def test_predict_proba(dataset):
color_seqs, word_seqs, vocab = dataset
mod = ContextualColorDescriber(vocab, max_iter=1)
mod.fit(color_seqs, word_seqs)
probs = mod.predict_proba(color_seqs, word_seqs)
assert all(np.round(t.sum(), 6) == 1.0 for seq in probs for t in seq)
# You can get the perplexities for test examles with `perpelexities`:
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toy_perp = toy_mod.perplexities(toy_color_seqs_test, toy_word_seqs_test)
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toy_perp[0]
# You can use `predict_proba` to see the full probability distributions assigned to test examples:
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toy_proba = toy_mod.predict_proba(toy_color_seqs_test, toy_word_seqs_test)
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toy_proba[0].shape
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for timestep in toy_proba[0]:
print(dict(zip(toy_vocab, timestep)))
# ### Cross-validation
# You can use `utils.fit_classifier_with_crossvalidation` to cross-validate these models. Just be sure to set `scoring=None` so that the sklearn model selection methods use the `score` method of `ContextualColorDescriber`, which is an alias for `listener_accuracy`:
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