TrainIQ 0.1.3

Universal trainiq Library – end-to-end ML/DL pipelines with a single call.

🤖 TrainIQ — Universal trainiq Library

End-to-end ML/DL pipelines with a single call.

Supports tabular, image, text, and time-series data with automatic model selection, hyperparameter tuning, and deployment.

Installation • Quick Start • Documentation • Examples

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✨ Features

• 🎯 Automatic Everything: Data type detection, task detection, model selection, preprocessing
• 🚀 One-Line Training: Train state-of-the-art models with a single function call
• 🔧 Hyperparameter Tuning: Built-in Optuna integration for automatic optimization
• 📊 Multiple Data Types: Tabular, Image, Text, Time-Series support
• 🎨 Rich Model Zoo: Neural networks, Random Forest, XGBoost, ResNet, BERT, and more
• 📦 Easy Deployment: Export to ONNX/TorchScript + FastAPI scaffold generation
• 💻 CLI & Python API: Use from command line or Python scripts
• 🔍 Comprehensive Metrics: Automatic evaluation with plots and reports

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🚀 Installation

Basic Installation

pip install TrainIQ

With All Features

pip install TrainIQ[all]

Optional Dependencies

# For text models (BERT, DistilBERT)
pip install TrainIQ[text]

# For XGBoost
pip install TrainIQ[xgboost]

# For deployment (FastAPI)
pip install TrainIQ[deploy]

# For ONNX export
pip install TrainIQ[onnx]

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⚡ Quick Start

Python API

from trainiq import trainiq, trainiqConfig

# Configure and train
config = trainiqConfig(
    data_path="data.csv",
    target_column="label",
    epochs=50
)

model = trainiq(config)
results = model.train()

# Make predictions
predictions = model.predict(new_data)

# Export model
model.export(format="onnx")

Command Line Interface

# Train a model
trainiq train --data data.csv --target label --epochs 50

# With hyperparameter tuning
trainiq train --data data.csv --target label --tune --tune-trials 50

# Check system info
trainiq info

# Get help
trainiq --help

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📚 Documentation

Core Concepts

1. Automatic Data Type Detection

TrainIQ automatically identifies your data modality:

• Tabular: CSV, Excel, Parquet, JSON
• Image: Folder structure with class subdirectories
• Text: CSV with text columns
• Time-Series: Sequential data with datetime index

# No need to specify data_type - it's auto-detected!
config = trainiqConfig(data_path="my_data.csv")

2. Automatic Task Detection

Identifies whether your problem is:

• Classification (binary or multi-class)
• Regression
• Forecasting (time-series)

# Task is automatically detected from your data
model = trainiq(config)
results = model.train()

3. Automatic Model Selection

Compares multiple models and selects the best:

• Tabular: MLP, Random Forest, XGBoost
• Image: ResNet18, ResNet50, EfficientNet-B0
• Text: TextCNN, DistilBERT
• Time-Series: LSTM, Transformer

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🎯 Examples

Tabular Classification

from trainiq import trainiq, trainiqConfig

config = trainiqConfig(
    data_path="iris.csv",
    target_column="species",
    epochs=50
)

model = trainiq(config)
results = model.train()

print(f"Accuracy: {results['best_val_acc']:.4f}")

Tabular Regression

config = trainiqConfig(
    data_path="housing.csv",
    target_column="price",
    task="regression",
    tune=True,  # Enable hyperparameter tuning
    tune_trials=30
)

model = trainiq(config)
results = model.train()

Image Classification

# Folder structure:
# images/
#   ├── cat/
#   ├── dog/
#   └── bird/

config = trainiqConfig(
    data_path="images/",
    data_type="image",
    model_name="resnet50",
    epochs=100,
    batch_size=64
)

model = trainiq(config)
results = model.train()
model.export(format="onnx")

Text Classification

config = trainiqConfig(
    data_path="reviews.csv",
    target_column="sentiment",
    data_type="text",
    model_name="distilbert",
    epochs=10
)

model = trainiq(config)
results = model.train()

Time-Series Forecasting

config = trainiqConfig(
    data_path="stock_prices.csv",
    data_type="timeseries",
    model_name="lstm",
    extra={
        "window": 30,    # Look back 30 time steps
        "horizon": 7     # Predict 7 steps ahead
    }
)

model = trainiq(config)
results = model.train()

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🔧 Configuration Options

Essential Parameters

config = trainiqConfig(
    # Data
    data_path="data.csv",           # Path to dataset (required)
    target_column="label",          # Target column name
    task="classification",          # "classification", "regression", "forecasting"
    data_type="tabular",            # "tabular", "image", "text", "timeseries"
    
    # Training
    epochs=50,                      # Number of epochs
    batch_size=32,                  # Batch size
    learning_rate=1e-3,             # Learning rate
    optimizer="adam",               # "adam", "adamw", "sgd"
    
    # Model
    model_name="resnet18",          # Specific model to use
    pretrained=True,                # Use pretrained weights
    
    # Hyperparameter Tuning
    tune=True,                      # Enable HPO
    tune_trials=30,                 # Number of trials
    
    # Output
    output_dir="trainiq_output",     # Output directory
    device="cuda",                  # "cpu", "cuda", "mps"
    seed=42                         # Random seed
)

