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updated notebookx
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@gAldeia
gAldeia committed Jun 26, 2024
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Showing 1 changed file with 198 additions and 18 deletions.
216 changes: 198 additions & 18 deletions docs/guide/saving_loading_populations.ipynb
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},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -40,9 +40,89 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generation 1/10 [////// ]\n",
"Train Loss (Med): 14.24093 (60.79966)\n",
"Val Loss (Med): 14.24093 (60.79966)\n",
"Median Size (Max): 3 (18)\n",
"Median complexity (Max): 9 (351)\n",
"Time (s): 0.12213\n",
"\n",
"Generation 2/10 [/////////// ]\n",
"Train Loss (Med): 13.86337 (20.54475)\n",
"Val Loss (Med): 13.86337 (20.54475)\n",
"Median Size (Max): 3 (20)\n",
"Median complexity (Max): 9 (651)\n",
"Time (s): 0.20873\n",
"\n",
"Generation 3/10 [//////////////// ]\n",
"Train Loss (Med): 13.58168 (17.94969)\n",
"Val Loss (Med): 13.58168 (17.94969)\n",
"Median Size (Max): 3 (18)\n",
"Median complexity (Max): 9 (216)\n",
"Time (s): 0.30500\n",
"\n",
"Generation 4/10 [///////////////////// ]\n",
"Train Loss (Med): 13.58167 (17.94969)\n",
"Val Loss (Med): 13.58167 (17.94969)\n",
"Median Size (Max): 3 (18)\n",
"Median complexity (Max): 9 (478)\n",
"Time (s): 0.44846\n",
"\n",
"Generation 5/10 [////////////////////////// ]\n",
"Train Loss (Med): 13.58167 (17.94969)\n",
"Val Loss (Med): 13.58167 (17.94969)\n",
"Median Size (Max): 3 (18)\n",
"Median complexity (Max): 8 (297)\n",
"Time (s): 0.58464\n",
"\n",
"Generation 6/10 [/////////////////////////////// ]\n",
"Train Loss (Med): 13.25836 (63.64049)\n",
"Val Loss (Med): 13.25836 (63.64049)\n",
"Median Size (Max): 3 (18)\n",
"Median complexity (Max): 7 (297)\n",
"Time (s): 0.71193\n",
"\n",
"Generation 7/10 [//////////////////////////////////// ]\n",
"Train Loss (Med): 10.63986 (63.64049)\n",
"Val Loss (Med): 10.63986 (63.64049)\n",
"Median Size (Max): 3 (18)\n",
"Median complexity (Max): 6 (567)\n",
"Time (s): 0.84199\n",
"\n",
"Generation 8/10 [///////////////////////////////////////// ]\n",
"Train Loss (Med): 10.28156 (63.64049)\n",
"Val Loss (Med): 10.28156 (63.64049)\n",
"Median Size (Max): 3 (18)\n",
"Median complexity (Max): 6 (270)\n",
"Time (s): 0.96073\n",
"\n",
"Generation 9/10 [////////////////////////////////////////////// ]\n",
"Train Loss (Med): 10.09177 (63.64049)\n",
"Val Loss (Med): 10.09177 (63.64049)\n",
"Median Size (Max): 3 (18)\n",
"Median complexity (Max): 6 (216)\n",
"Time (s): 1.06539\n",
"\n",
"Generation 10/10 [//////////////////////////////////////////////////]\n",
"Train Loss (Med): 10.09177 (63.64049)\n",
"Val Loss (Med): 10.09177 (63.64049)\n",
"Median Size (Max): 3 (20)\n",
"Median complexity (Max): 6 (510)\n",
"Time (s): 1.15215\n",
"\n",
"Saved population to file /tmp/tmp91lebnyr/population.json\n",
"saving final population as archive...\n",
"score: 0.8883469950530682\n"
]
}
],
"source": [
"import pickle\n",
"import os, tempfile\n",
Expand Down Expand Up @@ -74,9 +154,20 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded population from /tmp/tmp91lebnyr/population.json of size = 200\n",
"Completed 100% [====================]\n",
"saving final population as archive...\n",
"score: 0.8893989188100573\n"
]
}
],
"source": [
"est = BrushRegressor(\n",
" functions=['SplitBest','Add','Mul','Sin','Cos','Exp','Logabs'],\n",
