eazyml-insight 0.0.51

eazyml-insight from EazyML family to discover patterns, generate insights, or mine rules from your datasets.

EazyML Responsible-AI: Augmented Intelligence

A collection of APIs from EazyML family to discover patterns, generate insights, or mine rules from your datasets. Each discovered pattern is expressed as a set of conditions on feature variables - each with a trust-score to reflect confidence in the insight, allowing you to analyze and apply these insights to your data. Ideal for pattern recognition, interpretable AI, and augmented intelligence workflows.

Features

• Pattern Mining: Discover meaningful rules from the datasets.
• Insight Generation: Generate high-value insights with associated trust scores.
• Application of Rules: Apply discovered patterns to datasets for further analysis.

Ideal for use cases like interpretability, training data analysis, and building solutions with augmented intelligence.

Installation

To use the augmented intelligence, ensure you have Python installed on your system.

User installation

The easiest way to install this package for augmented intelligence is using pip:

pip install -U eazyml-insight

Dependencies

This package requires:

• werkzeug,
• unidecode,
• pandas,
• scikit-learn,
• nltk,
• pyyaml,
• requests

Usage

Here's an example of how you can use the APIs from this package.

from eazyml_insight import ez_init, ez_insight, ez_validate

# initialize: setup book-keeping, access_key if required 
_ = ez_init()

# discover insights for given dataset using EazyML.
insight_response = ez_insight(
                train_data = 'train.csv',
                outcome = 'target',
                options = {}
        )
# the insight_response object contains insights/patterns that you can explore to integrate in your augmented intelligence workflows.

# validate insights on given test dataset using EazyML.
validate_response = ez_validate(
                train_data = 'train.csv',
                outcome = 'target',
                insights = insight_response['insights'],
                test_data = 'test.csv',
                options = {
                    "record_number": [1,2,3]
            }
        )
# the validate_response object contains validation metrics on the insights provided by ez_insight, along with the filtered data from the test data for the given record number in the insights.

You can find more information in the documentation.

Useful links, other packages from EazyML family

• Documentation

• Homepage

• If you have questions or would like to discuss a use case, please contact us here

• Here are the other packages from EazyML suite:

  • eazyml-automl: eazyml-automl provides a suite of APIs for training, optimizing and validating machine learning models with built-in AutoML capabilities, hyperparameter tuning, and cross-validation.
  • eazyml-data-quality: eazyml-data-quality provides APIs for comprehensive data quality assessment, including bias detection, outlier identification, and drift analysis for both data and models.
  • eazyml-counterfactual: eazyml-counterfactual provides APIs for optimal prescriptive analytics, counterfactual explanations, and actionable insights to optimize predictive outcomes to align with your objectives.
  • eazyml-insight: eazyml-insight provides APIs to discover patterns, generate insights, and mine rules from your datasets.
  • eazyml-xai: eazyml-xai provides APIs for explainable AI (XAI), offering human-readable explanations, feature importance, and predictive reasoning.
  • eazyml-xai-image: eazyml-xai-image provides APIs for image explainable AI (XAI).

License

This project is licensed under the Proprietary License.

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