From f50e2d153a95d3d471f526198a11bee7329657f9 Mon Sep 17 00:00:00 2001 From: Hahnbee Lee <55263191+hahnbeelee@users.noreply.github.com> Date: Tue, 10 Oct 2023 21:52:58 -0700 Subject: [PATCH 01/22] Revert "undo all mintlify assets" This reverts commit e272e576e8bcd2fa2c66476fd9324f18ffcc9a24. --- .github/workflows/build-docs.yaml | 41 ++ README.md | 334 +++++++------- action_files/final-formatting.bash | 11 + nbs/_extensions/mintlify/_extension.yml | 30 ++ nbs/_extensions/mintlify/mintlify.lua | 71 +++ .../mintlify/mintlify_renderers.lua | 3 + nbs/_extensions/mintlify/mintlify_utils.lua | 47 ++ nbs/_extensions/mintlify/mintlify_writer.lua | 133 ++++++ nbs/_quarto.yml | 59 +-- nbs/custom.yml | 22 - nbs/favicon.png | Bin 0 -> 208747 bytes nbs/favicon.svg | 5 + nbs/favicon_png.png | Bin 0 -> 177981 bytes nbs/mint.json | 122 +++++ nbs/styles.css | 431 ------------------ preview-mintlify-docs.bash | 27 ++ settings.ini | 5 +- 17 files changed, 673 insertions(+), 668 deletions(-) create mode 100644 .github/workflows/build-docs.yaml create mode 100755 action_files/final-formatting.bash create mode 100644 nbs/_extensions/mintlify/_extension.yml create mode 100644 nbs/_extensions/mintlify/mintlify.lua create mode 100644 nbs/_extensions/mintlify/mintlify_renderers.lua create mode 100644 nbs/_extensions/mintlify/mintlify_utils.lua create mode 100644 nbs/_extensions/mintlify/mintlify_writer.lua delete mode 100644 nbs/custom.yml create mode 100644 nbs/favicon.png create mode 100644 nbs/favicon.svg create mode 100644 nbs/favicon_png.png create mode 100644 nbs/mint.json delete mode 100644 nbs/styles.css create mode 100644 preview-mintlify-docs.bash diff --git a/.github/workflows/build-docs.yaml b/.github/workflows/build-docs.yaml new file mode 100644 index 000000000..d50ea4104 --- /dev/null +++ b/.github/workflows/build-docs.yaml @@ -0,0 +1,41 @@ +name: "build-docs" +on: + push: + branches: ["main"] + workflow_dispatch: +jobs: + build-docs: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-python@v4 + with: + cache: "pip" + cache-dependency-path: settings.ini + - name: Install Dependencies + shell: bash + run: | + set -ux + python -m pip install --upgrade pip + pip install -Uq nbdev + test -f setup.py && pip install -e ".[dev]" + nbdev_docs + - name: Apply final formats + shell: bash + run: bash ./action_files/final-formatting.bash + - name: Copy over necessary assets + run: | + cp nbs/mint.json _docs/mint.json + cp nbs/imgs/logo/dark.png _docs/dark.png + cp nbs/imgs/logo/light.png _docs/light.png + cp nbs/favicon.svg _docs/favicon.svg + - name: Deploy to Mintlify Docs + uses: peaceiris/actions-gh-pages@v3 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_branch: docs + publish_dir: ./_docs + # The following lines assign commit authorship to the official GH-Actions bot for deploys to `docs` branch. + # You can swap them out with your own user credentials. + user_name: github-actions[bot] + user_email: 41898282+github-actions[bot]@users.noreply.github.com diff --git a/README.md b/README.md index 96a610ead..72627afbe 100644 --- a/README.md +++ b/README.md @@ -1,32 +1,46 @@ -# StatsForecast ⚡️ - - +# Nixtla   [![Tweet](https://img.shields.io/twitter/url/http/shields.io.svg?style=social)](https://twitter.com/intent/tweet?text=Statistical%20Forecasting%20Algorithms%20by%20Nixtla%20&url=https://github.com/Nixtla/statsforecast&via=nixtlainc&hashtags=StatisticalModels,TimeSeries,Forecasting)  [![Slack](https://img.shields.io/badge/Slack-4A154B?&logo=slack&logoColor=white)](https://join.slack.com/t/nixtlacommunity/shared_invite/zt-1pmhan9j5-F54XR20edHk0UtYAPcW4KQ) + +[![All Contributors](https://img.shields.io/badge/all_contributors-32-orange.svg?style=flat-square)](#contributors-) + + +
+ +

Statistical ⚡️ Forecast

+

Lightning fast forecasting with statistical and econometric models

+ +[![CI](https://github.com/Nixtla/statsforecast/actions/workflows/ci.yaml/badge.svg?branch=main)](https://github.com/Nixtla/statsforecast/actions/workflows/ci.yaml) +[![Python](https://img.shields.io/pypi/pyversions/statsforecast)](https://pypi.org/project/statsforecast/) +[![PyPi](https://img.shields.io/pypi/v/statsforecast?color=blue)](https://pypi.org/project/statsforecast/) +[![conda-nixtla](https://img.shields.io/conda/vn/conda-forge/statsforecast?color=seagreen&label=conda)](https://anaconda.org/conda-forge/statsforecast) +[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://github.com/Nixtla/statsforecast/blob/main/LICENSE) +[![docs](https://img.shields.io/website-up-down-green-red/http/nixtla.github.io/statsforecast.svg?label=docs)](https://nixtla.github.io/statsforecast/) +[![Downloads](https://pepy.tech/badge/statsforecast)](https://pepy.tech/project/statsforecast) + +**StatsForecast** offers a collection of widely used univariate time series forecasting models, including automatic `ARIMA`, `ETS`, `CES`, and `Theta` modeling optimized for high performance using `numba`. It also includes a large battery of benchmarking models. +
## Installation -You can install -[`StatsForecast`](https://Nixtla.github.io/statsforecast/src/core/core.html#statsforecast) -with: +You can install `StatsForecast` with: -``` python +```python pip install statsforecast ``` -or +or -``` python +```python conda install -c conda-forge statsforecast -``` +``` -Vist our [Installation -Guide](./docs/getting-started/0_Installation.ipynb) for further -instructions. + +Vist our [Installation Guide](https://nixtla.github.io/statsforecast/docs/getting-started/installation.html) for further instructions. ## Quick Start **Minimal Example** -``` python +```python from statsforecast import StatsForecast from statsforecast.models import AutoARIMA @@ -39,201 +53,144 @@ sf.fit(df) sf.predict(h=12, level=[95]) ``` -**Get Started with this [quick -guide](../nbs/docs/getting-started/1_Getting_Started_short.ipynb).** +**Get Started with this [quick guide](https://nixtla.github.io/statsforecast/docs/getting-started/getting_started_short.html).** -**Follow this [end-to-end -walkthrough](../nbs/docs/getting-started/2_Getting_Started_complete.ipynb) -for best practices.** +**Follow this [end-to-end walkthrough](https://nixtla.github.io/statsforecast/docs/getting-started/getting_started_complete.html) for best practices.** -## Why? +## Why? -Current Python alternatives for statistical models are slow, inaccurate -and don’t scale well. So we created a library that can be used to -forecast in production environments or as benchmarks. -[`StatsForecast`](https://Nixtla.github.io/statsforecast/src/core/core.html#statsforecast) -includes an extensive battery of models that can efficiently fit -millions of time series. +Current Python alternatives for statistical models are slow, inaccurate and don't scale well. So we created a library that can be used to forecast in production environments or as benchmarks. `StatsForecast` includes an extensive battery of models that can efficiently fit millions of time series. ## Features -- Fastest and most accurate implementations of - [`AutoARIMA`](https://Nixtla.github.io/statsforecast/src/core/models.html#autoarima), - [`AutoETS`](https://Nixtla.github.io/statsforecast/src/core/models.html#autoets), - [`AutoCES`](https://Nixtla.github.io/statsforecast/src/core/models.html#autoces), - [`MSTL`](https://Nixtla.github.io/statsforecast/src/core/models.html#mstl) - and - [`Theta`](https://Nixtla.github.io/statsforecast/src/core/models.html#theta) - in Python. -- Out-of-the-box compatibility with Spark, Dask, and Ray. -- Probabilistic Forecasting and Confidence Intervals. -- Support for exogenous Variables and static covariates. -- Anomaly Detection. -- Familiar sklearn syntax: `.fit` and `.predict`. +* Fastest and most accurate implementations of `AutoARIMA`, `AutoETS`, `AutoCES`, `MSTL` and `Theta` in Python. +* Out-of-the-box compatibility with Spark, Dask, and Ray. +* Probabilistic Forecasting and Confidence Intervals. +* Support for exogenous Variables and static covariates. +* Anomaly Detection. +* Familiar sklearn syntax: `.fit` and `.predict`. ## Highlights -- Inclusion of `exogenous variables` and `prediction intervals` for - ARIMA. -- 20x - [faster](https://github.com/Nixtla/statsforecast/tree/main/experiments/arima) - than `pmdarima`. -- 1.5x faster than `R`. -- 500x faster than `Prophet`. -- 4x - [faster](https://github.com/Nixtla/statsforecast/tree/main/experiments/ets) - than `statsmodels`. -- Compiled to high performance machine code through - [`numba`](https://numba.pydata.org/). -- 1,000,000 series in [30 - min](https://github.com/Nixtla/statsforecast/tree/main/experiments/ray) - with [ray](https://github.com/ray-project/ray). -- Replace FB-Prophet in two lines of code and gain speed and accuracy. - Check the experiments - [here](https://github.com/Nixtla/statsforecast/tree/main/experiments/arima_prophet_adapter). -- Fit 10 benchmark models on **1,000,000** series in [under **5 - min**](https://github.com/Nixtla/statsforecast/tree/main/experiments/benchmarks_at_scale). - -Missing something? Please open an issue or write us in -[![Slack](https://img.shields.io/badge/Slack-4A154B?&logo=slack&logoColor=white.png)](https://join.slack.com/t/nixtlaworkspace/shared_invite/zt-135dssye9-fWTzMpv2WBthq8NK0Yvu6A) +* Inclusion of `exogenous variables` and `prediction intervals` for ARIMA. +* 20x [faster](./experiments/arima/) than `pmdarima`. +* 1.5x faster than `R`. +* 500x faster than `Prophet`. +* 4x [faster](./experiments/ets/) than `statsmodels`. +* Compiled to high performance machine code through [`numba`](https://numba.pydata.org/). +* 1,000,000 series in [30 min](https://github.com/Nixtla/statsforecast/tree/main/experiments/ray) with [ray](https://github.com/ray-project/ray). +* Replace FB-Prophet in two lines of code and gain speed and accuracy. Check the experiments [here](https://github.com/Nixtla/statsforecast/tree/main/experiments/arima_prophet_adapter). +* Fit 10 benchmark models on **1,000,000** series in [under **5 min**](./experiments/benchmarks_at_scale/). + + +Missing something? Please open an issue or write us in [![Slack](https://img.shields.io/badge/Slack-4A154B?&logo=slack&logoColor=white)](https://join.slack.com/t/nixtlaworkspace/shared_invite/zt-135dssye9-fWTzMpv2WBthq8NK0Yvu6A) ## Examples and Guides -📚 [End to End -Walkthrough](https://nixtla.github.io/statsforecast/docs/getting-started/getting_started_complete.html): -Model training, evaluation and selection for multiple time series +📚 [End to End Walkthrough](https://nixtla.github.io/statsforecast/docs/getting-started/getting_started_complete.html): Model training, evaluation and selection for multiple time series + +🔎 [Anomaly Detection](https://nixtla.github.io/statsforecast/docs/tutorials/anomalydetection.html): detect anomalies for time series using in-sample prediction intervals. -🔎 [Anomaly -Detection](https://nixtla.github.io/statsforecast/docs/tutorials/anomalydetection.html): -detect anomalies for time series using in-sample prediction intervals. +👩‍🔬 [Cross Validation](https://nixtla.github.io/statsforecast/docs/tutorials/crossvalidation.html): robust model’s performance evaluation. -👩‍🔬 [Cross -Validation](https://nixtla.github.io/statsforecast/docs/tutorials/crossvalidation.html): -robust model’s performance evaluation. +❄️ [Multiple Seasonalities](https://nixtla.github.io/statsforecast/docs/tutorials/multipleseasonalities.html): how to forecast data with multiple seasonalities using an MSTL. -❄️ [Multiple -Seasonalities](https://nixtla.github.io/statsforecast/docs/tutorials/multipleseasonalities.html): -how to forecast data with multiple seasonalities using an MSTL. +🔌 [Predict Demand Peaks](https://nixtla.github.io/statsforecast/docs/tutorials/electricitypeakforecasting.html): electricity load forecasting for detecting daily peaks and reducing electric bills. -🔌 [Predict Demand -Peaks](https://nixtla.github.io/statsforecast/docs/tutorials/electricitypeakforecasting.html): -electricity load forecasting for detecting daily peaks and reducing -electric bills. +📈 [Intermittent Demand](https://nixtla.github.io/statsforecast/docs/tutorials/intermittentdata.html): forecast series with very few non-zero observations. -📈 [Intermittent -Demand](https://nixtla.github.io/statsforecast/docs/tutorials/intermittentdata.html): -forecast series with very few non-zero observations. +🌡️ [Exogenous Regressors](https://nixtla.github.io/statsforecast/docs/how-to-guides/exogenous.html): like weather or prices -🌡️ [Exogenous -Regressors](https://nixtla.github.io/statsforecast/docs/how-to-guides/exogenous.html): -like weather or prices ## Models ### Automatic Forecasting +Automatic forecasting tools search for the best parameters and select the best possible model for a group of time series. These tools are useful for large collections of univariate time series. -Automatic forecasting tools search for the best parameters and select -the best possible model for a group of time series. These tools are -useful for large collections of univariate time series. - -| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | -|:-----------------------------------------------------------------------------------|:--------------:|:----------------------:|:----------------------:|:---------------------------:| -| [AutoARIMA](https://nixtla.github.io/statsforecast/src/core/models.html#autoarima) | ✅ | ✅ | ✅ | ✅ | -| [AutoETS](https://nixtla.github.io/statsforecast/src/core/models.html#autoets) | ✅ | ✅ | ✅ | ✅ | -| [AutoCES](https://nixtla.github.io/statsforecast/src/core/models.html#autoces) | ✅ | ✅ | ✅ | ✅ | -| [AutoTheta](https://nixtla.github.io/statsforecast/src/core/models.html#autotheta) | ✅ | ✅ | ✅ | ✅ | +|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | +|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| +|[AutoARIMA](https://nixtla.github.io/statsforecast/src/core/models.html#autoarima)|✅|✅|✅|✅|✅| +|[AutoETS](https://nixtla.github.io/statsforecast/src/core/models.html#autoets)|✅|✅|✅|✅|✅| +|[AutoCES](https://nixtla.github.io/statsforecast/src/core/models.html#autoces)|✅|✅|✅|✅|✅| +|[AutoTheta](https://nixtla.github.io/statsforecast/src/core/models.html#autotheta)|✅|✅|✅|✅|✅| ## ARIMA Family - These models exploit the existing autocorrelations in the time series. -| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | -|:---------------------------------------------------------------------------------------------|:--------------:|:----------------------:|:----------------------:|:---------------------------:| -| [ARIMA](https://nixtla.github.io/statsforecast/src/core/models.html#arima) | ✅ | ✅ | ✅ | ✅ | -| [AutoRegressive](https://nixtla.github.io/statsforecast/src/core/models.html#autoregressive) | ✅ | ✅ | ✅ | ✅ | +|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | +|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| +|[ARIMA](https://nixtla.github.io/statsforecast/src/core/models.html#arima)|✅|✅|✅|✅|✅| +|[AutoRegressive](https://nixtla.github.io/statsforecast/src/core/models.html#autoregressive)|✅|✅|✅|✅|✅| ### Theta Family +Fit two theta lines to a deseasonalized time series, using different techniques to obtain and combine the two theta lines to produce the final forecasts. -Fit two theta lines to a deseasonalized time series, using different -techniques to obtain and combine the two theta lines to produce the -final forecasts. - -| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | -|:-----------------------------------------------------------------------------------------------------------|:--------------:|:----------------------:|:----------------------:|:---------------------------:| -| [Theta](https://nixtla.github.io/statsforecast/src/core/models.html#theta) | ✅ | ✅ | ✅ | ✅ | -| [OptimizedTheta](https://nixtla.github.io/statsforecast/src/core/models.html#optimizedtheta) | ✅ | ✅ | ✅ | ✅ | -| [DynamicTheta](https://nixtla.github.io/statsforecast/src/core/models.html#dynamictheta) | ✅ | ✅ | ✅ | ✅ | -| [DynamicOptimizedTheta](https://nixtla.github.io/statsforecast/src/core/models.html#dynamicoptimizedtheta) | ✅ | ✅ | ✅ | ✅ | +|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | +|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| +|[Theta](https://nixtla.github.io/statsforecast/src/core/models.html#theta)|✅|✅|✅|✅|✅| +|[OptimizedTheta](https://nixtla.github.io/statsforecast/src/core/models.html#optimizedtheta)|✅|✅|✅|✅|✅| +|[DynamicTheta](https://nixtla.github.io/statsforecast/src/core/models.html#dynamictheta)|✅|✅|✅|✅|✅| +|[DynamicOptimizedTheta](https://nixtla.github.io/statsforecast/src/core/models.html#dynamicoptimizedtheta)|✅|✅|✅|✅|✅| ### Multiple Seasonalities +Suited for signals with more than one clear seasonality. Useful for low-frequency data like electricity and logs. -Suited for signals with more than one clear seasonality. Useful for -low-frequency data like electricity and logs. +|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | +|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| +|[MSTL](https://nixtla.github.io/statsforecast/src/core/models.html#mstl)|✅|✅|✅|✅|✅| -| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | -|:-------------------------------------------------------------------------|:--------------:|:----------------------:|:----------------------:|:---------------------------:| -| [MSTL](https://nixtla.github.io/statsforecast/src/core/models.html#mstl) | ✅ | ✅ | ✅ | ✅ | +### GARCH and ARCH Models +Suited for modeling time series that exhibit non-constant volatility over time. The ARCH model is a particular case of GARCH. -### GARCH and ARCH Models +|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | +|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| +|[GARCH](https://nixtla.github.io/statsforecast/src/core/models.html#garch)|✅|✅|✅|✅|✅| +|[ARCH](https://nixtla.github.io/statsforecast/src/core/models.html#arch)|✅|✅|✅|✅|✅| -Suited for modeling time series that exhibit non-constant volatility -over time. The ARCH model is a particular case of GARCH. - -| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | -|:---------------------------------------------------------------------------|:--------------:|:----------------------:|:----------------------:|:---------------------------:| -| [GARCH](https://nixtla.github.io/statsforecast/src/core/models.html#garch) | ✅ | ✅ | ✅ | ✅ | -| [ARCH](https://nixtla.github.io/statsforecast/src/core/models.html#arch) | ✅ | ✅ | ✅ | ✅ | ### Baseline Models - Classical models for establishing baseline. -| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | -|:-----------------------------------------------------------------------------------------------------------|:--------------:|:----------------------:|:----------------------:|:---------------------------:| -| [HistoricAverage](https://nixtla.github.io/statsforecast/src/core/models.html#historicaverage) | ✅ | ✅ | ✅ | ✅ | -| [Naive](https://nixtla.github.io/statsforecast/src/core/models.html#naive) | ✅ | ✅ | ✅ | ✅ | -| [RandomWalkWithDrift](https://nixtla.github.io/statsforecast/src/core/models.html#randomwalkwithdrift) | ✅ | ✅ | ✅ | ✅ | -| [SeasonalNaive](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalnaive) | ✅ | ✅ | ✅ | ✅ | -| [WindowAverage](https://nixtla.github.io/statsforecast/src/core/models.html#windowaverage) | ✅ | | | | -| [SeasonalWindowAverage](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalwindowaverage) | ✅ | | | | +|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | +|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| +|[HistoricAverage](https://nixtla.github.io/statsforecast/src/core/models.html#historicaverage)|✅|✅|✅|✅|✅| +|[Naive](https://nixtla.github.io/statsforecast/src/core/models.html#naive)|✅|✅|✅|✅|✅| +|[RandomWalkWithDrift](https://nixtla.github.io/statsforecast/src/core/models.html#randomwalkwithdrift)|✅|✅|✅|✅|✅| +|[SeasonalNaive](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalnaive)|✅|✅|✅|✅|✅| +|[WindowAverage](https://nixtla.github.io/statsforecast/src/core/models.html#windowaverage)|✅||||| +|[SeasonalWindowAverage](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalwindowaverage)|✅||||| ### Exponential Smoothing +Uses a weighted average of all past observations where the weights decrease exponentially into the past. Suitable for data with clear trend and/or seasonality. Use the `SimpleExponential` family for data with no clear trend or seasonality. -Uses a weighted average of all past observations