{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# \"Hello, world\" —Stan\n", "\n", "
" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "nbsphinx": "hidden", "tags": [] }, "outputs": [], "source": [ "# 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\"collab-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", "
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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() {\n const el = document.getElementById(\"b90bde1d-d0ee-48d9-9408-6d55669f8d68\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 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 = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.0.min.js\", \"https://unpkg.com/@holoviz/panel@1.3.1/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 for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (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(\"b90bde1d-d0ee-48d9-9408-6d55669f8d68\")).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 pandas as pd\n", "import scipy.special\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. 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 ~ normal(0, 1);\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.\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": [ "10:42:25 - cmdstanpy - INFO - compiling stan file /Users/bois/Dropbox/git/bebi103_course/2024/b/content/lessons/08/hello_world.stan to exe file /Users/bois/Dropbox/git/bebi103_course/2024/b/content/lessons/08/hello_world\n", "10:42:34 - cmdstanpy - INFO - compiled model executable: /Users/bois/Dropbox/git/bebi103_course/2024/b/content/lessons/08/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": [ "10:42:35 - cmdstanpy - INFO - CmdStan start processing\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e7571f18b3d448ddac75b8b729e93473", "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": "cece63c0b4cc436f8d53bbba49e5786a", "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": "0a62937a6d32414caa88c493441de42d", "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": "850c357fb86f4f2e86fee20598b5423b", "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": [ "10:42:35 - 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/j_/c5r9ch0913v3h1w4bdwzm0lh0000gn/T/tmp3duzui3t/hello_world1oqwnzv3/hello_world-20240105104235_1.csv\n", "\t/var/folders/j_/c5r9ch0913v3h1w4bdwzm0lh0000gn/T/tmp3duzui3t/hello_world1oqwnzv3/hello_world-20240105104235_2.csv\n", "\t/var/folders/j_/c5r9ch0913v3h1w4bdwzm0lh0000gn/T/tmp3duzui3t/hello_world1oqwnzv3/hello_world-20240105104235_3.csv\n", "\t/var/folders/j_/c5r9ch0913v3h1w4bdwzm0lh0000gn/T/tmp3duzui3t/hello_world1oqwnzv3/hello_world-20240105104235_4.csv\n", " output_files:\n", "\t/var/folders/j_/c5r9ch0913v3h1w4bdwzm0lh0000gn/T/tmp3duzui3t/hello_world1oqwnzv3/hello_world-20240105104235_0-stdout.txt\n", "\t/var/folders/j_/c5r9ch0913v3h1w4bdwzm0lh0000gn/T/tmp3duzui3t/hello_world1oqwnzv3/hello_world-20240105104235_1-stdout.txt\n", "\t/var/folders/j_/c5r9ch0913v3h1w4bdwzm0lh0000gn/T/tmp3duzui3t/hello_world1oqwnzv3/hello_world-20240105104235_2-stdout.txt\n", "\t/var/folders/j_/c5r9ch0913v3h1w4bdwzm0lh0000gn/T/tmp3duzui3t/hello_world1oqwnzv3/hello_world-20240105104235_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\":\"0a68ba38-1e02-4475-9e27-7ba39528a188\",\"roots\":{\"p1002\":\"f3c2be32-adc5-4d7d-aee8-727b9a0206b0\"},\"root_ids\":[\"p1002\"]}];\n", " 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 posterior, 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\":\"4d523b9c-d0ea-4286-b7d4-ddf20cd418ef\",\"roots\":{\"p1059\":\"fde0cd9c-a39b-466c-ba08-b0099108f06b\"},\"root_ids\":[\"p1059\"]}];\n", " 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": "p1059" } }, "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 = normal_rng(0, 1);\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": [ "10:42:35 - cmdstanpy - INFO - compiling stan file /Users/bois/Dropbox/git/bebi103_course/2024/b/content/lessons/08/norm_rng.stan to exe file /Users/bois/Dropbox/git/bebi103_course/2024/b/content/lessons/08/norm_rng\n", "10:42:43 - cmdstanpy - INFO - compiled model executable: /Users/bois/Dropbox/git/bebi103_course/2024/b/content/lessons/08/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": [ "10:42:43 - cmdstanpy - INFO - CmdStan start processing\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a1224326a59b42f79c4279075f33faef", "version_major": 2, "version_minor": 0 }, "text/plain": [ "chain 1 | | 00:00 Status" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " " ] }, { "name": "stderr", "output_type": "stream", "text": [ "10:42:43 - cmdstanpy - INFO - CmdStan done processing.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "# Draw samples\n", "stan_samples = sm_rng.sample(\n", " chains=1,\n", " iter_sampling=300,\n", " fixed_param=True,\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\":\"a6a9e895-b30e-4c3e-91e7-1562052acaac\",\"roots\":{\"p1059\":\"a9755371-c27d-47ac-8b21-ed90cdd8f948\"},\"root_ids\":[\"p1059\"]}];\n", " 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": "p1059" } }, "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": [ "### Why are we using *that*?\n", "\n", "Yes, sampling using MCMC with Stan is a novel feature, and we used it to sample out of a trivial distribution (a standard Normal), but we can use it to sample out of very complex distributions. But with respect to the direct sampling we just did, you might be thinking, \"Sampling using Stan was **so** much harder than with Numpy! Why are we doing that?\" The answer is that for more complicated models, and doing things like prior predictive checks and posterior predictive checks, using Stan for all modeling is more convenient.\n", "\n", "Recalling also [last term's course](https://bebi103a.github.io/), here is a breakdown of when we will use the respective samplers.\n", "\n", "- We will use Numpy for sampling techniques in frequentist-based inference, that is for things like computing confidence intervals and p-values using resampling methods.\n", "- We will use `scipy.stats` when plotting distributions and using optimization methods in Bayesian inference.\n", "- We will occasionally use Numpy for prior predictive checks and posterior predictive checks (defined in coming lessons).\n", "- We will use Stan for everything else. This includes all Bayesian modeling that does not use optimization (and even some that does)." ] }, { "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 ~ normal(0, 1);\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 : 3.11.5\n", "IPython version : 8.15.0\n", "\n", "numpy : 1.26.2\n", "pandas : 2.1.4\n", "scipy : 1.11.4\n", "cmdstanpy : 1.2.0\n", "arviz : 0.17.0\n", "iqplot : 0.3.5\n", "bebi103 : 0.1.19\n", "bokeh : 3.3.0\n", "colorcet : 3.0.1\n", "jupyterlab: 4.0.10\n", "\n", "cmdstan : 2.33.1\n" ] } ], "source": [ "%load_ext watermark\n", "%watermark -v -p numpy,pandas,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": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 4 }