Skip to content

Commit

Permalink
add notebook
Browse files Browse the repository at this point in the history
  • Loading branch information
@thierrymoudiki
thierrymoudiki committed Feb 5, 2024
1 parent b12ba82 commit 4f2ccfd
Showing 1 changed file with 223 additions and 0 deletions.
223 changes: 223 additions & 0 deletions GPopt/demo/thierrymoudiki_20240206_tuning_BCN_classifier_Pt2.ipynb
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,223 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "ver1MWFiAIH1"
},
"source": [
"# 1 - Install"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip uninstall -y BCN GPopt "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "L7dr5u5k8hW9",
"outputId": "f26bd34b-d73b-4fee-b97a-3df46a0d8018"
},
"outputs": [],
"source": [
"!pip install BCN --upgrade --no-cache-dir"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "IBA5fko_BA4V",
"outputId": "122f3221-2fb1-409e-c4e8-17d9d1d8709c"
},
"outputs": [],
"source": [
"!pip install GPopt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FV7pttAu8pXP"
},
"outputs": [],
"source": [
"import BCN as bcn # takes a long time to run, ONLY the first time it's run\n",
"import GPopt as gp\n",
"import numpy as np\n",
"\n",
"from sklearn.gaussian_process import GaussianProcessRegressor\n",
"from sklearn.gaussian_process.kernels import Matern\n",
"from sklearn.datasets import load_iris, load_wine, load_breast_cancer\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn import metrics\n",
"from time import time"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YniHGO9AAac2"
},
"source": [
"# 2 - cv"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LIGGEBbxCznR"
},
"outputs": [],
"source": [
"from sklearn.model_selection import cross_val_score"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "fIgPu1vBAd7X"
},
"outputs": [],
"source": [
"def bcn_cv(X_train, y_train,\n",
" B = 10, nu = 0.335855,\n",
" lam = 10**0.7837525,\n",
" r = 1 - 10**(-5.470031),\n",
" tol = 10**-7,\n",
" col_sample=1,\n",
" n_clusters = 3):\n",
"\n",
" estimator = bcn.BCNClassifier(B = int(B),\n",
" nu = nu,\n",
" lam = lam,\n",
" r = r,\n",
" tol = tol,\n",
" col_sample = col_sample,\n",
" n_clusters = n_clusters,\n",
" activation=\"tanh\",\n",
" type_optim=\"nlminb\",\n",
" show_progress = False)\n",
"\n",
" return -cross_val_score(estimator, X_train, y_train,\n",
" scoring='accuracy',\n",
" cv=5, n_jobs=None,\n",
" verbose=0).mean()\n",
"\n",
"def optimize_bcn(X_train, y_train):\n",
" # objective function for hyperparams tuning\n",
" def crossval_objective(x):\n",
" return bcn_cv(X_train=X_train,\n",
" y_train=y_train,\n",
" B = int(x[0]),\n",
" nu = 10**x[1],\n",
" lam = 10**x[2],\n",
" r = 1 - 10**x[3],\n",
" tol = 10**x[4],\n",
" col_sample = np.ceil(x[5]),\n",
" n_clusters = np.ceil(x[6]))\n",
" gp_opt = gp.GPOpt(objective_func=crossval_objective,\n",
" lower_bound = np.array([ 3, -6, -10, -10, -6, 0.8, 1]),\n",
" upper_bound = np.array([ 100, -0.1, 10, -1, -0.1, 1, 4]),\n",
" params_names=[\"B\", \"nu\", \"lam\", \"r\", \"tol\", \"col_sample\", \"n_clusters\"],\n",
" gp_obj = GaussianProcessRegressor( # this is where the Gaussian Process can be chosen\n",
" kernel=Matern(nu=1.5),\n",
" alpha=1e-6,\n",
" normalize_y=True,\n",
" n_restarts_optimizer=25,\n",
" random_state=42,\n",
" ),\n",
" n_init=10, n_iter=190, seed=3137)\n",
" return gp_opt.optimize(verbose=2, abs_tol=1e-3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dU2gF9rYC_Q_",
"outputId": "600a5854-6c32-4603-d8b4-d4c0d4a44263"
},
"outputs": [],
"source": [
"dataset = load_wine()\n",
"X = dataset.data\n",
"y = dataset.target\n",
"\n",
"# split data into training test and test set\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
" test_size=0.2, random_state=3137)\n",
"\n",
"# hyperparams tuning\n",
"res_opt = optimize_bcn(X_train, y_train)\n",
"print(res_opt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "zo4LTC_PDEGp"
},
"outputs": [],
"source": [
"start = time()\n",
"estimator = bcn.BCNClassifier(B=int(parameters[0][0]),\n",
" nu=10**parameters[0][1],\n",
" lam=10**parameters[0][2],\n",
" r=1-10**parameters[0][3],\n",
" tol=10**parameters[0][4],\n",
" col_sample=np.ceil(parameters[0][5]),\n",
" n_clusters=np.ceil(parameters[0][6]),\n",
" activation=\"tanh\",\n",
" type_optim=\"nlminb\").fit(X_train, y_train)\n",
"print(f\"\\n Elapsed: {time() - start}\")\n",
"start = time()\n",
"print(f\"\\n\\n Test set accuracy: {estimator.score(X_test, y_test)}\")\n",
"print(f\"\\n Elapsed: {time() - start}\")"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"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": "3.11.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

0 comments on commit 4f2ccfd

Please sign in to comment.