def test_neighbor_list_vina(self):
"""Test under conditions closer to Vina usage."""
N_atoms = 5
M_nbrs = 2
ndim = 3
start = 0
stop = 4
nbr_cutoff = 1
X = NumpyDataset(start + np.random.rand(N_atoms, ndim) * (stop - start))
coords = Feature(shape=(N_atoms, ndim))
# Now an (N, M) shape
nbr_list = NeighborList(
N_atoms, M_nbrs, ndim, nbr_cutoff, start, stop, in_layers=[coords])
nbr_list = ToFloat(in_layers=[nbr_list])
flattened = Flatten(in_layers=[nbr_list])
dense = Dense(out_channels=1, in_layers=[flattened])
output = ReduceSum(in_layers=[dense])
tg = dc.models.TensorGraph(learning_rate=0.1, use_queue=False)
tg.set_loss(output)
databag = Databag({coords: X})
tg.fit_generator(databag.iterbatches(epochs=1))
def test_compute_model_performance_multitask_regressor(self):
random_seed = 42
n_data_points = 20
n_features = 2
np.random.seed(seed=random_seed)
X = np.random.rand(n_data_points, n_features)
y1 = np.expand_dims(np.array([0.5 for x in range(n_data_points)]),
axis=-1)
y2 = np.expand_dims(np.array([-0.5 for x in range(n_data_points)]),
axis=-1)
X = NumpyDataset(X)
ys = [NumpyDataset(y1), NumpyDataset(y2)]
databag = Databag()
features = Feature(shape=(None, n_features))
databag.add_dataset(features, X)
outputs = []
losses = []
labels = []
for i in range(2):
label = Label(shape=(None, 1))
dense = Dense(out_channels=1, in_layers=[features])
loss = ReduceSquareDifference(in_layers=[dense, label])
outputs.append(dense)
losses.append(loss)
labels.append(label)
databag.add_dataset(label, ys[i])
total_loss = ReduceMean(in_layers=losses)
tg = dc.models.TensorGraph(mode="regression",
batch_size=20,
random_seed=random_seed,
learning_rate=0.1)
for output in outputs:
tg.add_output(output)
tg.set_loss(total_loss)
tg.fit_generator(
databag.iterbatches(epochs=1000,
batch_size=tg.batch_size,
pad_batches=True))
metric = [
dc.metrics.Metric(dc.metrics.mean_absolute_error,
np.mean,
mode="regression"),
]
scores = tg.evaluate_generator(databag.iterbatches(),
metric,
labels=labels,
per_task_metrics=True)
scores = list(scores[1].values())
assert_true(np.all(np.isclose(scores, [0.0, 0.0], atol=1.0)))
def test_compute_model_performance_multitask_classifier(self):
n_data_points = 20
n_features = 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))
X = NumpyDataset(X)
ys = [NumpyDataset(y1), NumpyDataset(y2)]
databag = Databag()
features = Feature(shape=(None, n_features))
databag.add_dataset(features, X)
outputs = []
entropies = []
labels = []
for i in range(2):
label = Label(shape=(None, 2))
labels.append(label)
dense = Dense(out_channels=2, in_layers=[features])
output = SoftMax(in_layers=[dense])
smce = SoftMaxCrossEntropy(in_layers=[label, dense])
entropies.append(smce)
outputs.append(output)
databag.add_dataset(label, ys[i])
total_loss = ReduceMean(in_layers=entropies)
tg = dc.models.TensorGraph(learning_rate=0.1)
for output in outputs:
tg.add_output(output)
tg.set_loss(total_loss)
tg.fit_generator(
databag.iterbatches(epochs=1000,
batch_size=tg.batch_size,
pad_batches=True))
metric = dc.metrics.Metric(dc.metrics.roc_auc_score,
np.mean,
mode="classification")
scores = tg.evaluate_generator(databag.iterbatches(), [metric],
labels=labels,
per_task_metrics=True)
scores = list(scores[1].values())
# Loosening atol to see if tests stop failing sporadically
assert_true(np.all(np.isclose(scores, [1.0, 1.0], atol=0.20)))
def test_compute_model_performance_singletask_regressor_ordering(self):
n_data_points = 1000
n_features = 1
X = np.array(range(n_data_points))
