This example shows how to use the
to generate a radar plot to study the mean and variance
of learners’ interest in different subjects.
In this example, we use the
InterestClassifier to build
a representation of the learner’s interest. You could also use
other classifiers like
to build a representation of learner’s knowledge.
from truelearn import learning, datasets from truelearn.utils import visualisations import plotly.io as pio data, _, _ = datasets.load_peek_dataset(test_limit=0, verbose=False) # select a learner from data _, learning_events = data classifier = learning.InterestClassifier() for event, label in learning_events: classifier.fit(event, label) plotter = visualisations.RadarPlotter() # you can optionally set a title plotter.title("Mean and variance of interest in different topics.") # we could select topics we care via `topics` plotter.plot( classifier.get_learner_model().knowledge, topics=[ "Expected value", "Probability", "Sampling (statistics)", "Calculus of variations", "Dimension", "Computer virus", ], visualise_variance=False, ) # you can also use plotter.show() here # which is a shorthand for calling pio pio.show(plotter.figure)
Total running time of the script: (0 minutes 9.287 seconds)