PiePlotter Example

This example shows how to use the PiePlotter class to generate a pie chart to study the distribution of the learner’s knowledge.

In this example, we use the NoveltyClassifier to build a representation of the learner’s knowledge. You could also use other classifiers like KnowledgeClassifier (for building knowledge representation) or InterestClassifier (for building interest representation).

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

import plotly.io as pio

# You can also use a custom knowledge component
# if 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.NoveltyClassifier()
for event, label in learning_events:
    classifier.fit(event, label)

plotter = visualisations.PiePlotter()

# 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 plotter.show()
# which is a shorthand for calling pio
pio.show(plotter.figure)