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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Detected Jupyter notebook. Loading juliacall extension. Set `PYSR_AUTOLOAD_EXTENSIONS=no` to disable.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Precompiling SymbolicRegression\n",
"\u001b[32m ✓ \u001b[39mSymbolicRegression\n",
" 1 dependency successfully precompiled in 26 seconds. 106 already precompiled.\n",
"Precompiling SymbolicRegressionJSON3Ext\n",
"\u001b[32m ✓ \u001b[39m\u001b[90mSymbolicRegression → SymbolicRegressionJSON3Ext\u001b[39m\n",
" 1 dependency successfully precompiled in 2 seconds. 110 already precompiled.\n"
]
}
],
"source": [
"# NBVAL_IGNORE_OUTPUT\n",
"import numpy as np\n",
"from pysr import PySRRegressor, jl"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n"
]
}
],
"source": [
"%%julia\n",
"\n",
"# Automatically activates Julia magic\n",
"\n",
"x = 1\n",
"println(x + 2)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4\n"
]
}
],
"source": [
"%julia println(x + 3)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"my_loss (generic function with 1 method)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%julia\n",
"function my_loss(x)\n",
" x ^ 2\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%julia my_loss(2)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'PySRRegressor.equations_ = None'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rstate = np.random.RandomState(0)\n",
"X = np.random.randn(10, 2)\n",
"y = np.random.randn(10)\n",
"\n",
"model = PySRRegressor(deterministic=True, multithreading=False, procs=0, random_state=0, verbosity=0, progress=False, niterations=1, ncycles_per_iteration=1)\n",
"str(model)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mcranmer/PermaDocuments/SymbolicRegressionMonorepo/.venv/lib/python3.12/site-packages/pysr/sr.py:1297: UserWarning: Note: it looks like you are running in Jupyter. The progress bar will be turned off.\n",
" warnings.warn(\n"
]
},
{
"data": {
"text/plain": [
"pandas.core.frame.DataFrame"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(X, y)\n",
"type(model.equations_)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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