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‎CITATION.cff‎

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@@ -29,7 +29,6 @@ authors:
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given-names: "Tiki"
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email: "julian.t.gonzalez@usace.army.mil"
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affiliation: "U.S. Army Corps of Engineers, Risk Management Center"
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orcid: ""
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- family-names: "Smith"
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given-names: "C. Haden"
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email: "cole.h.smith@usace.army.mil"

‎notebooks/04_mcmc_bayesian_inference.ipynb‎

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‎notebooks/05_mcmc_adaptive.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 2,
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"id": "6f5439b8",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"WARNING (pytensor.configdefaults): g++ not available, if using conda: `conda install gxx`\n",
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"WARNING (pytensor.configdefaults): g++ not detected! PyTensor will be unable to compile C-implementations and will default to Python. Performance may be severely degraded. To remove this warning, set PyTensor flags cxx to an empty string.\n",
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"c:\\GIT\\Numerics-Python-Examples\\.venv\\Lib\\site-packages\\arviz\\__init__.py:39: FutureWarning: \n",
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"ArviZ is undergoing a major refactor to improve flexibility and extensibility while maintaining a user-friendly interface.\n",
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"Some upcoming changes may be backward incompatible.\n",
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"For details and migration guidance, visit: https://python.arviz.org/en/latest/user_guide/migration_guide.html\n",
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" warn(\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 3,
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"id": "9a147b4e",
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"metadata": {},
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"outputs": [
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"False"
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]
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},
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"execution_count": 2,
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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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": 3,
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"execution_count": 4,
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"id": "ef3f0173",
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"metadata": {},
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"outputs": [],
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"source": [
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"def extract_chain_samples(results, param_indx):\n",
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" \"\"\"Extract parameter samples from MCMC sampler.\"\"\"\n",
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" \"\"\"Extract parameter samples from MCMC sampler results.\"\"\"\n",
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" chains = []\n",
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" for c in range(len(results.MarkovChains)):\n",
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" chain_c = [results.MarkovChains[c][i].Values[param_indx]\n",
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" for i in range(len(results.MarkovChains[c]))]\n",
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" chains.append(chain_c)\n",
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" chain = []\n",
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" for i in range(len(results.MarkovChains[c])):\n",
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" chain.append(float(results.MarkovChains[c][i].Values[param_indx]))\n",
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" chains.append(chain)\n",
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" return chains\n",
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"\n",
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"def plot_trace(samples, param_names, title=\"Trace Plots\"):\n",
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 5,
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"id": "f230fcf0",
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"metadata": {},
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"outputs": [
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 6,
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"id": "031793f7",
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"metadata": {},
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"outputs": [
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 7,
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"id": "5840b6c1",
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"metadata": {},
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"outputs": [
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" <tr>\n",
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" <th>0</th>\n",
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" <td>ARWMH</td>\n",
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" <td>0.511</td>\n",
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" <td>0.528</td>\n",
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" <td>108.998</td>\n",
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" <td>2.674</td>\n",
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" <td>8.998</td>\n",
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" <td>202.499</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>DEMCzs</td>\n",
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" <td>0.914</td>\n",
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" <td>109.255</td>\n",
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" <td>2.693</td>\n",
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" <td>9.255</td>\n",
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" <td>0.193</td>\n",
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" <td>767.374</td>\n",
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" <td>768.685</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>DEMCz</td>\n",
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" <td>0.934</td>\n",
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" <td>0.885</td>\n",
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" <td>109.654</td>\n",
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" <td>2.694</td>\n",
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" <td>9.654</td>\n",
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" <td>581.106</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>DEMCzs</td>\n",
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" <td>0.910</td>\n",
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" <td>109.255</td>\n",
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" <td>2.693</td>\n",
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" <td>9.255</td>\n",
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" <td>0.193</td>\n",
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" <td>767.374</td>\n",
