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doc and dependencies update (#833)
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* doc and dependenies update
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edwinnglabs authored Jan 12, 2024
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39 changes: 13 additions & 26 deletions docs/tutorials/backtest.ipynb
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"start_time": "2022-04-28T21:48:26.206213Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/towinazure/opt/miniconda3/envs/orbit39/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
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"After seeing the results from the backtest, users may wish to fine tune the hyperparameters. Orbit also provide a `grid_search_orbit` utilities for parameter searching. It uses `Backtester` under the hood so users can compare backtest metrics for different parameters combination."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-info\">\n",
"\n",
"To have a consistent outlook here, users need to make sure an updated version of `ipywidgets` is installed.\n",
"\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": 29,
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},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9/9 [00:02<00:00, 4.48it/s]\n"
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"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d4818b1d0a8d45b7b66684eed8ec74db",
"version_major": 2,
"version_minor": 0
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"text/plain": [
" 0%| | 0/9 [00:00<?, ?it/s]"
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"metadata": {},
"output_type": "display_data"
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31 changes: 11 additions & 20 deletions docs/tutorials/build_your_own_model.ipynb
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"start_time": "2022-04-28T21:48:19.985960Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/towinazure/opt/miniconda3/envs/orbit39/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
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"name": "stdout",
"output_type": "stream",
"text": [
"1.1.4.2\n"
"1.1.4.3\n"
]
}
],
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{
"data": {
"text/plain": [
"<orbit.forecaster.svi.SVIForecaster at 0x2a61af520>"
"<orbit.forecaster.svi.SVIForecaster at 0x2a5da37c0>"
]
},
"execution_count": 11,
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"name": "stderr",
"output_type": "stream",
"text": [
"2023-12-25 15:20:13 - orbit - INFO - Using SVI (Pyro) with steps: 501, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
"2023-12-25 15:20:13 - orbit - INFO - step 0 loss = 27333, scale = 0.077497\n",
"2024-01-11 22:10:27 - orbit - INFO - Using SVI (Pyro) with steps: 501, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
"2024-01-11 22:10:28 - orbit - INFO - step 0 loss = 27333, scale = 0.077497\n",
"INFO:orbit:step 0 loss = 27333, scale = 0.077497\n",
"2023-12-25 15:20:15 - orbit - INFO - step 100 loss = 12594, scale = 0.00924\n",
"2024-01-11 22:10:29 - orbit - INFO - step 100 loss = 12594, scale = 0.00924\n",
"INFO:orbit:step 100 loss = 12594, scale = 0.00924\n",
"2023-12-25 15:20:17 - orbit - INFO - step 200 loss = 12596, scale = 0.0094562\n",
"2024-01-11 22:10:31 - orbit - INFO - step 200 loss = 12596, scale = 0.0094562\n",
"INFO:orbit:step 200 loss = 12596, scale = 0.0094562\n",
"2023-12-25 15:20:18 - orbit - INFO - step 300 loss = 12591, scale = 0.0092175\n",
"2024-01-11 22:10:33 - orbit - INFO - step 300 loss = 12591, scale = 0.0092175\n",
"INFO:orbit:step 300 loss = 12591, scale = 0.0092175\n",
"2023-12-25 15:20:20 - orbit - INFO - step 400 loss = 12594, scale = 0.0095741\n",
"2024-01-11 22:10:35 - orbit - INFO - step 400 loss = 12594, scale = 0.0095741\n",
"INFO:orbit:step 400 loss = 12594, scale = 0.0095741\n",
"2023-12-25 15:20:22 - orbit - INFO - step 500 loss = 12591, scale = 0.0095602\n",
"2024-01-11 22:10:36 - orbit - INFO - step 500 loss = 12591, scale = 0.0095602\n",
"INFO:orbit:step 500 loss = 12591, scale = 0.0095602\n"
]
},
{
"data": {
"text/plain": [
"<orbit.forecaster.svi.SVIForecaster at 0x2a61af520>"
"<orbit.forecaster.svi.SVIForecaster at 0x2a5da37c0>"
]
},
"execution_count": 12,
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15 changes: 3 additions & 12 deletions docs/tutorials/decompose_prediction.ipynb
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"start_time": "2022-04-28T21:48:09.278722Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/towinazure/opt/miniconda3/envs/orbit39/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
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"name": "stderr",
"output_type": "stream",
"text": [
"2024-01-07 16:49:08 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
"2024-01-11 22:10:53 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
]
},
{
"data": {
"text/plain": [
"<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x2a6a57c70>"
"<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x1058475b0>"
]
},
"execution_count": 4,
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62 changes: 22 additions & 40 deletions docs/tutorials/dlt.ipynb
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"start_time": "2022-04-28T21:51:56.242159Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/towinazure/opt/miniconda3/envs/orbit39/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
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"start_time": "2022-03-30T23:45:50.239137Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/towinazure/edwinnglabs/orbit/orbit/utils/dataset.py:27: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" df = pd.read_csv(url, parse_dates=[\"week\"])\n"
]
}
],
"outputs": [],
"source": [
"# load log-transformed data\n",
"df = load_iclaims()\n",
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"name": "stderr",
"output_type": "stream",
"text": [
"2024-01-07 16:49:37 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n"
"2024-01-11 22:11:09 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n"
]
},
{
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"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 615 ms, sys: 1.47 s, total: 2.09 s\n",
"Wall time: 455 ms\n"
"CPU times: user 795 ms, sys: 1.27 s, total: 2.06 s\n",
"Wall time: 481 ms\n"
]
}
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},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-01-07 16:50:15 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
"2024-01-11 22:11:10 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
]
},
{
"data": {
"text/plain": [
"<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x104e0b6a0>"
"<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x174876ca0>"
]
},
"execution_count": 14,
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
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"name": "stderr",
"output_type": "stream",
"text": [
"2024-01-07 16:49:39 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n"
"2024-01-11 22:11:12 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n"
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"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 724 ms, sys: 1.24 s, total: 1.96 s\n",
"Wall time: 466 ms\n"
"CPU times: user 1.01 s, sys: 956 ms, total: 1.97 s\n",
"Wall time: 501 ms\n"
]
}
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"name": "stderr",
"output_type": "stream",
"text": [
"2024-01-07 16:49:40 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n"
"2024-01-11 22:11:12 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 350 ms, sys: 184 ms, total: 535 ms\n",
"Wall time: 341 ms\n"
"CPU times: user 280 ms, sys: 110 ms, total: 391 ms\n",
"Wall time: 351 ms\n"
]
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{
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"name": "stderr",
"output_type": "stream",
"text": [
"2024-01-07 16:49:40 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n"
"2024-01-11 22:11:13 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n"
]
},
{
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"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 793 ms, sys: 1.38 s, total: 2.17 s\n",
"Wall time: 473 ms\n"
"CPU times: user 1.14 s, sys: 941 ms, total: 2.08 s\n",
"Wall time: 468 ms\n"
]
}
],
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},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2022-03-30T23:45:58.966922Z",
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"name": "stderr",
"output_type": "stream",
"text": [
"2024-01-07 16:50:03 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
"2024-01-11 22:11:14 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
]
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{
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{
"cell_type": "code",
"execution_count": 13,
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2022-03-30T23:45:58.983007Z",
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"1 0.139546 0.99 0.01 "
]
},
"execution_count": 13,
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
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