Metadata-Version: 2.4
Name: ai-mk-toolkit
Version: 0.5.0
Summary: Unified AI/ML toolkit providing a consistent API across sklearn, PyTorch, TensorFlow, and 150+ models.
Author-email: Manojna Karuparthi <manojnak03@gmail.com>
License-Expression: MIT
Keywords: machine-learning,deep-learning,nlp,computer-vision,reinforcement-learning,ai,unified-api
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
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Dynamic: license-file

# Ai_MK_Toolkit

A production-grade Python package providing a unified API for 150+ machine learning, deep learning, NLP, reinforcement learning, and computer vision models. All models are callable via intuitive dot-path imports like `ai_mk_toolkit.ml.ResNet50()`.

**This package is a unified interface wrapper** built on top of open-source libraries. It does not reimplement algorithms but provides convenient access to state-of-the-art models from leading frameworks.

## 🚀 Key Features

- **Unified API**: Single interface for models across frameworks (sklearn, PyTorch, TensorFlow)
- **Lazy Loading**: Heavy dependencies loaded only when used; base install stays lightweight (~10 MB)
- **150+ Models**: Pre-configured access to state-of-the-art models from trusted libraries
- **Framework Agnostic**: Switch backends seamlessly (PyTorch ↔ TensorFlow for deep learning)
- **Production Ready**: Type hints, error handling, PEP 561 compliance (py.typed)
- **License Compliant**: Respects licenses of all underlying libraries

## � About This Project

**What it is**: `ai-mk-toolkit` is a **unified Python interface** that wraps proven, production-grade machine learning libraries. It allows you to access 150+ models through a consistent, intuitive API.

**What it is NOT**: This package does NOT reimplement algorithms from scikit-learn, PyTorch, TensorFlow, or any other library. It solely provides a convenient interface layer for accessing these libraries.

**Design Philosophy**:
- Use battle-tested libraries (scikit-learn, PyTorch, TensorFlow, etc.)
- Provide a consistent API across different frameworks
- Let users install only what they need (modular dependencies)
- Respect and comply with all open-source licenses
- Be transparent about authorship and attribution

## �📦 Installation

### Base (ML only)
```bash
pip install ai-mk-toolkit
```

### With Deep Learning
```bash
pip install ai-mk-toolkit[dl,torch]      # PyTorch
pip install ai-mk-toolkit[dl,tensorflow]  # TensorFlow
```

### With NLP & Transformers
```bash
pip install ai-mk-toolkit[nlp]
```

### With Reinforcement Learning
```bash
pip install ai-mk-toolkit[rl]
```

### Everything
```bash
pip install ai-mk-toolkit[full]
```

## 🎯 Quick Start

### Classical Machine Learning

```python
from ai_mk_toolkit import ml

# 51 models: regression, classification, clustering, dimensionality reduction
regressor = ml.LinearRegression()
regressor.fit(X_train, y_train)
predictions = regressor.predict(X_test)

# Or by name
xgb = ml.get_model("XGBoost", n_estimators=50)
pca = ml.PCA(n_components=10)
svm = ml.SVC(kernel="rbf")
```

### Deep Learning (60+ Models)

```python
from ai_mk_toolkit import dl

resnet = dl.ResNet50(pretrained=True)  # CNN
yolo = dl.YOLOv8()  # Object Detection
lstm = dl.LSTM(units=128)  # Sequence
gan = dl.WGAN_GP()  # GAN (works with pytorch/tensorflow)
```

### Natural Language Processing (55+ Models)

```python
from ai_mk_toolkit import nlp

tokenizer = nlp.Tokenizer()  # Classical NLP
bert = nlp.BERT()  # Transformers
llama3 = nlp.Llama3()  # LLMs
text_gen = nlp.TextGeneration()  # NLP tasks
```

### Reinforcement Learning (13 Models)

```python
from ai_mk_toolkit import rl

ql = rl.QLearning(n_states=16, n_actions=4)
dqn = rl.DQN(policy="MlpPolicy", env=env)
sarsa = rl.SARSA(n_states=16, n_actions=4)
```

### Computer Vision (14 Tasks)

```python
from ai_mk_toolkit import cv

image_clf = cv.ImageClassification()
detector = cv.ObjectDetection()
segmenter = cv.ImageSegmentation()
```

## 📊 Complete Model Catalog

**📈 Machine Learning** (51 models): LinearRegression, Ridge, Lasso, ElasticNet, SVC, LogisticRegression, RandomForest, DecisionTree, XGBoost, LightGBM, CatBoost, KNN, PCA, TSNE, UMAP, and more

**🧠 Deep Learning** (60+ models): ResNet, VGG, MobileNet, EfficientNet, YOLOv5-v10, LSTM, GRU, GAN, StyleGAN, CycleGAN, and more

**🤖 NLP** (55+ models): BERT, GPT2, Llama3, Mistral, T5, Transformers, Classical NLP (NLTK, spaCy, gensim)

**🎮 Reinforcement Learning** (13 models): QLearning, SARSA, DQN, DoubleDQN, DuelingDQN, and more

**👁️ Computer Vision** (14 tasks): ImageClassification, ObjectDetection, ImageSegmentation, PoseEstimation, OCR, FaceRecognition, and more

See [CHANGELOG.md](CHANGELOG.md) for detailed model list.

