Histograms with Examples

Peering inside Histogram Bins with Spark and Bokeh

A histogram is one of the most effective tools for exploring a new dataset. In one graph, a histogram displays key information about the data's mean, variance, outliers, and periodic features. Histograms are so important than several libraries make histogramming extremely easy:

• In Pandas, the user can immediately plot a histogram from a Dataframe in a Jupyter notebook with a single function call.
• Tensorboard features a powerful temporal histogramming tool that can be critical for monitoring the weights of neural networks during training and debugging high-dimensional optimization problems.
• Bokeh provides a web-based histogram plotter with interactive tools and a nice Python API.

Histogram plots are often both surprising and boring: some bins have more items than expected, some bins have fewer, and a lot of bins are empty. It's not unreasonable to immediately want to ask: can we peer inside a bin? Which examples from my dataset are actually in there? And, since this histogram only shows one dimension of the data, what might be some other dimensions or factors that are common among most of the things in that bin?

This tutorial will show you how OarphPy's HistogramWithExamplesPlotter helps you do exactly that! What do you need?

1. A DataFrame (Pandas or Spark) with at least one numeric or categorical column.
2. A Python function for visualizing a row (or some portion of a row). For example, a function to convert a row to a pretty string or HTML visualization.
3. A Jupyter Notebook (like this one!) or a Python script to render the Bokeh HTML plot and display and/or save it to disk.

Why HistogramWithExamplesPlotter ?

• We'll use Spark to compute the actual histogram. Spark provides multi-cpu (and even multi-machine) processing to make histogramming scale linearly.
• We'll also use Spark to render visualizations for the bucket items. Spark's RDD API helps accomodate arbitrary user visualization functions and runs computation in parallel (even across many machines).
• We use Bokeh's Histogram tool because it supports the simple interactivity we need (click on a bucket to view examples) and plots work in any modern browser with no extra dependencies.

Notebook Setup

To run this notebook locally, try using the oarphpy/full dockerized environment:

docker run -it --rm --net=host oarphpy/full:0.1.1 jupyter notebook --allow-root --ip="*"

If you can't run the notebook locally, find an HTML-rendered copy here.

Google Colab You can also run this notebook in Google Colab. In the Colab environment, this notebook will install oarphpy, Spark, and Java. Running the cell below will take care of that for you. You might need to restart the runtime (Use the menu option: Runtime > Restart runtime ...) in order for Colab to recognize the new modules.

import os
import sys
if 'google.colab' in sys.modules:
    !pip install oarphpy[spark]==0.1.1
    !pip install pyspark==3.3.2
    !apt-get update && apt-get install -y openjdk-11-jdk
    os.environ['JAVA_HOME'] = '/usr/lib/jvm/java-11-openjdk-amd64'

A Motivating Example: Exploring Out-of-Distribution Robustness in MNIST

An MNIST example input fed into a LeNet Network

The MNIST dataset is well-studied in Computer Vision and consists of thousands of small pictures of hand-written digits. (New to MNIST? Suppose you're the Post Office and you want to train a Computer Vision model that can read the zipcode digits that people write on their mail. MNIST has a sample of such handwritten digits). Today, it's easy to train a convolutional neural network on MNIST and achieve over 98% accuracy. We're going to do exactly that in the next notebook cell!

But MNIST is a relatively small dataset versus all the digits people have ever written on paper. How robust is a trained MNIST model to new data? What if we don't have labels for that new data? In this tutorial, we're going to use HistogramWithExamplesPlotter to examine the scores that an MNIST-trained model gives to "corrupted" data never seen at training time.

First, let's train a basic MNIST model using Pytorch:

!pip install -v tqdm torch

Using pip 23.0.1 from /usr/local/lib/python3.10/dist-packages/pip (python 3.10)
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Successfully installed tqdm-4.65.0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

# Basic MNIST ConvNet c/o Pytorch (with some small modifications noted)
# https://github.com/pytorch/examples/blob/40289773aa4916fad0d50967917b3ae8aa534fd6/mnist/main.py#L1

model_ckpt = '/opt/mnist_cnn.pt'

import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
from tqdm.notebook import tqdm

torch.manual_seed(1337)
if 'google.colab' in sys.modules:
    DEVICE = 'cuda'
else:
    DEVICE = 'cpu'

# We need this many epochs to get a nice bimodal score distribution for the 7 class
N_EPOCHS = 15

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout(0.25)
        self.dropout2 = nn.Dropout(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = F.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        
        # Use softmax instead for easier interpretation of logits
        # output = F.log_softmax(x, dim=1)
        output = F.softmax(x, dim=1)

        return output
    
def train(model, train_loader, optimizer, epoch):
    model.train()
    iter_train = tqdm(enumerate(train_loader), desc='train_batches', total=len(train_loader))
    for batch_idx, (data, target) in iter_train:
        data, target = data.to(DEVICE), target.to(DEVICE)
        optimizer.zero_grad()
        output = model(data)
        
        # Push log into the loss instead of net output
        #loss = F.nll_loss(output, target)
        loss = F.nll_loss(torch.log(output), target)
        
        loss.backward()
        optimizer.step()
    print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
        epoch, batch_idx * len(data), len(train_loader.dataset),
        100. * batch_idx / len(train_loader), loss.item()))

def test(model, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        iter_test = tqdm(test_loader, desc='test_batches', total=len(test_loader))
        for data, target in iter_test:
            data, target = data.to(DEVICE), target.to(DEVICE)
            output = model(data)
            test_loss += F.nll_loss(output, target, reduction='sum').item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)

