`truelearn.learning`.PersistentClassifier#

class truelearn.learning.PersistentClassifier(*, engage_with_last: bool = False)[source]#

Bases: BaseClassifier

A classifier that makes predictions based on whether the learner has engaged with the last learnable unit.

Examples

>>> from truelearn.learning import PersistentClassifier
>>> from truelearn.models import EventModel
>>> persistent = PersistentClassifier()
>>> persistent
PersistentClassifier()
>>> # prepare an event model with empty knowledge
>>> events = [EventModel(), EventModel(), EventModel()]
>>> engage_stats = [False, True, False]
>>> for event, engage_stats in zip(events, engage_stats):
...     persistent = persistent.fit(event, engage_stats)
...     print(persistent.predict(event))
...
False
True
False

Methods

`__init__`(*[, engage_with_last])	Init PersistentClassifier object.
`fit`(x, y)	Train the model.
`get_params`([deep])	Get parameters for this Classifier.
`predict`(x)	Predict whether the learner will engage in the learning event.
`predict_proba`(x)	Predict the probability that the learner will engage in the learning event.
`set_params`(**args)	Set the parameters of this Classifier.

__init__(*, engage_with_last: bool = False) → None[source]#

Init PersistentClassifier object.

Parameters:

engage_with_last – whether the learner engages with the last learnable unit.

Raises:

TrueLearnTypeError – Types of parameters do not satisfy their constraints.
TrueLearnValueError – Values of parameters do not satisfy their constraints.

fit(x: EventModel, y: bool) → Self[source]#

Train the model.

Parameters:

x – A representation of a learning event.
y – A bool indicating whether the learner engages in the learning event.

Returns:

The updated classifier object.

get_params(deep: bool = True) → Dict[str, Any][source]#

Get parameters for this Classifier.

Parameters:: deep – If True, will return the parameters for this Classifier and contained sub-objects that inherit BaseClassifier class.
Returns:: A dict mapping variable names to the corresponding objects.

predict(x: EventModel) → bool[source]#

Predict whether the learner will engage in the learning event.

Parameters:: x – A representation of a learning event.
Returns:: A bool indicating whether the learner will engage in the learning event.

predict_proba(x: EventModel) → float[source]#

Predict the probability that the learner will engage in the learning event.

Parameters:: x – A representation of a learning event.
Returns:: A float indicating the probability that the learner will engage in the learning event.

set_params(**args) → Self[source]#

Set the parameters of this Classifier.

A value can be reset only if the given parameter has the same type as the original value.

Parameters:

**args – Keyword arguments. The key should match the parameter names of the classifier. The arguments should have the correct type.

Returns:

The updated classifier.

Raises:

TrueLearnTypeError – Types of parameters do not satisfy their constraints.
TrueLearnValueError – Values of parameters do not satisfy their constraints.
InvalidArgumentError – If the given argument name is not in the class.

truelearn.learning.PersistentClassifier#

`truelearn.learning`.PersistentClassifier#