Welcome to Neuraxle’s documentation, examples and tutorials!¶
Contents:
- About Neuraxle
- Neuraxle Pipelines
- Comparison to Other Machine Learning Pipeline Frameworks, and Compatibility
- Solutions to Scikit-Learn’s Biggest Problems
- Definitions
- Inability to Reasonably do Automatic Machine Learning (AutoML)
- Inability to Reasonably do Deep Learning Pipelines
- Not ready for Production nor for Complex Pipelines
- Problem: Processing 3D, 4D, or ND Data in your Pipeline with Steps Made for Lower-Dimensionnal Data
- Problem: Modify a Pipeline Along the Way, such as for Pre-Training or Fine-Tuning
- Problem: Getting Model Attributes from Scikit-Learn Pipeline
- Problem: You can’t Parallelize nor Save Pipelines Using Steps that Can’t be Serialized “as-is” by Joblib
- Awesome Neuraxle
- Contributing to Neuraxle
- Code of conduct
- License
- >>> Hands-On Walkthroughs
- Introduction
- Class diagrams and inheritance charts of Neuraxle objects
- The Mixin design pattern in machine learning
- Steps containing other steps as the composite design pattern in machine learning
- Scikit-learn’s pipeline.Pipeline class and how to shift to parallel deep learning
- FeatureUnion to compute steps in parallel and join their results
- AutoML module to automatically tune hyperparameters of your pipelines
- All the base classes of Neuraxle together
- Automatic Hyperparameter Tuning / AutoML
- AutoML loop
- Define your problem
- Define your pipeline
- Choose a validation splitter
- Define a the main scoring metric with a first MetricsCallback
- Add other metric callbacks with MetricCallback (optional)
- Select an hyperparams repository
- Select an hyperparams optimizer
- Create, and launch AutoML loop
- Get best model and measure test accuracy
- Additional note : model selection as an hyperparameter
- AutoML loop
- Handler Methods
- Step Saving & Lifecycle
- Time Series Processing Example
- Random Distributions
- REST API Serving
- >>> Practical Examples
- >>> Complete API Documentation
- neuraxle.base
- Neuraxle’s Base Classes
- Examples using
neuraxle.base.BaseStep - Examples using
neuraxle.base.ExecutionContext - Examples using
neuraxle.base.ForceHandleMixin - Examples using
neuraxle.base.Identity - Examples using
neuraxle.base.MetaStep - Examples using
neuraxle.base.NonFittableMixin - Examples using
neuraxle.base.NonTransformableMixin
- Examples using
- Neuraxle’s Base Classes
- neuraxle.pipeline
- neuraxle.data_container
- neuraxle.union
- neuraxle.steps.numpy
- neuraxle.steps.flow
- neuraxle.steps.data
- neuraxle.steps.column_transformer
- neuraxle.steps.features
- neuraxle.steps.sklearn
- neuraxle.steps.loop
- neuraxle.steps.output_handlers
- neuraxle.steps.misc
- neuraxle.hyperparams.distributions
- neuraxle.hyperparams.scipy_distributions
- neuraxle.hyperparams.space
- neuraxle.metaopt.auto_ml
- neuraxle.metaopt.callbacks
- neuraxle.metaopt.context
- neuraxle.metaopt.optimizer
- neuraxle.metaopt.validation
- neuraxle.metaopt.data.vanilla
- neuraxle.metaopt.data.reporting
- neuraxle.metaopt.data.aggregates
- neuraxle.metaopt.repositories.repo
- neuraxle.metaopt.repositories.json
- neuraxle.metaopt.repositories.db
- neuraxle.metaopt.hyperopt.tpe
- neuraxle.distributed.streaming
- neuraxle.logging.logging
- neuraxle.logging.warnings
- neuraxle.rest.flask
- neuraxle.base