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Fix loading datasets traffic_2008_10T and traffic_2008_hourly #462

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merged 4 commits into from
Aug 29, 2024
Merged

Fix loading datasets traffic_2008_10T and traffic_2008_hourly #462

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f0957c2
fix: fix loading traffic_2008_10T and traffic_2008_hourly
d-a-bunin Aug 28, 2024
4f6973d
chore: update changelog
d-a-bunin Aug 28, 2024
bd23912
fix: remove unnecessary comments
d-a-bunin Aug 29, 2024
baa4101
Merge branch 'master' into issue-455
d-a-bunin Aug 29, 2024
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2 changes: 1 addition & 1 deletion CHANGELOG.md
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Original file line number Diff line number Diff line change
Expand Up @@ -55,7 +55,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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-
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- Fix holidays during loading datasets `traffic_2008_10T` and `traffic_2008_hourly` ([#462](https://github.com/etna-team/etna/pull/462))
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-
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31 changes: 20 additions & 11 deletions etna/datasets/internal_datasets.py
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Expand Up @@ -15,7 +15,6 @@
from typing import Tuple
from typing import Union

import holidays
import numpy as np
import pandas as pd

Expand Down Expand Up @@ -162,7 +161,7 @@
data, dataset_hash = read_dataset(dataset_path=dataset_dir / f"{name}_{part}.csv.gz")
if dataset_hash != datasets_dict[name]["hash"][part]:
warnings.warn(
f"Local hash and expected hash are different for {name} record part {part}."
f"Local hash and expected hash are different for {name} record part {part}. "
"The first possible reason is that the local copy of the dataset is out of date. In this case you can "
"try setting rebuild_dataset=True to rebuild the dataset. The second possible reason is that the local "
"copy of the dataset reflects a more recent version of the data than your version of the library. "
Expand Down Expand Up @@ -348,11 +347,21 @@
targets = np.concatenate([targets_train, targets_test], axis=0)
targets = targets[np.argsort(ts_indecies)].reshape(-1, 963)

drop_days = (
list(holidays.country_holidays(country="US", years=2008).keys())
+ list(holidays.country_holidays(country="US", years=2009).keys())[:3]
+ [date(2009, 3, 8), date(2009, 3, 10)]
)
drop_days = [

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date(2008, 1, 1),
date(2008, 1, 21),
date(2008, 2, 18),
date(2008, 5, 26),
date(2008, 7, 4),
date(2008, 9, 1),
date(2008, 10, 13),
date(2008, 11, 11),
date(2008, 11, 27),
date(2008, 12, 25),
date(2009, 1, 1),
date(2009, 1, 19),
date(2009, 2, 16),
] + [date(2008, 3, 8), date(2009, 3, 9)]
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We can include this dates in the list above, maybe add a comment if needed


dates_df = pd.DataFrame({"timestamp": pd.date_range("2008-01-01 00:00:00", "2009-03-30 23:50:00", freq="10T")})
dates_df["dt"] = dates_df["timestamp"].dt.date
Expand Down Expand Up @@ -917,19 +926,19 @@
"freq": "10T",
"parts": ("train", "test", "full"),
"hash": {
"train": "4d8d1367fd5341475b852fe9779d0b05",
"train": "f22f77c170e698f4f51231b24e5bc9f0",
"test": "261ee7b09e50d1c7e1e74ccf08412f3f",
"full": "f0c9229d78cfa5b0abf5be950b6843b2",
"full": "d1d05602b15aa30d461e21148483a0c8",
},
},
"traffic_2008_hourly": {
"get_dataset_function": partial(get_traffic_2008_dataset, dataset_freq="hourly"),
"freq": "H",
"parts": ("train", "test", "full"),
"hash": {
"train": "7e6609cce30ae22004c7d7b1d39a35d5",
"train": "161748edc508b4e206344fcbb984bf9a",
"test": "adc3fa06ee856c6481faa400e9e9f602",
"full": "8d988a81e8c368164aada708be27a1c2",
"full": "899bc1fa3fc334868a9e41033a2c3a52",
},
},
"traffic_2015_hourly": {
Expand Down
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