where the weights -decrease exponentially into the past. Suitable for data with clear trend -and/or seasonality. Use the `SimpleExponential` family for data with no -clear trend or seasonality. +|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | +|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| +|[SimpleExponentialSmoothing](https://nixtla.github.io/statsforecast/src/core/models.html#simpleexponentialsmoothing)|✅||||| +|[SimpleExponentialSmoothingOptimized](https://nixtla.github.io/statsforecast/src/core/models.html#simpleexponentialsmoothingoptimized)|✅||||| +|[SeasonalExponentialSmoothing](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalexponentialsmoothing)|✅||||| +|[SeasonalExponentialSmoothingOptimized](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalexponentialsmoothingoptimized)|✅||||| +|[Holt](https://nixtla.github.io/statsforecast/src/core/models.html#holt)|✅|✅|✅|✅|✅| +|[HoltWinters](https://nixtla.github.io/statsforecast/src/core/models.html#holtwinters)|✅|✅|✅|✅|✅| -| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | -|:-------------------------------------------------------------------------------------------------------------------------------------------|:--------------:|:----------------------:|:----------------------:|:---------------------------:| -| [SimpleExponentialSmoothing](https://nixtla.github.io/statsforecast/src/core/models.html#simpleexponentialsmoothing) | ✅ | | | | -| [SimpleExponentialSmoothingOptimized](https://nixtla.github.io/statsforecast/src/core/models.html#simpleexponentialsmoothingoptimized) | ✅ | | | | -| [SeasonalExponentialSmoothing](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalexponentialsmoothing) | ✅ | | | | -| [SeasonalExponentialSmoothingOptimized](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalexponentialsmoothingoptimized) | ✅ | | | | -| [Holt](https://nixtla.github.io/statsforecast/src/core/models.html#holt) | ✅ | ✅ | ✅ | ✅ | -| [HoltWinters](https://nixtla.github.io/statsforecast/src/core/models.html#holtwinters) | ✅ | ✅ | ✅ | ✅ | - -### Sparse or Inttermitent +### Sparse or Intermittent Suited for series with very few non-zero observations -| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | -|:-------------------------------------------------------------------------------------------------|:--------------:|:----------------------:|:----------------------:|:---------------------------:| -| [ADIDA](https://nixtla.github.io/statsforecast/src/core/models.html#adida) | ✅ | | | | -| [CrostonClassic](https://nixtla.github.io/statsforecast/src/core/models.html#crostonclassic) | ✅ | | | | -| [CrostonOptimized](https://nixtla.github.io/statsforecast/src/core/models.html#crostonoptimized) | ✅ | | | | -| [CrostonSBA](https://nixtla.github.io/statsforecast/src/core/models.html#crostonsba) | ✅ | | | | -| [IMAPA](https://nixtla.github.io/statsforecast/src/core/models.html#imapa) | ✅ | | | | -| [TSB](https://nixtla.github.io/statsforecast/src/core/models.html#tsb) | ✅ | | | | - -## How to contribute +|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | +|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| +|[ADIDA](https://nixtla.github.io/statsforecast/src/core/models.html#adida)|✅||||| +|[CrostonClassic](https://nixtla.github.io/statsforecast/src/core/models.html#crostonclassic)|✅||||| +|[CrostonOptimized](https://nixtla.github.io/statsforecast/src/core/models.html#crostonoptimized)|✅||||| +|[CrostonSBA](https://nixtla.github.io/statsforecast/src/core/models.html#crostonsba)|✅||||| +|[IMAPA](https://nixtla.github.io/statsforecast/src/core/models.html#imapa)|✅||||| +|[TSB](https://nixtla.github.io/statsforecast/src/core/models.html#tsb)|✅||||| -See -[CONTRIBUTING.md](https://github.com/Nixtla/statsforecast/blob/main/CONTRIBUTING.md). +## 🔨 How to contribute +See [CONTRIBUTING.md](https://github.com/Nixtla/statsforecast/blob/main/CONTRIBUTING.md). ## Citing -``` bibtex +```bibtex @misc{garza2022statsforecast, author={Federico Garza, Max Mergenthaler Canseco, Cristian Challú, Kin G. Olivares}, title = {{StatsForecast}: Lightning fast forecasting with statistical and econometric models}, @@ -242,3 +199,64 @@ See url={https://github.com/Nixtla/statsforecast} } ``` + +## Contributors ✨ + +Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)): + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
fede
fede
💻 🚧		José Morales
José Morales
💻 🚧		Sugato Ray
Sugato Ray
💻		Jeff Tackes
Jeff Tackes
🐛		darinkist
darinkist
🤔		Alec Helyar
Alec Helyar
💬		Dave Hirschfeld
Dave Hirschfeld
💬
mergenthaler
mergenthaler
💻		Kin
Kin
💻		Yasslight90
Yasslight90
🤔		asinig
asinig
🤔		Philip Gillißen
Philip Gillißen
💻		Sebastian Hagn
Sebastian Hagn
🐛 📖		Han Wang
Han Wang
💻
Ben Jeffrey
Ben Jeffrey
🐛		Beliavsky
Beliavsky
📖		Mariana Menchero García
Mariana Menchero García
💻		Nikhil Gupta
Nikhil Gupta
🐛		JD
JD
🐛		josh attenberg
josh attenberg
💻		JeroenPeterBos
JeroenPeterBos
💻
Jeroen Van Der Donckt
Jeroen Van Der Donckt
💻		Roymprog
Roymprog
📖		Nelson Cárdenas Bolaño
Nelson Cárdenas Bolaño
📖		Kyle Schmaus
Kyle Schmaus
💻		Akmal Soliev
Akmal Soliev
💻		Nick To
Nick To
💻		Kevin Kho
Kevin Kho
💻
Yiben Huang
Yiben Huang
📖		Andrew Gross
Andrew Gross
📖		taniishkaaa
taniishkaaa
📖		Manuel Calzolari
Manuel Calzolari
💻
+ + + + + + +This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! diff --git a/action_files/final-formatting.bash b/action_files/final-formatting.bash new file mode 100755 index 000000000..451850326 --- /dev/null +++ b/action_files/final-formatting.bash @@ -0,0 +1,11 @@ +#!/usr/bin/env bash + +for file in $(find _docs -type f -name "*mdx"); do + if [[ "$OSTYPE" == "darwin"* ]]; then + sed -i '' -e 's/style="float:right; font-size:smaller"/style={{ float: "right", fontSize: "smaller" }}/g' $file + sed -i '' -e 's/
//g' $file + else + sed -i -e 's/style="float:right; font-size:smaller"/style={{ float: "right", fontSize: "smaller" }}/g' $file + sed -i -e 's/
//g' $file + fi +done \ No newline at end of file diff --git a/nbs/_extensions/mintlify/_extension.yml b/nbs/_extensions/mintlify/_extension.yml new file mode 100644 index 000000000..663084de9 --- /dev/null +++ b/nbs/_extensions/mintlify/_extension.yml @@ -0,0 +1,30 @@ +title: Mintlify +author: Mintlify, Inc +organization: Mintlify +contributes: + project: + project: + type: default + detect: + - ["mint.json"] + render: + - "**/*.md" + - "**/*.qmd" + - "**/*.ipynb" + format: mintlify-md + formats: + md: + # Although we use a custom writer, we still need the variants here the lua filters to render correctly. + # Ideally, we would forward the variants to the custom writer. + variant: gfm+pipe_tables+tex_math_dollars+raw_html+all_symbols_escapable+backtick_code_blocks+space_in_atx_header+intraword_underscores+lists_without_preceding_blankline+shortcut_reference_links + writer: mintlify_writer.lua + output-ext: mdx + inline-includes: true + preserve-yaml: true + wrap: none + fig-format: retina + fig-width: 8 + fig-height: 5 + html-math-method: webtex + filters: + - mintlify.lua diff --git a/nbs/_extensions/mintlify/mintlify.lua b/nbs/_extensions/mintlify/mintlify.lua new file mode 100644 index 000000000..a1e464013 --- /dev/null +++ b/nbs/_extensions/mintlify/mintlify.lua @@ -0,0 +1,71 @@ +-- mintlify.lua + +local kQuartoRawHtml = "quartoRawHtml" +local rawHtmlVars = pandoc.List() + +function Pandoc(doc) + -- insert exports at the top if we have them + if #rawHtmlVars > 0 then + local exports = ("export const %s =\n[%s];"):format(kQuartoRawHtml, + table.concat( + rawHtmlVars:map(function(var) return '`'.. var .. '`' end), + "," + ) + ) + doc.blocks:insert(1, pandoc.RawBlock("markdown", exports .. "\n")) + end + + return doc +end + + +-- strip image attributes (which may result from +-- fig-format: retina) as they will result in an +-- img tag which won't hit the asset pipeline +function Image(el) + el.attr = pandoc.Attr() + return el +end + +-- header attributes only support id +function Header(el) + el.attr = pandoc.Attr(el.identifier) + return el +end + +Block = function(node) + if node.text ~= nil and string.find(node.text, "") + :format(kQuartoRawHtml, #rawHtmlVars-1) .. "\n" + return pandoc.RawBlock("html", html) + end +end diff --git a/nbs/_extensions/mintlify/mintlify_renderers.lua b/nbs/_extensions/mintlify/mintlify_renderers.lua new file mode 100644 index 000000000..2597fbbf1 --- /dev/null +++ b/nbs/_extensions/mintlify/mintlify_renderers.lua @@ -0,0 +1,3 @@ +local codeBlock = require('mintlify_utils').codeBlock + +return {} -- return an empty table as a hack to pretend we're a shortcode handler for now diff --git a/nbs/_extensions/mintlify/mintlify_utils.lua b/nbs/_extensions/mintlify/mintlify_utils.lua new file mode 100644 index 000000000..42e8776c7 --- /dev/null +++ b/nbs/_extensions/mintlify/mintlify_utils.lua @@ -0,0 +1,47 @@ +-- local scriptCount = 0 +-- local printItem = 5 + +function codeBlock(el, filename) + local lang = el.attr.classes[1] + -- scriptCount = scriptCount + 1 + -- if printItem == scriptCount then + -- quarto.utils.dump(el) + -- end + -- quarto.log.output('---Code block---') + -- quarto.utils.dump(el.output) + -- quarto.utils.dump({}) + -- quarto.log.output('------') + local title = filename or el.attr.attributes["filename"] or el.attr.attributes["title"] + local showLineNumbers = el.attr.classes:includes('number-lines') + if lang or title or showLineNumbers then + if not lang then + lang = 'text' + end + local code = "\n```" .. lang + if showLineNumbers then + code = code .. " showLineNumbers" + end + if title then + code = code .. " title=\"" .. title .. "\"" + end + code = code .. "\n" .. el.text .. "\n```\n" + + -- quarto.log.output('------') + -- quarto.log.output(code) + -- quarto.log.output('------') + -- docusaures code block attributes don't conform to any syntax + -- that pandoc natively understands, so return the CodeBlock as + -- "raw" markdown (so it bypasses pandoc processing entirely) + return pandoc.RawBlock("markdown", code) + + elseif #el.attr.classes == 0 then + el.attr.classes:insert('text') + return el + end + + return nil +end + +return { + codeBlock = codeBlock +} \ No newline at end of file diff --git a/nbs/_extensions/mintlify/mintlify_writer.lua b/nbs/_extensions/mintlify/mintlify_writer.lua new file mode 100644 index 000000000..9fa2d7110 --- /dev/null +++ b/nbs/_extensions/mintlify/mintlify_writer.lua @@ -0,0 +1,133 @@ +local codeBlock = require('mintlify_utils').codeBlock + +local reactPreamble = pandoc.List() + +function capitalizeFirstLetter(str) + return (str:gsub("^%l", string.upper)) +end + +function castToMintlifyCallout(str) + if str == "caution" or str == "danger" then + return "Warning" + else + return capitalizeFirstLetter(str) + end +end + +local function addPreamble(preamble) + if not reactPreamble:includes(preamble) then + reactPreamble:insert(preamble) + end +end + +local function jsx(content) + return pandoc.RawBlock("markdown", content) +end + +local function tabset(node, filter) + -- note groupId + local groupId = "" + local group = node.attr.attributes["group"] + if group then + groupId = ([[ groupId="%s"]]):format(group) + end + + -- create tabs + local tabs = pandoc.Div({}) + tabs.content:insert(jsx("")) + + -- iterate through content + for i = 1, #node.tabs do + local content = node.tabs[i].content + local title = node.tabs[i].title + + tabs.content:insert(jsx(([[]]):format(pandoc.utils.stringify(title)))) + local result = quarto._quarto.ast.walk(content, filter) + if type(result) == "table" then + tabs.content:extend(result) + else + tabs.content:insert(result) + end + tabs.content:insert(jsx("")) + end + + -- end tab and tabset + tabs.content:insert(jsx("")) + + -- ensure we have required deps + addPreamble("import Tabs from '@theme/Tabs';") + addPreamble("import TabItem from '@theme/TabItem';") + + return tabs +end + +function Writer(doc, opts) + local filter + filter = { + CodeBlock = codeBlock, + + DecoratedCodeBlock = function(node) + local el = node.code_block + return codeBlock(el, node.filename) + end, + + Tabset = function(node) + return tabset(node, filter) + end, + + RawBlock = function (rawBlock) + -- We just "pass-through" raw blocks of type "confluence" + if(rawBlock.format == 'plotly') then + quarto.utils.dump("Plotly in filter") + return pandoc.RawBlock('html', rawBlock.text) + end + + -- Raw blocks inclding arbirtary HTML like JavaScript are not supported in CSF + return "" + end, + + Callout = function(node) + local admonition = pandoc.Div({}) + local mintlifyCallout = castToMintlifyCallout(node.type) + admonition.content:insert(jsx("<" .. mintlifyCallout .. ">")) + if node.title then + admonition.content:insert(pandoc.Header(3, node.title)) + end + local content = node.content + if type(content) == "table" then + admonition.content:extend(content) + else + admonition.content:insert(content) + end + admonition.content:insert(jsx("")) + return admonition + end + } + + doc = quarto._quarto.ast.walk(doc, filter) + + -- insert react preamble if we have it + if #reactPreamble > 0 then + local preamble = table.concat(reactPreamble, "\n") + doc.blocks:insert(1, pandoc.RawBlock("markdown", preamble .. "\n")) + end + + local extensions = { + yaml_metadata_block = true, + pipe_tables = true, + footnotes = true, + tex_math_dollars = true, + raw_html = true, + all_symbols_escapable = true, + backtick_code_blocks = true, + space_in_atx_header = true, + intraword_underscores = true, + lists_without_preceding_blankline = true, + shortcut_reference_links = true, + } + + return pandoc.write(doc, { + format = 'markdown_strict', + extensions = extensions + }, opts) +end diff --git a/nbs/_quarto.yml b/nbs/_quarto.yml index 60097036f..37fb3652a 100644 --- a/nbs/_quarto.yml +++ b/nbs/_quarto.yml @@ -1,59 +1,8 @@ project: - type: website + type: mintlify format: - html: - theme: cosmo - fontsize: 1em - linestretch: 1.7 - css: styles.css - toc: true + mintlify-md: + code-fold: true -website: - twitter-card: - image: "https://farm6.staticflickr.com/5510/14338202952_93595258ff_z.jpg" - site: "@Nixtlainc" - open-graph: - image: "https://github.com/Nixtla/styles/blob/2abf51612584169874c90cd7c4d347e3917eaf73/images/Banner%20Github.png" - google-analytics: "G-NXJNCVR18L" - repo-actions: [issue] - favicon: favicon_png.png - navbar: - background: primary - search: true - collapse-below: lg - left: - - text: "Get Started" - href: docs/getting-started/getting_started_short.html - - text: "NixtlaVerse" - menu: - - text: "StatsForecast ⚡️" - href: https://github.com/nixtla/statsforecast - - text: "MLForecast 🤖" - href: https://github.com/nixtla/mlforecast - - text: "NeuralForecast 🧠" - href: https://github.com/nixtla/neuralforecast - - text: "HierarchicalForecast 👑" - href: https://github.com/nixtla/hierarchicalforecast - - - text: "Help" - menu: - - text: "Report an Issue" - icon: bug - href: https://github.com/nixtla/statsforecast/issues/new/choose - - text: "Join our Slack" - icon: chat-right-text - href: https://join.slack.com/t/nixtlaworkspace/shared_invite/zt-135dssye9-fWTzMpv2WBthq8NK0Yvu6A - right: - - icon: github - href: "https://github.com/nixtla/statsforecast" - - icon: twitter - href: https://twitter.com/nixtlainc - aria-label: Nixtla Twitter - - sidebar: - style: floating - body-footer: | - Give us a ⭐ on [Github](https://github.com/nixtla/statsforecast) - -metadata-files: [nbdev.yml, sidebar.yml] +metadata-files: [nbdev.yml] diff --git a/nbs/custom.yml b/nbs/custom.yml deleted file mode 100644 index 1b2c854c2..000000000 --- a/nbs/custom.yml +++ /dev/null @@ -1,22 +0,0 @@ -website: - logo: https://github.com/Nixtla/styles/blob/b9ea432cfa2dae20fc84d8634cae6db902f9ca3f/images/Nixtla_Blanco.png - reader-mode: false - navbar: - collapse-below: lg - left: - - text: "Get Started" - href: examples/Getting_Started_with_Auto_Arima_and_ETS.ipynb - - text: "Experiments" - href: https://github.com/Nixtla/statsforecast/tree/main/experiments - - text: "Help" - menu: - - text: "Report an Issue" - icon: bug - href: https://github.com/nixtla/statsforecast/issues - - text: "Slack Nixtla" - icon: 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border-radius: 1rem 0 0 0; -} - -thead tr:first-child th:last-child { - border-radius: 0 1rem 0 0; -} - -th, -td { - padding: 0.5rem 1rem !important; - white-space: nowrap !important; -} - -td a, -td a code { - white-space: nowrap !important; -} - -tbody { - border-color: transparent !important; - border-top: none !important; -} - -tbody tr:last-child td:first-child { - border-radius: 0 0 0 1rem; -} - -tr.even, -tr.odd { - line-height: 2rem; -} - -tr:hover { - background-color: rgba(17, 24, 39, 0.05); -} - -td:first-child, -td:last-child { - padding: 0.25rem 1rem !important; -} - -.dropdown-menu.show { - background: white; - border: none; - border-radius: 0.5rem; - box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1); - padding-top: 0.5rem !important; - padding-bottom: 0.25rem !important; -} - -.dropdown-menu li { - padding: 0.25rem 1rem !important; -} - -.dropdown-menu li:hover { - background-color: #e9ecef; -} - -.js-plotly-plot .plotly { - border: none !important; -} - -.svg-container { - border: none !important; -} - -.svg-container > svg { - border-radius: 2rem; -} - -.plotly-graph-div { - border-radius: 5rem; -} - -@media (max-width: 991.98px) { - #quarto-sidebar-glass.show { - z-index: 10001; - } - - #quarto-sidebar { - top: 0 !important; - z-index: 10002 !important; - } - - #quarto-sidebar .sidebar-menu-container { - min-width: unset; - width: calc(100% - 32px); - } - - #quarto-sidebar.show { - max-width: calc(100vw - 32px); - width: 320px !important; - } -} diff --git a/preview-mintlify-docs.bash b/preview-mintlify-docs.bash new file mode 100644 index 000000000..fdde934c2 --- /dev/null +++ b/preview-mintlify-docs.bash @@ -0,0 +1,27 @@ +#!/usr/bin/env bash + +echo "Running nbdev_docs..." +nbdev_docs +echo "nbdev_docs is done" + +echo "Running final-formatting.bash..." +chmod +x final-formatting.bash +./final-formatting.bash +echo "final-formatting.bash is done" + +echo "Moving necessary assets..." +cp nbs/mint.json _docs/mint.json +cp nbs/imgs/logo/dark.png _docs/dark.png +cp nbs/imgs/logo/light.png _docs/light.png +cp nbs/favicon.svg _docs/favicon.svg +mkdir _docs/docs/contribute +cp _proc/docs/contribute/contribute.md _docs/docs/contribute/contribute.mdx +cp _proc/docs/contribute/docs.md _docs/docs/contribute/docs.mdx +cp _proc/docs/contribute/issue-labels.md _docs/docs/contribute/issue-labels.mdx +cp _proc/docs/contribute/issues.md _docs/docs/contribute/issues.mdx +cp _proc/docs/contribute/step-by-step.md _docs/docs/contribute/step-by-step.mdx +cp _proc/docs/contribute/techstack.md _docs/docs/contribute/techstack.mdx +echo "Done moving necessary assets" + +cd ./_docs +mintlify dev \ No newline at end of file diff --git a/settings.ini b/settings.ini index ab5f35046..7361c3603 100644 --- a/settings.ini +++ b/settings.ini @@ -15,7 +15,7 @@ language = English custom_sidebar = True license = apache2 status = 2 -requirements = numba>=0.55.0 numpy>=1.21.6 pandas>=1.3.5 polars scipy>=1.7.3 statsmodels>=0.13.2 tqdm fugue>=0.8.1 utilsforecast>=0.0.5 +requirements = numba>=0.55.0 numpy>=1.21.6 pandas>=1.3.5 polars scipy>=1.7.3 statsmodels>=0.13.2 tqdm fugue>=0.8.1 utilsforecast>=0.0.5 nbdev_plotly ray_requirements = fugue[ray]>=0.8.1 protobuf>=3.15.3,<4.0.0 dask_requirements = fugue[dask]>=0.8.1 spark_requirements = fugue[spark]>=0.8.1 @@ -32,4 +32,5 @@ title = %(lib_name)s black_formatting = True jupyter_hooks = True clean_ids = True -readme_nb = index.ipynb \ No newline at end of file +readme_nb = index.ipynb +procs = nbdev_plotly.plotly:PlotlyProc From 5a7418d3deb681df35b03b11092963595474e35a Mon Sep 17 00:00:00 2001 From: Hahnbee Lee <55263191+hahnbeelee@users.noreply.github.com> Date: Tue, 10 Oct 2023 22:22:04 -0700 Subject: [PATCH 02/22] delete contribute --- preview-mintlify-docs.bash | 7 ------- 1 file changed, 7 deletions(-) diff --git a/preview-mintlify-docs.bash b/preview-mintlify-docs.bash index fdde934c2..27aa8468d 100644 --- a/preview-mintlify-docs.bash +++ b/preview-mintlify-docs.bash @@ -14,13 +14,6 @@ cp nbs/mint.json _docs/mint.json cp nbs/imgs/logo/dark.png _docs/dark.png cp nbs/imgs/logo/light.png _docs/light.png cp nbs/favicon.svg _docs/favicon.svg -mkdir _docs/docs/contribute -cp _proc/docs/contribute/contribute.md _docs/docs/contribute/contribute.mdx -cp _proc/docs/contribute/docs.md _docs/docs/contribute/docs.mdx -cp _proc/docs/contribute/issue-labels.md _docs/docs/contribute/issue-labels.mdx -cp _proc/docs/contribute/issues.md _docs/docs/contribute/issues.mdx -cp _proc/docs/contribute/step-by-step.md _docs/docs/contribute/step-by-step.mdx -cp _proc/docs/contribute/techstack.md _docs/docs/contribute/techstack.mdx echo "Done moving necessary assets" cd ./_docs From 20bbcd0c678dd2164a58989be0b62fd847c78fca Mon Sep 17 00:00:00 2001 From: Dave Parr <8363743+DaveParr@users.noreply.github.com> Date: Thu, 12 Oct 2023 00:09:29 +0100 Subject: [PATCH 03/22] Correct broken url to docs (#660) --- nbs/src/core/distributed.fugue.