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The Convex Information Bottleneck Lagrangian

Code for the article "The Convex Information Bottleneck Lagrangian". This code is meant to verify and obtain the figures from the article.

This is a theoretical article. An updated implementation of the "Nonlinear Information Bottleneck, 2019", from Artemy Kolchinsky, Brendan D. Tracey and David H. Wolpert in PyTorch can be found "here" and in Tensorflow "here". In the PyTorch implementation we added the possibility of using the Convex Information Bottleneck Lagrangian.

Requirements

In order to be able to run the code gracefully you will need the following Python 3.6.8 packages. Probably older versions work too, but the code has been run and tested with:

  • matplotlib==3.1.1
  • numpy==1.17.2
  • scikit-learn==0.21.3
  • torch==1.2.0+cpu
  • torchvision==0.4.0+cpu
  • torchtext==0.4.0
  • scipy==1.3.1
  • autograd==1.3
  • progressbar2==3.43.1
  • jupyterlab==1.1.3
  • jupyterlab-server==1.0.6
  • scpacy==2.2.3

You can install them easily doing:

  • Linux:
pip3 install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html 
python3 -m spacy download en
  • Mac:
pip3 install -r requirements_mac.txt 
python3 -m spacy download en

Obtain the figures from the article

Please follow the notebook Guide to the code and figures.ipynb in the src folder. To open it just go to the src folder and write:

jupyter notebook Guide\ to\ the\ code\ and\ figures.ipynb

General Usage

Run either python3 train_model.pyor python3 study_behavior.py. The arguments are the following:

usage: [-h] [--logs_dir LOGS_DIR] [--figs_dir FIGS_DIR]
            [--models_dir MODELS_DIR] [--n_epochs N_EPOCHS]
            [--beta BETA] [--n_betas N_BETAS]
            [--beta_lim_min BETA_LIM_MIN]
            [--beta_lim_max BETA_LIM_MAX]
            [--u_func_name {pow,exp,shifted-exp,none}]
            [--hyperparameter HYPERPARAMETER]
            [--compression COMPRESSION] [--example_clusters]
            [--K K] [--logvar_kde LOGVAR_KDE]
            [--logvar_t LOGVAR_T]
            [--sgd_batch_size SGD_BATCH_SIZE]
            [--mi_batch_size MI_BATCH_SIZE] [--same_batch]
            [--dataset {mnist,fashion_mnist,california_housing,trec}]
            [--optimizer_name {sgd,rmsprop,adadelta,adagrad,adam,asgd}]
            [--method {nonlinear_IB,variational_IB}]
            [--learning_rate LEARNING_RATE]
            [--learning_rate_drop LEARNING_RATE_DROP]
            [--learning_rate_steps LEARNING_RATE_STEPS]
            [--train_logvar_t] [--eval_rate EVAL_RATE]
            [--visualize] [--verbose]

Run convex IB Lagrangian (with Pytorch)

optional arguments:
  -h, --help            show this help message and exit
  --logs_dir LOGS_DIR   folder to output the logs (default: ../results/logs/)
  --figs_dir FIGS_DIR   folder to output the images (default:
                        ../results/figures/)
  --models_dir MODELS_DIR
                        folder to save the models (default:
                        ../results/models/)
  --n_epochs N_EPOCHS   number of training epochs (default: 100)
  --beta BETA           Lagrange multiplier (only for train_model) (default:
                        0.0)
  --n_betas N_BETAS     Number of Lagrange multipliers (only for study
                        behavior) (default: 50)
  --beta_lim_min BETA_LIM_MIN
                        minimum value of beta for the study of the behavior
                        (default: 0.0)
  --beta_lim_max BETA_LIM_MAX
                        maximum value of beta for the study of the behavior
                        (default: 1.0)
  --u_func_name {pow,exp,shifted-exp,none}
                        monotonically increasing, strictly convex function
                        (default: exp)
  --hyperparameter HYPERPARAMETER
                        hyper-parameter of the h function (e.g., alpha in the
                        power and eta in the exponential case) (default: 1.0)
  --compression COMPRESSION
                        desired compression level (in bits). Only for the
                        shifted exponential. (default: 1.0)
  --example_clusters    plot example of the clusters obtained (only for study
                        behavior of power with alpha 1, otherwise change the
                        number of clusters to show) (default: False)
  --K K                 Dimensionality of the bottleneck varaible (default: 2)
  --logvar_kde LOGVAR_KDE
                        initial log variance of the KDE estimator (default:
                        -1.0)
  --logvar_t LOGVAR_T   initial log varaince of the bottleneck variable
                        (default: -1.0)
  --sgd_batch_size SGD_BATCH_SIZE
                        mini-batch size for the SGD on the error (default:
                        128)
  --mi_batch_size MI_BATCH_SIZE
                        mini-batch size for the I(X;T) estimation (default:
                        1000)
  --same_batch          use the same mini-batch for the SGD on the error and
                        I(X;T) estimation (default: False)
  --dataset {mnist,fashion_mnist,california_housing,trec}
                        dataset where to run the experiments. Classification:
                        MNIST or Fashion MNIST. Regression: California
                        housing. (default: mnist)
  --optimizer_name {sgd,rmsprop,adadelta,adagrad,adam,asgd}
                        optimizer (default: adam)
  --method {nonlinear_IB,variational_IB}
                        information bottleneck computation method (default:
                        nonlinear_IB)
  --learning_rate LEARNING_RATE
                        initial learning rate (default: 0.0001)
  --learning_rate_drop LEARNING_RATE_DROP
                        learning rate decay rate (step LR every
                        learning_rate_steps) (default: 0.6)
  --learning_rate_steps LEARNING_RATE_STEPS
                        number of steps (epochs) before decaying the learning
                        rate (default: 10)
  --train_logvar_t      train the log(variance) of the bottleneck variable
                        (default: False)
  --eval_rate EVAL_RATE
                        evaluate I(X;T), I(T;Y) and accuracies every eval_rate
                        epochs (default: 20)
  --visualize           visualize the results every eval_rate epochs (default:
                        False)
  --verbose             report the results every eval_rate epochs (default:
                        False)