semvec/pypi-packages huggingface dataset:
ruleminer 1.0.1
Python package to mine association rules in datasets
Requires Python: <3.13,>=3.9
Keywords: association rules, pandas

# ruleminer

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[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

Python package to discover association rules in Pandas DataFrames. 

This package implements the code of the paper [Discovering and ranking validation rules in supervisory data](https://github.com/wjwillemse/ruleminer/tree/main/docs/paper.pdf).

*   Free software: MIT/X license

*   Documentation: <https://ruleminer.readthedocs.io/en/latest>.

## Features

Here is what the package does:

* Generate human-readable validation rules using rule templates containing regular expressions and a Pandas DataFrame dataset

  - available functions: min, max, abs, quantile, sum, substr, split, count, sumif and countif
  - including parameters for metric filters and rule precisions (including XBRL tolerances)

* Evaluate rules and calculate association rules metrics

  - available metrics: abs support, abs exceptions, confidence, support, added value, casual confidence, casual support, conviction, lift and rule power factor

Here are some examples of rule templates with regexes with which you can generate validation rules:

  - *if ({"Type"} == ".*") then ({".*"} > 0)*

  - *if ({".*"} > 0) then (({".*"} == 0) & ({".*"} > 0))*

  - *(({".*"} + {".*"} + {".*"}) == {".*"})*

  - *({"Own funds"} <= quantile({"Own funds"}, 0.95))*

  - *(substr({"Type"}, 0, 1) in ["a", "b"])*

The first template generates (with the dataset described in the Usage section) rules like

  - *if ({"Type"} == "non-life_insurer") then ({"TP-nonlife"} > 0)*
  - *if ({"Type"} == "life_insurer") then ({"TP-life"} > 0)*

These generated validation rules can then be used to validate new datasets.

## Contributors

* Willem Jan Willemse <https://github.com/wjwillemse>
ibm-quantum-schemas 0.2.20260209
IBM Quantum Pydantic models.
Requires Python: >=3.10

# IBM Quantum Schemas

This repository contains the Pydantic models that describe the inputs and outputs of IBM Quantum
primitives and programs, to allow easier programmatic interfacing from Python.

The resulting `ibm-quantum-schemas` Python package contains the versioned definition of such
inputs and outputs, using the following structure:

```
ibm_quantum_schemas.models.<program>.version_<x_y>
```

Please note:
* this package contains multiple versions of the models, using semantic versioning in the last
  component of the module name (`.version_<x_y>`).
* when a new version is in development, it will contain a `_dev` suffix for signalling it
  (`.version_<x_y>_dev`). Such versions are not considered stable yet.
* other (non `_dev`) versions included in releases of this package are considered stable, albeit
  not all of them might be supported by the IBM Quantum platform at a given time. In particular,
  the programs included in the initial release of this library (`executor` and `noise_learner_v3`)
  are considered beta and not yet supported.
* please refer to the [Qiskit Runtime REST API] for the programs and their versions supported by
  the IBM Quantum platform.

## History

> [!WARNING]
> The purpose of this repository has changed. Prior to October 2025, the
> repository hosted JSONSchema files that dictated the request and response payloads
> for the primary IBM Quantum API payloads that allowed interacting with
> devices. Primarily, the Qobj, backend configuration, backend properties,
> pulse defaults, and result schemas.

[Qiskit Runtime REST API]: https://quantum.cloud.ibm.com/docs/en/api/qiskit-runtime-rest