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/nbs/src/core/distributed.fugue.ipynb b/nbs/src/core/distributed.fugue.ipynb index 0f1a6f892..992619618 100644 --- a/nbs/src/core/distributed.fugue.ipynb +++ b/nbs/src/core/distributed.fugue.ipynb @@ -443,7 +443,7 @@ "source": [ "### Distributed Forecast\n", "\n", - "For extremely fast distributed predictions we use FugueBackend as backend that operates like the original [StatsForecast.forecast](https://nixtla.github.io/statsforecast/core.html#statsforecast.forecast) method.\n", + "For extremely fast distributed predictions we use FugueBackend as backend that operates like the original [StatsForecast.forecast](https://nixtla.github.io/statsforecast/src/core/core.html#statsforecast.forecast) method.\n", "\n", "It receives as input a pandas.DataFrame with columns [`unique_id`,`ds`,`y`] and exogenous, where the `ds` (datestamp) column should be of a format expected by Pandas. The `y` column must be numeric, and represents the measurement we wish to forecast. And the `unique_id` uniquely identifies the series in the panel data." ] @@ -562,7 +562,7 @@ "source": [ "### Distributed Cross-Validation\n", "\n", - "For extremely fast distributed temporcal cross-validation we use `cross_validation` method that operates like the original [StatsForecast.cross_validation](https://nixtla.github.io/statsforecast/core.html#statsforecast) method." + "For extremely fast distributed temporcal cross-validation we use `cross_validation` method that operates like the original [StatsForecast.cross_validation](https://nixtla.github.io/statsforecast/src/core/core.html#statsforecast.cross_validation) method." ] }, { From 06fcb55463c06dda83db9080758c57402c5f4649 Mon Sep 17 00:00:00 2001 From: Hahnbee Lee <55263191+hahnbeelee@users.noreply.github.com> Date: Wed, 11 Oct 2023 18:01:18 -0700 Subject: [PATCH 04/22] final final formatting --- .github/workflows/build-docs.yaml | 2 +- ...atting.bash => docs-final-formatting.bash} | 4 ++- action_files/docs_replace_imgs.py | 30 +++++++++++++++++++ preview-mintlify-docs.bash | 8 ++--- 4 files changed, 38 insertions(+), 6 deletions(-) rename action_files/{final-formatting.bash => docs-final-formatting.bash} (89%) create mode 100644 action_files/docs_replace_imgs.py diff --git a/.github/workflows/build-docs.yaml b/.github/workflows/build-docs.yaml index d50ea4104..139ffd77b 100644 --- a/.github/workflows/build-docs.yaml +++ b/.github/workflows/build-docs.yaml @@ -22,7 +22,7 @@ jobs: nbdev_docs - name: Apply final formats shell: bash - run: bash ./action_files/final-formatting.bash + run: bash ./action_files/docs-final-formatting.bash - name: Copy over necessary assets run: | cp nbs/mint.json _docs/mint.json diff --git a/action_files/final-formatting.bash b/action_files/docs-final-formatting.bash similarity index 89% rename from action_files/final-formatting.bash rename to action_files/docs-final-formatting.bash index 451850326..07f0badb1 100755 --- a/action_files/final-formatting.bash +++ b/action_files/docs-final-formatting.bash @@ -8,4 +8,6 @@ for file in $(find _docs -type f -name "*mdx"); do sed -i -e 's/style="float:right; font-size:smaller"/style={{ float: "right", fontSize: "smaller" }}/g' $file sed -i -e 's/
    //g' $file fi -done \ No newline at end of file +done + +python3 "$(dirname "$0")/docs_replace_imgs.py" \ No newline at end of file diff --git a/action_files/docs_replace_imgs.py b/action_files/docs_replace_imgs.py new file mode 100644 index 000000000..7ce4b2f72 --- /dev/null +++ b/action_files/docs_replace_imgs.py @@ -0,0 +1,30 @@ +import os +from pathlib import Path +import re + +def main(): + dir_path = os.path.dirname(os.path.realpath(__file__)) + docs_path = os.path.abspath(os.path.join(dir_path, '../_docs')) + docs_path_obj = Path(docs_path) + paths_in_dir = docs_path_obj.glob('**/*.mdx') + pattern = r'!\[\]\(((?!https:\/\/).+?)\)' + + for file_path in paths_in_dir: + if file_path.is_file(): + folder_arr = file_path.parts[len(docs_path_obj.parts):-1] + content = file_path.read_text() + content = re.sub(pattern, url_replace(folder_arr), content) + file_path.write_text(content) + +def url_replace(folder_arr): + def url_replace_helper(match): + curr_url = Path(match[1]) + new_url = str(Path(*folder_arr).joinpath(curr_url)) + if not new_url.startswith('/'): + new_url = '/' + new_url + print('![](' + str(new_url) + ')') + return '![](' + str(new_url) + ')' + return url_replace_helper + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/preview-mintlify-docs.bash b/preview-mintlify-docs.bash index 27aa8468d..4c5e1c47e 100644 --- a/preview-mintlify-docs.bash +++ b/preview-mintlify-docs.bash @@ -4,10 +4,10 @@ echo "Running nbdev_docs..." nbdev_docs echo "nbdev_docs is done" -echo "Running final-formatting.bash..." -chmod +x final-formatting.bash -./final-formatting.bash -echo "final-formatting.bash is done" +echo "Running docs-final-formatting.bash..." +chmod +x docs-final-formatting.bash +./action_files/docs-final-formatting.bash +echo "docs-final-formatting.bash is done" echo "Moving necessary assets..." cp nbs/mint.json _docs/mint.json From bfd0c52af97e6b3de37b3e6bf869413caacae986 Mon Sep 17 00:00:00 2001 From: Hahnbee Lee <55263191+hahnbeelee@users.noreply.github.com> Date: Wed, 11 Oct 2023 18:13:00 -0700 Subject: [PATCH 05/22] delete all the other workflows lol --- .github/workflows/ci.yaml | 118 --------------------------- .github/workflows/deploy.yaml | 9 -- .github/workflows/lint.yaml | 28 ------- .github/workflows/no-response.yaml | 21 ----- .github/workflows/python-publish.yml | 25 ------ 5 files changed, 201 deletions(-) delete mode 100644 .github/workflows/ci.yaml delete mode 100644 .github/workflows/deploy.yaml delete mode 100644 .github/workflows/lint.yaml delete mode 100644 .github/workflows/no-response.yaml delete mode 100644 .github/workflows/python-publish.yml diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml deleted file mode 100644 index 59254ab31..000000000 --- a/.github/workflows/ci.yaml +++ /dev/null @@ -1,118 +0,0 @@ -name: CI - -on: - push: - branches: [main] - pull_request: - branches: [main] - -defaults: - run: - shell: bash -l {0} - -concurrency: - group: ${{ github.workflow }}-${{ github.ref }} - cancel-in-progress: true - -env: - NIXTLA_NUMBA_CACHE: '1' - -jobs: - nb-sync: - runs-on: ubuntu-latest - steps: - - name: Clone repo - uses: actions/checkout@v2 - - - name: Set up python - uses: actions/setup-python@v2 - - - name: Install nbdev - run: pip install nbdev - - - name: Check if all notebooks are cleaned - run: | - echo "Check we are starting with clean git checkout" - if [ -n "$(git status -uno -s)" ]; then echo "git status is not clean"; false; fi - echo "Trying to strip out notebooks" - ./action_files/clean_nbs - echo "Check that strip out was unnecessary" - git status -s # display the status to see which nbs need cleaning up - if [ -n "$(git status -uno -s)" ]; then echo -e "!!! Detected unstripped out notebooks\n!!!Remember to run nbdev_install_git_hooks and running ./action_files/clean_nbs before committing"; false; fi - - run-local-tests: - runs-on: windows-latest - strategy: - fail-fast: false - matrix: - python-version: [3.7, 3.8, 3.9, '3.10', '3.11'] - steps: - - name: Clone repo - uses: actions/checkout@v3 - - - name: Set up environment - uses: mamba-org/setup-micromamba@v1 - with: - environment-file: dev/local_environment.yml - create-args: python=${{ matrix.python-version }} - cache-environment: true - - - name: Install pip requirements - run: pip install ./ - - - name: Run local tests - run: nbdev_test --skip_file_re '(models|distributed|ets).*.ipynb' --pause 1.0 - - run-tests: - runs-on: ${{ matrix.os }} - timeout-minutes: 60 - strategy: - fail-fast: false - matrix: - os: [ubuntu-latest, macos-latest] - python-version: [3.7, 3.8, 3.9, '3.10', '3.11'] - steps: - - name: Clone repo - uses: actions/checkout@v3 - - - name: Set up environment - uses: mamba-org/setup-micromamba@v1 - with: - environment-file: dev/environment.yml - create-args: python=${{ matrix.python-version }} - cache-environment: true - - - name: Install pip requirements - run: pip install ./ - - - name: Run tests - run: nbdev_test --skip_file_re '(distributed).*.ipynb' --pause 1.0 - - - name: Run integration tests - run: | - pip install ".[dev]" pytest - pytest --durations=0 action_files - - test-m3-performance: - runs-on: ubuntu-latest - strategy: - fail-fast: false - steps: - - name: Clone repo - uses: actions/checkout@v3 - - - name: Set up environment - uses: mamba-org/setup-micromamba@v1 - with: - environment-file: dev/environment.yml - create-args: python=3.10 - cache-environment: true - - - name: Install library and extra deps - run: pip install ".[dev]" fire - - - name: Run M3 experiment - run: | - python -m src.experiment - python -m src.evaluation --test - working-directory: ./experiments/m3 diff --git a/.github/workflows/deploy.yaml b/.github/workflows/deploy.yaml deleted file mode 100644 index 8d077a768..000000000 --- a/.github/workflows/deploy.yaml +++ /dev/null @@ -1,9 +0,0 @@ -name: Deploy to GitHub Pages -on: - push: - branches: ["main"] - workflow_dispatch: -jobs: - deploy: - runs-on: ubuntu-latest - steps: [uses: fastai/workflows/quarto-ghp@master] diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml deleted file mode 100644 index 047073aa0..000000000 --- a/.github/workflows/lint.yaml +++ /dev/null @@ -1,28 +0,0 @@ -name: Lint - -on: - push: - branches: [main] - pull_request: - branches: [main] - -jobs: - lint: - runs-on: ubuntu-latest - steps: - - name: Clone repo - uses: actions/checkout@v2 - - - name: Set up python - uses: actions/setup-python@v2 - with: - python-version: 3.8 - - - name: Install linters - run: pip install mypy types-setuptools flake8 - - - name: mypy - run: mypy statsforecast/ - - - name: flake8 - run: flake8 --select=F statsforecast/ diff --git a/.github/workflows/no-response.yaml b/.github/workflows/no-response.yaml deleted file mode 100644 index a2b26e127..000000000 --- a/.github/workflows/no-response.yaml +++ /dev/null @@ -1,21 +0,0 @@ -name: No Response Bot - -on: - issue_comment: - types: [created] - schedule: - - cron: '0 4 * * *' - -jobs: - noResponse: - runs-on: ubuntu-latest - steps: - - uses: lee-dohm/no-response@v0.5.0 - with: - closeComment: > - This issue has been automatically closed because it has been awaiting a response for too long. - When you have time to to work with the maintainers to resolve this issue, please post a new comment and it will be re-opened. - If the issue has been locked for editing by the time you return to it, please open a new issue and reference this one. - daysUntilClose: 30 - responseRequiredLabel: awaiting response - token: ${{ github.token }} diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml deleted file mode 100644 index 58b53adf6..000000000 --- a/.github/workflows/python-publish.yml +++ /dev/null @@ -1,25 +0,0 @@ -name: Upload Python Package - -on: - release: - types: [published] - -jobs: - deploy: - - runs-on: ubuntu-latest - permissions: - id-token: write - - steps: - - uses: actions/checkout@v3 - - name: Set up Python - uses: actions/setup-python@v3 - with: - python-version: '3.10' - - name: Install dependencies - run: python -m pip install build wheel - - name: Build package - run: python -m build -sw - - name: Publish package - uses: pypa/gh-action-pypi-publish@release/v1 From 7037bc6241fd41c5071e41cbab47d883f65b7c13 Mon Sep 17 00:00:00 2001 From: Akmal Soliev Date: Fri, 13 Oct 2023 22:36:52 +0200 Subject: [PATCH 06/22] [CHORE] Updating `requirements.txt` (#666) --- dev/requirements.txt | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/dev/requirements.txt b/dev/requirements.txt index 4f4a21c28..4109358c4 100644 --- a/dev/requirements.txt +++ b/dev/requirements.txt @@ -1,17 +1,21 @@ -holidays<0.21 +holidays<0.21 jupyterlab matplotlib numba>=0.55.0 numpy>=1.21.6 pandas>=1.3.5 +pyspark>=3.3 pip prophet +pyarrow scipy>=1.7.3 statsmodels>=0.13.2 tabulate plotly +utilsforecast>=0.0.5 +fugue[dask,ray] nbdev -tqdm plotly-resampler polars -supersmoother \ No newline at end of file +supersmoother +tqdm From 041a697cd4829a1ad8e4d717f6a7193b721e29fd Mon Sep 17 00:00:00 2001 From: Kin Date: Fri, 13 Oct 2023 19:20:06 -0400 Subject: [PATCH 07/22] [Feat] Robustified crossvalidation (#668) --- .gitignore | 1 + nbs/src/core/core.ipynb | 70 +++++++++++++++++--- nbs/src/core/models.ipynb | 123 ++++++++++++++++++++++++++++++++++ statsforecast/_modidx.py | 4 ++ statsforecast/core.py | 50 +++++++++----- statsforecast/models.py | 135 +++++++++++++++++++++++++++++--------- 6 files changed, 327 insertions(+), 56 deletions(-) diff --git a/.gitignore b/.gitignore index adba6ab4f..06d74c19e 100644 --- a/.gitignore +++ b/.gitignore @@ -9,6 +9,7 @@ dist .vscode .idea *.gif +*.icloud *.csv */data/* *.parquet diff --git a/nbs/src/core/core.ipynb b/nbs/src/core/core.ipynb index 91d28c4f9..5c8308d38 100644 --- a/nbs/src/core/core.ipynb +++ b/nbs/src/core/core.ipynb @@ -166,14 +166,21 @@ " return False\n", " return np.allclose(self.data, other.data) and np.array_equal(self.indptr, other.indptr)\n", " \n", - " def fit(self, models):\n", + " def fit(self, models, fallback_model=None):\n", " fm = np.full((self.n_groups, len(models)), np.nan, dtype=object)\n", " for i, grp in enumerate(self):\n", " y = grp[:, 0] if grp.ndim == 2 else grp\n", " X = grp[:, 1:] if (grp.ndim == 2 and grp.shape[1] > 1) else None\n", " for i_model, model in enumerate(models):\n", - " new_model = model.new()\n", - " fm[i, i_model] = new_model.fit(y=y, X=X)\n", + " try:\n", + " new_model = model.new()\n", + " fm[i, i_model] = new_model.fit(y=y, X=X)\n", + " except Exception as error:\n", + " if fallback_model is not None:\n", + " new_fallback_model = fallback_model.new()\n", + " fm[i, i_model] = new_fallback_model.fit(y=y, X=X)\n", + " else:\n", + " raise error\n", " return fm\n", " \n", " def _get_cols(self, models, attr, h, X, level=tuple()):\n", @@ -354,9 +361,13 @@ " else:\n", " if i_window == 0:\n", " # for the first window we have to fit each model\n", - " model = model.fit(y=y_train, X=X_train)\n", - " if fallback_model is not None:\n", - " fallback_model = fallback_model.fit(y=y_train, X=X_train)\n", + " try:\n", + " model = model.fit(y=y_train, X=X_train)\n", + " except Exception as error:\n", + " if fallback_model is not None:\n", + " fallback_model = fallback_model.fit(y=y_train, X=X_train)\n", + " else:\n", + " raise error\n", " try:\n", " res_i = model.forward(h=h, y=y_train, X=X_train, \n", " X_future=X_future, fitted=fitted, **kwargs)\n", @@ -682,6 +693,47 @@ "np.testing.assert_array_equal(fcst_cv_f['fitted']['values'], fcst_cv_naive['fitted']['values'])" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "e05f8b34", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# test fallback model under failed fit for cross validation\n", + "class FailedFit:\n", + "\n", + " def __init__(self):\n", + " pass\n", + "\n", + " def forecast(self):\n", + " pass\n", + "\n", + " def fit(self, y, X):\n", + " raise Exception('Failed fit')\n", + "\n", + " def __repr__(self):\n", + " return \"FailedFit\"\n", + "\n", + "fcst_cv_f = ga.cross_validation(\n", + " models=[FailedFit()], \n", + " fallback_model=Naive(), h=2, \n", + " test_size=5,\n", + " refit=False,\n", + " fitted=True,\n", + ")\n", + "fcst_cv_naive = ga.cross_validation(\n", + " models=[Naive()], \n", + " h=2, \n", + " test_size=5,\n", + " refit=False,\n", + " fitted=True,\n", + ")\n", + "test_eq(fcst_cv_f['forecasts'], fcst_cv_naive['forecasts'])\n", + "np.testing.assert_array_equal(fcst_cv_f['fitted']['values'], fcst_cv_naive['fitted']['values'])" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1333,7 +1385,7 @@ " self._set_prediction_intervals(prediction_intervals=prediction_intervals)\n", " self._prepare_fit(df, sort_df)\n", " if self.n_jobs == 1:\n", - " self.fitted_ = self.ga.fit(models=self.models)\n", + " self.fitted_ = self.ga.fit(models=self.models, fallback_model=self.fallback_model)\n", " else:\n", " self.fitted_ = self._fit_parallel()\n", " return self\n", @@ -1725,10 +1777,10 @@ " with Pool(self.n_jobs, **pool_kwargs) as executor:\n", " futures = []\n", " for ga in gas:\n", - " future = executor.apply_async(ga.fit, (self.models,))\n", + " future = executor.apply_async(ga.fit, (self.models, self.fallback_model))\n", " futures.append(future)\n", " fm = np.vstack([f.get() for f in futures])\n", - " return fm\n", + " return fm \n", " \n", " def _get_gas_Xs(self, X):\n", " gas = self.ga.split(self.n_jobs)\n", diff --git a/nbs/src/core/models.ipynb b/nbs/src/core/models.ipynb index 5f86d9215..5b2ad2dcf 100644 --- a/nbs/src/core/models.ipynb +++ b/nbs/src/core/models.ipynb @@ -5412,6 +5412,40 @@ " residuals = y - out[\"fitted\"]\n", " sigma = _calculate_sigma(residuals, len(residuals) - 1)\n", " res = _add_fitted_pi(res=res, se=sigma, level=level)\n", + " return res\n", + " \n", + " def forward(\n", + " self, \n", + " y: np.ndarray,\n", + " h: int,\n", + " X: Optional[np.ndarray] = None,\n", + " X_future: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", + " fitted: bool = False,\n", + " ):\n", + " \"\"\" Apply fitted model to an new/updated series.\n", + "\n", + " Parameters\n", + " ----------\n", + " y : numpy.array \n", + " Clean time series of shape (n,). \n", + " h: int\n", + " Forecast horizon.\n", + " X : array-like\n", + " Optional insample exogenous of shape (t, n_x). \n", + " X_future : array-like\n", + " Optional exogenous of shape (h, n_x). \n", + " level : List[float]\n", + " Confidence levels (0-100) for prediction intervals.\n", + " fitted : bool\n", + " Whether or not to return insample predictions.\n", + "\n", + " Returns\n", + " -------\n", + " forecasts : dict\n", + " Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.\n", + " \"\"\"\n", + " res = self.forecast(y=y, h=h, X=X, X_future=X_future, level=level, fitted=fitted)\n", " return res" ] }, @@ -5472,6 +5506,23 @@ "_plot_insample_pi(fcst_naive)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# Unit test forward:=forecast\n", + "naive = Naive()\n", + "fcst_naive = naive.forward(ap,12,None,None,(80,95), True)\n", + "np.testing.assert_almost_equal(\n", + " fcst_naive['lo-80'],\n", + " np.array([388.7984, 370.9037, 357.1726, 345.5967, 335.3982, 326.1781, 317.6992, 309.8073, 302.3951, 295.3845, 288.7164, 282.3452]),\n", + " decimal=4\n", + ") # this is almost equal since Hyndman's forecasts are rounded up to 4 decimals" + ] + }, { "cell_type": "code", "execution_count": null, @@ -10880,6 +10931,40 @@ " if fitted:\n", " res[f'fitted-lo-{lv}'] = fitted_vals\n", " res[f'fitted-hi-{lv}'] = fitted_vals\n", + " return res\n", + " \n", + " def forward(\n", + " self,\n", + " y: np.ndarray,\n", + " h: int,\n", + " X: Optional[np.ndarray] = None,\n", + " X_future: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", + " fitted: bool = False,\n", + " ):\n", + " \"\"\"Apply Constant model predictions to a new/updated time series.\n", + "\n", + " Parameters\n", + " ----------\n", + " y : numpy.array \n", + " Clean time series of shape (n, ). \n", + " h : int \n", + " Forecast horizon.\n", + " X : array-like \n", + " Optional insample exogenous of shape (t, n_x). \n", + " X_future : array-like \n", + " Optional exogenous of shape (h, n_x). \n", + " level : List[float]\n", + " Confidence levels for prediction intervals.\n", + " fitted : bool \n", + " Whether or not returns insample predictions.\n", + "\n", + " Returns\n", + " -------\n", + " forecasts : dict \n", + " Dictionary with entries `constant` for point predictions and `level_*` for probabilistic predictions.