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June 27, 2018 01:23
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Eight Schools with PyMC4
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# The Eight Schools Problem with PyMC4" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import warnings\n", | |
| "warnings.filterwarnings(\"ignore\")\n", | |
| "import os\n", | |
| "import tensorflow as tf\n", | |
| "import pymc4 as pm\n", | |
| "from tensorflow_probability import edward2 as ed\n", | |
| "from tensorflow_probability import distributions as tfd\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import numpy as np\n", | |
| "import seaborn as sns\n", | |
| "from pymc4.inference.sampling.sample import sample\n", | |
| "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '0' " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 720x576 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "num_schools = 8 # number of schools\n", | |
| "treatment_effects = np.array(\n", | |
| " [28, 8, -3, 7, -1, 1, 18, 12], dtype=np.float32) # treatment effects\n", | |
| "treatment_stddevs = np.array(\n", | |
| " [15, 10, 16, 11, 9, 11, 10, 18], dtype=np.float32) # treatment SE\n", | |
| "\n", | |
| "fig, ax = plt.subplots()\n", | |
| "plt.bar(range(num_schools), treatment_effects, yerr=treatment_stddevs)\n", | |
| "plt.title(\"8 Schools treatment effects\")\n", | |
| "plt.xlabel(\"School\")\n", | |
| "plt.ylabel(\"Treatment effect\")\n", | |
| "fig.set_size_inches(10, 8)\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "model = pm.Model(num_schools=num_schools, treatment_effects=treatment_effects, treatment_stddevs=treatment_stddevs )\n", | |
| "@model.define\n", | |
| "def process(cfg):\n", | |
| " avg_effect = ed.Normal(loc=0., scale=10., name=\"avg_effect\") # `mu` above\n", | |
| " avg_stddev = ed.Normal(\n", | |
| " loc=5., scale=1., name=\"avg_stddev\") # `log(tau)` above\n", | |
| " school_effects_standard = ed.Normal(\n", | |
| " loc=tf.zeros(cfg.num_schools),\n", | |
| " scale=tf.ones(cfg.num_schools),\n", | |
| " name=\"school_effects_standard\") # `theta_prime` above\n", | |
| " school_effects = avg_effect + tf.exp(\n", | |
| " avg_stddev) * school_effects_standard # `theta` above\n", | |
| " treatment_effects = ed.Normal(\n", | |
| " loc=school_effects,\n", | |
| " scale=cfg.treatment_stddevs,\n", | |
| " name=\"treatment_effects\") # `y` above\n", | |
| " return treatment_effects" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "{'num_schools': 8,\n", | |
| " 'treatment_effects': array([28., 8., -3., 7., -1., 1., 18., 12.], dtype=float32),\n", | |
| " 'treatment_stddevs': array([15., 10., 16., 11., 9., 11., 10., 18.], dtype=float32)}" | |
| ] | |
| }, | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "model.observed" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "OrderedDict([('avg_effect',\n", | |
| " VariableDescription(Dist=<class 'tensorflow.python.ops.distributions.normal.Normal'>, shape=TensorShape([]), rv=<ed.RandomVariable 'avg_effect' shape=() dtype=float32>)),\n", | |
| " ('avg_stddev',\n", | |
| " VariableDescription(Dist=<class 'tensorflow.python.ops.distributions.normal.Normal'>, shape=TensorShape([]), rv=<ed.RandomVariable 'avg_stddev' shape=() dtype=float32>)),\n", | |
| " ('school_effects_standard',\n", | |
| " VariableDescription(Dist=<class 'tensorflow.python.ops.distributions.normal.Normal'>, shape=TensorShape([Dimension(8)]), rv=<ed.RandomVariable 'school_effects_standard' shape=(8,) dtype=float32>))])" | |
| ] | |
| }, | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "model.unobserved" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[ -32.547695 34.432945 40.320427 28.81765 115.495995 62.690655\n", | |
| " 26.68259 -112.8604 ]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "with tf.Session():\n", | |
| " print(model._f(model._cfg).eval())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n", | |
| "/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()\n", | |
| " if d.decorator_argspec is not None), _inspect.getargspec(target))\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Acceptance rate: 0.5994\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "trace = sample(model)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "{'avg_effect': array([-2.5910451, -2.5910451, -2.5910451, ..., 7.5722466, 5.421218 ,\n", | |
