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fix some imports

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This commit is contained in:
Dr. Kashif Rasul
2019-11-05 16:43:18 +01:00
parent 7d5e808a6c
commit 8415a522ec
8 changed files with 339 additions and 7 deletions
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pts/dataset/__init__.py
+1 -1
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@@ -1,6 +1,5 @@
from .common import DataEntry, FieldName
from .list_dataset import ListDataset
from .loader import DataLoader, InferenceDataLoader, TrainDataLoader
from .sampler import (
InstanceSampler,
BucketInstanceSampler,
@@ -8,4 +7,5 @@ from .sampler import (
TestSplitSampler,
UniformSplitSampler,
)
from .process import ProcessStartField
from .utils import to_pandas
pts/dataset/loader.py
+1 -1
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@@ -7,7 +7,7 @@ import numpy as np
# Third-party imports
import torch
from pts.feature.transform import Transformation
from pts.feature import Transformation
# First-party imports
from .common import DataEntry, Dataset
pts/feature/transform.py
+1 -2
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@@ -7,8 +7,7 @@ import numpy as np
import pandas as pd
from pts.exception import assert_pts
from pts.dataset import DataEntry
from pts.dataset.sampler import InstanceSampler
from pts.dataset import DataEntry, InstanceSampler
from .time_feature import TimeFeature
pts/model/estimator.py
+1 -2
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@@ -4,8 +4,7 @@ import numpy as np
import torch
import torch.nn as nn
from pts.dataset import TrainDataLoader
from pts.dataset.common import Dataset
from pts.dataset import Dataset, TrainDataLoader
from pts.feature import Transformation
from .predictor import Predictor
pts/model/predictor.py
+1 -1
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@@ -1,7 +1,7 @@
from abc import ABC, abstractmethod
from typing import Iterator
from pts.dataset.common import Dataset
from pts.dataset import Dataset
from .forecast import Forecast
from .predictor import Predictor
test/dataset/test_common.py
+15
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@@ -0,0 +1,15 @@
from pts.dataset import FieldName
def test_dataset_fields():
assert (
"feat_static_cat" == FieldName.FEAT_STATIC_CAT
), "Error in the FieldName 'feat_static_cat'."
assert (
"feat_static_real" == FieldName.FEAT_STATIC_REAL
), "Error in the FieldName 'feat_static_real'."
assert (
"feat_dynamic_cat" == FieldName.FEAT_DYNAMIC_CAT
), "Error in the FieldName 'feat_dynamic_cat'."
assert (
"feat_dynamic_real" == FieldName.FEAT_DYNAMIC_REAL
), "Error in the FieldName 'feat_dynamic_real'."
test/dataset/test_process.py
+20
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@@ -0,0 +1,20 @@
import pytest
import pandas as pd
from pts.dataset import ProcessStartField
@pytest.mark.parametrize(
"freq, expected",
[
("B", "2019-11-01"),
("W", "2019-11-03"),
("M", "2019-11-30"),
("12M", "2019-11-30"),
("A-DEC", "2019-12-31"),
],
)
def test_process_start_field(freq, expected):
process = ProcessStartField.process
given = "2019-11-01 12:34:56"
assert process(given, freq) == pd.Timestamp(expected, freq)
test/feature/test_lag.py
+299
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@@ -0,0 +1,299 @@
from pts.feature import get_lags_for_frequency
# These are the expected lags for common frequencies and corner cases.
# By default all frequencies have the following lags: [1, 2, 3, 4, 5, 6, 7].
# Remaining lags correspond to the same `season` (+/- `delta`) in previous `k` cycles.
expected_lags = {
# (apart from the default lags) centered around each of the last 3 hours (delta = 2)
"min": [
1,
2,
3,
4,
5,
6,
7,
58,
59,
60,
61,
62,
118,
119,
120,
121,
122,
178,
179,
180,
181,
182,
],
# centered around each of the last 3 hours (delta = 2) + last 7 days (delta = 1)
"15min": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
+ [
95,
96,
97,
191,
192,
193,
287,
288,
289,
383,
384,
385,
479,
480,
481,
575,
576,
577,
671,
672,
673,
],
# centered around each of the last 3 hours (delta = 2) + last 7 days (delta = 1) + 3 weeks (delta = 1)
"30min": [1, 2, 3, 4, 5, 6, 7, 8]
+ [
47,
48,
49,
95,
96,
97,
143,
144,
145,
191,
192,
193,
239,
240,
241,
287,
