Merge branch 'master' into zero_inflated_binomial

pymc-devs · May 31, 2017 · 0929f56 · 0929f56
2 parents 999be1e + 0e54ce6
commit 0929f56
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Showing 20 changed files with 2,265 additions and 2,069 deletions.
diff --git a/docs/source/notebooks/GLM-linear.ipynb b/docs/source/notebooks/GLM-linear.ipynb
diff --git a/docs/source/notebooks/GLM-logistic.ipynb b/docs/source/notebooks/GLM-logistic.ipynb
diff --git a/docs/source/notebooks/GLM-model-selection.ipynb b/docs/source/notebooks/GLM-model-selection.ipynb
@@ -96,9 +96,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from collections import OrderedDict\n",
@@ -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",
@@ -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",
@@ -356,9 +352,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -407,9 +401,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "n = 12\n",
@@ -427,9 +419,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -467,9 +457,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "dfs_lin = df_lin.copy()\n",
@@ -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",
@@ -530,9 +516,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -573,9 +557,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -620,9 +602,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -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)"
    ]
@@ -654,9 +634,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -728,9 +706,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -815,9 +791,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -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",
@@ -941,9 +913,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -987,9 +957,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1038,9 +1006,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1063,9 +1029,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1086,9 +1050,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1116,9 +1078,7 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1154,9 +1114,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1222,9 +1180,7 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1260,9 +1216,7 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -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",
@@ -1420,14 +1374,14 @@
     "87b986ac3e5a43ec859cf10e013f2955": {
      "views": [
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-       "cell_index": 9.0
+       "cell_index": 9
       }
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     "f1f05f8da738419e8e2c54ee1809c61c": {
      "views": [
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-       "cell_index": 47.0
+       "cell_index": 47
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@@ -1436,5 +1390,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
-}
+ "nbformat_minor": 1
+}
diff --git a/docs/source/notebooks/GLM-negative-binomial-regression.ipynb b/docs/source/notebooks/GLM-negative-binomial-regression.ipynb
diff --git a/docs/source/notebooks/GLM-robust-with-outlier-detection.ipynb b/docs/source/notebooks/GLM-robust-with-outlier-detection.ipynb
diff --git a/docs/source/notebooks/GLM-robust.ipynb b/docs/source/notebooks/GLM-robust.ipynb