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fonnesbeck/dependent_density_regression.ipynb

Created September 30, 2024 01:04
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dependent_density_regression.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(dependent_density_regression)=\n",
"# Dependent density regression\n",
":::{post} 2017\n",
":tags: mixture model, nonparametric\n",
":category: intermediate\n",
":author: Austin Rochford\n",
":::\n",
"\n",
"In another [example](dp_mix.ipynb), we showed how to use Dirichlet processes to perform Bayesian nonparametric density estimation. This example expands on the previous one, illustrating dependent density regression.\n",
"\n",
"Just as Dirichlet process mixtures can be thought of as infinite mixture models that select the number of active components as part of inference, dependent density regression can be thought of as infinite [mixtures of experts](https://en.wikipedia.org/wiki/Committee_machine) that select the active experts as part of inference. Their flexibility and modularity make them powerful tools for performing nonparametric Bayesian Data analysis."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on PyMC v5.16.2\n"
]
}
],
"source": [
"from io import StringIO\n",
"\n",
"import arviz as az\n",
"import numpy as np\n",
"import pandas as pd\n",
"import pymc as pm\n",
"import pytensor.tensor as pt\n",
"import requests\n",
"import seaborn as sns\n",
"\n",
"from matplotlib import animation as ani\n",
"from matplotlib import pyplot as plt\n",
"\n",
"print(f\"Running on PyMC v{pm.__version__}\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%config InlineBackend.figure_format = 'retina'\n",
"plt.rc(\"animation\", writer=\"ffmpeg\")\n",
"blue, *_ = sns.color_palette()\n",
"az.style.use(\"arviz-darkgrid\")\n",
"SEED = 1972917 # from random.org; for reproducibility\n",
"np.random.seed(SEED)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will use the LIDAR data set from Larry Wasserman's excellent book, [_All of Nonparametric Statistics_](http://www.stat.cmu.edu/~larry/all-of-nonpar/). We standardize the data set to improve the rate of convergence of our samples."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/home/fonnesbeck/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/urllib3/connectionpool.py:1099: InsecureRequestWarning: Unverified HTTPS request is being made to host 'www.stat.cmu.edu'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#tls-warnings\n",
" warnings.warn(\n"
]
}
],
"source": [
"DATA_URI = \"http://www.stat.cmu.edu/~larry/all-of-nonpar/=data/lidar.dat\"\n",
"\n",
"\n",
"def standardize(x):\n",
" return (x - x.mean()) / x.std()\n",
"\n",
"\n",
"response = requests.get(DATA_URI, verify=False)\n",
"df = pd.read_csv(StringIO(response.text), sep=r\"\\s{1,3}\", engine=\"python\").assign(\n",
" std_range=lambda df: standardize(df.range), std_logratio=lambda df: standardize(df.logratio)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>range</th>\n",
" <th>logratio</th>\n",
" <th>std_range</th>\n",
" <th>std_logratio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>390</td>\n",
" <td>-0.050356</td>\n",
" <td>-1.717725</td>\n",
" <td>0.852467</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>391</td>\n",
" <td>-0.060097</td>\n",
" <td>-1.707299</td>\n",
" <td>0.817981</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>393</td>\n",
" <td>-0.041901</td>\n",
" <td>-1.686447</td>\n",
" <td>0.882398</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>394</td>\n",
" <td>-0.050985</td>\n",
" <td>-1.676020</td>\n",
" <td>0.850240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>396</td>\n",
" <td>-0.059913</td>\n",
" <td>-1.655168</td>\n",
" <td>0.818631</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" range logratio std_range std_logratio\n",
"0 390 -0.050356 -1.717725 0.852467\n",
"1 391 -0.060097 -1.707299 0.817981\n",
"2 393 -0.041901 -1.686447 0.882398\n",
"3 394 -0.050985 -1.676020 0.850240\n",
"4 396 -0.059913 -1.655168 0.818631"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We plot the LIDAR data below."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 611,
"width": 811
}
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8, 6))\n",
"\n",
"ax.scatter(df.std_range, df.std_logratio, color=blue)\n",
"\n",
"ax.set_xticklabels([])\n",
"ax.set_xlabel(\"Standardized range\")\n",
"\n",
"ax.set_yticklabels([])\n",
"ax.set_ylabel(\"Standardized log ratio\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This data set has a two interesting properties that make it useful for illustrating dependent density regression.\n",
"\n",
"1. The relationship between range and log ratio is nonlinear, but has locally linear components.\n",
"2. The observation noise is [heteroskedastic](https://en.wikipedia.org/wiki/Heteroscedasticity); that is, the magnitude of the variance varies with the range.\n",
"\n",
"The intuitive idea behind dependent density regression is to reduce the problem to many (related) density estimates, conditioned on fixed values of the predictors. The following animation illustrates this intuition."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"fig, (scatter_ax, hist_ax) = plt.subplots(ncols=2, figsize=(16, 6))\n",
