Machine Learning with TuskLang Python SDK

Overview

TuskLang's Python SDK provides seamless integration with machine learning frameworks, enabling you to build AI-powered applications that leverage the revolutionary capabilities of the TuskLang ecosystem. From simple predictive models to complex neural networks, TuskLang makes ML accessible and powerful.

Installation & Setup

Core ML Dependencies

Install TuskLang Python SDK with ML extensions
pip install tuskml[full]
Or install specific ML frameworks
pip install tuskml[torch]    # PyTorch integration
pip install tuskml[tensorflow]  # TensorFlow integration
pip install tuskml[sklearn]  # Scikit-learn integration

Environment Configuration

peanu.tsk configuration for ML workloads
ml_config = {
    "gpu_enabled": true,
    "model_cache_dir": "/var/cache/tuskml/models",
    "batch_size": 32,
    "precision": "float16",  # or "float32", "bfloat16"
    "distributed_training": false,
    "model_registry": {
        "backend": "tuskdb",
        "versioning": true,
        "experiment_tracking": true
    }
}

Basic ML Operations

Data Loading & Preprocessing

from tuskml import DataLoader, Preprocessor
from tuskml.fujsen import @load_dataset
Load data using FUJSEN operators
@dataset = @load_dataset("customer_behavior")
@features = @extract_features(@dataset, ["age", "income", "purchase_history"])
TuskLang-native data loading
loader = DataLoader(
    source="@dataset",
    batch_size=32,
    shuffle=True,
    num_workers=4
)
Preprocessing pipeline
preprocessor = Preprocessor([
    "normalize",
    "encode_categorical",
    "handle_missing",
    "feature_selection"
])
@processed_data = preprocessor.fit_transform(@features)

Model Training

from tuskml import ModelTrainer, ModelRegistry
from tuskml.models import TuskNeuralNetwork
Define model architecture
model = TuskNeuralNetwork(
    layers=[128, 64, 32, 1],
    activation="relu",
    dropout=0.2,
    batch_norm=True
)
Training configuration
trainer = ModelTrainer(
    model=model,
    optimizer="adam",
    loss="binary_crossentropy",
    metrics=["accuracy", "f1_score"],
    callbacks=["early_stopping", "model_checkpoint"]
)
Train with TuskLang integration
@trained_model = trainer.fit(
    @processed_data,
    epochs=100,
    validation_split=0.2,
    use_tuskdb=True  # Store training logs in TuskDB
)
Register model in TuskLang registry
registry = ModelRegistry()
@model_id = registry.register(
    @trained_model,
    name="customer_churn_predictor",
    version="1.0.0",
    metadata={
        "accuracy": 0.89,
        "training_time": "2.5h",
        "dataset_size": 50000
    }
)

Advanced ML Features

Distributed Training

from tuskml.distributed import DistributedTrainer
from tuskml.flexequil import FlexEquilCluster
Set up distributed training cluster
cluster = FlexEquilCluster(
    nodes=["node1:5000", "node2:5000", "node3:5000"],
    gpu_per_node=2,
    memory_per_node="32GB"
)
Distributed trainer
dist_trainer = DistributedTrainer(
    model=model,
    cluster=cluster,
    strategy="data_parallel",
    sync_batch_norm=True
)
@distributed_model = dist_trainer.fit(
    @processed_data,
    epochs=50,
    batch_size=128
)

Model Serving & Inference

from tuskml.serving import ModelServer, InferenceEngine
Deploy model for serving
server = ModelServer(
    model="@trained_model",
    port=8080,
    workers=4,
    gpu_enabled=True
)
Start serving
server.start()
Inference engine for batch processing
engine = InferenceEngine(
    model="@trained_model",
    batch_size=64,
    preprocessors=[preprocessor]
)
Batch inference
@predictions = engine.predict(@new_data)

AutoML & Hyperparameter Optimization

from tuskml.automl import AutoMLPipeline, HyperparameterOptimizer
AutoML pipeline
automl = AutoMLPipeline(
    task="classification",
    time_limit=3600,  # 1 hour
    max_models=50,
    ensemble_method="stacking"
)
@best_model = automl.fit(@processed_data)
Hyperparameter optimization
optimizer = HyperparameterOptimizer(
    model_class=TuskNeuralNetwork,
    param_space={
        "layers": [[64, 32], [128, 64, 32], [256, 128, 64, 32]],
        "learning_rate": [0.001, 0.01, 0.1],
        "dropout": [0.1, 0.2, 0.3]
    },
    optimization_method="bayesian",
    n_trials=100
)
@optimized_model = optimizer.optimize(@processed_data)

ML Pipeline Integration

End-to-End ML Pipeline

from tuskml.pipeline import MLPipeline
from tuskml.monitoring import ModelMonitor
Complete ML pipeline
pipeline = MLPipeline([
    "data_loading",
    "preprocessing", 
    "feature_engineering",
    "model_training",
    "evaluation",
    "deployment"
])
Execute pipeline
@pipeline_result = pipeline.execute(
    config={
        "dataset": "@dataset",
        "target": "churn",
        "test_size": 0.2,
        "random_state": 42
    }
)
Model monitoring
monitor = ModelMonitor(
    model="@trained_model",
    metrics=["accuracy", "precision", "recall"],
    drift_detection=True,
    alert_threshold=0.05
)
monitor.start_monitoring()

