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edwinnglabs merged 4 commits into from
Jan 22, 2024
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Doc update 20240121 #838

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ca3c7c1
replacing pystan with cmdstanpy
edwinnglabs Jan 21, 2024
84fceaa
Merge branch 'dev' into doc-update-20240121
edwinnglabs Jan 21, 2024
13cb633
refresh notebooks docs and examples
edwinnglabs Jan 22, 2024
f2fda79
black format
edwinnglabs Jan 22, 2024
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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -41,7 +41,7 @@ The default page of the repo is on `dev` branch. To install the dev version, ple
This project

- is stable and being incubated for long-term support. It may contain new experimental code, for which APIs are subject to change.
- requires PyStan as a system dependency. PyStan is licensed under [GPLv3](https://www.gnu.org/licenses/gpl-3.0.html), which is a free, copyleft license for software.
- requires [cmdstanpy](https://mc-stan.org/cmdstanpy/) as one of the core dependencies for Bayesian sampling.

# Orbit: A Python Package for Bayesian Forecasting

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4 changes: 2 additions & 2 deletions docs/about.rst
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Expand Up @@ -20,8 +20,8 @@ It also supports the following sampling methods for model estimation:
- Stochastic Variational Inference (SVI) as a hybrid-sampling method on approximate
distribution

Under the hood, the package is leveraging probabilistic program such as `pyro <https://pyro.ai/>`__ and `PyStan 2.0
<https://pystan2.readthedocs.io/en/latest/>`__.
Under the hood, the package is leveraging probabilistic program such as `pyro <https://pyro.ai/>`__ and `cmdstanpy
<https://mc-stan.org/cmdstanpy/>`__.


Citation
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2 changes: 1 addition & 1 deletion docs/conf.py
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Expand Up @@ -50,7 +50,7 @@
]

# autodoc_mock_imports = [
# 'pandas', 'torch', 'pystan', 'tqdm', 'matplotlib.pyplot'
# 'pandas', 'torch', 'cmdstanpy', 'tqdm', 'matplotlib.pyplot'
# 'pyro', 'matplotlib', 'seaborn', 'scipy', 'sklearn']