    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))


train_kwargs = {'batch_size': 128}
test_kwargs = {'batch_size': 1024}
    
transform = transforms.Compose([
    transforms.ToTensor(),
#     transforms.Normalize((0.1307,), (0.3081,))
])
dataset1 = datasets.MNIST('/opt/mnist-data', train=True, download=True, transform=transform)
dataset2 = datasets.MNIST('/opt/mnist-data', train=False, transform=transform)
train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs)
test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)

model = Net().to(DEVICE)
optimizer = optim.Adadelta(model.parameters(), lr=1.0)

scheduler = StepLR(optimizer, step_size=1, gamma=0.7)

if os.path.exists(model_ckpt):
    print(f"Resuming from existing checkpoint {model_ckpt}")
    print(f"To re-train, delete the checkpoint: $ rm {model_ckpt}")
    model.load_state_dict(torch.load(model_ckpt))
else:
    print(f"Training and saving to {model_ckpt}")
    print("NOTE! If you'd like to skip training, you can download a copy of the model here:")
    print("https://drive.google.com/file/d/1rUey6jIW_duT20QNjnUL3i7LYI1u7IJW/view?usp=sharing")

    for epoch in tqdm(range(1, N_EPOCHS + 1), desc='epoch'):
        train(model, train_loader, optimizer, epoch)
        test(model, test_loader)
        scheduler.step()


    torch.save(model.state_dict(), model_ckpt)

x_test = torch.cat([xx[0] for xx in test_loader])

def model_predict(x):
    model.cpu().eval()
    with torch.no_grad():
        prob = model(x)
        pred = prob.argmax(dim=1, keepdim=True)
    return prob, pred

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Test set: Average loss: -0.9678, Accuracy: 9798/10000 (98%)

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Test set: Average loss: -0.9788, Accuracy: 9844/10000 (98%)

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Test set: Average loss: -0.9826, Accuracy: 9873/10000 (99%)

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Test set: Average loss: -0.9850, Accuracy: 9890/10000 (99%)

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Test set: Average loss: -0.9849, Accuracy: 9887/10000 (99%)

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Test set: Average loss: -0.9855, Accuracy: 9881/10000 (99%)

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Test set: Average loss: -0.9865, Accuracy: 9889/10000 (99%)

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Test set: Average loss: -0.9863, Accuracy: 9889/10000 (99%)

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Test set: Average loss: -0.9867, Accuracy: 9889/10000 (99%)

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Test set: Average loss: -0.9868, Accuracy: 9891/10000 (99%)

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Test set: Average loss: -0.9869, Accuracy: 9890/10000 (99%)

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Test set: Average loss: -0.9869, Accuracy: 9892/10000 (99%)

Now, let's run the trained model on the test set and pack the predictions into a Pandas DataFrame. For each input, the network outputs a score for each class (the numbers 0 through 9). We'll take a look at the raw network scores for the class "7," which is easy to confused for a "1".

prob, pred = model_predict(x_test)

rows = []
for i, (x_i, score_i, pred_i) in enumerate(zip(x_test, prob, pred)):
    row = {}
    for classname, score in enumerate(score_i):
        row[f'score_{classname}'] = score.item()
    row['x_i'] = x_i.squeeze().tolist()
    rows.append(row)

import pandas as pd
prediction_df = pd.DataFrame(rows)
prediction_df

	score_0	score_1	score_2	score_3	score_4	score_5	score_6	score_7	score_8	score_9	x_i
0	1.666295e-12	9.708602e-11	1.020098e-08	1.268901e-09	4.895701e-12	1.794280e-12	4.720299e-15	1.000000e+00	9.185045e-12	4.956310e-09	[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
1	6.385335e-08	1.281082e-06	9.999985e-01	4.398146e-10	7.451101e-12	7.687796e-13	1.296026e-07	7.235867e-11	1.650108e-09	9.231881e-14	[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
2	2.790225e-08	9.999918e-01	2.045216e-07	6.124344e-09	3.181621e-06	9.231777e-08	5.492074e-07	4.090763e-06	1.358098e-07	4.678723e-08	[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
3	9.999950e-01	3.951097e-08	1.086515e-06	3.036769e-09	1.050577e-08	1.097534e-08	3.404809e-06	1.830841e-07	6.096547e-08	9.567390e-08	[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
4	2.000482e-08	2.379246e-08	4.107488e-08	1.893190e-10	9.999777e-01	7.419485e-10	4.249667e-08	1.387315e-08	3.220784e-08	2.214572e-05	[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
...	...	...	...	...	...	...	...	...	...	...	...
9995	9.127882e-12	8.512246e-08	9.999999e-01	5.384519e-09	2.463138e-14	4.580554e-15	2.152786e-13	1.736553e-08	8.733217e-10	3.547919e-15	[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
9996	6.531591e-10	2.936270e-09	1.257551e-08	9.999963e-01	1.990298e-11	3.600434e-06	3.078520e-11	4.383115e-09	1.216664e-09	6.073076e-08	[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
9997	1.076957e-14	7.316457e-10	1.751892e-13	6.589362e-14	1.000000e+00	3.043836e-12	1.105804e-11	2.092271e-09	4.008249e-09	1.101974e-08	[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
9998	7.055724e-13	8.388026e-13	3.064934e-14	2.192952e-09	2.058131e-14	9.999999e-01	2.026460e-09	2.115985e-13	8.733659e-08	2.399424e-11	[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
9999	4.967735e-08	1.283627e-10	2.104551e-08	9.355691e-11	6.125577e-08	5.635767e-08	9.999999e-01	1.621125e-14	3.916712e-09	6.066733e-11	[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...

