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@feranick feranick released this 14 Sep 18:30
· 387 commits to master since this release
20180914a
d8fb37c

Changelog:

  1. DNN Regressor: The user can choose through a configuration flag (useRegressorDNNTF) which DNN Estimator to use, either the DNNClassifier or DNNRegressor
    • Warning: SpectraLearnPredict2.ini files created with previous version of SLP2 need to be updated by adding useRegressorDNNTF = False under the section [DNNClassifier]
  2. Bug fixes on HD5 I/O for plugins. Improved detection of file extensions, bug fixes.
  3. HD5 is now the default saving format for Datasets
  4. SpectraLearnPredict2 and SpectraKeras can now use either pure Keras APIs or tensorflow.keras APIs through a hardcoded flag (useTFKeras) in slp_config.py
  5. New and updated utilities:
    1. RandomCrossValidMaker: new way to do format subsetting for cross validation
    2. RangeToDataMaker: produce a large set of files from a single dataset with specific parameters given in a range
    3. RemoveLimitedDatasets and InfoLimitedDatasets: Remove or identify the number of spectra in a datasets belonging to a specific class that are below a specific threshold.
    4. MergeDatasets: Merge two different training sets. If the axis are not the same, they second will be normalized to the first
  6. SpectraKeras_MLP: Massive update
    • hidden layers can be defined via array in parameters (rather than hardcoded)
    • modularization: it will allow for easier implementation of future feature
  7. Updated Torque-PBS submission scripts (sub_slp2 and sub_slp2_cv) to accommodate training files not in the main folder. Previously if files were outside the working folder, no log was generated.
  8. Updated Slurm submission scripts (sub_ml and sub_ml_cv)
  9. Label encoder transformation matrix is now saved into a pkl file: this allow for encoders info for predictions done using software other than SpectraLearnPredict2
  10. Bug fixes and improvements:
  • Fixed bug prevented correct detection of x-axis with different scale.
  • Fixed bug that prevented to use training sets with less than 9 points per training data.
  • Fixed label encoding in SVM, NN
  • Training output lists more info on the training set
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