remove df_summary

RELEASE-NOTES.md

@@ -27,6 +27,7 @@
 ### Deprecations
 
 - DIC and BPIC calculations have been removed
+- df_summary have been removed, use summary instead
 - `njobs` and `nchains` kwarg are deprecated in favor of `cores` and `chains` for `sample`
 - `lag` kwarg in `pm.stats.autocorr` and `pm.stats.autocov` is deprecated.
 

docs/source/notebooks/GLM-model-selection.ipynb

@@ -16,7 +16,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Runing on PyMC3 v3.3\n"
+      "Running on PyMC3 v3.3\n"
      ]
     }
    ],
@@ -31,7 +31,7 @@
     "from ipywidgets import interactive, fixed\n",
     "\n",
     "plt.style.use('seaborn-darkgrid')\n",
-    "print('Runing on PyMC3 v{}'.format(pm.__version__))\n",
+    "print('Running on PyMC3 v{}'.format(pm.__version__))\n",
     "rndst = np.random.RandomState(0)"
    ]
   },

docs/source/notebooks/GLM-negative-binomial-regression.ipynb

@@ -4,52 +4,50 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# GLM: Negative Binomial Regression\n",
-    "\n",
-    "This notebook demos negative binomial regression using the `glm` submodule. It closely follows the GLM Poisson regression example by [Jonathan Sedar](https://github.com/jonsedar) (which is in turn insipired by [a project by Ian Osvald](http://ianozsvald.com/2016/05/07/statistically-solving-sneezes-and-sniffles-a-work-in-progress-report-at-pydatalondon-2016/)) except the data here is negative binomially distributed instead of Poisson distributed.\n",
-    "\n",
-    "Negative binomial regression is used to model count data for which the variance is higher than the mean. The [negative binomial distribution](https://en.wikipedia.org/wiki/Negative_binomial_distribution) can be thought of as a Poisson distribution whose rate parameter is gamma distributed, so that rate parameter can be adjusted to account for the increased variance.\n",
-    "\n",
-    "#### Contents\n",
-    "\n",
-    "+ [Setup](#Setup)\n",
-    "    + [Convenience Functions](#Convenience-Functions)\n",
-    "    + [Generate Data](#Generate-Data)\n",
-    "        + [Poisson Data](#Poisson-Data)\n",
-    "        + [Negative Binomial Data](#Negative-Binomial-Data)\n",
-    "        + [Visualize the Data](#Visualize-the-Data)\n",
-    "\n",
-    "\n",
-    "+ [Negative Binomial Regression](#Negative-Binomial-Regression)\n",
-    "    + [Create GLM Model](#Create-GLM-Model)\n",
-    "    + [View Results](#View-Results)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup"
+    "# GLM: Negative Binomial Regression"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/osvaldo/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on PyMC3 v3.3\n"
+     ]
+    }
+   ],
    "source": [
+    "%matplotlib inline\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import pymc3 as pm\n",
     "from scipy import stats\n",
-    "from scipy import optimize\n",
     "import matplotlib.pyplot as plt\n",
+    "plt.style.use('seaborn-darkgrid')\n",
     "import seaborn as sns\n",
     "import re\n",
+    "print('Running on PyMC3 v{}'.format(pm.__version__))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook demos negative binomial regression using the `glm` submodule. It closely follows the GLM Poisson regression example by [Jonathan Sedar](https://github.com/jonsedar) (which is in turn inspired by [a project by Ian Osvald](http://ianozsvald.com/2016/05/07/statistically-solving-sneezes-and-sniffles-a-work-in-progress-report-at-pydatalondon-2016/)) except the data here is negative binomially distributed instead of Poisson distributed.\n",
     "\n",
-    "%matplotlib inline"
+    "Negative binomial regression is used to model count data for which the variance is higher than the mean. The [negative binomial distribution](https://en.wikipedia.org/wiki/Negative_binomial_distribution) can be thought of as a Poisson distribution whose rate parameter is gamma distributed, so that rate parameter can be adjusted to account for the increased variance."
    ]
   },
   {
@@ -246,7 +244,9 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Gamma shape parameter\n",
@@ -670,7 +670,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.3"
   }
  },
  "nbformat": 4,