def test_ExpectedNumInstanceSampler():
N = 6
train_length = 2
pred_length = 1
ds = make_dataset(N, train_length)
t = transform.Chain(trans=[
transform.InstanceSplitter(
target_field=FieldName.TARGET,
is_pad_field=FieldName.IS_PAD,
start_field=FieldName.START,
forecast_start_field=FieldName.FORECAST_START,
train_sampler=transform.ExpectedNumInstanceSampler(
num_instances=4),
past_length=train_length,
future_length=pred_length,
pick_incomplete=True,
)
])
scale_hist = ScaleHistogram()
repetition = 2
for i in range(repetition):
for data in t(iter(ds), is_train=True):
target_values = data["past_target"]
# for simplicity, discard values that are zeros to avoid confusion with padding
target_values = target_values[target_values > 0]
scale_hist.add(target_values)
expected_values = {i: 2**i * repetition for i in range(1, N)}
assert expected_values == scale_hist.bin_counts
def test_Transformation():
train_length = 100
ds = ListDataset(
[{"start": "2012-01-01", "target": [0.2] * train_length}], freq="1D"
)
pred_length = 10
t = transform.Chain(
trans=[
transform.AddTimeFeatures(
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field="time_feat",
time_features=[
time_feature.DayOfWeek(),
time_feature.DayOfMonth(),
time_feature.MonthOfYear(),
],
pred_length=pred_length,
),
transform.AddAgeFeature(
target_field=FieldName.TARGET,
output_field="age",
pred_length=pred_length,
log_scale=True,
),
transform.AddObservedValuesIndicator(
target_field=FieldName.TARGET, output_field="observed_values"
),
transform.VstackFeatures(
output_field="dynamic_feat",
input_fields=["age", "time_feat"],
drop_inputs=True,
),
transform.InstanceSplitter(
target_field=FieldName.TARGET,
is_pad_field=FieldName.IS_PAD,
start_field=FieldName.START,
forecast_start_field=FieldName.FORECAST_START,
train_sampler=transform.ExpectedNumInstanceSampler(
num_instances=4
),
past_length=train_length,
future_length=pred_length,
time_series_fields=["dynamic_feat", "observed_values"],
),
]
)
for u in t(iter(ds), is_train=True):
print(u)
def test_BucketInstanceSampler():
N = 6
train_length = 2
pred_length = 1
ds = make_dataset(N, train_length)
dataset_stats = calculate_dataset_statistics(ds)
t = transform.Chain(
trans=[
transform.InstanceSplitter(
target_field=FieldName.TARGET,
is_pad_field=FieldName.IS_PAD,
start_field=FieldName.START,
forecast_start_field=FieldName.FORECAST_START,
train_sampler=transform.BucketInstanceSampler(
dataset_stats.scale_histogram
),
past_length=train_length,
future_length=pred_length,
pick_incomplete=True,
)
]
)
scale_hist = ScaleHistogram()
repetition = 200
for i in range(repetition):
for data in t(iter(ds), is_train=True):
target_values = data["past_target"]
# for simplicity, discard values that are zeros to avoid confusion with padding
target_values = target_values[target_values > 0]
scale_hist.add(target_values)
expected_values = {i: repetition for i in range(1, N)}
found_values = scale_hist.bin_counts
for i in range(1, N):
assert abs(
expected_values[i] - found_values[i] < expected_values[i] * 0.3
)
def test_target_dim_indicator():
target = np.array([0, 2, 3, 10]).tolist()
multi_dim_target = np.array([target, target, target, target])
dataset = ListDataset(
data_iter=[{"start": "2012-01-01", "target": multi_dim_target}],
freq="1D",
one_dim_target=False,
)
t = transform.Chain(
trans=[
transform.TargetDimIndicator(
target_field=FieldName.TARGET, field_name="target_dimensions"
)
]
)
for data_entry in t(dataset, is_train=True):
assert (data_entry["target_dimensions"] == np.array([0, 1, 2, 3])).all()
def test_cdf_to_gaussian_transformation():
def make_test_data():
target = np.array([
0,
0,
0,
0,
10,
10,
20,
20,
30,
30,
40,
50,
59,
60,
60,
70,
80,
90,
100,
]).tolist()
np.random.shuffle(target)
multi_dim_target = np.array([target, target]).transpose()
