kaizenstat 0.2.13

Zero-friction AutoML + Data Cleaning Toolkit

🚀 KaizenStat

Official Website: www.kaizenstat.com

KaizenStat is a zero-friction, production-grade AutoML, automated data cleaning, and model explanation engine. It allows you to audit datasets, repair data issues, benchmark models with hardware-aware optimization, export standalone pipeline code, and host web-based dashboards—all with a single command or Python import.

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🎯 Core Philosophy

• Zero-Friction AutoML: No complex configuration files. Pass your dataset, name your target, and KaizenStat does the rest.
• Production Crash-Proofing: Automatically handles messy real-world data issues: high-cardinality ID columns, datetime parsing, missing inputs, class imbalance, and label encoding.
• Explainable AI: Breaks open the "black box" by generating standalone, human-readable Python training code reproducing the best-found pipeline.
• Hybrid Interface: 100% parity between CLI and Python API.

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📦 Installation

Install the core package with zero heavy external dependencies:

pip install kaizenstat

Optional Drivers & Accelerators

Tailor KaizenStat to your specific workload:

pip install kaizenstat[ui]     # Install Streamlit for web dashboards
pip install kaizenstat[gpu]    # Install XGBoost with GPU/MPS support
pip install kaizenstat[fast]   # Install Polars for ultra-fast CSV parsing
pip install kaizenstat[all]    # Install all optional components

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⚔️ CLI & Python API Feature Matrix

KaizenStat is designed around a single unified vocabulary. Every CLI command has a direct, equivalent function in the Python SDK.

Command	Python API	Purpose
kz audit	KaizenStat.audit()	🔍 Runs a diagnostic sweep (missing values, duplicates, imbalance, dead features).
kz heal	KaizenStat.heal()	🩹 Clean, impute, parse datetimes, drop IDs, and encode string labels.
kz benchmark	KaizenStat.benchmark()	🚀 Automatically trains, optimizes, and ranks model pipelines.
kz auto	KaizenStat.auto()	⚡ Orchestrates the entire pipeline in sequence (Audit ➔ Heal ➔ Benchmark).
kz explain	KaizenStat.explain()	💬 Generates plain-English diagnostic summaries and model recommendations.
kz codegen	KaizenStat.codegen()	📝 Generates standalone, dependency-free Python code for the best model.
kz export-model	KaizenStat.save_model()	💾 Trains the top pipeline and saves it directly to a .joblib binary.
kz report	KaizenStat.report() / KaizenStat.serve_report()	📊 Generates HTML report (auto-opens browser) and serves it on a local web port (with --serve or serve_report()).
kz serve	KaizenStat.serve()	🖥️ Launches a local interactive Streamlit app dashboard.
kz analyze	KaizenStat.analyze()	🧠 Executes auto-intelligence analysis over dataset context using LLM reasoning.
kz ask	KaizenStat.ask()	🤖 Answers complex developer queries about accuracy, data quality, or anomalies.
kz ask --followup	KaizenStat.ask_followup()	🔁 Maintains multi-turn conversation memory with the data reasoning engine.
kz improve	KaizenStat.improve()	🚀 Query AI to get next best actions and improvement plans.
kz status	N/A (CLI Only)	📊 Show active system and dataset context status.
kz reset	N/A (CLI Only)	🧹 Reset conversational memory and active dataset context.

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💡 Quick Start Guide

1. Python SDK Usage

from kaizenstat import KaizenStat
import pandas as pd

# Load your dataset
df = pd.read_csv("dataset.csv")

# 1. Diagnose issues
findings = KaizenStat.audit(df, target="target_column")

# 2. Automatically heal dirty data
clean_df = KaizenStat.heal(df, target="target_column")

# 3. Benchmark models with cross-validation
leaderboard = KaizenStat.benchmark(clean_df, target="target_column")

# 4. Generate standalone code for reproduction
KaizenStat.codegen("dataset.csv", target="target_column", output_path="reproduce.py")

# 5. Generate and open interactive HTML profiling report
# By default, report() automatically opens in your default browser.
report_path = KaizenStat.report("dataset.csv", target="target_column")

# Or serve the HTML report temporarily on a local web port (great for remote/headless setups)
KaizenStat.serve_report(report_path)

# 6. Dual-Mode Conversational AI (OpenRouter powered)
# Runs automated structured AI analysis
analysis = KaizenStat.analyze(df, target="target_column")

# Ask custom developer queries about data or pipeline
KaizenStat.ask("Why is model accuracy lower or what are the dataset flaws?")

# Multi-turn conversation with memory context
KaizenStat.ask_followup("What should I do to handle the missing values or high cardinality?")

# 6. Get actionable next-step recommendations
KaizenStat.improve()

2. Command Line Interface (CLI)

# Get quick help and list commands
kz --help

# Run the full pipeline in one command
kz auto dataset.csv --target target_column

# Repair a dataset and save the clean file
kz heal dataset.csv --target target_column -o cleaned_dataset.csv

# Launch a local Streamlit app to preview and test model performance
kz serve dataset.csv --target target_column --port 8501

# Generate HTML report and automatically open it in your browser
kz report dataset.csv target_column

# Generate HTML report and serve it on a temporary local port with live web hosting
kz report dataset.csv target_column --serve

# Execute AI diagnostic analysis (saves context locally)
kz analyze dataset.csv --target target_column

# Ask conversational queries about data quality
kz ask "Why is model accuracy low?"

# Ask followup query with conversation memory persistence
kz ask "What should I do to handle the missing values?" --followup

# Get next best actions / actionable improvement plan
kz improve

# View active system and dataset context status
kz status

# Reset conversational memory and session cache
kz reset

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🧠 Behind the Scenes: Core Engines

1. Hardware-Aware Execution

KaizenStat automatically checks your environment using detect_device(). It leverages CUDA on Nvidia GPUs and MPS on Apple Silicon (M1/M2/M3 Mac) to accelerate training when optional dependencies (like xgboost) are installed.

2. Smart Model Selection

The benchmarking engine adjusts its logic dynamically based on the dataset properties:

• Large Datasets (>100k rows): Excludes slow estimators (like Gradient Boosting) on standard CPU hosts to prevent compute lockups.
• High-Cardinality Categoricals: Optimizes feature preprocessors and prioritizes tree-based models (Random Forests, Gradient Boosting, XGBoost).
• Float Targets: Detects values with a continuous numeric profile and switches the entire pipeline to regression mode automatically.

3. Automatic Imbalance Correction

During data healing, KaizenStat computes target ratios. If target class distribution has a skew larger than 65% / 35%, it adjusts model parameters dynamically (e.g. setting class_weight="balanced" in scikit-learn estimators).

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🛠 Developer Guide

Setting up a local workspace

To contribute or run local enhancements:

1. Clone the repository:

   git clone https://github.com/masuddarrahaman/KaizenStat-Library.git
   cd KaizenStat-Library
   

2. Install the package in editable mode with all optional drivers:

   pip install -e ".[all]"
   

3. Run tests or validation:

   python3 -m unittest discover -s tests
   

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📄 License

Distributed under the MIT License. See LICENSE for details.