{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# \"Hello, world\" —Stan {#sec-hello-world-stan}\n", "\n", "
" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "nbsphinx": "hidden", "tags": [] }, "outputs": [], "source": [ "#| code-fold: true\n", "\n", "# Colab setup ------------------\n", "import os, shutil, sys, subprocess, urllib.request\n", "if \"google.colab\" in sys.modules:\n", " cmd = \"pip install --upgrade iqplot colorcet datashader bebi103 arviz cmdstanpy watermark\"\n", " process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n", " stdout, stderr = process.communicate()\n", " from cmdstanpy.install_cmdstan import latest_version\n", " cmdstan_version = latest_version()\n", " cmdstan_url = f\"https://github.com/stan-dev/cmdstan/releases/download/v{cmdstan_version}/\"\n", " fname = f\"colab-cmdstan-{cmdstan_version}.tgz\"\n", " urllib.request.urlretrieve(cmdstan_url + fname, fname)\n", " shutil.unpack_archive(fname)\n", " os.environ[\"CMDSTAN\"] = f\"./cmdstan-{cmdstan_version}\"\n", " data_path = \"https://s3.amazonaws.com/bebi103.caltech.edu/data/\"\n", "else:\n", " data_path = \"../data/\"\n", "# ------------------------------" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", "
\n", " \n", " Loading BokehJS ...\n", "
\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "'use strict';\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " const force = true;\n", "\n", " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", "\n", "const JS_MIME_TYPE = 'application/javascript';\n", " const HTML_MIME_TYPE = 'text/html';\n", " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " const CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", " /**\n", " * Render data to the DOM node\n", " */\n", " function render(props, node) {\n", " const script = document.createElement(\"script\");\n", " node.appendChild(script);\n", " }\n", "\n", " /**\n", " * Handle when an output is cleared or removed\n", " */\n", " function handleClearOutput(event, handle) {\n", " function drop(id) {\n", " const view = Bokeh.index.get_by_id(id)\n", " if (view != null) {\n", " view.model.document.clear()\n", " Bokeh.index.delete(view)\n", " }\n", " }\n", "\n", " const cell = handle.cell;\n", "\n", " const id = cell.output_area._bokeh_element_id;\n", " const server_id = cell.output_area._bokeh_server_id;\n", "\n", " // Clean up Bokeh references\n", " if (id != null) {\n", " drop(id)\n", " }\n", "\n", " if (server_id !== undefined) {\n", " // Clean up Bokeh references\n", " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", " cell.notebook.kernel.execute(cmd_clean, {\n", " iopub: {\n", " output: function(msg) {\n", " const id = msg.content.text.trim()\n", " drop(id)\n", " }\n", " }\n", " });\n", " // Destroy server and session\n", " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", " cell.notebook.kernel.execute(cmd_destroy);\n", " }\n", " }\n", "\n", " /**\n", " * Handle when a new output is added\n", " */\n", " function handleAddOutput(event, handle) {\n", " const output_area = handle.output_area;\n", " const output = handle.output;\n", "\n", " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", " return\n", " }\n", "\n", " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", "\n", " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", " // store reference to embed id on output_area\n", " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " }\n", " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " const bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " const script_attrs = bk_div.children[0].attributes;\n", " for (let i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", " }\n", "\n", " function register_renderer(events, OutputArea) {\n", "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " const toinsert = this.create_output_subarea(\n", " metadata,\n", " CLASS_NAME,\n", " EXEC_MIME_TYPE\n", " );\n", " this.keyboard_manager.register_events(toinsert);\n", " // Render to node\n", " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[toinsert.length - 1]);\n", " element.append(toinsert);\n", " return toinsert\n", " }\n", "\n", " /* Handle when an output is cleared or removed */\n", " events.on('clear_output.CodeCell', handleClearOutput);\n", " events.on('delete.Cell', handleClearOutput);\n", "\n", " /* Handle when a new output is added */\n", " events.on('output_added.OutputArea', handleAddOutput);\n", "\n", " /**\n", " * Register the mime type and append_mime function with output_area\n", " */\n", " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", " /* Is output safe? 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\\n\"+\n", " \"