past_is_pad = np.array([[0] * len(target)]).transpose()
past_observed_target = np.array([[1] * len(target),
[1] * len(target)]).transpose()
ds = ListDataset(
# Mimic output from InstanceSplitter
data_iter=[{
"start":
"2012-01-01",
"target":
multi_dim_target,
"past_target":
multi_dim_target,
"future_target":
multi_dim_target,
"past_is_pad":
past_is_pad,
f"past_{FieldName.OBSERVED_VALUES}":
past_observed_target,
}],
freq="1D",
one_dim_target=False,
)
return ds
def make_fake_output(u: DataEntry):
fake_output = np.expand_dims(np.expand_dims(u["past_target_cdf"],
axis=0),
axis=0)
return fake_output
ds = make_test_data()
t = transform.Chain(trans=[
transform.CDFtoGaussianTransform(
target_field=FieldName.TARGET,
observed_values_field=FieldName.OBSERVED_VALUES,
max_context_length=20,
target_dim=2,
)
])
for u in t(iter(ds), is_train=False):
fake_output = make_fake_output(u)
# Fake transformation chain output
u["past_target_sorted"] = torch.tensor(
np.expand_dims(u["past_target_sorted"], axis=0))
u["slopes"] = torch.tensor(np.expand_dims(u["slopes"], axis=0))
u["intercepts"] = torch.tensor(np.expand_dims(u["intercepts"], axis=0))
back_transformed = transform.cdf_to_gaussian_forward_transform(
u, fake_output)
# Get any sample/batch (slopes[i][:, d]they are all the same)
back_transformed = back_transformed[0][0]
original_target = u["target"]
# Original target and back-transformed target should be the same
assert np.allclose(original_target, back_transformed)
def test_multi_dim_transformation(is_train):
train_length = 10
first_dim: list = list(np.arange(1, 11, 1))
first_dim[-1] = "NaN"
second_dim: list = list(np.arange(11, 21, 1))
second_dim[0] = "NaN"
ds = ListDataset(
data_iter=[{
"start": "2012-01-01",
"target": [first_dim, second_dim]
}],
freq="1D",
one_dim_target=False,
)
pred_length = 2
# Looks weird - but this is necessary to assert the nan entries correctly.
first_dim[-1] = np.nan
second_dim[0] = np.nan
t = transform.Chain(trans=[
transform.AddTimeFeatures(
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field="time_feat",
time_features=[
time_feature.DayOfWeek(),
time_feature.DayOfMonth(),
time_feature.MonthOfYear(),
],
pred_length=pred_length,
),
transform.AddAgeFeature(
target_field=FieldName.TARGET,
output_field="age",
pred_length=pred_length,
log_scale=True,
),
transform.AddObservedValuesIndicator(
target_field=FieldName.TARGET,
output_field="observed_values",
convert_nans=False,
),
transform.VstackFeatures(
output_field="dynamic_feat",
input_fields=["age", "time_feat"],
drop_inputs=True,
),
transform.InstanceSplitter(
target_field=FieldName.TARGET,
is_pad_field=FieldName.IS_PAD,
start_field=FieldName.START,
forecast_start_field=FieldName.FORECAST_START,
train_sampler=transform.ExpectedNumInstanceSampler(
num_instances=4),
past_length=train_length,
future_length=pred_length,
time_series_fields=["dynamic_feat", "observed_values"],
time_first=False,
),
])
if is_train:
for u in t(iter(ds), is_train=True):
assert_shape(u["past_target"], (2, 10))
assert_shape(u["past_dynamic_feat"], (4, 10))
assert_shape(u["past_observed_values"], (2, 10))
assert_shape(u["future_target"], (2, 2))
assert_padded_array(
u["past_observed_values"],
np.array([[1.0] * 9 + [0.0], [0.0] + [1.0] * 9]),
u["past_is_pad"],
)
assert_padded_array(
u["past_target"],
np.array([first_dim, second_dim]),
u["past_is_pad"],
)
else:
for u in t(iter(ds), is_train=False):
assert_shape(u["past_target"], (2, 10))
assert_shape(u["past_dynamic_feat"], (4, 10))
assert_shape(u["past_observed_values"], (2, 10))
assert_shape(u["future_target"], (2, 0))
assert_padded_array(
u["past_observed_values"],
np.array([[1.0] * 9 + [0.0], [0.0] + [1.0] * 9]),
u["past_is_pad"],
)
assert_padded_array(
u["past_target"],
np.array([first_dim, second_dim]),
u["past_is_pad"],
)