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Dr. Kashif Rasul
2019-12-21 14:59:50 +01:00
parent 032f32faf0
commit 16a31f0b53
8 changed files with 31 additions and 75 deletions
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test/dataset/test_common.py
+1
View File
@@ -1,5 +1,6 @@
from pts.dataset import FieldName
def test_dataset_fields():
assert (
"feat_static_cat" == FieldName.FEAT_STATIC_CAT
test/dataset/test_process.py
+1
View File
@@ -3,6 +3,7 @@ import pandas as pd
from pts.dataset import ProcessStartField, ProcessDataEntry
@pytest.mark.parametrize(
"freq, expected",
[
test/feature/test_lag.py
+2 -4
View File
@@ -225,11 +225,9 @@ expected_lags = {
+ [28, 42]
+ [181, 182, 183, 363, 364, 365, 545, 546, 547],
# centered around each of the last 3 months (delta = 0) + last 3 years (delta = 1) (assuming 52 weeks per year)
"6D": [1, 2, 3, 4, 5, 6, 7, 9, 14]
+ [59, 60, 61, 120, 121, 122, 181, 182, 183],
"6D": [1, 2, 3, 4, 5, 6, 7, 9, 14] + [59, 60, 61, 120, 121, 122, 181, 182, 183],
# centered around each of the last 3 months (delta = 0) + last 3 years (delta = 1) (assuming 52 weeks per year)
"W": [1, 2, 3, 4, 5, 6, 7, 8, 12]
+ [51, 52, 53, 103, 104, 105, 155, 156, 157],
"W": [1, 2, 3, 4, 5, 6, 7, 8, 12] + [51, 52, 53, 103, 104, 105, 155, 156, 157],
# centered around each of the last 3 months (delta = 0) + last 3 years (delta = 1) (assuming 52 weeks per year)
"8D": [1, 2, 3, 4, 5, 6, 7, 10] + [44, 45, 46, 90, 91, 92, 135, 136, 137],
# centered around each of the last 3 years (delta = 1)
test/model/deepar/test_auxillary_outputs.py
+2 -6
View File
@@ -51,7 +51,7 @@ def test_distribution():
transform=train_output.transformation,
batch_size=batch_size,
num_batches_per_epoch=estimator.trainer.num_batches_per_epoch,
device=torch.device("cpu")
device=torch.device("cpu"),
)
seq_len = 2 * ds_info.prediction_length
@@ -63,8 +63,4 @@ def test_distribution():
*[data_entry[k] for k in input_names]
)
assert distr.sample((num_samples,)).shape == (
num_samples,
batch_size,
seq_len,
)
assert distr.sample((num_samples,)).shape == (num_samples, batch_size, seq_len,)
test/model/deepar/test_lags.py
+1 -4
View File
@@ -43,10 +43,7 @@ def test_lagged_subsequences():
# checks that lags value behave as described as in the get_lagged_subsequences contract
for i, j, k in itertools.product(range(N), range(S), range(I)):
assert (
(
lagged_subsequences[i, j, :, k]
== sequence[i, -lags[k] - S + j, :]
)
(lagged_subsequences[i, j, :, k] == sequence[i, -lags[k] - S + j, :])
.numpy()
.all()
)
test/model/test_forecast.py
+1 -3
View File
@@ -38,9 +38,7 @@ FORECASTS = {
forecast_keys=np.array(QUANTILES, str),
freq=FREQ,
),
"SampleForecast": SampleForecast(
samples=SAMPLES, start_date=START_DATE, freq=FREQ
),
"SampleForecast": SampleForecast(samples=SAMPLES, start_date=START_DATE, freq=FREQ),
"DistributionForecast": DistributionForecast(
distribution=Uniform(low=torch.zeros(1), high=torch.ones(1)),
start_date=START_DATE,
test/modules/test_feature.py
+23 -57
View File
@@ -8,45 +8,33 @@ from torch.distributions import Uniform
from pts.modules import FeatureEmbedder, FeatureAssembler
@pytest.mark.parametrize(
"config",
(
lambda N, T: [
# single static feature
dict(
shape=(N, 1),
kwargs=dict(cardinalities=[50], embedding_dims=[10]),
),
dict(shape=(N, 1), kwargs=dict(cardinalities=[50], embedding_dims=[10]),),
# single dynamic feature
dict(
shape=(N, T, 1),
kwargs=dict(cardinalities=[2], embedding_dims=[10]),
),
dict(shape=(N, T, 1), kwargs=dict(cardinalities=[2], embedding_dims=[10]),),
# multiple static features
dict(
shape=(N, 4),
kwargs=dict(
cardinalities=[50, 50, 50, 50],
embedding_dims=[10, 20, 30, 40],
cardinalities=[50, 50, 50, 50], embedding_dims=[10, 20, 30, 40],
),
),
# multiple dynamic features
dict(
shape=(N, T, 3),
kwargs=dict(
cardinalities=[30, 30, 30], embedding_dims=[10, 20, 30]
),
kwargs=dict(cardinalities=[30, 30, 30], embedding_dims=[10, 20, 30]),
),
]
)(10, 20),
)
def test_feature_embedder(config):
out_shape = config["shape"][:-1] + (
sum(config["kwargs"]["embedding_dims"]),
)
embed_feature = FeatureEmbedder(
**config["kwargs"]
)
out_shape = config["shape"][:-1] + (sum(config["kwargs"]["embedding_dims"]),)