Real-time ML with TuskLang

from tuskml.streaming import StreamingMLPipeline
from tuskml.fujsen import @stream_data
Streaming ML pipeline
stream_pipeline = StreamingMLPipeline(
    model="@trained_model",
    preprocessor=preprocessor,
    window_size=1000,
    update_frequency=100
)
Process streaming data
@stream_predictions = stream_pipeline.process_stream(
    @stream_data,
    output_format="json"
)
Real-time feature engineering
@real_time_features = @extract_streaming_features(
    @stream_data,
    features=["user_behavior", "transaction_patterns"],
    window_size=300
)

ML Model Management

Model Versioning & Deployment

from tuskml.deployment import ModelDeployer
from tuskml.versioning import ModelVersionManager
Model versioning
version_manager = ModelVersionManager()
@new_version = version_manager.create_version(
    model="@trained_model",
    changes="Improved feature engineering",
    performance_metrics={"accuracy": 0.92}
)
Model deployment
deployer = ModelDeployer(
    model="@new_version",
    environment="production",
    scaling_config={
        "min_replicas": 2,
        "max_replicas": 10,
        "cpu_request": "500m",
        "memory_request": "1Gi"
    }
)
@deployment = deployer.deploy()

Model Explainability

from tuskml.explainability import ModelExplainer
from tuskml.visualization import MLVisualizer
Model explainability
explainer = ModelExplainer(
    model="@trained_model",
    method="shap",
    background_data="@processed_data"
)
@explanations = explainer.explain(@test_data)
Visualize explanations
visualizer = MLVisualizer()
@explanation_plot = visualizer.plot_shap_values(
    @explanations,
    feature_names=@feature_names
)

ML with TuskLang Ecosystem

Integration with TuskDB

from tuskml.storage import TuskDBStorage
from tuskml.fujsen import @store_model, @load_model
Store model in TuskDB
@model_storage = TuskDBStorage(
    database="ml_models",
    collection="trained_models"
)
@store_model(@trained_model, "customer_churn_v1")
Load model from TuskDB
@loaded_model = @load_model("customer_churn_v1")
Store training metrics
@store_metrics = @store_training_metrics(
    model_id="@model_id",
    metrics={
        "accuracy": 0.89,
        "precision": 0.87,
        "recall": 0.91,
        "f1_score": 0.89
    }
)

ML with FUJSEN Intelligence

from tuskml.fujsen import @ml_predict, @ml_train, @ml_evaluate
FUJSEN-powered ML operations
@prediction = @ml_predict(
    model="@trained_model",
    data="@new_data",
    confidence_threshold=0.8
)
@training_result = @ml_train(
    model="@model_architecture",
    data="@training_data",
    hyperparameters={
        "learning_rate": 0.001,
        "batch_size": 32,
        "epochs": 100
    }
)
@evaluation = @ml_evaluate(
    model="@trained_model",
    test_data="@test_data",
    metrics=["accuracy", "precision", "recall", "f1"]
)

Best Practices

Performance Optimization

GPU acceleration
import torch
if torch.cuda.is_available():
    model = model.cuda()
    data = data.cuda()
Mixed precision training
from tuskml.optimization import MixedPrecisionTrainer
mp_trainer = MixedPrecisionTrainer(
    model=model,
    precision="float16"
)
Model quantization
from tuskml.optimization import ModelQuantizer
quantizer = ModelQuantizer(
    model="@trained_model",
    quantization="int8"
)
@quantized_model = quantizer.quantize()

Security & Privacy

from tuskml.security import SecureMLPipeline
from tuskml.privacy import DifferentialPrivacy
Secure ML pipeline
secure_pipeline = SecureMLPipeline(
    model="@trained_model",
    encryption="aes256",
    access_control=True
)
Differential privacy
dp_trainer = DifferentialPrivacy(
    model=model,
    epsilon=1.0,
    delta=1e-5
)
@dp_model = dp_trainer.fit(@sensitive_data)

Example: Customer Churn Prediction

Complete customer churn prediction system
from tuskml import *
Load and preprocess data
@customer_data = @load_dataset("customer_behavior")
@features = @extract_features(@customer_data, [
    "age", "income", "purchase_frequency", 
    "support_tickets", "last_login_days"
])
Train model
model = TuskNeuralNetwork(layers=[64, 32, 16, 1])
trainer = ModelTrainer(model=model, optimizer="adam", loss="binary_crossentropy")
@churn_model = trainer.fit(@features, epochs=50)
Deploy and monitor
deployer = ModelDeployer(@churn_model, environment="production")
@deployment = deployer.deploy()
monitor = ModelMonitor(@churn_model, metrics=["accuracy", "f1_score"])
monitor.start_monitoring()
Real-time predictions
@churn_predictions = @ml_predict(
    model="@churn_model",
    data="@live_customer_data"
)

Conclusion

TuskLang's Python SDK provides a comprehensive machine learning ecosystem that integrates seamlessly with the revolutionary TuskLang framework. From basic model training to advanced distributed systems, TuskLang makes ML accessible, powerful, and production-ready.

The combination of FUJSEN intelligence, TuskDB storage, and FlexEquil distributed computing creates a unique ML platform that scales from simple predictions to enterprise-grade AI systems. Whether you're building recommendation engines, predictive analytics, or real-time AI applications, TuskLang provides the tools and infrastructure you need to succeed.

Embrace the future of machine learning with TuskLang - where AI meets revolutionary technology.