Mercurial > repos > bgruening > sklearn_regression_metrics
annotate keras_train_and_eval.py @ 29:9c8ede554b7c draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"
author | bgruening |
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date | Tue, 13 Apr 2021 22:10:06 +0000 |
parents | 0cc5098a9bff |
children | 70c5a9127f99 |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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1 import argparse |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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2 import json |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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3 import os |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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4 import pickle |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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5 import warnings |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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6 from itertools import chain |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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7 |
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8 import joblib |
9c8ede554b7c
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"
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9 import numpy as np |
9c8ede554b7c
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"
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10 import pandas as pd |
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11 from galaxy_ml.externals.selene_sdk.utils import compute_score |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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12 from galaxy_ml.keras_galaxy_models import _predict_generator |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"
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13 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 e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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14 from galaxy_ml.utils import ( |
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15 clean_params, |
9c8ede554b7c
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"
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16 get_main_estimator, |
9c8ede554b7c
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"
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17 get_module, |
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18 get_scoring, |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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19 load_model, |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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20 read_columns, |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"
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21 SafeEval, |
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22 try_get_attr, |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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23 ) |
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24 from scipy.io import mmread |
9c8ede554b7c
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"
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25 from sklearn.metrics.scorer import _check_multimetric_scoring |
9c8ede554b7c
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"
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26 from sklearn.model_selection import _search, _validation |
9c8ede554b7c
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"
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27 from sklearn.model_selection._validation import _score |
9c8ede554b7c
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"
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28 from sklearn.pipeline import Pipeline |
9c8ede554b7c
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"
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29 from sklearn.utils import indexable, safe_indexing |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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30 |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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31 |
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32 _fit_and_score = try_get_attr("galaxy_ml.model_validations", "_fit_and_score") |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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33 setattr(_search, "_fit_and_score", _fit_and_score) |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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34 setattr(_validation, "_fit_and_score", _fit_and_score) |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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35 |
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36 N_JOBS = int(os.environ.get("GALAXY_SLOTS", 1)) |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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37 CACHE_DIR = os.path.join(os.getcwd(), "cached") |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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38 del os |
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39 NON_SEARCHABLE = ("n_jobs", "pre_dispatch", "memory", "_path", "nthread", "callbacks") |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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40 ALLOWED_CALLBACKS = ( |
0cc5098a9bff
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41 "EarlyStopping", |
0cc5098a9bff
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42 "TerminateOnNaN", |
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43 "ReduceLROnPlateau", |
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44 "CSVLogger", |
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45 "None", |
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46 ) |
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47 |
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48 |
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49 def _eval_swap_params(params_builder): |
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50 swap_params = {} |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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51 |
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52 for p in params_builder["param_set"]: |
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53 swap_value = p["sp_value"].strip() |
0cc5098a9bff
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54 if swap_value == "": |
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55 continue |
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56 |
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57 param_name = p["sp_name"] |
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58 if param_name.lower().endswith(NON_SEARCHABLE): |
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59 warnings.warn("Warning: `%s` is not eligible for search and was " "omitted!" % param_name) |
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60 continue |
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61 |
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62 if not swap_value.startswith(":"): |
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63 safe_eval = SafeEval(load_scipy=True, load_numpy=True) |
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64 ev = safe_eval(swap_value) |
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65 else: |
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66 # Have `:` before search list, asks for estimator evaluatio |
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67 safe_eval_es = SafeEval(load_estimators=True) |
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68 swap_value = swap_value[1:].strip() |
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69 # TODO maybe add regular express check |
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70 ev = safe_eval_es(swap_value) |
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71 |
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72 swap_params[param_name] = ev |
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73 |
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74 return swap_params |
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75 |
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76 |
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77 def train_test_split_none(*arrays, **kwargs): |
a152c1d23379
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78 """extend train_test_split to take None arrays |
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79 and support split by group names. |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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80 """ |
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81 nones = [] |
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82 new_arrays = [] |
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83 for idx, arr in enumerate(arrays): |
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84 if arr is None: |
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85 nones.append(idx) |
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86 else: |
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87 new_arrays.append(arr) |
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88 |
28
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89 if kwargs["shuffle"] == "None": |
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90 kwargs["shuffle"] = None |
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91 |
28
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92 group_names = kwargs.pop("group_names", None) |
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93 |
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94 if group_names is not None and group_names.strip(): |
