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feature(Package): added keywords, install guide to README (#72)
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almostintuitive authored Apr 19, 2023
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26 changes: 13 additions & 13 deletions README.md
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# Available models

| | Name | Link | Supports<br />Online <br />updating | Wrapper Name<br />& Import Location |
| :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| :------------------------------------- | :----------------------------------------------------: | :---------------------------------: | ------------------------------------------------------------------------------ |
| <img alt='StatsDorecast Logo' src='https://raw.githubusercontent.com/Nixtla/neuralforecast/main/nbs/imgs_indx/logo_mid.png' height=64> | StatsForecast | [GitHub](https://github.com/Nixtla/statsforecast) | ❌ | **WrapStatsForecast**<br />`from fold_wrappers import WrapStatsForecast` |
| <img alt='NeuralForecast Logo' src='https://raw.githubusercontent.com/Nixtla/neuralforecast/main/nbs/imgs_indx/logo_mid.png' height=64> | NeuralForecast (beta) | [GitHub](https://github.com/Nixtla/neuralforecast) | ❌ | **WrapNeuralForecast**<br />`from fold_wrappers import WrapNeuralForecast` |
| <img alt='XGBoost Logo' src='https://camo.githubusercontent.com/0ea6e7814dd771f740509bbb668d251d485a6e21f12e287be7cc2275e0eab1d1/68747470733a2f2f7867626f6f73742e61692f696d616765732f6c6f676f2f7867626f6f73742d6c6f676f2e737667' height=64> | XGBoost | [GitHub](https://github.com/dmlc/xgboost) | ✅ | **WrapXGB**<br />`from fold_wrappers import WrapXGB` |
| <img alt='LightGBM Logo' src='https://lightgbm.readthedocs.io/en/latest/_images/LightGBM_logo_black_text.svg' height=64> | LightGBM | [GitHub](https://github.com/Microsoft/LightGBM) | ✅ | **WrapLGBM**<br />`from fold_wrappers import WrapLGBM` |
| <img alt='Sktime Logo' src='https://github.com/sktime/sktime/raw/main/docs/source/images/sktime-logo.jpg?raw=true' height=64> | SKTime (beta) | [GitHub](https://github.com/sktime/sktime) | ✅ | **WrapSktime**<br />`from fold_wrappers import WrapSktime` |
| <img alt='Statsmodels Logo' src='https://github.com/statsmodels/statsmodels/raw/main/docs/source/images/statsmodels-logo-v2-horizontal.svg' width=160> | Statsmodels | [GitHub](https://github.com/statsmodels/statsmodels) | ✅ | **WrapStatsModels**<br />`from fold_wrappers import WrapStatsModels` |
| <img alt='Prophet Logo' src='https://miro.medium.com/v2/resize:fit:964/0*tVCene42rgUTNv9Q.png' width=160> | Prophet | [GitHub](https://github.com/facebook/prophet) | ✅ | **WrapProphet**<br />`from fold_wrappers import WrapProphet` |
| <img alt='Scikit-Learn Logo' src='https://raw.githubusercontent.com/scikit-learn/scikit-learn/main/doc/logos/scikit-learn-logo.png' width=160> | Sklearn <br/>(natively available in `fold`) | [GitHub](https://github.com/scikit-learn/scikit-learn) | 🟡<br/>(some) | Sklearn doesn't need to be wrapped,<br />just pass in the models. |
| | Name | Link | Supports<br />Online <br />updating | Wrapper Name<br />& Import Location |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------|:------------------------------------------------------:|:-----------------------------------:|----------------------------------------------------------------------------|
