Merge latest experiments into main

README.md

@@ -1,38 +1,77 @@
 # tree_diff
 
-To get started:
+This repository contains code for the paper [Minimising changes to audit when updating decision trees](https://arxiv.org/abs/2408.16321).
+
+# Interactive notebooks
+
+We provide notebooks that can be used to explore how the algorithm works without running the full experiment.
+
+To get started with the interactive notebooks:
 * Install poetry (https://python-poetry.org/)
 * `poetry install`
 * `export PYTHONPATH="$PWD"`
 * `poetry run jupyter notebook`
 * Navigate to `tree_diff/notebooks` in the browser
 * Done!
 
-# Run pipeline
-* `poetry run python -m tree_diff assembler=baseline mode=train input_path=<project directory>/input`
-
 # Additional dependencies
 
+We use the `dot` command line tool for generating tree figures:
 * `dot` command line tool on path (brew install graphviz / conda install graphviz)
-* `rulecosi` algorithm needs to be git pulled into a `3rdparty` directory:
-  * Run `mkdir 3rdparty` from the project directory
-  * `cd 3rdparty`
-  * `git clone https://github.com/jobregon1212/rulecosi.git`
 
-In order to run the experiments in the `tree-dff/notebooks/Evaluation.ipynb`. 
-
-Install the dependencies using the command below: 
+The full experiment was run in a conda environment. In addition to standard conda dependencies (sklearn, pandas, numpy, matplotlib), comparisons to EFDT need the river library. Install the additional dependencies using the command below: 
 
 ```console
 $ pip install -r requirements.txt
 ```
 
 # Datasets
-The following datasets are used in the experiments and evaluation for our model with other decision trees:
 
-* [`Adult`](https://www.kaggle.com/datasets/wenruliu/adult-income-dataset)
-* [`Mushroom`](https://www.kaggle.com/datasets/uciml/mushroom-classification)
-* [`HIGGS`](https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz)
-* [`Android Malware`](https://archive.ics.uci.edu/ml/machine-learning-databases/00622/TUANDROMD.csv)
+Datasets for the paper are available from the UCI Machine Learning Repository at https://archive.ics.uci.edu. Extract the datasets to a directory called `datasets`. Your folder structure should look like this: 
+
+```
+tree_diff/
+└...
+
+datasets/
+├── covertype
+│   ├── covtype.data
+│   ├── covtype.info
+│   └── old_covtype.info
+├── covertype.zip
+├── hepmass
+│   ├── 1000_test.csv
+│   ├── 1000_train.csv
+│   ├── all_test.csv
+│   ├── all_train.csv
+│   ├── not1000_test.csv
+│   └── not1000_train.csv
+├── hepmass.zip
+├── higgs
+│   └── HIGGS.csv
+├── higgs.zip
+├── poker+hand
+│   ├── poker-hand.names
+│   ├── poker-hand-testing.data
+│   └── poker-hand-training-true.data
+├── poker+hand.zip
+├── skin+segmentation
+│   └── Skin_NonSkin.txt
+├── skin+segmentation.zip
+├── susy
+│   └── SUSY.csv
+└── susy.zip
+```
+
+# Running the experiments
+
+After downloading the datasets, you can run the experiment in the paper:
+
+```
+python3 experiment2.py
+```
+
+# Analysis of experiment results
+
+The experiment will write results to an output directory (starting with `out` followed by a number). The notebook used to analyse these experiment results is `notebooks/experiment2_analysis.ipynb`
 
-After downloading these datasets, place them in the `tree-diff/notebooks` folder to run the experiments.

data_scripts.sh

@@ -0,0 +1,3 @@
+head -n 10000 ../datasets/higgs/HIGGS.csv > HIGGS10000.csv
+head -n 10000 ../datasets/susy/SUSY.csv > SUSY10000.csv
+head -n 10000 ../datasets/hepmass/all_train.csv > hepmass10000.csv