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95 group_names = [name.strip() for name in group_names.split(",")] |
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96 new_arrays = indexable(*new_arrays) |
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97 groups = kwargs["labels"] |
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98 n_samples = new_arrays[0].shape[0] |
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99 index_arr = np.arange(n_samples) |
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100 test = index_arr[np.isin(groups, group_names)] |
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101 train = index_arr[~np.isin(groups, group_names)] |
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102 rval = list(chain.from_iterable((safe_indexing(a, train), safe_indexing(a, test)) for a in new_arrays)) |
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103 else: |
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104 rval = train_test_split(*new_arrays, **kwargs) |
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105 |
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106 for pos in nones: |
29
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107 rval[pos * 2: 2] = [None, None] |
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108 |
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109 return rval |
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110 |
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111 |
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112 def _evaluate(y_true, pred_probas, scorer, is_multimetric=True): |
28
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113 """output scores based on input scorer |
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114 |
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115 Parameters |
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116 ---------- |
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117 y_true : array |
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118 True label or target values |
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119 pred_probas : array |
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120 Prediction values, probability for classification problem |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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121 scorer : dict |
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122 dict of `sklearn.metrics.scorer.SCORER` |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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123 is_multimetric : bool, default is True |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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124 """ |
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125 if y_true.ndim == 1 or y_true.shape[-1] == 1: |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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126 pred_probas = pred_probas.ravel() |
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127 pred_labels = (pred_probas > 0.5).astype("int32") |
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128 targets = y_true.ravel().astype("int32") |
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129 if not is_multimetric: |
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130 preds = pred_labels if scorer.__class__.__name__ == "_PredictScorer" else pred_probas |
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131 score = scorer._score_func(targets, preds, **scorer._kwargs) |
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132 |
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133 return score |
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134 else: |
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135 scores = {} |
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136 for name, one_scorer in scorer.items(): |
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137 preds = pred_labels if one_scorer.__class__.__name__ == "_PredictScorer" else pred_probas |
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138 score = one_scorer._score_func(targets, preds, **one_scorer._kwargs) |
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139 scores[name] = score |
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140 |
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141 # TODO: multi-class metrics |
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142 # multi-label |
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143 else: |
28
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144 pred_labels = (pred_probas > 0.5).astype("int32") |
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145 targets = y_true.astype("int32") |
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146 if not is_multimetric: |
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147 preds = pred_labels if scorer.__class__.__name__ == "_PredictScorer" else pred_probas |
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148 score, _ = compute_score(preds, targets, scorer._score_func) |
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149 return score |
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150 else: |
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151 scores = {} |
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152 for name, one_scorer in scorer.items(): |
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153 preds = pred_labels if one_scorer.__class__.__name__ == "_PredictScorer" else pred_probas |
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154 score, _ = compute_score(preds, targets, one_scorer._score_func) |
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155 scores[name] = score |
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156 |
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157 return scores |
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158 |
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159 |
28
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160 def main( |
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161 inputs, |
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162 infile_estimator, |
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163 infile1, |
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164 infile2, |
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165 outfile_result, |
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166 outfile_object=None, |
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167 outfile_weights=None, |
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168 outfile_y_true=None, |
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169 outfile_y_preds=None, |
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170 groups=None, |
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171 ref_seq=None, |
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172 intervals=None, |
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173 targets=None, |
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174 fasta_path=None, |
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175 ): |
24
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176 """ |
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177 Parameter |
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178 --------- |
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179 inputs : str |
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180 File path to galaxy tool parameter |
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181 |
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182 infile_estimator : str |
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183 File path to estimator |
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184 |
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185 infile1 : str |
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186 File path to dataset containing features |
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187 |
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188 infile2 : str |
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189 File path to dataset containing target values |
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190 |
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191 outfile_result : str |
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192 File path to save the results, either cv_results or test result |
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193 |
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194 outfile_object : str, optional |
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195 File path to save searchCV object |
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196 |
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197 outfile_weights : str, optional |
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198 File path to save deep learning model weights |
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199 |
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200 outfile_y_true : str, optional |
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201 File path to target values for prediction |
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202 |
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203 outfile_y_preds : str, optional |
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204 File path to save deep learning model weights |
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205 |
a152c1d23379
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206 groups : str |
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207 File path to dataset containing groups labels |
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208 |
a152c1d23379
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209 ref_seq : str |
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210 File path to dataset containing genome sequence file |
