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توضیحات

FOST Python Package

ویژگی	مقدار
سیستم عامل	-
نام فایل	fostool-0.0.4
نام	fostool
نسخه کتابخانه	0.0.4
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Microsoft
ایمیل نویسنده	fostool@microsoft.com
آدرس صفحه اصلی	https://github.com/microsoft/FOST
آدرس اینترنتی	https://pypi.org/project/fostool/
مجوز	The MIT License (Microsoft)

<p align="center"> <img src="https://dsm01pap002files.storage.live.com/y4mueD2H6WE6Df3edTW6YE_KLeND5ROVCKksKxGarweSuk9VW2m4jrY8EBTVN5qXiQEnuyfSQZ2t9HOtrsLjXSPqKkmMrMtrmncb3xVzITPl0pu7mwESEjF1CooSkvtdTNPBW237K1bTNqyA9cD-opu_ISObWFLusFpAFJQk_tSxRAYi-mp4QI9fcXUUTYgndae?width=4248&height=1324&cropmode=none" width=50% /> </p> - [**Fost**](#fost) - [Framework of Fost](#framework-of-fost) - [Quick Start](#quick-start) - [Installation](#installation) - [Train with FOST](#train-with-fost) - [Data Format](#data-format) - [Examples](#examples) - [Contact Us](#contact-us) # FOST  FOST(Forecasting open source tool) aims to provide an easy-use tool for spatial-temporal forecasting. The users only need to organize their data into a certain format and then get the prediction results with one command. FOST automatically handles the missing and abnormal values, and captures both spatial and temporal correlations efficiently. # Framework of FOST Following is the framework of FOST, basically it contains 4 main components: ![FOST framework](https://dsm01pap002files.storage.live.com/y4mqv6c15r0vEfpNGcpMnUa4sOxYZFDDBL6h47EdLlVuKZcGTUw8LKrseJnZ2Q8hlJK3VB0lj13TJmF5pvrC5LeiKHR4cfSIGJT3YmV2D_-O6HpG8VFVKM5Alx9hEhAvc0fOAXFkthsC5qAccx8_eJsoKj8eTHvAns0z72v811JOVbswqGLWOeGNyUIjgQiL52F?width=1050&height=268&cropmode=none) | Module name | Description | | ------------- | ------------------------------------------------------------ | | Preprocessing | Preprocessing module aims at handle varies data situation, currently FOST designed sub-module to handle issues such as missing value, unalignment timestamp and feature selection. | | Modeling | FOST contains implements for different mainstream deep learning models such as RNN, MLP and GNN, for better performance on varies custom data. Further model implements such as Transformer, N-beats are in progress. | | Fusion | Fusion module aims at automatically select and ensemble model predictions. | | Utils | There are many other utils in FOST, such as neural-network trainer and predictor, result plotter and so on. | # Quick Start ## Installation ### Installation of dependency packages #### 1. Prerequisites This project relies on `pytorch >= 1.8` and `torch-geometric >= 1.7.2` - torch installation reference link：https://pytorch.org/get-started/previous-versions/ - torch-geometric installation reference link: https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html #### 2. Installation You can install fost with pip: ``` pip install fostool ``` ## Train with FOST #### 1. Import forecasting pipeline ```python from fostool.pipeline import Pipeline ``` #### 2. Setting data path and lookahead You need to pass your `train.csv` and `graph.csv` for model training, see [dataformat](#data-format) for data preparing. ```python train_path = '/path/to/your/train.csv' graph_path = '/path/to/your/graph.csv' # graph_path is alternative lookahead = 7 # Forward steps you would like to predict. ``` #### 3. Fit and predict We provide a default config file in config/default.yaml. You could use your config file through config_path augment. ```python fost = Pipeline(lookahead=lookahead, train_path=train_path, graph_path=graph_path) fost.fit() result = fost.predict() ``` #### 4. Plot results ```python fost.plot(result) ``` # Data Format > You can fetch sample data on `/examples` ### 1. train.csv 3 columns are required for `train.csv`: + Node: node name for current data + Date: date or timestamp for current data + TARGET: target for prediction A valid format may look like: | Node | Date | TARGET | | ------- | ---------- | ---------- | | Alaska | 1960-01-01 | 800592.0 | | Alaska | 1961-01-01 | 933600.0 | | Alabama | 1960-01-01 | 10141633.0 | | Alabama | 1961-01-01 | 9885992.0 | | Alabama | 1962-01-01 | 10497917.0 | Columns except above will be regarded as feature columns. ### 2. graph.csv (option) `graph.csv` should only contains 3 columns: + node_0: node name for fist node, node name should align with node name in `train.csv`. + node_1: node name for second node, node name should align with node name in `train.csv`. + weight: weight on connection for node_0 to node_1. If `graph.csv` is not provided, identity graph will be used. # Examples We prepared several examples on `examples`: 1. [Predict simulation cosine curve](/examples/1.%20Cosine%20prediction.ipynb) 2. [Predict States Energy Data](/examples/2.%20Predict%20States%20Energy%20Data.ipynb) 3. [Save and load model](/examples/3.%20Save%20and%20load.ipynb) # Contact Us - If you have any issues, please create issue [here](https://github.com/microsoft/fost/issues/new/choose) or send messages in [gitter](https://gitter.im/Microsoft/fost). - For other reasons, you are welcome to contact us by email([fostool@microsoft.com](mailto:fostool@microsoft.com)).

نیازمندی

مقدار	نام
-	scikit-learn
-	matplotlib
-	numpy
-	pandas
>=5.1	pyyaml
-	addict

نحوه نصب

نصب پکیج whl fostool-0.0.4:

pip install fostool-0.0.4.whl

نصب پکیج tar.gz fostool-0.0.4:

pip install fostool-0.0.4.tar.gz