TreePlotter Example#

This example shows how to use the TreePlotter class to generate a treemap to study the distribution of the learner’s knowledge.

In this example, we use the KnowledgeClassifier to build a representation of the learner’s knowledge. You could also use other classifiers like NoveltyClassifier.

from truelearn import learning, datasets, models
from truelearn.utils import visualisations

import as pio

# use a custom knowledge component
# you can always use your knowledge component here
# as soon as it follows the protocol of history aware knowledge component
data, _, _ = datasets.load_peek_dataset(
    test_limit=0, kc_init_func=models.HistoryAwareKnowledgeComponent, verbose=False

# select a learner from data
_, learning_events = data[12]

classifier = learning.KnowledgeClassifier()
for event, label in learning_events:, label)

plotter = visualisations.TreePlotter()

# you can control whether to include history data
# in the plot. If you use `history=True`, it requires
# the knowledge contains a history attribute.
# This is why we use models.HistoryAwareKnowledgeComponent above
plotter.plot(classifier.get_learner_model().knowledge, top_n=10, history=True)

# you can also use
# which is a shorthand for calling pio

Total running time of the script: (0 minutes 12.432 seconds)