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211 |
a152c1d23379
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212 intervals : str |
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213 File path to dataset containing interval file |
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214 |
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215 targets : str |
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216 File path to dataset compressed target bed file |
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217 |
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218 fasta_path : str |
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219 File path to dataset containing fasta file |
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220 """ |
28
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221 warnings.simplefilter("ignore") |
24
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222 |
28
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223 with open(inputs, "r") as param_handler: |
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224 params = json.load(param_handler) |
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225 |
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226 # load estimator |
28
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227 with open(infile_estimator, "rb") as estimator_handler: |
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228 estimator = load_model(estimator_handler) |
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229 |
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230 estimator = clean_params(estimator) |
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231 |
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232 # swap hyperparameter |
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233 swapping = params["experiment_schemes"]["hyperparams_swapping"] |
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234 swap_params = _eval_swap_params(swapping) |
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235 estimator.set_params(**swap_params) |
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236 |
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237 estimator_params = estimator.get_params() |
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238 |
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239 # store read dataframe object |
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240 loaded_df = {} |
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241 |
28
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242 input_type = params["input_options"]["selected_input"] |
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243 # tabular input |
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244 if input_type == "tabular": |
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245 header = "infer" if params["input_options"]["header1"] else None |
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246 column_option = params["input_options"]["column_selector_options_1"]["selected_column_selector_option"] |
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247 if column_option in [ |
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248 "by_index_number", |
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249 "all_but_by_index_number", |
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250 "by_header_name", |
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251 "all_but_by_header_name", |
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252 ]: |
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253 c = params["input_options"]["column_selector_options_1"]["col1"] |
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254 else: |
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255 c = None |
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256 |
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257 df_key = infile1 + repr(header) |
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258 df = pd.read_csv(infile1, sep="\t", header=header, parse_dates=True) |
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259 loaded_df[df_key] = df |
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260 |
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261 X = read_columns(df, c=c, c_option=column_option).astype(float) |
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262 # sparse input |
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263 elif input_type == "sparse": |
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264 X = mmread(open(infile1, "r")) |
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265 |
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266 # fasta_file input |
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267 elif input_type == "seq_fasta": |
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268 pyfaidx = get_module("pyfaidx") |
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269 sequences = pyfaidx.Fasta(fasta_path) |
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270 n_seqs = len(sequences.keys()) |
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271 X = np.arange(n_seqs)[:, np.newaxis] |
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272 for param in estimator_params.keys(): |
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273 if param.endswith("fasta_path"): |
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274 estimator.set_params(**{param: fasta_path}) |
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275 break |
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276 else: |
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277 raise ValueError( |
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278 "The selected estimator doesn't support " |
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279 "fasta file input! Please consider using " |
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280 "KerasGBatchClassifier with " |
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281 "FastaDNABatchGenerator/FastaProteinBatchGenerator " |
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282 "or having GenomeOneHotEncoder/ProteinOneHotEncoder " |
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283 "in pipeline!" |
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284 ) |
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285 |
28
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286 elif input_type == "refseq_and_interval": |
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287 path_params = { |
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288 "data_batch_generator__ref_genome_path": ref_seq, |
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289 "data_batch_generator__intervals_path": intervals, |
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290 "data_batch_generator__target_path": targets, |
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291 } |
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292 estimator.set_params(**path_params) |
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293 n_intervals = sum(1 for line in open(intervals)) |
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294 X = np.arange(n_intervals)[:, np.newaxis] |
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295 |
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296 # Get target y |
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297 header = "infer" if params["input_options"]["header2"] else None |
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298 column_option = params["input_options"]["column_selector_options_2"]["selected_column_selector_option2"] |
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299 if column_option in [ |
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300 "by_index_number", |
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301 "all_but_by_index_number", |
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302 "by_header_name", |
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303 "all_but_by_header_name", |
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304 ]: |
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305 c = params["input_options"]["column_selector_options_2"]["col2"] |
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306 else: |
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307 c = None |
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308 |
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309 df_key = infile2 + repr(header) |
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310 if df_key in loaded_df: |
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311 infile2 = loaded_df[df_key] |
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312 else: |
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313 infile2 = pd.read_csv(infile2, sep="\t", header=header, parse_dates=True) |
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314 loaded_df[df_key] = infile2 |
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315 |
28
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316 y = read_columns(infile2, |
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317 c=c, |
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318 c_option=column_option, |
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319 sep='\t', |
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320 header=header, |
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321 parse_dates=True) |
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322 if len(y.shape) == 2 and y.shape[1] == 1: |
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323 y = y.ravel() |
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324 if input_type == "refseq_and_interval": |
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325 estimator.set_params(data_batch_generator__features=y.ravel().tolist()) |
