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ericmjlericmjl
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[FINALIZED] nb7 is ready for prime time
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notebooks/08-hierarchical-finches.ipynb renamed to notebooks/07-instructor-hierarchical-finches.ipynb

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"import pandas as pd\n",
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"import pymc3 as pm\n",
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"from data import load_finches_2012\n",
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"from utils import despine_traceplot\n",
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"\n",
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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" trace = pm.sample(2000, nuts_kwargs={'target_accept': 0.95})"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Visualize the traceplots to check for convergence."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"pm.traceplot(trace, varnames=['mean'])"
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"traces = pm.traceplot(trace, varnames=['mean'])\n",
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"despine_traceplot(traces)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Visualize the posterior distributions using the `plot_posterior` or `forestplot` functions."
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]
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},
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{
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" trace = pm.sample(2000, nuts_kwargs={'target_accept': 0.95})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"traces = pm.traceplot(trace)\n",
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"despine_traceplot(traces)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Discuss\n",
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"**Discuss:** \n",
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"\n",
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"- Are the estimates for the unknown species' beak depth and beak length more reasonable? "
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]

‎notebooks/07-student-hierarchical-finches.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Introduction\n",
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"\n",
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"This notebook is designed to be the \"exercise\" notebook for you to practice defining hierarchical models. We will do this with the finches dataset again."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import pymc3 as pm\n",
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"from data import load_finches_2012\n",
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"\n",
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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"%matplotlib inline\n",
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"%config InlineBackend.figure_format = 'retina'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = load_finches_2012()\n",
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"df.groupby('species').size()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df.sample(5)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df.groupby('species')['beak_depth'].describe()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"fortis_filter = df['species'] == 'fortis'\n",
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"scandens_filter = df['species'] == 'scandens'\n",
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"unknown_filter = df['species'] == 'unknown'"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Exercise:** Define a hierarchical model for the finches beak depths. For bonus points, use NumPy-like fancy indexing!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"with pm.Model() as beak_depth_model:\n",
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" # SD can only be positive, therefore it is reasonable to constrain to >0\n",
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" # Likewise for betas.\n",
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" sd_hyper = pm._________('sd_hyper', _________)\n",
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" beta_hyper = pm._________('beta_hyper', _________)\n",
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" \n",
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" # Beaks cannot be of \"negative\" mean, therefore, HalfNormal is \n",
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" # a reasonable, constrained prior.\n",
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" mean = pm._________('mean', _________, shape=_________)\n",
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" sd = pm._________('sd', _________, shape=_________)\n",
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" nu = pm._________('nu', _________) + 1\n",
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" \n",
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" # Define the likelihood distribution for the data.\n",
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" "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Sample from the posterior distribution!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Your code below.\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Visualize the traceplots to check for convergence."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Your code below\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Visualize the posterior distributions using the `plot_posterior` or `forestplot` functions."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ax1, ax2, ax3 = pm.plot_posterior(trace, varnames=['mean'])\n",
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"ax1.set_title('fortis')\n",
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"ax2.set_title('scandens')\n",
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"ax3.set_title('unknown')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now, repeat the model specification for beak length."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Model definition"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Sample from posterior"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Check for convergence"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Plot posterior distribution"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Discuss:** \n",
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"\n",
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"- Are the estimates for the unknown species' beak depth and beak length more reasonable? "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "bayesian-modelling-tutorial",
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"language": "python",
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"name": "bayesian-modelling-tutorial"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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