10000 rows × 11 columns

Now let's histogram the network's scores for the 7 class. Note that since the network has high accuracy, the scores are rather cleanly bi-modal. For this stage, we'll use the pandas built-in hist() feature, which gives us a histogram (though without examples or other visualization).

%matplotlib inline 
prediction_df['score_7'].hist()

<Axes: >

Simulating Out-of-Distribution Samples using MNIST-C: Corrupted MNIST

MNIST-C is a benchmark dataset derived from MNIST that has synthetic corruptions. For example, in MNIST-C, digits are rotated, blurred, speckled, etc. We will take the normal MNIST model we trained above, run inference on MNIST-C examples, and examine how well the corruption-unaware network generalizes. This scenario simulates a common situation in production where one has a trained model, lots of unlabeled data, and little tooling for measuring or inspecting error in the wild. We'll see how HistogramWithExamplesPlotter can be a useful tool for quick exploration.

First, let's get the code for using MNIST-C:

!cd /opt && ((git clone https://github.com/pwais/oarphpy-mirror-mnist-c && \
              cd oarphpy-mirror-mnist-c && git checkout bba57e4ccc282f106907c5239958e72298451ea7) || echo "have mnist-c")
import sys
sys.path.append('/opt/oarphpy-mirror-mnist-c')

# These are hard requirements of the package we need from above
!pip3 install scikit-image==0.19.3
!pip3 install wand scipy
!ln -s /opt/oarphpy-mirror-mnist-c/pessimal_noise_matrix ./pessimal_noise_matrix || echo "symlink placed"
!apt-get install -y libmagickwand-dev

Cloning into 'oarphpy-mirror-mnist-c'...
remote: Enumerating objects: 22, done.
remote: Total 22 (delta 0), reused 0 (delta 0), pack-reused 22
Receiving objects: 100% (22/22), 1.91 MiB | 5.01 MiB/s, done.
Resolving deltas: 100% (4/4), done.
Note: switching to 'bba57e4ccc282f106907c5239958e72298451ea7'.

You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by switching back to a branch.

If you want to create a new branch to retain commits you create, you may
do so (now or later) by using -c with the switch command. Example:

  git switch -c <new-branch-name>

Or undo this operation with:

  git switch -

Turn off this advice by setting config variable advice.detachedHead to false

HEAD is now at bba57e4 updated readme
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Collecting networkx>=2.2
  Downloading networkx-3.0-py3-none-any.whl (2.0 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.0/2.0 MB 11.2 MB/s eta 0:00:00a 0:00:01
Requirement already satisfied: numpy>=1.17.0 in /usr/lib/python3/dist-packages (from scikit-image==0.19.3) (1.21.5)
Requirement already satisfied: scipy>=1.4.1 in /usr/lib/python3/dist-packages (from scikit-image==0.19.3) (1.8.0)
Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /usr/lib/python3/dist-packages (from scikit-image==0.19.3) (9.0.1)
Requirement already satisfied: packaging>=20.0 in /usr/lib/python3/dist-packages (from scikit-image==0.19.3) (21.3)
Installing collected packages: tifffile, PyWavelets, networkx, scikit-image
Successfully installed PyWavelets-1.4.1 networkx-3.0 scikit-image-0.19.3 tifffile-2023.2.28
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
Collecting wand
  Downloading Wand-0.6.11-py2.py3-none-any.whl (143 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 143.6/143.6 kB 3.2 MB/s eta 0:00:0000:01
Requirement already satisfied: scipy in /usr/lib/python3/dist-packages (1.8.0)
Installing collected packages: wand
Successfully installed wand-0.6.11
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
ln: failed to create symbolic link './pessimal_noise_matrix': File exists
symlink placed
Reading package lists... Done
Building dependency tree... Done
Reading state information... Done
The following additional packages will be installed:
  autoconf automake autotools-dev bzip2-doc file fonts-droid-fallback
  fonts-noto-mono fonts-urw-base35 ghostscript gir1.2-freedesktop
  gir1.2-gdkpixbuf-2.0 gir1.2-rsvg-2.0 gsfonts icu-devtools
  imagemagick-6-common libblkid-dev libbrotli-dev libbz2-dev
  libcairo-script-interpreter2 libcairo2-dev libdeflate-dev libdjvulibre-dev
  libdjvulibre-text libdjvulibre21 libexif-dev libexif-doc libffi-dev
  libfftw3-double3 libfontconfig-dev libfontconfig1-dev libfreetype-dev
  libfreetype6-dev libgdk-pixbuf-2.0-dev libgdk-pixbuf2.0-bin libglib2.0-dev
  libglib2.0-dev-bin libgs9 libgs9-common libicu-dev libidn12 libijs-0.35
  libilmbase-dev libjbig-dev libjbig2dec0 libjpeg-dev libjpeg-turbo8-dev
  libjpeg8-dev libjxr-tools libjxr0 liblcms2-dev liblqr-1-0 liblqr-1-0-dev
  libltdl-dev liblzma-dev liblzo2-2 libmagic-mgc libmagic1
  libmagickcore-6-arch-config libmagickcore-6-headers libmagickcore-6.q16-6
  libmagickcore-6.q16-6-extra libmagickcore-6.q16-dev libmagickwand-6-headers
  libmagickwand-6.q16-6 libmagickwand-6.q16-dev libmount-dev libopenexr-dev
  libopenjp2-7-dev libpaper-utils libpaper1 libpcre16-3 libpcre2-16-0
  libpcre2-32-0 libpcre2-dev libpcre2-posix3 libpcre3-dev libpcre32-3
  libpcrecpp0v5 libpixman-1-dev libpng-dev libpng-tools librsvg2-common
  librsvg2-dev libselinux1-dev libsepol-dev libsigsegv2 libtiff-dev libtiffxx5
  libtool libwmf-0.2-7 libwmf-dev libwmflite-0.2-7 libxcb-render0-dev
  libxcb-shm0-dev libxext-dev libxml2-dev libxrender-dev m4 pkg-config
  poppler-data uuid-dev
Suggested packages:
  autoconf-archive gnu-standards autoconf-doc gettext fonts-noto
  fonts-freefont-otf | fonts-freefont-ttf fonts-texgyre ghostscript-x
  libcairo2-doc libfftw3-bin libfftw3-dev freetype2-doc libgirepository1.0-dev
  libglib2.0-doc libxml2-utils icu-doc libtool-doc liblzma-doc inkscape
  librsvg2-doc gcj-jdk libwmf-0.2-7-gtk libwmf-doc libxext-doc m4-doc
  poppler-utils fonts-japanese-mincho | fonts-ipafont-mincho
  fonts-japanese-gothic | fonts-ipafont-gothic fonts-arphic-ukai
  fonts-arphic-uming fonts-nanum
The following NEW packages will be installed:
  autoconf automake autotools-dev bzip2-doc file fonts-droid-fallback
  fonts-noto-mono fonts-urw-base35 ghostscript gir1.2-freedesktop
  gir1.2-gdkpixbuf-2.0 gir1.2-rsvg-2.0 gsfonts icu-devtools
  imagemagick-6-common libblkid-dev libbrotli-dev libbz2-dev
  libcairo-script-interpreter2 libcairo2-dev libdeflate-dev libdjvulibre-dev
  libdjvulibre-text libdjvulibre21 libexif-dev libexif-doc libffi-dev
  libfftw3-double3 libfontconfig-dev libfontconfig1-dev libfreetype-dev
  libfreetype6-dev libgdk-pixbuf-2.0-dev libgdk-pixbuf2.0-bin libglib2.0-dev
  libglib2.0-dev-bin libgs9 libgs9-common libicu-dev libidn12 libijs-0.35
  libilmbase-dev libjbig-dev libjbig2dec0 libjpeg-dev libjpeg-turbo8-dev
  libjpeg8-dev libjxr-tools libjxr0 liblcms2-dev liblqr-1-0 liblqr-1-0-dev
  libltdl-dev liblzma-dev liblzo2-2 libmagic-mgc libmagic1
  libmagickcore-6-arch-config libmagickcore-6-headers libmagickcore-6.q16-6
  libmagickcore-6.q16-6-extra libmagickcore-6.q16-dev libmagickwand-6-headers
  libmagickwand-6.q16-6 libmagickwand-6.q16-dev libmagickwand-dev libmount-dev
  libopenexr-dev libopenjp2-7-dev libpaper-utils libpaper1 libpcre16-3
  libpcre2-16-0 libpcre2-32-0 libpcre2-dev libpcre2-posix3 libpcre3-dev
  libpcre32-3 libpcrecpp0v5 libpixman-1-dev libpng-dev libpng-tools
  librsvg2-common librsvg2-dev libselinux1-dev libsepol-dev libsigsegv2
  libtiff-dev libtiffxx5 libtool libwmf-0.2-7 libwmf-dev libwmflite-0.2-7
  libxcb-render0-dev libxcb-shm0-dev libxext-dev libxml2-dev libxrender-dev m4
  pkg-config poppler-data uuid-dev
0 upgraded, 102 newly installed, 0 to remove and 1 not upgraded.
Need to get 54.3 MB of archives.
After this operation, 208 MB of additional disk space will be used.
