## Eazyml Augmented Intelligence
  

A collection of APIs to discover patterns, generate insights, and uncover
rules in training datasets. Each discovered pattern is expressed as a set of
rules/conditions on feature variables, allowing users to analyze and apply
these insights to their data. Ideal for pattern recognition, interpretable AI,
and augmented intelligence workflows
### Features
- **Pattern Mining**: Discover meaningful rules in datasets.
- **Insight Generation**: Generate high-value insights with associated scores.
- **Application of Rules**: Apply discovered patterns to datasets for further analysis.
Ideal for use cases like interpretability, training data analysis, and building augmented intelligence solutions.
## Installation
### User installation
The easiest way to install augmented intelligence is using pip:
```bash
pip install -U eazyml-augi
```
### Dependencies
Eazyml Augmented Intelligence requires :
- werkzeug,
- unidecode,
- pandas,
- scikit-learn,
- nltk,
- pyyaml,
- requests
## Usage
Augmented Intelligence can be used by initalizing EazyML and then getting insights for given training data.
```python
from eazyml_augi import ez_init, ez_augi
# Replace 'your_license_key' with your actual EazyML license key
ez_init(license_key="your_license_key")
# Perform data insights for given training data using EazyML.
response = ez_augi(mode='classification',
outcome='target',
train_file_path='train.csv')
```
You can find more information in the [documentation](https://eazyml.readthedocs.io/en/latest/packages/eazyml_augi.html).
## Useful links and similar projects
- [Documentation](https://docs.eazyml.com)
- [Homepage](https://eazyml.com)
- If you have more questions or want to discuss a specific use case please book an appointment [here](https://eazyml.com/trust-in-ai)
- Here are some other EazyML's packages :
- [eazyml](https://pypi.org/project/eazyml/): Eazyml provides a suite of APIs for training, testing and optimizing machine learning models with built-in AutoML capabilities, hyperparameter tuning, and cross-validation.
- [eazyml-dq](https://pypi.org/project/eazyml-dq/): `eazyml-dq` provides APIs for comprehensive data quality assessment, including bias detection, outlier identification, and data drift analysis.
- [eazyml-cf](https://pypi.org/project/eazyml-cf/): `eazyml-cf` provides APIs for counterfactual explanations, prescriptive analytics, and actionable insights to optimize predictive outcomes.
- [eazyml-augi](https://pypi.org/project/eazyml-augi/): `eazyml-augi` provides APIs to uncover patterns, generate insights, and discover rules from training datasets.
- [eazyml-xai](https://pypi.org/project/eazyml-xai/): `eazyml-xai` provides APIs for explainable AI (XAI), offering human-readable explanations, feature importance, and predictive reasoning.
- [eazyml-xai-image](https://pypi.org/project/eazyml-xai-image/): eazyml-xai-image provides APIs for image explainable AI (XAI).
## License
This project is licensed under the [Proprietary License](https://github.com/EazyML/eazyml-docs/blob/master/LICENSE).
---
*Maintained by [EazyML](https://eazyml.com)*
*© 2025 EazyML. All rights reserved.*
Raw data
{
"_id": null,
"home_page": "https://eazyml.com/",
"name": "eazyml-augmented-insights",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": null,
"keywords": "pattern-discovery, rule-mining, data-insights, insight-generation, augmented-intelligence, data-analysis, rule-discovery, data-patterns, machine-learning, data-science, ml-api, training-data-analysis, interpretable-ai",
"author": "Eazyml",
"author_email": "admin@ipsoftlabs.com",
"download_url": "https://files.pythonhosted.org/packages/2f/a0/9adaf6c8b49bc60e38df250ae5904ba29061daf3cbaba01e2171c86e7202/eazyml-augmented-insights-0.0.35.tar.gz",
"platform": null,
"description": "## Eazyml Augmented Intelligence\n  \n\n\n\nA collection of APIs to discover patterns, generate insights, and uncover\nrules in training datasets. Each discovered pattern is expressed as a set of\nrules/conditions on feature variables, allowing users to analyze and apply\nthese insights to their data. Ideal for pattern recognition, interpretable AI,\nand augmented intelligence workflows\n\n### Features\n- **Pattern Mining**: Discover meaningful rules in datasets.\n- **Insight Generation**: Generate high-value insights with associated scores.\n- **Application of Rules**: Apply discovered patterns to datasets for further analysis.\n\nIdeal for use cases like interpretability, training data analysis, and building augmented intelligence solutions.\n\n## Installation\n### User installation\nThe easiest way to install augmented intelligence is using pip:\n```bash\npip install -U eazyml-augi\n```\n### Dependencies\nEazyml Augmented Intelligence requires :\n- werkzeug,\n- unidecode,\n- pandas,\n- scikit-learn,\n- nltk,\n- pyyaml,\n- requests\n\n## Usage\nAugmented Intelligence can be used by initalizing EazyML and then getting insights for given training data.