## 🔧 Advanced Usage

### Unified Classifiers (sklearn-backed)

```python
from ai_mk_toolkit import UnifiedClassifier

clf = UnifiedClassifier(model="svc", kernel="rbf")
clf.fit(X_train, y_train)
print(clf.backend_name)
```

### Deep Learning with Backend Selection

```python
from ai_mk_toolkit import UnifiedModel

model = UnifiedModel(backend="pytorch")
model.add_dense(128, activation="relu")
model.compile(task="classification")
model.fit(X_train, y_train, epochs=10)
predictions = model.predict(X_test)
```

## ✅ Quality Assurance & Code Cleanliness

This package maintains strict code quality standards:

- **Zero Dead Code**: All modules are functionally active and referenced. No orphaned imports, unused variables, or unreachable code.
- **Comprehensive Testing**: 87 tests passing, 2 conditional skips. Full coverage of core functionality.
- **Static Analysis**: Passes ruff checks (F401 unused imports, F841 unused variables).
- **Build & Packaging**: Passes PyPI validation via twine. Builds successfully for both sdist and wheel distributions.
- **Compliance Verified**: No algorithm reimplementations. Pure wrapper architecture respecting open-source licenses.
- **Dependencies Optimized**: Core dependencies (`numpy`, `scikit-learn`, `torch`, `tensorflow`, etc.) automatically installed. Optional extras available for specialized use cases.
- **Documentation**: Centralized, current, and comprehensive. See [CHANGELOG.md](CHANGELOG.md) for version history and [CONTRIBUTING.md](CONTRIBUTING.md) for development guidelines.

## 📝 Contributing

Found a bug? Want to contribute? See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

### Normalized Input (any format works)

```python
# These are identical:
dl.get_model("RT-DETR")
dl.get_model("RT_DETR")
dl.RTDETR()

# Same for WGAN variants:
dl.WGAN_GP()
dl.get_model("wgan-gp")
```

## 📋 Testing

```bash
pytest                    # All tests
pytest -v                 # Verbose
pytest tests/test_classical_models.py  # Specific test
```

## 📄 License

MIT License — See [LICENSE](LICENSE)

## 🙏 Acknowledgments & Attribution

This package is built on top of the following open-source libraries. We are grateful for their contributions to the ML community and comply with each project's license:

**Core Dependencies** (always installed):
- **scikit-learn** (BSD-3-Clause) — Classical ML algorithms (regression, classification, clustering, decomposition)
- **NumPy** (BSD) — Numerical computing foundation

**Optional Deep Learning Frameworks** (install via extras):
- **PyTorch** (BSD) — Deep learning models (CNNs, RNNs, GANs, transformers)
- **TensorFlow/Keras** (Apache 2.0) — Alternative deep learning backend
- **torchvision** (BSD) — Pre-trained computer vision models
- **timm** (Apache 2.0) — PyTorch image models library
- **Ultralytics** (AGPL-3.0) — YOLO object detection models

**Optional ML Boosting** (install via `[ml_boosting]` extra):
- **XGBoost** (Apache 2.0) — Gradient boosting
- **LightGBM** (MIT) — Fast gradient boosting
- **CatBoost** (Apache 2.0) — Categorical gradient boosting
- **UMAP** (BSD-3-Clause) — Dimensionality reduction

**Optional NLP** (install via `[nlp]` extra):
- **Transformers** (Apache 2.0) — HuggingFace transformer models (BERT, GPT, Llama, Mistral, etc.)
- **NLTK** (Apache 2.0) — Classical NLP tools
- **spaCy** (MIT) — Industrial-strength NLP
- **Gensim** (LGPL-2.1+) — Topic modeling and embeddings
- **sentence-transformers** (Apache 2.0) — Semantic embeddings

**Optional Reinforcement Learning** (install via `[rl]` extra):
- **Stable Baselines 3** (MIT) — RL algorithms (DQN, PPO, A2C, etc.)
- **sb3-contrib** (MIT) — Additional RL algorithms

**All licenses are permissive and allow commercial use.**