Expand Down Expand Up @@ -114,9 +205,90 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generation 1/10 [////// ]\n",
"Train Loss (Med): 0.54930 (0.69315)\n",
"Val Loss (Med): 0.54930 (0.69315)\n",
"Median Size (Max): 5 (10)\n",
"Median complexity (Max): 6 (270)\n",
"Time (s): 0.05091\n",
"\n",
"Generation 2/10 [/////////// ]\n",
"Train Loss (Med): 0.54848 (0.69315)\n",
"Val Loss (Med): 0.54848 (0.69315)\n",
"Median Size (Max): 5 (10)\n",
"Median complexity (Max): 6 (54)\n",
"Time (s): 0.08855\n",
"\n",
"Generation 3/10 [//////////////// ]\n",
"Train Loss (Med): 0.54847 (0.69315)\n",
"Val Loss (Med): 0.54847 (0.69315)\n",
"Median Size (Max): 5 (10)\n",
"Median complexity (Max): 6 (54)\n",
"Time (s): 0.12188\n",
"\n",
"Generation 4/10 [///////////////////// ]\n",
"Train Loss (Med): 0.54847 (0.69315)\n",
"Val Loss (Med): 0.54847 (0.69315)\n",
"Median Size (Max): 1 (10)\n",
"Median complexity (Max): 2 (52)\n",
"Time (s): 0.15164\n",
"\n",
"Generation 5/10 [////////////////////////// ]\n",
"Train Loss (Med): 0.54846 (0.69315)\n",
"Val Loss (Med): 0.54846 (0.69315)\n",
"Median Size (Max): 1 (9)\n",
"Median complexity (Max): 1 (52)\n",
"Time (s): 0.18853\n",
"\n",
"Generation 6/10 [/////////////////////////////// ]\n",
"Train Loss (Med): 0.50911 (0.69315)\n",
"Val Loss (Med): 0.50911 (0.69315)\n",
"Median Size (Max): 1 (12)\n",
"Median complexity (Max): 1 (52)\n",
"Time (s): 0.23546\n",
"\n",
"Generation 7/10 [//////////////////////////////////// ]\n",
"Train Loss (Med): 0.50911 (0.69315)\n",
"Val Loss (Med): 0.50911 (0.69315)\n",
"Median Size (Max): 1 (12)\n",
"Median complexity (Max): 1 (44)\n",
"Time (s): 0.28747\n",
"\n",
"Generation 8/10 [///////////////////////////////////////// ]\n",
"Train Loss (Med): 0.50911 (0.69315)\n",
"Val Loss (Med): 0.50911 (0.69315)\n",
"Median Size (Max): 1 (14)\n",
"Median complexity (Max): 1 (120)\n",
"Time (s): 0.34002\n",
"\n",
"Generation 9/10 [////////////////////////////////////////////// ]\n",
"Train Loss (Med): 0.50911 (0.69315)\n",
"Val Loss (Med): 0.50911 (0.69315)\n",
"Median Size (Max): 1 (14)\n",
"Median complexity (Max): 1 (80)\n",
"Time (s): 0.39083\n",
"\n",
"Generation 10/10 [//////////////////////////////////////////////////]\n",
"Train Loss (Med): 0.50911 (0.69315)\n",
"Val Loss (Med): 0.50911 (0.69315)\n",
"Median Size (Max): 1 (12)\n",
"Median complexity (Max): 1 (44)\n",
"Time (s): 0.45610\n",
"\n",
"Saved population to file /tmp/tmpqcnwu35t/population.json\n",
"saving final population as archive...\n",
"If(AIDS>15890.50,Logistic(13.52),If(Total>1572255.50,0.22,0.52))\n",
"score: 0.7\n"
]
}
],
"source": [
"from pybrush import BrushClassifier\n",
"\n",
Expand Down Expand Up @@ -145,9 +317,26 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generation 1/1 [//////////////////////////////////////////////////]\n",
"Train Loss (Med): 0.46115 (0.31675)\n",
"Val Loss (Med): 0.46115 (0.31675)\n",
"Median Size (Max): 5 (9)\n",
"Median complexity (Max): 6 (120)\n",
"Time (s): 0.04622\n",
"\n",
"saving final population as archive...\n",
"Logistic(Sum(-1.2485952,Add(-1.25,0.08*AIDS)))\n",
"score: 0.54\n"
]
}
],
"source": [
"est = BrushClassifier(\n",
" functions=['SplitBest','Add','Mul','Sin','Cos','Exp','Logabs'],\n",
Expand All @@ -164,15 +353,6 @@
"y_pred = est.predict(X)\n",
"print('score:', est.score(X,y))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"est.best_estimator_.get_model()"
]
}
],
"metadata": {
Expand Down

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