Clustering: the example #1 (and most probably the first one)
a machine learning expert will give you if you ask "What are
examples of unsupervised learning?". Clustering is also a closet of shame of machine learning as
a scientific domain. Nobody really knows what a good clustering is.
There's no algorithmic way to optimally decide on the good
initialization of clustering algorithms, the optimal number of
clusters, the metric to compare the similarity/dissimilarity of
points within one cluster. Only heuristics and advice of kind "try
this/try that".
Classification, regression, sequence modeling, and reinforcement
learning are all living a boom of new discoveries and new problems
being solved. Clustering desperately got stick in the 1980's.
source: Andriy Burkov on LinkedinIn this article, we suggest TensorBoard interactive
visualization as an additional tool to help visualize higher
dimensional data and understand unsupervised models and results

Introduction

With data increasing at an exponential rate, the datasets have
million observations and attributes/features. One might argue, more
the data the merrier. But this is not the case always. Datasets with
high dimensions/features are subjected to what is called as curse of dimensionality Medical images
generate thousands of features and are subjected to curse of
dimensionality. This problem pushed researchers to explore
dimensionality reduction procedures such as Principal Component
Analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE),
Linear discriminant analysis (LDA) etc. In this article, we will
concentrate on t-SNE.

Now that we established some understanding of visualizing
higher dimensional data. Let's understand how one could leverage
this to understand unsupervised model performance.

...

Project Description

Multi-source, heterogeneous and multifaceted data for 11,000
participants were acquired for a large scale study. Imaging,
genetics, clinical assessments and demographic information was
recorded at multiple times. The data was preprocessed, derived
neuroimaging morphometry measures were computed, and a single
computable data object was created by harmonizing and aggregating
all the available information. The final sample size was reduced to
9,914, as some cases were removed due to preprocessing errors,
extreme missingness, or inconsistencies.

The goal of the study was to examine thousands
of data elements (3,300), predict specific clinical outcomes,
determine the most salient features associated with computable
clinical phenotypes, and interpret the joint data holistically,
in a lower dimensional space

Pipeline

Feature Extraction

Description: For each participant with a structural MRI brain scan,
we derived a set of
3,000 neuroimaging biomarkerss. These represent a quantitative
signature vector of the 3D stereotactic brain anatomy. Additionally,
each participant had clinical
assessment, demographic, and phenotypic data, which was harmonized
and integrated with the derived neuroimaging biomarkers.

Data after feature extraction

Number of Observations: 9,914

Number of features: 3,297

Machine Learning

After all the data pre-processing and feature extraction, it's
time to find hidden patterns in the data. Since we do not have
ground truth labels, unsupervised learning techniques are used. We
would not go in depth on these machine learning models in this
article.

Before training an unsupervised model, we need to note that
data has 3,297 features which can results in poor performance of our
model. So the first step employed is dimensionality reduction using
PCA to get a minimum number of features which can explain 80% of the
variance. As seen in the graph below, approx. 300 features can help
explain 80% variance in the data. Hence, our final data that is fed
into machine learning model has 9,914 Observations and 300
Features/attributes.

Machine Learning algorithms

Model Performance To evaluate model performance
in absence of information about ground truth labels, very few
metrics are available to evaluate the model. These metrics are:

Silhouette Coefficient

Interpretation: The silhouette value is a measure of how
similar an object is to its own cluster (cohesion) compared to
other clusters (separation). The score is bounded between -1 for
incorrect clustering and +1 for highly dense clustering. Scores
around zero indicate overlapping clusters. The score is higher when
clusters are dense and well separated, which relates to a standard
concept of a cluster.

Calinski-Harabaz Index

Interpretation: The score is higher when clusters are dense
and well separated, which relates to a standard concept of a
cluster. The score is fast to compute

Note: Since the focus of this article is on
understanding the results of unsupervised learning using TensorBoard
visualizations, we would not do much in terms of hyper-parameter
tuning of machine learning models.

K-Means++

The parameter of interest in this model is choosing optimal
'K' i.e., Number of Clusters. Elbow graph as shown below is the
popular method to estimate the value of 'K'

We are looking for a sharp bend in the plot of inertia vs.
number of clusters where beyond that point change in inertia is
very less and that point of the bend can be considered as optimal
cluster number. In this case, we do not see such sharp bend.
However, we see that after 3 clusters the variation in inertia is
less/decreasing gradually. Thus, we will fit our data to 3
clusters.

Result of K-Means++ model

As seen from the above label distribution plot, 85% of the
observations are in clusters 1 and 3 with Silhouette
Coefficient of 0.0907435709364 . Based on this, it can be inferred
that the model performed poorly resulting in overlapping clusters. This can be seen using t-SNE generated in python. Later
we will use TensorBoard to generate 3D visualization of t-SNE

Note: All the models below are trained with
default parameters

Though we a metric to evaluate different model performance,
without ground truth label we cannot ascertain that a particular
model is performing well. Thus, one way to solve this is
visualization of the underlying clusters formed by each model. Such
visualizations can put our doubts at ease and also provide
meaningful insights on model performance and lot being limited by
Silhouette Coefficient

What is TensorBoard?

The computations you'll use TensorFlow
for - like training a massive deep neural network - can be complex
and confusing. To make it easier to understand, debug, and optimize
TensorFlow programs, we've included a suite of visualization tools
called TensorBoard. You can use TensorBoard to visualize your
TensorFlow graph, plot quantitative metrics about the execution of
your graph, and show additional data like images that pass through
it.

