xaura 0.1.1

eXtendable Automated Unified Research & Analytics — an intelligent AI library

XAURA — eXtendable Automated Unified Research & Analytics

An intelligent, dataset-aware Python ML library with built-in profiling, experiment tracking, model-specific visualisations, and a local web UI — all in one pip install.

XAURA is designed to make machine learning workflows faster, smarter, and fully reproducible. Instead of writing boilerplate for every project, you call profile() to understand your data and run_model() to train with dataset-aware defaults. Every run is automatically logged, visualised, and exportable.

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Table of Contents

• Installation
• Quick Start
  • Python API
  • Web UI
  • CLI
• Key Features
• Supported Models
• API Reference
• Architecture
• Contributing
• License

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Installation

Install XAURA directly from PyPI:

pip install xaura

Install from Source

If you want to contribute or run the latest development version:

git clone https://github.com/Vinamra3215/Xaura.git
cd Xaura
pip install -e ".[dev]"

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Quick Start

Python API

import pandas as pd
from xaura import profile, run_model

# Load data
df = pd.read_csv("data.csv")

# Step 1: Profile
data_profile = profile(df)
print(data_profile.warnings)  # ["High imbalance: 3.2:1"]

# Step 2: Run a model (dataset-aware defaults applied automatically)
result = run_model("rf_classifier", df, data_profile, target_col="target")

# Step 3: Inspect results
print(result.metrics)       # {'accuracy': 0.91, 'f1': 0.85, 'recall': 0.78, ...}
print(result.config)        # Full config with all defaults resolved
print(result.run_id)        # 'a3f8c21d-...' — logged to SQLite automatically

# Step 4: Override defaults if needed
result2 = run_model("xgboost_cls", df, data_profile, target_col="target", config={
    "n_estimators": 500,
    "max_depth": 8,
    "learning_rate": 0.01
})

Web UI

XAURA comes with a beautiful local web interface. Start it by running:

xaura serve

Then open http://localhost:7070 in your browser.

• Upload page — drag-and-drop CSV upload
• Profile view — dataset summary with interactive charts
• Model runner — select model, configure params, run, view results
• Experiment log — sortable/filterable table of all past runs
• Run comparison — side-by-side diff of two runs
• Export buttons — one-click download of plots, bundles, logs

CLI

You can also use XAURA directly from the terminal:

xaura profile data.csv
xaura run rf_classifier data.csv --config '{"n_estimators": 200}'
xaura export a3f8c21d

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Key Features

• 🔍 Automatic Dataset Profiling — shape, types, class balance, correlations, missing values, and warnings
• 🎯 Dataset-Aware Defaults — hyperparameters adapt based on your data's characteristics
• 📊 Model-Specific Visualisations — confusion matrices for classifiers, residual plots for regressors, silhouette plots for clusterers
• 💾 Silent Experiment Tracking — every run auto-logged to SQLite with full reproducibility
• 🌐 Local Web UI — FastAPI-powered dashboard served at localhost:7070
• 📦 One-Click Export — PNG plots, JSON configs, ZIP run bundles, CSV experiment logs
• 🖥️ CLI Interface — profile, run models, and export from the terminal
• ⚡ CPU-Optimised — all models run efficiently on CPU (no GPU required)

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Supported Models

Classification

Model	Identifier
Logistic Regression	logistic_regression
Random Forest	rf_classifier
XGBoost	xgboost_cls
LightGBM	lightgbm_cls

Regression

Model	Identifier
Linear Regression	linear_regression
Ridge	ridge
Lasso	lasso
Random Forest	random_forest_reg
XGBoost	xgboost_reg

Clustering

Model	Identifier
K-Means	kmeans
DBSCAN	dbscan
Agglomerative	hierarchical

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API Reference

profile(data) → DataProfile

Parameter	Type	Description
data	pd.DataFrame, str, np.ndarray	Dataset or path to CSV

run_model(model_name, data, profile, config=None, target_col=None) → Result

Parameter	Type	Description
model_name	str	Model identifier (e.g., "rf_classifier")
data	pd.DataFrame	Dataset
profile	DataProfile	From profile() call
config	dict, optional	Hyperparameter overrides
target_col	str, optional	The target column to predict

Result Object

Attribute	Type	Description
metrics	dict	Evaluation metrics
plots	list	Plotly JSON chart objects
model_object	object	Trained model
run_id	str	UUID in experiment log
config	dict	Full resolved config

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Architecture

XAURA is built with strict layer separation. Each component has a clear responsibility:

1. User Interfaces: Web UI (FastAPI/JS), CLI (Click), Python API
2. Core Library: Profiler, Model Wrappers, Registry, Visualisation Engines
3. Storage: Local SQLite database for zero-config experiment tracking

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Contributing

1. Fork the repo
2. Create a feature branch (git checkout -b feature/model-name)
3. Write tests for your changes
4. Ensure all tests pass (pytest tests/ -v)
5. Submit a PR with a clear description

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License

MIT License — see LICENSE for details.

XAURA — Because ML should be intelligent about your data, not just your model.