Advanced Features

config = trainiqConfig(
    # Advanced Training
    early_stopping_patience=7,      # Early stopping
    gradient_clip=1.0,              # Gradient clipping
    mixed_precision=True,           # AMP training
    scheduler="cosine",             # LR scheduler
    
    # Model Architecture
    layers=[512, 256, 128],         # Custom layer sizes
    dropout=0.3,                    # Dropout rate
    activations="relu",             # Activation function
    
    # Data Augmentation
    val_split=0.2,                  # Validation split
    cv_folds=5,                     # K-fold CV
    class_weights="auto",           # Handle imbalance
    
    # Advanced Features
    lr_finder=True,                 # Auto-find LR
    ensemble=True,                  # Model ensembling
    ensemble_top_n=3                # Top N models
)

---

💻 CLI Usage

Training Commands

# Basic training
trainiq train --data data.csv --target label

# With custom parameters
trainiq train \
  --data housing.csv \
  --target price \
  --task regression \
  --epochs 100 \
  --batch-size 64 \
  --lr 0.001

# With hyperparameter tuning
trainiq train \
  --data data.csv \
  --target label \
  --tune \
  --tune-trials 50

# Image classification
trainiq train \
  --data images/ \
  --data-type image \
  --model resnet50 \
  --epochs 200

# Export after training
trainiq train \
  --data data.csv \
  --target label \
  --export onnx

Other Commands

# System information
trainiq info

# Make predictions
trainiq predict \
  --model-path trainiq_output/checkpoints/best_model.pt \
  --data test.csv

# Export model
trainiq export \
  --model-path model.pt \
  --format onnx

# Generate API
trainiq deploy \
  --model-path model.onnx \
  --output my_api/

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📦 Model Zoo

Tabular Models

Model	Type	Description
tabular_net	Neural Network	Fully-connected MLP
sklearn_rf	Random Forest	Fast, interpretable
sklearn_xgb	XGBoost	High performance

Image Models

Model	Type	Parameters	Description
resnet18	CNN	11M	Fast, good accuracy
resnet50	CNN	25M	Higher accuracy
efficientnet_b0	CNN	5M	Efficient

Text Models

Model	Type	Parameters	Description
text_cnn	CNN	<1M	Fast, lightweight
distilbert	Transformer	66M	High accuracy

Time-Series Models

Model	Type	Description
lstm	RNN	Handles sequences
transformer_ts	Transformer	Long-range dependencies

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🚢 Deployment

Export Models

# Export to ONNX
paths = model.export(format="onnx")

# Export to TorchScript
paths = model.export(format="torchscript")

# Export to both
paths = model.export(format="both")

Generate FastAPI App

# Generate API scaffold
api_path = model.deploy(output_dir="my_api")

# Then run:
# cd my_api
# pip install -r requirements.txt
# uvicorn app:app --reload

API Endpoints

# Health check
GET http://localhost:8000/health

# Prediction
POST http://localhost:8000/predict
{
    "data": [[5.1, 3.5, 1.4, 0.2]]
}

# Documentation
GET http://localhost:8000/docs

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🔍 Evaluation & Metrics

Classification Metrics

results = model.train()
metrics = results['eval_metrics']

print(f"Accuracy: {metrics['accuracy']:.4f}")
print(f"F1 Score: {metrics['f1_macro']:.4f}")
print(f"Precision: {metrics['precision_macro']:.4f}")
print(f"Recall: {metrics['recall_macro']:.4f}")

Regression Metrics

print(f"RMSE: {metrics['rmse']:.4f}")
print(f"MAE: {metrics['mae']:.4f}")
print(f"R²: {metrics['r2']:.4f}")

Automatic Visualizations

• Training curves (loss & accuracy)
• Confusion matrix (classification)
• Feature importance (tabular models)

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🐛 Troubleshooting

Common Issues

Out of Memory

config = trainiqConfig(
    batch_size=16,  # Reduce batch size
    mixed_precision=True  # Enable AMP
)

Slow Training

config = trainiqConfig(
    device="cuda",  # Use GPU
    num_workers=8,  # More data loading workers
    mixed_precision=True
)

Poor Performance

config = trainiqConfig(
    tune=True,  # Enable hyperparameter tuning
    tune_trials=50
)

PyTorch DLL Error (Windows)

This is a Windows-specific PyTorch installation issue, not a TrainIQ bug.

Solution:

# Reinstall PyTorch with proper dependencies
pip uninstall torch torchvision
pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu

Or install CUDA version if you have NVIDIA GPU:

pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

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📖 Full Documentation

For complete documentation, visit: Full API Reference

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🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📄 License

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

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🙏 Acknowledgments

Built with:

• PyTorch - Deep learning framework
• Scikit-learn - Machine learning library
• Optuna - Hyperparameter optimization
• FastAPI - API framework
• Transformers - NLP models

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📞 Support

• PyPI: https://pypi.org/project/TrainIQ/
• Issues: GitHub Issues
• Documentation: GitHub README

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🌟 Star History

If you find TrainIQ useful, please consider giving it a star ⭐

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Made with ❤️ by Mickey2004

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