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Bidirectional Active Processing (BAP) Implementation

Reimplementation of the BAP algorithm from Bidirectional Active Processing.md as specified in our Electronics paper available both online and locally. Single-file implementations in Python and Julia with TOML configuration.

Requirements

Python

  • Python 3.11
  • numpy, pandas, scikit-learn
  • tomli (for Python < 3.11)

Use the project venv when present (dependencies are installed there):

source .venv311/bin/activate   # macOS / Linux
python bap.py -c examples/configs/config_iris.toml

Or run without activating:

.venv311/bin/python bap.py -c examples/configs/config_iris.toml

To create or refresh the venv:

python3.11 -m venv .venv311
.venv311/bin/python -m pip install -r requirements.txt

Configs point at CSVs under computing/machine learning datasets (not copies inside other app repos).

Julia

  • Julia 1.8+
  • Packages: CSV, DataFrames, ScikitLearn, TOML
    (ScikitLearn requires Python with scikit-learn)
julia -e 'using Pkg; Pkg.activate("./julia"); Pkg.instantiate()'

(Run from the repository root.)

Quick Start

Single CSV (train/test split)

Example: Fisher Iris dataset

# Python (after: source .venv311/bin/activate)
python bap.py -c examples/configs/config_iris.toml

# Or with flags
python bap.py --train "../../machine learning datasets/default/fisher_iris.csv" --testing split --split 0.8,0.2 --classifier dt -t 0.95 -n 10 -m 5

Separate train and test CSVs

Example: MNIST

# Python
python bap.py -c examples/configs/config_mnist.toml

# Or with flags
python bap.py --train "../../machine learning datasets/default/mnist_train.csv" --test "../../machine learning datasets/default/mnist_test.csv" --testing fixed --classifier knn --k 5 -n 5 -m 100

Julia

julia julia/bap.jl -c examples/configs/config_iris.toml
julia julia/bap.jl -c examples/configs/config_mnist.toml
julia julia/bap.jl --train "../../machine learning datasets/default/fisher_iris.csv" -c examples/configs/config_iris.toml  # override train path

Configuration

All options can be set via TOML config (preferred) or CLI flags. TOML overrides defaults; flags override TOML.

TOML structure

Parameter TOML Definition
train train = "path.csv" Training CSV (or single dataset to split)
test test = "path.csv" Test CSV (for testing.fixed)
testing [testing.fixed] or [testing.split] How to obtain test set
testing.fixed [testing.fixed] + test = "..." Use separate test file
testing.split [testing.split] + split = [0.8, 0.2] Split train ratio : test ratio
classifier classifier = "dt" dt, knn, or svm
parameters [parameters] + k = 5 Classifier hyperparameters (e.g. k for KNN)
distance distance = "euclidean" Distance metric for KNN
goal.t [goal] + t = 0.95 Accuracy threshold (0–1)
direction [direction.forward] or [direction.backward] Forward (additive) or backward (subtractive)
splits splits = 1 Number of train/test splits
n n = 10 Iterations per split
m m = 5 Cases added/removed per iteration
sampling [sampling.stratified] or [sampling.random] Sampling method
seed seed = 42 PRNG seed
output_dir output_dir = "results" Output directory

CLI flags (Python)

Flag Description
-c, --config TOML config file
--train Training CSV
--test Test CSV (for fixed)
--testing fixed | split | cv
--split Train,test ratio, e.g. 0.8,0.2
--classifier dt | knn | svm
--k K for KNN (default 3)
--distance Metric for KNN
-t, --threshold Accuracy threshold
--direction forward | backward
--splits Number of splits
-n, --iterations Iterations per split
-m Cases per iteration
--sampling random | stratified
--seed Random seed
-o, --output-dir Output directory

Data format

CSVs must have a class column whose header matches class, label, or target (case-insensitive).
All other columns are features (column order may follow your benchmark file, e.g. fisher_iris.csv).

  • fisher_iris.csv: class column
  • mnist_train.csv, mnist_test.csv: label column

Classifiers

Code Classifier
dt Decision Tree
knn K-Nearest Neighbors
svm Support Vector Machine (RBF)
hb_vis Hyperblock (VisCanvas-style)
hb_dv Hyperblock (DV-style, interval-based)

Output

Results are written to {output_dir}/bap_{timestamp}/ (default results/bap_YYYYMMDD_HHMMSS/).

Exported case and hyperblock CSVs use the same tabular shape as the shared datasets (e.g. computing/machine learning datasets/default/fisher_iris.csv):

  • One header class (lowercase), case label for data rows.
  • One column per attribute (same names as the training CSV). No separate *_min / *_max columns and no extra ID column.
  • split_N/converged_exp_{id}_seed{seed}.csv – converged training cases: class holds the dataset label (e.g. Setosa).
  • split_N/converged_exp_{id}_seed{seed}_hyperblocks.csv – when using hb_vis or hb_dv: two rows per hyperblock. Each row has the same attribute columns; values are the box minimum (…__bottom) and maximum (…__top) corners. The class cell encodes label, HB id, and edge, e.g. Setosa__HB0__bottom / Setosa__HB0__top. Any __ in the dataset label is replaced by _ so the suffix pattern stays parseable.

Other output:

  • config.txt – Settings used
  • statistics.csv – Aggregate statistics (mean/min/max cases, convergence rate, etc.)

Every converged result CSV has a matching _hyperblocks.csv in the same directory when using hyperblock classifiers.

Algorithm (summary)

  1. Set PRNG seed
  2. For each split: load data (fixed test or split)
  3. For each iteration: start with empty set (forward) or full set (backward)
  4. While accuracy < threshold and cases remain: add/remove m cases via sampling
  5. Record converged subsets and compute statistics

Bidirectional Processing Definition Notes

This repository originally exposed BAP behavior through rebuild.py; that interface has now been folded into the TOML/CLI model used by bap.py and julia/bap.jl.

Parameter mapping

  • Legacy --data maps to train
  • Legacy --test-data maps to test with testing.fixed
  • Legacy train/test split flags map to testing.split with split = [train_ratio, test_ratio]
  • Legacy --action {additive,subtractive} maps to direction {forward,backward}
  • Legacy --iterations maps to n
  • Legacy --threshold maps to goal.t

Formal algorithm input

Core BAP input fields are:

  • train, test/testing, classifier, parameters, distance
  • goal.t, direction, splits, n, m
  • sampling, seed

Formal algorithm output

  • Run configuration export (config.txt)
  • Converged case-set CSV artifacts per successful iteration
  • Aggregate statistics including convergence rate and sureness-related measures

Procedure (expanded)

  1. Initialize PRNG with seed.
  2. Repeat for each split:
    • Build train/test partitions (testing.fixed or testing.split).
  3. Repeat n times:
    • Start with empty set (direction.forward) or full train set (direction.backward).
    • Train/test until threshold goal.t is met, adding/removing m cases each step.
    • Mark iteration as failed if no cases remain before meeting threshold.
    • Persist converged set and increment seed on success.