# Add any paths that contain templates here, relative to this directory.
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22 changes: 11 additions & 11 deletions docs/tutorials/build_your_own_model.ipynb
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Expand Up @@ -42,11 +42,11 @@
"\n",
"### Model\n",
"\n",
"**Model** is an object defined by a class inherited from `BaseTemplate` a.k.a **Model Template** in the diagram below. It mainly turns the logic of `fit()` and `predict()` concrete by supplying the `fitter` as a file (**PyStan**) or a callable class (**Pyro**) and the internal `predict()` method. This object defines the overall inputs, model structure, parameters and likelihoods.\n",
"**Model** is an object defined by a class inherited from `BaseTemplate` a.k.a **Model Template** in the diagram below. It mainly turns the logic of `fit()` and `predict()` concrete by supplying the `fitter` as a file (**CmdStanPy**) or a callable class (**Pyro**) and the internal `predict()` method. This object defines the overall inputs, model structure, parameters and likelihoods.\n",
"\n",
"### Estimator\n",
"\n",
"Meanwhile, there are different APIs implement slightly different ways of sampling and optimization (for **MAP**). `orbit` is designed to support various APIs such as **PyStan** and **Pyro** (hopefully PyMC3, Numpyro in the future!). The logic separating the call of different APIs with different interface is done by the **Estimator** class which is further inherited in `PyroEstimator` and `StanEstimator`.\n"
"Meanwhile, there are different APIs implement slightly different ways of sampling and optimization (for **MAP**). `orbit` is designed to support various APIs such as **CmdStanPy** and **Pyro** (hopefully PyMC3, Numpyro in the future!). The logic separating the call of different APIs with different interface is done by the **Estimator** class which is further inherited in `PyroEstimator` and `StanEstimator`.\n"
]
},
{
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{
"data": {
"text/plain": [
"<orbit.forecaster.svi.SVIForecaster at 0x2a5da37c0>"
"<orbit.forecaster.svi.SVIForecaster at 0x17f0f6070>"
]
},
"execution_count": 11,
Expand Down Expand Up @@ -531,25 +531,25 @@
"name": "stderr",
"output_type": "stream",
"text": [
"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",
"2024-01-21 13:47:27 - orbit - INFO - Using SVI (Pyro) with steps: 501, samples: 100, learning rate: 0.1, learning_rate_total_decay: 1.0 and particles: 100.\n",
"2024-01-21 13:47:27 - orbit - INFO - step 0 loss = 27333, scale = 0.077497\n",
"INFO:orbit:step 0 loss = 27333, scale = 0.077497\n",
"2024-01-11 22:10:29 - orbit - INFO - step 100 loss = 12594, scale = 0.00924\n",
"2024-01-21 13:47:29 - orbit - INFO - step 100 loss = 12594, scale = 0.00924\n",
"INFO:orbit:step 100 loss = 12594, scale = 0.00924\n",
"2024-01-11 22:10:31 - orbit - INFO - step 200 loss = 12596, scale = 0.0094562\n",
"2024-01-21 13:47:31 - orbit - INFO - step 200 loss = 12596, scale = 0.0094562\n",
"INFO:orbit:step 200 loss = 12596, scale = 0.0094562\n",
"2024-01-11 22:10:33 - orbit - INFO - step 300 loss = 12591, scale = 0.0092175\n",
"2024-01-21 13:47:32 - orbit - INFO - step 300 loss = 12591, scale = 0.0092175\n",
"INFO:orbit:step 300 loss = 12591, scale = 0.0092175\n",
"2024-01-11 22:10:35 - orbit - INFO - step 400 loss = 12594, scale = 0.0095741\n",
"2024-01-21 13:47:34 - orbit - INFO - step 400 loss = 12594, scale = 0.0095741\n",
"INFO:orbit:step 400 loss = 12594, scale = 0.0095741\n",
"2024-01-11 22:10:36 - orbit - INFO - step 500 loss = 12591, scale = 0.0095602\n",
"2024-01-21 13:47:36 - 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 0x2a5da37c0>"
"<orbit.forecaster.svi.SVIForecaster at 0x17f0f6070>"
]
},
"execution_count": 12,
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4 changes: 2 additions & 2 deletions docs/tutorials/decompose_prediction.ipynb
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Expand Up @@ -111,13 +111,13 @@
"name": "stderr",
"output_type": "stream",
"text": [
"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"
"2024-01-21 14:39:49 - orbit - INFO - Sampling (CmdStanPy) 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 0x1058475b0>"
"<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x108f9ca60>"
]
},
"execution_count": 4,
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41 changes: 24 additions & 17 deletions docs/tutorials/dlt.ipynb
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Expand Up @@ -201,7 +201,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"2024-01-11 22:11:09 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n"
"2024-01-21 13:46:06 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n"
]
},
{
Expand All @@ -218,8 +218,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 795 ms, sys: 1.27 s, total: 2.06 s\n",
"Wall time: 481 ms\n"
"CPU times: user 613 ms, sys: 1.34 s, total: 1.96 s\n",
"Wall time: 729 ms\n"
]
}
],
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"name": "stderr",
"output_type": "stream",
"text": [
"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"
"2024-01-21 13:46:06 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
]
},
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"data": {
"text/plain": [
"<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x174876ca0>"
"<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x175098400>"
]
},
"execution_count": 5,
Expand Down Expand Up @@ -333,7 +333,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"2024-01-11 22:11:12 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n"
"2024-01-21 13:46:08 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n"
]
},
{
Expand All @@ -350,8 +350,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1.01 s, sys: 956 ms, total: 1.97 s\n",
"Wall time: 501 ms\n"
"CPU times: user 593 ms, sys: 1.38 s, total: 1.97 s\n",
"Wall time: 468 ms\n"
]
}
],
Expand Down Expand Up @@ -403,15 +403,15 @@
"name": "stderr",
"output_type": "stream",
"text": [
"2024-01-11 22:11:12 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n"
"2024-01-21 13:46:09 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 280 ms, sys: 110 ms, total: 391 ms\n",
"Wall time: 351 ms\n"
"CPU times: user 324 ms, sys: 268 ms, total: 592 ms\n",
"Wall time: 344 ms\n"
]
},
{
Expand Down Expand Up @@ -479,7 +479,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"2024-01-11 22:11:13 - orbit - INFO - Optimizing (PyStan) with algorithm: LBFGS.\n"
"2024-01-21 13:46:10 - orbit - INFO - Optimizing (CmdStanPy) with algorithm: LBFGS.\n"
]
},
{
Expand All @@ -496,8 +496,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1.14 s, sys: 941 ms, total: 2.08 s\n",
"Wall time: 468 ms\n"
"CPU times: user 735 ms, sys: 1.51 s, total: 2.24 s\n",
"Wall time: 478 ms\n"
]
}
],
Expand Down Expand Up @@ -552,7 +552,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"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"
"2024-01-21 13:46:10 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
]
},
{
Expand Down Expand Up @@ -684,11 +684,13 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"tags": []
},
"source": [
"### High Dimensional and Fourier Series Regression\n",
"\n",
"In case of high dimensional regression, users can consider fixing the smoothness with a relatively small levels smoothing values e.g. setting `level_sm_input=0.01`. This is particularly useful in modeling higher frequency time-series such as daily and hourly data using regression on Fourier series. Check out the `examples/` folder for more details."
"In case of high dimensional regression, users can consider fixing the smoothness with a relatively small levels smoothing values e.g. setting `level_sm_input=0.01`. This is particularly useful in modeling higher frequency time-series such as daily and hourly data using regression on Fourier series. Check out the `examples/` folder for the details."
]
}
],
Expand Down Expand Up @@ -722,6 +724,11 @@
"toc_position": {},
"toc_section_display": true,
"toc_window_display": true
},
"vscode": {
"interpreter": {
"hash": "091dbfa208428afaf5ec1bce184d496f586fd9ce3f39e55cfff16a9adc2d3379"
}
}
},
"nbformat": 4,
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32 changes: 16 additions & 16 deletions docs/tutorials/ets_lgt_dlt_missing_response.ipynb
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Expand Up @@ -297,13 +297,13 @@
"name": "stderr",
"output_type": "stream",
"text": [
"2024-01-11 22:12:15 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
"2024-01-21 14:38:49 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
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Expand All @@ -317,7 +317,7 @@
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Expand All @@ -331,7 +331,7 @@
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Expand All @@ -345,7 +345,7 @@
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Expand Down Expand Up @@ -397,13 +397,13 @@
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"2024-01-11 22:12:15 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
"2024-01-21 14:38:50 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
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Expand All @@ -417,7 +417,7 @@
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Expand All @@ -431,7 +431,7 @@
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Expand All @@ -445,7 +445,7 @@
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Expand Down Expand Up @@ -497,13 +497,13 @@
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"2024-01-11 22:12:19 - orbit - INFO - Sampling (PyStan) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
"2024-01-21 14:38:53 - orbit - INFO - Sampling (CmdStanPy) with chains: 4, cores: 8, temperature: 1.000, warmups (per chain): 225 and samples(per chain): 25.\n"
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Expand All @@ -517,7 +517,7 @@
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Expand All @@ -531,7 +531,7 @@
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Expand All @@ -545,7 +545,7 @@
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Expand Down Expand Up @@ -811,7 +811,7 @@
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