Logo Neuraxio/ Neuraxle
0.6.0

Contents:

  • About
    • Neuraxle Pipelines
      • Documentation
      • Installation
        • Examples
        • License
        • Citation
        • Contributors
        • Supported By
    • Solutions to Scikit-Learn’s Biggest Problems
      • Definitions
      • Inability to Reasonably do Automatic Machine Learning (AutoML)
        • Problem: Defining the Search Space (Hyperparameter Distributions)
        • Problem: Defining Hyperparameters in the Constructor is Limiting
        • Problem: Different Train and Test Behavior
        • Problem: You trained a Pipeline and You Want Feedback Statistics on its Learning
      • Inability to Reasonably do Deep Learning Pipelines
        • Problem: Scikit-Learn Hardly Allows for Mini-Batch Gradient Descent (Incremental Fit)
        • Problem: Initializing the Pipeline and Deallocating Resources
        • Problem: It is Difficult to Use Other Deep Learning (DL) Libraries in Scikit-Learn
        • Problem: The Ability to Transform Output Labels
      • 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
  • >>> Hands-On Walkthroughs
    • Introduction
      • Encapsulate Models and Data Transformers
      • Pipe and Filter
      • Features
      • Wrapper (a.k.a. Decorator) classes
      • Pipelines for Minibatching and Parallel Processing
      • Repository for lazy data loading
      • Training your pipeline
      • Serializing your pipeline
      • Conclusion
    • Automatic Hyperparameter Tuning / AutoML
      • AutoML loop
        • Define your problem
        • Define your pipeline
        • Choose a validation splitter
        • Define a the main scoring metric with ScoringCallback
        • 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
    • Handler Methods
      • handle_fit_transform
      • handle_fit
      • handle_transform
      • When to use handler methods ?
      • HandleOnlyMixin
      • ForceHandleMixin
      • ForceHandleOnlyMixin
        • Examples
      • ForEach
      • ToNumpy
      • Transform Expected Outputs
      • Expand The DataContainer
      • Reversible Pipeline
    • Step Saving & Lifecycle
      • Lifecycle
      • Step Saving
        • Saver
        • Custom Saver Example
      • Saving Example
        • Pipeline
      • Full Dump Saving
      • Full Dump Loading
    • Random Distributions
      • Plotting Each Hyperparameter Distribution
      • Discrete Distributions
        • RandInt
        • Boolean
        • Choice
        • Priority Choice
      • Continuous Distributions
        • Continuous Uniform
        • Continuous Loguniform
        • Continuous Normal
        • Continuous Lognormal
        • Continuous Normal Clipped
        • Continuous Lognormal Clipped
      • Quantized Hyperparameter Distributions
        • Quantized Uniform
        • Repaired Quantized Uniform
        • Quantized Log Uniform
        • Quantized Normal
        • Quantized Lognormal
      • Creating your own distributions
      • Using Scipy Distributions
      • Creating your own distributions using scipy
        • BaseCustomContinuousScipyDistribution
        • BaseCustomDiscreteScipyDistribution
    • REST API Serving
      • Import Packages
      • Load your Dataset
      • Create your Pipeline
      • Let’s Train and Test
      • Deploy the Pipeline
        • Write a step to decode the accepted JSON as data inputs
        • Write a step to encode the returned JSON response
        • Finally Serve Predictions
      • API Call Example
      • Next Steps
        • Pipeline Serialization
        • Data Transformation Caching
        • Checkpoints
  • >>> Practical Examples
    • AutoML
      • Usage of AutoML loop, and hyperparams with sklearn models.
    • Caching
      • Usage of ValueCachingWrapper in Neuraxle.
      • Usage of Checkpoints in Automatic Machine Learning (AutoML)
    • REST API Model Serving
      • Easy REST API Model Serving with Neuraxle
    • Getting started
      • Inverse Transforms in Neuraxle: How to Reverse a Prediction
      • Create Nested Pipelines in Neuraxle
      • Create label encoder across multiple columns
      • Create Pipeline Steps in Neuraxle that doesn’t fit or transform
      • Create Pipeline Steps that require implementing only handler methods
    • Hyperparameters
      • Manipulate Hyperparameter Spaces for Hyperparameter Tuning
    • Parallel
      • Parallel processing in Neuraxle
    • Neuraxle hyperparameter examples
      • Boston Housing Regression
      • Boston Housing Regression with Meta Optimization
  • >>> Complete API Documentation
    • neuraxle.base
      • Neuraxle’s Base Classes
    • neuraxle.pipeline
      • Neuraxle’s Pipeline Classes
    • neuraxle.data_container
      • Neuraxle’s DataContainer classes
    • neuraxle.union
      • Union of Features
    • neuraxle.checkpoints
      • Neuraxle’s Checkpoint Classes
    • neuraxle.metrics
      • Neuraxle’s metrics classes
    • neuraxle.plotting
      • Notebook matplotlib plotting functions
    • neuraxle.steps.numpy
      • Pipeline Steps Based on NumPy
    • neuraxle.steps.flow
      • Neuraxle’s Flow Steps
    • neuraxle.steps.data
      • Data Steps
    • neuraxle.steps.column_transformer
      • Neuraxle’s Column Transformer Steps
    • neuraxle.steps.features
      • Featurization Steps
    • neuraxle.steps.sklearn
      • Pipeline Steps Based on Scikit-Learn
    • neuraxle.steps.loop
      • Pipeline Steps For Looping
    • neuraxle.steps.caching
      • Pipeline Steps For Caching
    • neuraxle.steps.output_handlers
      • Output Handlers Steps
    • neuraxle.steps.misc
      • Miscelaneous Pipeline Steps
    • neuraxle.hyperparams.distributions
      • Hyperparameter Distributions
    • neuraxle.hyperparams.scipy_distributions
    • neuraxle.hyperparams.space
      • Hyperparameter Dictionary Conversions
    • neuraxle.metaopt.auto_ml
      • Neuraxle’s AutoML Classes
    • neuraxle.metaopt.trial
      • Neuraxle’s Trial Classes
    • neuraxle.metaopt.callbacks
      • Neuraxle’s training callbacks classes.
    • neuraxle.metaopt.random
      • Random
    • neuraxle.metaopt.tpe
      • Tree parzen estimator
    • neuraxle.metaopt.observable
      • Neuraxle’s Observable Classes
    • neuraxle.metaopt.deprecated
      • Neuraxle’s Automatic Machine Learning Classes
    • neuraxle.distributed.streaming
      • Streaming Parallel Data Processing
    • neuraxle.rest.flask
      • Neuraxle’s Flask Wrapper classes
  • Awesome Neuraxle
    • Contents
    • Examples & Articles
    • Courses & Training
    • Videos
    • Projects
    • Community
    • License
  • Contributing to Neuraxle
    • First steps
    • Before coding
    • Pull Requests
    • Code Reviews
    • Reviewing other’s code
    • Publishing project to PyPI
  • Deep Learning & RNN Course [↗️]
    • Neuraxio/Neuraxle
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