"\n",
"scatter_ax.scatter(df.std_range, df.std_logratio, color=blue, zorder=2)\n",
"\n",
"scatter_ax.set_xticklabels([])\n",
"scatter_ax.set_xlabel(\"Standardized range\")\n",
"\n",
"scatter_ax.set_yticklabels([])\n",
"scatter_ax.set_ylabel(\"Standardized log ratio\")\n",
"\n",
"bins = np.linspace(df.std_range.min(), df.std_range.max(), 25)\n",
"\n",
"hist_ax.hist(df.std_logratio, bins=bins, color=\"k\", lw=0, alpha=0.25, label=\"All data\")\n",
"\n",
"hist_ax.set_xticklabels([])\n",
"hist_ax.set_xlabel(\"Standardized log ratio\")\n",
"\n",
"hist_ax.set_yticklabels([])\n",
"hist_ax.set_ylabel(\"Frequency\")\n",
"\n",
"hist_ax.legend(loc=2)\n",
"\n",
"endpoints = np.linspace(1.05 * df.std_range.min(), 1.05 * df.std_range.max(), 15)\n",
"\n",
"frame_artists = []\n",
"\n",
"for low, high in zip(endpoints[:-1], endpoints[2:]):\n",
" interval = scatter_ax.axvspan(low, high, color=\"k\", alpha=0.5, lw=0, zorder=1)\n",
" *_, bars = hist_ax.hist(\n",
" df[df.std_range.between(low, high)].std_logratio, bins=bins, color=\"k\", lw=0, alpha=0.5\n",
" )\n",
"\n",
" frame_artists.append((interval,) + tuple(bars))\n",
"\n",
"animation = ani.ArtistAnimation(fig, frame_artists, interval=500, repeat_delay=3000, blit=True)\n",
"plt.close()\n",
"# prevent the intermediate figure from showing"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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"\">\n",
" Your browser does not support the video tag.\n",
"</video>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import HTML\n",
"\n",
"HTML(animation.to_html5_video())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we slice the data with a window sliding along the x-axis in the left plot, the empirical distribution of the y-values of the points in the window varies in the right plot. An important aspect of this approach is that the density estimates that correspond to close values of the predictor are similar.\n",
"\n",
"In the previous example, we saw that a Dirichlet process estimates a probability density as a mixture model with infinitely many components. In the case of normal component distributions,\n",
"\n",
"$$y \\sim \\sum_{i = 1}^{\\infty} w_i \\cdot N(\\mu_i, \\tau_i^{-1}),$$\n",
"\n",
"where the mixture weights, $w_1, w_2, \\ldots$, are generated by a [stick-breaking process](https://en.wikipedia.org/wiki/Dirichlet_process#The_stick-breaking_process).\n",
"\n",
"Dependent density regression generalizes this representation of the Dirichlet process mixture model by allowing the mixture weights and component means to vary conditioned on the value of the predictor, $x$. That is,\n",
"\n",
"$$y\\ |\\ x \\sim \\sum_{i = 1}^{\\infty} w_i\\ |\\ x \\cdot N(\\mu_i\\ |\\ x, \\tau_i^{-1}).$$\n",
"\n",
"In this example, we will follow Chapter 23 of [_Bayesian Data Analysis_](http://www.stat.columbia.edu/~gelman/book/) and use a probit stick-breaking process to determine the conditional mixture weights, $w_i\\ |\\ x$. The probit stick-breaking process starts by defining\n",
"\n",
"$$v_i\\ |\\ x = \\Phi(\\alpha_i + \\beta_i x),$$\n",
"\n",
"where $\\Phi$ is the cumulative distribution function of the standard normal distribution. We then obtain $w_i\\ |\\ x$ by applying the stick breaking process to $v_i\\ |\\ x$. That is,\n",
"\n",
"$$w_i\\ |\\ x = v_i\\ |\\ x \\cdot \\prod_{j = 1}^{i - 1} (1 - v_j\\ |\\ x).$$\n",
"\n",
"For the LIDAR data set, we use independent normal priors $\\alpha_i \\sim N(0, 5^2)$ and $\\beta_i \\sim N(0, 5^2)$. We now express this this model for the conditional mixture weights using `PyMC`."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def norm_cdf(z):\n",
" return 0.5 * (1 + pt.erf(z / np.sqrt(2)))\n",
"\n",
"\n",
"def stick_breaking(v):\n",
" return v * pt.concatenate(\n",
" [pt.ones_like(v[:, :1]), pt.extra_ops.cumprod(1 - v[:, :-1], axis=1)], axis=1\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"N = len(df)\n",
"K = 20\n",
"\n",
"std_range = df.std_range.values\n",
"std_logratio = df.std_logratio.values\n",
"\n",
"with pm.Model(coords={\"N\": np.arange(N), \"K\": np.arange(K) + 1}) as model:\n",
" alpha = pm.Normal(\"alpha\", 0, 5, dims=\"K\")\n",
" beta = pm.Normal(\"beta\", 0, 5, dims=\"K\")\n",
" x = pm.Data(\"x\", std_range, dims=\"N\")\n",
" v = norm_cdf(alpha + pt.outer(x, beta))\n",
" w = pm.Deterministic(\"w\", stick_breaking(v), dims=[\"N\", \"K\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have defined `x` as a `pm.Data` container in order to use `PyMC`'s posterior prediction capabilities later.\n",
"\n",
"While the dependent density regression model theoretically has infinitely many components, we must truncate the model to finitely many components (in this case, twenty) in order to express it using `PyMC`. After sampling from the model, we will verify that truncation did not unduly influence our results.\n",
"\n",
"Since the LIDAR data seems to have several linear components, we use the linear models\n",
"\n",
"$$\n",
"\\begin{align*}\n",
"\\mu_i\\ |\\ x\n",
" & \\sim \\gamma_i + \\delta_i x \\\\\n",
"\\gamma_i\n",
" & \\sim N(0, 10^2) \\\\\n",
"\\delta_i\n",
