fix diagnosing biased inference until variational api quickstart (#4080)

Author: MarcoGorelli

docs/source/notebooks/Diagnosing_biased_Inference_with_Divergences.ipynb

@@ -21,14 +21,14 @@
     }
    ],
    "source": [
+    "from collections import defaultdict\n",
+    "\n",
     "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import pymc3 as pm\n",
     "\n",
-    "from collections import defaultdict\n",
-    "\n",
     "print('Runing on PyMC3 v{}'.format(pm.__version__))"
    ]
   },

docs/source/notebooks/GP-Marginal.ipynb

@@ -137,12 +137,12 @@
    },
    "outputs": [],
    "source": [
-    "import pymc3 as pm\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "import scipy as sp\n",
     "import pandas as pd\n",
+    "import pymc3 as pm\n",
+    "import scipy as sp\n",
     "\n",
-    "import matplotlib.pyplot as plt\n",
     "%matplotlib inline"
    ]
   },
@@ -481,6 +481,7 @@
     "\n",
     "# plot the samples from the gp posterior with samples and shading\n",
     "from pymc3.gp.util import plot_gp_dist\n",
+    "\n",
     "plot_gp_dist(ax, pred_samples[\"f_pred\"], X_new);\n",
     "\n",
     "# plot the data and the true latent function\n",

docs/source/notebooks/GP-smoothing.ipynb

@@ -162,9 +162,10 @@
    "outputs": [],
    "source": [
     "import pymc3 as pm\n",
-    "from theano import shared\n",
+    "\n",
     "from pymc3.distributions.timeseries import GaussianRandomWalk\n",
-    "from scipy import optimize"
+    "from scipy import optimize\n",
+    "from theano import shared"
    ]
   },
   {

docs/source/notebooks/MvGaussianRandomWalk_demo.ipynb

@@ -13,13 +13,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "from scipy.linalg import cholesky\n",
-    "\n",
+    "import numpy as np\n",
     "import pymc3 as pm\n",
     "import theano\n",
     "\n",
+    "from scipy.linalg import cholesky\n",
+    "\n",
     "np.random.seed(42)\n",
     "%matplotlib inline"
    ]

docs/source/notebooks/bayesian_neural_network_advi.ipynb

@@ -76,6 +76,8 @@
     }
    ],
    "source": [
+    "from warnings import filterwarnings\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pymc3 as pm\n",
@@ -85,10 +87,9 @@
     "import theano.tensor as T\n",
     "\n",
     "from sklearn import datasets\n",
-    "from sklearn.preprocessing import scale\n",
-    "from sklearn.model_selection import train_test_split\n",
     "from sklearn.datasets import make_moons\n",
-    "from warnings import filterwarnings\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import scale\n",
     "\n",
     "print('Running on PyMC3 v{}'.format(pm.__version__))"
    ]
@@ -252,7 +253,8 @@
    },
    "outputs": [],
    "source": [
-    "from pymc3.theanof import set_tt_rng, MRG_RandomStreams\n",
+    "from pymc3.theanof import MRG_RandomStreams, set_tt_rng\n",
+    "\n",
     "set_tt_rng(MRG_RandomStreams(42))"
    ]
   },

