semiq-ml 0.4.5

A collection of reusable machine learning pipeline helpers

semiq-ml - Machine Learning Workflow Simplifier

Welcome to the semiq-ml documentation. This package provides helper functions and classes to simplify common machine learning workflows, including baseline model training, evaluation, hyperparameter tuning, and image processing.

Overview

semiq-ml is designed to:

• Quickly compare multiple machine learning models on your dataset
• Automate hyperparameter tuning with Optuna
• Provide consistent preprocessing and evaluation
• Support both classification and regression tasks
• Handle categorical features correctly, especially for tree-based models
• Offer flexible model selection with 'all', 'trees', or 'gbm' options
• Simplify image dataset preparation for computer vision tasks

Key Components

BaselineModel

The BaselineModel class automates the training and evaluation of multiple ML models, providing:

• Automatic handling of preprocessing (scaling, encoding, imputation)
• Performance comparison across standard algorithms
• Support for common evaluation metrics
• Special handling for boosting libraries (LightGBM, XGBoost, CatBoost)
• Visualization of ROC curves and precision-recall curves
• Flexible model selection with 'all', 'trees', or 'gbm' options

OptunaOptimizer

The OptunaOptimizer class enhances the BaselineModel by adding:

• Efficient hyperparameter tuning with Optuna
• Smart parameter space sampling for all supported models
• Detailed tuning results and best parameter reporting
• Visualization of optimization history and parameter importance
• Flexible control over trials and cross-validation
• GPU acceleration for XGBoost, LightGBM, and CatBoost (set gpu=True)

GPU

If you have a compatible GPU and the required libraries installed, you can enable GPU acceleration for supported models (XGBoost, LightGBM, CatBoost) by passing gpu=True to the OptunaOptimizer constructor. This will automatically inject the correct GPU parameters for each library during hyperparameter search.

Example:

from semiq_ml.tuning import OptunaOptimizer

tuner = OptunaOptimizer(
    task_type="classification",
    metric="f1_weighted",
    n_trials=20,
    gpu=True  # Enable GPU acceleration for supported models
)

If gpu=True, the following parameters are set automatically:

• XGBoost: tree_method='gpu_hist', predictor='gpu_predictor'
• LightGBM: device='gpu', gpu_platform_id=0, gpu_device_id=0
• CatBoost: task_type='GPU', devices='0'

Image Module

The image module provides utilities for working with image datasets:

• Easy scanning of directory structures to create image DataFrames
• Automatic label inference from directory hierarchies
• Convenient image loading with resizing, normalization and format conversion
• Batch image loading from DataFrames with detailed control over transformations
• Image visualization tools for single images or batches, with optional label and prediction display
• Image sampling utilities for exploring large datasets

Getting Started

Please refer to these guides to get started with semiq-ml:

• Installation Guide - Setup instructions and requirements
• Basic Usage Examples - Simple examples to get you started
• API Reference - Complete documentation of all classes and methods

Example Usage

The following example demonstrates a typical semiq-ml workflow:

# Import required libraries
from semiq_ml import BaselineModel
from semiq_ml.tuning import OptunaOptimizer
import pandas as pd
from sklearn.model_selection import train_test_split

# 1. Load your dataset
data = pd.read_csv('your_data.csv')
X = data.drop('target', axis=1)  # Features
y = data['target']               # Target variable

# 2. Split data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# 3. Train and evaluate baseline models
baseline = BaselineModel(
    task_type="classification",  # Use "regression" for regression tasks
    metric="f1_weighted",        # Choose an appropriate evaluation metric
    models="trees"               # Only use tree-based models (options: 'all', 'trees', 'gbm')
)
baseline.fit(X_train, y_train)
results = baseline.get_results()
print(results)

# 4. Tune the best performing model with OptunaOptimizer
best_model_name = results.iloc[0]['model']
tuner = OptunaOptimizer(
    task_type="classification", 
    metric="f1_weighted",
    n_trials=20,                 # Number of parameter combinations to try
    gpu=True                     # Enable GPU acceleration for supported models
)
tuned_model = tuner.tune_model(best_model_name, X_train, y_train)
tuning_results = tuner.get_tuning_results()
print(tuning_results)

Image Processing Example

# Import the image module
import semiq_ml.image as img_utils
import matplotlib.pyplot as plt

# Create a DataFrame from a directory of images (e.g., for classification)
# Assumes a folder structure like: dataset/class_name/image.jpg
image_df = img_utils.path_to_dataframe_with_labels('path/to/dataset')
print(f"Found {len(image_df)} images with labels: {image_df['label'].unique()}")

# Load images with preprocessing
images, labels = img_utils.load_images_from_dataframe(
    image_df,
    size=(224, 224),  # Resize all images to 224x224
    normalize=True,   # Normalize pixel values to [0,1]
    show_progress=True
)

# Display a sample of images with labels
img_utils.display_images(
    images[:5], 
    labels=labels[:5],
    n_cols=5
)

For more examples and advanced usage, see the Basic Usage Examples guide.

Support

If you encounter issues or have questions about semiq-ml:

• Bug Reports: Please open an issue with a detailed description of the problem, steps to reproduce it, and your environment details.
• Feature Requests: Submit your ideas through the issue tracker using the "Feature Request" template.
• Questions: For usage questions, reach out via GitHub Discussions

Contributing

We welcome contributions to semiq-ml! Here's how you can help:

1. Code Contributions: Fork the repository, create a feature branch, and submit a pull request.
2. Documentation: Help improve or translate documentation.
3. Bug Reports: Report bugs or suggest features via the issue tracker.

Please review our Contributing Guidelines for more details on code style, testing requirements, and the pull request process.

License

This project is licensed under the MIT License - see the LICENSE file for details.