| " 5.421218 ], dtype=float32),\n", | |
| " 'avg_stddev': array([3.1497786, 3.1497786, 3.1497786, ..., 2.819227 , 2.3189976,\n", | |
| " 2.3189976], dtype=float32),\n", | |
| " 'school_effects_standard': array([[ 1.9479568 , 0.6442069 , -0.875897 , ..., 0.18446806,\n", | |
| " 0.6115229 , 1.6996222 ],\n", | |
| " [ 1.9479568 , 0.6442069 , -0.875897 , ..., 0.18446806,\n", | |
| " 0.6115229 , 1.6996222 ],\n", | |
| " [ 1.9479568 , 0.6442069 , -0.875897 , ..., 0.18446806,\n", | |
| " 0.6115229 , 1.6996222 ],\n", | |
| " ...,\n", | |
| " [ 1.7085497 , -0.5002298 , 0.7994229 , ..., 0.534858 ,\n", | |
| " 1.0336227 , 0.25752133],\n", | |
| " [ 0.1164161 , 0.5631517 , 0.21413967, ..., -0.08510438,\n", | |
| " 0.14131531, 0.37988228],\n", | |
| " [ 0.1164161 , 0.5631517 , 0.21413967, ..., -0.08510438,\n", | |
| " 0.14131531, 0.37988228]], dtype=float32)}" | |
| ] | |
| }, | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "trace" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "school_effects_samples = (\n", | |
| " trace['avg_effect'][:, np.newaxis] +\n", | |
| " np.exp(trace['avg_stddev'])[:, np.newaxis] * trace['school_effects_standard'])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "E[avg_effect] = [-2.5910451 -2.5910451 -2.5910451 ... 7.5722466 5.421218 5.421218 ]\n", | |
| "E[avg_stddev] = [3.1497786 3.1497786 3.1497786 ... 2.819227 2.3189976 2.3189976]\n", | |
| "E[school_effects_standard] =\n", | |
| "[ 0.676412 0.13477032 -0.20579918 0.1252802 -0.26034215 -0.13445626\n", | |
| " 0.6088453 0.17541133]\n", | |
| "E[school_effects] =\n", | |
| "[14.047517 6.444718 1.7609924 6.1226816 1.209603 2.9346652\n", | |
| " 12.386038 6.989637 ]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(\"E[avg_effect] = {}\".format(trace['avg_effect']))\n", | |
| "print(\"E[avg_stddev] = {}\".format(trace['avg_stddev']))\n", | |
| "print(\"E[school_effects_standard] =\")\n", | |
| "print(trace['school_effects_standard'].mean(0))\n", | |
| "print(\"E[school_effects] =\")\n", | |
| "print(school_effects_samples[:, ].mean(0))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 864x720 with 16 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "import warnings\n", | |
| "warnings.filterwarnings('ignore')\n", | |
| "fig, axes = plt.subplots(8, 2, sharex='col', sharey='col')\n", | |
| "fig.set_size_inches(12, 10)\n", | |
| "for i in range(num_schools):\n", | |
| " axes[i][0].plot(school_effects_samples[:,i])\n", | |
| " axes[i][0].title.set_text(\"School {} treatment effect chain\".format(i))\n", | |
| " sns.kdeplot(school_effects_samples[:,i], ax=axes[i][1], shade=True)\n", | |
| " axes[i][1].title.set_text(\"School {} treatment effect distribution\".format(i))\n", | |
| "axes[num_schools - 1][0].set_xlabel(\"Iteration\")\n", | |
| "axes[num_schools - 1][1].set_xlabel(\"School effect\")\n", | |
| "fig.tight_layout()\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "school_effects_low = np.array([\n", | |
| " np.percentile(school_effects_samples[:, i], 2.5) for i in range(num_schools)\n", | |
| "])\n", | |
| "school_effects_med = np.array([\n", | |
| " np.percentile(school_effects_samples[:, i], 50) for i in range(num_schools)\n", | |
| "])\n", | |
| "school_effects_hi = np.array([\n", | |
| " np.percentile(school_effects_samples[:, i], 97.5)\n", | |
| " for i in range(num_schools)\n", | |
| "])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Inferred posterior mean: 6.49\n", | |
| "Inferred posterior mean se: 10.22\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(\"Inferred posterior mean: {0:.2f}\".format(\n", | |
| " np.mean(school_effects_samples[:,])))\n", | |
| "print(\"Inferred posterior mean se: {0:.2f}\".format(\n", | |
| " np.std(school_effects_samples[:,])))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python (pymc3)", | |
| "language": "python", | |
| "name": "pymc3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.6.5" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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