docs/source/notebooks/blackbox_external_likelihood.ipynb

@@ -36,6 +36,9 @@
    "source": [
     "%load_ext Cython\n",
     "\n",
+    "import os\n",
+    "import platform\n",
+    "\n",
     "import arviz as az\n",
     "import corner\n",
     "import cython\n",
@@ -44,8 +47,6 @@
     "import matplotlib\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "import os\n",
-    "import platform\n",
     "import pymc3 as pm\n",
     "import theano\n",
     "import theano.tensor as tt\n",
@@ -104,9 +105,11 @@
     "%%cython -I/usr/include -L/usr/lib/x86_64-linux-gnu -lgsl -lgslcblas -lm\n",
     "\n",
     "import cython\n",
+    "\n",
     "cimport cython\n",
     "\n",
     "import numpy as np\n",
+    "\n",
     "cimport numpy as np\n",
     "\n",
     "### STUFF FOR USING GSL (FEEL FREE TO IGNORE!) ###\n",
@@ -340,13 +343,16 @@
     "%%cython\n",
     "\n",
     "import cython\n",
+    "\n",
     "cimport cython\n",
     "\n",
     "import numpy as np\n",
+    "\n",
     "cimport numpy as np\n",
     "\n",
     "import warnings\n",
     "\n",
+    "\n",
     "def gradients(vals, func, releps=1e-3, abseps=None, mineps=1e-9, reltol=1e-3,\n",
     "              epsscale=0.5):\n",
     "    \"\"\"\n",
@@ -661,6 +667,7 @@
    "outputs": [],
    "source": [
     "import warnings\n",
+    "\n",
     "warnings.simplefilter(action='ignore', category=FutureWarning)  # supress emcee autocorr FutureWarning\n",
     "\n",
     "matplotlib.rcParams['font.size'] = 18\n",

docs/source/notebooks/conditional-autoregressive-model.ipynb

@@ -14,10 +14,8 @@
     "import theano\n",
     "import theano.tensor as tt\n",
     "\n",
-    "from pymc3.distributions import continuous\n",
-    "from pymc3.distributions import distribution\n",
-    "from theano import scan\n",
-    "from theano import shared"
+    "from pymc3.distributions import continuous, distribution\n",
+    "from theano import scan, shared"
    ]
   },
   {

docs/source/notebooks/gaussian-mixture-model-advi.ipynb

@@ -40,7 +40,8 @@
     "import seaborn as sns\n",
     "import theano.tensor as tt\n",
     "\n",
-    "from pymc3 import Normal, Metropolis, sample, MvNormal, Dirichlet, DensityDist, find_MAP, NUTS, Slice\n",
+    "from pymc3 import (NUTS, DensityDist, Dirichlet, Metropolis, MvNormal, Normal,\n",
+    "                   Slice, find_MAP, sample)\n",
     "from pymc3.math import logsumexp\n",
     "from theano.tensor.nlinalg import det\n",
     "\n",
@@ -112,6 +113,7 @@
    "source": [
     "from pymc3.math import logsumexp\n",
     "\n",
+    "\n",
     "# Log likelihood of normal distribution\n",
     "def logp_normal(mu, tau, value):\n",
     "    # log probability of individual samples\n",

docs/source/notebooks/survival_analysis.ipynb

@@ -28,12 +28,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from matplotlib import pyplot as plt\n",
     "import numpy as np\n",
+    "import pandas as pd\n",
     "import pymc3 as pm\n",
-    "from pymc3.distributions.timeseries import GaussianRandomWalk\n",
     "import seaborn as sns\n",
-    "import pandas as pd\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from pymc3.distributions.timeseries import GaussianRandomWalk\n",
     "from theano import tensor as T"
    ]
   },

docs/source/notebooks/variational_api_quickstart.ipynb

@@ -26,9 +26,9 @@
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import pymc3 as pm\n",
     "import theano\n",
-    "import numpy as np\n",
     "\n",
     "np.random.seed(42)\n",
     "pm.set_tt_rng(42)"
@@ -853,6 +853,7 @@
    ],
    "source": [
     "import seaborn as sns\n",
+    "\n",
     "ax = sns.kdeplot(trace['x'], label='NUTS');\n",
     "sns.kdeplot(approx.sample(10000)['x'], label='ADVI');"
    ]
@@ -1327,10 +1328,11 @@
     }
    ],
    "source": [
+    "import pandas as pd\n",
+    "import theano.tensor as tt\n",
+    "\n",
     "from sklearn.datasets import load_iris\n",
     "from sklearn.model_selection import train_test_split\n",
-    "import theano.tensor as tt\n",
-    "import pandas as pd\n",
     "\n",
     "X, y = load_iris(True)\n",
     "X_train, X_test, y_train, y_test = train_test_split(X, y)"