def test_XY_dataset_sparse_y():
X = ["One and two", "One only", "Two nothing else", "Two and three"]
Y = np.array([[1, 1, 0, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 1, 1, 0]])
Y_sparse = csr_matrix(Y)
vec = KerasVectorizer()
X_vec = vec.fit_transform(X)
data = tf.data.Dataset.from_tensor_slices((X_vec, Y))
data = data.shuffle(100, seed=42)
clf = CNNClassifier(batch_size=2, sparse_y=True, multilabel=True)
clf.fit(data)
assert clf.score(data, Y_sparse) > 0.3
def test_XY_dataset():
X = ["One", "One only", "Two nothing else", "Two and three"]
Y = np.array([0, 0, 1, 1])
vec = KerasVectorizer()
X_vec = vec.fit_transform(X)
data = tf.data.Dataset.from_tensor_slices((X_vec, Y))
data = data.shuffle(100, seed=42)
clf = CNNClassifier(batch_size=2)
clf.fit(data)
assert clf.score(data, Y) > 0.3
def test_vanilla():
X = ["One", "One only", "Two nothing else", "Two and three"]
Y = np.array([0, 0, 1, 1])
model = Pipeline([('vec', KerasVectorizer()), ('clf', CNNClassifier())])
model.fit(X, Y)
assert model.score(X, Y) > 0.6
def test_sparse():
X = [
"One and two",
"One only",
"Three and four, nothing else",
"Two nothing else",
"Two and three"
]
Y = csr_matrix(np.array([
[1, 1, 0, 0],
[1, 0, 0, 0],
[0, 0, 1, 1],
[0, 1, 0, 0],
[0, 1, 1, 0]
]))
model = Pipeline([
('vec', KerasVectorizer()),
('clf', CNNClassifier(
multilabel=True,
batch_size=2,
sparse_y=True))
])
model.fit(X, Y)
assert model.score(X, Y) > 0.4
assert model.predict(X).shape == (5, 4)
def test_XY_list():
X = ["One", "One only", "Two nothing else", "Two and three"]
Y = [0, 0, 1, 1]
model = Pipeline([('vec', KerasVectorizer()),
('clf', CNNClassifier(batch_size=2))])
model.fit(X, Y)
assert model.score(X, Y) > 0.6
def test_attention():
X = ["One", "One only", "Two nothing else", "Two and three"]
Y = np.array([0, 0, 1, 1])
model = Pipeline([('vec', KerasVectorizer()),
('clf', CNNClassifier(attention=True,
attention_heads=10))])
model.fit(X, Y)
assert model.score(X, Y) > 0.6
def test_threshold():
X = ["One", "One only", "Two nothing else", "Two and three"]
Y = np.array([0, 0, 1, 1])
model = Pipeline([('vec', KerasVectorizer()),
('clf', CNNClassifier(batch_size=2, threshold=0.1))])
model.fit(X, Y)
Y_pred_expected = model.predict_proba(X) > 0.1
Y_pred = model.predict(X)
assert np.array_equal(Y_pred_expected, Y_pred)
def test_predict_proba():
X = ["One", "One only", "Two nothing else", "Two and three"]
Y = np.array([0, 0, 1, 1])
model = Pipeline([('vec', KerasVectorizer()),
('clf', CNNClassifier(batch_size=2))])
model.fit(X, Y)
Y_pred_prob = model.predict_proba(X)
assert sum(Y_pred_prob >= 0) == Y.shape[0]
assert sum(Y_pred_prob <= 1) == Y.shape[0]
def test_early_stopping():
X = ["One", "One only", "Two nothing else", "Two and three"]
Y = np.array([0, 0, 1, 1])
model = Pipeline([('vec', KerasVectorizer()),
('clf',
CNNClassifier(early_stopping=True, nb_epochs=10000))])
# if early_stopping is not working it will take
# a lot of time to finish running this test
model.fit(X, Y)
assert model.score(X, Y) > 0.6
def test_save_load():
X = ["One", "One only", "Two nothing else", "Two and three"]
Y = np.array([0, 0, 1, 1])
vec = KerasVectorizer()
X_vec = vec.fit_transform(X)
model = CNNClassifier()
model.fit(X_vec, Y)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save(tmp_dir)
loaded_model = CNNClassifier()
loaded_model.load(tmp_dir)
assert hasattr(loaded_model, 'model')
assert loaded_model.score(X_vec, Y) > 0.6
from wellcomeml.ml import CNNClassifier, KerasVectorizer
from sklearn.pipeline import Pipeline
import numpy as np
X = ["One", "three", "one", "two", "four"]
Y = np.array([1, 0, 1, 0, 0])
cnn_pipeline = Pipeline([("vec", KerasVectorizer()), ("clf", CNNClassifier())])
cnn_pipeline.fit(X, Y)
print(cnn_pipeline.score(X, Y))
X = ["One, three", "one", "two, three"]
Y = np.array([[1, 0, 1], [1, 0, 0], [0, 1, 1]])
cnn_pipeline = Pipeline([("vec", KerasVectorizer()),
("clf", CNNClassifier(multilabel=True))])
cnn_pipeline.fit(X, Y)
print(cnn_pipeline.score(X, Y))
