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bytehub-0.4.0
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ByteHub Timeseries Feature Store
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | bytehub-0.4.0 |
| نام | bytehub |
| نسخه کتابخانه | 0.4.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | ByteHub AI Limited |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://bytehub.ai |
| آدرس اینترنتی | https://pypi.org/project/bytehub/ |
| مجوز | - |
# ByteHub [](https://pypi.org/project/bytehub/) [](https://github.com/bytehub-ai/bytehub/actions?query=workflow%3ATests) [](https://github.com/bytehub-ai/bytehub/issues) [](https://github.com/psf/black)
<img src="https://uploads-ssl.webflow.com/5f187c12c1b99c41557b035e/6026e99dad5c3cf816547670_bytehub-rect-logo.png" align="right" alt="ByteHub logo" width="120" height="60">
An easy-to-use feature store.
## 💾 What is a feature store?
A feature store is a data storage system for data science and machine-learning. It can store _raw data_ and also transformed _features_, which can be fed straight into an ML model or training script.
Feature stores allow data scientists and engineers to be **more productive** by organising the flow of data into models.
The [Bytehub Feature Store](https://www.bytehub.ai) is designed to:
* Be simple to use, with a Pandas-like API;
* Require no complicated infrastructure, running on a local Python installation or in a cloud environment;
* Be optimised towards timeseries operations, making it highly suited to applications such as those in finance, energy, forecasting; and
* Support simple time/value data as well as complex structures, e.g. dictionaries.
It is built on [Dask](https://dask.org/) to support large datasets and cluster compute environments.
## 🦉 Features
* Searchable **feature information** and **metadata** can be stored locally using SQLite or in a remote database.
* Timeseries data is saved in [Parquet format](https://parquet.apache.org/) using Dask, making it readable from a wide range of other tools. Data can reside either on a local filesystem or in a [cloud storage service](https://docs.dask.org/en/latest/remote-data-services.html), e.g. AWS S3.
* Supports **timeseries joins**, along with **filtering** and **resampling** operations to make it easy to load and prepare datasets for ML training.
* Feature engineering steps can be implemented as **transforms**. These are saved within the feature store, and allows for simple, resusable preparation of raw data.
* **Time travel** can retrieve feature values based on when they were created, which can be useful for forecasting applications.
* Simple APIs to retrieve timeseries dataframes for training, or a dictionary of the most recent feature values, which can be used for inference.
Also available as **[☁️ ByteHub Cloud](https://bytehub.ai)**: a ready-to-use, cloud-hosted feature store.
## 📖 Documentation and tutorials
See the [ByteHub documentation](https://docs.bytehub.ai/) and [notebook tutorials](https://github.com/bytehub-ai/code-examples/tree/main/tutorials) to learn more and get started.
## 🚀 Quick-start
Install using pip:
```sh
pip install bytehub
```
Create a local SQLite feature store by running:
```python
import bytehub as bh
import pandas as pd
fs = bh.FeatureStore()
```
Data lives inside _namespaces_ within each feature store. They can be used to separate projects or environments. Create a namespace as follows:
```python
fs.create_namespace(
'tutorial', url='/tmp/featurestore/tutorial', description='Tutorial datasets'
)
```
Create a _feature_ inside this namespace which will be used to store a timeseries of pre-prepared data:
```python
fs.create_feature('tutorial/numbers', description='Timeseries of numbers')
```
Now save some data into the feature store:
```python
dts = pd.date_range('2020-01-01', '2021-02-09')
df = pd.DataFrame({'time': dts, 'value': list(range(len(dts)))})
fs.save_dataframe(df, 'tutorial/numbers')
```
The data is now stored, ready to be transformed, resampled, merged with other data, and fed to machine-learning models.
We can engineer new features from existing ones using the _transform_ decorator. Suppose we want to define a new feature that contains the squared values of `tutorial/numbers`:
```python
@fs.transform('tutorial/squared', from_features=['tutorial/numbers'])
def squared_numbers(df):
# This transform function receives dataframe input, and defines a transform operation
return df ** 2 # Square the input
```
Now both features are saved in the feature store, and can be queried using:
```python
df_query = fs.load_dataframe(
['tutorial/numbers', 'tutorial/squared'],
from_date='2021-01-01', to_date='2021-01-31'
)
```
To connect to ByteHub Cloud, first [register for an account](https://www.bytehub.ai/feature-store/request-access), then use:
```python
fs = bh.FeatureStore("https://api.bytehub.ai")
```
This will allow you to store features in your own private namespace on ByteHub Cloud, and save datasets to an AWS S3 storage bucket.
## 🐾 Roadmap
* _Tasks_ to automate updates to features using orchestration tools like [Airflow](https://airflow.apache.org/)
نیازمندی
| مقدار | نام |
|---|---|
| >=1.1 | pandas |
| >=3.0 | pyarrow |
| >=2021 | dask[dataframe,delayed] |
| >=1.3.0 | cloudpickle |
| >=1.3 | SQLAlchemy |
| >=1.5 | alembic |
| >=2 | requests |
| >=1.1 | requests-oauthlib |
| ==0.4 | s3fs |
| >=0.6 | adlfs |
| ==0.4 | s3fs |
| - | pytest |
| - | black |
| - | bump2version |
| - | jupyterlab |
| - | pre-commit |
| - | pdoc3 |
| ==0.8 | gcsfs |
نحوه نصب
نصب پکیج whl bytehub-0.4.0:
pip install bytehub-0.4.0.whl
نصب پکیج tar.gz bytehub-0.4.0:
pip install bytehub-0.4.0.tar.gz