In order to do data analysis, you must download all .pkl files from https://imperialcollegelondon.app.box.com/s/mwdgg4uz7hdz56bx6w4loc04qvzb7tmy and place them in the /data/ folder.
These files are too large to upload to github.
By default .pkl will not be uploaded to github when you upload your code.
Code to load and save .pkl files, as well as an example selector function, are in core.py .
Example of how to use this is in example_of_using_core.py .
In order to import and run the machine learning selector, use the code
from ml_selector import remove_all_bks
from core import load_file, RAWFILES
data = load_file(RAWFILES.TOTAL_DATASET)
subset, notsubset = remove_all_bk(data) acceptance_func_parameters
data
Raw data files as give by Mitesh
data_combinatorial_background_sample_histograms
Histograms showing the properties of the data selected for ML training
on the combinatorial background
data_histograms
Histograms of total_dataset vs. signal
data_histograms_with_jpsi
Histograms of total_dataset vs. signal vs. jpsi
ES_functions
Manual selector functions
ml_models
Saved machine learning models
optimisation_models_comb
Saved machine learning models from hyperparameter optimisation for
combinatorial background
optimisation_models_peaking
Saved machine learning models from hyperparameter optimisation for
peaking background
_ml_histograms_on_total
Histograms of main ML selector run on total_dataset
_ml_hist_individual_bks
Histograms of main ML selector run on individual backgrounds
acceptance_plot_tool.py
Plots histograms in the angular quantities
binning.py
Binning for ???
core.py
Code to load raw data
example_combining_arbitrary_selectors.py
Code to combine N selectors of choice
example_of_using_core.py
duh
histrogram_plots.py
Code to make histogram plots
ml_bivariant_example.py
Simple illustrative example of benefit of ML as a multivariate method
ml_combinatorial_extraction.py
Code for loading combinatorial background training data
ml_count_bk_in_total.py
Code for estimating remaining peaking background events after main ML
selector
ml_recreate.py
Core ML code to manipulate data, train models and interact with models
ml_selector.py
Selectors to remove peaking bk, combinatorial bk or all bk based on ML
models
ml_tools.py
Tools for ML, mainly testing / visulisation
ml_train.py
Code for hyperparameter optimisation of ML models
selection_cuts_hist.py
Plotting historgrams of selection cuts
starter_notebook-Copy1.ipynb
starter_notebook.ipynb
Summed_dataset2d_ratio_Legendre_polinomial.py
test_candidates_example.py
- Copy the project to somewhere on H: drive
- (If using bayesian optimisation) Copy the directory
bayes_optfrom https://github.com/fmfn/BayesianOptimization into the project root directory - Open Anaconda Navigator
- Open console_shortcut
- Navigate to the directory with your python files (using
h:andcd /path/to/dir) - IMPORTANT: Updata pandas -
conda update pandas- this fixes bugs in the python install IT has decided to use... - Install xgboost -
conda install xgboost - Run via
python filename.py