Out of vast majority of features TensorBoard offers we will
use Embedding Projector. TensorBoard includes the Embedding
Projector, a tool that lets you interactively visualize embeddings.
This tool can read embeddings from your model and render them in two
or three dimensions.

The Embedding Projector has three panels:

Data panel on the top left, where you can choose the run,
the embedding variable and data columns to color and label points
by.

Projections panel on the bottom left, where you can choose
the type of projection.

Inspector panel on the right side, where you can search for
particular points and see a list of nearest neighbors.

Projections

The Embedding Projector provides three ways to reduce the
dimensionality of a data set.

t-SNE: A nonlinear nondeterministic
algorithm (T-distributed stochastic neighbor embedding) that tries
to preserve local neighborhoods in the data, often at the expense
of distorting global structure. You can choose whether to compute
two- or three-dimensional projections.

PCA: A linear deterministic algorithm
(principal component analysis) that tries to capture as much of the
data variability in as few dimensions as possible. PCA tends to
highlight large-scale structure in the data, but can distort local
neighborhoods. The Embedding Projector computes the top 10
principal components, from which you can choose two or three to
view.

Custom:A linear projection onto horizontal
and vertical axes that you specify using labels in the data. You
define the horizontal axis, for instance, by giving text patterns
for "Left" and "Right". The Embedding Projector finds all points
whose label matches the "Left" pattern and computes the centroid of
that set; and similarly for "Right". The line passing through these two
centroids defines the horizontal axis. The vertical axis is
likewise computed from the centroids for points matching the "Up"
and "Down" text patterns

Let's get started generating t-SNE visualization on
tensorboard with our own data. Steps involved

Required Libraries: TensorFlow, Pandas, Numpy,
sklearn (PCA, StandardScaler). You can also create an environment
using the .yml file found here
here. To run the .yml, run the following command conda
env create -f filename.yml in terminal(mac) or conda
prompt(windows)

Before jumping into code to visualize higher dimensional data

Apply standard scaler and Create dummy variable for
categorical data

For better results with t-SNE, apply dimensionality
reduction to reduce your data set to 50 features or PCA components
that explain at least 80% of the variance in your data

If your data is not labeled, predict clusters/labels using
unsupervised learning methods. In fact, this visualization method
helps immensely in understanding our clustering results.

Pythonic Way

Running the code below generates necessary files such as
embeddings for data, metadata, checkpoints and TensorFlow variables
that TensorBoard reads during startup

CODE

## Importing required Libraries
import os
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
## Get working directory
PATH = os.getcwd()
## Path to save the embedding and checkpoints generated
LOG_DIR = PATH + '/project-tensorboard/log-1/'
## Load data
df = pd.read_csv("scaled_data.csv",index_col =0)
## Load the metadata file. Metadata consists your labels. This is optional. Metadata helps us visualize(color) different clusters that form t-SNE
metadata = os.path.join(LOG_DIR, 'df_labels.tsv')
# Generating PCA and
pca = PCA(n_components=50,
random_state = 123,
svd_solver = 'auto'
)
df_pca = pd.DataFrame(pca.fit_transform(df))
df_pca = df_pca.values
## TensorFlow Variable from data
tf_data = tf.Variable(df_pca)
## Running TensorFlow Session
with tf.Session() as sess:
saver = tf.train.Saver([tf_data])
sess.run(tf_data.initializer)
saver.save(sess, os.path.join(LOG_DIR, 'tf_data.ckpt'))
config = projector.ProjectorConfig()
# One can add multiple embeddings.
embedding = config.embeddings.add()
embedding.tensor_name = tf_data.name
# Link this tensor to its metadata(Labels) file
embedding.metadata_path = metadata
# Saves a config file that TensorBoard will read during startup.
projector.visualize_embeddings(tf.summary.FileWriter(LOG_DIR), config)

Now, open terminal and run the following command

tensorboard --logdir= "where the log files are stored"(without quotes) --port=6006

Result

Let's summarize few of our observations from the plot. In the
above visualization, different colors from metadata(label) that are
predicted using unsupervised model in this case K-Means++. We see
four clusters being formed. However, our unsupervised learning model
was trained with 3 clusters. We also see blue and orange cluster
seem to share observations while the rest are share few
observations. This shows that a good parameter tuning and careful
study of observations we can identify/predict clusters that are
separated nicely from one another.

Another important feature is visualizing data points and their
associated images. With minimal effort a subject matter expert can
carefully study clusters and deduct insights on model performance.
Thus, this helps us really on visual aid along side popular
unsupervised performance metrics to improve our model.

Try-It-Now experiment 1: Now, itâ€™s your turn to complete a hands-on activity using the UK Biobank data.

Mind that this app runs best in Chome browser and is a very computationally intensive demonstration.
It will require up to a minute to load, utilize 1GB of RAM, demand a reasonable graphics card.
Please try the SOCR UKBB t-SNE app directly.

Try-It-Now experiment 2: Interactive activity using user-specified data.

You can also try to run similar analysis to the UK Biobank study above using your own dataset.
This will require you to provide a pair of ASCII text files that can be loaded from your computer.

The first file contains tab-delimited (TSV) data including the predictor vectors (row=case * column=features).
The second file is an optional TSV file including metadata like labels for each case (row), if any.
Then you can run the same dimensionality reduction on your own data in 2D or 3D. This link provides a direct access to the User-Data-driven application ...