{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Chapter 6. Overfitting, Regularization, and Information Criteria" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "import torch\n", "from torch.distributions import constraints\n", "\n", "import pyro\n", "import pyro.distributions as dist\n", "import pyro.ops.stats as stats\n", "\n", "from rethinking import (LM, MAP, coef, compare, ensemble, extract_samples,\n", " link, precis, replicate)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.1" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "sppnames = [\"afarensis\", \"africanus\", \"habilis\", \"boisei\", \"rudolfensis\", \"ergaster\",\n", " \"sapiens\"]\n", "brainvolcc = torch.tensor([438., 452, 612, 521, 752, 871, 1350])\n", "masskg = torch.tensor([37.0, 35.5, 34.5, 41.5, 55.5, 61.0, 53.5])\n", "d = pd.DataFrame({\"species\": sppnames, \"brain\": brainvolcc, \"mass\": masskg})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.2" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "m6_1 = LM(\"brain ~ mass\", data=d)\n", "m6_1.model = pyro.do(m6_1.model, data={\"sigma\": 1})\n", "m6_1.run();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.3" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor(0.4902)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "resid = brainvolcc - (coef(m6_1)[\"Intercept\"] + coef(m6_1)[\"mass\"] * masskg)\n", "1 - resid.var() / brainvolcc.var()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.4" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "m6_2 = LM(\"brain ~ mass + I(mass ** 2)\", data=d).run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.5" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "m6_3 = LM(\"brain ~ mass + I(mass ** 2) + I(mass ** 3)\", data=d).run()\n", "m6_4 = LM(\"brain ~ mass + I(mass ** 2) + I(mass ** 3) + I(mass ** 4)\", data=d).run()\n", "torch.set_default_dtype(torch.double)\n", "m6_5 = LM(\"brain ~ mass + I(mass ** 2) + I(mass ** 3) + I(mass ** 4) \"\n", " \"+ I(mass ** 5)\", data=d)\n", "m6_5.model = pyro.do(m6_5.model, data={\"sigma\": 1})\n", "m6_5.run()\n", "m6_6 = LM(\"brain ~ mass + I(mass ** 2) + I(mass ** 3) + I(mass ** 4) \"\n", " \"+ I(mass ** 5) + I(mass ** 6)\",\n", " data=d)\n", "m6_6.model = pyro.do(m6_6.model, data={\"sigma\": 1})\n", "m6_6.run()\n", "torch.set_default_dtype(torch.float)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.6" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "m6_7 = LM(\"brain ~ 1\", data=d).run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.7" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "d_new = d.drop(index=i)\n", "d_new.index = range(d_new.shape[0])\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.8" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.scatterplot(\"mass\", \"brain\", data=d)\n", "for i in range(d.shape[0]):\n", " d_new = d.drop(index=i)\n", " d_new.index = range(d_new.shape[0])\n", " m0 = LM(\"brain ~ mass\", data=d_new)\n", " m0.model = pyro.do(m0.model, data={\"sigma\": 1})\n", " m0.run()\n", " x = torch.linspace(34, 62, 101)\n", " sns.lineplot(x, coef(m0)[\"Intercept\"] + coef(m0)[\"mass\"] * x, color=\"k\", alpha=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.9" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor(0.6109)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p = torch.tensor([0.3, 0.7])\n", "-(p * p.log()).sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.10" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor(94.9250)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# fit model with lm\n", "m6_1 = LM(\"brain ~ mass\", data=d).run()\n", "m6_1.model = pyro.do(m6_1.model, data={\"sigma\": 1})\n", "m6_1.run()\n", "\n", "# compute deviance by cheating\n", "coefs = coef(m6_1)\n", "resid = brainvolcc - (coefs[\"Intercept\"] + coefs[\"mass\"] * masskg)\n", "sigma = resid.pow(2).mean().sqrt()\n", "coefs[\"sigma\"] = sigma\n", "model = pyro.do(LM(\"brain ~ mass\", data=d, centering=False).model, data=coefs)\n", "(-2) * pyro.poutine.trace(model).get_trace().log_prob_sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.11" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor(94.9536)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# standardize the mass before fitting\n", "mass_s = (masskg - masskg.mean()) / masskg.std()\n", "\n", "def model(mass, brain):\n", " a = pyro.param(\"a\", brainvolcc.mean())\n", " b = pyro.param(\"b\", torch.tensor(0.))\n", " mu = a + b * mass\n", " sigma = pyro.param(\"sigma\", brainvolcc.std(), constraints.positive)\n", " with pyro.plate(\"plate\"):\n", " pyro.sample(\"brain\", dist.Normal(mu, sigma), obs=brain)\n", "\n", "m6_8 = MAP(model).run(mass_s, brainvolcc)\n", "\n", "# extract MAP estimates\n", "theta = list(coef(m6_8).values())\n", "\n", "# compute deviance\n", "dev = (-2) * dist.Normal(theta[0] + theta[1] * mass_s,\n", " theta[2]).log_prob(brainvolcc).sum()\n", "dev" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.12" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n", "4\n", "5\n" ] } ], "source": [ "def model(mm, y, b_sigma):\n", " a = pyro.param(\"a\", torch.tensor(0.))\n", " Bvec = a.unsqueeze(0)\n", " k = mm.size(1)\n", " if k > 1:\n", " with pyro.plate(\"plate_b\", k - 1):\n", " b = pyro.sample(\"b\", dist.Normal(0, b_sigma))\n", " Bvec = torch.cat([Bvec, b])\n", " mu = mm.matmul(Bvec)\n", " with pyro.plate(\"plate\"):\n", " pyro.sample(\"y\", dist.Normal(mu, 1), obs=y)\n", "\n", "def sim_train_test(N=20, k=3, rho=[0.15, -0.4], b_sigma=100):\n", " n_dim = max(k, 3)\n", " Rho = torch.eye(n_dim)\n", " Rho[1:len(rho) + 1, 0] = torch.tensor(rho)\n", " Rho[0, 1:len(rho) + 1] = torch.tensor(rho)\n", "\n", " X_train = dist.MultivariateNormal(torch.zeros(n_dim), Rho).sample(torch.Size([N]))\n", " mm_train = torch.ones(N, 1)\n", " if k > 1:\n", " mm_train = torch.cat([mm_train, X_train[:, 1:k]], dim=1)\n", "\n", " m = MAP(model, start={\"b\": torch.zeros(k - 1)}, num_samples=10)\n", " m.run(mm_train, X_train[:, 0], b_sigma)\n", " coefs = torch.cat([c.reshape(-1) for c in coef(m).values()])\n", " dev_train = (-2) * dist.Normal(mm_train.matmul(coefs),\n", " 1).log_prob(X_train[:, 0]).sum()\n", "\n", " X_test = dist.MultivariateNormal(torch.zeros(n_dim), Rho).sample(torch.Size([N]))\n", " mm_test = torch.ones(N, 1)\n", " if k > 1:\n", " mm_test = torch.cat([mm_test, X_test[:, 1:k]], dim=1)\n", " dev_test = (-2) * dist.Normal(mm_test.matmul(coefs),\n", " 1).log_prob(X_test[:, 0]).sum()\n", " return torch.stack([dev_train, dev_test])\n", "\n", "def dev_fn(N, k):\n", " print(k)\n", " r = torch.stack(replicate(int(1e4), sim_train_test, (N, k)), dim=1)\n", " return torch.stack([r[0].mean(), r[1].mean(), r[0].std(), r[1].std()])\n", "\n", "N = 20\n", "kseq = range(1, 6)\n", "dev = torch.stack([dev_fn(N, k) for k in kseq], dim=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.13" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "r = torch.stack(replicate(int(1e4), sim_train_test, (N, k), mc_cores=4), dim=1)\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.14" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.scatterplot(torch.arange(1, 6), dev[0], s=80, color=\"b\")\n", "ax.set(xlabel=\"number of parameters\", ylabel=\"deviance\", title=\"N = {}\".format(N))\n", "sns.scatterplot(torch.arange(1.1, 6), dev[1], s=80, color=\"k\")\n", "pts_int = (dev[0] - dev[2], dev[0] + dev[2])\n", "pts_out = (dev[1] - dev[3], dev[1] + dev[3])\n", "ax.vlines(torch.arange(1, 6), pts_int[0], pts_int[1], color=\"b\")\n", "ax.vlines(torch.arange(1.1, 6), pts_out[0], pts_out[1], color=\"k\")\n", "ax.annotate(\"in\", (2, dev[0][1]), xytext=(-25, -5),\n", " textcoords=\"offset pixels\", color=\"b\")\n", "ax.annotate(\"out\", (2.1, dev[1][1]), xytext=(10, -5), textcoords=\"offset pixels\")\n", "ax.annotate(\"-1SD\", (2.1, pts_out[1][1]), xytext=(10, -5),\n", " textcoords=\"offset pixels\", fontsize=12)\n", "ax.annotate(\"+1SD\", (2.1, pts_out[0][1]), xytext=(10, -5),\n", " textcoords=\"offset pixels\", fontsize=12);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.15" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "cars = pd.read_csv(\"../data/cars.csv\", sep=\",\")\n", "speed = torch.tensor(cars[\"speed\"], dtype=torch.float)\n", "distance = torch.tensor(cars[\"dist\"], dtype=torch.float)\n", "\n", "def model(speed, distance):\n", " a = pyro.sample(\"a\", dist.Normal(0, 100))\n", " b = pyro.sample(\"b\", dist.Normal(0, 10))\n", " mu = a + b * speed\n", " sigma = pyro.sample(\"sigma\", dist.Uniform(0, 