X = np.expand_dims(X, axis=-1)
y1 = X + 1
X = NumpyDataset(X)
ys = [NumpyDataset(y1)]
databag = Databag()
features = Feature(shape=(None, n_features))
databag.add_dataset(features, X)
outputs = []
losses = []
labels = []
for i in range(1):
label = Label(shape=(None, 1))
dense = Dense(out_channels=1, in_layers=[features])
loss = ReduceSquareDifference(in_layers=[dense, label])
outputs.append(dense)
losses.append(loss)
labels.append(label)
databag.add_dataset(label, ys[i])
total_loss = ReduceMean(in_layers=losses)
tg = dc.models.TensorGraph(mode="regression", learning_rate=0.1)
for output in outputs:
tg.add_output(output)
tg.set_loss(total_loss)
tg.fit_generator(
databag.iterbatches(epochs=1000,
batch_size=tg.batch_size,
pad_batches=True))
metric = [
dc.metrics.Metric(dc.metrics.mean_absolute_error,
np.mean,
mode="regression"),
dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression")
]
scores = tg.evaluate_generator(databag.iterbatches(batch_size=1),
metric,
labels=labels,
per_task_metrics=True)
print(scores)
scores = list(scores[1].values())
assert_true(np.all(np.isclose(scores, [0.0], atol=0.5)))
def test_weighted_combo(self):
"""Tests that weighted linear combinations can be built"""
N = 10
n_features = 5
X1 = NumpyDataset(np.random.rand(N, n_features))
X2 = NumpyDataset(np.random.rand(N, n_features))
y = NumpyDataset(np.random.rand(N))
features_1 = Feature(shape=(None, n_features))
features_2 = Feature(shape=(None, n_features))
labels = Label(shape=(None,))
combo = WeightedLinearCombo(in_layers=[features_1, features_2])
out = ReduceSum(in_layers=[combo], axis=1)
loss = ReduceSquareDifference(in_layers=[out, labels])
databag = Databag({features_1: X1, features_2: X2, labels: y})
tg = dc.models.TensorGraph(learning_rate=0.1, use_queue=False)
tg.set_loss(loss)
tg.fit_generator(databag.iterbatches(epochs=1))
def test_weighted_combo(self):
"""Tests that weighted linear combinations can be built"""
N = 10
n_features = 5
X1 = NumpyDataset(np.random.rand(N, n_features))
X2 = NumpyDataset(np.random.rand(N, n_features))
y = NumpyDataset(np.random.rand(N))
features_1 = Feature(shape=(None, n_features))
features_2 = Feature(shape=(None, n_features))
labels = Label(shape=(None,))
combo = WeightedLinearCombo(in_layers=[features_1, features_2])
out = ReduceSum(in_layers=[combo], axis=1)
loss = ReduceSquareDifference(in_layers=[out, labels])
databag = Databag({features_1: X1, features_2: X2, labels: y})
tg = dc.models.TensorGraph(learning_rate=0.1, use_queue=False)
tg.set_loss(loss)
tg.fit_generator(databag.iterbatches(epochs=1))
assert len(tg.get_layer_variables(combo)) >= 2
assert len(tg.get_layer_variables(out)) == 0
def test_multi_task_regressor(self):
n_data_points = 20
n_features = 2
X = np.random.rand(n_data_points, n_features)
y1 = np.expand_dims(np.array([0.5 for x in range(n_data_points)]),
axis=-1)
y2 = np.expand_dims(np.array([-0.5 for x in range(n_data_points)]),
axis=-1)
X = NumpyDataset(X)
ys = [NumpyDataset(y1), NumpyDataset(y2)]
databag = Databag()
features = Feature(shape=(None, n_features))
databag.add_dataset(features, X)
outputs = []
losses = []
for i in range(2):
label = Label(shape=(None, 1))
dense = Dense(out_channels=1, in_layers=[features])
loss = ReduceSquareDifference(in_layers=[dense, label])
outputs.append(dense)
losses.append(loss)
databag.add_dataset(label, ys[i])
total_loss = ReduceMean(in_layers=losses)
tg = dc.models.TensorGraph(learning_rate=0.01)
for output in outputs:
tg.add_output(output)
