Mercurial > repos > bgruening > sklearn_nn_classifier
annotate keras_deep_learning.py @ 12:d0efc68a3ddb draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
author | bgruening |
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date | Fri, 09 Aug 2019 07:19:24 -0400 |
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children | fbc38059bb8f |
rev | line source |
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12
d0efc68a3ddb
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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1 import argparse |
d0efc68a3ddb
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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2 import json |
d0efc68a3ddb
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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3 import keras |
d0efc68a3ddb
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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4 import pandas as pd |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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5 import pickle |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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6 import six |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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7 import warnings |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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8 |
d0efc68a3ddb
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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9 from ast import literal_eval |
d0efc68a3ddb
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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10 from keras.models import Sequential, Model |
d0efc68a3ddb
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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11 from galaxy_ml.utils import try_get_attr, get_search_params |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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12 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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13 |
d0efc68a3ddb
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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14 def _handle_shape(literal): |
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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 """Eval integer or list/tuple of integers from string |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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16 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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17 Parameters: |
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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 ----------- |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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19 literal : str. |
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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 """ |
d0efc68a3ddb
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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21 literal = literal.strip() |
d0efc68a3ddb
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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22 if not literal: |
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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 return 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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24 try: |
d0efc68a3ddb
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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25 return literal_eval(literal) |
d0efc68a3ddb
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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26 except NameError as e: |
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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 print(e) |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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28 return literal |
d0efc68a3ddb
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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29 |
d0efc68a3ddb
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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30 |
d0efc68a3ddb
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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31 def _handle_regularizer(literal): |
d0efc68a3ddb
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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32 """Construct regularizer from string literal |
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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 |
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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 Parameters |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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35 ---------- |
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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 literal : str. E.g. '(0.1, 0)' |
d0efc68a3ddb
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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37 """ |
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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 literal = literal.strip() |
d0efc68a3ddb
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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39 if not literal: |
d0efc68a3ddb
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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40 return None |
d0efc68a3ddb
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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41 |
d0efc68a3ddb
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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42 l1, l2 = literal_eval(literal) |
d0efc68a3ddb
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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43 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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44 if not l1 and not l2: |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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45 return 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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46 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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47 if l1 is None: |
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48 l1 = 0. |
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49 if l2 is 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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50 l2 = 0. |
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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 |
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52 return keras.regularizers.l1_l2(l1=l1, l2=l2) |
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53 |
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54 |
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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 def _handle_constraint(config): |
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56 """Construct constraint from galaxy tool parameters. |
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57 Suppose correct dictionary format |
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58 |
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59 Parameters |
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60 ---------- |
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61 config : dict. E.g. |
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62 "bias_constraint": |
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63 {"constraint_options": |
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64 {"max_value":1.0, |
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65 "min_value":0.0, |
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66 "axis":"[0, 1, 2]" |
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67 }, |
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68 "constraint_type": |
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69 "MinMaxNorm" |
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70 } |
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71 """ |
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72 constraint_type = config['constraint_type'] |
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73 if constraint_type == 'None': |
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74 return None |
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75 |
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76 klass = getattr(keras.constraints, constraint_type) |
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77 options = config.get('constraint_options', {}) |
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78 if 'axis' in options: |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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79 options['axis'] = literal_eval(options['axis']) |
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80 |
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81 return klass(**options) |
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82 |
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83 |
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84 def _handle_lambda(literal): |
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85 return None |
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86 |
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87 |
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88 def _handle_layer_parameters(params): |
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89 """Access to handle all kinds of parameters |
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90 """ |
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91 for key, value in six.iteritems(params): |
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92 if value == 'None': |
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93 params[key] = None |
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94 continue |
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95 |
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96 if type(value) in [int, float, bool]\ |
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97 or (type(value) is str and value.isalpha()): |
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98 continue |
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99 |
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100 if key in ['input_shape', 'noise_shape', 'shape', 'batch_shape', |
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101 'target_shape', 'dims', 'kernel_size', 'strides', |
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102 'dilation_rate', 'output_padding', 'cropping', 'size', |