30))\n", " with pyro.plate(\"plate\"):\n", " pyro.sample(\"dist\", dist.Normal(mu, sigma), obs=distance)\n", "\n", "m = MAP(model).run(speed, distance)\n", "idx = m._categorical.sample(torch.Size([1000]))\n", "post = {latent: samples[idx] for latent, samples in extract_samples(m).items()}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.16" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "n_samples = 1000\n", "\n", "def ll_fn(s):\n", " mu = post[\"a\"][s] + post[\"b\"][s] * speed\n", " return dist.Normal(mu, post[\"sigma\"][s]).log_prob(distance)\n", "\n", "ll = torch.stack([ll_fn(s) for s in range(n_samples)], dim=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.17" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "n_cases = cars.shape[0]\n", "lppd = ll.logsumexp(1) - torch.tensor(float(n_samples)).log()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.18" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "pWAIC = ll.var(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.19" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor(421.0318)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "-2 * (lppd.sum() - pWAIC.sum())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.20" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor(14.3545)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "waic_vec = -2 * (lppd - pWAIC)\n", "(n_cases * waic_vec.var()).sqrt()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.21" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(17, 9)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "milk = pd.read_csv(\"../data/milk.csv\", sep=\";\")\n", "d = milk.dropna().copy()\n", "d[\"neocortex\"] = d[\"neocortex.perc\"] / 100\n", "d.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.22" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "kcal_per_g = torch.tensor(d[\"kcal.per.g\"], dtype=torch.float)\n", "a_start = kcal_per_g.mean()\n", "sigma_start = kcal_per_g.std().log()\n", "\n", "def model(kcal_per_g):\n", " a = pyro.param(\"a\", a_start)\n", " log_sigma = pyro.param(\"log.sigma\", sigma_start)\n", " with pyro.plate(\"plate\"):\n", " pyro.sample(\"kcal.per.g\", dist.Normal(a, log_sigma.exp()), obs=kcal_per_g)\n", "\n", "m6_11 = MAP(model).run(kcal_per_g)\n", "\n", "def model(neocortex, kcal_per_g):\n", " a = pyro.param(\"a\", a_start)\n", " bn = pyro.param(\"bn\", torch.tensor(0.))\n", " mu = a + bn * neocortex\n", " log_sigma = pyro.param(\"log.sigma\", sigma_start)\n", " with pyro.plate(\"plate\"):\n", " pyro.sample(\"kcal.per.g\", dist.Normal(mu, log_sigma.exp()), obs=kcal_per_g)\n", "\n", "neocortex = torch.tensor(d[\"neocortex\"], dtype=torch.float)\n", "m6_12 = MAP(model).run(neocortex, kcal_per_g)\n", "\n", "def model(mass, kcal_per_g):\n", " a = pyro.param(\"a\", a_start)\n", " bm = pyro.param(\"bm\", torch.tensor(0.))