This example shows how to use the
to generate a bar chart to study the estimated mean of
the learner’s knowledge and our confidence level (via error bars).
It is similar to the previous example of the
The difference is that the bars have been replaced with dots.
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
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 classifier = learning.NoveltyClassifier() for event, label in learning_events: classifier.fit(event, label) plotter = visualisations.DotPlotter() # you can control whether to include history data # in the plot. If you use `history=True`, when you hover # your mouse over the dot, you can see statistics about # the total number of videos watched and the time the learner watched the last video 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)
Total running time of the script: (0 minutes 13.404 seconds)