\n", + " \"\"\"\n", + " res = self.forecast(y=y, h=h, X=X, X_future=X_future, level=level, fitted=fitted)\n", " return res" ] }, @@ -10904,6 +10989,16 @@ "constant_model.forecast(ap, 12, level=[90, 80])" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "constant_model.forward(ap, 12, level=[90, 80])" + ] + }, { "cell_type": "code", "execution_count": null, @@ -10967,6 +11062,15 @@ "show_doc(ConstantModel.predict_in_sample, title_level=3)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "show_doc(ConstantModel.forward, title_level=3)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -11035,6 +11139,16 @@ "zero_model.forecast(ap, 12, level=[90, 80])" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "zero_model.forward(ap, 12, level=[90, 80])" + ] + }, { "cell_type": "code", "execution_count": null, @@ -11098,6 +11212,15 @@ "show_doc(ZeroModel.predict_in_sample, title_level=3, name='ZeroModel.predict_in_sample')" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "show_doc(ZeroModel.forward, title_level=3, name='ZeroModel.forward')" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/statsforecast/_modidx.py b/statsforecast/_modidx.py index 30f335f72..91c4471ea 100644 --- a/statsforecast/_modidx.py +++ b/statsforecast/_modidx.py @@ -371,6 +371,8 @@ 'statsforecast/models.py'), 'statsforecast.models.ConstantModel.forecast': ( 'src/core/models.html#constantmodel.forecast', 'statsforecast/models.py'), + 'statsforecast.models.ConstantModel.forward': ( 'src/core/models.html#constantmodel.forward', + 'statsforecast/models.py'), 'statsforecast.models.ConstantModel.predict': ( 'src/core/models.html#constantmodel.predict', 'statsforecast/models.py'), 'statsforecast.models.ConstantModel.predict_in_sample': ( 'src/core/models.html#constantmodel.predict_in_sample', @@ -498,6 +500,8 @@ 'statsforecast.models.Naive.fit': ('src/core/models.html#naive.fit', 'statsforecast/models.py'), 'statsforecast.models.Naive.forecast': ( 'src/core/models.html#naive.forecast', 'statsforecast/models.py'), + 'statsforecast.models.Naive.forward': ( 'src/core/models.html#naive.forward', + 'statsforecast/models.py'), 'statsforecast.models.Naive.predict': ( 'src/core/models.html#naive.predict', 'statsforecast/models.py'), 'statsforecast.models.Naive.predict_in_sample': ( 'src/core/models.html#naive.predict_in_sample', diff --git a/statsforecast/core.py b/statsforecast/core.py index 44f1e70d7..1b9829190 100644 --- a/statsforecast/core.py +++ b/statsforecast/core.py @@ -61,14 +61,21 @@ def __eq__(self, other): self.indptr, other.indptr ) - def fit(self, models): + def fit(self, models, fallback_model=None): fm = np.full((self.n_groups, len(models)), np.nan, dtype=object) for i, grp in enumerate(self): y = grp[:, 0] if grp.ndim == 2 else grp X = grp[:, 1:] if (grp.ndim == 2 and grp.shape[1] > 1) else None for i_model, model in enumerate(models): - new_model = model.new() - fm[i, i_model] = new_model.fit(y=y, X=X) + try: + new_model = model.new() + fm[i, i_model] = new_model.fit(y=y, X=X) + except Exception as error: + if fallback_model is not None: + new_fallback_model = fallback_model.new() + fm[i, i_model] = new_fallback_model.fit(y=y, X=X) + else: + raise error return fm def _get_cols(self, models, attr, h, X, level=tuple()): @@ -331,11 +338,15 @@ def cross_validation( else: if i_window == 0: # for the first window we have to fit each model - model = model.fit(y=y_train, X=X_train) - if fallback_model is not None: - fallback_model = fallback_model.fit( - y=y_train, X=X_train - ) + try: + model = model.fit(y=y_train, X=X_train) + except Exception as error: + if fallback_model is not None: + fallback_model = fallback_model.fit( + y=y_train, X=X_train + ) + else: + raise error try: res_i = model.forward( h=h, @@ -401,7 +412,7 @@ def split_fm(self, fm, n_chunks): if x.size ] -# %% ../nbs/src/core/core.ipynb 22 +# %% ../nbs/src/core/core.ipynb 23 class DataFrameProcessing: """ A utility to process Pandas or Polars dataframes for time series forecasting. @@ -442,6 +453,7 @@ def __init__( sort_dataframe: bool, validate: Optional[bool] = True, ): + self.dataframe = dataframe self.sort_dataframe = sort_dataframe self.validate = validate @@ -692,7 +704,7 @@ def _check_datetime(self, arr: np.ndarray) -> Union[pd.DatetimeIndex, np.ndarray raise Exception(msg) from e return arr -# %% ../nbs/src/core/core.ipynb 25 +# %% ../nbs/src/core/core.ipynb 26 def _cv_dates(last_dates, freq, h, test_size, step_size=1): # assuming step_size = 1 if (test_size - h) % step_size: @@ -731,7 +743,7 @@ def _cv_dates(last_dates, freq, h, test_size, step_size=1): dates = dates.reset_index(drop=True) return dates -# %% ../nbs/src/core/core.ipynb 29 +# %% ../nbs/src/core/core.ipynb 30 def _get_n_jobs(n_groups, n_jobs): if n_jobs == -1 or (n_jobs is None): actual_n_jobs = cpu_count() @@ -739,7 +751,7 @@ def _get_n_jobs(n_groups, n_jobs): actual_n_jobs = n_jobs return min(n_groups, actual_n_jobs) -# %% ../nbs/src/core/core.ipynb 32 +# %% ../nbs/src/core/core.ipynb 33 def _parse_ds_type(df): dt_col = df["ds"] dt_check = pd.api.types.is_datetime64_any_dtype(dt_col) @@ -757,7 +769,7 @@ def _parse_ds_type(df): raise Exception(msg) from e return df -# %% ../nbs/src/core/core.ipynb 33 +# %% ../nbs/src/core/core.ipynb 34 class _StatsForecast: def __init__( self, @@ -871,7 +883,9 @@ def fit( self._set_prediction_intervals(prediction_intervals=prediction_intervals) self._prepare_fit(df, sort_df) if self.n_jobs == 1: - self.fitted_ = self.ga.fit(models=self.models) + self.fitted_ = self.ga.fit( + models=self.models, fallback_model=self.fallback_model + ) else: self.fitted_ = self._fit_parallel() return self @@ -1300,7 +1314,9 @@ def _fit_parallel(self): with Pool(self.n_jobs, **pool_kwargs) as executor: futures = [] for ga in gas: - future = executor.apply_async(ga.fit, (self.models,)) + future = executor.apply_async( + ga.fit, (self.models, self.fallback_model) + ) futures.append(future) fm = np.vstack([f.get() for f in futures]) return fm @@ -1533,7 +1549,7 @@ def plot( def __repr__(self): return f"StatsForecast(models=[{','.join(map(repr, self.models))}])" -# %% ../nbs/src/core/core.ipynb 34 +# %% ../nbs/src/core/core.ipynb 35 class ParallelBackend: def forecast(self, df, models, freq, fallback_model=None, **kwargs: Any) -> Any: model = _StatsForecast( @@ -1554,7 +1570,7 @@ def cross_validation( def make_backend(obj: Any, *args: Any, **kwargs: Any) -> ParallelBackend: return ParallelBackend() -# %% ../nbs/src/core/core.ipynb 35 +# %% ../nbs/src/core/core.ipynb 36 class StatsForecast(_StatsForecast): """Train statistical models. diff --git a/statsforecast/models.py b/statsforecast/models.py index 479757de2..8d7a81037 100644 --- a/statsforecast/models.py +++ b/statsforecast/models.py @@ -2550,6 +2550,7 @@ def __init__( alias: str = "Holt", prediction_intervals: Optional[ConformalIntervals] = None, ): + self.season_length = season_length self.error_type = error_type self.alias = alias @@ -2748,6 +2749,7 @@ def forecast( level: Optional[List[int]] = None, fitted: bool = False, ): + """Memory Efficient HistoricAverage predictions. This method avoids memory burden due from object storage. @@ -2969,7 +2971,43 @@ def forecast( res = _add_fitted_pi(res=res, se=sigma, level=level) return res -# %% ../nbs/src/core/models.ipynb 217 + def forward( + self, + y: np.ndarray, + h: int, + X: Optional[np.ndarray] = None, + X_future: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, + fitted: bool = False, + ): + """Apply fitted model to an new/updated series. + + Parameters + ---------- + y : numpy.array + Clean time series of shape (n,). + h: int + Forecast horizon. + X : array-like + Optional insample exogenous of shape (t, n_x). + X_future : array-like + Optional exogenous of shape (h, n_x). + level : List[float] + Confidence levels (0-100) for prediction intervals. + fitted : bool + Whether or not to return insample predictions. + + Returns + ------- + forecasts : dict + Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions. + """ + res = self.forecast( + y=y, h=h, X=X, X_future=X_future, level=level, fitted=fitted + ) + return res + +# %% ../nbs/src/core/models.ipynb 218 @njit(nogil=NOGIL, cache=CACHE) def _random_walk_with_drift( y: np.ndarray, # time series @@ -2989,7 +3027,7 @@ def _random_walk_with_drift( fcst["fitted"] = fitted_vals return fcst -# %% ../nbs/src/core/models.ipynb 218 +# %% ../nbs/src/core/models.ipynb 219 class RandomWalkWithDrift(_TS): def __init__( self, @@ -3166,7 +3204,7 @@ def forecast( return res -# %% ../nbs/src/core/models.ipynb 232 +# %% ../nbs/src/core/models.ipynb 233 class SeasonalNaive(_TS): def __init__( self, @@ -3355,7 +3393,7 @@ def forecast( return res -# %% ../nbs/src/core/models.ipynb 246 +# %% ../nbs/src/core/models.ipynb 247 @njit(nogil=NOGIL, cache=CACHE) def _window_average( y: np.ndarray, # time series @@ -3371,7 +3409,7 @@ def _window_average( mean = _repeat_val(val=wavg, h=h) return {"mean": mean} -# %% ../nbs/src/core/models.ipynb 247 +# %% ../nbs/src/core/models.ipynb 248 class WindowAverage(_TS): def __init__( self, @@ -3529,7 +3567,7 @@ def forecast( raise Exception("You must pass `prediction_intervals` to " "compute them.") return res -# %% ../nbs/src/core/models.ipynb 258 +# %% ../nbs/src/core/models.ipynb 259 @njit(nogil=NOGIL, cache=CACHE) def _seasonal_window_average( y: np.ndarray, @@ -3550,7 +3588,7 @@ def _seasonal_window_average( out = _repeat_val_seas(season_vals=season_avgs, h=h, season_length=season_length) return {"mean": out} -# %% ../nbs/src/core/models.ipynb 259 +# %% ../nbs/src/core/models.ipynb 260 class SeasonalWindowAverage(_TS): def __init__( self, @@ -3724,7 +3762,7 @@ def forecast( raise Exception("You must pass `prediction_intervals` to compute them.") return res -# %% ../nbs/src/core/models.ipynb 271 +# %% ../nbs/src/core/models.ipynb 272 def _adida( y: np.ndarray, # time series h: int, # forecasting horizon @@ -3745,7 +3783,7 @@ def _adida( mean = _repeat_val(val=forecast, h=h) return {"mean": mean} -# %% ../nbs/src/core/models.ipynb 272 +# %% ../nbs/src/core/models.ipynb 273 class ADIDA(_TS): def __init__( self, @@ -3907,7 +3945,7 @@ def forecast( ) return res -# %% ../nbs/src/core/models.ipynb 284 +# %% ../nbs/src/core/models.ipynb 285 @njit(nogil=NOGIL, cache=CACHE) def _croston_classic( y: np.ndarray, # time series @@ -3929,7 +3967,7 @@ def _croston_classic( mean = _repeat_val(val=mean, h=h) return {"mean": mean} -# %% ../nbs/src/core/models.ipynb 285 +# %% ../nbs/src/core/models.ipynb 286 class CrostonClassic(_TS): def __init__( self, @@ -4086,7 +4124,7 @@ def forecast( ) return res -# %% ../nbs/src/core/models.ipynb 296 +# %% ../nbs/src/core/models.ipynb 297 def _croston_optimized( y: np.ndarray, # time series h: int, # forecasting horizon @@ -4107,7 +4145,7 @@ def _croston_optimized( mean = _repeat_val(val=mean, h=h) return {"mean": mean} -# %% ../nbs/src/core/models.ipynb 297 +# %% ../nbs/src/core/models.ipynb 298 class CrostonOptimized(_TS): def __init__( self, @@ -4260,7 +4298,7 @@ def forecast( raise Exception("You must pass `prediction_intervals` to compute them.") return res -# %% ../nbs/src/core/models.ipynb 308 +# %% ../nbs/src/core/models.ipynb 309 @njit(nogil=NOGIL, cache=CACHE) def _croston_sba( y: np.ndarray, # time series @@ -4273,7 +4311,7 @@ def _croston_sba( mean["mean"] *= 0.95 return mean -# %% ../nbs/src/core/models.ipynb 309 +# %% ../nbs/src/core/models.ipynb 310 class CrostonSBA(_TS): def __init__( self, @@ -4432,7 +4470,7 @@ def forecast( ) return res -# %% ../nbs/src/core/models.ipynb 320 +# %% ../nbs/src/core/models.ipynb 321 def _imapa( y: np.ndarray, # time series h: int, # forecasting horizon @@ -4456,7 +4494,7 @@ def _imapa( mean = _repeat_val(val=forecast, h=h) return {"mean": mean} -# %% ../nbs/src/core/models.ipynb 321 +# %% ../nbs/src/core/models.ipynb 322 class IMAPA(_TS): def __init__( self, @@ -4612,7 +4650,7 @@ def forecast( ) return res -# %% ../nbs/src/core/models.ipynb 332 +# %% ../nbs/src/core/models.ipynb 333 @njit(nogil=NOGIL, cache=CACHE) def _tsb( y: np.ndarray, # time series @@ -4633,7 +4671,7 @@ def _tsb( mean = _repeat_val(val=forecast, h=h) return {"mean": mean} -# %% ../nbs/src/core/models.ipynb 333 +# %% ../nbs/src/core/models.ipynb 334 class TSB(_TS): def __init__( self, @@ -4800,7 +4838,7 @@ def forecast( raise Exception("You must pass `prediction_intervals` to compute them.") return res -# %% ../nbs/src/core/models.ipynb 345 +# %% ../nbs/src/core/models.ipynb 346 def _predict_mstl_seas(mstl_ob, h, season_length): seasoncolumns = mstl_ob.filter(regex="seasonal*").columns nseasons = len(seasoncolumns) @@ -4817,7 +4855,7 @@ def _predict_mstl_seas(mstl_ob, h, season_length): lastseas = seascomp.sum(axis=1) return lastseas -# %% ../nbs/src/core/models.ipynb 346 +# %% ../nbs/src/core/models.ipynb 347 class MSTL(_TS): """MSTL model. @@ -4853,6 +4891,7 @@ def __init__( alias: str = "MSTL", prediction_intervals: Optional[ConformalIntervals] = None, ): + # check ETS model doesnt have seasonality if repr(trend_forecaster) == "AutoETS": if trend_forecaster.model[2] != "N": @@ -5091,7 +5130,7 @@ def forward( } return res -# %% ../nbs/src/core/models.ipynb 362 +# %% ../nbs/src/core/models.ipynb 363 class Theta(AutoTheta): """Standard Theta Method. @@ -5127,7 +5166,7 @@ def __init__( prediction_intervals=prediction_intervals, ) -# %% ../nbs/src/core/models.ipynb 375 +# %% ../nbs/src/core/models.ipynb 376 class OptimizedTheta(AutoTheta): """Optimized Theta Method. @@ -5163,7 +5202,7 @@ def __init__( prediction_intervals=prediction_intervals, ) -# %% ../nbs/src/core/models.ipynb 388 +# %% ../nbs/src/core/models.ipynb 389 class DynamicTheta(AutoTheta): """Dynamic Standard Theta Method. @@ -5199,7 +5238,7 @@ def __init__( prediction_intervals=prediction_intervals, ) -# %% ../nbs/src/core/models.ipynb 401 +# %% ../nbs/src/core/models.ipynb 402 class DynamicOptimizedTheta(AutoTheta): """Dynamic Optimized Theta Method. @@ -5235,7 +5274,7 @@ def __init__( prediction_intervals=prediction_intervals, ) -# %% ../nbs/src/core/models.ipynb 415 +# %% ../nbs/src/core/models.ipynb 416 class GARCH(_TS): """Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. @@ -5429,7 +5468,7 @@ def forecast( res = _add_fitted_pi(res=res, se=se, level=level) return res -# %% ../nbs/src/core/models.ipynb 428 +# %% ../nbs/src/core/models.ipynb 429 class ARCH(GARCH): """Autoregressive Conditional Heteroskedasticity (ARCH) model. @@ -5475,7 +5514,7 @@ def __init__( def __repr__(self): return self.alias -# %% ../nbs/src/core/models.ipynb 439 +# %% ../nbs/src/core/models.ipynb 440 class ConstantModel(_TS): def __init__(self, constant: float, alias: str = "ConstantModel"): """Constant Model. @@ -5624,7 +5663,43 @@ def forecast( res[f"fitted-hi-{lv}"] = fitted_vals return res -# %% ../nbs/src/core/models.ipynb 450 + def forward( + self, + y: np.ndarray, + h: int, + X: Optional[np.ndarray] = None, + X_future: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, + fitted: bool = False, + ): + """Apply Constant model predictions to a new/updated time series. + + Parameters + ---------- + y : numpy.array + Clean time series of shape (n, ). + h : int + Forecast horizon. + X : array-like + Optional insample exogenous of shape (t, n_x). + X_future : array-like + Optional exogenous of shape (h, n_x). + level : List[float] + Confidence levels for prediction intervals. + fitted : bool + Whether or not returns insample predictions. + + Returns + ------- + forecasts : dict + Dictionary with entries `constant` for point predictions and `level_*` for probabilistic predictions. + """ + res = self.forecast( + y=y, h=h, X=X, X_future=X_future, level=level, fitted=fitted + ) + return res + +# %% ../nbs/src/core/models.ipynb 453 class ZeroModel(ConstantModel): def __init__(self, alias: str = "ZeroModel"): """Returns Zero forecasts. @@ -5638,7 +5713,7 @@ def __init__(self, alias: str = "ZeroModel"): """ super().__init__(constant=0, alias=alias) -# %% ../nbs/src/core/models.ipynb 461 +# %% ../nbs/src/core/models.ipynb 466 class NaNModel(ConstantModel): def __init__(self, alias: str = "NaNModel"): """NaN Model. From 0d05a56452d6bffca319f8be933954318b6303c3 Mon Sep 17 00:00:00 2001 From: Hahnbee Lee <55263191+hahnbeelee@users.noreply.github.com> Date: Mon, 16 Oct 2023 15:11:43 -0700 Subject: [PATCH 08/22] Fix formatting issues in docs (#662) --- .gitignore | 2 +- README.md | 2 +- nbs/_quarto.yml | 4 +- nbs/docs/contribute/issues.md | 21 ++- nbs/docs/contribute/step-by-step.md | 50 +++---- .../how-to-guides/AmazonStatsForecast.ipynb | 2 +- nbs/docs/how-to-guides/migrating_R.qmd | 4 +- nbs/docs/models/ARCH.ipynb | 4 +- nbs/docs/models/ARIMA.ipynb | 4 +- nbs/docs/models/AutoCES.ipynb | 4 +- nbs/docs/models/AutoETS.ipynb | 2 +- nbs/docs/models/AutoTheta.ipynb | 6 +- nbs/docs/models/Holt.ipynb | 2 +- nbs/docs/models/OptimizedTheta.ipynb | 6 +- .../models/SeasonalExponentialSmoothing.ipynb | 4 +- nbs/docs/tutorials/IntermittentData.ipynb | 4 +- nbs/src/adapters.prophet.ipynb | 8 +- nbs/src/core/distributed.fugue.ipynb | 6 +- nbs/src/core/models.ipynb | 124 +++++++++++------- nbs/src/utils.ipynb | 6 +- nbs/styles.css | 69 ++++++---- settings.ini | 2 +- 22 files changed, 205 insertions(+), 131 deletions(-) diff --git a/.gitignore b/.gitignore index 06d74c19e..92ef65b65 100644 --- a/.gitignore +++ b/.gitignore @@ -16,10 +16,10 @@ dist tmp _docs/ _proc/ -sidebar.yml .DS_Store .gitattributes .gitconfig nbs/.last_checked .venv .idea +.luarc.json diff --git a/README.md b/README.md index 72627afbe..d6aefd25f 100644 --- a/README.md +++ b/README.md @@ -259,4 +259,4 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d -This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! +This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! \ No newline at end of file diff --git a/nbs/_quarto.yml b/nbs/_quarto.yml index f708a5614..60097036f 100644 --- a/nbs/_quarto.yml +++ b/nbs/_quarto.yml @@ -10,7 +10,7 @@ format: toc: true website: - twitter-card: + twitter-card: image: "https://farm6.staticflickr.com/5510/14338202952_93595258ff_z.jpg" site: "@Nixtlainc" open-graph: @@ -35,7 +35,7 @@ website: href: https://github.com/nixtla/neuralforecast - text: "HierarchicalForecast 👑" href: https://github.com/nixtla/hierarchicalforecast - + - text: "Help" menu: - text: "Report an Issue" diff --git a/nbs/docs/contribute/issues.md b/nbs/docs/contribute/issues.md index 65b79b608..d0f78b248 100644 --- a/nbs/docs/contribute/issues.md +++ b/nbs/docs/contribute/issues.md @@ -26,7 +26,9 @@ The form to report the bug will appear. 5. You can also add links, references, logs, screenshots, and so on. - Please ensure that your contributions abide by the [contributing guidelines](https://github.com/nixtla/nixtla/blob/staging/CONTRIBUTING.md) and [code of conduct](https://github.com/nixtla/nixtla/blob/staging/CODE_OF_CONDUCT.md). + +Please ensure that your contributions abide by the [contributing guidelines](https://github.com/nixtla/nixtla/blob/staging/CONTRIBUTING.md) and [code of conduct](https://github.com/nixtla/nixtla/blob/staging/CODE_OF_CONDUCT.md). + Thank you for your contribution! Your report aids in refining Nixtla for current and future users. @@ -43,7 +45,9 @@ The feature request form will appear. 4. Additional references, links, logs, and screenshots are welcome! - Please ensure that your contributions abide by the [contributing guidelines](https://github.com/nixtla/nixtla/blob/staging/CONTRIBUTING.md) and [code of conduct](https://github.com/nixtla/nixtla/blob/staging/CODE_OF_CONDUCT.md). + +Please ensure that your contributions abide by the [contributing guidelines](https://github.com/nixtla/nixtla/blob/staging/CONTRIBUTING.md) and [code of conduct](https://github.com/nixtla/nixtla/blob/staging/CODE_OF_CONDUCT.md). + Thank you for your feature request! It will help us enhance Nixtla for all users. @@ -59,7 +63,9 @@ A form for suggesting improvements will appear. 3. Any additional references, links, logs, screenshots are appreciated! - Please ensure that your contributions abide by the [contributing guidelines](https://github.com/nixtla/nixtla/blob/staging/CONTRIBUTING.md) and [code of conduct](https://github.com/nixtla/nixtla/blob/staging/CODE_OF_CONDUCT.md). + +Please ensure that your contributions abide by the [contributing guidelines](https://github.com/nixtla/nixtla/blob/staging/CONTRIBUTING.md) and [code of conduct](https://github.com/nixtla/nixtla/blob/staging/CODE_OF_CONDUCT.md). + Thank you for your valuable suggestions! Your input helps us refine Nixtla's documentation. @@ -80,7 +86,9 @@ A form for your proposal will appear. 