" & \\sim N(0, 10^2)\n",
"\\end{align*}\n",
"$$\n",
"\n",
"for the conditional component means."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"with model:\n",
" gamma = pm.Normal(\"gamma\", 0, 3, dims=\"K\")\n",
" delta = pm.Normal(\"delta\", 0, 3, dims=\"K\")\n",
" mu = pm.Deterministic(\"mu\", gamma + pt.outer(x, delta), dims=(\"N\", \"K\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we specify a `NormalMixture` likelihood function, using the weights we have modeled above."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
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"source": [
"with model:\n",
" sigma = pm.HalfNormal(\"sigma\", 3, dims=\"K\")\n",
" y = pm.Data(\"y\", std_logratio, dims=\"N\")\n",
" obs = pm.NormalMixture(\"obs\", w, mu, sigma=sigma, observed=y, dims=\"N\")\n",
"\n",
"pm.model_to_graphviz(model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now sample from the dependent density regression model."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
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"Multiprocess sampling (4 chains in 4 jobs)\n",
"CompoundStep\n",
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"\u001b[0;31mConnectionResetError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[12], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m model:\n\u001b[0;32m----> 2\u001b[0m trace \u001b[38;5;241m=\u001b[39m \u001b[43mpm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mMetropolis\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtune\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m3000\u001b[39;49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pymc/sampling/mcmc.py:846\u001b[0m, in \u001b[0;36msample\u001b[0;34m(draws, tune, chains, cores, random_seed, progressbar, progressbar_theme, step, var_names, nuts_sampler, initvals, init, jitter_max_retries, n_init, trace, discard_tuned_samples, compute_convergence_checks, keep_warning_stat, return_inferencedata, idata_kwargs, nuts_sampler_kwargs, callback, mp_ctx, blas_cores, model, **kwargs)\u001b[0m\n\u001b[1;32m 844\u001b[0m _print_step_hierarchy(step)\n\u001b[1;32m 845\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 846\u001b[0m \u001b[43m_mp_sample\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msample_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mparallel_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 847\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m pickle\u001b[38;5;241m.\u001b[39mPickleError:\n\u001b[1;32m 848\u001b[0m _log\u001b[38;5;241m.\u001b[39mwarning(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not pickle model, sampling singlethreaded.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pymc/sampling/mcmc.py:1259\u001b[0m, in \u001b[0;36m_mp_sample\u001b[0;34m(draws, tune, step, chains, cores, random_seed, start, progressbar, progressbar_theme, traces, model, callback, blas_cores, mp_ctx, **kwargs)\u001b[0m\n\u001b[1;32m 1257\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1258\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m sampler:\n\u001b[0;32m-> 1259\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43msampler\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 1260\u001b[0m \u001b[43m \u001b[49m\u001b[43mstrace\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mtraces\u001b[49m\u001b[43m[\u001b[49m\u001b[43mdraw\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchain\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 1261\u001b[0m \u001b[43m \u001b[49m\u001b[43mstrace\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecord\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdraw\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpoint\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdraw\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstats\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pymc/sampling/parallel.py:471\u001b[0m, in \u001b[0;36mParallelSampler.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 464\u001b[0m task \u001b[38;5;241m=\u001b[39m progress\u001b[38;5;241m.\u001b[39madd_task(\n\u001b[1;32m 465\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_desc\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mself\u001b[39m),\n\u001b[1;32m 466\u001b[0m completed\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_completed_draws,\n\u001b[1;32m 467\u001b[0m total\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_total_draws,\n\u001b[1;32m 468\u001b[0m )\n\u001b[1;32m 470\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_active:\n\u001b[0;32m--> 471\u001b[0m draw \u001b[38;5;241m=\u001b[39m \u001b[43mProcessAdapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecv_draw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_active\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 472\u001b[0m proc, is_last, draw, tuning, stats \u001b[38;5;241m=\u001b[39m draw\n\u001b[1;32m 473\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_completed_draws \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