\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"

\\n\"+\n", " \"\\n\"+\n", " \"\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"\\n\"+\n", " \"
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\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded(error = null) {\n const el = document.getElementById(\"c2fb624d-6e7e-440d-bc38-f01d07e1886a\");\n if (el != null) {\n const html = (() => {\n if (typeof root.Bokeh === \"undefined\") {\n if (error == null) {\n return \"BokehJS is loading ...\";\n } else {\n return \"BokehJS failed to load.\";\n }\n } else {\n const prefix = `BokehJS ${root.Bokeh.version}`;\n if (error == null) {\n return `${prefix} successfully loaded.`;\n } else {\n return `${prefix} encountered errors while loading and may not function as expected.`;\n }\n }\n })();\n el.innerHTML = html;\n\n if (error != null) {\n const wrapper = document.createElement(\"div\");\n wrapper.style.overflow = \"auto\";\n wrapper.style.height = \"5em\";\n wrapper.style.resize = \"vertical\";\n const content = document.createElement(\"div\");\n content.style.fontFamily = \"monospace\";\n content.style.whiteSpace = \"pre-wrap\";\n content.style.backgroundColor = \"rgb(255, 221, 221)\";\n content.textContent = error.stack ?? error.toString();\n wrapper.append(content);\n el.append(wrapper);\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(() => display_loaded(error), 100);\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"static/extensions/panel/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.6.2.min.js\", \"https://unpkg.com/@holoviz/panel@1.6.0/dist/panel.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n try {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n\n } catch (error) {display_loaded(error);throw error;\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"c2fb624d-6e7e-440d-bc38-f01d07e1886a\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import scipy.stats as st\n", "\n", "import cmdstanpy\n", "import arviz as az\n", "\n", "import iqplot\n", "\n", "import bebi103\n", "\n", "import colorcet\n", "\n", "import bokeh.io\n", "import bokeh.plotting\n", "bokeh.io.output_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When getting familiar with a new programming language, we often write a [\"Hello, world\" program](https://en.wikipedia.org/wiki/%22Hello,_World!%22_program). This is a simple, often minimal, to demonstrate some of the basic syntax of the language. Python's \"Hello, world\" program is:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello, world.\n" ] } ], "source": [ "print(\"Hello, world.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we introduce Stan, and write a \"Hello, world\" program for it.\n", "\n", "Before we do, we note that you may run Stan on your own machine if you have managed to get Stan and CmdStanPy installed. Otherwise, you can use AWS using the `bebi103` Amazon Machine Image, available in the Oregon region. If you wish, you may also use Google Colab, though you will be limited in how many cores you can use and how long you can use them." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basics of Stan programs\n", "\n", "This is our first introduction to [Stan](http://mc-stan.org/), a **probabilistic programming language** that we will use for much of our statistical modeling. Stan is a separate language. It is *not* Python. It has a command line interface and interfaces for R, Python, Julia, Matlab, and Stata.\n", "\n", "We will be using one of the two Python interfaces, [CmdStanPy](https://mc-stan.org/cmdstanpy/). [PyStan](https://pystan.readthedocs.io) is another popular interface. Remember, though, that Stan is a separate language, and any Stan program you write works across all of these interfaces.\n", "\n", "Before we dive in and write our first Stan program to draw samples out of the Normal distribution, I want to tell you a few things about Stan. Briefly, Stan works as follows when using the CmdStanPy interface.\n", "\n", "1. A user writes a model using the Stan language. This is usually stored in a `.stan` text file.\n", "2. The model is compiled in two steps. First, Stan translates the model in the `.stan` file into [C++ code](https://en.wikipedia.org/wiki/C%2B%2B). Then, that C++ code is compiled into [machine code](https://en.wikipedia.org/wiki/Machine_code).\n", "3. Once the machine code is built, the user can, via the CmdStanPy interface, sample out of the distribution defined by the model and perform other calculations (such as optimization and variational inference) with the model.