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_InstanceSplitter(
start, target, is_train: bool, pick_incomplete: bool
):
train_length = 100
pred_length = 13
t = 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.UniformSplitSampler(p=1.0),
past_length=train_length,
future_length=pred_length,
time_series_fields=["some_time_feature"],
pick_incomplete=pick_incomplete,
)
other_feat = np.arange(len(target) + 100)
data = {
"start": start,
"target": target,
"some_time_feature": other_feat,
"some_other_col": "ABC",
}
if not is_train and not pick_incomplete and len(target) < train_length:
with pytest.raises(AssertionError):
out = list(t.flatmap_transform(data, is_train=is_train))
return
else:
out = list(t.flatmap_transform(data, is_train=is_train))
if is_train:
assert len(out) == max(
0,
len(target)
- pred_length
+ 1
- (0 if pick_incomplete else train_length),
)
else:
assert len(out) == 1
for o in out:
assert "target" not in o
assert "some_time_feature" not in o
assert "some_other_col" in o
assert len(o["past_some_time_feature"]) == train_length
assert len(o["past_target"]) == train_length
if is_train:
assert len(o["future_target"]) == pred_length
assert len(o["future_some_time_feature"]) == pred_length
else:
assert len(o["future_target"]) == 0
assert len(o["future_some_time_feature"]) == pred_length
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_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"],
)