4. Any additional references, links, logs, screenshots, and so on, are welcome! - Please ensure that your contributions abide by the [contributing guidelines](https://github.com/nixtla/nixtla/blob/staging/CONTRIBUTING.md) and [code of conduct](https://github.com/nixtla/nixtla/blob/staging/CODE_OF_CONDUCT.md). + +Please ensure that your contributions abide by the [contributing guidelines](https://github.com/nixtla/nixtla/blob/staging/CONTRIBUTING.md) and [code of conduct](https://github.com/nixtla/nixtla/blob/staging/CODE_OF_CONDUCT.md). + Thank you for your proposal! Your suggestion helps us extend the capabilities of Nixtla. @@ -92,6 +100,7 @@ Thank you for your proposal! Your suggestion helps us extend the capabilities of - Please be ready to respond to our feedback or questions regarding your issue. - This document is based on the documentation from [MindsDB](https://github.com/mindsdb/mindsdb) - +This document is based on the documentation from [MindsDB](https://github.com/mindsdb/mindsdb) + + diff --git a/nbs/docs/contribute/step-by-step.md b/nbs/docs/contribute/step-by-step.md index 18dc705a2..49848ff9d 100644 --- a/nbs/docs/contribute/step-by-step.md +++ b/nbs/docs/contribute/step-by-step.md @@ -120,11 +120,11 @@ You can find a detailed step by step buide with screen-shots below. ### 1. Create a fork of the mlforecast repo The first thing you need to do is create a fork of the GitHub repository to your own account: -image +image Your fork on your account will look like this: -image +image In that repository, you can make your changes and then request to have them added to the main repo. @@ -134,99 +134,99 @@ In this tutorial, we are using Mac (also compatible with other Linux distributio Once the repository is created, you need to clone it to your own computer. Simply copy the repository URL from GitHub as shown below: -image +image Then open Visual Studio Code, click on "Clone Git Repository," and paste the line you just copied into the top part of the window, as shown below: -image +image Select the folder where you want to copy the repository: -image +image And choose to open the cloned repository: -image +image You will end up with something like this: -image +image ### 3. Create the Conda environment Open a terminal within Visual Studio Code, as shown in the image: -image +image You can use conda but we highly recommend using Mamba to speed up the creation of the Conda environment. To install it, simply use `conda install mamba -c conda-forge` in the terminal you just opened: -image +image Create an empty environment named `mlforecast` with the following command: `mamba create -n mlforecast python=3.10`: -image +image Activate the newly created environment using `conda activate mlforecast`: -image +image Install the libraries within the environment file `environment.yml` using `mamba env update -f environment.yml`: -image +image Now install the library to make interactive changes and other additional dependencies using `pip install -e ".[dev]"`: -image +image ### 4. Make the changes you want. In this section, we assume that we want to increase the default number of windows used to create prediction intervals from 2 to 3. The first thing we need to do is create a specific branch for that change using `git checkout -b [new_branch]` like this: -image +image Once created, open the notebook you want to modify. In this case, it's `nbs/utils.ipynb`, which contains the metadata for the prediction intervals. After opening it, click on the environment you want to use (top right) and select the `mlforecast` environment: -image +image Next, execute the notebook and make the necessary changes. In this case, we want to modify the `PredictionIntervals` class: -image +image We will change the default value of `n_window` from 2 to 3: -image +image Once you have made the change and performed any necessary validations, it's time to convert the notebook to Python modules. To do this, simply use `nbdev_export` in the terminal. You will see that the `mlforecast/utils.py` file has been modified (the changes from `nbs/utils.ipynb` are reflected in that module). Before committing the changes, we need to clean the notebooks using the command `./action_files/clean_nbs` and verify that the linters pass using `./action_files/lint`: -image +image Once you have done the above, simply add the changes using `git add nbs/utils.ipynb mlforecast/utils.py`: -image +image Create a descriptive commit message for the changes using `git commit -m "[description of changes]"`: -image +image Finally, push your changes using `git push`: -image +image ### 5. Create a pull request. In GitHub, open your repository that contains your fork of the original repo. Once inside, you will see the changes you just pushed. Click on "Compare and pull request": -image +image Include an appropriate title for your pull request and fill in the necessary information. Once you're done, click on "Create pull request". -image +image Finally, you will see something like this: -image +image ## Notes -- This file was generated using [this file](https://github.com/Nixtla/nixtla-commons/blob/main/docs/contribute/step-by-step.md). Please change that file if you want to enhance the document. +- This file was generated using [this file](https://github.com/Nixtla/nixtla-commons/blob/main/docs/contribute/step-by-step.md). Please change that file if you want to enhance the document. \ No newline at end of file diff --git a/nbs/docs/how-to-guides/AmazonStatsForecast.ipynb b/nbs/docs/how-to-guides/AmazonStatsForecast.ipynb index 6a2a6e536..b06d24aa5 100644 --- a/nbs/docs/how-to-guides/AmazonStatsForecast.ipynb +++ b/nbs/docs/how-to-guides/AmazonStatsForecast.ipynb @@ -611,7 +611,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\"image\"" + "\"image\"" ] } ], diff --git a/nbs/docs/how-to-guides/migrating_R.qmd b/nbs/docs/how-to-guides/migrating_R.qmd index 9a0c50bfb..352be78f5 100644 --- a/nbs/docs/how-to-guides/migrating_R.qmd +++ b/nbs/docs/how-to-guides/migrating_R.qmd @@ -1,4 +1,6 @@ -# Migrating from R +--- +title: Migrating from R +--- ## 🚧 We are working on this site. This site is currently in development. If you are particularly interested in this section, please open a GitHub Issue, and we will prioritize it. \ No newline at end of file diff --git a/nbs/docs/models/ARCH.ipynb b/nbs/docs/models/ARCH.ipynb index 090c71f23..0cd6018f8 100644 --- a/nbs/docs/models/ARCH.ipynb +++ b/nbs/docs/models/ARCH.ipynb @@ -213,7 +213,9 @@ "source": [ "## Loading libraries and data     \n", "\n", - ":::{.callout-tip} Statsforecast will be needed. To install, see [instructions](../getting-started/0_Installation.ipynb). :::\n", + ":::{.callout-tip}\n", + "Statsforecast will be needed. To install, see [instructions](../getting-started/0_Installation.ipynb).\n", + ":::\n", "\n", "Next, we import plotting libraries and configure the plotting style." ] diff --git a/nbs/docs/models/ARIMA.ipynb b/nbs/docs/models/ARIMA.ipynb index f4854579e..91066e627 100644 --- a/nbs/docs/models/ARIMA.ipynb +++ b/nbs/docs/models/ARIMA.ipynb @@ -296,7 +296,9 @@ "source": [ "## Loading libraries and data \n", "\n", - ":::{.callout-tip} Statsforecast will be needed. To install, see [instructions](../getting-started/0_Installation.ipynb). :::\n", + ":::{.callout-tip}\n", + "Statsforecast will be needed. To install, see [instructions](../getting-started/0_Installation.ipynb). \n", + ":::\n", "\n", "Next, we import plotting libraries and configure the plotting style." ] diff --git a/nbs/docs/models/AutoCES.ipynb b/nbs/docs/models/AutoCES.ipynb index 4c36482af..3d1b8e5e3 100644 --- a/nbs/docs/models/AutoCES.ipynb +++ b/nbs/docs/models/AutoCES.ipynb @@ -197,7 +197,9 @@ "source": [ "## Loading libraries and data \n", "\n", - ":::{.callout-tip} Statsforecast will be needed. To install, see [instructions](../getting-started/0_Installation.ipynb). :::\n", + ":::{.callout-tip}\n", + "Statsforecast will be needed. To install, see [instructions](../getting-started/0_Installation.ipynb).\n", + ":::\n", "\n", "Next, we import plotting libraries and configure the plotting style." ] diff --git a/nbs/docs/models/AutoETS.ipynb b/nbs/docs/models/AutoETS.ipynb index 60b76ec3a..c42e4dd95 100644 --- a/nbs/docs/models/AutoETS.ipynb +++ b/nbs/docs/models/AutoETS.ipynb @@ -111,7 +111,7 @@ "\n", "$$s_{t} = \\gamma^*(1-\\alpha) (y_{t}-\\ell_{t-1}-b_{t-1})+ [1-\\gamma^*(1-\\alpha)]s_{t-m},$$\n", "\n", - "Thus, we obtain identical forecasts using this approach by replacing $\\gamma$ in (1c) with $\\gamma^{*} (1-\\alpha) $. The modification given in (1c) was proposed by Ord et al. (1997) to make the state space formulation simpler. It is equivalent to Archibald’s (1990) variation of the Holt-Winters’ method." + "Thus, we obtain identical forecasts using this approach by replacing $\\gamma$ in (1c) with $\\gamma^{*} (1-\\alpha)$. The modification given in (1c) was proposed by Ord et al. (1997) to make the state space formulation simpler. It is equivalent to Archibald’s (1990) variation of the Holt-Winters’ method." ] }, { diff --git a/nbs/docs/models/AutoTheta.ipynb b/nbs/docs/models/AutoTheta.ipynb index b7a66624d..e372091c3 100644 --- a/nbs/docs/models/AutoTheta.ipynb +++ b/nbs/docs/models/AutoTheta.ipynb @@ -148,13 +148,13 @@ "\n", "$$\\hat Y_{n+h|n} = (1-\\frac{1}{\\theta}) [\\text{A}_n+\\text{B}_n (n+h)]+ \\frac{1}{\\theta} \\tilde {\\text{Z}}_{n+h|n} (\\theta) \\tag 6$$\n", "\n", - "where $\\tilde {\\text{Z}}_{n+h|n} (\\theta)=\\tilde {\\text{Z}}_{n+1|n} (\\theta)=\\alpha \\sum_{i=0}^{n-1}(1-\\alpha)^i \\text{Z}_{n-i}(\\theta)+(1-\\alpha)^n \\ell_{0}^{*} $ is the extrapolation of $\\text{Z}_t(\\theta)$ by an SES model with $\\ell_{0}^{*} \\in \\mathbb{R}$ as the initial level parameter and $\\alpha \\in (0,1)$ as the smoothing parameter. Note that for $\\theta=2$ Eq. (6) corresponds to Step 4 of the STheta algorithm. After some algebra, we can write\n", + "where $\\tilde {\\text{Z}}_{n+h|n} (\\theta)=\\tilde {\\text{Z}}_{n+1|n} (\\theta)=\\alpha \\sum_{i=0}^{n-1}(1-\\alpha)^i \\text{Z}_{n-i}(\\theta)+(1-\\alpha)^n \\ell_{0}^{*}$ is the extrapolation of $\\text{Z}_t(\\theta)$ by an SES model with $\\ell_{0}^{*} \\in \\mathbb{R}$ as the initial level parameter and $\\alpha \\in (0,1)$ as the smoothing parameter. Note that for $\\theta=2$ Eq. (6) corresponds to Step 4 of the STheta algorithm. After some algebra, we can write\n", "\n", "$$\\tilde {\\text{Z}}_{n+1|n} (\\theta)=\\theta \\ell{n}+(1-\\theta) \\{ \\text{A}_n [1-(1-\\alpha)^n] + \\text{B}_n [n+(1-\\frac{1}{\\alpha}) [1-(1-\\alpha)^n] ] \\} \\tag 7$$\n", "\n", - "where $\\ell_{t}=\\alpha Y_t +(1-\\alpha) \\ell_{t-1} $ for $t=1, \\cdots, n$ and $\\ell_{0}=\\ell_{0}^{*}/\\theta$.\n", + "where $\\ell_{t}=\\alpha Y_t +(1-\\alpha) \\ell_{t-1}$ for $t=1, \\cdots, n$ and $\\ell_{0}=\\ell_{0}^{*}/\\theta$.\n", "\n", - "In the light of Eqs. (6), (7), we suggest four stochastic approaches. These approaches differ due to the parameter $\\theta$ which may be either fixed at two or optimised, and the coefficients $\\text{A}_n$ and $\\text{B}_n$, which can be either fixed or dynamic functions. To formulate the state space models, it is helpful to adopt $\\mu_{t}$ as the one-step-ahead forecast at origin $t-1$ and $\\varepsilon_{t} $ as the respective additive error, i.e., $\\varepsilon_{t}=Y_t - \\mu_{t} $ if $\\mu_{t}= \\hat Y_{t|t-1} $. We assume $\\{ \\varepsilon_{t} \\} $ to be a Gaussian white noise process with mean zero and variance $\\sigma^2$. " + "In the light of Eqs. (6), (7), we suggest four stochastic approaches. These approaches differ due to the parameter $\\theta$ which may be either fixed at two or optimised, and the coefficients $\\text{A}_n$ and $\\text{B}_n$, which can be either fixed or dynamic functions. To formulate the state space models, it is helpful to adopt $\\mu_{t}$ as the one-step-ahead forecast at origin $t-1$ and $\\varepsilon_{t}$ as the respective additive error, i.e., $\\varepsilon_{t}=Y_t - \\mu_{t}$ if $\\mu_{t}= \\hat Y_{t|t-1}$. We assume $\\{ \\varepsilon_{t} \\}$ to be a Gaussian white noise process with mean zero and variance $\\sigma^2$. " ] }, { diff --git a/nbs/docs/models/Holt.ipynb b/nbs/docs/models/Holt.ipynb index 065900df9..2705fc903 100644 --- a/nbs/docs/models/Holt.ipynb +++ b/nbs/docs/models/Holt.ipynb @@ -112,7 +112,7 @@ "\n", "For each method there exist two models: one with additive errors and one with multiplicative errors. The point forecasts produced by the models are identical if they use the same smoothing parameter values. They will, however, generate different prediction intervals.\n", "\n", - "To distinguish between a model with additive errors and one with multiplicative errors. We label each state space model as ETS( .,.,.) for (Error, Trend, Seasonal). This label can also be thought of as ExponenTial Smoothing. Using the same notation as in Table 7.5, the possibilities for each component are: Error ={A,M } , Trend ={ N,A,A d} and Seasonal ={ N,A,M }\n", + "To distinguish between a model with additive errors and one with multiplicative errors. We label each state space model as ETS( .,.,.) for (Error, Trend, Seasonal). This label can also be thought of as ExponenTial Smoothing. Using the same notation as in Table 7.5, the possibilities for each component are: $Error=\\{A,M\\}$, $Trend=\\{N,A,A_d\\}$ and $Seasonal=\\{N,A,M\\}$\n", "\n", "For our case, the linear Holt model with a trend, we are going to see two cases, both for the additive and the multiplicative\n", "\n", diff --git a/nbs/docs/models/OptimizedTheta.ipynb b/nbs/docs/models/OptimizedTheta.ipynb index dc4dd7c6c..34e0434d6 100644 --- a/nbs/docs/models/OptimizedTheta.ipynb +++ b/nbs/docs/models/OptimizedTheta.ipynb @@ -127,13 +127,13 @@ "\n", "$$\\hat Y_{n+h|n} = (1-\\frac{1}{\\theta}) [\\text{A}_n+\\text{B}_n (n+h)]+ \\frac{1}{\\theta} \\tilde {\\text{Z}}_{n+h|n} (\\theta) \\tag 3$$\n", "\n", - "where $\\tilde {\\text{Z}}_{n+h|n} (\\theta)=\\tilde {\\text{Z}}_{n+1|n} (\\theta)=\\alpha \\sum_{i=0}^{n-1}(1-\\alpha)^i \\text{Z}_{n-i}(\\theta)+(1-\\alpha)^n \\ell_{0}^{*} $ is the extrapolation of $\\text{Z}_t(\\theta)$ by an SES model with $\\ell_{0}^{*} \\in \\mathbb{R}$ as the initial level parameter and $\\alpha \\in (0,1)$ as the smoothing parameter. Note that for $\\theta=2$ Eq. (3) corresponds to Step 4 of the STheta algorithm. After some algebra, we can write\n", + "where $\\tilde {\\text{Z}}_{n+h|n} (\\theta)=\\tilde {\\text{Z}}_{n+1|n} (\\theta)=\\alpha \\sum_{i=0}^{n-1}(1-\\alpha)^i \\text{Z}_{n-i}(\\theta)+(1-\\alpha)^n \\ell_{0}^{*}$ is the extrapolation of $\\text{Z}_t(\\theta)$ by an SES model with $\\ell_{0}^{*} \\in \\mathbb{R}$ as the initial level parameter and $\\alpha \\in (0,1)$ as the smoothing parameter. Note that for $\\theta=2$ Eq. (3) corresponds to Step 4 of the STheta algorithm. After some algebra, we can write\n", "\n", "$$\\tilde {\\text{Z}}_{n+1|n} (\\theta)=\\theta \\ell{n}+(1-\\theta) \\{ \\text{A}_n [1-(1-\\alpha)^n] + \\text{B}_n [n+(1-\\frac{1}{\\alpha}) [1-(1-\\alpha)^n] ] \\} \\tag 4$$\n", "\n", "where $\\ell_{t}=\\alpha Y_t +(1-\\alpha) \\ell_{t-1} $ for $t=1, \\cdots, n$ and $\\ell_{0}=\\ell_{0}^{*}/\\theta$.\n", "\n", - "In the light of Eqs. (3), (4), we suggest four stochastic approaches. These approaches differ due to the parameter $\\theta$ which may be either fixed at two or optimised, and the coefficients $\\text{A}_n$ and $\\text{B}_n$, which can be either fixed or dynamic functions. To formulate the state space models, it is helpful to adopt $\\mu_{t}$ as the one-step-ahead forecast at origin $t-1$ and $\\varepsilon_{t} $ as the respective additive error, i.e., $\\varepsilon_{t}=Y_t - \\mu_{t} $ if $\\mu_{t}= \\hat Y_{t|t-1} $. We assume $\\{ \\varepsilon_{t} \\} $ to be a Gaussian white noise process with mean zero and variance $\\sigma^2$. " + "In the light of Eqs. (3), (4), we suggest four stochastic approaches. These approaches differ due to the parameter $\\theta$ which may be either fixed at two or optimised, and the coefficients $\\text{A}_n$ and $\\text{B}_n$, which can be either fixed or dynamic functions. To formulate the state space models, it is helpful to adopt $\\mu_{t}$ as the one-step-ahead forecast at origin $t-1$ and $\\varepsilon_{t}$ as the respective additive error, i.e., $\\varepsilon_{t}=Y_t - \\mu_{t}$ if $\\mu_{t}= \\hat Y_{t|t-1}$. We assume $\\{ \\varepsilon_{t} \\}$ to be a Gaussian white noise process with mean zero and variance $\\sigma^2$. " ] }, { @@ -161,7 +161,7 @@ "\n", "For $\\theta=2$ OTM reproduces the forecasts of the STheta method; hereafter, we will refer to this particular case as the standard Theta model (STM). \n", "\n", - "**Theorem 2:** The SES-d $(\\ell_{0}^{**}, \\alpha, b)$ model, where $\\ell_{0}^{**} \\in \\mathbb{R}, \\alpha \\in (0,1) $ and $b \\in \\mathbb{R} $ is equivalent to $\\text{OTM} (\\ell_{0}, \\alpha, \\theta )$ where $\\ell_{0} \\in \\mathbb{R}$ and $\\theta \\geq 1$, if \n", + "**Theorem 2:** The SES-d $(\\ell_{0}^{**}, \\alpha, b)$ model, where $\\ell_{0}^{**} \\in \\mathbb{R}, \\alpha \\in (0,1)$ and $b \\in \\mathbb{R}$ is equivalent to $\\text{OTM} (\\ell_{0}, \\alpha, \\theta )$ where $\\ell_{0} \\in \\mathbb{R}$ and $\\theta \\geq 1$, if \n", "\n", "$$\\ell_{0}^{**} = \\ell_{0} + (1- \\frac{1}{\\theta} )A_n \\ \\ and \\ \\ b=(1-\\frac{1}{\\theta} )B_n $$ \n", "\n", diff --git a/nbs/docs/models/SeasonalExponentialSmoothing.ipynb b/nbs/docs/models/SeasonalExponentialSmoothing.ipynb index dcea25030..2dd9bae47 100644 --- a/nbs/docs/models/SeasonalExponentialSmoothing.ipynb +++ b/nbs/docs/models/SeasonalExponentialSmoothing.ipynb @@ -146,7 +146,9 @@ "source": [ "## Loading libraries and data \n", "\n", - ":::{.callout-tip} Statsforecast will be needed. To install, see [instructions](../getting-started/0_Installation.ipynb). :::\n", + ":::{.callout-tip}\n", + "Statsforecast will be needed. To install, see [instructions](../getting-started/0_Installation.ipynb).\n", + ":::\n", "\n", "Next, we import plotting libraries and configure the plotting style." ] diff --git a/nbs/docs/tutorials/IntermittentData.ipynb b/nbs/docs/tutorials/IntermittentData.ipynb index 624f095f8..5fab2c5eb 100644 --- a/nbs/docs/tutorials/IntermittentData.ipynb +++ b/nbs/docs/tutorials/IntermittentData.ipynb @@ -9606,7 +9606,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - " [Croston, J. D. (1972). Forecasting and stock control for intermittent demands. Journal of the Operational Research Society, 23(3), 289-303.](https://link.springer.com/article/10.1057/jors.1972.50) " + "[Croston, J. D. (1972). Forecasting and stock control for intermittent\n", + " demands. Journal of the Operational Research Society, 23(3),\n", + " 289-303.](https://link.springer.com/article/10.1057/jors.1972.50)" ] } ], diff --git a/nbs/src/adapters.prophet.ipynb b/nbs/src/adapters.prophet.ipynb index 07208823b..2cdf836f5 100644 --- a/nbs/src/adapters.prophet.ipynb +++ b/nbs/src/adapters.prophet.ipynb @@ -77,7 +77,7 @@ "id": "cd013cee", "metadata": {}, "source": [ - "# 1. AutoARIMA Adapter " + "# 1. AutoARIMA Adapter" ] }, { @@ -349,7 +349,7 @@ "id": "016a5fd2", "metadata": {}, "source": [ - "# 2. Prophet Substitution Example " + "# 2. Prophet Substitution Example" ] }, { @@ -386,7 +386,7 @@ "id": "312bd903-a199-4412-926f-33d57a68d2b8", "metadata": {}, "source": [ - "## 2.1 Univariate Prophet \n", + "## 2.1 Univariate Prophet\n", "\n", "Here we forecast with `Prophet` without external regressors. We first instantiate a new `Prophet` object, and define its forecasting procedure into its constructor. After that a classic sklearn `fit` and `predict` is used to obtain the predictions." ] @@ -454,7 +454,7 @@ "id": "6c7034db-3440-43ce-98c0-54a598da04d6", "metadata": {}, "source": [ - "## 2.2 Holiday Prophet " + "## 2.2 Holiday Prophet" ] }, { diff --git a/nbs/src/core/distributed.fugue.ipynb b/nbs/src/core/distributed.fugue.ipynb index 992619618..0b9c39cc6 100644 --- a/nbs/src/core/distributed.fugue.ipynb +++ b/nbs/src/core/distributed.fugue.ipynb @@ -29,7 +29,11 @@ "source": [ "# FugueBackend\n", "\n", - "> The computational efficiency of `StatsForecast` can be tracked to its two core components:
    1. Its `models` written in NumBa that optimizes Python code to reach C speeds.
    2. Its `core.StatsForecast` class that enables distributed computing.

    Here we use [Fugue](https://github.com/fugue-project/fugue) which is a unified interface for `Dask` and `Spark`.

