Mercurial > repos > bgruening > sklearn_build_pipeline
annotate keras_deep_learning.py @ 27:7442b94d6a1e draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 80417bf0158a9b596e485dd66408f738f405145a
author | bgruening |
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date | Mon, 02 Oct 2023 09:28:19 +0000 |
parents | 118e230e85ce |
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775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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1 import argparse |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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2 import json |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"
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3 import warnings |
664666bd14ec
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"
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4 from ast import literal_eval |
664666bd14ec
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"
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5 |
10
775b004b7920
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 9981e25b00de29ed881b2229a173a8c812ded9bb
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7 from galaxy_ml.model_persist import dump_model_to_h5 |
118e230e85ce
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb
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8 from galaxy_ml.utils import SafeEval, try_get_attr |
118e230e85ce
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb
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9 from tensorflow import keras |
118e230e85ce
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb
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10 from tensorflow.keras.models import Model, Sequential |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ba6a47bdf76bbf4cb276206ac1a8cbf61332fd16"
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11 |
653be9c354ec
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ba6a47bdf76bbf4cb276206ac1a8cbf61332fd16"
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12 safe_eval = SafeEval() |
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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 |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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14 |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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15 def _handle_shape(literal): |
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118e230e85ce
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb
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16 """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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17 |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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18 Parameters: |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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19 ----------- |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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20 literal : str. |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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21 """ |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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22 literal = literal.strip() |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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23 if not literal: |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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24 return None |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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25 try: |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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26 return literal_eval(literal) |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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27 except NameError as e: |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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28 print(e) |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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29 return literal |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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30 |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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31 |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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32 def _handle_regularizer(literal): |
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118e230e85ce
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb
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33 """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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34 |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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35 Parameters |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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36 ---------- |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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37 literal : str. E.g. '(0.1, 0)' |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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38 """ |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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39 literal = literal.strip() |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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40 if not literal: |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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41 return None |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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42 |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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43 l1, l2 = literal_eval(literal) |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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44 |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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45 if not l1 and not l2: |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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46 return None |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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47 |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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48 if l1 is None: |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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49 l1 = 0.0 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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50 if l2 is None: |
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0c74d3164ac8
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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51 l2 = 0.0 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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52 |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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53 return keras.regularizers.l1_l2(l1=l1, l2=l2) |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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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 |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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56 def _handle_constraint(config): |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb
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57 """Construct constraint from galaxy tool 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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58 Suppose correct dictionary format |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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59 |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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60 Parameters |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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61 ---------- |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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62 config : dict. E.g. |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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63 "bias_constraint": |
775b004b7920
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64 {"constraint_options": |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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65 {"max_value":1.0, |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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66 "min_value":0.0, |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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67 "axis":"[0, 1, 2]" |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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68 }, |
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69 "constraint_type": |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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70 "MinMaxNorm" |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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71 } |
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72 """ |
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73 constraint_type = config["constraint_type"] |
0c74d3164ac8
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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74 if constraint_type in ("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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75 return None |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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76 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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77 klass = getattr(keras.constraints, constraint_type) |
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0c74d3164ac8
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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78 options = config.get("constraint_options", {}) |
0c74d3164ac8
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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79 if "axis" in options: |
0c74d3164ac8
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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80 options["axis"] = literal_eval(options["axis"]) |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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81 |
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82 return klass(**options) |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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83 |
775b004b7920
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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84 |
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85 def _handle_lambda(literal): |
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86 return None |
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87 |
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88 |
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89 def _handle_layer_parameters(params): |
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90 """Access to handle all kinds of parameters""" |
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91 for key, value in six.iteritems(params): |
