Improve preprocessing

Fix bugs:

• Don't suppress all warnings in full_pipeline.ipynb (cc9e4a71, b0aa27fa)

Update:

• Update libraries to last version (and pytorch to >=2) (06f9a14d, c6726100, 1e70774b, f6ae9298, e880aeca, e061054d, 5839a6db)
• Update Docker image

Improvements

• Preprocessing:
  • Split the pre-processing and the processing (7f548663)
  • Only 2 files / event out of the preprocessing: hits_particles and particles (can be filtered, combined like before) (43bf4f8e, fba1d670)
    • These two samples can be used by the evaluation
    • These two samples are used in the processing step to build the files used in the metric-learning
  • Only open and save columns that are used in the next steps (24bebbca, dfdd643b, ...)
  • Don't compute or save unnecessary columns: px, py and pz (c89b8d6f)
  • Add plane-wise edges (already implemented in another branch) (6644c96c, 63c15258)
  • Add Mean Squared Error (MSE) between a particle trajectory and a line (already implemented in another branch) (0ff4bc26)
  • Add utilities to compute weights to balance dataset (53ea0264)
  • Split datasets into train and val from processing to make sure that we indeed have the exact same splitting all the way to the final evaluation (c9940046, a454c268, ..., b0982458)
• Processing
  • Remove selection in processing: all selections will be done in the pre-processing step so that we know what we're doing (6b55fe7e, 1ca487b9)
  • Rename pid into particle_id in the processing step (4f3785d2)
• Configuration
  • Be able to control the features in the configuration file (dfdd643b)
  • Remove distinction between cell features and space features for now (45e3cc92, 4fd1a3c9, 1d667389)
  • Every output in the same output directory: avoid to save pictures or repository in a common folder (759f4e3d, 4471297c, bedc028d, 74f5824d)
• Evaluation
  • Montetracko will use the files produced out of the pre-processing (b0982458)
  • Separate evaluation in train / dev / test sample (b0982458)
  • A fixed test sample will be used: the same for every sample, benchmarked using Allen
  • Find a way of splitting the train and dev samples or know the splitting before processing (9769accf)

Move to a later merge request:

• In preprocessing, only and only if this can be useful
  • Add partial multi-processing to speed up the pre-processing
  • Add an example of pre-processing that can be used to augment data with electrons in a consistent and efficient way
  • Be able to loop through events located in different parquet files (already partially implemented in another branch)
• Evaluation after the metric learning of the best result that can be obtained (already partially implemented in another branch)