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" <td>768.685</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>HMC</td>\n",
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" <td>8.857</td>\n",
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" <td>8.497</td>\n",
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" <td>111.574</td>\n",
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" <td>2.769</td>\n",
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" <td>11.574</td>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>NUTS</td>\n",
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" <td>36.850</td>\n",
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" <td>37.273</td>\n",
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" <td>108.194</td>\n",
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" <td>2.695</td>\n",
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" <td>8.194</td>\n",
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],
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"text/plain": [
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" Sampler Runtime (s) λ Estimate κ Estimate λ Error κ Error ESS (λ) \\\n",
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"0 ARWMH 0.511 108.998 2.674 8.998 0.174 182.686 \n",
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"2 DEMCzs 0.914 109.255 2.693 9.255 0.193 767.374 \n",
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"1 DEMCz 0.934 109.654 2.694 9.654 0.194 726.493 \n",
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"3 HMC 8.857 111.574 2.769 11.574 0.269 10.068 \n",
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"4 NUTS 36.850 108.194 2.695 8.194 0.195 73.592 \n",
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"0 ARWMH 0.528 108.998 2.674 8.998 0.174 182.686 \n",
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"1 DEMCz 0.885 109.654 2.694 9.654 0.194 726.493 \n",
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"2 DEMCzs 0.910 109.255 2.693 9.255 0.193 767.374 \n",
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"3 HMC 8.497 111.574 2.769 11.574 0.269 10.068 \n",
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"4 NUTS 37.273 108.194 2.695 8.194 0.195 73.592 \n",
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"\n",
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" ESS (κ) \n",
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"0 202.499 \n",
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"2 768.685 \n",
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"1 581.106 \n",
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"2 768.685 \n",
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"3 102.772 \n",
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"4 709.799 "
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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": 7,
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"execution_count": 8,
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"id": "2a1a1ca1",
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"metadata": {},
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"outputs": [
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"\n",
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"Running DEMCzs...\n",
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"Numerics DEMCzs Performance:\n",
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" Runtime: 0.243 seconds\n",
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" Runtime: 0.241 seconds\n",
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" Mean μ: 12660.51\n",
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" Mean σ: 4831.53\n",
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"\n",
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"Running Numerics NUTS...\n",
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"Numerics NUTS Performance:\n",
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" Runtime: 7.619 seconds\n",
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" Runtime: 7.685 seconds\n",
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" Mean μ: 12650.87\n",
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" Mean σ: 4845.18\n",
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"\n",
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"Initializing NUTS using jitter+adapt_diag...\n",
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"Sequential sampling (1 chains in 1 job)\n",
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"NUTS: [mu, sigma]\n",
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"Sampling 1 chain for 1_000 tune and 2_000 draw iterations (1_000 + 2_000 draws total) took 23 seconds.\n",
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"Sampling 1 chain for 1_000 tune and 2_000 draw iterations (1_000 + 2_000 draws total) took 25 seconds.\n",
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"Only one chain was sampled, this makes it impossible to run some convergence checks\n"
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]
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},
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"output_type": "stream",
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"text": [
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"Numerics PyMC Performance:\n",
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" Runtime: 26.254 seconds\n",
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" Runtime: 27.552 seconds\n",
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" Mean μ: 12641.05\n",
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" Mean σ: 4826.64\n",
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"\n",
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"PERFORMANCE COMPARISON: Numerics DEMCzs vs Numerics NUTS vs PyMC\n",
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"Numerics DEMCzs: 0.243 seconds\n",
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"Numerics NUTS: 7.619 seconds\n",
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"PyMC (NUTS): 26.254 seconds\n",
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"Numerics DEMCzs: 0.241 seconds\n",
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"Numerics NUTS: 7.685 seconds\n",
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"PyMC (NUTS): 27.552 seconds\n",
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"\n",
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" Numerics Speedup vs PyMC:\n",
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" DEMCzs: 108.17x faster\n",
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" NUTS: 3.45x faster\n"
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" DEMCzs: 114.41x faster\n",
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" NUTS: 3.59x faster\n"
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]
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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": 8,
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"execution_count": 9,
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"id": "689c6a99",
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"metadata": {},
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"outputs": [
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"Initializing NUTS using jitter+adapt_diag...\n",
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"Multiprocess sampling (3 chains in 3 jobs)\n",
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"NUTS: [xi, alpha]\n",