| <img alt='StatsDorecast Logo' src='https://raw.githubusercontent.com/Nixtla/neuralforecast/main/nbs/imgs_indx/logo_mid.png' height=64> | StatsForecast | [GitHub](https://github.com/Nixtla/statsforecast) | ❌ | **WrapStatsForecast**<br />`from fold_wrappers import WrapStatsForecast` |
| <img alt='NeuralForecast Logo' src='https://raw.githubusercontent.com/Nixtla/neuralforecast/main/nbs/imgs_indx/logo_mid.png' height=64> | NeuralForecast (beta) | [GitHub](https://github.com/Nixtla/neuralforecast) | ❌ | **WrapNeuralForecast**<br />`from fold_wrappers import WrapNeuralForecast` |
| <img alt='XGBoost Logo' src='https://camo.githubusercontent.com/0ea6e7814dd771f740509bbb668d251d485a6e21f12e287be7cc2275e0eab1d1/68747470733a2f2f7867626f6f73742e61692f696d616765732f6c6f676f2f7867626f6f73742d6c6f676f2e737667' height=64> | XGBoost | [GitHub](https://github.com/dmlc/xgboost) | ✅ | **WrapXGB**<br />`from fold_wrappers import WrapXGB` |
| <img alt='LightGBM Logo' src='https://lightgbm.readthedocs.io/en/latest/_images/LightGBM_logo_black_text.svg' height=64> | LightGBM | [GitHub](https://github.com/Microsoft/LightGBM) | ✅ | **WrapLGBM**<br />`from fold_wrappers import WrapLGBM` |
| <img alt='Sktime Logo' src='https://github.com/sktime/sktime/raw/main/docs/source/images/sktime-logo.jpg?raw=true' height=64> | SKTime (beta) | [GitHub](https://github.com/sktime/sktime) | ✅ | **WrapSktime**<br />`from fold_wrappers import WrapSktime` |
| <img alt='Statsmodels Logo' src='https://github.com/statsmodels/statsmodels/raw/main/docs/source/images/statsmodels-logo-v2-horizontal.svg' width=160> | Statsmodels | [GitHub](https://github.com/statsmodels/statsmodels) | ✅ | **WrapStatsModels**<br />`from fold_wrappers import WrapStatsModels` |
| <img alt='Prophet Logo' src='https://miro.medium.com/v2/resize:fit:964/0*tVCene42rgUTNv9Q.png' width=160> | Prophet | [GitHub](https://github.com/facebook/prophet) | ✅ | **WrapProphet**<br />`from fold_wrappers import WrapProphet` |
| <img alt='Scikit-Learn Logo' src='https://raw.githubusercontent.com/scikit-learn/scikit-learn/main/doc/logos/scikit-learn-logo.png' width=160> | Sklearn <br/>(natively available in `fold`) | [GitHub](https://github.com/scikit-learn/scikit-learn) | 🟡<br/>(some) | Sklearn doesn't need to be wrapped,<br />just pass in the models. |

# Installation

- Prerequisites: `python >= 3.7` and `pip`

- Install from git directly:
- Install from pypi:
```
pip install https://github.com/dream-faster/fold-wrappers/archive/main.zip
pip install fold-wrappers
```
- Depending on what model you'd like to wrap, you can either install the library directly or run
```
pip install "git+https://github.com/dream-faster/fold-wrappers.git#egg=fold-wrappers[<your_library_name>]"
pip install "fold-wrappers[<your_library_name>]"
```

# Quickstart
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6 changes: 3 additions & 3 deletions pyproject.toml
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Expand Up @@ -23,10 +23,10 @@ classifiers = [
]
dependencies = [
"numpy",
"fold-core~=0.0.4",
"fold-core~=0.1",
]
description = "3rd party model wrappers for fold."
keywords = []
keywords = ["time-series", "machine-learning", "forecasting", "forecast", "nowcast", "models", "time-series-regression", "time-series-classification", "financial-machine-learning"]
license = { file="LICENSE" }
readme = "README.md"
requires-python = ">=3.7"
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]
tests = [
"pytest~=7.1.2",
"fold-core~=0.0.4",
"fold-core~=0.1",
]
docs = [
"mkdocs-material",
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