dc4eba996e003be0331cd78e7ba7c477f30f4e67,deepchem/utils/test/test_generator_evaluator.py,TestGeneratorEvaluator,test_compute_model_performance_multitask_classifier,#TestGeneratorEvaluator#,17

Before Change

    y1 = np.concatenate((class_0, class_1))
    y2 = np.concatenate((class_1, class_0))
    X = NumpyDataset(X)
    ys = [NumpyDataset(y1), NumpyDataset(y2)]

    databag = Databag()

After Change

  def test_compute_model_performance_multitask_classifier(self):
    n_data_points = 20
    n_features = 1
    n_tasks = 2
    n_classes = 2

    X = np.ones(shape=(n_data_points // 2, n_features)) * -1
    X1 = np.ones(shape=(n_data_points // 2, n_features))
    X = np.concatenate((X, X1))
    class_1 = np.array([[0.0, 1.0] for x in range(int(n_data_points / 2))])
    class_0 = np.array([[1.0, 0.0] for x in range(int(n_data_points / 2))])
    y1 = np.concatenate((class_0, class_1))
    y2 = np.concatenate((class_1, class_0))
    y = np.stack([y1, y2], axis=1)
    dataset = NumpyDataset(X, y)

    features = Feature(shape=(None, n_features))
    label = Label(shape=(None, n_tasks, n_classes))
    dense = Dense(out_channels=n_tasks * n_classes, in_layers=[features])
    logits = Reshape(shape=(None, n_tasks, n_classes), in_layers=dense)
    output = SoftMax(in_layers=[logits])
    smce = SoftMaxCrossEntropy(in_layers=[label, logits])
    total_loss = ReduceMean(in_layers=smce)

Instances