Get:1 http://archive.ubuntu.com/ubuntu jammy/main amd64 fonts-droid-fallback all 1:6.0.1r16-1.1build1 [1805 kB]
Get:2 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 imagemagick-6-common all 8:6.9.11.60+dfsg-1.3ubuntu0.22.04.1 [64.5 kB]
Get:3 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libmagickcore-6-headers all 8:6.9.11.60+dfsg-1.3ubuntu0.22.04.1 [52.8 kB]
Get:4 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libmagickcore-6-arch-config amd64 8:6.9.11.60+dfsg-1.3ubuntu0.22.04.1 [26.5 kB]
Get:5 http://archive.ubuntu.com/ubuntu jammy/main amd64 libfftw3-double3 amd64 3.3.8-2ubuntu8 [770 kB]
Get:6 http://archive.ubuntu.com/ubuntu jammy/universe amd64 liblqr-1-0 amd64 0.4.2-2.1 [27.7 kB]
Get:7 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libmagickcore-6.q16-6 amd64 8:6.9.11.60+dfsg-1.3ubuntu0.22.04.1 [1789 kB]
Get:8 http://archive.ubuntu.com/ubuntu jammy/main amd64 libdjvulibre-text all 3.5.28-2build2 [50.9 kB]
Get:9 http://archive.ubuntu.com/ubuntu jammy/main amd64 libdjvulibre21 amd64 3.5.28-2build2 [624 kB]
Get:10 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libmagickwand-6.q16-6 amd64 8:6.9.11.60+dfsg-1.3ubuntu0.22.04.1 [328 kB]
Get:11 http://archive.ubuntu.com/ubuntu jammy/main amd64 libwmflite-0.2-7 amd64 0.2.12-5ubuntu1 [68.9 kB]
Get:12 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libmagickcore-6.q16-6-extra amd64 8:6.9.11.60+dfsg-1.3ubuntu0.22.04.1 [70.1 kB]
Get:13 http://archive.ubuntu.com/ubuntu jammy/main amd64 libbz2-dev amd64 1.0.8-5build1 [32.5 kB]
Get:14 http://archive.ubuntu.com/ubuntu jammy/main amd64 libjpeg-turbo8-dev amd64 2.1.2-0ubuntu1 [257 kB]
Get:15 http://archive.ubuntu.com/ubuntu jammy/main amd64 libjpeg8-dev amd64 8c-2ubuntu10 [1476 B]
Get:16 http://archive.ubuntu.com/ubuntu jammy/main amd64 libjpeg-dev amd64 8c-2ubuntu10 [1472 B]
Get:17 http://archive.ubuntu.com/ubuntu jammy/main amd64 libdjvulibre-dev amd64 3.5.28-2build2 [2463 kB]
Get:18 http://archive.ubuntu.com/ubuntu jammy/main amd64 libexif-dev amd64 0.6.24-1build1 [113 kB]
Get:19 http://archive.ubuntu.com/ubuntu jammy/main amd64 libbrotli-dev amd64 1.0.9-2build6 [337 kB]
Get:20 http://archive.ubuntu.com/ubuntu jammy/main amd64 libpng-dev amd64 1.6.37-3build5 [192 kB]
Get:21 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libfreetype-dev amd64 2.11.1+dfsg-1ubuntu0.1 [555 kB]
Get:22 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libfreetype6-dev amd64 2.11.1+dfsg-1ubuntu0.1 [8298 B]
Get:23 http://archive.ubuntu.com/ubuntu jammy/main amd64 libopenjp2-7-dev amd64 2.4.0-6 [245 kB]
Get:24 http://archive.ubuntu.com/ubuntu jammy/main amd64 liblcms2-dev amd64 2.12~rc1-2build2 [1887 kB]
Get:25 http://archive.ubuntu.com/ubuntu jammy/universe amd64 liblqr-1-0-dev amd64 0.4.2-2.1 [69.1 kB]
Get:26 http://archive.ubuntu.com/ubuntu jammy/main amd64 libsigsegv2 amd64 2.13-1ubuntu3 [14.6 kB]
Get:27 http://archive.ubuntu.com/ubuntu jammy/main amd64 m4 amd64 1.4.18-5ubuntu2 [199 kB]
Get:28 http://archive.ubuntu.com/ubuntu jammy/main amd64 autoconf all 2.71-2 [338 kB]
Get:29 http://archive.ubuntu.com/ubuntu jammy/main amd64 autotools-dev all 20220109.1 [44.9 kB]
Get:30 http://archive.ubuntu.com/ubuntu jammy/main amd64 automake all 1:1.16.5-1.3 [558 kB]
Get:31 http://archive.ubuntu.com/ubuntu jammy/main amd64 libltdl-dev amd64 2.4.6-15build2 [169 kB]
Get:32 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libilmbase-dev amd64 2.5.7-2 [78.4 kB]
Get:33 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libopenexr-dev amd64 2.5.7-1 [73.3 kB]
Get:34 http://archive.ubuntu.com/ubuntu jammy/main amd64 gir1.2-freedesktop amd64 1.72.0-1 [22.3 kB]
Get:35 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 gir1.2-gdkpixbuf-2.0 amd64 2.42.8+dfsg-1ubuntu0.2 [9482 B]
Get:36 http://archive.ubuntu.com/ubuntu jammy/main amd64 gir1.2-rsvg-2.0 amd64 2.52.5+dfsg-3 [16.5 kB]
Get:37 http://archive.ubuntu.com/ubuntu jammy/main amd64 liblzo2-2 amd64 2.10-2build3 [53.7 kB]
Get:38 http://archive.ubuntu.com/ubuntu jammy/main amd64 libcairo-script-interpreter2 amd64 1.16.0-5ubuntu2 [62.0 kB]
Get:39 http://archive.ubuntu.com/ubuntu jammy/main amd64 uuid-dev amd64 2.37.2-4ubuntu3 [33.1 kB]
Get:40 http://archive.ubuntu.com/ubuntu jammy/main amd64 pkg-config amd64 0.29.2-1ubuntu3 [48.2 kB]
Get:41 http://archive.ubuntu.com/ubuntu jammy/main amd64 libfontconfig-dev amd64 2.13.1-4.2ubuntu5 [151 kB]
Get:42 http://archive.ubuntu.com/ubuntu jammy/main amd64 libfontconfig1-dev amd64 2.13.1-4.2ubuntu5 [1836 B]
Get:43 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxrender-dev amd64 1:0.9.10-1build4 [26.7 kB]
Get:44 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxext-dev amd64 2:1.3.4-1build1 [84.7 kB]
Get:45 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libpixman-1-dev amd64 0.40.0-1ubuntu0.22.04.1 [280 kB]
Get:46 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb-render0-dev amd64 1.14-3ubuntu3 [19.6 kB]
Get:47 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb-shm0-dev amd64 1.14-3ubuntu3 [6848 B]
Get:48 http://archive.ubuntu.com/ubuntu jammy/main amd64 libffi-dev amd64 3.4.2-4 [63.7 kB]
Get:49 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libglib2.0-dev-bin amd64 2.72.4-0ubuntu1 [117 kB]
Get:50 http://archive.ubuntu.com/ubuntu jammy/main amd64 libblkid-dev amd64 2.37.2-4ubuntu3 [185 kB]
Get:51 http://archive.ubuntu.com/ubuntu jammy/main amd64 libsepol-dev amd64 3.3-1build1 [378 kB]