    " + "The computational efficiency of `StatsForecast` can be tracked to its two core components:\n", + "1. Its `models` written in NumBa that optimizes Python code to reach C speeds.\n", + "2. Its `core.StatsForecast` class that enables distributed computing.\n", + "\n", + "Here we use [Fugue](https://github.com/fugue-project/fugue) which is a unified interface for `Dask` and `Spark`." ] }, { diff --git a/nbs/src/core/models.ipynb b/nbs/src/core/models.ipynb index 5b2ad2dcf..3c14b4f69 100644 --- a/nbs/src/core/models.ipynb +++ b/nbs/src/core/models.ipynb @@ -356,7 +356,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Automatic Forecasting " + "# Automatic Forecasting" ] }, { @@ -379,10 +379,12 @@ " Automatically selects the best ARIMA (AutoRegressive Integrated Moving Average) \n", " model using an information criterion. Default is Akaike Information Criterion (AICc). \n", " \n", - " **Note:**
    \n", + " **Note:**\n", + "\n", " This implementation is a mirror of Hyndman's [forecast::auto.arima](https://github.com/robjhyndman/forecast).\n", " \n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Rob J. Hyndman, Yeasmin Khandakar (2008). \"Automatic Time Series Forecasting: The forecast package for R\"](https://www.jstatsoft.org/article/view/v027i03).\n", " \n", " Parameters\n", @@ -1042,10 +1044,12 @@ " If the component is selected as 'Z', it operates as a placeholder to ask the AutoETS model\n", " to figure out the best parameter.\n", " \n", - " **Note:**
    \n", + " **Note:**\n", + "\n", " This implementation is a mirror of Hyndman's [forecast::ets](https://github.com/robjhyndman/forecast).\n", " \n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Rob J. Hyndman, Yeasmin Khandakar (2008). \"Automatic Time Series Forecasting: The forecast package for R\"](https://www.jstatsoft.org/article/view/v027i03).\n", " \n", " [Hyndman, Rob, et al (2008). \"Forecasting with exponential smoothing: the state space approach\"](https://robjhyndman.com/expsmooth/).\n", @@ -1548,7 +1552,8 @@ " If the component is selected as 'Z', it operates as a placeholder to ask the AutoCES model\n", " to figure out the best parameter.\n", " \n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Svetunkov, Ivan & Kourentzes, Nikolaos. (2015). \"Complex Exponential Smoothing\". 10.13140/RG.2.1.3757.2562. ](https://onlinelibrary.wiley.com/doi/full/10.1002/nav.22074).\n", " \n", " Parameters\n", @@ -1986,7 +1991,8 @@ " Optimized Theta Model ('OTM'), Dynamic Standard Theta Model ('DSTM'),\n", " Dynamic Optimized Theta Model ('DOTM')) model using mse. \n", " \n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Jose A. Fiorucci, Tiago R. Pellegrini, Francisco Louzada, Fotios Petropoulos, Anne B. Koehler (2016). \"Models for optimising the theta method and their relationship to state space models\". International Journal of Forecasting](https://www.sciencedirect.com/science/article/pii/S0169207016300243)\n", " \n", " Parameters\n", @@ -2338,7 +2344,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# ARIMA family " + "# ARIMA family" ] }, { @@ -2360,7 +2366,8 @@ "\n", " AutoRegressive Integrated Moving Average model.\n", "\n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Rob J. Hyndman, Yeasmin Khandakar (2008). \"Automatic Time Series Forecasting: The forecast package for R\"](https://www.jstatsoft.org/article/view/v027i03).\n", " \n", " Parameters\n", @@ -3030,7 +3037,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# ExponentialSmoothing " + "# ExponentialSmoothing" ] }, { @@ -3176,7 +3183,8 @@ "\n", " The rate $0 \\leq \\\\alpha \\leq 1$ at which the weights decrease is called the smoothing parameter. When $\\\\alpha = 1$, SES is equal to the naive method.\n", "\n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Charles C Holt (1957). “Forecasting seasonals and trends by exponentially weighted moving averages”](https://doi.org/10.1016/j.ijforecast).\n", "\n", " Parameters\n", @@ -3492,7 +3500,8 @@ "\n", " The smoothing parameter $\\\\alpha^*$ is optimized by square error minimization.\n", "\n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Charles C Holt (1957). “Forecasting seasonals and trends by exponentially weighted moving averages”](https://doi.org/10.1016/j.ijforecast).\n", "\n", " Parameters\n", @@ -3804,11 +3813,13 @@ " Assuming there are $t$ observations and season $s$, the one-step forecast is given by: \n", " $\\hat{y}_{t+1,s} = \\\\alpha y_t + (1-\\\\alpha) \\hat{y}_{t-1,s}$\n", "\n", - " **Note:**
    \n", + " **Note:**\n", + "\n", " This method is an extremely simplified of Holt-Winter's method where the trend and level are set to zero.\n", " And a single seasonal smoothing parameter $\\\\alpha$ is shared across seasons.\n", "\n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Charles. C. Holt (1957). \"Forecasting seasonals and trends by exponentially weighted moving averages\", ONR Research Memorandum, Carnegie Institute of Technology 52.](https://www.sciencedirect.com/science/article/abs/pii/S0169207003001134).\n", " [Peter R. Winters (1960). \"Forecasting sales by exponentially weighted moving averages\". Management Science](https://pubsonline.informs.org/doi/abs/10.1287/mnsc.6.3.324).\n", "\n", @@ -4182,11 +4193,13 @@ " \n", " The smoothing parameter $\\\\alpha^*$ is optimized by square error minimization. \n", "\n", - " **Note:**
    \n", + " **Note:**\n", + "\n", " This method is an extremely simplified of Holt-Winter's method where the trend and level are set to zero.\n", " And a single seasonal smoothing parameter $\\\\alpha$ is shared across seasons.\n", "\n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Charles. C. Holt (1957). \"Forecasting seasonals and trends by exponentially weighted moving averages\", ONR Research Memorandum, Carnegie Institute of Technology 52.](https://www.sciencedirect.com/science/article/abs/pii/S0169207003001134).\n", " [Peter R. Winters (1960). \"Forecasting sales by exponentially weighted moving averages\". Management Science](https://pubsonline.informs.org/doi/abs/10.1287/mnsc.6.3.324).\n", " \n", @@ -4486,7 +4499,8 @@ " Also known as double exponential smoothing, Holt's method is an extension of exponential smoothing for series with a trend.\n", " This implementation returns the corresponding `ETS` model with additive (A) or multiplicative (M) errors (so either 'AAN' or 'MAN'). \n", "\n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Rob J. Hyndman and George Athanasopoulos (2018). \"Forecasting principles and practice, Methods with trend\"](https://otexts.com/fpp3/holt.html).\n", "\n", " Parameters\n", @@ -4692,7 +4706,8 @@ " Also known as triple exponential smoothing, Holt-Winters' method is an extension of exponential smoothing for series that contain both trend and seasonality.\n", " This implementation returns the corresponding `ETS` model with additive (A) or multiplicative (M) errors (so either 'AAA' or 'MAM'). \n", " \n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Rob J. Hyndman and George Athanasopoulos (2018). \"Forecasting principles and practice, Methods with seasonality\"](https://otexts.com/fpp3/holt-winters.html).\n", " \n", " Parameters\n", @@ -4880,7 +4895,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Baseline Models " + "# Baseline Models" ] }, { @@ -4929,7 +4944,8 @@ " Assuming there are $t$ observations, the one-step forecast is given by: \n", " $$ \\hat{y}_{t+1} = \\\\frac{1}{t} \\sum_{j=1}^t y_j $$\n", "\n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Rob J. Hyndman and George Athanasopoulos (2018). \"Forecasting principles and practice, Simple Methods\"](https://otexts.com/fpp3/simple-methods.html).\n", "\n", " Parameters \n", @@ -5254,7 +5270,8 @@ " All forecasts have the value of the last observation: \n", " $\\hat{y}_{t+1} = y_t$ for all $t$\n", " \n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Rob J. Hyndman and George Athanasopoulos (2018). \"forecasting principles and practice, Simple Methods\"](https://otexts.com/fpp3/simple-methods.html). \n", " \n", " Parameters \n", @@ -5674,7 +5691,8 @@ " From the previous equation, we can see that this is equivalent to extrapolating a line between \n", " the first and the last observation. \n", "\n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Rob J. Hyndman and George Athanasopoulos (2018). \"forecasting principles and practice, Simple Methods\"](https://otexts.com/fpp3/simple-methods.html).\n", "\n", " Parameters\n", @@ -6022,7 +6040,8 @@ "\n", " A method similar to the naive, but uses the last known observation of the same period (e.g. the same month of the previous year) in order to capture seasonal variations.\n", "\n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Rob J. Hyndman and George Athanasopoulos (2018). \"forecasting principles and practice, Simple Methods\"](https://otexts.com/fpp3/simple-methods.html#seasonal-na%C3%AFve-method).\n", "\n", " Parameters\n", @@ -6410,7 +6429,8 @@ " The length of the window selected should take into account the importance of past\n", " observations and how fast the series changes.\n", "\n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Rob J. Hyndman and George Athanasopoulos (2018). \"forecasting principles and practice, Simple Methods\"](https://otexts.com/fpp3/simple-methods.html).\n", "\n", " Parameters\n", @@ -6722,7 +6742,8 @@ "\n", " An average of the last $k$ observations of the same period, with $k$ the length of the window.\n", "\n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Rob J. Hyndman and George Athanasopoulos (2018). \"forecasting principles and practice, Simple Methods\"](https://otexts.com/fpp3/simple-methods.html).\n", "\n", " Parameters\n", @@ -6998,7 +7019,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Sparse or Intermittent " + "# Sparse or Intermittent" ] }, { @@ -7057,7 +7078,8 @@ " They are notoriously hard to forecast, and so, different methods have been developed \n", " especifically for them.\n", " \n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Nikolopoulos, K., Syntetos, A. A., Boylan, J. E., Petropoulos, F., & Assimakopoulos, V. (2011). An aggregate–disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis. Journal of the Operational Research Society, 62(3), 544-554.](https://researchportal.bath.ac.uk/en/publications/an-aggregate-disaggregate-intermittent-demand-approach-adida-to-f).\n", " \n", " Parameters\n", @@ -7378,7 +7400,8 @@ " where $\\hat{z}_t$ and $\\hat{p}_t$ are forecasted using SES. The smoothing parameter \n", " of both components is set equal to 0.1\n", "\n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Croston, J. D. (1972). Forecasting and stock control for intermittent demands. Journal of the Operational Research Society, 23(3), 289-303.](https://link.springer.com/article/10.1057/jors.1972.50)\n", " \n", " Parameters\n", @@ -7687,7 +7710,8 @@ " selected from the range $[0.1,0.3]$. Both the non-zero demand $z_t$ and the inter-demand \n", " intervals $p_t$ are smoothed separately, so their smoothing parameters can be different.\n", "\n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Croston, J. D. (1972). Forecasting and stock control for intermittent demands. Journal of the Operational Research Society, 23(3), 289-303.](https://link.springer.com/article/10.1057/jors.1972.50).\n", "\n", " Parameters\n", @@ -7984,7 +8008,8 @@ " forecast is given by:\n", " $$ \\hat{y}_t = 0.95 \\\\frac{\\hat{z}_t}{\\hat{p}_t} $$\n", "\n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Croston, J. D. (1972). Forecasting and stock control for intermittent demands. Journal of the Operational Research Society, 23(3), 289-303.](https://link.springer.com/article/10.1057/jors.1972.50).\n", "\n", " Parameters\n", @@ -8294,7 +8319,8 @@ " dynamics of the data. Uses the optimized SES to generate the forecasts at the new levels\n", " and then combines them using a simple average.\n", " \n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Syntetos, A. A., & Boylan, J. E. (2021). Intermittent demand forecasting: Context, methods and applications. John Wiley & Sons.](https://www.ifors.org/intermittent-demand-forecasting-context-methods-and-applications/).\n", "\n", " Parameters\n", @@ -8619,7 +8645,8 @@ " Both $d_t$ and $z_t$ are forecasted using SES. The smooting paramaters of each may differ, \n", " like in the optimized Croston's method.\n", "\n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Teunter, R. H., Syntetos, A. A., & Babai, M. Z. (2011). Intermittent demand: Linking forecasting to inventory obsolescence. European Journal of Operational Research, 214(3), 606-615.](https://www.sciencedirect.com/science/article/abs/pii/S0377221711004437)\n", "\n", " Parameters\n", @@ -8885,7 +8912,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Multiple Seasonalities " + "# Multiple Seasonalities" ] }, { @@ -8929,7 +8956,8 @@ " in multiple seasonalities using LOESS. Then forecasts the trend using \n", " a custom non-seaonal model and each seasonality using a SeasonalNaive model.\n", " \n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Bandara, Kasun & Hyndman, Rob & Bergmeir, Christoph. (2021). \"MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple Seasonal Patterns\".](https://arxiv.org/abs/2107.13462).\n", " \n", " Parameters\n", @@ -9435,7 +9463,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Theta Family " + "# Theta Family" ] }, { @@ -9455,7 +9483,8 @@ "class Theta(AutoTheta): \n", " \"\"\" Standard Theta Method. \n", "\n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Jose A. Fiorucci, Tiago R. Pellegrini, Francisco Louzada, Fotios Petropoulos, Anne B. Koehler (2016). \"Models for optimising the theta method and their relationship to state space models\". International Journal of Forecasting](https://www.sciencedirect.com/science/article/pii/S0169207016300243)\n", "\n", " Parameters\n", @@ -9657,7 +9686,8 @@ "class OptimizedTheta(AutoTheta): \n", " \"\"\" Optimized Theta Method. \n", "\n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Jose A. Fiorucci, Tiago R. Pellegrini, Francisco Louzada, Fotios Petropoulos, Anne B. Koehler (2016). \"Models for optimising the theta method and their relationship to state space models\". International Journal of Forecasting](https://www.sciencedirect.com/science/article/pii/S0169207016300243)\n", "\n", " Parameters\n", @@ -9861,7 +9891,8 @@ "class DynamicTheta(AutoTheta): \n", " \"\"\" Dynamic Standard Theta Method. \n", "\n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Jose A. Fiorucci, Tiago R. Pellegrini, Francisco Louzada, Fotios Petropoulos, Anne B. Koehler (2016). \"Models for optimising the theta method and their relationship to state space models\". International Journal of Forecasting](https://www.sciencedirect.com/science/article/pii/S0169207016300243)\n", "\n", " Parameters\n", @@ -10065,7 +10096,8 @@ "class DynamicOptimizedTheta(AutoTheta): \n", " \"\"\" Dynamic Optimized Theta Method. \n", " \n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Jose A. Fiorucci, Tiago R. Pellegrini, Francisco Louzada, Fotios Petropoulos, Anne B. Koehler (2016). \"Models for optimising the theta method and their relationship to state space models\". International Journal of Forecasting](https://www.sciencedirect.com/science/article/pii/S0169207016300243)\n", "\n", " Parameters\n", @@ -10255,7 +10287,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# ARCH Family" + "# ARCH Family" ] }, { @@ -10292,7 +10324,8 @@ " \n", " The ARCH model is a particular case of the GARCH model when $q=0$. \n", " \n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the econometric society, 987-1007.](http://www.econ.uiuc.edu/~econ508/Papers/engle82.pdf) \n", " \n", " [Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=7da8bfa5295375c1141d797e80065a599153c19d)\n", @@ -10635,10 +10668,11 @@ " \n", " $$ \\sigma_t^2 = w0 + \\sum_{i=1}^p a_i y_{t-i}^2$$. \n", " \n", - " Here {$\\epsilon_t$} is a sequence of iid random variables with zero mean and unit variance. \n", + " Here $\\epsilon_t$ is a sequence of iid random variables with zero mean and unit variance. \n", " The coefficients $w$ and $a_i$, $i=1,...,p$ must be nonnegative and $\\sum_{k=1}^p a_k < 1$. \n", " \n", - " **References:**
    \n", + " **References:**\n", + "\n", " [Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the econometric society, 987-1007.](http://www.econ.uiuc.edu/~econ508/Papers/engle82.pdf) \n", "\n", " [James D. Hamilton. Time Series Analysis Princeton University Press, Princeton, New Jersey, 1st Edition, 1994.](https://press.princeton.edu/books/hardcover/9780691042893/time-series-analysis)\n", @@ -10767,7 +10801,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Fallback Models " + "# Fallback Models" ] }, { @@ -11364,7 +11398,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# References " + "# References" ] }, { diff --git a/nbs/src/utils.ipynb b/nbs/src/utils.ipynb index f2c8bcea4..c11fbcdc2 100644 --- a/nbs/src/utils.ipynb +++ b/nbs/src/utils.ipynb @@ -27,7 +27,7 @@ "source": [ "# Utils\n", "\n", - "> The `core.StatsForecast` class allows you to efficiently fit multiple `StatsForecast` models for large sets of time series. It operates with pandas DataFrame `df` that identifies individual series and datestamps with the `unique_id` and `ds` columns, and the `y` column denotes the target time series variable. To assist development, we declare useful datasets that we use throughout all `StatsForecast`'s unit tests.