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92 if value in ("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] or ( |
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97 type(value) is str and value.isalpha() |
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98 ): |
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99 continue |
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100 |
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101 if key in [ |
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102 "input_shape", |
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103 "noise_shape", |
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104 "shape", |
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105 "batch_shape", |
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106 "target_shape", |
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107 "dims", |
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108 "kernel_size", |
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109 "strides", |
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110 "dilation_rate", |
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111 "output_padding", |
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112 "cropping", |
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113 "size", |
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114 "padding", |
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115 "pool_size", |
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116 "axis", |
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117 "shared_axes", |
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118 ] and isinstance(value, str): |
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119 params[key] = _handle_shape(value) |
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120 |
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121 elif key.endswith("_regularizer") and isinstance(value, dict): |
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122 params[key] = _handle_regularizer(value) |
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123 |
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124 elif key.endswith("_constraint") and isinstance(value, dict): |
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125 params[key] = _handle_constraint(value) |
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126 |
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127 elif key == "function": # No support for lambda/function eval |
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128 params.pop(key) |
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129 |
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130 return params |
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131 |
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132 |
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133 def get_sequential_model(config): |
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134 """Construct keras Sequential model from Galaxy tool parameters |
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135 |
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136 Parameters: |
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137 ----------- |
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138 config : dictionary, galaxy tool parameters loaded by JSON |
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139 """ |
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140 model = Sequential() |
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141 input_shape = _handle_shape(config["input_shape"]) |
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142 layers = config["layers"] |
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143 for layer in layers: |
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144 options = layer["layer_selection"] |
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145 layer_type = options.pop("layer_type") |
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146 klass = getattr(keras.layers, layer_type) |
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147 kwargs = options.pop("kwargs", "") |
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148 |
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149 # parameters needs special care |
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150 options = _handle_layer_parameters(options) |
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151 |
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152 if kwargs: |
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153 kwargs = safe_eval("dict(" + kwargs + ")") |
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154 options.update(kwargs) |
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155 |
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156 # add input_shape to the first layer only |
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157 if not model.get_config()["layers"] and input_shape is not None: |
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158 options["input_shape"] = input_shape |
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159 |
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160 model.add(klass(**options)) |
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161 |
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162 return model |
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163 |
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164 |
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165 def get_functional_model(config): |
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166 """Construct keras functional model from Galaxy tool parameters |
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167 |
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168 Parameters |
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169 ----------- |
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170 config : dictionary, galaxy tool parameters loaded by JSON |
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171 """ |
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172 layers = config["layers"] |
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173 all_layers = [] |
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174 for layer in layers: |
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175 options = layer["layer_selection"] |
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176 layer_type = options.pop("layer_type") |
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177 klass = getattr(keras.layers, layer_type) |
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178 inbound_nodes = options.pop("inbound_nodes", None) |
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179 kwargs = options.pop("kwargs", "") |
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180 |
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181 # parameters needs special care |
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182 options = _handle_layer_parameters(options) |
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183 |
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184 if kwargs: |
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185 kwargs = safe_eval("dict(" + kwargs + ")") |
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186 options.update(kwargs) |
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187 |
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188 # merge layers |
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189 if "merging_layers" in options: |
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190 idxs = literal_eval(options.pop("merging_layers")) |
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191 merging_layers = [all_layers[i - 1] for i in idxs] |
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192 new_layer = klass(**options)(merging_layers) |
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193 # non-input layers |
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194 elif inbound_nodes is not None: |
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195 new_layer = klass(**options)(all_layers[inbound_nodes - 1]) |
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196 # input layers |
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197 else: |
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198 new_layer = klass(**options) |
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199 |
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200 all_layers.append(new_layer) |
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201 |
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202 input_indexes = _handle_shape(config["input_layers"]) |
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203 input_layers = [all_layers[i - 1] for i in input_indexes] |
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204 |
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205 output_indexes = _handle_shape(config["output_layers"]) |
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206 output_layers = [all_layers[i - 1] for i in output_indexes] |
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207 |
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208 return Model(inputs=input_layers, outputs=output_layers) |
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209 |
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210 |
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211 def get_batch_generator(config): |
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212 """Construct keras online data generator from Galaxy tool parameters |
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213 |
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214 Parameters |
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215 ----------- |
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216 config : dictionary, galaxy tool parameters loaded by JSON |
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217 """ |
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218 generator_type = config.pop("generator_type") |
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219 if generator_type == "none": |
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220 return None |
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221 |
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222 klass = try_get_attr("galaxy_ml.preprocessors", generator_type) |
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223 |
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224 if generator_type == "GenomicIntervalBatchGenerator": |
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225 config["ref_genome_path"] = "to_be_determined" |
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226 config["intervals_path"] = "to_be_determined" |
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227 config["target_path"] = "to_be_determined" |
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228 config["features"] = "to_be_determined" |
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229 else: |
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230 config["fasta_path"] = "to_be_determined" |
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231 |
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232 return klass(**config) |
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233 |
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234 |
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235 def config_keras_model(inputs, outfile): |
