jarvais 0.15.1

jarvAIs: just a really versatile AI service

jarvAIs

Just A Really Versatile AI Service

jarvAIs is a Python package designed to automate and enhance machine learning workflows. The primary goal of this project is to reduce redundancy in repetitive tasks, improve consistency, and elevate the quality of standardized processes in oncology research.

Installation

$ pip install jarvais

(recommended) Create new pixi environment for a project

mkdir my_project
cd my_project
pixi init
pixi add --pypi jarvais

(recommended) Create new conda virtual environment

conda create -n jarvais python=3.11
conda activate jarvais
pip install jarvais

Modules

This package consists of 3 different modules:

• Analyzer: A module that analyzes and processes data, providing valuable insights for downstream tasks.
• Trainer: A module for training machine learning models, designed to be flexible and efficient.
• Explainer: A module that explains model predictions, offering interpretability and transparency in decision-making.

Analyzer

The Analyzer module is designed for data visualization and exploration. It helps to gain insights into the data, identify patterns, and assess relationships between different features, which is essential for building effective models.

Example Usage

from jarvais.analyzer import Analyzer

analyzer = Analyzer(
    data, 
    output_dir="./analyzer_outputs",
    categorical_columns=['Gender', 'Disease Type', 'Treatment', 'Target'] 
    target_variable="Target", 
    task="classification"
)

analyzer.run()

Example Output

Feature Types:
  - Categorical: ['Gender', 'Disease Type', 'Treatment', 'Target']
  - Continuous: ['Age', 'Tumor Size']

Outlier Detection:
  - Outliers found in Gender: ['Male: 5 out of 1000']
  - Outliers found in Disease Type: ['Lung Cancer: 10 out of 1000']
  - No Outliers found in Treatment
  - No Outliers found in Target

TableOne(Data Summary):

Feature	Category	Missing	Overall
n			1000
Age, mean (SD)		0	58.2 (12.3)
Tumor Size, mean (SD)		0	4.5 (1.2)
Gender, n (%)	Female		520 (52%)
	Male		480 (48%)
Disease Type, n (%)	Breast Cancer		300 (30%)
	Lung Cancer		150 (15%)
	Prostate Cancer		100 (10%)
Target	True		560 (56%)
	False		440 (44%)

Output Files:

The Analyzer module generates the following files and directories:

• analysis_report.pdf: A PDF report summarizing the analysis results.
• config.yaml: Configuration file for the analysis setup.
• analyzer_settings.json: JSON file that contains the settings used for the analysis.
• analyzer_settings.schema.json: JSON schema file that documents how the settings can be modified.

Figures:

• frequency_tables: Contains visualizations comparing different categorical features.

• multiplots: Visualizations showing combinations of features for deeper analysis.

• Additional Figures:

  • pairplot.png: Pairwise relationships between continuous variables.
  • pearson_correlation.png: Pearson correlation matrix.
  • spearman_correlation.png: Spearman correlation matrix.
  • umap_continuous_data.png: UMAP visualization of continuous data.

• Data Files:

  • tableone.csv: CSV file containing summary statistics for the dataset.
  • updated_data.csv: CSV file with the cleaned and processed data.

Check out the Analyzer Quick Start for more details.

Trainer Module

The Trainer module simplifies and automates the process of feature reduction, model training, and evaluation for various machine learning tasks, ensuring flexibility and efficiency.

Key Features

1. Feature Reduction:
   • Supports methods such as mrmr, variance_threshold, corr, and chi2 to identify and retain relevant features.
2. Automated Model Training:
   • Integrates with AutoGluon for model training, selection, and optimization.
   • Handles tasks such as binary classification, multiclass classification, regression, and survival.

Example Usage

from jarvais.trainer import TrainerSupervised

trainer = TrainerSupervised(task='binary', output_dir='./trainer_outputs')
trainer.run(data=data, target_variable='target', save_data=True)

Example Output

Training fold 1/5...  
Fold 1 score: `0.8467207586933614`

Training fold 2/5...  
Fold 2 score: `0.8487846136306914`
...

Model Leaderboard

Displays values in mean [min, max] format across training folds.

Model	Score Test	Score Val	Score Train
WeightedEnsemble_L2	AUROC: 0.82 [0.82, 0.83]	AUROC: 0.85 [0.85, 0.85]	AUROC: 1.0 [1.0, 1.0]
	F1: 0.13 [0.11, 0.14]	F1: 0.09 [0.07, 0.12]	F1: 0.95 [0.9, 1.0]
	AUPRC: 0.48 [0.45, 0.52]	AUPRC: 0.47 [0.44, 0.49]	AUPRC: 0.96 [0.91, 1.0]
ExtraTreesGini	AUROC: 0.82 [0.82, 0.82]	AUROC: 0.84 [0.84, 0.84]	AUROC: 1.0 [1.0, 1.0]
	F1: 0.21 [0.19, 0.22]	F1: 0.16 [0.14, 0.18]	F1: 1.0 [1.0, 1.0]
	AUPRC: 0.45 [0.45, 0.45]	AUPRC: 0.43 [0.41, 0.45]	AUPRC: 1.0 [1.0, 1.0]

Check out the Trainer Quick Start for more details.

Explainer Module

The Explainer module is designed to evaluate trained models by generating diagnostic plots, auditing bias, and producing comprehensive reports. It supports various supervised learning tasks, including classification, regression, and survival models.

The module provides an easy-to-use interface for model diagnostics, bias analysis, and feature importance visualization, facilitating deeper insights into the model's performance and fairness.

Features

• Diagnostic Plots: Generates performance diagnostics, including classification metrics, regression plots, and SHAP value visualizations.
• Bias Audit: Identifies potential biases in model predictions with respect to sensitive features.
• Feature Importance: Calculates and visualizes feature importance using permutation importance or model-specific methods.
• Comprehensive Reports: Creates a detailed PDF report summarizing all diagnostic results.

Example Usage

from jarvais.explainer import Explainer

# Prefered method is to initialize from trainer
exp = Explainer.from_trainer(trainer)
exp.run()

Output Files:

The Explainer module generates the following files and directories:

• explainer_report.pdf: A PDF report summarizing the model diagnostics, bias audit results, and feature importance.
• bias/: Contains CSV files with bias metrics for different sensitive features.
• figures/: Contains diagnostic plots for model evaluation and feature importance.
  • confusion_matrix.png: Visual representation of the model’s confusion matrix.
  • feature_importance.png: A plot visualizing the importance of features used by the model.
  • model_evaluation.png: A visual summary of model evaluation.
  • shap_barplot.png: SHAP value bar plot for model interpretability.
  • shap_heatmap.png: SHAP value heatmap for model interpretability.

Check out the Explainer Quick Start for more details.

Contributing

Please use the following angular commit message format:

<type>(optional scope): short summary in present tense

(optional body: explains motivation for the change)

(optional footer: note BREAKING CHANGES here, and issues to be closed)

<type> refers to the kind of change made and is usually one of:

• feat: A new feature.
• fix: A bug fix.
• docs: Documentation changes.
• style: Changes that do not affect the meaning of the code (white-space, formatting, missing semi-colons, etc).
• refactor: A code change that neither fixes a bug nor adds a feature.
• perf: A code change that improves performance.
• test: Changes to the test framework.
• build: Changes to the build process or tools.

scope is an optional keyword that provides context for where the change was made. It can be anything relevant to your package or development workflow (e.g., it could be the module or function - name affected by the change).

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.