    " + "> The `core.StatsForecast` class allows you to efficiently fit multiple `StatsForecast` models for large sets of time series. It operates with pandas DataFrame `df` that identifies individual series and datestamps with the `unique_id` and `ds` columns, and the `y` column denotes the target time series variable. To assist development, we declare useful datasets that we use throughout all `StatsForecast`'s unit tests." ] }, { @@ -85,7 +85,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 1. Synthetic Panel Data " + "# 1. Synthetic Panel Data" ] }, { @@ -157,7 +157,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 2. AirPassengers Data \n", + "# 2. AirPassengers Data\n", "\n", "The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.\n", "\n", diff --git a/nbs/styles.css b/nbs/styles.css index ef52213db..17f7c8a54 100644 --- a/nbs/styles.css +++ b/nbs/styles.css @@ -11,7 +11,9 @@ margin-bottom: 0; } -.cell-output > pre, .cell-output > .sourceCode > pre, .cell-output-stdout > pre { +.cell-output > pre, +.cell-output > .sourceCode > pre, +.cell-output-stdout > pre { margin-left: 0.8rem; margin-top: 0; background: none; @@ -60,8 +62,8 @@ blockquote > pre { /* new */ @font-face { - font-family: 'Inter'; - src: url('./assets/Inter-VariableFont.ttf') format('ttf') + font-family: "Inter"; + src: url("./assets/Inter-VariableFont.ttf") format("ttf"); } :root { @@ -69,9 +71,10 @@ blockquote > pre { --secondary: rgb(255, 112, 0); } -html, body { +html, +body { color: #374151; - font-family: 'Inter', sans-serif; + font-family: "Inter", sans-serif; } header { @@ -110,20 +113,20 @@ a > code:hover { background: rgba(255, 255, 255, 0.95); backdrop-filter: blur(8px); -webkit-backdrop-filter: blur(8px); /* For Safari support */ - border-bottom: 1px solid rgba(17, 24,39, 0.05); + border-bottom: 1px solid rgba(17, 24, 39, 0.05); } .nav-link { - color:rgba(17, 24,39, 0.6) !important; + color: rgba(17, 24, 39, 0.6) !important; font-size: 0.875rem; } .nav-link.active { - color:rgb(17, 24,39) !important; + color: rgb(17, 24, 39) !important; } .aa-SubmitIcon { - fill: rgba(17, 24,39, 0.6) !important; + fill: rgba(17, 24, 39, 0.6) !important; height: 20px !important; margin-top: -2px; } @@ -138,7 +141,7 @@ a > code:hover { } .navbar-brand { - background-image: url('./assets/logo.png'); + background-image: url("./assets/logo.png"); background-size: contain; background-repeat: no-repeat; background-position: center; @@ -154,8 +157,13 @@ a > code:hover { width: 100%; } -h1, h2, h3, h4, h5, h6 { - color: rgb(17, 24,39); +h1, +h2, +h3, +h4, +h5, +h6 { + color: rgb(17, 24, 39); margin-top: 3rem; } @@ -167,7 +175,7 @@ h1.title { div.description { font-style: normal; - font-size: .875rem; + font-size: 0.875rem; line-height: 1.25rem; } @@ -176,7 +184,12 @@ p { } /* menu */ -.sidebar-menu-container > ul > li:first-child > .sidebar-item-container > a > span { +.sidebar-menu-container + > ul + > li:first-child + > .sidebar-item-container + > a + > span { font-weight: 600 !important; font-size: 0.875rem; color: var(--secondary); @@ -187,7 +200,7 @@ div.sidebar-item-container { } .sidebar-divider.hi { - color: rgb(0,0,0, 0.2); + color: rgb(0, 0, 0, 0.2); margin-top: 0.5rem; margin-bottom: 1rem; } @@ -200,7 +213,6 @@ div.sidebar-item-container { font-weight: 400; } - ul.sidebar-section { padding-left: 0; } @@ -236,12 +248,12 @@ ul.sidebar-section .sidebar-link { } .callout-tip { - background: rgba(63,182,24, 0.05); - border: 1px solid rgba(63,182,24, 0.25) !important; + background: rgba(63, 182, 24, 0.05); + border: 1px solid rgba(63, 182, 24, 0.25) !important; } .callout-note { - background: rgba(59 , 130, 246, 0.05); + background: rgba(59, 130, 246, 0.05); border: 1px solid rgba(59, 130, 246, 0.25) !important; } @@ -296,8 +308,6 @@ code { overflow-x: scroll !important; } - - .cell-output > div { border: 1px solid rgba(100, 116, 139, 0.2) !important; border-radius: 1rem; @@ -305,7 +315,8 @@ code { margin-top: 3rem; } -table, .table { +table, +.table { border-radius: 1rem; font-size: 0.875rem; margin-bottom: 0; @@ -331,12 +342,14 @@ thead tr:first-child th:last-child { border-radius: 0 1rem 0 0; } -th, td { +th, +td { padding: 0.5rem 1rem !important; white-space: nowrap !important; } -td a, td a code { +td a, +td a code { white-space: nowrap !important; } @@ -349,7 +362,8 @@ tbody tr:last-child td:first-child { border-radius: 0 0 0 1rem; } -tr.even, tr.odd { +tr.even, +tr.odd { line-height: 2rem; } @@ -357,7 +371,8 @@ tr:hover { background-color: rgba(17, 24, 39, 0.05); } -td:first-child, td:last-child { +td:first-child, +td:last-child { padding: 0.25rem 1rem !important; } @@ -365,7 +380,7 @@ td:first-child, td:last-child { background: white; border: none; border-radius: 0.5rem; - box-shadow: 0 2px 4px rgba(0,0,0,0.1); + box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1); padding-top: 0.5rem !important; padding-bottom: 0.25rem !important; } diff --git a/settings.ini b/settings.ini index 56e2f0b6a..ab5f35046 100644 --- a/settings.ini +++ b/settings.ini @@ -32,4 +32,4 @@ title = %(lib_name)s black_formatting = True jupyter_hooks = True clean_ids = True -readme_nb = index.ipynb +readme_nb = index.ipynb \ No newline at end of file From 48fbe911205c8a0a34c2bb7a37abf9f0cd061f44 Mon Sep 17 00:00:00 2001 From: Hahnbee Lee <55263191+hahnbeelee@users.noreply.github.com> Date: Wed, 18 Oct 2023 14:31:24 -0700 Subject: [PATCH 09/22] re-add deleted ci check --- .github/workflows/ci.yaml | 118 +++++++++++++++++++++++++++ .github/workflows/deploy.yaml | 9 ++ .github/workflows/lint.yaml | 28 +++++++ .github/workflows/no-response.yaml | 21 +++++ .github/workflows/python-publish.yml | 25 ++++++ 5 files changed, 201 insertions(+) create mode 100644 .github/workflows/ci.yaml create mode 100644 .github/workflows/deploy.yaml create mode 100644 .github/workflows/lint.yaml create mode 100644 .github/workflows/no-response.yaml create mode 100644 .github/workflows/python-publish.yml diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml new file mode 100644 index 000000000..59254ab31 --- /dev/null +++ b/.github/workflows/ci.yaml @@ -0,0 +1,118 @@ +name: CI + +on: + push: + branches: [main] + pull_request: + branches: [main] + +defaults: + run: + shell: bash -l {0} + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +env: + NIXTLA_NUMBA_CACHE: '1' + +jobs: + nb-sync: + runs-on: ubuntu-latest + steps: + - name: Clone repo + uses: actions/checkout@v2 + + - name: Set up python + uses: actions/setup-python@v2 + + - name: Install nbdev + run: pip install nbdev + + - name: Check if all notebooks are cleaned + run: | + echo "Check we are starting with clean git checkout" + if [ -n "$(git status -uno -s)" ]; then echo "git status is not clean"; false; fi + echo "Trying to strip out notebooks" + ./action_files/clean_nbs + echo "Check that strip out was unnecessary" + git status -s # display the status to see which nbs need cleaning up + if [ -n "$(git status -uno -s)" ]; then echo -e "!!! Detected unstripped out notebooks\n!!!Remember to run nbdev_install_git_hooks and running ./action_files/clean_nbs before committing"; false; fi + + run-local-tests: + runs-on: windows-latest + strategy: + fail-fast: false + matrix: + python-version: [3.7, 3.8, 3.9, '3.10', '3.11'] + steps: + - name: Clone repo + uses: actions/checkout@v3 + + - name: Set up environment + uses: mamba-org/setup-micromamba@v1 + with: + environment-file: dev/local_environment.yml + create-args: python=${{ matrix.python-version }} + cache-environment: true + + - name: Install pip requirements + run: pip install ./ + + - name: Run local tests + run: nbdev_test --skip_file_re '(models|distributed|ets).*.ipynb' --pause 1.0 + + run-tests: + runs-on: ${{ matrix.os }} + timeout-minutes: 60 + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest, macos-latest] + python-version: [3.7, 3.8, 3.9, '3.10', '3.11'] + steps: + - name: Clone repo + uses: actions/checkout@v3 + + - name: Set up environment + uses: mamba-org/setup-micromamba@v1 + with: + environment-file: dev/environment.yml + create-args: python=${{ matrix.python-version }} + cache-environment: true + + - name: Install pip requirements + run: pip install ./ + + - name: Run tests + run: nbdev_test --skip_file_re '(distributed).*.ipynb' --pause 1.0 + + - name: Run integration tests + run: | + pip install ".[dev]" pytest + pytest --durations=0 action_files + + test-m3-performance: + runs-on: ubuntu-latest + strategy: + fail-fast: false + steps: + - name: Clone repo + uses: actions/checkout@v3 + + - name: Set up environment + uses: mamba-org/setup-micromamba@v1 + with: + environment-file: dev/environment.yml + create-args: python=3.10 + cache-environment: true + + - name: Install library and extra deps + run: pip install ".[dev]" fire + + - name: Run M3 experiment + run: | + python -m src.experiment + python -m src.evaluation --test + working-directory: ./experiments/m3 diff --git a/.github/workflows/deploy.yaml b/.github/workflows/deploy.yaml new file mode 100644 index 000000000..8d077a768 --- /dev/null +++ b/.github/workflows/deploy.yaml @@ -0,0 +1,9 @@ +name: Deploy to GitHub Pages +on: + push: + branches: ["main"] + workflow_dispatch: +jobs: + deploy: + runs-on: ubuntu-latest + steps: [uses: fastai/workflows/quarto-ghp@master] diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml new file mode 100644 index 000000000..047073aa0 --- /dev/null +++ b/.github/workflows/lint.yaml @@ -0,0 +1,28 @@ +name: Lint + +on: + push: + branches: [main] + pull_request: + branches: [main] + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - name: Clone repo + uses: actions/checkout@v2 + + - name: Set up python + uses: actions/setup-python@v2 + with: + python-version: 3.8 + + - name: Install linters + run: pip install mypy types-setuptools flake8 + + - name: mypy + run: mypy statsforecast/ + + - name: flake8 + run: flake8 --select=F statsforecast/ diff --git a/.github/workflows/no-response.yaml b/.github/workflows/no-response.yaml new file mode 100644 index 000000000..a2b26e127 --- /dev/null +++ b/.github/workflows/no-response.yaml @@ -0,0 +1,21 @@ +name: No Response Bot + +on: + issue_comment: + types: [created] + schedule: + - cron: '0 4 * * *' + +jobs: + noResponse: + runs-on: ubuntu-latest + steps: + - uses: lee-dohm/no-response@v0.5.0 + with: + closeComment: > + This issue has been automatically closed because it has been awaiting a response for too long. + When you have time to to work with the maintainers to resolve this issue, please post a new comment and it will be re-opened. + If the issue has been locked for editing by the time you return to it, please open a new issue and reference this one. + daysUntilClose: 30 + responseRequiredLabel: awaiting response + token: ${{ github.token }} diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml new file mode 100644 index 000000000..58b53adf6 --- /dev/null +++ b/.github/workflows/python-publish.yml @@ -0,0 +1,25 @@ +name: Upload Python Package + +on: + release: + types: [published] + +jobs: + deploy: + + runs-on: ubuntu-latest + permissions: + id-token: write + + steps: + - uses: actions/checkout@v3 + - name: Set up Python + uses: actions/setup-python@v3 + with: + python-version: '3.10' + - name: Install dependencies + run: python -m pip install build wheel + - name: Build package + run: python -m build -sw + - name: Publish package + uses: pypa/gh-action-pypi-publish@release/v1 From cca00b0e92afcee7204f4782793f63e89a276813 Mon Sep 17 00:00:00 2001 From: Hahnbee Lee <55263191+hahnbeelee@users.noreply.github.com> Date: Wed, 18 Oct 2023 14:43:50 -0700 Subject: [PATCH 10/22] revive deleted files --- nbs/custom.yml | 22 ++++++++++++++++ nbs/docs/contribute/step-by-step.md | 40 ++++++++++++++--------------- nbs/sidebar.yml | 17 ++++++++++++ 3 files changed, 59 insertions(+), 20 deletions(-) create mode 100644 nbs/custom.yml create mode 100644 nbs/sidebar.yml diff --git a/nbs/custom.yml b/nbs/custom.yml new file mode 100644 index 000000000..1b2c854c2 --- /dev/null +++ b/nbs/custom.yml @@ -0,0 +1,22 @@ +website: + logo: https://github.com/Nixtla/styles/blob/b9ea432cfa2dae20fc84d8634cae6db902f9ca3f/images/Nixtla_Blanco.png + reader-mode: false + navbar: + collapse-below: lg + left: + - text: "Get Started" + href: examples/Getting_Started_with_Auto_Arima_and_ETS.ipynb + - text: "Experiments" + href: https://github.com/Nixtla/statsforecast/tree/main/experiments + - text: "Help" + menu: + - text: "Report an Issue" + icon: bug + href: https://github.com/nixtla/statsforecast/issues + - text: "Slack Nixtla" + icon: chat-right-text + href: https://join.slack.com/t/nixtlaworkspace/shared_invite/zt-135dssye9-fWTzMpv2WBthq8NK0Yvu6A + right: + - icon: twitter + href: https://twitter.com/nixtlainc + aria-label: Nixtla Twitter diff --git a/nbs/docs/contribute/step-by-step.md b/nbs/docs/contribute/step-by-step.md index 90634190e..49848ff9d 100644 --- a/nbs/docs/contribute/step-by-step.md +++ b/nbs/docs/contribute/step-by-step.md @@ -15,35 +15,35 @@ Sometimes, diving into a new technology can be challenging and overwhelming. We' 7. [Start Coding](#start-coding) 8. [Example with Screen-shots](#example-with-screen-shots) -## Prerequisites +## Prerequisites -- _GitHub_: You should already have a GitHub account and a basic understanding of its functionalities. Alternatively check [this guide](https://docs.github.com/en/get-started). -- _Python_: Python should be installed on your system. Alternatively check [this guide](https://www.python.org/downloads/). -- _conda_: You need to have conda installed, along with a good grasp of fundamental operations such as creating environments, and activating and deactivating them. Alternatively check [this guide](https://conda.io/projects/conda/en/latest/user-guide/install/index.html). +- *GitHub*: You should already have a GitHub account and a basic understanding of its functionalities. Alternatively check [this guide](https://docs.github.com/en/get-started). +- *Python*: Python should be installed on your system. Alternatively check [this guide](https://www.python.org/downloads/). +- *conda*: You need to have conda installed, along with a good grasp of fundamental operations such as creating environments, and activating and deactivating them. Alternatively check [this guide](https://conda.io/projects/conda/en/latest/user-guide/install/index.html). ## Git `fork-and-pull` worklow -**1. Fork the Project:** +**1. Fork the Project:** Start by forking the Nixtla repository to your own GitHub account. This creates a personal copy of the project where you can make changes without affecting the main repository. **2. Clone the Forked Repository** -Clone the forked repository to your local machine using `git clone https://github.com//nixtla.git`. This allows you to work with the code directly on your system. +Clone the forked repository to your local machine using `git clone https://github.com//nixtla.git`. This allows you to work with the code directly on your system. -**3. Create a Branch:** +**3. Create a Branch:** Branching in GitHub is a key strategy for effectively managing and isolating changes to your project. It allows you to segregate work on different features, fixes, and issues without interfering with the main, production-ready codebase. -1. _Main Branch_: The default branch with production-ready code. +1. *Main Branch*: The default branch with production-ready code. -2. _Feature Branches_: For new features, create branches prefixed with 'feature/', like `git checkout -b feature/new-model`. +2. *Feature Branches*: For new features, create branches prefixed with 'feature/', like `git checkout -b feature/new-model`. -3. _Fix Branches_: For bug fixes, use 'fix/' prefix, like `git checkout -b fix/forecasting-bug`. +3. *Fix Branches*: For bug fixes, use 'fix/' prefix, like `git checkout -b fix/forecasting-bug`. -4. _Issue Branches_: For specific issues, use `git checkout -b issue/issue-number` or `git checkout -b issue/issue-description`. +4. *Issue Branches*: For specific issues, use `git checkout -b issue/issue-number` or `git checkout -b issue/issue-description`. After testing, branches are merged back into the main branch via a pull request, and then typically deleted to maintain a clean repository. You can read more about github and branching [here](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-and-deleting-branches-within-your-repository). -## Set Up a Conda Environment +## Set Up a Conda Environment > If you want to use Docker or Codespaces, let us know opening an issue and we will set you up. @@ -51,13 +51,13 @@ Next, you'll need to set up a [Conda](https://docs.conda.io/en/latest/) environm First, ensure you have Anaconda or Miniconda installed on your system. Alternatively checkout these guides: [Anaconda](https://www.anaconda.com/), [Miniconda](https://docs.conda.io/en/latest/miniconda.html), and [Mamba](https://mamba.readthedocs.io/en/latest/). -Then, you can create a new environment using `conda create -n nixtla-env python=3.10`. +Then, you can create a new environment using `conda create -n nixtla-env python=3.10`. -You can also use mamba for creating the environment (mamba is faster than Conda) using `mamba create -n nixtla-env python=3.10`. +You can also use mamba for creating the environment (mamba is faster than Conda) using `mamba create -n nixtla-env python=3.10`. You can replace `nixtla-env` for something more meaningful to you. Eg. `statsforecast-env` or `mlforecast-env`. You can always check the list of environments in your system using `conda env list`. -Activate your new environment with `conda activate nixtla-env`. +Activate your new environment with `conda activate nixtla-env`. ## Install required libraries for development @@ -71,7 +71,7 @@ Sometimes (e.g. StatsForecast) the `enviorment.yml` is sometimes inside a folder ## Start editable mode -Install the library in editable mode using `pip install -e ".[dev]"`. +Install the library in editable mode using `pip install -e ".[dev]"`. This means the package is linked directly to the source code, allowing any changes made to the source code to be immediately reflected in your Python environment without the need to reinstall the package. This is useful for testing changes during package development. @@ -101,8 +101,9 @@ Open a jupyter notebook using `jupyter lab` (or VS Code). 2. **Commit Your Changes:** Add the changed files using `git add [your_modified_file_0.ipynb] [your_modified_file_1.ipynb]`, then commit these changes using `git commit -m ": "`. Please use [Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0/) -3. **Push Your Changes:** - Push your changes to the remote repository on GitHub with `git push origin feature/your-feature-name`. + +3. **Push Your Changes:** +Push your changes to the remote repository on GitHub with `git push origin feature/your-feature-name`. 4. **Open a Pull Request:** Open a pull request from your new branch on the Nixtla repository on GitHub. Provide a thorough description of your changes when creating the pull request. @@ -117,7 +118,6 @@ You can find a detailed step by step buide with screen-shots below. ## Example with Screen-shots ### 1. Create a fork of the mlforecast repo - The first thing you need to do is create a fork of the GitHub repository to your own account: image @@ -229,4 +229,4 @@ Finally, you will see something like this: image ## Notes -- This file was generated using [this file](https://github.com/Nixtla/nixtla-commons/blob/main/docs/contribute/step-by-step.md). Please change that file if you want to enhance the document. +- This file was generated using [this file](https://github.com/Nixtla/nixtla-commons/blob/main/docs/contribute/step-by-step.md). Please change that file if you want to enhance the document. \ No newline at end of file diff --git a/nbs/sidebar.yml b/nbs/sidebar.yml new file mode 100644 index 000000000..e9f29d299 --- /dev/null +++ b/nbs/sidebar.yml @@ -0,0 +1,17 @@ +website: + reader-mode: false + sidebar: + collapse-level: 1 + contents: + - index.ipynb + - text: "--" + - section: "Getting Started" + contents: docs/getting-started/* + - section: "Tutorials" + contents: docs/tutorials/* + - section: "How-To Guides" + contents: docs/how-to-guides/* + - section: "API Reference" + contents: src/core/* + - section: "Contributing" + contents: docs/contribute/* From c5dc6f766fad7a4ae27eac374089dd1fb1a57dd2 Mon Sep 17 00:00:00 2001 From: Hahnbee Lee <55263191+hahnbeelee@users.noreply.github.com> Date: Wed, 18 Oct 2023 15:19:41 -0700 Subject: [PATCH 11/22] fix sidebar.yml --- nbs/sidebar.