Mercurial > repos > bgruening > sklearn_numeric_clustering
annotate train_test_eval.py @ 36:836ba896e2be draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
author | bgruening |
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date | Thu, 07 Nov 2019 05:13:53 -0500 |
parents | fbd849199283 |
children | 80bb86a40de6 |
rev | line source |
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32
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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1 import argparse |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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2 import joblib |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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3 import json |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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4 import numpy as np |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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5 import pandas as pd |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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6 import pickle |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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7 import warnings |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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8 from itertools import chain |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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9 from scipy.io import mmread |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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10 from sklearn.base import clone |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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11 from sklearn import (cluster, compose, decomposition, ensemble, |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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12 feature_extraction, feature_selection, |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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13 gaussian_process, kernel_approximation, metrics, |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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14 model_selection, naive_bayes, neighbors, |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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15 pipeline, preprocessing, svm, linear_model, |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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16 tree, discriminant_analysis) |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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17 from sklearn.exceptions import FitFailedWarning |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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18 from sklearn.metrics.scorer import _check_multimetric_scoring |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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19 from sklearn.model_selection._validation import _score, cross_validate |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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20 from sklearn.model_selection import _search, _validation |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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21 from sklearn.utils import indexable, safe_indexing |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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22 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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23 from galaxy_ml.model_validations import train_test_split |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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24 from galaxy_ml.utils import (SafeEval, get_scoring, load_model, |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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25 read_columns, try_get_attr, get_module) |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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26 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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27 |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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28 _fit_and_score = try_get_attr('galaxy_ml.model_validations', '_fit_and_score') |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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29 setattr(_search, '_fit_and_score', _fit_and_score) |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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30 setattr(_validation, '_fit_and_score', _fit_and_score) |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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31 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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32 N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1)) |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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33 CACHE_DIR = './cached' |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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34 NON_SEARCHABLE = ('n_jobs', 'pre_dispatch', 'memory', '_path', |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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35 'nthread', 'callbacks') |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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36 ALLOWED_CALLBACKS = ('EarlyStopping', 'TerminateOnNaN', 'ReduceLROnPlateau', |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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37 'CSVLogger', 'None') |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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38 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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39 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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40 def _eval_swap_params(params_builder): |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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41 swap_params = {} |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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42 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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43 for p in params_builder['param_set']: |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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44 swap_value = p['sp_value'].strip() |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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45 if swap_value == '': |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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46 continue |
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47 |
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48 param_name = p['sp_name'] |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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49 if param_name.lower().endswith(NON_SEARCHABLE): |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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50 warnings.warn("Warning: `%s` is not eligible for search and was " |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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51 "omitted!" % param_name) |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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52 continue |
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53 |
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54 if not swap_value.startswith(':'): |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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55 safe_eval = SafeEval(load_scipy=True, load_numpy=True) |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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56 ev = safe_eval(swap_value) |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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57 else: |
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58 # Have `:` before search list, asks for estimator evaluatio |
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59 safe_eval_es = SafeEval(load_estimators=True) |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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60 swap_value = swap_value[1:].strip() |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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61 # TODO maybe add regular express check |
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62 ev = safe_eval_es(swap_value) |
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63 |
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64 swap_params[param_name] = ev |
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65 |
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66 return swap_params |
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67 |
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68 |
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69 def train_test_split_none(*arrays, **kwargs): |
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70 """extend train_test_split to take None arrays |
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71 and support split by group names. |
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72 """ |
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73 nones = [] |
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74 new_arrays = [] |
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75 for idx, arr in enumerate(arrays): |
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76 if arr is None: |
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77 nones.append(idx) |
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78 else: |
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79 new_arrays.append(arr) |
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80 |
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81 if kwargs['shuffle'] == 'None': |
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82 kwargs['shuffle'] = None |
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83 |
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84 group_names = kwargs.pop('group_names', None) |
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85 |
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86 if group_names is not None and group_names.strip(): |
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87 group_names = [name.strip() for name in |
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88 group_names.split(',')] |
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89 new_arrays = indexable(*new_arrays) |
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90 groups = kwargs['labels'] |
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91 n_samples = new_arrays[0].shape[0] |
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92 index_arr = np.arange(n_samples) |
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93 test = index_arr[np.isin(groups, group_names)] |
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94 train = index_arr[~np.isin(groups, group_names)] |