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103 'padding', 'pool_size', 'axis', 'shared_axes']: |
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104 params[key] = _handle_shape(value) |
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105 |
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106 elif key.endswith('_regularizer'): |
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107 params[key] = _handle_regularizer(value) |
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108 |
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109 elif key.endswith('_constraint'): |
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110 params[key] = _handle_constraint(value) |
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111 |
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112 elif key == 'function': # No support for lambda/function eval |
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113 params.pop(key) |
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114 |
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115 return params |
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116 |
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117 |
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118 def get_sequential_model(config): |
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119 """Construct keras Sequential model from Galaxy tool parameters |
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120 |
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121 Parameters: |
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122 ----------- |
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123 config : dictionary, galaxy tool parameters loaded by JSON |
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124 """ |
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125 model = Sequential() |
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126 input_shape = _handle_shape(config['input_shape']) |
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127 layers = config['layers'] |
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128 for layer in layers: |
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129 options = layer['layer_selection'] |
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130 layer_type = options.pop('layer_type') |
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131 klass = getattr(keras.layers, layer_type) |
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132 other_options = options.pop('layer_options', {}) |
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133 options.update(other_options) |
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134 |
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135 # parameters needs special care |
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136 options = _handle_layer_parameters(options) |
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137 |
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138 # add input_shape to the first layer only |
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139 if not getattr(model, '_layers') and input_shape is not None: |
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140 options['input_shape'] = input_shape |
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141 |
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142 model.add(klass(**options)) |
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143 |
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144 return model |
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145 |
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146 |
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147 def get_functional_model(config): |
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148 """Construct keras functional model from Galaxy tool parameters |
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149 |
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150 Parameters |
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151 ----------- |
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152 config : dictionary, galaxy tool parameters loaded by JSON |
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153 """ |
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154 layers = config['layers'] |
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155 all_layers = [] |
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156 for layer in layers: |
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157 options = layer['layer_selection'] |
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158 layer_type = options.pop('layer_type') |
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159 klass = getattr(keras.layers, layer_type) |
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160 inbound_nodes = options.pop('inbound_nodes', None) |
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161 other_options = options.pop('layer_options', {}) |
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162 options.update(other_options) |
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163 |
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164 # parameters needs special care |
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165 options = _handle_layer_parameters(options) |
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166 # merge layers |
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167 if 'merging_layers' in options: |
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168 idxs = literal_eval(options.pop('merging_layers')) |
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169 merging_layers = [all_layers[i-1] for i in idxs] |
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170 new_layer = klass(**options)(merging_layers) |
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171 # non-input layers |
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172 elif inbound_nodes is not None: |
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173 new_layer = klass(**options)(all_layers[inbound_nodes-1]) |
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174 # input layers |
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175 else: |
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176 new_layer = klass(**options) |
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177 |
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178 all_layers.append(new_layer) |
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179 |
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180 input_indexes = _handle_shape(config['input_layers']) |
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181 input_layers = [all_layers[i-1] for i in input_indexes] |
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182 |
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183 output_indexes = _handle_shape(config['output_layers']) |
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184 output_layers = [all_layers[i-1] for i in output_indexes] |
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185 |
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186 return Model(inputs=input_layers, outputs=output_layers) |
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187 |
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188 |
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189 def get_batch_generator(config): |
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190 """Construct keras online data generator from Galaxy tool parameters |
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191 |
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192 Parameters |
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193 ----------- |
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194 config : dictionary, galaxy tool parameters loaded by JSON |
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195 """ |
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196 generator_type = config.pop('generator_type') |
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197 klass = try_get_attr('galaxy_ml.preprocessors', generator_type) |
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198 |
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199 if generator_type == 'GenomicIntervalBatchGenerator': |
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200 config['ref_genome_path'] = 'to_be_determined' |
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201 config['intervals_path'] = 'to_be_determined' |
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202 config['target_path'] = 'to_be_determined' |
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203 config['features'] = 'to_be_determined' |
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204 else: |
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205 config['fasta_path'] = 'to_be_determined' |
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206 |
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207 return klass(**config) |
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208 |
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209 |
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210 def config_keras_model(inputs, outfile): |
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211 """ config keras model layers and output JSON |
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212 |
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213 Parameters |
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214 ---------- |
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215 inputs : dict |
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216 loaded galaxy tool parameters from `keras_model_config` |
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217 tool. |
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218 outfile : str |
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219 Path to galaxy dataset containing keras model JSON. |
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220 """ |
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221 model_type = inputs['model_selection']['model_type'] |
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222 layers_config = inputs['model_selection'] |
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223 |
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224 if model_type == 'sequential': |
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225 model = get_sequential_model(layers_config) |
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226 else: |
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227 model = get_functional_model(layers_config) |
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228 |
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229 json_string = model.to_json() |
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230 |
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231 with open(outfile, 'w') as f: |
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232 f.write(json_string) |
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233 |
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234 |
