def test_multilabel_Y_list():
X = [
"One and two", "One only", "Three and four, nothing else",
"Two nothing else", "Two and three"
]
Y = [[1, 1, 0, 0], [1, 0, 0, 0], [0, 0, 1, 1], [0, 1, 0, 0], [0, 1, 1, 0]]
model = SpacyClassifier()
model.fit(X, Y)
assert model.score(X, Y) > 0.3
assert model.predict(X).shape == (5, 4)
def test_partial_fit():
X = [
"One and two", "One only", "Three and four, nothing else",
"Two nothing else", "Two and three"
]
Y = [[1, 1, 0, 0], [1, 0, 0, 0], [0, 0, 1, 1], [0, 1, 0, 0], [0, 1, 1, 0]]
model = SpacyClassifier()
for x, y in zip(X, Y):
model.partial_fit([x], [y])
assert model.score(X, Y) > 0.2
assert model.predict(X).shape == (5, 4)
from wellcomeml.ml import SpacyClassifier X = ["One, three", "one", "two, three"] Y = [[1, 0, 1], [1, 0, 0], [0, 1, 1]] spacy_classifier = SpacyClassifier() spacy_classifier.fit(X, Y) print(spacy_classifier.score(X, Y))