tg.set_loss(total_loss)
tg.fit_generator(
databag.iterbatches(epochs=1000,
batch_size=tg.batch_size,
pad_batches=True))
predictions = tg.predict_on_generator(databag.iterbatches())
for i in range(2):
y_real = ys[i].X
y_pred = predictions[i]
assert_true(np.all(np.isclose(y_pred, y_real, atol=1.5)))
def test_multi_task_classifier(self):
n_data_points = 20
n_features = 2
X = np.random.rand(n_data_points, n_features)
y1 = np.array([[0, 1] for x in range(n_data_points)])
y2 = np.array([[1, 0] for x in range(n_data_points)])
X = NumpyDataset(X)
ys = [NumpyDataset(y1), NumpyDataset(y2)]
databag = Databag()
features = Feature(shape=(None, n_features))
databag.add_dataset(features, X)
outputs = []
entropies = []
for i in range(2):
label = Label(shape=(None, 2))
dense = Dense(out_channels=2, in_layers=[features])
output = SoftMax(in_layers=[dense])
smce = SoftMaxCrossEntropy(in_layers=[label, dense])
entropies.append(smce)
outputs.append(output)
databag.add_dataset(label, ys[i])
total_loss = ReduceMean(in_layers=entropies)
tg = dc.models.TensorGraph(learning_rate=0.01)
for output in outputs:
tg.add_output(output)
tg.set_loss(total_loss)
tg.fit_generator(
databag.iterbatches(epochs=1000,
batch_size=tg.batch_size,
pad_batches=True))
predictions = tg.predict_on_generator(databag.iterbatches())
for i in range(2):
y_real = ys[i].X
y_pred = predictions[i]
assert_true(np.all(np.isclose(y_pred, y_real, atol=0.6)))
def test_compute_model_performance_singletask_regressor(self):
n_data_points = 20
n_features = 2
X = np.random.rand(n_data_points, n_features)
y1 = np.expand_dims(np.array([0.5 for x in range(n_data_points)]), axis=-1)
X = NumpyDataset(X)
ys = [NumpyDataset(y1)]
databag = Databag()
features = Feature(shape=(None, n_features))
databag.add_dataset(features, X)
outputs = []
losses = []
labels = []
for i in range(1):
label = Label(shape=(None, 1))
dense = Dense(out_channels=1, in_layers=[features])
loss = ReduceSquareDifference(in_layers=[dense, label])
outputs.append(dense)
losses.append(loss)
labels.append(label)
databag.add_dataset(label, ys[i])
total_loss = ReduceMean(in_layers=losses)
tg = dc.models.TensorGraph(mode="regression", learning_rate=0.1)
for output in outputs:
tg.add_output(output)
tg.set_loss(total_loss)
tg.fit_generator(
databag.iterbatches(
epochs=1000, batch_size=tg.batch_size, pad_batches=True))
metric = [
dc.metrics.Metric(
dc.metrics.mean_absolute_error, np.mean, mode="regression"),
]
scores = tg.evaluate_generator(
databag.iterbatches(batch_size=tg.batch_size),
metric,
labels=labels,
per_task_metrics=True)
scores = list(scores[1].values())
assert_true(np.all(np.isclose(scores, [0.0], atol=0.5)))
def test_shared_layer(self):
n_data_points = 20
n_features = 2
X = np.random.rand(n_data_points, n_features)
y1 = np.array([[0, 1] for x in range(n_data_points)])
X = NumpyDataset(X)
ys = [NumpyDataset(y1)]
databag = Databag()
features = Feature(shape=(None, n_features))
databag.add_dataset(features, X)
outputs = []
label = Label(shape=(None, 2))
dense1 = Dense(out_channels=2, in_layers=[features])
dense2 = dense1.shared(in_layers=[features])
output1 = SoftMax(in_layers=[dense1])
output2 = SoftMax(in_layers=[dense2])
smce = SoftMaxCrossEntropy(in_layers=[label, dense1])
outputs.append(output1)
outputs.append(output2)
databag.add_dataset(label, ys[0])
total_loss = ReduceMean(in_layers=[smce])
tg = dc.models.TensorGraph(learning_rate=0.01)
for output in outputs:
tg.add_output(output)
tg.set_loss(total_loss)
tg.fit_generator(