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326 y = None |
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327 # end y |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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328 |
a152c1d23379
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329 # load groups |
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330 if groups: |
28
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331 groups_selector = (params["experiment_schemes"]["test_split"]["split_algos"]).pop("groups_selector") |
24
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332 |
28
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333 header = "infer" if groups_selector["header_g"] else None |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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334 column_option = groups_selector["column_selector_options_g"]["selected_column_selector_option_g"] |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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335 if column_option in [ |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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336 "by_index_number", |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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337 "all_but_by_index_number", |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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338 "by_header_name", |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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339 "all_but_by_header_name", |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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340 ]: |
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341 c = groups_selector["column_selector_options_g"]["col_g"] |
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342 else: |
a152c1d23379
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343 c = None |
a152c1d23379
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344 |
a152c1d23379
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345 df_key = groups + repr(header) |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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346 if df_key in loaded_df: |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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347 groups = loaded_df[df_key] |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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348 |
28
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349 groups = read_columns(groups, |
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350 c=c, |
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351 c_option=column_option, |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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352 sep='\t', |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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353 header=header, |
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354 parse_dates=True) |
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355 groups = groups.ravel() |
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356 |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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357 # del loaded_df |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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358 del loaded_df |
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359 |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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360 # cache iraps_core fits could increase search speed significantly |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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361 memory = joblib.Memory(location=CACHE_DIR, verbose=0) |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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362 main_est = get_main_estimator(estimator) |
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363 if main_est.__class__.__name__ == "IRAPSClassifier": |
24
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364 main_est.set_params(memory=memory) |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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365 |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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366 # handle scorer, convert to scorer dict |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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367 scoring = params['experiment_schemes']['metrics']['scoring'] |
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368 if scoring is not None: |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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369 # get_scoring() expects secondary_scoring to be a comma separated string (not a list) |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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370 # Check if secondary_scoring is specified |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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371 secondary_scoring = scoring.get("secondary_scoring", None) |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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372 if secondary_scoring is not None: |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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373 # If secondary_scoring is specified, convert the list into comman separated string |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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374 scoring["secondary_scoring"] = ",".join(scoring["secondary_scoring"]) |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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375 |
24
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376 scorer = get_scoring(scoring) |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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377 scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer) |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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378 |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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379 # handle test (first) split |
28
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380 test_split_options = params["experiment_schemes"]["test_split"]["split_algos"] |
24
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381 |
28
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382 if test_split_options["shuffle"] == "group": |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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383 test_split_options["labels"] = groups |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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384 if test_split_options["shuffle"] == "stratified": |
24
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385 if y is not None: |
28
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386 test_split_options["labels"] = y |
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387 else: |
28
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388 raise ValueError("Stratified shuffle split is not " "applicable on empty target values!") |
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389 |
28
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390 ( |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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391 X_train, |
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392 X_test, |
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393 y_train, |
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394 y_test, |
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395 groups_train, |
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396 _groups_test, |
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397 ) = train_test_split_none(X, y, groups, **test_split_options) |
24
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398 |
28
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399 exp_scheme = params["experiment_schemes"]["selected_exp_scheme"] |
24
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400 |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
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401 # handle validation (second) split |
28
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402 if exp_scheme == "train_val_test": |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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403 val_split_options = params["experiment_schemes"]["val_split"]["split_algos"] |
24
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404 |
28
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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405 if val_split_options["shuffle"] == "group": |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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406 val_split_options["labels"] = groups_train |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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407 if val_split_options["shuffle"] == "stratified": |
24
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
408 if y_train is not None: |
28
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
409 val_split_options["labels"] = y_train |
24
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
410 else: |
28
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
411 raise ValueError("Stratified shuffle split is not " "applicable on empty target values!") |
24
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
412 |
28
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
413 ( |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
414 X_train, |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
