Logo Neuraxio/ Neuraxle
0.5.7

Contents:

  • 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
  • Neuraxle Pipelines
    • Documentation
    • Installation
      • Examples
        • Why Neuraxle ?
        • Community
      • 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)
        • Solution: Define Hyperparameter Spaces Within the Steps
      • Problem: Defining Hyperparameters in the Constructor is Limiting
        • Solution: Separate Steps’s Constructors From the get_params Method
      • Problem: Different Train and Test Behavior
        • Solution: use the Set Train Special Method and use Step Wrappers
      • Problem: You trained a Pipeline and You Want Feedback Statistics on its Learning
        • Solution: the Introspect Special Method
    • Inability to Reasonably do Deep Learning Pipelines
      • Problem: Scikit-Learn Hardly Allows for Mini-Batch Gradient Descent (Incremental Fit)
        • Solution: Minibatch Pipeline Class and the Ability to Incrementally Fit Pipeline Steps
      • Problem: Initializing the Pipeline and Deallocating Resources
        • Solution: Add Setup and Teardown Lifecycle Methods to Your Steps
      • Problem: It is Difficult to Use Other Deep Learning (DL) Libraries in Scikit-Learn
        • Solution: Moar Steps Lifecycle Methods
      • Problem: The Ability to Transform Output Labels
        • Solution: OutputTransformerWrapper and InputAndOutputTransformerMixin
    • 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
        • Solution: use a ForEachDataInputs Wrapper to Loop from ND Data to N(D-1) Data
      • Problem: Modify a Pipeline Along the Way, such as for Pre-Training or Fine-Tuning
        • Solution: the Mutate Special Method
        • Another Solution: the Apply Special Method
      • Problem: Getting Model Attributes from Scikit-Learn Pipeline
        • Solution: Simpler Nested Pipelines __getitem__ Methods
      • Problem: You can’t Parallelize nor Save Pipelines Using Steps that Can’t be Serialized “as-is” by Joblib
        • Solution: Use a Chain of Savers in each Step
        • About Cluster Computing and Parallelism in Python
  • Handler Methods
    • handle_fit_transform
    • handle_fit
    • handle_transform
    • When to use handler methods ?
    • HandleOnlyMixin
    • ForceHandleMixin
    • ForceHandleOnlyMixin
      • Examples
    • ForEachDataInput
    • ToNumpy
    • Transform Expected Outputs
    • Expand The DataContainer
    • Reversible Pipeline
  • Introduction to Automatic Hyperparameter Tuning
    • AutoML loop
      • 1. Define your pipeline
      • 2. Choose a validation splitter
      • 3. Define a the main scoring metric with ScoringCallback
      • 4. Add metric callbacks with MetricCallback (optional)
      • 5. Select an hyperparams repository
      • 6. Select an hyperparams optimizer
      • 7. Create, and launch AutoML loop
    • Run 10 trials
    • Get best model, and predict
  • Introduction to 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
  • Step Saving And Lifecycle
    • Lifecycle
    • Step Saving
      • Saver
      • Custom Saver Example
    • Saving Example
      • Pipeline
    • Full Dump Saving
    • Full Dump Loading
  • 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
  • Usage 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
  • Awesome Neuraxle
    • Contents
    • Examples & Articles
    • Courses & Training
    • Videos
    • Projects
    • Community
    • License
  • 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.sklearn
      • Scikit-learn metaoptimizers
    • 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
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