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236 """config keras model layers and output JSON |
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237 |
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238 Parameters |
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239 ---------- |
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240 inputs : dict |
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241 loaded galaxy tool parameters from `keras_model_config` |
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242 tool. |
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243 outfile : str |
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244 Path to galaxy dataset containing keras model JSON. |
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245 """ |
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246 model_type = inputs["model_selection"]["model_type"] |
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247 layers_config = inputs["model_selection"] |
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248 |
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249 if model_type == "sequential": |
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250 model = get_sequential_model(layers_config) |
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251 else: |
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252 model = get_functional_model(layers_config) |
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253 |
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254 json_string = model.to_json() |
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255 |
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256 with open(outfile, "w") as f: |
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257 json.dump(json.loads(json_string), f, indent=2) |
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258 |
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259 |
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260 def build_keras_model(inputs, outfile, model_json, batch_mode=False): |
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261 """for `keras_model_builder` tool |
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262 |
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263 Parameters |
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264 ---------- |
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265 inputs : dict |
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266 loaded galaxy tool parameters from `keras_model_builder` tool. |
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267 outfile : str |
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268 Path to galaxy dataset containing the keras_galaxy model output. |
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269 model_json : str |
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270 Path to dataset containing keras model JSON. |
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271 batch_mode : bool, default=False |
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272 Whether to build online batch classifier. |
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273 """ |
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274 with open(model_json, "r") as f: |
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275 json_model = json.load(f) |
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276 |
21
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277 config = json_model["config"] |
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278 |
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279 options = {} |
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280 |
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281 if json_model["class_name"] == "Sequential": |
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282 options["model_type"] = "sequential" |
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283 klass = Sequential |
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284 elif json_model["class_name"] == "Functional": |
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285 options["model_type"] = "functional" |
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286 klass = Model |
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287 else: |
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288 raise ValueError("Unknow Keras model class: %s" % json_model["class_name"]) |
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289 |
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290 # load prefitted model |
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291 if inputs["mode_selection"]["mode_type"] == "prefitted": |
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292 # estimator = klass.from_config(config) |
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293 # estimator.load_weights(infile_weights) |
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294 raise Exception("Prefitted was deprecated!") |
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295 # build train model |
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296 else: |
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297 cls_name = inputs["mode_selection"]["learning_type"] |
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298 klass = try_get_attr("galaxy_ml.keras_galaxy_models", cls_name) |
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299 |
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300 options["loss"] = inputs["mode_selection"]["compile_params"]["loss"] |
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301 options["optimizer"] = ( |
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302 inputs["mode_selection"]["compile_params"]["optimizer_selection"][ |
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303 "optimizer_type" |
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304 ] |
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305 ).lower() |
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306 |
21
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307 options.update( |
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308 ( |
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309 inputs["mode_selection"]["compile_params"]["optimizer_selection"][ |
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310 "optimizer_options" |
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311 ] |
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312 ) |
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313 ) |
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314 |
21
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315 train_metrics = inputs["mode_selection"]["compile_params"]["metrics"] |
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316 if not isinstance(train_metrics, list): # for older galaxy |
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317 train_metrics = train_metrics.split(",") |
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318 if train_metrics[-1] == "none": |
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319 train_metrics.pop() |
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320 options["metrics"] = train_metrics |
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321 |
21
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322 options.update(inputs["mode_selection"]["fit_params"]) |
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323 options["seed"] = inputs["mode_selection"]["random_seed"] |
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324 |
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325 if batch_mode: |
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326 generator = get_batch_generator( |
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327 inputs["mode_selection"]["generator_selection"] |
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328 ) |
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329 options["data_batch_generator"] = generator |
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330 options["prediction_steps"] = inputs["mode_selection"]["prediction_steps"] |
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331 options["class_positive_factor"] = inputs["mode_selection"][ |
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332 "class_positive_factor" |
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333 ] |
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334 estimator = klass(config, **options) |
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335 |
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336 print(repr(estimator)) |
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337 # save model |
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338 dump_model_to_h5(estimator, outfile, verbose=1) |
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339 |
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340 |
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341 if __name__ == "__main__": |
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342 warnings.simplefilter("ignore") |
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343 |
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344 aparser = argparse.ArgumentParser() |
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345 aparser.add_argument("-i", "--inputs", dest="inputs", required=True) |
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346 aparser.add_argument("-m", "--model_json", dest="model_json") |
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347 aparser.add_argument("-t", "--tool_id", dest="tool_id") |
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348 aparser.add_argument("-o", "--outfile", dest="outfile") |
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349 args = aparser.parse_args() |
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350 |
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351 input_json_path = args.inputs |
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352 with open(input_json_path, "r") as param_handler: |
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353 inputs = json.load(param_handler) |
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354 |
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355 tool_id = args.tool_id |
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356 outfile = args.outfile |
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357 model_json = args.model_json |
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358 |
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359 # for keras_model_config tool |
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360 if tool_id == "keras_model_config": |
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361 config_keras_model(inputs, outfile) |
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362 |
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363 # for keras_model_builder tool |
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364 else: |
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365 batch_mode = False |
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366 if tool_id == "keras_batch_models": |
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367 batch_mode = True |
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368 |
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369 build_keras_model( |
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370 inputs=inputs, model_json=model_json, batch_mode=batch_mode, outfile=outfile |
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371 ) |