SKLearnReconstructor#

class ctapipe.reco.sklearn.SKLearnReconstructor(**kwargs: Any)[source]#

Base Class for a Machine Learning Based Reconstructor.

Keeps a dictionary of sklearn models, the current tools are designed to train one model per telescope type.

Attributes Summary

`features`	Features to use for this model.
`instrument_table`
`load_path`	If given, load serialized model from this path.
`model_cls`	Which scikit-learn model to use.
`model_config`	kwargs for the sklearn model.
`prefix`	Prefix for the output of this model.
`property`	Property predicted, overridden in subclass.
`stereo_combiner_cls`	Which stereo combination method to use.
`target`	Name of the target table column for training.

Methods Summary

`__call__`(event)	Event-wise prediction for the EventSource-Loop.
`fit`(key, table)	Create and fit a new model for `key` using the data in `table`.
`predict_table`(key, table)	Predict on a table of events.
`write`(path[, overwrite])

Attributes Documentation

stereo_combiner_cls#: Which stereo combination method to use.. Possible values: []

Methods Documentation

abstract __call__(event: ArrayEventContainer) → None[source]#

Event-wise prediction for the EventSource-Loop.

Fills the event.dl2.<your-feature>[name] container.

Parameters:

fit(key, table)[source]#: Create and fit a new model for key using the data in table.

abstract predict_table(key, table: Table) → Table[source]#

Predict on a table of events.

Parameters:

keyHashable: Key of the model. Currently always a TelescopeDescription as we train models per telescope type.
tableTable: Table of features

Returns:

tableTable: Table(s) with predictions, matches the corresponding container definition(s)