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95 rval = list(chain.from_iterable( |
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96 (safe_indexing(a, train), |
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97 safe_indexing(a, test)) for a in new_arrays)) |
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98 else: |
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99 rval = train_test_split(*new_arrays, **kwargs) |
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100 |
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101 for pos in nones: |
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102 rval[pos * 2: 2] = [None, None] |
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103 |
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104 return rval |
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105 |
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106 |
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107 def main(inputs, infile_estimator, infile1, infile2, |
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108 outfile_result, outfile_object=None, |
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109 outfile_weights=None, groups=None, |
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110 ref_seq=None, intervals=None, targets=None, |
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111 fasta_path=None): |
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112 """ |
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113 Parameter |
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114 --------- |
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115 inputs : str |
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116 File path to galaxy tool parameter |
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117 |
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118 infile_estimator : str |
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119 File path to estimator |
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120 |
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121 infile1 : str |
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122 File path to dataset containing features |
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123 |
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124 infile2 : str |
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125 File path to dataset containing target values |
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126 |
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127 outfile_result : str |
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128 File path to save the results, either cv_results or test result |
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129 |
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130 outfile_object : str, optional |
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131 File path to save searchCV object |
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132 |
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133 outfile_weights : str, optional |
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134 File path to save deep learning model weights |
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135 |
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136 groups : str |
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137 File path to dataset containing groups labels |
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138 |
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139 ref_seq : str |
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140 File path to dataset containing genome sequence file |
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141 |
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142 intervals : str |
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143 File path to dataset containing interval file |
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144 |
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145 targets : str |
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146 File path to dataset compressed target bed file |
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147 |
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148 fasta_path : str |
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149 File path to dataset containing fasta file |
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150 """ |
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151 warnings.simplefilter('ignore') |
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152 |
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153 with open(inputs, 'r') as param_handler: |
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154 params = json.load(param_handler) |
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155 |
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156 # load estimator |
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157 with open(infile_estimator, 'rb') as estimator_handler: |
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158 estimator = load_model(estimator_handler) |
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159 |
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160 # swap hyperparameter |
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161 swapping = params['experiment_schemes']['hyperparams_swapping'] |
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162 swap_params = _eval_swap_params(swapping) |
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163 estimator.set_params(**swap_params) |
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164 |
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165 estimator_params = estimator.get_params() |
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166 |
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167 # store read dataframe object |
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168 loaded_df = {} |
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169 |
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170 input_type = params['input_options']['selected_input'] |
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171 # tabular input |
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172 if input_type == 'tabular': |
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173 header = 'infer' if params['input_options']['header1'] else None |
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174 column_option = (params['input_options']['column_selector_options_1'] |
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175 ['selected_column_selector_option']) |
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176 if column_option in ['by_index_number', 'all_but_by_index_number', |
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177 'by_header_name', 'all_but_by_header_name']: |
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178 c = params['input_options']['column_selector_options_1']['col1'] |
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179 else: |
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180 c = None |
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181 |
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182 df_key = infile1 + repr(header) |
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183 df = pd.read_csv(infile1, sep='\t', header=header, |
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184 parse_dates=True) |
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185 loaded_df[df_key] = df |
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186 |
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187 X = read_columns(df, c=c, c_option=column_option).astype(float) |
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188 # sparse input |
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189 elif input_type == 'sparse': |
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190 X = mmread(open(infile1, 'r')) |
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191 |
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192 # fasta_file input |
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193 elif input_type == 'seq_fasta': |
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194 pyfaidx = get_module('pyfaidx') |
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195 sequences = pyfaidx.Fasta(fasta_path) |
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196 n_seqs = len(sequences.keys()) |
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197 X = np.arange(n_seqs)[:, np.newaxis] |
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198 for param in estimator_params.keys(): |
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199 if param.endswith('fasta_path'): |
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200 estimator.set_params( |
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201 **{param: fasta_path}) |
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202 break |
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203 else: |
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204 raise ValueError( |
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205 "The selected estimator doesn't support " |
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206 "fasta file input! Please consider using " |
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207 "KerasGBatchClassifier with " |
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208 "FastaDNABatchGenerator/FastaProteinBatchGenerator " |
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209 "or having GenomeOneHotEncoder/ProteinOneHotEncoder " |
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210 "in pipeline!") |
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211 |
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212 elif input_type == 'refseq_and_interval': |
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213 path_params = { |
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214 'data_batch_generator__ref_genome_path': ref_seq, |
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215 'data_batch_generator__intervals_path': intervals, |
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216 'data_batch_generator__target_path': targets |
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217 } |
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218 estimator.set_params(**path_params) |
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219 n_intervals = sum(1 for line in open(intervals)) |
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220 X = np.arange(n_intervals)[:, np.newaxis] |
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221 |
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222 # Get target y |
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223 header = 'infer' if params['input_options']['header2'] else None |
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224 column_option = (params['input_options']['column_selector_options_2'] |
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225 ['selected_column_selector_option2']) |
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226 if column_option in ['by_index_number', 'all_but_by_index_number', |
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227 'by_header_name', 'all_but_by_header_name']: |