"File \u001b[0;32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pymc/sampling/parallel.py:328\u001b[0m, in \u001b[0;36mProcessAdapter.recv_draw\u001b[0;34m(processes, timeout)\u001b[0m\n\u001b[1;32m 326\u001b[0m idxs \u001b[38;5;241m=\u001b[39m {\u001b[38;5;28mid\u001b[39m(proc\u001b[38;5;241m.\u001b[39m_msg_pipe): proc \u001b[38;5;28;01mfor\u001b[39;00m proc \u001b[38;5;129;01min\u001b[39;00m processes}\n\u001b[1;32m 327\u001b[0m proc \u001b[38;5;241m=\u001b[39m idxs[\u001b[38;5;28mid\u001b[39m(ready[\u001b[38;5;241m0\u001b[39m])]\n\u001b[0;32m--> 328\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[43mready\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecv\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 330\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m msg[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124merror\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 331\u001b[0m old_error \u001b[38;5;241m=\u001b[39m msg[\u001b[38;5;241m1\u001b[39m]\n",
"File \u001b[0;32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/multiprocessing/connection.py:250\u001b[0m, in \u001b[0;36m_ConnectionBase.recv\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_closed()\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_readable()\n\u001b[0;32m--> 250\u001b[0m buf \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_recv_bytes\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 251\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _ForkingPickler\u001b[38;5;241m.\u001b[39mloads(buf\u001b[38;5;241m.\u001b[39mgetbuffer())\n",
"File \u001b[0;32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/multiprocessing/connection.py:430\u001b[0m, in \u001b[0;36mConnection._recv_bytes\u001b[0;34m(self, maxsize)\u001b[0m\n\u001b[1;32m 429\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_recv_bytes\u001b[39m(\u001b[38;5;28mself\u001b[39m, maxsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m--> 430\u001b[0m buf \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_recv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 431\u001b[0m size, \u001b[38;5;241m=\u001b[39m struct\u001b[38;5;241m.\u001b[39munpack(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m!i\u001b[39m\u001b[38;5;124m\"\u001b[39m, buf\u001b[38;5;241m.\u001b[39mgetvalue())\n\u001b[1;32m 432\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m size \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m:\n",
"File \u001b[0;32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/multiprocessing/connection.py:395\u001b[0m, in \u001b[0;36mConnection._recv\u001b[0;34m(self, size, read)\u001b[0m\n\u001b[1;32m 393\u001b[0m remaining \u001b[38;5;241m=\u001b[39m size\n\u001b[1;32m 394\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m remaining \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 395\u001b[0m chunk \u001b[38;5;241m=\u001b[39m \u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mremaining\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 396\u001b[0m n \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(chunk)\n\u001b[1;32m 397\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
"\u001b[0;31mConnectionResetError\u001b[0m: [Errno 104] Connection reset by peer"
]
}
],
"source": [
"with model:\n",
" trace = pm.sample(step=pm.Metropolis(), tune=3000)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([<Axes: title={'center': '94.0% HDI'}>], dtype=object)"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 600x1200 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 1211,
"width": 611
}
},
"output_type": "display_data"
}
],
"source": [
"az.plot_forest(trace, var_names=[\"gamma\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To verify that truncation did not unduly influence our results, we plot the largest posterior expected mixture weight for each component. (In this model, each point has a mixture weight for each component, so we plot the maximum mixture weight for each component across all data points in order to judge if the component exerts any influence on the posterior.)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 611,
"width": 811
}
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8, 6))\n",
"\n",
"max_mixture_weights = trace.posterior[\"w\"].mean((\"chain\", \"draw\")).max(\"N\")\n",
"ax.bar(max_mixture_weights.coords.to_index(), max_mixture_weights)\n",
"\n",
"ax.set_xlim(1 - 0.5, K + 0.5)\n",
"ax.set_xticks(np.arange(0, K, 2) + 1) \n",
"ax.set_xlabel(\"Mixture component\")\n",
"\n",
"ax.set_ylabel(\"Largest posterior expected\\nmixture weight\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since only three mixture components have appreciable posterior expected weight for any data point, we can be fairly certain that truncation did not unduly influence our results. (If most components had appreciable posterior expected weight, truncation may have influenced the results, and we would have increased the number of components and sampled again.)\n",
"\n",
"Visually, it is reasonable that the LIDAR data has three linear components, so these posterior expected weights seem to have identified the structure of the data well. We now sample from the posterior predictive distribution to get a better understand the model's performance."