288,
289,
335,
336,
337,
]
+ [671, 672, 673, 1007, 1008, 1009],
# centered around each of the last 3 hours (delta = 2) + last 7 days (delta = 1) + last 6 weeks (delta = 1)
"59min": [1, 2, 3, 4, 5, 6, 7]
+ [
23,
24,
25,
47,
48,
49,
72,
73,
74,
96,
97,
98,
121,
122,
123,
145,
146,
147,
169,
170,
171,
]
+ [340, 341, 342, 511, 512, 513, 682, 683, 684, 731, 732, 733],
# centered around each of the last 3 hours (delta = 2) + last 7 days (delta = 1) + last 6 weeks (delta = 1)
"61min": [1, 2, 3, 4, 5, 6, 7]
+ [
22,
23,
24,
46,
47,
48,
69,
70,
71,
93,
94,
95,
117,
118,
119,
140,
141,
142,
164,
165,
166,
]
+ [329, 330, 331, 494, 495, 496, 659, 660, 661, 707, 708, 709],
# centered around each of the last 3 hours (delta = 2) + last 7 days (delta = 1) + last 6 weeks (delta = 1)
"H": [1, 2, 3, 4, 5, 6, 7]
+ [
23,
24,
25,
47,
48,
49,
71,
72,
73,
95,
96,
97,
119,
120,
121,
143,
144,
145,
167,
168,
169,
]
+ [335, 336, 337, 503, 504, 505, 671, 672, 673, 719, 720, 721],
# centered around each of the last 7 days (delta = 1) + last 4 weeks (delta = 1) + last 1 month (delta = 1) +
# last 8th and 12th weeks (delta = 0)
"6H": [
1,
2,
3,
4,
5,
6,
7,
8,
9,
11,
12,
13,
15,
16,
17,
19,
20,
21,
23,
24,
25,
27,
28,
29,
]
+ [55, 56, 57, 83, 84, 85, 111, 112, 113]
+ [119, 120, 121]
+ [224, 336],
# centered around each of the last 7 days (delta = 1) + last 4 weeks (delta = 1) + last 1 month (delta = 1) +
# last 8th and 12th weeks (delta = 0) + last year (delta = 1)
"12H": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
+ [27, 28, 29, 41, 42, 43, 55, 56, 57]
+ [59, 60, 61]
+ [112, 168]
+ [727, 728, 729],
# centered around each of the last 7 days (delta = 1) + last 4 weeks (delta = 1) + last 1 month (delta = 1) +
# last 8th and 12th weeks (delta = 0) + last 3 years (delta = 1)
"23H": [1, 2, 3, 4, 5, 6, 7, 8]
+ [13, 14, 15, 20, 21, 22, 28, 29]
+ [30, 31, 32]
+ [58, 87]
+ [378, 379, 380, 758, 759, 760, 1138, 1139, 1140],
# centered around each of the last 7 days (delta = 1) + last 4 weeks (delta = 1) + last 1 month (delta = 1) +
# last 8th and 12th weeks (delta = 0) + last 3 years (delta = 1)
"25H": [1, 2, 3, 4, 5, 6, 7]
+ [12, 13, 14, 19, 20, 21, 25, 26, 27]
+ [28, 29]
+ [53, 80]
+ [348, 349, 350, 697, 698, 699, 1047, 1048, 1049],
# centered around each of the last 7 days (delta = 1) + last 4 weeks (delta = 1) + last 1 month (delta = 1) +
# last 8th and 12th weeks (delta = 0) + last 3 years (delta = 1)
"D": [1, 2, 3, 4, 5, 6, 7, 8]
+ [13, 14, 15, 20, 21, 22, 27, 28, 29]
+ [30, 31]
+ [56, 84]
+ [363, 364, 365, 727, 728, 729, 1091, 1092, 1093],
# centered around each of the last 7 days (delta = 1) + last 4 weeks (delta = 1) + last 1 month (delta = 1) +
# last 8th and 12th weeks (delta = 0) + last 3 years (delta = 1)
"2D": [1, 2, 3, 4, 5]
+ [6, 7, 8, 9, 10, 11, 13, 14, 15]
+ [16]
+ [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],
# 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],
# 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)
"4W": [1, 2, 3, 4, 5, 6, 7, 12, 13, 14, 25, 26, 27, 38, 39, 40],
# centered around each of the last 3 years (delta = 1)
"3W": [1, 2, 3, 4, 5, 6, 7, 16, 17, 18, 33, 34, 35, 51, 52, 53],
# centered around each of the last 3 years (delta = 1)
"5W": [1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 19, 20, 21, 30, 31, 32],
# centered around each of the last 3 years (delta = 1)
"M": [1, 2, 3, 4, 5, 6, 7, 11, 12, 13, 23, 24, 25, 35, 36, 37],
# default
"6M": [1, 2, 3, 4, 5, 6, 7],
# default
"12M": [1, 2, 3, 4, 5, 6, 7],
}
# For the default multiple (1)
for freq in ["min", "H", "D", "W", "M"]:
expected_lags["1" + freq] = expected_lags[freq]
# For frequencies that do not have unique form
expected_lags["60min"] = expected_lags["1H"]
expected_lags["24H"] = expected_lags["1D"]
expected_lags["7D"] = expected_lags["1W"]
def test_lags():
freq_strs = [
"min",
"1min",
"15min",
"30min",
"59min",
"60min",
"61min",
"H",
"1H",
"6H",
"12H",
"23H",
"24H",
"25H",
"D",
"1D",
"2D",
"6D",
"7D",
"8D",
"W",
"1W",
"3W",
"4W",
"5W",
"M",
"6M",
"12M",
]
for freq_str in freq_strs:
lags = get_lags_for_frequency(freq_str)
assert (
lags == expected_lags[freq_str]
), "lags do not match for the frequency '{}':\nexpected: {},\nprovided: {}".format(
freq_str, expected_lags[freq_str], lags
)