\n", "4. The results from the sampling are written to disk as CSV and txt files. As demonstrated below, we conveniently access these files using [ArviZ](https://python.arviz.org/), so we do not directly interact with them.\n", "\n", "We will learn the Stan language structure and syntax as we go along. To start with, a Stan program consists of seven sections, called **blocks**. They are, in order\n", "\n", "- `functions`: Any user-defined functions that can be used in other blocks.\n", "- `data`: Any inputs from the user. Most commonly, these are measured data themselves. You can also put user-adjustable parameters in this block as well, but nothing you intend to sample.\n", "- `transformed data`: Any transformations that need to be done on the data.\n", "- `parameters`: The parameters of the model. Stan will give you samples of the variables described in this block. In the context of Bayesian inference, these are the $\\theta$ in the posterior $g(\\theta\\mid y)$.\n", "- `transformed parameters`: Any transformations that need to be done on the parameters.\n", "- `model`: Specification of the generative model. The sampler will sample the parameters $\\theta$ out of this model.\n", "- `generated quantities`: Any other quantities you want to calculate with each sample.\n", "\n", "Not all blocks need to be in a Stan program, but they must be in this order. Some other important points to keep in mind as we venture into Stan:\n", "\n", "1. The [Stan documentation](https://mc-stan.org/users/documentation/) will be a very good friend of yours, both the user's guide and reference manual.\n", "2. The index origin of Stan is `1`, not `0` as in Python.\n", "3. Stan is strongly statically typed, which means that you need to declare the data type of a variable explicitly before using it.\n", "4. All Stan commands must end with a semicolon.\n", "5. Blocks of code are separated using curly braces.\n", "6. Stan programs are stored outside of your notebook in a `.stan` file. These are text files, which you can prepare with your favorite text editor, including the one included in JupyterLab." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Say hi, Stan\n", "\n", "With this groundwork laid, let's just go ahead and write our \"Hello, world\" Stan program to generate samples out of a standard Normal distribution (with zero mean and unit variance). (Note that this is *not* sampling out of a posterior.) Here is the code, which I have stored in the file `hello_world.stan`.\n", "\n", "```stan\n", "parameters {\n", " real x;\n", "}\n", "\n", "\n", "model {\n", " x ~ std_normal();\n", "}\n", "```\n", "\n", "Note that there are two blocks in this particular Stan code, the `parameters` block and the `model` block. These are two of the seven possible blocks in a Stan code, and we will explore others in the next part of the lesson when we learn more about Stan after we complete our Hello, world program. \n", "\n", "In the `parameters` block, we have the names and types of parameters we want to obtain samples for. In this case, we want to obtain samples of a real number we will call `x`.\n", "\n", "In the `model` block, we have our statistical model. The syntax is similar to how we would write the model on paper. We specify that `x`, the parameter we want to get samples of, is Normally distributed with location parameter zero and scale parameter one, the standard normal.\n", "\n", "Now that we have our code (which I have stored in a file named `hello_world.stan`), we can use CmdStanPy to compile it and get `CmdStanModel`, which is a Python object that provides access to the compiled Stan executable that we can conveniently access using Python syntax." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "00:52:11 - cmdstanpy - INFO - compiling stan file /Users/bois/Dropbox/git/datasai/2025/content/content/lessons/sampling/hello_world.stan to exe file /Users/bois/Dropbox/git/datasai/2025/content/content/lessons/sampling/hello_world\n", "00:52:14 - cmdstanpy - INFO - compiled model executable: /Users/bois/Dropbox/git/datasai/2025/content/content/lessons/sampling/hello_world\n" ] } ], "source": [ "sm = cmdstanpy.CmdStanModel(stan_file='hello_world.stan')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have the Stan model, stored as the variable `sm`, we can collect samples from it using the `sm.sample()` method. We pass in the number of chains; that is, the number of Markov chains to use in sampling. We can also pass in the number of sampling iterations to do. We'll do four chains, which each taking 1000 samples. Let's do it!" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "00:52:14 - cmdstanpy - INFO - CmdStan start processing\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d298f2f12d6842a08a2f2915d31298d5", "version_major": 2, "version_minor": 0 }, "text/plain": [ "chain 1 | | 00:00 Status" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1bdb8e94ac0344dcb5310f686da94db7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "chain 2 | | 00:00 Status" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8cfb4c2dc3d940f2900901493fb1e875", "version_major": 2, "version_minor": 0 }, "text/plain": [ "chain 3 | | 00:00 Status" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3cce3e06dbd14465a571db34c1cf7c55", "version_major": 2, "version_minor": 0 }, "text/plain": [ "chain 4 | | 00:00 Status" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " " ] }, { "name": "stderr", "output_type": "stream", "text": [ "00:52:14 - cmdstanpy - INFO - CmdStan done processing.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "samples = sm.sample(\n", " chains=4,\n", " iter_sampling=1000,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that CmdStanPy conveniently gave us progress bars for the sampling. We can turn those off using the `show_progress=False` kwarg of `sm.sample()`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parsing output with ArviZ\n", "\n", "At this point, Stan did its job and acquired the samples. So, it said \"hello, world.\"\n", "\n", "Let's take a look at the samples. They are stored as a `CmdStanMCMC` instance." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "CmdStanMCMC: model=hello_world chains=4['method=sample', 'num_samples=1000', 'algorithm=hmc', 'adapt', 'engaged=1']\n", " csv_files:\n", "\t/var/folders/8h/qwnxpqcx6vldhxr71n1582d00000gn/T/tmpc164di3v/hello_world5eza7drh/hello_world-20250515005214_1.csv\n", "\t/var/folders/8h/qwnxpqcx6vldhxr71n1582d00000gn/T/tmpc164di3v/hello_world5eza7drh/hello_world-20250515005214_2.csv\n", "\t/var/folders/8h/qwnxpqcx6vldhxr71n1582d00000gn/T/tmpc164di3v/hello_world5eza7drh/hello_world-20250515005214_3.csv\n", "\t/var/folders/8h/qwnxpqcx6vldhxr71n1582d00000gn/T/tmpc164di3v/hello_world5eza7drh/hello_world-20250515005214_4.csv\n", " output_files:\n", "\t/var/folders/8h/qwnxpqcx6vldhxr71n1582d00000gn/T/tmpc164di3v/hello_world5eza7drh/hello_world-20250515005214_0-stdout.txt\n", "\t/var/folders/8h/qwnxpqcx6vldhxr71n1582d00000gn/T/tmpc164di3v/hello_world5eza7drh/hello_world-20250515005214_1-stdout.txt\n", "\t/var/folders/8h/qwnxpqcx6vldhxr71n1582d00000gn/T/tmpc164di3v/hello_world5eza7drh/hello_world-20250515005214_2-stdout.txt\n", "\t/var/folders/8h/qwnxpqcx6vldhxr71n1582d00000gn/T/tmpc164di3v/hello_world5eza7drh/hello_world-20250515005214_3-stdout.txt" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "samples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This object that was returned by CmdStanPy points to CSV and text files Stan generated while running. We can load them into a more convenient format using [ArviZ](https://python.arviz.org/) (pronounced like \"RVs\", the abbreviation for \"recreational vehicles\" or \"random variables\"). " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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arviz.InferenceData
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\n", " " ], "text/plain": [ "Inference data with groups:\n", "\t> posterior\n", "\t> sample_stats" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "samples = az.from_cmdstanpy(samples)\n", "\n", "# Take a look\n", "samples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We used ArviZ to convert the data type to an ArviZ `InferenceData` data type. This has two groups, `posterior`, which contains the samples, and `sample_stats` which gives information about the sampling. (Note that ArviZ named the group \"posterior,\" which it does by default, even though these samples are out of a standard Normal distribution and not out of a posterior distribution for some model we may have built.) We'll start by looking at the samples themselves. Since the samples were taken using the `model` block, they are assumed to be samples out of a posterior distribution, and are therefore present in the `samples.posterior` group." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" const render_items = [{\"docid\":\"b37ef68c-6f9a-442b-92c3-d6a7fca315c4\",\"roots\":{\"p1002\":\"f914bc5f-2e25-40ae-aa04-859584b96e28\"},\"root_ids\":[\"p1002\"]}];\n", " void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", " }\n", " if (root.Bokeh !== undefined) {\n", " embed_document(root);\n", " } else {\n", " let attempts = 0;\n", " const timer = setInterval(function(root) {\n", " if (root.Bokeh !