Get:52 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libpcre2-16-0 amd64 10.39-3ubuntu0.1 [203 kB]
Get:53 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libpcre2-32-0 amd64 10.39-3ubuntu0.1 [194 kB]
Get:54 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libpcre2-posix3 amd64 10.39-3ubuntu0.1 [6130 B]
Get:55 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libpcre2-dev amd64 10.39-3ubuntu0.1 [730 kB]
Get:56 http://archive.ubuntu.com/ubuntu jammy/main amd64 libselinux1-dev amd64 3.3-1build2 [158 kB]
Get:57 http://archive.ubuntu.com/ubuntu jammy/main amd64 libmount-dev amd64 2.37.2-4ubuntu3 [14.5 kB]
Get:58 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libpcre16-3 amd64 2:8.39-13ubuntu0.22.04.1 [164 kB]
Get:59 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libpcre32-3 amd64 2:8.39-13ubuntu0.22.04.1 [155 kB]
Get:60 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libpcrecpp0v5 amd64 2:8.39-13ubuntu0.22.04.1 [16.5 kB]
Get:61 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libpcre3-dev amd64 2:8.39-13ubuntu0.22.04.1 [579 kB]
Get:62 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libglib2.0-dev amd64 2.72.4-0ubuntu1 [1735 kB]
Get:63 http://archive.ubuntu.com/ubuntu jammy/main amd64 libcairo2-dev amd64 1.16.0-5ubuntu2 [692 kB]
Get:64 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libgdk-pixbuf2.0-bin amd64 2.42.8+dfsg-1ubuntu0.2 [14.2 kB]
Get:65 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libjbig-dev amd64 2.1-3.1ubuntu0.22.04.1 [27.4 kB]
Get:66 http://archive.ubuntu.com/ubuntu jammy/main amd64 liblzma-dev amd64 5.2.5-2ubuntu1 [159 kB]
Get:67 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libtiffxx5 amd64 4.3.0-6ubuntu0.3 [5738 B]
Get:68 http://archive.ubuntu.com/ubuntu jammy/main amd64 libdeflate-dev amd64 1.10-2 [59.2 kB]
Get:69 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libtiff-dev amd64 4.3.0-6ubuntu0.3 [314 kB]
Get:70 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libgdk-pixbuf-2.0-dev amd64 2.42.8+dfsg-1ubuntu0.2 [47.8 kB]
Get:71 http://archive.ubuntu.com/ubuntu jammy/main amd64 librsvg2-common amd64 2.52.5+dfsg-3 [17.7 kB]
Get:72 http://archive.ubuntu.com/ubuntu jammy/main amd64 librsvg2-dev amd64 2.52.5+dfsg-3 [49.7 kB]
Get:73 http://archive.ubuntu.com/ubuntu jammy/main amd64 libwmf-0.2-7 amd64 0.2.12-5ubuntu1 [94.2 kB]
Get:74 http://archive.ubuntu.com/ubuntu jammy/main amd64 libwmf-dev amd64 0.2.12-5ubuntu1 [236 kB]
Get:75 http://archive.ubuntu.com/ubuntu jammy/main amd64 icu-devtools amd64 70.1-2 [197 kB]
Get:76 http://archive.ubuntu.com/ubuntu jammy/main amd64 libicu-dev amd64 70.1-2 [11.6 MB]
Get:77 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libxml2-dev amd64 2.9.13+dfsg-1ubuntu0.2 [804 kB]
Get:78 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libmagickcore-6.q16-dev amd64 8:6.9.11.60+dfsg-1.3ubuntu0.22.04.1 [1115 kB]
Get:79 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libmagickwand-6-headers all 8:6.9.11.60+dfsg-1.3ubuntu0.22.04.1 [10.4 kB]
Get:80 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libmagickwand-6.q16-dev amd64 8:6.9.11.60+dfsg-1.3ubuntu0.22.04.1 [357 kB]
Get:81 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libmagickwand-dev all 8:6.9.11.60+dfsg-1.3ubuntu0.22.04.1 [1176 B]
Get:82 http://archive.ubuntu.com/ubuntu jammy/main amd64 poppler-data all 0.4.11-1 [2171 kB]
Get:83 http://archive.ubuntu.com/ubuntu jammy/main amd64 libmagic-mgc amd64 1:5.41-3 [257 kB]
Get:84 http://archive.ubuntu.com/ubuntu jammy/main amd64 libmagic1 amd64 1:5.41-3 [87.2 kB]
Get:85 http://archive.ubuntu.com/ubuntu jammy/main amd64 file amd64 1:5.41-3 [21.5 kB]
Get:86 http://archive.ubuntu.com/ubuntu jammy/main amd64 bzip2-doc all 1.0.8-5build1 [500 kB]
Get:87 http://archive.ubuntu.com/ubuntu jammy/main amd64 fonts-noto-mono all 20201225-1build1 [397 kB]
Get:88 http://archive.ubuntu.com/ubuntu jammy/main amd64 fonts-urw-base35 all 20200910-1 [6367 kB]
Get:89 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libgs9-common all 9.55.0~dfsg1-0ubuntu5.1 [751 kB]
Get:90 http://archive.ubuntu.com/ubuntu jammy/main amd64 libidn12 amd64 1.38-4build1 [60.6 kB]
Get:91 http://archive.ubuntu.com/ubuntu jammy/main amd64 libijs-0.35 amd64 0.35-15build2 [16.5 kB]
Get:92 http://archive.ubuntu.com/ubuntu jammy/main amd64 libjbig2dec0 amd64 0.19-3build2 [64.7 kB]
Get:93 http://archive.ubuntu.com/ubuntu jammy/main amd64 libpaper1 amd64 1.1.28build2 [13.8 kB]
Get:94 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libgs9 amd64 9.55.0~dfsg1-0ubuntu5.1 [5037 kB]
Get:95 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 ghostscript amd64 9.55.0~dfsg1-0ubuntu5.1 [49.5 kB]
Get:96 http://archive.ubuntu.com/ubuntu jammy/universe amd64 gsfonts all 1:8.11+urwcyr1.0.7~pre44-4.5 [3120 kB]
Get:97 http://archive.ubuntu.com/ubuntu jammy/main amd64 libexif-doc all 0.6.24-1build1 [317 kB]
Get:98 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libjxr0 amd64 1.2~git20170615.f752187-5 [174 kB]
Get:99 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libjxr-tools amd64 1.2~git20170615.f752187-5 [16.0 kB]
Get:100 http://archive.ubuntu.com/ubuntu jammy/main amd64 libpaper-utils amd64 1.1.28build2 [8674 B]
Get:101 http://archive.ubuntu.com/ubuntu jammy/main amd64 libpng-tools amd64 1.6.37-3build5 [28.7 kB]
Get:102 http://archive.ubuntu.com/ubuntu jammy/main amd64 libtool all 2.4.6-15build2 [164 kB]
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Let's take a look at a single corrupted example:

import corruptions

# See more corruptions here: 
# https://github.com/google-research/mnist-c/blob/bba57e4ccc282f106907c5239958e72298451ea7/corruptions.py#L57 

x_to_corrupt = x_test[100].squeeze() * 255
# x_corrupted = corruptions.speckle_noise(x_to_corrupt)
# x_corrupted = corruptions.glass_blur(x_to_corrupt, severity=4)
x_corrupted = corruptions.rotate(x_to_corrupt, severity=4)

x_corrupted.shape

(28, 28)

sys.path.append('/opt/oarphpy')
from oarphpy.plotting import img_to_img_tag

img_html = img_to_img_tag(x_to_corrupt)
img_html_c = img_to_img_tag(x_corrupted)

def show_html(html):
    from IPython.core.display import display, HTML
    display(HTML(html))
show_html('<b>Original:</b>' + img_html)
show_html('<b>Corrupted:</b>' + img_html_c)

Lossy conversion from float32 to uint8. Range [0.0, 255.0]. Convert image to uint8 prior to saving to suppress this warning.
Lossy conversion from float32 to uint8. Range [0.0, 233.87545776367188]. Convert image to uint8 prior to saving to suppress this warning.

Original:

Corrupted:

Now, let's generated corrupted versions for the entire MNIST test set, and run inference of our earlier model on these corrupted examples:

import numpy as np
x_test_c = np.zeros_like(x_test)
for i in tqdm(range(len(x_test_c)), total=len(x_test_c)):
    # xform = corruptions.glass_blur
    xform = corruptions.rotate
    x_test_c[i][0] = (1. / 255) * xform(x_test[i].squeeze() * 255, severity=4)

  0%|          | 0/10000 [00:00<?, ?it/s]

Let's make sure that worked and also declare a utility function for visualizing digits:

NUM_TO_SHOW = 10

def unit_digit_to_img_tag(x):
    img_char = (255 * x).astype('uint8')
    return img_to_img_tag(img_char)

for r in x_test_c[:NUM_TO_SHOW, ...]:
    x_i = r.squeeze()
    show_html(unit_digit_to_img_tag(x_i))

Now let's run inference on the corrupted data!

prob_c, pred_c = model_predict(torch.from_numpy(x_test_c))

# Aggregate inference results into a dataframe
rows = []
for i, (x_i, score_i, pred_i) in enumerate(zip(x_test_c, prob_c, pred_c)):
    row = {}
    for classname, score in enumerate(score_i):
        row[f'score_{classname}'] = score.item()
    row['x_i'] = x_i.squeeze().tolist()
    rows.append(row)

import pandas as pd
prediction_c_df = pd.DataFrame(rows)
prediction_c_df

	score_0	score_1	score_2	score_3	score_4	score_5	score_6	score_7	score_8	score_9	x_i
0	0.023189	3.499873e-02	7.924204e-02	2.462936e-03	7.328708e-01	2.511773e-03	0.000694	5.893948e-02	0.004421	0.060670	[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
1	0.136875	1.656399e-01	1.804453e-02	8.661019e-04	2.014344e-02	1.060956e-02	0.185269	3.871693e-02	0.423418	0.000418	[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
2	0.000057	2.777056e-02	4.605581e-01	4.355606e-02	1.046089e-03	8.797347e-03	0.001094	1.946782e-02	0.437414	0.000240	[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
3	0.999870	4.705473e-07	1.019485e-05	6.493735e-08	1.318491e-06	8.314926e-06	0.000059	3.484946e-05	0.000013	0.000002	[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
4	0.002216	1.544213e-05	2.452351e-03	3.282306e-04	8.513890e-03	5.292688e-01	0.022211	1.086944e-03	0.264774	0.169133	[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
...	...	...	...	...	...	...	...	...	...	...	...
9995	0.000160	2.077016e-06	9.966429e-01	1.492914e-04	7.414887e-07	9.567250e-07	0.000006	4.912279e-05	0.002986	0.000003	[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
9996	0.002529	5.961329e-05	8.055447e-04	9.899822e-01	2.514886e-06	6.705057e-04	0.000006	3.017469e-04	0.000128	0.005515	[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
9997	0.001643	2.136260e-04	8.218874e-01	5.305554e-03	7.188972e-04	1.871666e-02	0.063834	5.108909e-04	0.086146	0.001024	[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
9998	0.000001	3.017144e-07	2.192112e-08	1.729715e-04	4.796895e-05	3.358821e-01	0.663164	5.775211e-09	0.000685	0.000047	[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
9999	0.000223	1.686272e-07	4.811619e-07	7.268485e-06	6.574192e-08	9.839876e-01	0.001034	5.640553e-08	0.014691	0.000057	[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...