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228 c = params['input_options']['column_selector_options_2']['col2'] |
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229 else: |
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230 c = None |
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231 |
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232 df_key = infile2 + repr(header) |
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233 if df_key in loaded_df: |
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234 infile2 = loaded_df[df_key] |
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235 else: |
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236 infile2 = pd.read_csv(infile2, sep='\t', |
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237 header=header, parse_dates=True) |
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238 loaded_df[df_key] = infile2 |
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239 |
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240 y = read_columns( |
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241 infile2, |
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242 c=c, |
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243 c_option=column_option, |
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244 sep='\t', |
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245 header=header, |
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246 parse_dates=True) |
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247 if len(y.shape) == 2 and y.shape[1] == 1: |
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248 y = y.ravel() |
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249 if input_type == 'refseq_and_interval': |
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250 estimator.set_params( |
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251 data_batch_generator__features=y.ravel().tolist()) |
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252 y = None |
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253 # end y |
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254 |
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255 # load groups |
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256 if groups: |
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257 groups_selector = (params['experiment_schemes']['test_split'] |
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258 ['split_algos']).pop('groups_selector') |
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259 |
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260 header = 'infer' if groups_selector['header_g'] else None |
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261 column_option = \ |
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262 (groups_selector['column_selector_options_g'] |
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263 ['selected_column_selector_option_g']) |
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264 if column_option in ['by_index_number', 'all_but_by_index_number', |
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265 'by_header_name', 'all_but_by_header_name']: |
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266 c = groups_selector['column_selector_options_g']['col_g'] |
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267 else: |
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268 c = None |
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269 |
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270 df_key = groups + repr(header) |
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271 if df_key in loaded_df: |
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272 groups = loaded_df[df_key] |
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273 |
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274 groups = read_columns( |
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275 groups, |
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276 c=c, |
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277 c_option=column_option, |
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278 sep='\t', |
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279 header=header, |
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280 parse_dates=True) |
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281 groups = groups.ravel() |
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282 |
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283 # del loaded_df |
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284 del loaded_df |
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285 |
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286 # handle memory |
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287 memory = joblib.Memory(location=CACHE_DIR, verbose=0) |
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288 # cache iraps_core fits could increase search speed significantly |
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289 if estimator.__class__.__name__ == 'IRAPSClassifier': |
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290 estimator.set_params(memory=memory) |
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291 else: |
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292 # For iraps buried in pipeline |
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293 new_params = {} |
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294 for p, v in estimator_params.items(): |
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295 if p.endswith('memory'): |
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296 # for case of `__irapsclassifier__memory` |
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297 if len(p) > 8 and p[:-8].endswith('irapsclassifier'): |
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298 # cache iraps_core fits could increase search |
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299 # speed significantly |
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300 new_params[p] = memory |
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301 # security reason, we don't want memory being |
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302 # modified unexpectedly |
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303 elif v: |
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304 new_params[p] = None |
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305 # handle n_jobs |
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306 elif p.endswith('n_jobs'): |
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307 # For now, 1 CPU is suggested for iprasclassifier |
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308 if len(p) > 8 and p[:-8].endswith('irapsclassifier'): |
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309 new_params[p] = 1 |
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310 else: |
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311 new_params[p] = N_JOBS |
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312 # for security reason, types of callback are limited |
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313 elif p.endswith('callbacks'): |
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314 for cb in v: |
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315 cb_type = cb['callback_selection']['callback_type'] |
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316 if cb_type not in ALLOWED_CALLBACKS: |
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317 raise ValueError( |
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318 "Prohibited callback type: %s!" % cb_type) |
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319 |
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320 estimator.set_params(**new_params) |
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321 |
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322 # handle scorer, convert to scorer dict |
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323 scoring = params['experiment_schemes']['metrics']['scoring'] |
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324 scorer = get_scoring(scoring) |
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325 scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer) |
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326 |
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327 # handle test (first) split |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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328 test_split_options = (params['experiment_schemes'] |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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329 ['test_split']['split_algos']) |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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330 |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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331 if test_split_options['shuffle'] == 'group': |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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332 test_split_options['labels'] = groups |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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333 if test_split_options['shuffle'] == 'stratified': |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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334 if y is not None: |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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335 test_split_options['labels'] = y |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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336 else: |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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337 raise ValueError("Stratified shuffle split is not " |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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338 "applicable on empty target values!") |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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339 |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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parents:
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340 X_train, X_test, y_train, y_test, groups_train, groups_test = \ |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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341 train_test_split_none(X, y, groups, **test_split_options) |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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342 |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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343 exp_scheme = params['experiment_schemes']['selected_exp_scheme'] |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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344 |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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345 # handle validation (second) split |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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346 if exp_scheme == 'train_val_test': |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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347 val_split_options = (params['experiment_schemes'] |