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Sampling: [obs, w]\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ac6275b929644f0f8ca1d0d57701929d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Output()"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
},
{
"ename": "ValueError",
"evalue": "shape mismatch: objects cannot be broadcast to a single shape. Mismatch is between arg 0 with shape (100, 19) and arg 2 with shape (221, 1).\nApply node that caused the error: stick_breaking_weights_rv{\"(),()->(k)\"}(RNG(<Generator(PCG64) at 0x7F2158D37760>), MakeVector{dtype='int64'}.0, alpha, [19])\nToposort index: 1\nInputs types: [RandomGeneratorType, TensorType(int64, shape=(1,)), TensorType(float64, shape=(221,)), TensorType(int64, shape=(1,))]\nInputs shapes: ['No shapes', (1,), (221,), (1,)]\nInputs strides: ['No strides', (8,), (8,), (8,)]\nInputs values: [Generator(PCG64) at 0x7F2158D37760, array([100]), 'not shown', array([19])]\nOutputs clients: [[output[2](stick_breaking_weights_rv{\"(),()->(k)\"}.0)], [categorical_rv{\"(p)->()\"}(RNG(<Generator(PCG64) at 0x7F2158D37D80>), NoneConst{None}, w)]]\n\nBacktrace when the node is created (use PyTensor flag traceback__limit=N to make it longer):\n File \"/tmp/ipykernel_172049/2226113595.py\", line 14, in <module>\n w = pm.StickBreakingWeights(\"w\", alpha=alpha, K=K-1, dims=[\"N\", \"K\"])\n File \"/var/home/fonnesbeck/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pymc/distributions/distribution.py\", line 536, in __new__\n rv_out = cls.dist(*args, **kwargs)\n File \"/var/home/fonnesbeck/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pymc/distributions/multivariate.py\", line 2521, in dist\n return super().dist([alpha, K], **kwargs)\n File \"/var/home/fonnesbeck/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pymc/distributions/distribution.py\", line 618, in dist\n rv_out = cls.rv_op(*dist_params, size=create_size, **kwargs)\n File \"/var/home/fonnesbeck/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/tensor/random/op.py\", line 312, in __call__\n return new_op.__call__(\n File \"/var/home/fonnesbeck/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/tensor/random/op.py\", line 316, in __call__\n res = super().__call__(rng, size, *args, **kwargs)\n File \"/var/home/fonnesbeck/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/graph/op.py\", line 293, in __call__\n node = self.make_node(*inputs, **kwargs)\n File \"/var/home/fonnesbeck/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pymc/distributions/multivariate.py\", line 2440, in make_node\n return super().make_node(rng, size, alpha, K)\n\nHINT: Use the PyTensor flag `exception_verbosity=high` for a debug print-out and storage map footprint of this Apply node.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/compile/function/types.py:959\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 957\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 958\u001b[0m outputs \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 959\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvm\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 960\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_subset \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 961\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvm(output_subset\u001b[38;5;241m=\u001b[39moutput_subset)\n\u001b[1;32m 962\u001b[0m )\n\u001b[1;32m 963\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n",
"File \u001b[0;32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/graph/op.py:524\u001b[0m, in \u001b[0;36mOp.make_py_thunk.<locals>.rval\u001b[0;34m(p, i, o, n)\u001b[0m\n\u001b[1;32m 522\u001b[0m \u001b[38;5;129m@is_thunk_type\u001b[39m\n\u001b[1;32m 523\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrval\u001b[39m(p\u001b[38;5;241m=\u001b[39mp, i\u001b[38;5;241m=\u001b[39mnode_input_storage, o\u001b[38;5;241m=\u001b[39mnode_output_storage, n\u001b[38;5;241m=\u001b[39mnode):\n\u001b[0;32m--> 524\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43mp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 525\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m o \u001b[38;5;129;01min\u001b[39;00m node\u001b[38;5;241m.\u001b[39moutputs:\n",
"File \u001b[0;32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/tensor/random/op.py:403\u001b[0m, in \u001b[0;36mRandomVariable.perform\u001b[0;34m(self, node, inputs, outputs)\u001b[0m\n\u001b[1;32m 402\u001b[0m size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(size)\n\u001b[0;32m--> 403\u001b[0m smpl_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrng_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrng\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 405\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(smpl_val, np\u001b[38;5;241m.\u001b[39mndarray) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(smpl_val\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype:\n",
"File \u001b[0;32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pymc/distributions/multivariate.py:2456\u001b[0m, in \u001b[0;36mStickBreakingWeightsRV.rng_fn\u001b[0;34m(cls, rng, alpha, K, size)\u001b[0m\n\u001b[1;32m 2454\u001b[0m alpha \u001b[38;5;241m=\u001b[39m alpha[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, np\u001b[38;5;241m.\u001b[39mnewaxis]\n\u001b[0;32m-> 2456\u001b[0m betas \u001b[38;5;241m=\u001b[39m \u001b[43mrng\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbeta\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2458\u001b[0m sticks \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate(\n\u001b[1;32m 2459\u001b[0m (\n\u001b[1;32m 2460\u001b[0m np\u001b[38;5;241m.\u001b[39mones(shape\u001b[38;5;241m=\u001b[39m(size[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m+\u001b[39m (\u001b[38;5;241m1\u001b[39m,))),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2463\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 2464\u001b[0m )\n",