415 X_val, |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
416 y_train, |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
417 y_val, |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
418 groups_train, |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
419 _groups_val, |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
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420 ) = train_test_split_none(X_train, y_train, groups_train, **val_split_options) |
24
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
421 |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
422 # train and eval |
28
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
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423 if hasattr(estimator, "validation_data"): |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
424 if exp_scheme == "train_val_test": |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
425 estimator.fit(X_train, y_train, validation_data=(X_val, y_val)) |
24
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
426 else: |
28
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
427 estimator.fit(X_train, y_train, validation_data=(X_test, y_test)) |
24
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
428 else: |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
429 estimator.fit(X_train, y_train) |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
430 |
28
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
431 if hasattr(estimator, "evaluate"): |
24
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
432 steps = estimator.prediction_steps |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
433 batch_size = estimator.batch_size |
28
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
434 generator = estimator.data_generator_.flow(X_test, y=y_test, batch_size=batch_size) |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
435 predictions, y_true = _predict_generator(estimator.model_, generator, steps=steps) |
24
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
436 scores = _evaluate(y_true, predictions, scorer, is_multimetric=True) |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
437 |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
438 else: |
28
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
439 if hasattr(estimator, "predict_proba"): |
24
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
440 predictions = estimator.predict_proba(X_test) |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
441 else: |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
442 predictions = estimator.predict(X_test) |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
443 |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
444 y_true = y_test |
28
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
445 scores = _score(estimator, X_test, y_test, scorer, is_multimetric=True) |
24
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
446 if outfile_y_true: |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
447 try: |
28
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
448 pd.DataFrame(y_true).to_csv(outfile_y_true, sep="\t", index=False) |
24
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
449 pd.DataFrame(predictions).astype(np.float32).to_csv( |
28
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
450 outfile_y_preds, |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
451 sep="\t", |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
452 index=False, |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
453 float_format="%g", |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
454 chunksize=10000, |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
455 ) |
24
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
456 except Exception as e: |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
457 print("Error in saving predictions: %s" % e) |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
458 |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
459 # handle output |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
460 for name, score in scores.items(): |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
461 scores[name] = [score] |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
462 df = pd.DataFrame(scores) |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
463 df = df[sorted(df.columns)] |
28
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
464 df.to_csv(path_or_buf=outfile_result, sep="\t", header=True, index=False) |
24
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
465 |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
466 memory.clear(warn=False) |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
467 |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
468 if outfile_object: |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
469 main_est = estimator |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
470 if isinstance(estimator, Pipeline): |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
471 main_est = estimator.steps[-1][-1] |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
472 |
28
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
473 if hasattr(main_est, "model_") and hasattr(main_est, "save_weights"): |
24
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
474 if outfile_weights: |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
475 main_est.save_weights(outfile_weights) |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
476 del main_est.model_ |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
477 del main_est.fit_params |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
478 del main_est.model_class_ |
28
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
479 if getattr(main_est, "validation_data", None): |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
480 del main_est.validation_data |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
481 if getattr(main_est, "data_generator_", None): |
24
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
482 del main_est.data_generator_ |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
483 |
28
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
484 with open(outfile_object, "wb") as output_handler: |
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
changeset
|
485 pickle.dump(estimator, output_handler, pickle.HIGHEST_PROTOCOL) |
24
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
486 |
a152c1d23379
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
bgruening
parents:
diff
changeset
|
487 |
28
0cc5098a9bff
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
bgruening
parents:
24
diff
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488 if __name__ == "__main__": |
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489 aparser = argparse.ArgumentParser() |
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490 aparser.add_argument("-i", "--inputs", dest="inputs", required=True) |
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491 aparser.add_argument("-e", "--estimator", dest="infile_estimator") |
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492 aparser.add_argument("-X", "--infile1", dest="infile1") |
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493 aparser.add_argument("-y", "--infile2", dest="infile2") |
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494 aparser.add_argument("-O", "--outfile_result", dest="outfile_result") |
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495 aparser.add_argument("-o", "--outfile_object", dest="outfile_object") |
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496 aparser.add_argument("-w", "--outfile_weights", dest="outfile_weights") |
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497 aparser.add_argument("-l", "--outfile_y_true", dest="outfile_y_true") |
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498 aparser.add_argument("-p", "--outfile_y_preds", dest="outfile_y_preds") |
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499 aparser.add_argument("-g", "--groups", dest="groups") |
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500 aparser.add_argument("-r", "--ref_seq", dest="ref_seq") |
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501 aparser.add_argument("-b", "--intervals", dest="intervals") |
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502 aparser.add_argument("-t", "--targets", dest="targets") |
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503 aparser.add_argument("-f", "--fasta_path", dest="fasta_path") |
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504 args = aparser.parse_args() |
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505 |
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506 main( |
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507 args.inputs, |
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508 args.infile_estimator, |
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509 args.infile1, |
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510 args.infile2, |
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511 args.outfile_result, |
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512 outfile_object=args.outfile_object, |
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513 outfile_weights=args.outfile_weights, |
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514 outfile_y_true=args.outfile_y_true, |
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515 outfile_y_preds=args.outfile_y_preds, |
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516 groups=args.groups, |
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517 ref_seq=args.ref_seq, |
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518 intervals=args.intervals, |
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519 targets=args.targets, |
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520 fasta_path=args.fasta_path, |
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521 ) |