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"Sampling 3 chains for 1_750 tune and 3_500 draw iterations (5_250 + 10_500 draws total) took 69 seconds.\n",
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"Sampling 3 chains for 1_750 tune and 3_500 draw iterations (5_250 + 10_500 draws total) took 68 seconds.\n",
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"We recommend running at least 4 chains for robust computation of convergence diagnostics\n"
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]
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},
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" <tr>\n",
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" <th>10</th>\n",
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" <td>Runtime (secs)</td>\n",
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" <td>8.7408</td>\n",
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" <td>69.8270</td>\n",
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" <td>-61.0863</td>\n",
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" <td>8.5154</td>\n",
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" <td>68.8552</td>\n",
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" <td>-60.3398</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"7 Alpha Lower CI 2297.817553 2305.096694 -0.3158%\n",
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"8 Alpha Median 2795.192763 2793.589666 0.0574%\n",
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"9 Alpha Upper CI 3447.047966 3453.778878 -0.1949%\n",
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"10 Runtime (secs) 8.7408 69.8270 -61.0863"
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"10 Runtime (secs) 8.5154 68.8552 -60.3398"
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]
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},
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"metadata": {},
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"name": "stdout",
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"text": [
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"Speedup: 7.99x faster with Numerics\n",
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"Speedup: 8.09x faster with Numerics\n",
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"\n",
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"Gumbel Distribution [PyMC Comparison]\n"
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]
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"Initializing NUTS using jitter+adapt_diag...\n",
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"Multiprocess sampling (3 chains in 3 jobs)\n",
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"NUTS: [xi, alpha]\n",
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"Sampling 3 chains for 1_750 tune and 3_500 draw iterations (5_250 + 10_500 draws total) took 52 seconds.\n",
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"Sampling 3 chains for 1_750 tune and 3_500 draw iterations (5_250 + 10_500 draws total) took 54 seconds.\n",
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"We recommend running at least 4 chains for robust computation of convergence diagnostics\n"
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]
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},
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" <tr>\n",
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" <th>10</th>\n",
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" <td>Runtime (secs)</td>\n",
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" <td>6.9611</td>\n",
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" <td>53.2323</td>\n",
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" <td>-46.2712</td>\n",
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" <td>6.9768</td>\n",
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" <td>55.1851</td>\n",
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" <td>-48.2083</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"7 Alpha Lower CI 3752.989571 3752.668837 0.0085%\n",
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"8 Alpha Median 4463.712922 4476.005183 -0.2746%\n",
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"9 Alpha Upper CI 5392.367283 5419.541733 -0.5014%\n",
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"10 Runtime (secs) 6.9611 53.2323 -46.2712"
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"10 Runtime (secs) 6.9768 55.1851 -48.2083"
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]
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},
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"metadata": {},
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Speedup: 7.65x faster with Numerics\n",
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"Speedup: 7.91x faster with Numerics\n",
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"\n",
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"Weibull Distribution [PyMC Comparison]\n"
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]
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"Initializing NUTS using jitter+adapt_diag...\n",
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"Multiprocess sampling (3 chains in 3 jobs)\n",
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"NUTS: [kappa, lambda]\n",
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"Sampling 3 chains for 1_750 tune and 3_500 draw iterations (5_250 + 10_500 draws total) took 58 seconds.\n",
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"Sampling 3 chains for 1_750 tune and 3_500 draw iterations (5_250 + 10_500 draws total) took 61 seconds.\n",
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"We recommend running at least 4 chains for robust computation of convergence diagnostics\n"
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]
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},
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" <tr>\n",
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" <th>10</th>\n",
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" <td>Runtime (secs)</td>\n",
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" <td>7.6491</td>\n",
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" <td>59.4641</td>\n",
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" <td>-51.8150</td>\n",
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" <td>7.8432</td>\n",
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" <td>61.8657</td>\n",
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" <td>-54.0226</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"7 Lambda Lower CI 2.445549 13122.431346 -99.9814%\n",
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"8 Lambda Median 2.973241 14278.392251 -99.9792%\n",
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"9 Lambda Upper CI 3.547739 15514.805284 -99.9771%\n",
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"10 Runtime (secs) 7.6491 59.4641 -51.8150"
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"10 Runtime (secs) 7.8432 61.8657 -54.0226"
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]
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},
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"metadata": {},
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Speedup: 7.77x faster with Numerics\n",
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"Speedup: 7.89x faster with Numerics\n",
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"\n"
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]
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}

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