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Fix classification notebook to download FordA dataset without error v2 #1299

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Jun 29, 2023
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Fix classification notebook to download FordA dataset without error v2 #1299

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151da62
fix: fix classification notebook to download dataset without error
Jun 27, 2023
62164fb
chore: update changelog
Jun 27, 2023
1a3a8b2
chore: update changelog
Jun 27, 2023
04ad3c1
style: reformat code
Jun 27, 2023
f29224e
fix: fix comments on PR
Jun 28, 2023
02c2b3d
fix: remove notion about version of pyts
Jun 28, 2023
7052481
Merge branch 'master' into fix-classification-notebook
Mr-Geekman Jun 28, 2023
955e09c
fix: fix downloading archive
Jun 28, 2023
f843c85
Merge remote-tracking branch 'origin/master' into fix-classification-…
Jun 28, 2023
427fd56
fix: temp fix to check notebooks ci
Jun 28, 2023
586f542
fix: temp fix to check notebooks ci
Jun 28, 2023
65b8503
fix: temp fix to check notebooks ci
Jun 29, 2023
23c9fa4
chore: update changelog
Jun 29, 2023
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2 changes: 1 addition & 1 deletion CHANGELOG.md
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Expand Up @@ -62,7 +62,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
- Fix `BasePipeline.forecast` when prediction intervals are estimated on history data with presence of NaNs ([#1291](https://github.com/tinkoff-ai/etna/pull/1291))
- Teach `BaseMixin.set_params` to work with nested `list` and `tuple` ([#1201](https://github.com/tinkoff-ai/etna/pull/1201))
- Fix `get_anomalies_prediction_interval` to work when segments have different start date ([#1296](https://github.com/tinkoff-ai/etna/pull/1296))
- Fix `classification` notebook to download `FordA` dataset without error ([#1298](https://github.com/tinkoff-ai/etna/pull/1298))
- Fix `classification` notebook to download `FordA` dataset without error ([#1299](https://github.com/tinkoff-ai/etna/pull/1299))

