https://github.com/sintel-dev/ml-stars
Primitives and Pipelines for Time Series Data
Science Score: 26.0%
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○Scientific vocabulary similarity
Low similarity (19.4%) to scientific vocabulary
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Repository
Primitives and Pipelines for Time Series Data
Basic Info
- Host: GitHub
- Owner: sintel-dev
- License: mit
- Language: Python
- Default Branch: master
- Homepage: https://sintel.dev/ml-stars/
- Size: 2.33 MB
Statistics
- Stars: 4
- Watchers: 2
- Forks: 1
- Open Issues: 2
- Releases: 8
Topics
Metadata Files
README.md
An Open Source Project from the Data to AI Lab, at MIT
ml-stars
Primitives for machine learning and time series.
Overview
This repository contains primitive annotations to be used by the MLBlocks library, as well as the necessary Python code to make some of them fully compatible with the MLBlocks API requirements.
There is also a collection of custom primitives contributed directly to this library, which either combine third party tools or implement new functionalities from scratch.
Installation
Requirements
ml-stars has been developed and tested on Python 3.8, 3.9, 3.10, 3.11, and 3.12
Also, although it is not strictly required, the usage of a virtualenv is highly recommended in order to avoid interfering with other software installed in the system where ml-stars is run.
Install with pip
The easiest and recommended way to install ml-stars is using pip:
bash
pip install ml-stars
This will pull and install the latest stable release from PyPi.
If you want to install from source or contribute to the project please read the Contributing Guide.
Quickstart
This section is a short series of tutorials to help you getting started with ml-stars.
We will be executing a single primitive for data transformation.
1. Load a Primitive
The first step in order to run a primitive is to load it.
This will be done using the mlstars.load_primitive function, which will
load the indicated primitive as an MLBlock Object from MLBlocks
In this case, we will load the sklearn.preprocessing.MinMaxScaler primitive.
```python3 from mlstars import load_primitive
primitive = load_primitive('sklearn.preprocessing.MinMaxScaler') ```
2. Load some data
The StandardScaler is a transformation primitive which scales your data into a given range.
To use this primtives, we generate a synthetic data with some numeric values. ```python3 import numpy as np
data = np.array([10, 1, 3, -1, 5, 6, 0, 4, 13, 4]).reshape(-1, 1) ```
The data is a list of integers where their original range is between [-1, 13].
3. Fit the primitive
In order to run our primitive, we first need to fit it.
This is the process where it analyzes the data to detect what is the original range of the data.
This is done by calling its fit method and passing the data as X.
python3
primitive.fit(X=data)
4. Produce results
Once the pipeline is fit, we can process the data by calling the produce method of the
primitive instance and passing agin the data as X.
python3
transformed = primitive.produce(X=data)
transformed
After this is done, we can see how the transformed data contains the transformed values:
array([[0.78571429],
[0.14285714],
[0.28571429],
[0. ],
[0.42857143],
[0.5 ],
[0.07142857],
[0.35714286],
[1. ],
[0.35714286]])
The data is now in [0, 1] range.
What's Next?
Documentation
Owner
- Name: The Signal Intelligence Project
- Login: sintel-dev
- Kind: organization
- Email: dai-lab@mit.edu
- Website: https://dai.lids.mit.edu/
- Repositories: 11
- Profile: https://github.com/sintel-dev
Systems and tools to design, develop and deploy AI applications on top of signals.
GitHub Events
Total
- Create event: 5
- Release event: 3
- Issues event: 4
- Delete event: 2
- Issue comment event: 1
- Push event: 13
- Pull request event: 4
Last Year
- Create event: 5
- Release event: 3
- Issues event: 4
- Delete event: 2
- Issue comment event: 1
- Push event: 13
- Pull request event: 4
Committers
Last synced: about 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Sarah Alnegheimish | s****h@g****m | 38 |
Issues and Pull Requests
Last synced: 7 months ago
All Time
- Total issues: 4
- Total pull requests: 13
- Average time to close issues: 2 days
- Average time to close pull requests: 4 days
- Total issue authors: 2
- Total pull request authors: 2
- Average comments per issue: 0.25
- Average comments per pull request: 0.0
- Merged pull requests: 12
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 2
- Pull requests: 2
- Average time to close issues: 3 days
- Average time to close pull requests: about 12 hours
- Issue authors: 1
- Pull request authors: 1
- Average comments per issue: 0.5
- Average comments per pull request: 0.0
- Merged pull requests: 2
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- sarahmish (3)
- graceyesong (1)
Pull Request Authors
- sarahmish (14)
- boom90lb (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 1,751 last-month
- Total dependent packages: 2
- Total dependent repositories: 1
- Total versions: 19
- Total maintainers: 3
pypi.org: ml-stars
Primitives and Pipelines for Time Series Data.
- Homepage: https://github.com/sintel-dev/ml-stars
- Documentation: https://ml-stars.readthedocs.io/
- License: MIT license
-
Latest release: 0.2.3
published about 1 year ago
Rankings
Maintainers (3)
Dependencies
- actions/checkout v2 composite
- actions/setup-python v1 composite
- peaceiris/actions-gh-pages v3 composite
- actions/checkout v1 composite
- actions/setup-python v2 composite
- Keras >=2.4,<2.5
- fix *
- mlblocks >=0.4,<0.6
- numpy <1.21.0,>=1.16.0
- pandas >=1,<2
- protobuf <4
- scikit-learn >=0.21
- scipy >=1.1.0,<2
- statsmodels >=0.9.0,<0.13
- tensorflow >=2,<2.5
- xgboost >=0.72.1,<1