\n", " mu = a + bm * mass.log()\n", " log_sigma = pyro.param(\"log.sigma\", sigma_start)\n", " with pyro.plate(\"plate\"):\n", " pyro.sample(\"kcal.per.g\", dist.Normal(mu, log_sigma.exp()), obs=kcal_per_g)\n", "\n", "mass = torch.tensor(d[\"mass\"], dtype=torch.float)\n", "m6_13 = MAP(model).run(mass, kcal_per_g)\n", "\n", "def model(neocortex, mass, kcal_per_g):\n", " a = pyro.param(\"a\", a_start)\n", " bn = pyro.param(\"bn\", torch.tensor(0.))\n", " bm = pyro.param(\"bm\", torch.tensor(0.))\n", " mu = a + bn * neocortex + bm * mass.log()\n", " log_sigma = pyro.param(\"log.sigma\", sigma_start)\n", " with pyro.plate(\"plate\"):\n", " pyro.sample(\"kcal.per.g\", dist.Normal(mu, log_sigma.exp()), obs=kcal_per_g)\n", "\n", "m6_14 = MAP(model).run(neocortex, mass, kcal_per_g)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.23" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OrderedDict([('waic', tensor(-15.1608)), ('p_waic', tensor(4.7740))])\n" ] } ], "source": [ "with torch.no_grad():\n", " print(m6_14.information_criterion())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.24" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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WAICpWAICdWAICweightSEdSE
m6.14-15.164.770.000.937.730.00
m6.11-8.221.856.940.034.677.23
m6.13-8.012.977.160.035.815.26
m6.12-6.452.798.710.014.377.52
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" ], "text/plain": [ " WAIC pWAIC dWAIC weight SE dSE\n", "m6.14 -15.16 4.77 0.00 0.93 7.73 0.00\n", "m6.11 -8.22 1.85 6.94 0.03 4.67 7.23\n", "m6.13 -8.01 2.97 7.16 0.03 5.81 5.26\n", "m6.12 -6.45 2.79 8.71 0.01 4.37 7.52" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "milk_models = compare({\"m6.11\": m6_11, \"m6.12\": m6_12, \"m6.13\": m6_13, \"m6.14\": m6_14})\n", "milk_models.round(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.25" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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AYAQBAAAMRhAAAMBgBAEAAAxGEAAAwGAEAQAADEYQAIBmclacVkX283JWnPZ2KUCTEQQAoJmqtq+X48R+Ve3I9nYpQJP5e7sAAN5VseEFb5fgMUUB/qqurmnVfVqOGjm/OyTJUvVXW+Q4eVQ2v7b1o9UbfatPx/hnvF2CcVgRAIBmcJ47Kcn65yPrn4+BtqNtxVYAHtee3oGFhXVWcfHZVtufs+K0ytc8VXuwskIdxsyUvWPXVqujuVq7b/AtrAgAgJuqtq+XLGftQcvJvQJoUwgCAOAmx3cHJaej9qDTIce3B7xTEOAGLg0AgJs6TXrW2yUAzcaKAAAABiMIAABgMIIAAAAGIwgAAGAwggAAAAYjCAAAYDCCAAAABiMIAABgMIIAAAAGIwgAAGAwggAAAAYjCAAAYDCCAAAABiMIAABgMIIAAAAGIwgAAGAwggAAAAYjCAAAYDCCAAAABiMIAABgMIIAAAAGIwgAAGAwggAAAAYjCAAAYDCCAAAABiMIAABgMIIAAAAGIwgAAGAwggAAAAYjCAAAYDCCAAAABiMIAABgMIIAAAAGIwgAAGAwggAAAAYjCAAAYDCCAAAABiMIAABgMIIAAAAG8/f0Bjdv3qxly5bp2LFj6tGjh+bOnavRo0df8TVHjhzRj3/8Y61bt07XX3+9JKm6ulrJycl67733VF5erhtvvFFz5sxRbGysp0sG2oVz587p7bcz9OGHm1Refk4DBkTqwQcf0uDBUd4urd0qv1Ctj3YUase+YlXVONSnR6juuu169ekR6u3SgEbzaBDIzc3V/PnztXz5csXExGjTpk2aNWuWNm/erB49etT5mi1btmjhwoUqKyurNZ6SkqLPP/9cmZmZuuaaa/Tmm29q5syZ+vTTT9WpUydPlg20eadOndLPfvYTHTp0wDV25Mhhbdr0nhYuXKLExAe8WF37dOpspX6Tvl3Fpy+4xo6XVOhvX53Qf4yPVOzNdf/MA3xNg5cGCgoKFBkZqczMTI0aNUpRUVFatGiR8vPzFR8fr6ioKE2fPl2lpaVavXq1pk2bptjYWNlsNo0bN05r165VSEhIndtOSUlRcnKyZs+efdnc448/rjVr1qh79+46f/68Tp8+rdDQUAUEBDT/qIF25ve/f7FWCPhXv/nNr1RYWNDKFbV/b374Ta0QcIllSSs3fq2y8iovVAU0XaPuEXA4HMrJydHGjRuVkZGhzMxM/frXv1ZqaqpycnJUVFSkNWvWaM+ePQoJCdGMGTM0fPhwJSYm6uzZs/UGgYkTJ+rdd9+tc7nfz89PwcHBWrdunYYOHaqUlBQ9/fTTCgwMbN4RA+1MRUW5/vKX9+uddzgcys5+pxUrav/OVFTpi/3F9c7XOCz9be+JVqwIcF+jLw3MnDlTwcHBioiIUFhYmBISEtS9e3dJ0pAhQ1RQUKCysjKtWrVKS5cu1cCBA5WVlaWkpCRlZ2erV69el23z0uuvZPz48YqPj9cHH3yg+fPn65prrlF0dHQTDhFo306ePKmqqrrfffYf/V+SpC9Lr9aL6TtasyyvCAj0U3WVo8X3c6HKIad15ef8Zds/tHP/yRavxRNaq2+esmDqrd4uoV1pdBDo2rWr62s/Pz+Fhv7vzTB2u12WZSkoKEiJiYkaNGiQJCkxMVFpaWnKzc3V1KlT3SowKChI0sVAsH79em3atKlJQaBbt7pXI5oqLKyzR7ZjInrXsDNnLt738q+9amzfOnToLX9/f9XU1FzhOUEKCPRrXpFtRGscp81ua/A5QYH+barnbalWX/qZ4ku1uKvRQcBma/jEDw8PV2VlZa0xh8O9lLlgwQKFh4frkUcecY1VVlaqS5cuTdpOSck5ORuK7g0IC+us4uKzzdqGqehd45w6VS5Jrl41tW9jxtxd5+WB/R+9IpvNpuSs99W7d1/PFOvDWvN8e+WtL7XrYEmdc3a7TfMfjNJVnYNapZbmamvfp75Sq6/1zW63ufXm16N/R2Dy5MlKT09XXl6eHA6HMjIyVFhYqLFjxzZ5W9HR0fqf//kf/f3vf1dNTY0yMzO1e/duJSQkeLJkoF2YM2eBrruuZ51zs2bNMSIEtLYpd0WoS0jd9yw9OKZ/mwkBgEf/+2BiYqIkacmSJSoqKlKfPn20YsUK170AixcvVlFRkVJTUxu1rbNnz+qxxx7TmTNnNGDAAL3xxhu64YYbPFky0C50795daWlvKS3tDX3wwUaVl5dr4MBITZnysG6//QfeLq9duqZrsBZPu00f5P1D2/cVq7LaoX7Xhuqu23opss/3vF0e0Gg2y7Kat27u47g04F30rnGOHj0sSa537vTNPfTNPfTNPb7WN5+4NAAAANoWggAAAAYjCAAAYDCCAAAABiMIAABgMIIAAAAGIwgAAGAwggAAAAYjCAAAYDCCAAAABiMIAABgMIIAAAAGIwgAAGAwggAAAAYjCAAAYDCCAAAABiMIAABgMIIAAAAGIwgAAGAwggAAAAYjCAAAYDCCAAAABiMIAABgMIIAAAAGIwgAAGAwggAAAAYjCAAAYDCCAAAABiMIAABgMIIAAAAGIwgAAGAwggAAAAYjCAAAYDCCAAAABiMIAABgMIIAAAAGIwgAAGAwggAAAAYjCAAAYDCCAAAABiMIAICPc1acVkX283JWnPZ2KWiHCAIA4OOqtq+X48R+Ve3I9nYpaIf8vV0AAM+r2PCCt0vwiqIAf1VX13i7DI+yHDVyfndIkqXqr7bIcfKobH6e/dHdHvvWGjzRt47xz3ioGvexIgAAPsx57qQk65+PrH8+BjyHFQGgHfKFdxneEBbWWcXFZ71dhsc4K06rfM1TtQcrK9RhzEzZO3b12H7aW99aS3vpGysCAOCjqravlyxn7UHLyb0C8CiCAAD4KMd3ByWno/ag0yHHtwe8UxDaJS4NAICP6jTpWW+XAAOwIgAAgMEIAgAAGIwgAACAwQgCAAAYjCAAAIDBCAIAABiMIAAAgMEIAgAAGIwgAACAwQgCAAAYjCAAAIDBCAIAABiMIAAAgMEIAgAAGIwgAACAwQgCAAAYjCAAAIDBCAIAABiMIAAAgMEIAgAAGIwgAACAwQgCAAAYjCAAAIDBCAIAABiMIAAAgMEIAgAAGIwgAACAwQgCAAAYjCAAAIDBCAIAABiMIAAAgMEIAgAAGIwgAACAwQgCAAAYjCAAAIDBCAIAABiMIAAAgMEIAgAAGMzf2wUAaPtqamp04MB+ORw1Cg/vrw4dOni7pHal9MwFnTpbqe+FdtBVnYO8XQ7aGY8Hgc2bN2vZsmU6duyYevTooblz52r06NFXfM2RI0f04x//WOvWrdP111/vGn/jjTeUnp6ukpIS9ezZU4899pjGjRvn6ZIBNENWVqZWrFimEyeOS5K6dOmihx6arp/97FHZ7Sw6Nsd3pyqU9sE32nO41DV2S79ueujuCIV1DfZiZWhPPPpdmpubq/nz52vBggXKz8/XE088oVmzZunEiRP1vmbLli2aMmWKysrKao2vX79eqampWrp0qbZv3645c+ZowYIF2rVrlydLBtAMb7+doSVLFrpCgCSVlZVp+fJXlZz8Gy9W1vadPlepF9J31AoBkrT7UIl+k75DZ8qrvFQZ2psGg0BBQYEiIyOVmZmpUaNGKSoqSosWLVJ+fr7i4+MVFRWl6dOnq7S0VKtXr9a0adMUGxsrm82mcePGae3atQoJCalz2ykpKUpOTtbs2bMvmyspKdHMmTM1YMAA2Ww23XnnnQoPD9eOHTuaf9QAmq2yslLLl79S7/yf/5ym48eLWrGi9uXDvGMqO1f3L/tTZyu1eXtBK1eE9qpRlwYcDodycnK0ceNGHTt2TBMmTNCePXuUmpqqgIAAPfDAA1qzZo327NmjmJgYzZgxQ3v37tUNN9ygefPm1RsEJk6cqKSkJBUWFl42N2PGjFqPDx06pP379+vmm2924zABeNrOnTt06tSpeuedTqe2bv1IDzzwkFvbfzG96aE/INBP1VUOt/bnaw4dP3PF+Q+2/UP7j532yL7aat8WTL3V2yW0C42+R2DmzJkKDg5WRESEwsLClJCQoO7du0uShgwZooKCApWVlWnVqlVaunSpBg4cqKysLCUlJSk7O1u9evW6bJuXXt+QY8eO6dFHH1VCQoKio6MbW7IkqVu3ukNIU4WFdfbIdkxE7xp25kwnSbV75et969Ch4SuL/v7uH0dAoF+rvs7XWFYD8/LssbbFvvnC94gv1NBcjQ4CXbt2dX3t5+en0NBQ12O73S7LshQUFKTExEQNGjRIkpSYmKi0tDTl5uZq6tSpbhX48ccfa968ebrvvvv0zDPPNPn1JSXn5HQ28B3VgLCwziouPtusbZiK3jXOqVPlkuTqVVvo23XX9ZWfn58cjvrfSfbrN8Dt45iTOLjJr2kLfWus//vObuXvK653/taIMD163/c9sq+22jdv1+xrfbPbbW69+W30zYI2m63B54SHh6uysrLW2JV+SDRk5cqVevLJJzV//nwtXLiQO5ABHxIWdo3uvTe+3vmbbx6kqKihrVhR+3L3bTeovp+6Npt0922Xr7IC7vDob9bJkycrPT1deXl5cjgcysjIUGFhocaOHdvkba1bt06vvvqqVq5cqUmTJnmyTAAe8t//vVi33z7ysvGbbhqo3/9+aaPeQKBuN17fRT+9d6D8/Wr3MMDfrv8YH6m+14bW80qgaTz6dwQSExMlSUuWLFFRUZH69OmjFStWuO4FWLx4sYqKipSamtrgtpYvX66qqqrLbhp89NFHlZSU5MmyAbgpOLijli5doV27durjj7eqpqZGQ4fephEj7mAFzwNuH3StBt3YTX/b+61Onb2g74V2UExkd3XuGOjt0tCO2CyroVtS2jbuEfAuetc4R48eliT17t1XEn1zF31zD31zj6/1rcXvEQAAAO0PQQAAAIMRBAAAMBhBAAAAgxEEAAAwGEEAAACDEQQAADAYQQAAAIMRBAAAMBhBAAA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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.pointplot(milk_models[\"WAIC\"], milk_models.index, join=False)\n", "ax.errorbar(milk_models[\"WAIC\"], milk_models.index, xerr=milk_models[\"SE\"], fmt=\"none\")\n", "sns.pointplot(milk_models[\"WAIC\"] - 2 * milk_models[\"pWAIC\"],\n", " milk_models.index, color=\"k\", join=False)\n", "ax.errorbar(milk_models[\"WAIC\"][1:], [0.5, 1.5, 2.5],\n", " xerr=milk_models[\"dSE\"][1:], fmt=\"^\")\n", "ax.axvline(milk_models[\"WAIC\"][0], c=\"k\", alpha=0.2)\n", "ax.yaxis.grid(True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.26" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor(0.1719)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dWAIC = milk_models.loc[\"m6.11\", \"dWAIC\"]\n", "dSE = milk_models.loc[\"m6.11\", \"dSE\"]\n", "diff = torch.empty(int(1e5)).normal_(dWAIC, dSE)\n", "(diff < 0).float().sum() / 1e5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.27" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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m6.11m6.12m6.13m6.14
a0.660.350.71-1.09
bmNaNNaN-0.03-0.10
bnNaN0.45NaN2.80
log.sigma-1.78-1.79-1.85-2.17
\n", "