databag.iterbatches(epochs=1,
batch_size=tg.batch_size,
pad_batches=True))
prediction = tg.predict_on_generator(databag.iterbatches())
assert_true(np.all(np.isclose(prediction[0], prediction[1],
atol=0.01)))
def test_multi_task_classifier(self):
n_data_points = 20
n_features = 2
X = np.random.rand(n_data_points, n_features)
y1 = np.array([[0, 1] for x in range(n_data_points)])
y2 = np.array([[1, 0] for x in range(n_data_points)])
X = NumpyDataset(X)
ys = [NumpyDataset(y1), NumpyDataset(y2)]
databag = Databag()
features = Feature(shape=(None, n_features))
databag.add_dataset(features, X)
outputs = []
entropies = []
for i in range(2):
label = Label(shape=(None, 2))
dense = Dense(out_channels=2, in_layers=[features])
output = SoftMax(in_layers=[dense])
smce = SoftMaxCrossEntropy(in_layers=[label, dense])
entropies.append(smce)
outputs.append(output)
databag.add_dataset(label, ys[i])
total_loss = ReduceMean(in_layers=entropies)
tg = dc.models.TensorGraph(learning_rate=0.01)
for output in outputs:
tg.add_output(output)
tg.set_loss(total_loss)
tg.fit_generator(
databag.iterbatches(
epochs=1000, batch_size=tg.batch_size, pad_batches=True))
predictions = tg.predict_on_generator(databag.iterbatches())
for i in range(2):
y_real = ys[i].X
y_pred = predictions[i]
assert_true(np.all(np.isclose(y_pred, y_real, atol=0.6)))
def test_multi_task_regressor(self):
n_data_points = 20
n_features = 2
X = np.random.rand(n_data_points, n_features)
y1 = np.expand_dims(np.array([0.5 for x in range(n_data_points)]), axis=-1)
y2 = np.expand_dims(np.array([-0.5 for x in range(n_data_points)]), axis=-1)
X = NumpyDataset(X)
ys = [NumpyDataset(y1), NumpyDataset(y2)]
databag = Databag()
features = Feature(shape=(None, n_features))
databag.add_dataset(features, X)
outputs = []
losses = []
for i in range(2):
label = Label(shape=(None, 1))
dense = Dense(out_channels=1, in_layers=[features])
loss = ReduceSquareDifference(in_layers=[dense, label])
outputs.append(dense)
losses.append(loss)
databag.add_dataset(label, ys[i])
total_loss = ReduceMean(in_layers=losses)
tg = dc.models.TensorGraph(learning_rate=0.01)
for output in outputs:
tg.add_output(output)
tg.set_loss(total_loss)
tg.fit_generator(
databag.iterbatches(
epochs=1000, batch_size=tg.batch_size, pad_batches=True))
predictions = tg.predict_on_generator(databag.iterbatches())
for i in range(2):
y_real = ys[i].X
y_pred = predictions[i]
assert_true(np.all(np.isclose(y_pred, y_real, atol=1.5)))
def test_shared_layer(self):
n_data_points = 20
n_features = 2
X = np.random.rand(n_data_points, n_features)
y1 = np.array([[0, 1] for x in range(n_data_points)])
X = NumpyDataset(X)
ys = [NumpyDataset(y1)]
databag = Databag()
features = Feature(shape=(None, n_features))
databag.add_dataset(features, X)
outputs = []
label = Label(shape=(None, 2))
dense1 = Dense(out_channels=2, in_layers=[features])
dense2 = dense1.shared(in_layers=[features])
output1 = SoftMax(in_layers=[dense1])
output2 = SoftMax(in_layers=[dense2])
smce = SoftMaxCrossEntropy(in_layers=[label, dense1])
outputs.append(output1)
outputs.append(output2)
databag.add_dataset(label, ys[0])
total_loss = ReduceMean(in_layers=[smce])
tg = dc.models.TensorGraph(learning_rate=0.01)
for output in outputs:
tg.add_output(output)
tg.set_loss(total_loss)
tg.fit_generator(
databag.iterbatches(
epochs=1, batch_size=tg.batch_size, pad_batches=True))
prediction = tg.predict_on_generator(databag.iterbatches())
assert_true(np.all(np.isclose(prediction[0], prediction[1], atol=0.01)))
