ProgLearn

Using ProgLearn

  • Overview
    • The Library
    • The Learners
    • Wrap Up
  • Install
    • Install from PyPi
    • Install from Github
    • Python package dependencies
    • Hardware requirements
    • OS Requirements
    • Testing
  • Tutorials
    • Installation Tutorial
      • 1. Installation
      • 2: Package Setup
    • Estimation of Bayes’ Error for Gaussian Parity
      • What is Bayes’ Error?
      • Problem Definition: Gaussian Parity
      • Estimation of Bayes’ Error (Analytically)
      • Estimation of Bayes’ Error (Numerically)
    • How to Run UncertaintyForest
      • Import required packages and set parameters for the forest
      • Create and train our UncertaintyForest
      • Produce a metric of accuracy for our learner
      • What’s next?
    • Analyzing the UncertaintyForest Class by Reproducing Posterior Estimates
      • Import Required Packages
      • Specify Parameters
      • Specify Learners
      • Generate predicted posteriors
      • Create Figure 1
    • Analyzing the UncertaintyForest Class by Reproducing Conditional Entropy Estimates
      • Import Required Packages
      • Specify Parameters
      • Specify Learners
      • Plot Figure 2
    • Analyzing the UncertaintyForest Class by Reproducing Mutual Information Estimates
      • Import Required Packages
      • Specify Parameters
      • Specify Learners
      • Plot Figure 3
    • Scene Segmentation with Random Forests
      • I. Preprocessing of Images
      • II. Scene Segmentation: Scikit-Image
    • Proglearn: Scene Segmentation of ISIC using Scikit-Image
      • 0. Environment Setup
      • I. Preprocessing of Images
      • II. Scene Segmentation using Scikit
      • III. Conclusion
    • Scene Segmentation with Neural Networks
      • Preprocessing
      • Neural Network
  • Experiments
    • FTE/BTE Experiment for food-101
      • Import necessary packages and modules
      • Experimental Set-up
      • food-101 Data Generation
      • Train the model and perform validation
      • Calculating FTE, BTE, TE, and Accuracy
      • Plotting FTE, BTE, TE, and Accuracy
    • FTE/BTE Experiment for MNIST & Fashion-MNIST
      • Benchmark Individual Datasets
      • FTE/BTE Between Datasets
    • Label Shuffle Experiment
      • Import necessary packages and modules
      • Load CIFAR100 data
      • Define hyperparameters for the model and preprocess data
      • Train the model and perform validation
      • Function to calculate backward transfer efficiency
      • Plotting the backward transfer efficiency
    • Random Classification Experiment
      • Synergistic Learning
      • Choosing hyperparameters
      • Loading datasets
      • Running experiment
      • Plotting backward transfer efficiency
    • Rotation CIFAR Experiment
      • Hyperparameters
      • Algorithms
      • Experiment
      • Rotation CIFAR Plot
      • Expected Results
      • FAQs
    • Recruitment Experiment
      • Recruitment Within Datasets: CIFAR10x10
      • Recruitment Between Datasets: MNIST/Fashion-MNIST
    • Recruitment Across Datasets
      • FTE/BTE Experiment
      • Recruitment Experiment
      • Other Experiments
    • Spiral Experiment
      • Function Import
      • Data Creation
      • Training/Experiment
      • Results
    • Spoken Digit Experiment
      • Import necessary packages and modules
      • Load spoken_digit data and extract features
      • Inspect data
      • Run Synergistic Learning
      • Calculate and plot transfer efficiency
      • Shuffled speaker
    • Gaussian XOR and Gaussian R-XOR Experiment
      • Classification Problem
      • The Experiment
      • Visualizing the Results
      • Various Angles vs BTE
      • Number of training samples vs BTE
    • Gaussian XOR and Gaussian R-XOR BTE with CPD Experiment
      • Classification Problem with Domain Adaptation
      • The Experiment (Various Angles vs BTE)
    • Gaussian XOR and Gaussian R-XOR BTE with Supervised Alignment
      • Classification Problem with Domain Adaptation
      • The Experiment (Various Angles vs BTE)
    • Gaussian XOR and Gaussian R-XOR Experiment with Task Unaware Settings
      • Ksample test
      • Task aware BTE and generalization error (XOR)
      • Task Unaware: K-sample testing “dcorr”
    • Gaussian XOR and Gaussian XNOR Experiment
      • Classification Problem
      • The Experiment
      • Visualizing the Results
    • Double-descent phenomena on Random Forest
      • Discussion
  • API Reference
    • Transformers
      • NeuralClassificationTransformer
      • TreeClassificationTransformer
    • Voters
      • TreeClassificationVoter
      • KNNClassificationVoter
    • Deciders
      • SimpleArgmaxAverage
    • Progressive_Learner
      • ClassificationProgressiveLearner
    • Forest
      • LifelongClassificationForest
    • Network
      • LifelongClassificationNetwork
  • Contributing to ProgLearn
    • Submitting a bug report or a feature request
    • How to make a good bug report
    • Contributing Code
    • Pull Request Checklist
    • Coding Guidelines
    • Docstring Guidelines
    • Tutorial Guidelines
  • License

Useful Links

  • proglearn @ GitHub
  • proglearn @ PyPi
  • Issue Tracker
ProgLearn
  • Docs »
  • Overview: module code

All modules for which code is available

  • proglearn.deciders
  • proglearn.forest
  • proglearn.network
  • proglearn.progressive_learner
  • proglearn.sims.gaussian_sim
  • proglearn.sims.spiral_sim
  • proglearn.transformers
  • proglearn.voters

© Copyright 2020, Will LeVine, Jayanta Dey, Hayden Helm

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