Machine learning algorithms implemented in Sci-kit learn Estimators for the purposes of program synthesis (aka automatic programming). Programs are expressed in the Push language and executed using the Plushi interpreter.
- Genetic programming
- Simmulated annealing
The below Python snippet attempts to evolve the ReLU function, and prints the best program found throughout evolution and its training MSE.
from sklearn.metrics import mean_squared_error
from sklearn_program_synthesis.estimators import ProgramSynthesizer
X = np.arange(-2, 2, 0.1).reshape(-1, 1)
y = np.array([[x if x > 0 else 0 for x in X]]).flatten()
prog_synth = ProgramSynthesizer(arity=X.shape[1],
metric=mean_squared_error,
min_or_max="min",
types=['exec', 'float'],
search_method="evolution",
verbose=2)
prog_synth.fit(X, y)
y_hat_train = prog_synth.predict(X)
print(prog_synth._fit_program)
print(mean_squared_error(y, y_hat_train))To run the benchmark, you will need a Plushi standalone .jar (see next section).
pip install -r requirements.pip
java -jar plushi-standalone.jar --start
python run_benchmarks.py [problem name] [search method]
| Problem/Dataset | Type |
|---|---|
relu |
regression |
iris |
classification |
diabetes |
regression |
| Search Method | Argument |
|---|---|
| Genetic Programming | evoltuion |
| Simulated annealing | annealing |
Plushi is an embeddable, language agnostic Push language interpreter. This repo requires the use of a standalone .jar file. You can read more about the project on its GitHub page.