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Update example
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‎examples/GP_sGP.ipynb

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"cell_type": "code",
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"id": "136rQl-Z67Xf"
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"cell_type": "code",
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},
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"cell_type": "code",
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"id": "LAvbGDom67Xl"
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},
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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],
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"source": [
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"# Get random number generator keys (see JAX documentation for why it is neccessary)\n",
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"rng_key, rng_keposterior_meanict = gpax.utils.get_keys()\n",
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"rng_key, rng_key_predict = gpax.utils.get_keys()\n",
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"\n",
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"# Initialize model\n",
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"gp_model = gpax.ExactGP(1, kernel='Matern')\n",
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"gp_model.fit(rng_key, X, y, num_chains=1)\n",
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"\n",
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"# Get GP prediction\n",
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"posterior_mean, f_samples = gp_model.predict(rng_keposterior_meanict, X_test, n=200)"
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"posterior_mean, f_samples = gp_model.predict(rng_key_predict, X_test, n=200)"
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]
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},
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "lnxdYcLL67Xm",
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"outputId": "584a3a74-32e5-4f13-d3e5-0d2bcdf9cfe7",
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},
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"cell_type": "code",
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"metadata": {
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"id": "OjxPG_gY3U2c"
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},
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"cell_type": "code",
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"metadata": {
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"id": "zdrtXqGPKzUe"
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "lqXxUSGeqGhm"
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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" gp_model.fit(rng_key, X, y, num_warmup=NUM_WARMUP, num_samples=NUM_SAMPLES)\n",
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"\n",
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" # Get GP prediction\n",
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" posterior_mean, f_samples = gp_model.predict(rng_keposterior_meanict, X_test, n=200)\n",
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" posterior_mean, f_samples = gp_model.predict(rng_key_predict, X_test, n=200)\n",
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"\n",
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" # Plot results\n",
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" _, ax = plt.subplots(dpi=100)\n",
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "qRocZMUIVsp4"
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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}
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],
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"source": [
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"rng_key, rng_keposterior_meanict = gpax.utils.get_keys(1)\n",
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"rng_key, rng_key_predict = gpax.utils.get_keys(1)\n",
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"\n",
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"for i in range(6):\n",
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" print(\"\\nExploration step {}\".format(i+1))\n",
@@ -754,7 +754,7 @@
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" gp_model.fit(rng_key, X, y, print_summary=1, num_warmup=NUM_WARMUP, num_samples=NUM_SAMPLES)\n",
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" # Compute acquisition function (here it is simply the uncertinty in prediciton)\n",
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" # and get the coordinate of the next point to measure\n",
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" obj = gpax.acquisition.UE(rng_keposterior_meanict, gp_model, X_test)\n",
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" obj = gpax.acquisition.UE(rng_key_predict, gp_model, X_test)\n",
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" next_point_idx = obj.argmax()\n",
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" # Append the 'suggested' point\n",
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" X = np.append(X, X_test[next_point_idx])\n",
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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}
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],
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"source": [
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"rng_key, rng_keposterior_meanict = gpax.utils.get_keys(1)\n",
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"rng_key, rng_key_predict = gpax.utils.get_keys(1)\n",
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"# Update GP posterior\n",
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"gp_model = gpax.ExactGP(1, kernel='Matern', mean_fn=piecewise1, mean_fn_prior=piecewise1_priors)\n",
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"gp_model.fit(rng_key, X, y)\n",
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"# Get GP prediction\n",
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"posterior_mean, f_samples = gp_model.predict(rng_keposterior_meanict, X_test, n=200)"
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"posterior_mean, f_samples = gp_model.predict(rng_key_predict, X_test, n=200)"
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]
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},
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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"source": [
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"X, y = Xo, yo # start from the same set of observations\n",
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"\n",
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"rng_key, rng_keposterior_meanict = gpax.utils.get_keys(1)\n",
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"rng_key, rng_key_predict = gpax.utils.get_keys(1)\n",
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"\n",
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"for i in range(9):\n",
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" print(\"\\nExploration step {}\".format(i+1))\n",
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" # Obtain/update GP posterior\n",
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" gp_model = gpax.ExactGP(1, kernel='Matern', mean_fn=piecewise2, mean_fn_prior=piecewise2_priors)\n",
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" gp_model.fit(rng_key, X, y, print_summary=1, num_warmup=NUM_WARMUP, num_samples=NUM_SAMPLES)\n",
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" # Compute acquisition function and get coordinate of the next point\n",
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" obj = gpax.acquisition.UE(rng_keposterior_meanict, gp_model, X_test)\n",
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" obj = gpax.acquisition.UE(rng_key_predict, gp_model, X_test)\n",
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" next_point_idx = obj.argmax()\n",
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" # Append the 'suggested' point\n",
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" X = np.append(X, X_test[next_point_idx])\n",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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}
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],
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"source": [
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"rng_key, rng_keposterior_meanict = gpax.utils.get_keys(1)\n",
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"rng_key, rng_key_predict = gpax.utils.get_keys(1)\n",
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"# Update GP posterior\n",
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"gp_model = gpax.ExactGP(1, kernel='Matern', mean_fn=piecewise2, mean_fn_prior=piecewise2_priors)\n",
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"gp_model.fit(rng_key, X, y, num_warmup=NUM_WARMUP, num_samples=NUM_SAMPLES)\n",
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"# Get GP prediction\n",
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"posterior_mean, f_samples = gp_model.predict(rng_keposterior_meanict, X_test, n=200)"
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"posterior_mean, f_samples = gp_model.predict(rng_key_predict, X_test, n=200)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",

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