== undefined) {\n", " clearInterval(timer);\n", " embed_document(root);\n", " } else {\n", " attempts++;\n", " if (attempts > 100) {\n", " clearInterval(timer);\n", " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", " }\n", " }\n", " }, 10, root)\n", " }\n", "})(window);" ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { "id": "p1002" } }, "output_type": "display_data" } ], "source": [ "bokeh.io.show(\n", " iqplot.ecdf(\n", " samples.posterior['x'].values.ravel()\n", " )\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Indeed it does! We have just verified that Stan properly said, \"Hello, world.\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Direct sampling\n", "\n", "Stan can also draw samples out of probability distributions without using MCMC, just as Numpy and Scipy can. For a generic distribution, we use MCMC, but for many named distributions we can directly sample.\n", "\n", "Let's draw 300 random numbers from a Normal distribution with location parameter zero and scale parameter one using Numpy and Scipy." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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" const render_items = [{\"docid\":\"11e2dbe8-7b61-4bd8-8d5a-da66866dcda8\",\"roots\":{\"p1065\":\"f51d8363-f8e8-46ad-a24a-5f2730c4ce8e\"},\"root_ids\":[\"p1065\"]}];\n", " void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", " }\n", " if (root.Bokeh !== undefined) {\n", " embed_document(root);\n", " } else {\n", " let attempts = 0;\n", " const timer = setInterval(function(root) {\n", " if (root.Bokeh !== undefined) {\n", " clearInterval(timer);\n", " embed_document(root);\n", " } else {\n", " attempts++;\n", " if (attempts > 100) {\n", " clearInterval(timer);\n", " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", " }\n", " }\n", " }, 10, root)\n", " }\n", "})(window);" ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { "id": "p1065" } }, "output_type": "display_data" } ], "source": [ "rng = np.random.default_rng()\n", "np_samples = rng.normal(0, 1, size=300)\n", "\n", "sp_samples = st.norm.rvs(0, 1, size=300)\n", "\n", "# Plot samples\n", "p = iqplot.ecdf(\n", " np_samples,\n", " style='staircase',\n", " palette=[colorcet.b_glasbey_category10[0]],\n", ")\n", "\n", "p = iqplot.ecdf(\n", " sp_samples,\n", " style='staircase',\n", " palette=[colorcet.b_glasbey_category10[1]],\n", " p=p,\n", ")\n", "\n", "bokeh.io.show(p)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To generate random draws from a standard Normal distribution without using Markov chain Monte Carlo, we use the following Stan code.\n", "\n", "```stan\n", "generated quantities {\n", " real x;\n", "\n", " x = std_normal_rng();\n", "}\n", "```\n", "\n", "Let's compile it, and then comment on the code." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "00:52:14 - cmdstanpy - INFO - compiling stan file /Users/bois/Dropbox/git/datasai/2025/content/content/lessons/sampling/norm_rng.stan to exe file /Users/bois/Dropbox/git/datasai/2025/content/content/lessons/sampling/norm_rng\n", "00:52:16 - cmdstanpy - INFO - compiled model executable: /Users/bois/Dropbox/git/datasai/2025/content/content/lessons/sampling/norm_rng\n" ] } ], "source": [ "sm_rng = cmdstanpy.CmdStanModel(stan_file='norm_rng.stan')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is just one block in this particular Stan code, the `generated quantities` block. In the `generated quantities` block, we have code for that tells Stan what to generate for each set of parameters it encountered while doing Markov chain Mote Carlo. Here, we are not performing Markov chain Monte Carlo, so we do the \"sampling\" in **fixed parameter mode** when we call `sm_rng.sample()` by setting the `fixed_param` kwarg to `True`." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "00:52:16 - cmdstanpy - INFO - CmdStan start processing\n", "00:52:16 - cmdstanpy - INFO - Chain [1] start processing\n", "00:52:16 - cmdstanpy - INFO - Chain [1] done processing\n" ] } ], "source": [ "# Draw samples\n", "stan_samples = sm_rng.sample(\n", " chains=1,\n", " iter_sampling=300,\n", " fixed_param=True,\n", " show_progress=False,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To convert this sampling object to a Numpy array, we can first convert it to an ArviZ `InferenceData` instance and then extract the Numpy array. Note that we will define the samples as coming from a \"posterior,\" even though it is not a posterior, since that's the default for ArviZ." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Convert to ArviZ InferenceData\n", "stan_samples = az.from_cmdstanpy(\n", " posterior=stan_samples\n", ")\n", "\n", "# Extract Numpy array\n", "stan_samples = stan_samples.posterior['x'].values.flatten()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we can add the ECDF of these samples to the plot of Numpy and Scipy samples." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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" const render_items = [{\"docid\":\"93675263-8fa5-4982-93bd-794f22c7b349\",\"roots\":{\"p1065\":\"f0191b6f-0f8d-4929-a213-745c78444749\"},\"root_ids\":[\"p1065\"]}];\n", " void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", " }\n", " if (root.Bokeh !== undefined) {\n", " embed_document(root);\n", " } else {\n", " let attempts = 0;\n", " const timer = setInterval(function(root) {\n", " if (root.Bokeh !== undefined) {\n", " clearInterval(timer);\n", " embed_document(root);\n", " } else {\n", " attempts++;\n", " if (attempts > 100) {\n", " clearInterval(timer);\n", " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", " }\n", " }\n", " }, 10, root)\n", " }\n", "})(window);" ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { "id": "p1065" } }, "output_type": "display_data" } ], "source": [ "p = iqplot.ecdf(\n", " stan_samples,\n", " style='staircase',\n", " palette=[colorcet.b_glasbey_category10[2]],\n", " p=p,\n", ")\n", "\n", "bokeh.io.show(p)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we expect, sampling with Stan gives the same results." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Displaying your Stan code\n", "\n", "When you are working on assignments, your Stan models are written as separate files. It is instructive to display the Stan code in the Jupyter notebook. This is easily accomplished for any CmdStanPy model using the `code()` method." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "parameters {\n", " real x;\n", "}\n", "\n", "\n", "model {\n", " x ~ std_normal();\n", "}\n" ] } ], "source": [ "print(sm.code())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You should do this in your notebooks so the code is visible." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Saving samples\n", "\n", "While your samples are saved in CSV and text files by Stan, is is convenient to save the sampling information in a format the can immediately be read into an ArviZ InferenceData object. The [NetCDF format](https://en.wikipedia.org/wiki/NetCDF) is useful for this. ArviZ enables saving as NetCDF as follows." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'stan_hello_world.nc'" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "samples.to_netcdf('stan_hello_world.nc')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When calling the function, it returns the string of the filename to which the NetCDF file is written. The samples can be read from the NetCDF file using `az.from_netcdf()`." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "samples = az.from_netcdf('stan_hello_world.nc')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cleaning up the shrapnel\n", "\n", "When using Stan, CmdStanPy leaves a lot of files on your file system.\n", "\n", "1. Your stan model is translated into C++, and the result is stored in a `.hpp` file.\n", "2. The `.hpp` file is compiled into an object file (`.o` file).\n", "3. The `.o` file is used to build an executable.\n", "\n", "All of these files are deposited in your present working directory, and can get annoying for version control purposes and can add clutter. To clean them up after you are finished running your models, you can run the function below." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "bebi103.stan.clean_cmdstan()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When doing sampling the results are stored in a `/var/` directory in various CSV and text files. We never work with these directly, but rather read them into RAM in a convenience `az.InferenceData` object using ArviZ. When exiting your session, CmdStanPy deletes all of these CSV files, etc., unless you specifically say which directory to store the results in your call to `sm.sample()` using the `outpur_dir` kwarg." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Computing environment" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python implementation: CPython\n", "Python version : 3.12.9\n", "IPython version : 9.1.0\n", "\n", "numpy : 2.1.3\n", "scipy : 1.15.2\n", "cmdstanpy : 1.2.5\n", "arviz : 0.21.0\n", "iqplot : 0.3.7\n", "bebi103 : 0.1.27\n", "bokeh : 3.6.2\n", "colorcet : 3.1.0\n", "jupyterlab: 4.4.2\n", "\n", "cmdstan : 2.36.0\n" ] } ], "source": [ "%load_ext watermark\n", "%watermark -v -p numpy,scipy,cmdstanpy,arviz,iqplot,bebi103,bokeh,colorcet,jupyterlab\n", "print(\"cmdstan :\", bebi103.stan.cmdstan_version())" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.12.9" } }, "nbformat": 4, "nbformat_minor": 4 }