## [2.0.0] - 2023-04-11
### Added
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62 changes: 30 additions & 32 deletions examples/classification.ipynb
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Expand Up @@ -77,15 +77,13 @@
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"100 34.6M 100 34.6M 0 0 521k 0 0:01:08 0:01:08 --:--:-- 562k 0 0 346k 0 0:01:42 0:00:09 0:01:33 388k 0 0:01:13 0:00:24 0:00:49 658k 0 0:01:08 0:00:58 0:00:10 481k\n",
"Archive: data/ford_a.zip\n",
"caution: filename not matched: -q\n"
"100 34.6M 100 34.6M 0 0 364k 0 0:01:37 0:01:37 --:--:-- 471k6 585k 0 389k 0 0:01:31 0:00:40 0:00:51 326k77k 0 0:01:33 0:00:42 0:00:51 236k 376k 0 0:01:34 0:00:49 0:00:45 388k51k 0 0:01:41 0:01:19 0:00:22 325k 359k 0 0:01:38 0:01:33 0:00:05 399k\n"
]
}
],
"source": [
"!curl \"http://www.timeseriesclassification.com/ClassificationDownloads/FordA.zip\" -o data/ford_a.zip\n",
"!unzip data/ford_a.zip -d data/ford_a -q"
"!unzip -q data/ford_a.zip -d data/ford_a"
]
},
{
Expand Down Expand Up @@ -378,16 +376,16 @@
"name": "stderr",
"output_type": "stream",
"text": [
"Feature Extraction: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2880/2880 [00:00<00:00, 3198.77it/s]\n",
"Feature Extraction: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 721/721 [00:00<00:00, 3384.68it/s]\n",
"Feature Extraction: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2881/2881 [00:00<00:00, 3294.64it/s]\n",
"Feature Extraction: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 720/720 [00:00<00:00, 3469.26it/s]\n",
"Feature Extraction: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2881/2881 [00:00<00:00, 3324.07it/s]\n",
"Feature Extraction: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 720/720 [00:00<00:00, 3412.13it/s]\n",
"Feature Extraction: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2881/2881 [00:00<00:00, 2980.69it/s]\n",
"Feature Extraction: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 720/720 [00:00<00:00, 2783.82it/s]\n",
"Feature Extraction: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2881/2881 [00:00<00:00, 3105.03it/s]\n",
"Feature Extraction: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 720/720 [00:00<00:00, 2932.66it/s]\n"
"Feature Extraction: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2880/2880 [00:01<00:00, 2075.39it/s]\n",
"Feature Extraction: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 721/721 [00:00<00:00, 3087.24it/s]\n",
"Feature Extraction: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2881/2881 [00:00<00:00, 2972.92it/s]\n",
"Feature Extraction: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 720/720 [00:00<00:00, 3097.62it/s]\n",
"Feature Extraction: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2881/2881 [00:01<00:00, 2690.83it/s]\n",
"Feature Extraction: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 720/720 [00:00<00:00, 3024.67it/s]\n",
"Feature Extraction: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2881/2881 [00:00<00:00, 3021.04it/s]\n",
"Feature Extraction: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 720/720 [00:00<00:00, 2807.53it/s]\n",
"Feature Extraction: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2881/2881 [00:00<00:00, 2891.77it/s]\n",
"Feature Extraction: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 720/720 [00:00<00:00, 2813.56it/s]\n"
]
}
],
Expand Down Expand Up @@ -604,7 +602,7 @@
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"100 91.3M 100 91.3M 0 0 1132k 0 0:01:22 0:01:22 --:--:-- 1373k-:--:-- 0:00:01 --:--:-- 0 0 1025k 0 0:01:31 0:00:38 0:00:53 1215k\n"
"100 91.3M 100 91.3M 0 0 519k 0 0:03:00 0:03:00 --:--:-- 591k0:02:03 625k 0 0:02:44 0:00:47 0:01:57 449k 0 566k 0 0:02:45 0:01:45 0:01:00 764k 0 0 563k 0 0:02:46 0:01:47 0:00:59 696k0 0:02:49 0:01:55 0:00:54 344k8k 0 0:02:53 0:02:11 0:00:42 497k 0 530k 0 0:02:56 0:02:20 0:00:36 488k 0 530k 0 0:02:56 0:02:21 0:00:35 495k0:00:24 612k 0 0 528k 0 0:02:57 0:02:36 0:00:21 529k 0 523k 0 0:02:58 0:02:42 0:00:16 402k 0 523k 0 0:02:58 0:02:43 0:00:15 434k\n"
]
}
],
Expand All @@ -631,7 +629,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8e52dcb4ef5e4955baf35f677edd7b5d",
"model_id": "fd3aa4fabbf7469388b856fe0c26e083",
"version_major": 2,
"version_minor": 0
},
Expand Down Expand Up @@ -754,26 +752,26 @@
"output_type": "stream",
"text": [
"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
"[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 1.5s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 3.0s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 4.9s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 4.9s finished\n",
"[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 1.9s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 3.8s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 5.3s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 5.3s finished\n",
"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
"/Users/d.a.binin/Documents/tasks/etna-github/etna/datasets/tsdataset.py:279: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.\n",
" future_dates = pd.date_range(\n",
"[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 5.0s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 4.0s remaining: 0.0s\n",
"/Users/d.a.binin/Documents/tasks/etna-github/etna/datasets/tsdataset.py:279: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.\n",
" future_dates = pd.date_range(\n",
"[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 8.3s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 7.3s remaining: 0.0s\n",
"/Users/d.a.binin/Documents/tasks/etna-github/etna/datasets/tsdataset.py:279: FutureWarning: Argument `closed` is deprecated in favor of `inclusive`.\n",
" future_dates = pd.date_range(\n",
"[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 11.4s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 11.4s finished\n",
"[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 10.6s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 10.7s finished\n",
"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
"[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 9.7s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 20.5s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 31.5s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 31.6s finished\n"
"[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 10.5s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 25.4s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 37.0s remaining: 0.0s\n",
"[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 37.1s finished\n"
]
}
],
Expand Down Expand Up @@ -999,8 +997,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 11.5 s, sys: 1.08 s, total: 12.6 s\n",
"Wall time: 12.7 s\n"
"CPU times: user 12.8 s, sys: 1.45 s, total: 14.3 s\n",
"Wall time: 14.3 s\n"
]
}
],
Expand Down Expand Up @@ -1045,8 +1043,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 11.2 s, sys: 1 s, total: 12.2 s\n",
"Wall time: 12.1 s\n"
"CPU times: user 12 s, sys: 1.12 s, total: 13.1 s\n",
"Wall time: 13.1 s\n"
]
}
],
Expand Down