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15 failing tests

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This commit is contained in:
Kashif Rasul
2019-11-14 21:29:38 +01:00
parent 05c6e73068
commit cbf5fe314a
2 changed files with 59 additions and 97 deletions
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pts/dataset/__init__.py
+1
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@@ -9,3 +9,4 @@ from .sampler import (
)
from .process import ProcessStartField, ProcessDataEntry
from .utils import to_pandas
from .stat import ScaleHistogram
test/feature/test_transformation.py
+58 -97
View File
@@ -5,8 +5,27 @@ import pytest
import numpy as np
import pandas as pd
from pts.dataset import ProcessStartField, FieldName, ListDataset
from pts.feature import AddTimeFeatures, DayOfWeek, DayOfMonth
from pts.dataset import (
ProcessStartField,
FieldName,
ListDataset,
ExpectedNumInstanceSampler,
UniformSplitSampler,
ScaleHistogram,
BucketInstanceSampler,
)
from pts.feature import (
Chain,
AddTimeFeatures,
DayOfWeek,
DayOfMonth,
MonthOfYear,
AddAgeFeature,
VstackFeatures,
InstanceSplitter,
CanonicalInstanceSplitter,
AddObservedValuesIndicator,
)
FREQ = "1D"
@@ -22,64 +41,30 @@ TEST_VALUES = {
"allow_target_padding": [True, False],
}
def test_align_timestamp():
def aligned_with(date_str, freq):
return str(ProcessStartField.process(date_str, freq=freq))
for _ in range(2):
assert (
aligned_with("2012-03-05 09:13:12", "min") == "2012-03-05 09:13:00"
)
assert (
aligned_with("2012-03-05 09:13:12", "2min")
== "2012-03-05 09:12:00"
)
assert (
aligned_with("2012-03-05 09:13:12", "H") == "2012-03-05 09:00:00"
)
assert (
aligned_with("2012-03-05 09:13:12", "D") == "2012-03-05 00:00:00"
)
assert (
aligned_with("2012-03-05 09:13:12", "W") == "2012-03-11 00:00:00"
)
assert (
aligned_with("2012-03-05 09:13:12", "4W") == "2012-03-11 00:00:00"
)
assert (
aligned_with("2012-03-05 09:13:12", "M") == "2012-03-31 00:00:00"
)
assert (
aligned_with("2012-03-05 09:13:12", "3M") == "2012-03-31 00:00:00"
)
assert (
aligned_with("2012-03-05 09:13:12", "Y") == "2012-12-31 00:00:00"
)
assert (
aligned_with("2012-03-05 09:14:11", "min") == "2012-03-05 09:14:00"
)
assert (
aligned_with("2012-03-05 09:14:11", "2min")
== "2012-03-05 09:14:00"
)
assert (
aligned_with("2012-03-05 09:14:11", "H") == "2012-03-05 09:00:00"
)
assert (
aligned_with("2012-03-05 09:14:11", "D") == "2012-03-05 00:00:00"
)
assert (
aligned_with("2012-03-05 09:14:11", "W") == "2012-03-11 00:00:00"
)
assert (
aligned_with("2012-03-05 09:14:11", "4W") == "2012-03-11 00:00:00"
)
assert (
aligned_with("2012-03-05 09:14:11", "M") == "2012-03-31 00:00:00"
)
assert (
aligned_with("2012-03-05 09:14:11", "3M") == "2012-03-31 00:00:00"
)
assert aligned_with("2012-03-05 09:13:12", "min") == "2012-03-05 09:13:00"
assert aligned_with("2012-03-05 09:13:12", "2min") == "2012-03-05 09:12:00"
assert aligned_with("2012-03-05 09:13:12", "H") == "2012-03-05 09:00:00"
assert aligned_with("2012-03-05 09:13:12", "D") == "2012-03-05 00:00:00"
assert aligned_with("2012-03-05 09:13:12", "W") == "2012-03-11 00:00:00"
assert aligned_with("2012-03-05 09:13:12", "4W") == "2012-03-11 00:00:00"
assert aligned_with("2012-03-05 09:13:12", "M") == "2012-03-31 00:00:00"
assert aligned_with("2012-03-05 09:13:12", "3M") == "2012-03-31 00:00:00"
assert aligned_with("2012-03-05 09:13:12", "Y") == "2012-12-31 00:00:00"
assert aligned_with("2012-03-05 09:14:11", "min") == "2012-03-05 09:14:00"
assert aligned_with("2012-03-05 09:14:11", "2min") == "2012-03-05 09:14:00"
assert aligned_with("2012-03-05 09:14:11", "H") == "2012-03-05 09:00:00"
assert aligned_with("2012-03-05 09:14:11", "D") == "2012-03-05 00:00:00"
assert aligned_with("2012-03-05 09:14:11", "W") == "2012-03-11 00:00:00"
assert aligned_with("2012-03-05 09:14:11", "4W") == "2012-03-11 00:00:00"
assert aligned_with("2012-03-05 09:14:11", "M") == "2012-03-31 00:00:00"
assert aligned_with("2012-03-05 09:14:11", "3M") == "2012-03-31 00:00:00"
@pytest.mark.parametrize("is_train", TEST_VALUES["is_train"])
@pytest.mark.parametrize("target", TEST_VALUES["target"])
@@ -102,9 +87,7 @@ def test_AddTimeFeatures(start, target, is_train):
mat = res["myout"]
expected_length = len(target) + (0 if is_train else pred_length)
assert mat.shape == (2, expected_length)
tmp_idx = pd.date_range(
start=start, freq=start.freq, periods=expected_length