"File \u001b[0;32mnumpy/random/_generator.pyx:400\u001b[0m, in \u001b[0;36mnumpy.random._generator.Generator.beta\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32m_common.pyx:600\u001b[0m, in \u001b[0;36mnumpy.random._common.cont\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32m_common.pyx:517\u001b[0m, in \u001b[0;36mnumpy.random._common.cont_broadcast_2\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32m__init__.cython-30.pxd:780\u001b[0m, in \u001b[0;36mnumpy.PyArray_MultiIterNew3\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: shape mismatch: objects cannot be broadcast to a single shape. Mismatch is between arg 0 with shape (100, 19) and arg 2 with shape (221, 1).",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[68], line 7\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m model:\n\u001b[1;32m 4\u001b[0m pm\u001b[38;5;241m.\u001b[39mset_data({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m: lidar_pp_x, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m: np\u001b[38;5;241m.\u001b[39mzeros_like(lidar_pp_x)},\n\u001b[1;32m 5\u001b[0m coords\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mN\u001b[39m\u001b[38;5;124m\"\u001b[39m: np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;28mlen\u001b[39m(lidar_pp_x))})\n\u001b[0;32m----> 7\u001b[0m \u001b[43mpm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample_posterior_predictive\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpredictions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextend_inferencedata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandom_seed\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mSEED\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pymc/sampling/forward.py:885\u001b[0m, in \u001b[0;36msample_posterior_predictive\u001b[0;34m(trace, model, var_names, sample_dims, random_seed, progressbar, progressbar_theme, return_inferencedata, extend_inferencedata, predictions, idata_kwargs, compile_kwargs)\u001b[0m\n\u001b[1;32m 880\u001b[0m \u001b[38;5;66;03m# there's only a single chain, but the index might hit it multiple times if\u001b[39;00m\n\u001b[1;32m 881\u001b[0m \u001b[38;5;66;03m# the number of indices is greater than the length of the trace.\u001b[39;00m\n\u001b[1;32m 882\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 883\u001b[0m param \u001b[38;5;241m=\u001b[39m _trace[idx \u001b[38;5;241m%\u001b[39m len_trace]\n\u001b[0;32m--> 885\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[43msampler_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mparam\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 887\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(vars_, values):\n\u001b[1;32m 888\u001b[0m ppc_trace_t\u001b[38;5;241m.\u001b[39minsert(k\u001b[38;5;241m.\u001b[39mname, v, idx)\n",
"File \u001b[0;32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pymc/util.py:395\u001b[0m, in \u001b[0;36mpoint_wrapper.<locals>.wrapped\u001b[0;34m(**kwargs)\u001b[0m\n\u001b[1;32m 393\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 394\u001b[0m input_point \u001b[38;5;241m=\u001b[39m {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m ins}\n\u001b[0;32m--> 395\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcore_function\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minput_point\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/compile/function/types.py:972\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 970\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvm, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthunks\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 971\u001b[0m thunk \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvm\u001b[38;5;241m.\u001b[39mthunks[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvm\u001b[38;5;241m.\u001b[39mposition_of_error]\n\u001b[0;32m--> 972\u001b[0m \u001b[43mraise_with_op\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 973\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaker\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfgraph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 974\u001b[0m \u001b[43m \u001b[49m\u001b[43mnode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnodes\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mposition_of_error\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 975\u001b[0m \u001b[43m \u001b[49m\u001b[43mthunk\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mthunk\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 976\u001b[0m \u001b[43m \u001b[49m\u001b[43mstorage_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 977\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 978\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 979\u001b[0m \u001b[38;5;66;03m# old-style linkers raise their own exceptions\u001b[39;00m\n\u001b[1;32m 980\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n",
"File \u001b[0;32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/link/utils.py:524\u001b[0m, in \u001b[0;36mraise_with_op\u001b[0;34m(fgraph, node, thunk, exc_info, storage_map)\u001b[0m\n\u001b[1;32m 519\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 520\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexc_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m error does not allow us to add an extra error message\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 521\u001b[0m )\n\u001b[1;32m 522\u001b[0m \u001b[38;5;66;03m# Some exception need extra parameter in inputs. So forget the\u001b[39;00m\n\u001b[1;32m 523\u001b[0m \u001b[38;5;66;03m# extra long error message in that case.\u001b[39;00m\n\u001b[0;32m--> 524\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc_value\u001b[38;5;241m.\u001b[39mwith_traceback(exc_trace)\n",
"File \u001b[0;32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/compile/function/types.py:959\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 956\u001b[0m t0_fn \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mperf_counter()\n\u001b[1;32m 957\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 958\u001b[0m outputs \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 959\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvm\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 960\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_subset \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 961\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvm(output_subset\u001b[38;5;241m=\u001b[39moutput_subset)\n\u001b[1;32m 962\u001b[0m )\n\u001b[1;32m 963\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m 964\u001b[0m restore_defaults()\n",