10000 rows × 11 columns

Ok, so how did the network score the 7 class in the corrupted data?

prediction_c_df['score_7'].hist()

<Axes: >

Huh, that score distribution is still bi-modal, but is much more uniform than the plot we saw earlier. Clearly the model is making mistakes due to the corruptions. But what sorts of mistakes? If we needed to select some of these examples to label, which would we choose? Let's use HistogramWithExamplesPlotter to "peer inside" the histogram buckets of the plot above.

from oarphpy.spark import NBSpark
spark = NBSpark.getOrCreate()

2023-03-06 08:13:58,093	oarph 195 : Using source root /usr/lib/python3/dist-packages/IPython/core 
2023-03-06 08:13:58,094	oarph 195 : Using source root /usr/lib/python3/dist-packages/IPython 
2023-03-06 08:13:58,127	oarph 195 : Generating egg to /tmp/tmp6gziyi2u_oarphpy_eggbuild ...
2023-03-06 08:13:58,366	oarph 195 : ... done.  Egg at /tmp/tmp6gziyi2u_oarphpy_eggbuild/core-0.0.0-py3.10.egg
23/03/06 08:14:00 WARN Utils: Your hostname, mrskylake resolves to a loopback address: 127.0.1.1; using 192.168.0.202 instead (on interface enp59s0)
23/03/06 08:14:00 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address
23/03/06 08:14:01 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
INFO:SparkMonitorKernel:Client Connected ('127.0.0.1', 49758)

from bokeh.plotting import figure
from bokeh.io import output_notebook
from bokeh.io import show as bokeh_show
output_notebook()

Loading BokehJS ...

prediction_c_sdf = spark.createDataFrame(prediction_c_df)

# To see the text representation of a Spark Dataframe try:
# prediction_c_sdf.show()

from oarphpy import plotting as pl
class MyPlotter(pl.HistogramWithExamplesPlotter):
    NUM_BINS = 20
    def display_bucket(self, sub_pivot, bucket_id, irows):
        MAX_TO_VIZ = 50
        
        from oarphpy.plotting import img_to_img_tag
        htmls = []
        for row in irows:
            htmls.append(unit_digit_to_img_tag(np.array(row['x_i'])))
            
            if len(htmls) > MAX_TO_VIZ:
                break
        
        # Make a nice table
        N_COLS = 25
        from oarphpy.util import ichunked
        trs = [
            "<tr>%s</tr>" % ''.join("<td>%s</td>" % ihtml for ihtml in row)
            for row in ichunked(htmls, n=N_COLS) 
        ]
        table_html = "<table>%s</table>" % ''.join(trs)
        
        return bucket_id, table_html

plotter = MyPlotter()
fig = plotter.run(prediction_c_sdf, 'score_7')
bokeh_show(fig)

2023-03-06 08:14:58,873	oarph 195 : Plotting histogram for score_7 of DataFrame[score_0: double, score_1: double, score_2: double, score_3: double, score_4: double, score_5: double, score_6: double, score_7: double, score_8: double, score_9: double, x_i: array<array<double>>] ...
2023-03-06 08:14:58,875	oarph 195 : ... building data source for ALL ...
2023-03-06 08:14:58,876	oarph 195 : ... histogramming ALL ...
2023-03-06 08:15:04,343	oarph 195 : ... display-ifying examples for ALL ...     
                                                                                

fig = plotter.run(spark.createDataFrame(prediction_c_df), 'score_1')
bokeh_show(fig)

2023-03-06 08:14:29,657	oarph 195 : Plotting histogram for score_1 of DataFrame[score_0: double, score_1: double, score_2: double, score_3: double, score_4: double, score_5: double, score_6: double, score_7: double, score_8: double, score_9: double, x_i: array<array<double>>] ...
2023-03-06 08:14:29,659	oarph 195 : ... building data source for ALL ...
2023-03-06 08:14:29,660	oarph 195 : ... histogramming ALL ...
2023-03-06 08:14:34,922	oarph 195 : ... display-ifying examples for ALL ...     
                                                                                

The two figures above examine the model's inferences on the corrupted dataset. Let's use HistogramWithExamplesPlotter to visualize the inference results on the original MNIST dataset to compare:

fig = plotter.run(spark.createDataFrame(prediction_df), 'score_7')
bokeh_show(fig)

2023-03-06 08:14:42,200	oarph 195 : Plotting histogram for score_7 of DataFrame[score_0: double, score_1: double, score_2: double, score_3: double, score_4: double, score_5: double, score_6: double, score_7: double, score_8: double, score_9: double, x_i: array<array<double>>] ...
2023-03-06 08:14:42,201	oarph 195 : ... building data source for ALL ...
2023-03-06 08:14:42,202	oarph 195 : ... histogramming ALL ...
2023-03-06 08:14:47,644	oarph 195 : ... display-ifying examples for ALL ...     
                                                                                

For more examples of HistogramWithExamplesPlotter, see these rendered HTML pages: https://drive.google.com/drive/folders/1dOmkPvdFiGBMaYEddx1KK5vmCeYl2CyV?usp=sharing