)
tmp_idx = pd.date_range(start=start, freq=start.freq, periods=expected_length)
assert np.alltrue(mat[0] == DayOfWeek()(tmp_idx))
assert np.alltrue(mat[1] == DayOfMonth()(tmp_idx))
@@ -149,9 +132,7 @@ def test_AddAgeFeatures(start, target, is_train):
assert out["age"].shape[-1] == expected_length
assert np.allclose(
out["age"],
np.log10(2.0 + np.arange(expected_length)).reshape(
(1, expected_length)
),
np.log10(2.0 + np.arange(expected_length)).reshape((1, expected_length)),
)
@@ -216,9 +197,7 @@ def test_InstanceSplitter(start, target, is_train):
@pytest.mark.parametrize(
"use_prediction_features", TEST_VALUES["use_prediction_features"]
)
@pytest.mark.parametrize(
"allow_target_padding", TEST_VALUES["allow_target_padding"]
)
@pytest.mark.parametrize("allow_target_padding", TEST_VALUES["allow_target_padding"])
def test_CanonicalInstanceSplitter(
start, target, is_train, use_prediction_features, allow_target_padding
):
@@ -251,9 +230,7 @@ def test_CanonicalInstanceSplitter(
min_num_instances = 1 if allow_target_padding else 0
if is_train:
assert len(out) == max(
min_num_instances, len(target) - train_length + 1
)
assert len(out) == max(min_num_instances, len(target) - train_length + 1)
else:
assert len(out) == 1
@@ -285,9 +262,9 @@ def test_Transformation():
target_field=FieldName.TARGET,
output_field="time_feat",
time_features=[
time_feature.DayOfWeek(),
time_feature.DayOfMonth(),
time_feature.MonthOfYear(),
DayOfWeek(),
DayOfMonth(),
MonthOfYear(),
],
pred_length=pred_length,
),
@@ -310,9 +287,7 @@ def test_Transformation():
is_pad_field=FieldName.IS_PAD,
start_field=FieldName.START,
forecast_start_field=FieldName.FORECAST_START,
train_sampler=ExpectedNumInstanceSampler(
num_instances=4
),
train_sampler=ExpectedNumInstanceSampler(num_instances=4),
past_length=train_length,
future_length=pred_length,
time_series_fields=["dynamic_feat", "observed_values"],
@@ -354,9 +329,9 @@ def test_multi_dim_transformation(is_train):
target_field=FieldName.TARGET,
output_field="time_feat",
time_features=[
time_feature.DayOfWeek(),
time_feature.DayOfMonth(),
time_feature.MonthOfYear(),
DayOfWeek(),
DayOfMonth(),
MonthOfYear(),
],
pred_length=pred_length,
),
@@ -381,9 +356,7 @@ def test_multi_dim_transformation(is_train):
is_pad_field=FieldName.IS_PAD,
start_field=FieldName.START,
forecast_start_field=FieldName.FORECAST_START,
train_sampler=ExpectedNumInstanceSampler(
num_instances=4
),
train_sampler=ExpectedNumInstanceSampler(num_instances=4),
past_length=train_length,
future_length=pred_length,
time_series_fields=["dynamic_feat", "observed_values"],
@@ -407,9 +380,7 @@ def test_multi_dim_transformation(is_train):
u["past_is_pad"],
)
assert_padded_array(
u["past_target"],
np.array([first_dim, second_dim]),
u["past_is_pad"],
u["past_target"], np.array([first_dim, second_dim]), u["past_is_pad"],
)
else:
for u in t(iter(ds), is_train=False):
@@ -424,9 +395,7 @@ def test_multi_dim_transformation(is_train):
u["past_is_pad"],
)
assert_padded_array(
u["past_target"],
np.array([first_dim, second_dim]),
u["past_is_pad"],
u["past_target"], np.array([first_dim, second_dim]), u["past_is_pad"],
)
@@ -443,9 +412,7 @@ def test_ExpectedNumInstanceSampler():
is_pad_field=FieldName.IS_PAD,
start_field=FieldName.START,
forecast_start_field=FieldName.FORECAST_START,
train_sampler=ExpectedNumInstanceSampler(
num_instances=4
),
train_sampler=ExpectedNumInstanceSampler(num_instances=4),
past_length=train_length,
future_length=pred_length,
pick_incomplete=True,
@@ -485,9 +452,7 @@ def test_BucketInstanceSampler():
is_pad_field=FieldName.IS_PAD,
start_field=FieldName.START,
forecast_start_field=FieldName.FORECAST_START,
train_sampler=BucketInstanceSampler(
dataset_stats.scale_histogram
),
train_sampler=BucketInstanceSampler(dataset_stats.scale_histogram),
past_length=train_length,
future_length=pred_length,
pick_incomplete=True,
@@ -511,9 +476,7 @@ def test_BucketInstanceSampler():
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
)
assert abs(expected_values[i] - found_values[i] < expected_values[i] * 0.3)
def make_dataset(N, train_length):
@@ -524,9 +487,7 @@ def make_dataset(N, train_length):
targets[i, :] = targets[i, :] * i
ds = ListDataset(
data_iter=[
{"start": "2012-01-01", "target": targets[i, :]} for i in range(n)
],
data_iter=[{"start": "2012-01-01", "target": targets[i, :]} for i in range(n)],
freq="1D",
)