"File \u001b[0;32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/graph/op.py:524\u001b[0m, in \u001b[0;36mOp.make_py_thunk.<locals>.rval\u001b[0;34m(p, i, o, n)\u001b[0m\n\u001b[1;32m 522\u001b[0m \u001b[38;5;129m@is_thunk_type\u001b[39m\n\u001b[1;32m 523\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrval\u001b[39m(p\u001b[38;5;241m=\u001b[39mp, i\u001b[38;5;241m=\u001b[39mnode_input_storage, o\u001b[38;5;241m=\u001b[39mnode_output_storage, n\u001b[38;5;241m=\u001b[39mnode):\n\u001b[0;32m--> 524\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43mp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 525\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m o \u001b[38;5;129;01min\u001b[39;00m node\u001b[38;5;241m.\u001b[39moutputs:\n\u001b[1;32m 526\u001b[0m compute_map[o][\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
"File \u001b[0;32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/tensor/random/op.py:403\u001b[0m, in \u001b[0;36mRandomVariable.perform\u001b[0;34m(self, node, inputs, outputs)\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 402\u001b[0m size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(size)\n\u001b[0;32m--> 403\u001b[0m smpl_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrng_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrng\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 405\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(smpl_val, np\u001b[38;5;241m.\u001b[39mndarray) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(smpl_val\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype:\n\u001b[1;32m 406\u001b[0m smpl_val \u001b[38;5;241m=\u001b[39m _asarray(smpl_val, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype)\n",
"File \u001b[0;32m~/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pymc/distributions/multivariate.py:2456\u001b[0m, in \u001b[0;36mStickBreakingWeightsRV.rng_fn\u001b[0;34m(cls, rng, alpha, K, size)\u001b[0m\n\u001b[1;32m 2453\u001b[0m size \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m*\u001b[39msize, K)\n\u001b[1;32m 2454\u001b[0m alpha \u001b[38;5;241m=\u001b[39m alpha[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, np\u001b[38;5;241m.\u001b[39mnewaxis]\n\u001b[0;32m-> 2456\u001b[0m betas \u001b[38;5;241m=\u001b[39m \u001b[43mrng\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbeta\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2458\u001b[0m sticks \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate(\n\u001b[1;32m 2459\u001b[0m (\n\u001b[1;32m 2460\u001b[0m np\u001b[38;5;241m.\u001b[39mones(shape\u001b[38;5;241m=\u001b[39m(size[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m+\u001b[39m (\u001b[38;5;241m1\u001b[39m,))),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2463\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 2464\u001b[0m )\n\u001b[1;32m 2466\u001b[0m weights \u001b[38;5;241m=\u001b[39m sticks \u001b[38;5;241m*\u001b[39m betas\n",
"File \u001b[0;32mnumpy/random/_generator.pyx:400\u001b[0m, in \u001b[0;36mnumpy.random._generator.Generator.beta\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32m_common.pyx:600\u001b[0m, in \u001b[0;36mnumpy.random._common.cont\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32m_common.pyx:517\u001b[0m, in \u001b[0;36mnumpy.random._common.cont_broadcast_2\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32m__init__.cython-30.pxd:780\u001b[0m, in \u001b[0;36mnumpy.PyArray_MultiIterNew3\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: shape mismatch: objects cannot be broadcast to a single shape. Mismatch is between arg 0 with shape (100, 19) and arg 2 with shape (221, 1).\nApply node that caused the error: stick_breaking_weights_rv{\"(),()->(k)\"}(RNG(<Generator(PCG64) at 0x7F2158D37760>), MakeVector{dtype='int64'}.0, alpha, [19])\nToposort index: 1\nInputs types: [RandomGeneratorType, TensorType(int64, shape=(1,)), TensorType(float64, shape=(221,)), TensorType(int64, shape=(1,))]\nInputs shapes: ['No shapes', (1,), (221,), (1,)]\nInputs strides: ['No strides', (8,), (8,), (8,)]\nInputs values: [Generator(PCG64) at 0x7F2158D37760, array([100]), 'not shown', array([19])]\nOutputs clients: [[output[2](stick_breaking_weights_rv{\"(),()->(k)\"}.0)], [categorical_rv{\"(p)->()\"}(RNG(<Generator(PCG64) at 0x7F2158D37D80>), NoneConst{None}, w)]]\n\nBacktrace when the node is created (use PyTensor flag traceback__limit=N to make it longer):\n File \"/tmp/ipykernel_172049/2226113595.py\", line 14, in <module>\n w = pm.StickBreakingWeights(\"w\", alpha=alpha, K=K-1, dims=[\"N\", \"K\"])\n File \"/var/home/fonnesbeck/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pymc/distributions/distribution.py\", line 536, in __new__\n rv_out = cls.dist(*args, **kwargs)\n File \"/var/home/fonnesbeck/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pymc/distributions/multivariate.py\", line 2521, in dist\n return super().dist([alpha, K], **kwargs)\n File \"/var/home/fonnesbeck/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pymc/distributions/distribution.py\", line 618, in dist\n rv_out = cls.rv_op(*dist_params, size=create_size, **kwargs)\n File \"/var/home/fonnesbeck/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/tensor/random/op.py\", line 312, in __call__\n return new_op.__call__(\n File \"/var/home/fonnesbeck/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/tensor/random/op.py\", line 316, in __call__\n res = super().__call__(rng, size, *args, **kwargs)\n File \"/var/home/fonnesbeck/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pytensor/graph/op.py\", line 293, in __call__\n node = self.make_node(*inputs, **kwargs)\n File \"/var/home/fonnesbeck/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/pymc/distributions/multivariate.py\", line 2440, in make_node\n return super().make_node(rng, size, alpha, K)\n\nHINT: Use the PyTensor flag `exception_verbosity=high` for a debug print-out and storage map footprint of this Apply node."
