def test_tf_rnn_classifier_cheese_disease(cheese_disease_dataset):
mod = tf_rnn_classifier.TfRNNClassifier(
vocab=cheese_disease_dataset['vocab'],
embed_dim=20,
hidden_dim=20,
max_iter=20)
mod.fit(cheese_disease_dataset['X_train'],
cheese_disease_dataset['y_train'])
pred = mod.predict(cheese_disease_dataset['X_test'])
assert accuracy_score(cheese_disease_dataset['y_test'], pred) > 0.80
def test_tf_rnn_classifier(X_sequence):
"""Just makes sure that this code will run; it doesn't check that
it is creating good models.
"""
train, test, vocab = X_sequence
mod = tf_rnn_classifier.TfRNNClassifier(vocab=vocab, max_iter=100)
X, y = zip(*train)
X_test, _ = zip(*test)
mod.fit(X, y)
mod.predict(X_test)
mod.predict_proba(X_test)
vocab=[], max_iter=10, hidden_dim=5, eta=0.1), {
'hidden_dim': 10,
'eta': 1.0,
'max_iter': 100
}
],
[
torch_rnn_classifier.TorchRNNClassifier(
vocab=[], max_iter=10, hidden_dim=5, eta=0.1), {
'hidden_dim': 10,
'eta': 1.0,
'max_iter': 100
}
],
[
tf_rnn_classifier.TfRNNClassifier(
vocab=[], max_iter=10, hidden_dim=5, eta=0.1), {
'hidden_dim': 10,
'eta': 1.0,
'max_iter': 100
}
],
[
np_tree_nn.TreeNN(vocab=[], max_iter=10, hidden_dim=5, eta=0.1), {
'embed_dim': 5,
'hidden_dim': 10,
'eta': 1.0,
'max_iter': 100
}
],
[
torch_tree_nn.TorchTreeNN(