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348 ['val_split']['split_algos']) |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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349 |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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350 if val_split_options['shuffle'] == 'group': |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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351 val_split_options['labels'] = groups_train |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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352 if val_split_options['shuffle'] == 'stratified': |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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353 if y_train is not None: |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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354 val_split_options['labels'] = y_train |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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355 else: |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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356 raise ValueError("Stratified shuffle split is not " |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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|
357 "applicable on empty target values!") |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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358 |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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359 X_train, X_val, y_train, y_val, groups_train, groups_val = \ |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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changeset
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360 train_test_split_none(X_train, y_train, groups_train, |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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|
361 **val_split_options) |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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362 |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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changeset
|
363 # train and eval |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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364 if hasattr(estimator, 'validation_data'): |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
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changeset
|
365 if exp_scheme == 'train_val_test': |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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366 estimator.fit(X_train, y_train, |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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367 validation_data=(X_val, y_val)) |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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|
368 else: |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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369 estimator.fit(X_train, y_train, |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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370 validation_data=(X_test, y_test)) |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff
changeset
|
371 else: |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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372 estimator.fit(X_train, y_train) |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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373 |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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374 if hasattr(estimator, 'evaluate'): |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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375 scores = estimator.evaluate(X_test, y_test=y_test, |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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376 scorer=scorer, |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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377 is_multimetric=True) |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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|
378 else: |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
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379 scores = _score(estimator, X_test, y_test, scorer, |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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380 is_multimetric=True) |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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381 # handle output |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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382 for name, score in scores.items(): |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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383 scores[name] = [score] |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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384 df = pd.DataFrame(scores) |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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385 df = df[sorted(df.columns)] |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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386 df.to_csv(path_or_buf=outfile_result, sep='\t', |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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387 header=True, index=False) |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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|
388 |
a36e1455971d
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389 memory.clear(warn=False) |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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parents:
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|
390 |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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|
391 if outfile_object: |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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392 main_est = estimator |
a36e1455971d
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parents:
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393 if isinstance(estimator, pipeline.Pipeline): |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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394 main_est = estimator.steps[-1][-1] |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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parents:
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changeset
|
395 |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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|
396 if hasattr(main_est, 'model_') \ |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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parents:
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397 and hasattr(main_est, 'save_weights'): |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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parents:
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changeset
|
398 if outfile_weights: |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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parents:
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|
399 main_est.save_weights(outfile_weights) |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff
changeset
|
400 del main_est.model_ |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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parents:
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changeset
|
401 del main_est.fit_params |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
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changeset
|
402 del main_est.model_class_ |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
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changeset
|
403 del main_est.validation_data |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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parents:
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changeset
|
404 if getattr(main_est, 'data_generator_', None): |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
diff
changeset
|
405 del main_est.data_generator_ |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
bgruening
parents:
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changeset
|
406 |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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changeset
|
407 with open(outfile_object, 'wb') as output_handler: |
a36e1455971d
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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408 pickle.dump(estimator, output_handler, |
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409 pickle.HIGHEST_PROTOCOL) |
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410 |
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411 |
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412 if __name__ == '__main__': |
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413 aparser = argparse.ArgumentParser() |
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414 aparser.add_argument("-i", "--inputs", dest="inputs", required=True) |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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415 aparser.add_argument("-e", "--estimator", dest="infile_estimator") |
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416 aparser.add_argument("-X", "--infile1", dest="infile1") |
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417 aparser.add_argument("-y", "--infile2", dest="infile2") |
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418 aparser.add_argument("-O", "--outfile_result", dest="outfile_result") |
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419 aparser.add_argument("-o", "--outfile_object", dest="outfile_object") |
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420 aparser.add_argument("-w", "--outfile_weights", dest="outfile_weights") |
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421 aparser.add_argument("-g", "--groups", dest="groups") |
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422 aparser.add_argument("-r", "--ref_seq", dest="ref_seq") |
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423 aparser.add_argument("-b", "--intervals", dest="intervals") |
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424 aparser.add_argument("-t", "--targets", dest="targets") |
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425 aparser.add_argument("-f", "--fasta_path", dest="fasta_path") |
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426 args = aparser.parse_args() |
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427 |
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428 main(args.inputs, args.infile_estimator, args.infile1, args.infile2, |
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429 args.outfile_result, outfile_object=args.outfile_object, |
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430 outfile_weights=args.outfile_weights, groups=args.groups, |
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431 ref_seq=args.ref_seq, intervals=args.intervals, |
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432 targets=args.targets, fasta_path=args.fasta_path) |