* aict-tools can now apply models stored using onnx or pmml. However, application of models stored to pickle is still the fastest option for most models.
* Fixing the configuration of the output names. These can now be set using `output_name: ` in the models section
* Use onnxruntime v1.0
* Fix modification of input file timestamp in copy runs
* All but the `apply_` scripts should now not modify their input filestamps now
* Fix for apply_cuts: modification date of input file is not modified anymore
* Add support for most sklearn models, before only `sklearn.ensemble` was supported
* Update to sklearn ~0.21.0, use joblib directly (fixes sklearn's DeprecationWarning)
* Improve feature importance plot
* Add citing instructions
* Enable zenodo doi
* Refactored IO, removed the already sparse support for csv completely
* Testing pmml on travis
* Improved log output
* ade81cb Merge pull request 78 from fact-project/tables_compability
* b6a41f3 Merge pull request 87 from fact-project/logging
* c087f54 Merge pull request 88 from fact-project/enable_pmml
* f930c2a Merge pull request 89 from fact-project/zenodo
Add optional support for serializing models into the onnx format.
Switch from pyyaml to ruamel.yaml
* Add random source position sampling for theta calculation in `fact_to_dl3`
* Write the samplefraction into files created by `split_data` and copy it in `fact_to_dl3` if present
* Merge pull request 81 from fact-project/add_sample_fraction
* Merge pull request 80 from fact-project/random_source
Add chunked version of `split_data` for single telescope h5py files to deal with larger than memory files.
* Add long description and classifiers for PyPI
* Publish to PyPI
* Require scikit-learn 0.20.*
* Fix progress bars
Fixes an issue where files would not get opened in read-only mode essentially breaking make files.
The module and all executables got renamed to `aict_tools` and `aict_train_...` and `aict_apply_...`
* Configuration was completely refactored, check the examples folder, especially `full_config.yaml`
* Added executable `klaas_fact_to_dl3` to apply all three models and calculate ra/dec and theta in one go.
* First support for multiple telescopes (cta)
We forgot to merge a PR apparently. This times source dependent features are actually removed.
This allows setting random seeds to be used everywhere. The split_data script can now keep together telescope events that belong to the same array-wide event.
* The disp regressor does not calculate theta anymore, to make analysis more flexibel
* Output columns are now `source_x_prediction`, `source_y_prediction` and `disp_prediction`
* `klaas_split_data` and `klaas_apply_cuts` use h5py by default
* Switch to sklearn 0.19.0
This release features two new klaas scripts
To train models to predict the origin of gamma rays
This release fixes the helptext in the `klaas_apply_separation_model` command line utility and fixes the matplotlib depenedency to at least 2.0 for plotting bias and resolution.