embed_feature = FeatureEmbedder(**config["kwargs"])
for embed in embed_feature._FeatureEmbedder__embedders:
nn.init.constant_(embed.weight, 1.0)
@@ -54,17 +42,18 @@ def test_feature_embedder(config):
exp_params_len = len([p for p in embed_feature.parameters()])
act_params_len = len(config["kwargs"]["embedding_dims"])
assert exp_params_len == act_params_len
def test_forward_pass():
act_output = embed_feature(torch.ones(config["shape"]).to(torch.long))
exp_output = torch.ones(out_shape)
assert act_output.shape == exp_output.shape
assert torch.abs(torch.sum(act_output - exp_output)) < 1e-20
test_parameters_length()
test_forward_pass()
@pytest.mark.parametrize(
"config",
(
@@ -76,13 +65,9 @@ def test_feature_embedder(config):
static_real=dict(C=5),
dynamic_cat=dict(C=3),
dynamic_real=dict(C=4),
embed_static=dict(
cardinalities=[2, 4],
embedding_dims=[3, 6],
),
embed_static=dict(cardinalities=[2, 4], embedding_dims=[3, 6],),
embed_dynamic=dict(
cardinalities=[30, 30, 30],
embedding_dims=[10, 20, 30],
cardinalities=[30, 30, 30], embedding_dims=[10, 20, 30],
),
)
]
@@ -97,17 +82,15 @@ def test_feature_assembler(config):
"dynamic_real",
}
feature_combs = chain.from_iterable(
combinations(feature_types, r)
for r in range(1, len(feature_types) + 1)
combinations(feature_types, r) for r in range(1, len(feature_types) + 1)
)
# iterate over the power-set of all possible feature types, including the empty set
embedder_types = {"embed_static", "embed_dynamic"}
embedder_combs = chain.from_iterable(
combinations(embedder_types, r)
for r in range(0, len(embedder_types) + 1)
combinations(embedder_types, r) for r in range(0, len(embedder_types) + 1)
)
for enabled_embedders in embedder_combs:
embed_static = (
FeatureEmbedder(**config["embed_static"])
@@ -122,13 +105,10 @@ def test_feature_assembler(config):
for enabled_features in feature_combs:
assemble_feature = FeatureAssembler(
T=config["T"],
embed_static=embed_static,
embed_dynamic=embed_dynamic,
T=config["T"], embed_static=embed_static, embed_dynamic=embed_dynamic,
)
# assemble_feature.collect_params().initialize(mx.initializer.One())
def test_parameters_length():
exp_params_len = sum(
[
@@ -166,11 +146,7 @@ def test_feature_assembler(config):
)
out_features.append(
torch.ones(
(
N,
T,
sum(config["embed_static"]["embedding_dims"]),
)
(N, T, sum(config["embed_static"]["embedding_dims"]),)
)
)
else: # not embed_static and 'static_cat' in enabled_features
@@ -197,11 +173,9 @@ def test_feature_assembler(config):
out_features.append(torch.zeros((N, T, 1)))
else:
C = config["static_real"]["C"]
static_real = torch.empty((N,C)).uniform_(0,100)
static_real = torch.empty((N, C)).uniform_(0, 100)
inp_features.append(static_real)
out_features.append(
static_real.unsqueeze(-2).expand(-1, T, -1)
)
out_features.append(static_real.unsqueeze(-2).expand(-1, T, -1))
if "dynamic_cat" not in enabled_features:
inp_features.append(torch.zeros((N, T, 1)))
@@ -213,9 +187,7 @@ def test_feature_assembler(config):
[
torch.randint(
0,
config["embed_dynamic"]["cardinalities"][
c
],
config["embed_dynamic"]["cardinalities"][c],
(N, T, 1),
)
for c in range(C)
@@ -225,11 +197,7 @@ def test_feature_assembler(config):
)
out_features.append(
torch.ones(
(
N,
T,
sum(config["embed_dynamic"]["embedding_dims"]),
)
(N, T, sum(config["embed_dynamic"]["embedding_dims"]),)
)
)
else: # not embed_dynamic and 'dynamic_cat' in enabled_features
@@ -239,9 +207,7 @@ def test_feature_assembler(config):
[
torch.randint(
0,
config["embed_dynamic"]["cardinalities"][
c
],
config["embed_dynamic"]["cardinalities"][c],
(N, T, 1),
)
for c in range(C)
@@ -256,7 +222,7 @@ def test_feature_assembler(config):
out_features.append(torch.zeros((N, T, 1)))
else:
C = config["dynamic_real"]["C"]
dynamic_real = torch.empty((N, T, C)).uniform_(0,100)
dynamic_real = torch.empty((N, T, C)).uniform_(0, 100)
inp_features.append(dynamic_real)
out_features.append(dynamic_real)
test/test_transform.py
-1
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@@ -805,4 +805,3 @@ def assert_padded_array(
f"Sampled and reference arrays do not match. '"
f"Got {sampled_no_padding} but should be {reference_no_padding}."
)