" ], "text/plain": [ " m6.11 m6.12 m6.13 m6.14\n", "a 0.66 0.35 0.71 -1.09\n", "bm NaN NaN -0.03 -0.10\n", "bn NaN 0.45 NaN 2.80\n", "log.sigma -1.78 -1.79 -1.85 -2.17" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.concat([precis(m6_11), precis(m6_12), precis(m6_13), precis(m6_14)],\n", " keys=[\"m6.11\", \"m6.12\", \"m6.13\", \"m6.14\"]).unstack(level=0)[\"Mean\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.28" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "coeftab = pd.concat([precis(m6_11), precis(m6_12), precis(m6_13), precis(m6_14)],\n", " keys=[\"m6.11\", \"m6.12\", \"m6.13\", \"m6.14\"]).unstack(level=0)\n", "mean = coeftab[\"Mean\"].iloc[:, ::-1].stack(dropna=False)\n", "error = mean - coeftab[\"|0.89\"].iloc[:, ::-1].stack(dropna=False)\n", "ax = sns.pointplot(mean, mean.index, join=False, facecolors=\"none\")\n", "ax.errorbar(mean, range(mean.shape[0]), xerr=error, fmt=\"none\", c=\"k\")\n", "ax.set(xlabel=\"Estimate\")\n", "for i in range(1, coeftab.shape[0]):\n", " ax.axhline(i * coeftab[\"Mean\"].shape[1] - 0.5, c=\"k\", alpha=0.2, linestyle=\"--\")\n", "ax.yaxis.grid(True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.29" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# compute counterfactual predictions\n", "# neocortex from 0.5 to 0.8\n", "nc_seq = torch.linspace(start=0.5, end=0.8, steps=30)\n", "d_predict = {\n", " \"kcal_per_g\": None, # empty outcome\n", " \"neocortex\": nc_seq, # sequence of neocortex\n", " \"mass\": torch.tensor(4.5).repeat(30) # average mass\n", "}\n", "pred_m6_14 = link(m6_14, data=d_predict)\n", "mu = pred_m6_14.mean(0)\n", "mu_PI = stats.pi(pred_m6_14, 0.89, dim=0)\n", "\n", "# plot it all\n", "fig, ax = sns.mpl.pyplot.subplots()\n", "sns.scatterplot(\"neocortex\", \"kcal.per.g\", data=d, s=80)\n", "sns.lineplot(nc_seq, mu, color=\"k\", linestyle=\"--\")\n", "ax.lines[-1].set_linestyle(\"--\")\n", "sns.lineplot(nc_seq, mu_PI[0], color=\"k\", linestyle=\"--\")\n", "ax.lines[-1].set_linestyle(\"--\")\n", "sns.lineplot(nc_seq, mu_PI[1], color=\"k\", linestyle=\"--\")\n", "ax.lines[-1].set_linestyle(\"--\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.30" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "milk_ensemble = ensemble({\"m6.11\": m6_11, \"m6.12\": m6_12,\n", " \"m6.13\": m6_13, \"m6.14\": m6_14}, data=d_predict)\n", "mu = milk_ensemble[\"link\"].mean(0)\n", "mu_PI = stats.pi(milk_ensemble[\"link\"], 0.89, dim=0)\n", "sns.lineplot(nc_seq, mu, color=\"k\", ax=ax)\n", "ax.fill_between(nc_seq, mu_PI[0], mu_PI[1], color=\"k\", alpha=0.2)\n", "fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code 6.31" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "howell1 = pd.read_csv(\"../data/Howell1.csv\", sep=\";\")\n", "d = howell1\n", "d[\"age\"] = (d[\"age\"] - d[\"age\"].mean()) / d[\"age\"].std()\n", "torch.manual_seed(1000)\n", "i = torch.multinomial(torch.ones(d.shape[0]), num_samples=(d.shape[0] // 2)).tolist()\n", "d1 = d.iloc[i]\n", "d2 = d.drop(index=i)" ] } ], "metadata": { "anaconda-cloud": {}, "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": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 4 }