aiqclib 0.3.1

This package aims to offer helper functions that simplify model building and evaluation

aiqclib

aiqclib is a Python library that provides a configuration-driven workflow for machine learning, simplifying dataset preparation, model training, and data classification. It is a core component of the AIQC project that aims to enhance anomaly detection in CTD (Conductivity, Temperature, Depth) data.

ML Algorithms Supported by aiqclib

Category	Algorithm	Short Name	Method
Tree-Based & Ensemble	XGBoost	XGB	Ensemble (Boosting)
	Random Forest	RF	Ensemble (Bagging)
	Decision Tree	DT	Tree
Linear & Geometric	Logistic Regression	Logit	Linear
	Linear Discriminant Analysis	LDA	Linear / Statistical
	Support Vector Machine	SVM	Geometric
Instance-Based (Distance-Based)	K-Nearest Neighbors	KNN	Distance-based
Probabilistic	Gaussian Naive Bayes	GNB	Probabilistic
Neural Network	Multilayer Perceptron	MLP	Neural Network

Installation

The package is available on PyPI and conda-forge.

Using pip:

pip install aiqclib

Using conda:

conda install -c conda-forge aiqclib

Documentation

Project documentation is hosted on Read the Docs.

Core Concepts

The library is designed around a three-stage workflow:

1. Dataset Preparation: Prepare feature datasets from raw data and generate training, validation, and test data sets.
2. Training & Evaluation: Train machine learning models and evaluate their performance using cross-validation.
3. Classification: Apply a trained model to classify new, unseen data.

Each stage is controlled by a YAML configuration file, allowing you to define and reproduce your entire workflow with ease.

Usage

The general workflow for any task in aiqclib follows these steps:

1. Generate a Configuration Template: Create a starter YAML file for the task (e.g., prepare, train, classify).
2. Customize the Configuration: Edit the YAML file to specify paths, dataset names, and other parameters.
3. Run the Task: Load the configuration and execute the main function for the task.

1. Dataset Preparation

This workflow processes your input data and creates training, validation, and test sets.

Step 1: Generate a configuration template.

import aiqclib as aq

aq.write_config_template(file_name="/path/to/prepare_config.yaml", stage="prepare")

Step 2: Customize prepare_config.yaml. You must edit the file to set the correct input/output paths and define your dataset. See the Configuration section for details.

Step 3: Run the preparation process.

import aiqclib as aq

config = aq.read_config("/path/to/prepare_config.yaml")
aq.create_training_dataset(config)

This generates the following output folders:

• summary: Statistics of input data used for normalization.
• select: Profiles with bad observation flags (positive samples) and good profiles (negative samples).
• locate: Observation records for both positive and negative profiles.
• extract: Features extracted from the observation records.
• training: The final training, validation, and test datasets.

2. Model Training and Evaluation

This workflow uses the prepared dataset to train a model and evaluate its performance.

Step 1: Generate a training configuration template.

import aiqclib as aq

aq.write_config_template(file_name="/path/to/training_config.yaml", stage="train")

Step 2: Customize training_config.yaml. Edit the file to point to your prepared dataset and define training parameters.

Step 3: Train and evaluate the model.

import aiqclib as aq

config = aq.read_config("/path/to/training_config.yaml")
aq.train_and_evaluate(config)

This generates the following output folders:

• validate: Results from the cross-validation process.
• build: The final trained models and their evaluation results on the test dataset.

3. Data Classification

This workflow applies a trained model to classify all observations in a dataset.

Step 1: Generate a classification configuration template.

import aiqclib as aq

aq.write_config_template(file_name="/path/to/classification_config.yaml", stage="classify")

Step 2: Customize classification_config.yaml. Edit the file to point to the input data and the trained model.

Step 3: Run classification.

import aiqclib as aq

config = aq.read_config("/path/to/classification_config.yaml")
aq.classify_dataset(config)

This workflow processes a dataset using a trained model and generates:

• classify: The final classification results and a summary report.

Configuration

Configuration is managed via YAML files. The write_config_template function provides a starting point that you must customize for each module.

1. Dataset Preparation (stage="prepare")

The preparation config requires you to modify two key sections:

• path_info_sets: Defines the location of input and output data.

  path_info_sets:
    - name: data_set_1
      common:
        base_path: /path/to/data # EDIT: Root output directory
      input:
        base_path: /path/to/input # EDIT: Directory with input files
        step_folder_name: ""
      split:
        step_folder_name: training
  

• data_sets: Defines a specific dataset to be processed.

  data_sets:
    - name: dataset_0001  # EDIT: Your data set name
      dataset_folder_name: dataset_0001  # EDIT: Your output folder
      input_file_name: nrt_cora_bo_4.parquet # EDIT: Your input filename
  

2. Training and Evaluation (stage="train")

The training config links the prepared data to the model training process.

• path_info_sets: Defines where to find the prepared dataset and where to save model artifacts.

  path_info_sets:
    - name: data_set_1
      common:
        base_path: /path/to/data # EDIT: Root output directory
      input:
        step_folder_name: training
  

• training_sets: Links to a dataset prepared in the previous workflow.

  training_sets:
    - name: training_0001  # EDIT: Your training name
      dataset_folder_name: dataset_0001  # EDIT: Your output folder
  

3. Classification (stage="classify")

The classification config uses a trained model to classify new data.

• path_info_sets: Defines paths for raw data, models, and classification results.

  path_info_sets:
    - name: data_set_1
      common:
        base_path: /path/to/data # EDIT: Root output directory
      input:
        base_path: /path/to/input # EDIT: Directory with input files
        step_folder_name: ""
      model:
        base_path: /path/to/model  # EDIT: Directory with model files
        step_folder_name: model
      concat:
        step_folder_name: classification # EDIT: Directory with classification results
  

• classification_sets: Defines a specific dataset to be classified.

  classification_sets:
    - name: classification_0001  # EDIT: Your classification name
      dataset_folder_name: dataset_0001  # EDIT: Your output folder
      input_file_name: nrt_cora_bo_4.parquet   # EDIT: Your input filename
  

Contributing & Development

We welcome contributions! Development setup (uv environment, test data), running tests, and code style are documented in CONTRIBUTING.md.

Releasing & Deployment (for Maintainers)

The release process (versioning checklist), building the docs, and deployment to PyPI, conda-forge, and Anaconda.org are documented in RELEASING.md.