\n\n```python\nfrom eazyml_augi import ez_init, ez_augi\n# Replace 'your_license_key' with your actual EazyML license key\nez_init(license_key=\"your_license_key\")\n# Perform data insights for given training data using EazyML.\nresponse = ez_augi(mode='classification',\n outcome='target',\n train_file_path='train.csv')\n```\nYou can find more information in the [documentation](https://eazyml.readthedocs.io/en/latest/packages/eazyml_augi.html).\n\n\n## Useful links and similar projects\n- [Documentation](https://docs.eazyml.com)\n- [Homepage](https://eazyml.com)\n- If you have more questions or want to discuss a specific use case please book an appointment [here](https://eazyml.com/trust-in-ai)\n- Here are some other EazyML's packages :\n\n - [eazyml](https://pypi.org/project/eazyml/): Eazyml provides a suite of APIs for training, testing and optimizing machine learning models with built-in AutoML capabilities, hyperparameter tuning, and cross-validation.\n - [eazyml-dq](https://pypi.org/project/eazyml-dq/): `eazyml-dq` provides APIs for comprehensive data quality assessment, including bias detection, outlier identification, and data drift analysis.\n - [eazyml-cf](https://pypi.org/project/eazyml-cf/): `eazyml-cf` provides APIs for counterfactual explanations, prescriptive analytics, and actionable insights to optimize predictive outcomes.\n - [eazyml-augi](https://pypi.org/project/eazyml-augi/): `eazyml-augi` provides APIs to uncover patterns, generate insights, and discover rules from training datasets.\n - [eazyml-xai](https://pypi.org/project/eazyml-xai/): `eazyml-xai` provides APIs for explainable AI (XAI), offering human-readable explanations, feature importance, and predictive reasoning.\n - [eazyml-xai-image](https://pypi.org/project/eazyml-xai-image/): eazyml-xai-image provides APIs for image explainable AI (XAI).\n\n## License\nThis project is licensed under the [Proprietary License](https://github.com/EazyML/eazyml-docs/blob/master/LICENSE).\n\n---\n\n*Maintained by [EazyML](https://eazyml.com)* \n*\u00c2\u00a9 2025 EazyML. All rights reserved.*\n",
"bugtrack_url": null,
"license": null,
"summary": "eazyml-augi provides APIs to uncover patterns, generate insights, and discover rules from training datasets.",
"version": "0.0.35",
"project_urls": {
"Contact Us": "https://eazyml.com/trust-in-ai",
"Documentation": "https://docs.eazyml.com/",
"Homepage": "https://eazyml.com/",
"eazyml": "https://pypi.org/project/eazyml/",
"eazyml-augi": "https://pypi.org/project/eazyml-augi/",
"eazyml-cf": "https://pypi.org/project/eazyml-cf/",
"eazyml-dq": "https://pypi.org/project/eazyml-dq/",
"eazyml-xai": "https://pypi.org/project/eazyml-xai/",
"eazyml-xai-image": "https://pypi.org/project/eazyml-xai-image/"
},
"split_keywords": [
"pattern-discovery",
" rule-mining",
" data-insights",
" insight-generation",
" augmented-intelligence",
" data-analysis",
" rule-discovery",
" data-patterns",
" machine-learning",
" data-science",
" ml-api",
" training-data-analysis",
" interpretable-ai"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "e646239045a525dc92b0ceb5c9a8bb6b36cc77ea8019bb76f1e6105bb05c699f",
"md5": "1e0a017b15e34a135011f1da2c09dd5e",
"sha256": "f7cb8e6ba2f60798c0f68fb575fc75c6d4acec1612f1758f3b9c4592b2829de6"
},
"downloads": -1,
"filename": "eazyml_augmented_insights-0.0.35-py2.py3-none-any.whl",
"has_sig": false,
"md5_digest": "1e0a017b15e34a135011f1da2c09dd5e",
"packagetype": "bdist_wheel",
"python_version": "py2.py3",
"requires_python": ">=3.7",
"size": 55241627,
"upload_time": "2025-02-02T01:42:19",
"upload_time_iso_8601": "2025-02-02T01:42:19.594913Z",
"url": "https://files.pythonhosted.org/packages/e6/46/239045a525dc92b0ceb5c9a8bb6b36cc77ea8019bb76f1e6105bb05c699f/eazyml_augmented_insights-0.0.35-py2.py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "2fa09adaf6c8b49bc60e38df250ae5904ba29061daf3cbaba01e2171c86e7202",
"md5": "c41a33f8a4396d8fe0ecc1c8d81d45a3",
"sha256": "1d07563fc01d186248b07a01b4161295b5bb59741fb6146ca4899e9ffcc4e643"
},
"downloads": -1,
"filename": "eazyml-augmented-insights-0.0.35.tar.gz",
"has_sig": false,
"md5_digest": "c41a33f8a4396d8fe0ecc1c8d81d45a3",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 54408423,
"upload_time": "2025-02-02T01:42:45",
"upload_time_iso_8601": "2025-02-02T01:42:45.492746Z",
"url": "https://files.pythonhosted.org/packages/2f/a0/9adaf6c8b49bc60e38df250ae5904ba29061daf3cbaba01e2171c86e7202/eazyml-augmented-insights-0.0.35.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-02-02 01:42:45",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "eazyml-augmented-insights"
}