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Add command to scrub output from ipynb files in the Makefile (pyro-ppl#428)
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8 files changed

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-154
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8 files changed

+54
-154
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‎.gitattributes‎

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‎.gitconfig‎

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‎.travis.yml‎

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@@ -34,9 +34,12 @@ jobs:
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include:
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- stage: lint
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python: 2.7
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before_install: pip install flake8
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before_install: pip install flake8 nbstripout nbformat
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install:
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script: make lint
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script:
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- make lint
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#- make scrub;
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# git diff-index HEAD
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- stage: unit test
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python: 2.7
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env: STAGE=docs

‎CONTRIBUTING.md‎

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@@ -16,12 +16,6 @@ or explicitly
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pip install -e .[dev]
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```
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Add the repository's git configuration to your local project `.gitconfig` file.
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```sh
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git config --local include.path ../.gitconfig
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```
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# Testing
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Before submitting a pull request, please autoformat code and ensure that unit tests pass locally

‎Makefile‎

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@@ -14,6 +14,9 @@ apidoc: FORCE
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lint: FORCE
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flake8
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scrub: FORCE
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find tutorial -name "*.ipynb" | xargs python -m nbstripout
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format: FORCE
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# yapf -i -p *.py pyro/*.py pyro/*/*.py
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isort -rc *.py pyro/ tests/

‎tutorial/source/air.ipynb‎

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@@ -18,9 +18,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"%pylab inline\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"fn = '../../examples/air/data/multi_mnist_train_uint8.npz'\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create the neural network. This takes a latent code, z_what, to pixel intensities.\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"def expand_z_where(z_where):\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"pyro.set_rng_seed(0)\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"pyro.set_rng_seed(0)\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"def geom_prior(x, step=0):\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"pyro.set_rng_seed(13)\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"def model_step(n, t, prev_x, prev_z_pres):\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"def prior(n):\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"pyro.set_rng_seed(87678)\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"def model(data):\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"def guide_step_basic(t, data, prev):\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"rnn = nn.LSTMCell(2554, 256)\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"def z_where_inv(z_where):\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"bl_rnn = nn.LSTMCell(2554, 256)\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"GuideState = namedtuple('GuideState', ['h', 'c', 'bl_h', 'bl_c', 'z_pres', 'z_where', 'z_what'])\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"data = mnist.view(-1, 50 * 50)\n",

‎tutorial/source/bayesian_regression.ipynb‎

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@@ -24,9 +24,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"N = 100 # size of toy data\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"class RegressionModel(nn.Module):\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"loss_fn = torch.nn.MSELoss(size_average=False)\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"mu = Variable(torch.zeros(1, 1))\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"def model(data):\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"optim = Adam({\"lr\": 0.01})\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"metadata": {},
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"outputs": [],
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"source": [
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"print pyro.get_param_store()._params"
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"X = np.linspace(8, 12, num=20)\n",

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