Merge pull request #838 from uber/doc-update-20240121

Doc update 20240121
uber · Jan 22, 2024 · c853d61 · c853d61
2 parents 447487f + f2fda79
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diff --git a/README.md b/README.md
@@ -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
 

diff --git a/docs/about.rst b/docs/about.rst
@@ -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

diff --git a/docs/conf.py b/docs/conf.py
@@ -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.

diff --git a/docs/tutorials/build_your_own_model.ipynb b/docs/tutorials/build_your_own_model.ipynb
@@ -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"
  ]
  },
  {
@@ -496,7 +496,7 @@
  {
  "data": {
  "text/plain": [
- "<orbit.forecaster.svi.SVIForecaster at 0x2a5da37c0>"
+ "<orbit.forecaster.svi.SVIForecaster at 0x17f0f6070>"
  ]
  },
  "execution_count": 11,
@@ -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,

diff --git a/docs/tutorials/decompose_prediction.ipynb b/docs/tutorials/decompose_prediction.ipynb
@@ -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,

diff --git a/docs/tutorials/dlt.ipynb b/docs/tutorials/dlt.ipynb
@@ -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"
  ]
  },
  {
@@ -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"
  ]
  }
  ],
@@ -252,13 +252,13 @@
  "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"
  ]
  },
  {
  "data": {
  "text/plain": [
- "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x174876ca0>"
+ "<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x175098400>"
  ]
  },
  "execution_count": 5,
@@ -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"
  ]
  },
  {
@@ -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"
  ]
  }
  ],
@@ -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"
  ]
  },
  {
@@ -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"
  ]
  },
  {
@@ -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"
  ]
  }
  ],
@@ -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"
  ]
  },
  {
@@ -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."
  ]
  }
  ],
@@ -722,6 +724,11 @@
  "toc_position": {},
  "toc_section_display": true,
  "toc_window_display": true
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "091dbfa208428afaf5ec1bce184d496f586fd9ce3f39e55cfff16a9adc2d3379"
+ }
  }
  },
  "nbformat": 4,

diff --git a/docs/tutorials/ets_lgt_dlt_missing_response.ipynb b/docs/tutorials/ets_lgt_dlt_missing_response.ipynb
@@ -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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- "model_id": "675e617a68644e69bc19461f72afcb76",
+ "model_id": "7127326a60cb455a92d2f29cc00a7a3d",
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  "version_minor": 0
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@@ -317,7 +317,7 @@
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- "model_id": "e61ef67e9c1349a391fa6d7dd8049409",
+ "model_id": "788443932419449f9e398df74e8180ab",
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@@ -331,7 +331,7 @@
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+ "model_id": "f75584f0370742288f15096a09a23ee2",
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@@ -345,7 +345,7 @@
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+ "model_id": "ee67293605f9471c876b59bd14d63cc0",
  "version_major": 2,
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@@ -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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+ "model_id": "91631a6519be4f9eb191e0655fbac846",
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@@ -417,7 +417,7 @@
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+ "model_id": "5117ba9e278f4c93915daa1cade2411e",
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@@ -431,7 +431,7 @@
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+ "model_id": "e047da76e6a3451dbfb3895656ccc731",
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@@ -445,7 +445,7 @@
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@@ -497,13 +497,13 @@
  "name": "stderr",
  "output_type": "stream",
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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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+ "model_id": "c51672ad186240cc80ddffdcddff9c3d",
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@@ -517,7 +517,7 @@
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+ "model_id": "956c2e23d6514668bf16bceac75fbbc8",
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@@ -531,7 +531,7 @@
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@@ -811,7 +811,7 @@
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- "execution_count": 15,
+ "execution_count": 14,
  "metadata": {
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