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Clean up GLM doc #2241

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merged 4 commits into from
May 30, 2017
Merged

Clean up GLM doc #2241

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e10d9f1
[WIP] clean up GLM doc
May 30, 2017
3a07aaa
More clean up
May 30, 2017
15ea96b
more clean up
May 30, 2017
eff3ad6
GLM doc clean up
May 30, 2017
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94 changes: 35 additions & 59 deletions docs/source/notebooks/GLM-linear.ipynb
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1,677 changes: 297 additions & 1,380 deletions docs/source/notebooks/GLM-logistic.ipynb
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108 changes: 31 additions & 77 deletions docs/source/notebooks/GLM-model-selection.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -96,9 +96,7 @@
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"from collections import OrderedDict\n",
Expand Down Expand Up @@ -135,9 +133,7 @@
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"def generate_data(n=20, p=0, a=1, b=1, c=0, latent_sigma_y=20):\n",
Expand Down Expand Up @@ -267,7 +263,7 @@
" with pm.Model() as models[nm]:\n",
"\n",
" print('\\nRunning: {}'.format(nm))\n",
" pm.glm.glm(fml, df, family=pm.glm.families.Normal())\n",
" pm.glm.GLM.from_formula(fml, df, family=pm.glm.families.Normal())\n",
"\n",
" # For speed, we're using Metropolis here\n",
" traces[nm] = pm.sample(5000, pm.Metropolis())[1000::5]\n",
Expand Down Expand Up @@ -356,9 +352,7 @@
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"data": {
Expand Down Expand Up @@ -407,9 +401,7 @@
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"n = 12\n",
Expand All @@ -427,9 +419,7 @@
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"data": {
Expand Down Expand Up @@ -467,9 +457,7 @@
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"dfs_lin = df_lin.copy()\n",
Expand All @@ -489,9 +477,7 @@
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"dfs_lin_xlims = (dfs_lin['x'].min() - np.ptp(dfs_lin['x'])/10,\n",
Expand Down Expand Up @@ -530,9 +516,7 @@
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"name": "stderr",
Expand Down Expand Up @@ -573,9 +557,7 @@
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"data": {
Expand Down Expand Up @@ -620,9 +602,7 @@
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"name": "stderr",
Expand All @@ -639,7 +619,7 @@
"source": [
"with pm.Model() as mdl_ols_glm:\n",
" # setup model with Normal likelihood (which uses HalfCauchy for error prior)\n",
" pm.glm.glm('y ~ 1 + x', df_lin, family=pm.glm.families.Normal())\n",
" pm.glm.GLM.from_formula('y ~ 1 + x', df_lin, family=pm.glm.families.Normal())\n",
" \n",
" traces_ols_glm = pm.sample(2000)"
]
Expand All @@ -654,9 +634,7 @@
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"data": {
Expand Down Expand Up @@ -728,9 +706,7 @@
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"name": "stdout",
Expand Down Expand Up @@ -815,9 +791,7 @@
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"name": "stdout",
Expand Down Expand Up @@ -916,9 +890,7 @@
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"dfll = pd.DataFrame(index=['k1','k2','k3','k4','k5'], columns=['lin','quad'])\n",
Expand All @@ -941,9 +913,7 @@
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"data": {
Expand Down Expand Up @@ -987,9 +957,7 @@
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"data": {
Expand Down Expand Up @@ -1038,9 +1006,7 @@
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"data": {
Expand All @@ -1063,9 +1029,7 @@
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"data": {
Expand All @@ -1086,9 +1050,7 @@
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"data": {
Expand Down Expand Up @@ -1116,9 +1078,7 @@
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"data": {
Expand Down Expand Up @@ -1154,9 +1114,7 @@
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"data": {
Expand Down Expand Up @@ -1222,9 +1180,7 @@
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"data": {
Expand Down Expand Up @@ -1260,9 +1216,7 @@
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"data": {
Expand Down Expand Up @@ -1399,14 +1353,14 @@
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3.0
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
Expand All @@ -1420,14 +1374,14 @@
"87b986ac3e5a43ec859cf10e013f2955": {
"views": [
{
"cell_index": 9.0
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"f1f05f8da738419e8e2c54ee1809c61c": {
"views": [
{
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}
]
}
Expand All @@ -1436,5 +1390,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 0
}
"nbformat_minor": 1
}
171 changes: 91 additions & 80 deletions docs/source/notebooks/GLM-negative-binomial-regression.ipynb
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192 changes: 88 additions & 104 deletions docs/source/notebooks/GLM-robust-with-outlier-detection.ipynb
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101 changes: 50 additions & 51 deletions docs/source/notebooks/GLM-robust.ipynb
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