]
}
],
"source": [
"lidar_pp_x = np.linspace(std_range.min() - 0.05, std_range.max() + 0.05, 100)\n",
"\n",
"with model:\n",
" pm.set_data({\"x\": lidar_pp_x, \"y\": np.zeros_like(lidar_pp_x)},\n",
" coords={\"N\": np.arange(len(lidar_pp_x))})\n",
"\n",
" pm.sample_posterior_predictive(\n",
" trace, predictions=True, extend_inferencedata=True, random_seed=SEED\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below we plot the posterior expected value and the 95% posterior credible interval."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 611,
"width": 811
}
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8, 6))\n",
"\n",
"ax.scatter(df.std_range, df.std_logratio, color=blue, zorder=10, label=None)\n",
"\n",
"low, high = np.percentile(az.extract(trace.predictions)[\"obs\"].T, [2.5, 97.5], axis=0)\n",
"ax.fill_between(\n",
" lidar_pp_x, low, high, color=\"k\", alpha=0.35, zorder=5, label=\"95% posterior credible interval\"\n",
")\n",
"\n",
"ax.plot(\n",
" lidar_pp_x,\n",
" trace.predictions[\"obs\"].mean((\"chain\", \"draw\")).values,\n",
" c=\"k\",\n",
" zorder=6,\n",
" label=\"Posterior expected value\",\n",
")\n",
"\n",
"ax.set_xticklabels([])\n",
"ax.set_xlabel(\"Standardized range\")\n",
"\n",
"ax.set_yticklabels([])\n",
"ax.set_ylabel(\"Standardized log ratio\")\n",
"\n",
"ax.legend(loc=1)\n",
"ax.set_title(\"LIDAR Data\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The model has fit the linear components of the data well, and also accommodated its heteroskedasticity. This flexibility, along with the ability to modularly specify the conditional mixture weights and conditional component densities, makes dependent density regression an extremely useful nonparametric Bayesian model.\n",
"\n",
"To learn more about dependent density regression and related models, consult [_Bayesian Data Analysis_](http://www.stat.columbia.edu/~gelman/book/), [_Bayesian Nonparametric Data Analysis_](http://www.springer.com/us/book/9783319189673), or [_Bayesian Nonparametrics_](https://www.google.com/webhp?sourceid=chrome-instant&ion=1&espv=2&ie=UTF-8#q=bayesian+nonparametrics+book).\n",
"\n",
"This example first appeared [here](http://austinrochford.com/posts/2017-01-18-ddp-pymc3.html)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Authors\n",
"* authored by Austin Rochford in 2017\n",
"* updated to PyMC v5 by Christopher Fonnesbeck in September2024"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n",
"\n",
":::{bibliography}\n",
":filter: docname in docnames\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Last updated: Sat Sep 28 2024\n",
"\n",
"Python implementation: CPython\n",
"Python version : 3.12.5\n",
"IPython version : 8.27.0\n",
"\n",
"pymc : 5.16.2\n",
"arviz : 0.19.0\n",
"numpy : 1.26.4\n",
"pytensor : 2.25.4\n",
"requests : 2.32.3\n",
"pandas : 2.2.2\n",
"matplotlib: 3.9.2\n",
"seaborn : 0.13.2\n",
"\n",
"Watermark: 2.4.3\n",
"\n"
]
}
],
"source": [
"%load_ext watermark\n",
"%watermark -n -u -v -iv -w"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.12.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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