diagnostipy 0.1.2

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Diagnostipy

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Table of Contents

1. About The Project
2. Getting Started
3. Usage
4. Roadmap
5. Contributing
6. License

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About The Project

Diagnostipy is a open-source, Python library for rule-based diagnosis and evaluation. It allows users to define rules with conditions and weights, manage rulesets, and evaluate input data using customizable scoring and confidence functions.

Features

• Define rules using SymptomRule with customizable conditions, weights, and criticality.
• Manage collections of rules via SymptomRuleset.
• Evaluate data using built-in or custom scoring and confidence functions with Evaluator.
• Flexible and extensible API.

When Should I Use Diagnostipy?

• When interpretability is more important than accuracy, and you need a clear, rules-based system to explain decision-making
• If you lack access to large datasets for training machine learning models but still require a structured evaluation or diagnostic tool
• When collaborating with domain experts to formalize diagnostic or evaluation criteria without a data-driven approach
• When working in regulated industries or domains where decisions must be auditable and transparent
• For quick prototyping of logic-based systems without the overhead of complex data pipelines

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Getting Started

To get a local copy up and running, follow these steps.

Prerequisites

• Python 3.10 or later

Installation

Install Diagnostipy using pip:

pip install diagnostipy

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Usage

Define Rules

from diagnostipy.core.models.symptom_rule import SymptomRule

rule_high_fever = SymptomRule(
    name="High Fever",
    weight=6.0,
    critical=True,
    apply_condition=lambda data: data.get("temperature", 0) >= 39,
)

rule_cough = SymptomRule(
    name="Persistent Cough",
    weight=4.0,
    critical=False,
    apply_condition=lambda data: data.get("cough", False),
)

rule_sore_throat = SymptomRule(
    name="Sore Throat",
    weight=5.0,
    critical=True,
    apply_condition=lambda data: data.get("sore_throat", False),
)

Create Ruleset

from diagnostipy import SymptomRuleset

ruleset = SymptomRuleset([rule_high_fever, rule_cough, rule_sore_throat])

Evaluate data

from diagnostipy import Evaluator

data = {
    "temperature": 39.5,
    "cough": True,
    "sore_throat": False,
}

evaluator = Evaluator(ruleset)
results = evaluator.run(data=data)

print(results)

total_score=10.0 label='High' confidence=0.8333333333333334 metadata=None

Explanation of Output:

• total_score: Sum of weights from applicable rules based on the input data. Here, 6.0 (High Fever) + 4.0 (Persistent Cough) = 10.0.

• label: Categorical result based on total_score (e.g., 'Low', 'High'). Determined by the evaluation function used.

• confidence: A measure of certainty about the evaluation result, computed based on the logic defined in the confidence_function parameter when creating the Evaluator

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Roadmap

• Add support for more built-in confidence functions.
• Improve error handling.
• Expand the API to include more user-friendly options.

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Contributing

We are open to, and grateful for, any contributions made by the community.

A huge thank you to all the contributors.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/some-feature)
3. Commit your Changes (git commit -m 'Add some feature')
4. Push to the Branch (git push origin feature/some-feature)
5. Open a Pull Request

Top contributors:

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License

Diagnostipy is distributed under the MIT License.

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