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235 def build_keras_model(inputs, outfile, model_json, infile_weights=None, |
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236 batch_mode=False, outfile_params=None): |
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237 """ for `keras_model_builder` tool |
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238 |
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239 Parameters |
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240 ---------- |
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241 inputs : dict |
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242 loaded galaxy tool parameters from `keras_model_builder` tool. |
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243 outfile : str |
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244 Path to galaxy dataset containing the keras_galaxy model output. |
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245 model_json : str |
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246 Path to dataset containing keras model JSON. |
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247 infile_weights : str or None |
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248 If string, path to dataset containing model weights. |
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249 batch_mode : bool, default=False |
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250 Whether to build online batch classifier. |
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251 outfile_params : str, default=None |
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252 File path to search parameters output. |
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253 """ |
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254 with open(model_json, 'r') as f: |
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255 json_model = json.load(f) |
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256 |
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257 config = json_model['config'] |
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258 |
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259 options = {} |
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260 |
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261 if json_model['class_name'] == 'Sequential': |
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262 options['model_type'] = 'sequential' |
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263 klass = Sequential |
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264 elif json_model['class_name'] == 'Model': |
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265 options['model_type'] = 'functional' |
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266 klass = Model |
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267 else: |
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268 raise ValueError("Unknow Keras model class: %s" |
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269 % json_model['class_name']) |
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270 |
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271 # load prefitted model |
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272 if inputs['mode_selection']['mode_type'] == 'prefitted': |
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273 estimator = klass.from_config(config) |
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274 estimator.load_weights(infile_weights) |
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275 # build train model |
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276 else: |
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277 cls_name = inputs['mode_selection']['learning_type'] |
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278 klass = try_get_attr('galaxy_ml.keras_galaxy_models', cls_name) |
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279 |
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280 options['loss'] = (inputs['mode_selection'] |
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281 ['compile_params']['loss']) |
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282 options['optimizer'] =\ |
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283 (inputs['mode_selection']['compile_params'] |
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284 ['optimizer_selection']['optimizer_type']).lower() |
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285 |
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286 options.update((inputs['mode_selection']['compile_params'] |
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287 ['optimizer_selection']['optimizer_options'])) |
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288 |
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289 train_metrics = (inputs['mode_selection']['compile_params'] |
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290 ['metrics']).split(',') |
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291 if train_metrics[-1] == 'none': |
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292 train_metrics = train_metrics[:-1] |
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293 options['metrics'] = train_metrics |
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294 |
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295 options.update(inputs['mode_selection']['fit_params']) |
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296 options['seed'] = inputs['mode_selection']['random_seed'] |
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297 |
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298 if batch_mode: |
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299 generator = get_batch_generator(inputs['mode_selection'] |
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300 ['generator_selection']) |
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301 options['data_batch_generator'] = generator |
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302 options['prediction_steps'] = \ |
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303 inputs['mode_selection']['prediction_steps'] |
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304 options['class_positive_factor'] = \ |
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305 inputs['mode_selection']['class_positive_factor'] |
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306 estimator = klass(config, **options) |
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307 if outfile_params: |
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308 hyper_params = get_search_params(estimator) |
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309 # TODO: remove this after making `verbose` tunable |
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310 for h_param in hyper_params: |
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311 if h_param[1].endswith('verbose'): |
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312 h_param[0] = '@' |
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313 df = pd.DataFrame(hyper_params, columns=['', 'Parameter', 'Value']) |
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314 df.to_csv(outfile_params, sep='\t', index=False) |
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315 |
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316 print(repr(estimator)) |
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317 # save model by pickle |
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318 with open(outfile, 'wb') as f: |
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319 pickle.dump(estimator, f, pickle.HIGHEST_PROTOCOL) |
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320 |
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321 |
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322 if __name__ == '__main__': |
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323 warnings.simplefilter('ignore') |
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324 |
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325 aparser = argparse.ArgumentParser() |
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326 aparser.add_argument("-i", "--inputs", dest="inputs", required=True) |
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327 aparser.add_argument("-m", "--model_json", dest="model_json") |
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328 aparser.add_argument("-t", "--tool_id", dest="tool_id") |
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329 aparser.add_argument("-w", "--infile_weights", dest="infile_weights") |
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330 aparser.add_argument("-o", "--outfile", dest="outfile") |
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331 aparser.add_argument("-p", "--outfile_params", dest="outfile_params") |
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332 args = aparser.parse_args() |
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333 |
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334 input_json_path = args.inputs |
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335 with open(input_json_path, 'r') as param_handler: |
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336 inputs = json.load(param_handler) |
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337 |
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338 tool_id = args.tool_id |
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339 outfile = args.outfile |
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340 outfile_params = args.outfile_params |
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341 model_json = args.model_json |
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342 infile_weights = args.infile_weights |
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343 |
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344 # for keras_model_config tool |
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345 if tool_id == 'keras_model_config': |
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346 config_keras_model(inputs, outfile) |
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347 |
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348 # for keras_model_builder tool |
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349 else: |
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350 batch_mode = False |
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351 if tool_id == 'keras_batch_models': |
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352 batch_mode = True |
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353 |
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354 build_keras_model(inputs=inputs, |
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355 model_json=model_json, |
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356 infile_weights=infile_weights, |
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357 batch_mode=batch_mode, |
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358 outfile=outfile, |
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359 outfile_params=outfile_params) |