README.md

Postprocessing scripts

If you want to computer event-based metrics, analysis or store informations, this is the right place to do so.

Existing scripts

To be filled in...

• analysis Higher level scripts that take the output of the full chain and do some physics analysis with it. For example, finding Michel electrons.
• metrics Reproducible metrics scripts for various stages / models, to check and study their performance.
• store If you ever need to store in a CSV raw information/predictions from an event (code may be obsolete, to double check).

How to write your own script

The bare minimum for a postprocessing script that feeds on the input data seg_label and the network output segmentation would look like this:

from mlreco.post_processing import post_processing

@post_processing('my-metrics',
                ['seg_label'],
                ['segmentation'])
def my_metrics(cfg, module_cfg, data_blob, res, logdir, iteration,
                data_idx=None, seg_label=None, segmentation=None, **kwargs):
    # Do your metrics
    row_names = ('metric1',)
    row_values = (0.5,)

    return row_names, row_values

The function my_metrics runs on a single event. seg_label[data_idx] and segmentation[data_idx] contain the requested data and output. This file should be named my_metrics.py and placed in the appropriate folder among store, metrics and analysis. If placed in a custom location, manually add it to post_processing/__init__.py folder.

The decorator @post_processing takes 3 arguments: filenames, data input capture, network output capture. It performs the necessary boilerplate to create/write into the CSV files, save iteration and event id, fetch the data/output quantities, and applies a deghosting mask in the background if necessary.

In the configuration, your script would go under the post_processing section:

post_processing:
  ppn_metrics:
    store_method: per-iteration
    ghost: True

This will create in the log folder corresponding CSV files named my-metrics-*.csv.