle.
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126 For str, one of 'simple', 'stratified' and 'group', corresponding to
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127 `ShuffleSplit`, `StratifiedShuffleSplit` and `GroupShuffleSplit`,
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128 respectively.
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129
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130 labels : array-like or None (default=None)
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131 Ignored if shuffle is None or 'simple'.
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132 When shuffle='stratified', this array is used as class labels.
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133 When shuffle='group', this array is used as groups.
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134
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135 Returns
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136 -------
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137 splitting : list, length=2 * len(arrays)
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138 List containing train-test split of inputs.
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139
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140 """
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141 n_arrays = len(arrays)
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142 if n_arrays == 0:
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143 raise ValueError("At least one array required as input")
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144 test_size = options.pop('test_size', None)
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145 train_size = options.pop('train_size', None)
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146 random_state = options.pop('random_state', None)
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147 shuffle = options.pop('shuffle', 'simple')
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148 labels = options.pop('labels', None)
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149
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150 if options:
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151 raise TypeError("Invalid parameters passed: %s" % str(options))
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152
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153 arrays = indexable(*arrays)
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154
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155 n_samples = _num_samples(arrays[0])
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156 if shuffle == 'group':
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157 if labels is None:
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158 raise ValueError("When shuffle='group', "
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159 "labels should not be None!")
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160 labels = check_array(labels, ensure_2d=False, dtype=None)
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161 uniques = np.unique(labels)
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162 n_samples = uniques.size
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163
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164 n_train, n_test = _validate_shuffle_split(n_samples, test_size, train_size,
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165 default_test_size=0.25)
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166
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167 shuffle_options = dict(test_size=n_test,
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168 train_size=n_train,
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169 random_state=random_state)
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170
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171 if shuffle is None:
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172 if labels is not None:
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173 warnings.warn("The `labels` is ignored for "
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174 "shuffle being None!")
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175
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176 train = np.arange(n_train)
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177 test = np.arange(n_train, n_train + n_test)
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178
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179 elif shuffle == 'simple':
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180 if labels is not None:
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181 warnings.warn("The `labels` is not needed and therefore "
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182 "ignored for ShuffleSplit, as shuffle='simple'!")
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183
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184 cv = ShuffleSplit(**shuffle_options)
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185 train, test = next(cv.split(X=arrays[0], y=None))
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186
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187 elif shuffle == 'stratified':
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188 cv = StratifiedShuffleSplit(**shuffle_options)
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189 train, test = next(cv.split(X=arrays[0], y=labels))
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190
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191 elif shuffle == 'group':
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192 cv = GroupShuffleSplit(**shuffle_options)
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193 train, test = next(cv.split(X=arrays[0], y=None, groups=labels))
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194
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195 else:
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196 raise ValueError("The argument `shuffle` only supports None, "
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197 "'simple', 'stratified' and 'group', but got `%s`!"
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198 % shuffle)
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199
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200 return list(chain.from_iterable((safe_indexing(a, train),
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201 safe_indexing(a, test)) for a in arrays))
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202
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203
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204 class OrderedKFold(_BaseKFold):
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205 """
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206 Split into K fold based on ordered target value
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207
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208 Parameters
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209 ----------
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210 n_splits : int, default=3
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211 Number of folds. Must be at least 2.
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212 shuffle: bool
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213 random_state: None or int
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214 """
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215
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216 def __init__(self, n_splits=3, shuffle=False, random_state=None):
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217 super(OrderedKFold, self).__init__(n_splits, shuffle, random_state)
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218
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219 def _iter_test_indices(self, X, y, groups=None):
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220 n_samples = _num_samples(X)
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221 n_splits = self.n_splits
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222 y = np.asarray(y)
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223 sorted_index = np.argsort(y)
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224 if self.shuffle:
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225 current = 0
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226 rng = check_random_state(self.random_state)
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227 for i in range(n_samples // int(n_splits)):
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228 start, stop = current, current + n_splits
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229 rng.shuffle(sorted_index[start:stop])
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230 current = stop
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231 rng.shuffle(sorted_index[current:])
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232
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233 for i in range(n_splits):
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234 yield sorted_index[i:n_samples:n_splits]
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235
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236
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237 class RepeatedOrderedKFold(_RepeatedSplits):
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238 """ Repeated OrderedKFold runs mutiple times with different randomization.
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239
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240 Parameters
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241 ----------
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242 n_splits : int, default=5
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243 Number of folds. Must be at least 2.
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244
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245 n_repeats : int, default=5
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246 Number of times cross-validator to be repeated.
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247
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248 random_state: int, RandomState instance or None. Optional
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249 """
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250 def __init__(self, n_splits=5, n_repeats=5, random_state=None):
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251 super(RepeatedOrderedKFold, self).__init__(
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252 OrderedKFold, n_repeats, random_state, n_splits=n_splits)