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/nbs/sidebar.yml b/nbs/sidebar.yml index e9f29d299..ce8e9088f 100644 --- a/nbs/sidebar.yml +++ b/nbs/sidebar.yml @@ -11,6 +11,8 @@ website: contents: docs/tutorials/* - section: "How-To Guides" contents: docs/how-to-guides/* + - section: "Model References" + contents: docs/models/* - section: "API Reference" contents: src/core/* - section: "Contributing" From 80c274e018314d36e3da7b539eb5278a1411b370 Mon Sep 17 00:00:00 2001 From: Hahnbee Lee <55263191+hahnbeelee@users.noreply.github.com> Date: Wed, 18 Oct 2023 15:25:21 -0700 Subject: [PATCH 12/22] clean up code --- action_files/docs_replace_imgs.py | 1 - nbs/_extensions/mintlify/mintlify_renderers.lua | 3 --- nbs/_extensions/mintlify/mintlify_utils.lua | 14 -------------- 3 files changed, 18 deletions(-) delete mode 100644 nbs/_extensions/mintlify/mintlify_renderers.lua diff --git a/action_files/docs_replace_imgs.py b/action_files/docs_replace_imgs.py index 7ce4b2f72..9920c2da7 100644 --- a/action_files/docs_replace_imgs.py +++ b/action_files/docs_replace_imgs.py @@ -22,7 +22,6 @@ def url_replace_helper(match): new_url = str(Path(*folder_arr).joinpath(curr_url)) if not new_url.startswith('/'): new_url = '/' + new_url - print('![](' + str(new_url) + ')') return '![](' + str(new_url) + ')' return url_replace_helper diff --git a/nbs/_extensions/mintlify/mintlify_renderers.lua b/nbs/_extensions/mintlify/mintlify_renderers.lua deleted file mode 100644 index 2597fbbf1..000000000 --- a/nbs/_extensions/mintlify/mintlify_renderers.lua +++ /dev/null @@ -1,3 +0,0 @@ -local codeBlock = require('mintlify_utils').codeBlock - -return {} -- return an empty table as a hack to pretend we're a shortcode handler for now diff --git a/nbs/_extensions/mintlify/mintlify_utils.lua b/nbs/_extensions/mintlify/mintlify_utils.lua index 42e8776c7..ae076ddaa 100644 --- a/nbs/_extensions/mintlify/mintlify_utils.lua +++ b/nbs/_extensions/mintlify/mintlify_utils.lua @@ -1,16 +1,5 @@ --- local scriptCount = 0 --- local printItem = 5 - function codeBlock(el, filename) local lang = el.attr.classes[1] - -- scriptCount = scriptCount + 1 - -- if printItem == scriptCount then - -- quarto.utils.dump(el) - -- end - -- quarto.log.output('---Code block---') - -- quarto.utils.dump(el.output) - -- quarto.utils.dump({}) - -- quarto.log.output('------') local title = filename or el.attr.attributes["filename"] or el.attr.attributes["title"] local showLineNumbers = el.attr.classes:includes('number-lines') if lang or title or showLineNumbers then @@ -26,9 +15,6 @@ function codeBlock(el, filename) end code = code .. "\n" .. el.text .. "\n```\n" - -- quarto.log.output('------') - -- quarto.log.output(code) - -- quarto.log.output('------') -- docusaures code block attributes don't conform to any syntax -- that pandoc natively understands, so return the CodeBlock as -- "raw" markdown (so it bypasses pandoc processing entirely) From e36e72e47a5191b64207dc65c7f2c69a8e1a2125 Mon Sep 17 00:00:00 2001 From: Hahnbee Lee <55263191+hahnbeelee@users.noreply.github.com> Date: Wed, 18 Oct 2023 15:35:20 -0700 Subject: [PATCH 13/22] delete unneeded comments --- nbs/_extensions/mintlify/mintlify.lua | 12 +----------- 1 file changed, 1 insertion(+), 11 deletions(-) diff --git a/nbs/_extensions/mintlify/mintlify.lua b/nbs/_extensions/mintlify/mintlify.lua index a1e464013..b95554781 100644 --- a/nbs/_extensions/mintlify/mintlify.lua +++ b/nbs/_extensions/mintlify/mintlify.lua @@ -41,23 +41,13 @@ Block = function(node) return node end --- local scriptCount = 0 --- local printItem = 1 - -- transform 'mdx' into passthrough content, transform 'html' -- into raw commamark to pass through via dangerouslySetInnerHTML function RawBlock(el) - -- quarto.log.output(el.format) - -- scriptCount = scriptCount + 1 if el.format == 'mdx' then - -- quarto.log.output('---A---') return pandoc.CodeBlock(el.text, pandoc.Attr("", {"mdx-code-block"})) elseif el.format == 'html' then - -- quarto.utils.dump(el.text) - -- quarto.log.output('---B---') - -- if printItem == scriptCount then - -- quarto.utils.dump(el) - -- end + -- track the raw html vars (we'll insert them at the top later on as -- mdx requires all exports be declared together) local html = string.gsub(el.text, "\n+", "\n") From 9ecf43b3ff0bfa9844439116989d7e0db600fc10 Mon Sep 17 00:00:00 2001 From: Hahnbee Lee <55263191+hahnbeelee@users.noreply.github.com> Date: Thu, 19 Oct 2023 00:11:36 -0700 Subject: [PATCH 14/22] move nbdev_plotly to dev_reqs --- settings.ini | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/settings.ini b/settings.ini index 7361c3603..00bcc3f86 100644 --- a/settings.ini +++ b/settings.ini @@ -15,12 +15,12 @@ language = English custom_sidebar = True license = apache2 status = 2 -requirements = numba>=0.55.0 numpy>=1.21.6 pandas>=1.3.5 polars scipy>=1.7.3 statsmodels>=0.13.2 tqdm fugue>=0.8.1 utilsforecast>=0.0.5 nbdev_plotly +requirements = numba>=0.55.0 numpy>=1.21.6 pandas>=1.3.5 polars scipy>=1.7.3 statsmodels>=0.13.2 tqdm fugue>=0.8.1 utilsforecast>=0.0.5 ray_requirements = fugue[ray]>=0.8.1 protobuf>=3.15.3,<4.0.0 dask_requirements = fugue[dask]>=0.8.1 spark_requirements = fugue[spark]>=0.8.1 plotly_requirements = plotly plotly-resampler -dev_requirements = nbdev black mypy flake8 ray protobuf>=3.15.3,<4.0.0 matplotlib pmdarima prophet scikit-learn fugue[dask,ray,spark]>=0.8.1 datasetsforecast supersmoother +dev_requirements = nbdev black mypy flake8 ray protobuf>=3.15.3,<4.0.0 matplotlib pmdarima prophet scikit-learn fugue[dask,ray,spark]>=0.8.1 datasetsforecast supersmoother nbdev_plotly nbs_path = nbs doc_path = _docs recursive = True From 88e39edda49cabb021efbfe732c30d2072e3bbe5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Morales?= Date: Thu, 19 Oct 2023 14:31:20 -0600 Subject: [PATCH 15/22] move scripts to submodule --- .github/workflows/build-docs.yaml | 15 ++- .gitmodules | 4 + action_files/docs-final-formatting.bash | 13 -- action_files/docs_replace_imgs.py | 29 ---- docs-scripts | 1 + nbs/_extensions/mintlify/_extension.yml | 30 ----- nbs/_extensions/mintlify/mintlify.lua | 61 --------- nbs/_extensions/mintlify/mintlify_utils.lua | 33 ----- nbs/_extensions/mintlify/mintlify_writer.lua | 133 ------------------- nbs/_quarto.yml | 10 +- nbs/favicon.svg | 5 - nbs/imgs/logo/dark.png | Bin 2883 -> 0 bytes nbs/imgs/logo/light.png | Bin 2851 -> 0 bytes preview-mintlify-docs.bash | 20 --- 14 files changed, 22 insertions(+), 332 deletions(-) create mode 100644 .gitmodules delete mode 100755 action_files/docs-final-formatting.bash delete mode 100644 action_files/docs_replace_imgs.py create mode 160000 docs-scripts delete mode 100644 nbs/_extensions/mintlify/_extension.yml delete mode 100644 nbs/_extensions/mintlify/mintlify.lua delete mode 100644 nbs/_extensions/mintlify/mintlify_utils.lua delete mode 100644 nbs/_extensions/mintlify/mintlify_writer.lua delete mode 100644 nbs/favicon.svg delete mode 100644 nbs/imgs/logo/dark.png delete mode 100644 nbs/imgs/logo/light.png delete mode 100644 preview-mintlify-docs.bash diff --git a/.github/workflows/build-docs.yaml b/.github/workflows/build-docs.yaml index 139ffd77b..4492b0d82 100644 --- a/.github/workflows/build-docs.yaml +++ b/.github/workflows/build-docs.yaml @@ -2,6 +2,8 @@ name: "build-docs" on: push: branches: ["main"] + pull_request: + branches: ["main"] workflow_dispatch: jobs: build-docs: @@ -12,24 +14,27 @@ jobs: with: cache: "pip" cache-dependency-path: settings.ini + submodules: true - name: Install Dependencies shell: bash run: | set -ux python -m pip install --upgrade pip pip install -Uq nbdev - test -f setup.py && pip install -e ".[dev]" + test -f setup.py && pip install ".[dev]" + mkdir nbs/_extensions + cp -r docs-scripts/mintlify/ nbs/_extensions/ + python docs-scripts/update-quarto.py nbdev_docs - name: Apply final formats shell: bash - run: bash ./action_files/docs-final-formatting.bash + run: bash ./docs-scripts/docs-final-formatting.bash - name: Copy over necessary assets run: | cp nbs/mint.json _docs/mint.json - cp nbs/imgs/logo/dark.png _docs/dark.png - cp nbs/imgs/logo/light.png _docs/light.png - cp nbs/favicon.svg _docs/favicon.svg + cp docs-scripts/imgs/* _docs/ - name: Deploy to Mintlify Docs + if: github.event_name == 'push' uses: peaceiris/actions-gh-pages@v3 with: github_token: ${{ secrets.GITHUB_TOKEN }} diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 000000000..b09d038f8 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,4 @@ +[submodule "docs-scripts"] + path = docs-scripts + url = git@github.com:Nixtla/docs.git + branch = scripts diff --git a/action_files/docs-final-formatting.bash b/action_files/docs-final-formatting.bash deleted file mode 100755 index 07f0badb1..000000000 --- a/action_files/docs-final-formatting.bash +++ /dev/null @@ -1,13 +0,0 @@ -#!/usr/bin/env bash - -for file in $(find _docs -type f -name "*mdx"); do - if [[ "$OSTYPE" == "darwin"* ]]; then - sed -i '' -e 's/style="float:right; font-size:smaller"/style={{ float: "right", fontSize: "smaller" }}/g' $file - sed -i '' -e 's/
    //g' $file - else - sed -i -e 's/style="float:right; font-size:smaller"/style={{ float: "right", fontSize: "smaller" }}/g' $file - sed -i -e 's/
    //g' $file - fi -done - -python3 "$(dirname "$0")/docs_replace_imgs.py" \ No newline at end of file diff --git a/action_files/docs_replace_imgs.py b/action_files/docs_replace_imgs.py deleted file mode 100644 index 9920c2da7..000000000 --- a/action_files/docs_replace_imgs.py +++ /dev/null @@ -1,29 +0,0 @@ -import os -from pathlib import Path -import re - -def main(): - dir_path = os.path.dirname(os.path.realpath(__file__)) - docs_path = os.path.abspath(os.path.join(dir_path, '../_docs')) - docs_path_obj = Path(docs_path) - paths_in_dir = docs_path_obj.glob('**/*.mdx') - pattern = r'!\[\]\(((?!https:\/\/).+?)\)' - - for file_path in paths_in_dir: - if file_path.is_file(): - folder_arr = file_path.parts[len(docs_path_obj.parts):-1] - content = file_path.read_text() - content = re.sub(pattern, url_replace(folder_arr), content) - file_path.write_text(content) - -def url_replace(folder_arr): - def url_replace_helper(match): - curr_url = Path(match[1]) - new_url = str(Path(*folder_arr).joinpath(curr_url)) - if not new_url.startswith('/'): - new_url = '/' + new_url - return '![](' + str(new_url) + ')' - return url_replace_helper - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/docs-scripts b/docs-scripts new file mode 160000 index 000000000..a1f3370b6 --- /dev/null +++ b/docs-scripts @@ -0,0 +1 @@ +Subproject commit a1f3370b64071e6926acb389f8af8fb5d80cb892 diff --git a/nbs/_extensions/mintlify/_extension.yml b/nbs/_extensions/mintlify/_extension.yml deleted file mode 100644 index 663084de9..000000000 --- a/nbs/_extensions/mintlify/_extension.yml +++ /dev/null @@ -1,30 +0,0 @@ -title: Mintlify -author: Mintlify, Inc -organization: Mintlify -contributes: - project: - project: - type: default - detect: - - ["mint.json"] - render: - - "**/*.md" - - "**/*.qmd" - - "**/*.ipynb" - format: mintlify-md - formats: - md: - # Although we use a custom writer, we still need the variants here the lua filters to render correctly. - # Ideally, we would forward the variants to the custom writer. - variant: gfm+pipe_tables+tex_math_dollars+raw_html+all_symbols_escapable+backtick_code_blocks+space_in_atx_header+intraword_underscores+lists_without_preceding_blankline+shortcut_reference_links - writer: mintlify_writer.lua - output-ext: mdx - inline-includes: true - preserve-yaml: true - wrap: none - fig-format: retina - fig-width: 8 - fig-height: 5 - html-math-method: webtex - filters: - - mintlify.lua diff --git a/nbs/_extensions/mintlify/mintlify.lua b/nbs/_extensions/mintlify/mintlify.lua deleted file mode 100644 index b95554781..000000000 --- a/nbs/_extensions/mintlify/mintlify.lua +++ /dev/null @@ -1,61 +0,0 @@ --- mintlify.lua - -local kQuartoRawHtml = "quartoRawHtml" -local rawHtmlVars = pandoc.List() - -function Pandoc(doc) - -- insert exports at the top if we have them - if #rawHtmlVars > 0 then - local exports = ("export const %s =\n[%s];"):format(kQuartoRawHtml, - table.concat( - rawHtmlVars:map(function(var) return '`'.. var .. '`' end), - "," - ) - ) - doc.blocks:insert(1, pandoc.RawBlock("markdown", exports .. "\n")) - end - - return doc -end - - --- strip image attributes (which may result from --- fig-format: retina) as they will result in an --- img tag which won't hit the asset pipeline -function Image(el) - el.attr = pandoc.Attr() - return el -end - --- header attributes only support id -function Header(el) - el.attr = pandoc.Attr(el.identifier) - return el -end - -Block = function(node) - if node.text ~= nil and string.find(node.text, "") - :format(kQuartoRawHtml, #rawHtmlVars-1) .. "\n" - return pandoc.RawBlock("html", html) - end -end diff --git a/nbs/_extensions/mintlify/mintlify_utils.lua b/nbs/_extensions/mintlify/mintlify_utils.lua deleted file mode 100644 index ae076ddaa..000000000 --- a/nbs/_extensions/mintlify/mintlify_utils.lua +++ /dev/null @@ -1,33 +0,0 @@ -function codeBlock(el, filename) - local lang = el.attr.classes[1] - local title = filename or el.attr.attributes["filename"] or el.attr.attributes["title"] - local showLineNumbers = el.attr.classes:includes('number-lines') - if lang or title or showLineNumbers then - if not lang then - lang = 'text' - end - local code = "\n```" .. lang - if showLineNumbers then - code = code .. " showLineNumbers" - end - if title then - code = code .. " title=\"" .. title .. "\"" - end - code = code .. "\n" .. el.text .. "\n```\n" - - -- docusaures code block attributes don't conform to any syntax - -- that pandoc natively understands, so return the CodeBlock as - -- "raw" markdown (so it bypasses pandoc processing entirely) - return pandoc.RawBlock("markdown", code) - - elseif #el.attr.classes == 0 then - el.attr.classes:insert('text') - return el - end - - return nil -end - -return { - codeBlock = codeBlock -} \ No newline at end of file diff --git a/nbs/_extensions/mintlify/mintlify_writer.lua b/nbs/_extensions/mintlify/mintlify_writer.lua deleted file mode 100644 index 9fa2d7110..000000000 --- a/nbs/_extensions/mintlify/mintlify_writer.lua +++ /dev/null @@ -1,133 +0,0 @@ -local codeBlock = require('mintlify_utils').codeBlock - -local reactPreamble = pandoc.List() - -function capitalizeFirstLetter(str) - return (str:gsub("^%l", string.upper)) -end - -function castToMintlifyCallout(str) - if str == "caution" or str == "danger" then - return "Warning" - else - return capitalizeFirstLetter(str) - end -end - -local function addPreamble(preamble) - if not reactPreamble:includes(preamble) then - reactPreamble:insert(preamble) - end -end - -local function jsx(content) - return pandoc.RawBlock("markdown", content) -end - -local function tabset(node, filter) - -- note groupId - local groupId = "" - local group = node.attr.attributes["group"] - if group then - groupId = ([[ groupId="%s"]]):format(group) - end - - -- create tabs - local tabs = pandoc.Div({}) - tabs.content:insert(jsx("")) - - -- iterate through content - for i = 1, #node.tabs do - local content = node.tabs[i].content - local title = node.tabs[i].title - - tabs.content:insert(jsx(([[]]):format(pandoc.utils.stringify(title)))) - local result = quarto._quarto.ast.walk(content, filter) - if type(result) == "table" then - tabs.content:extend(result) - else - tabs.content:insert(result) - end - tabs.content:insert(jsx("")) - end - - -- end tab and tabset - tabs.content:insert(jsx("")) - - -- ensure we have required deps - addPreamble("import Tabs from '@theme/Tabs';") - addPreamble("import TabItem from '@theme/TabItem';") - - return tabs -end - -function Writer(doc, opts) - local filter - filter = { - CodeBlock = codeBlock, - - DecoratedCodeBlock = function(node) - local el = node.code_block - return codeBlock(el, node.filename) - end, - - Tabset = function(node) - return tabset(node, filter) - end, - - RawBlock = function (rawBlock) - -- We just "pass-through" raw blocks of type "confluence" - if(rawBlock.format == 'plotly') then - quarto.utils.dump("Plotly in filter") - return pandoc.RawBlock('html', rawBlock.text) - end - - -- Raw blocks inclding arbirtary HTML like JavaScript are not supported in CSF - return "" - end, - - Callout = function(node) - local admonition = pandoc.Div({}) - local mintlifyCallout = castToMintlifyCallout(node.type) - admonition.content:insert(jsx("<" .. mintlifyCallout .. ">")) - if node.title then - admonition.content:insert(pandoc.Header(3, node.title)) - end - local content = node.content - if type(content) == "table" then - admonition.content:extend(content) - else - admonition.content:insert(content) - end - admonition.content:insert(jsx("")) - return admonition - end - } - - doc = quarto._quarto.ast.walk(doc, filter) - - -- insert react preamble if we have it - if #reactPreamble > 0 then - local preamble = table.concat(reactPreamble, "\n") - doc.blocks:insert(1, pandoc.RawBlock("markdown", preamble .. "\n")) - end - - local extensions = { - yaml_metadata_block = true, - pipe_tables = true, - footnotes = true, - tex_math_dollars = true, - raw_html = true, - all_symbols_escapable = true, - backtick_code_blocks = true, - space_in_atx_header = true, - intraword_underscores = true, - lists_without_preceding_blankline = true, - shortcut_reference_links = true, - } - - return pandoc.write(doc, { - format = 'markdown_strict', - extensions = extensions - }, opts) -end diff --git a/nbs/_quarto.yml b/nbs/_quarto.yml index 26cbcb454..60097036f 100644 --- a/nbs/_quarto.yml +++ b/nbs/_quarto.yml @@ -1,9 +1,13 @@ project: - type: mintlify + type: website format: - mintlify-md: - code-fold: true + html: + theme: cosmo + fontsize: 1em + linestretch: 1.7 + css: styles.css + toc: true website: twitter-card: diff --git a/nbs/favicon.svg b/nbs/favicon.svg deleted file mode 100644 index e5f333428..000000000 --- a/nbs/favicon.svg +++ /dev/null @@ -1,5 +0,0 @@ - - - - - diff --git a/nbs/imgs/logo/dark.png 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cache-dependency-path: settings.ini - submodules: true + submodules: 'recursive' - name: Install Dependencies shell: bash run: | From 719fc49016ed730eb7a63daa7a50d8c4dcff008e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Morales?= Date: Thu, 19 Oct 2023 14:54:49 -0600 Subject: [PATCH 17/22] place recursive in right action --- .github/workflows/build-docs.yaml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build-docs.yaml b/.github/workflows/build-docs.yaml index 65f5d50e7..7f1cb926e 100644 --- a/.github/workflows/build-docs.yaml +++ b/.github/workflows/build-docs.yaml @@ -10,11 +10,12 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v3 + with: + submodules: 'recursive' - uses: actions/setup-python@v4 with: cache: "pip" - cache-dependency-path: settings.ini - submodules: 'recursive' + cache-dependency-path: dev/requirements.txt - name: Install Dependencies shell: bash run: | From fdf37e66884cecf062f06cc5138fffab18f00d6c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Morales?= Date: Thu, 19 Oct 2023 14:59:45 -0600 Subject: [PATCH 18/22] set python version --- .github/workflows/build-docs.yaml | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/.github/workflows/build-docs.yaml b/.github/workflows/build-docs.yaml index 7f1cb926e..cdf70629e 100644 --- a/.github/workflows/build-docs.yaml +++ b/.github/workflows/build-docs.yaml @@ -5,6 +5,11 @@ on: pull_request: branches: ["main"] workflow_dispatch: + +defaults: + run: + shell: bash -l {0} + jobs: build-docs: runs-on: ubuntu-latest @@ -15,20 +20,17 @@ jobs: - uses: actions/setup-python@v4 with: cache: "pip" + python-version: '3.10' cache-dependency-path: dev/requirements.txt - name: Install Dependencies - shell: bash + run: pip install -r dev/requirements.txt . + - name: Build docs run: | - set -ux - python -m pip install --upgrade pip - pip install -Uq nbdev - test -f setup.py && pip install ".[dev]" mkdir nbs/_extensions cp -r docs-scripts/mintlify/ nbs/_extensions/ python docs-scripts/update-quarto.py nbdev_docs - name: Apply final formats - shell: bash run: bash ./docs-scripts/docs-final-formatting.bash - name: Copy over necessary assets run: | From 0d95521783599c859917a3ce8ffe393437daa392 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Morales?= Date: Thu, 19 Oct 2023 15:09:49 -0600 Subject: [PATCH 19/22] updates --- .github/workflows/build-docs.yaml | 2 +- docs-scripts | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build-docs.yaml b/.github/workflows/build-docs.yaml index cdf70629e..f53865669 100644 --- a/.github/workflows/build-docs.yaml +++ b/.github/workflows/build-docs.yaml @@ -23,7 +23,7 @@ jobs: python-version: '3.10' cache-dependency-path: dev/requirements.txt - name: Install Dependencies - run: pip install -r dev/requirements.txt . + run: pip install ".[dev]" - name: Build docs run: | mkdir nbs/_extensions diff --git a/docs-scripts b/docs-scripts index a1f3370b6..d63d02696 160000 --- a/docs-scripts +++ b/docs-scripts @@ -1 +1 @@ -Subproject commit a1f3370b64071e6926acb389f8af8fb5d80cb892 +Subproject commit d63d02696ad23a3104636207152d2ac393291315 From 9c3de0cbc2e998febaafa8713d9af3976c0c602e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Morales?= Date: Thu, 19 Oct 2023 15:15:07 -0600 Subject: [PATCH 20/22] restore editable --- .github/workflows/build-docs.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build-docs.yaml b/.github/workflows/build-docs.yaml index f53865669..bb5569b40 100644 --- a/.github/workflows/build-docs.yaml +++ b/.github/workflows/build-docs.yaml @@ -23,7 +23,7 @@ jobs: python-version: '3.10' cache-dependency-path: dev/requirements.txt - name: Install Dependencies - run: pip install ".[dev]" + run: pip install -e ".[dev]" - name: Build docs run: | mkdir nbs/_extensions From a928c757eb2bebb5014e45a3fba2879058316fd8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Morales?= Date: Thu, 19 Oct 2023 15:22:46 -0600 Subject: [PATCH 21/22] restore commands --- .github/workflows/build-docs.yaml | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/.github/workflows/build-docs.yaml b/.github/workflows/build-docs.yaml index bb5569b40..e132568f7 100644 --- a/.github/workflows/build-docs.yaml +++ b/.github/workflows/build-docs.yaml @@ -8,7 +8,7 @@ on: defaults: run: - shell: bash -l {0} + shell: bash jobs: build-docs: @@ -21,11 +21,13 @@ jobs: with: cache: "pip" python-version: '3.10' - cache-dependency-path: dev/requirements.txt - - name: Install Dependencies - run: pip install -e ".[dev]" + cache-dependency-path: settings.ini - name: Build docs run: | + set -ux + python -m pip install --upgrade pip + pip install -Uq nbdev + pip install -e ".[dev]" mkdir nbs/_extensions cp -r docs-scripts/mintlify/ nbs/_extensions/ python docs-scripts/update-quarto.py From e750b63858aaa55ca853a7f92bb8e35a9e2d6a4c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Morales?= Date: Thu, 19 Oct 2023 15:34:23 -0600 Subject: [PATCH 22/22] move proc to script --- .github/workflows/build-docs.yaml | 1 + settings.ini | 1 - 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build-docs.yaml b/.github/workflows/build-docs.yaml index e132568f7..8209bdabe 100644 --- a/.github/workflows/build-docs.yaml +++ b/.github/workflows/build-docs.yaml @@ -31,6 +31,7 @@ jobs: mkdir nbs/_extensions cp -r docs-scripts/mintlify/ nbs/_extensions/ python docs-scripts/update-quarto.py + echo "procs = nbdev_plotly.plotly:PlotlyProc" >> settings.ini nbdev_docs - name: Apply final formats run: bash ./docs-scripts/docs-final-formatting.bash diff --git a/settings.ini b/settings.ini index 00bcc3f86..abdcc7497 100644 --- a/settings.ini +++ b/settings.ini @@ -33,4 +33,3 @@ black_formatting = True jupyter_hooks = True clean_ids = True readme_nb = index.ipynb -procs = nbdev_plotly.plotly:PlotlyProc