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SciPy and NumPy by Eli Bressert

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Chapter 4. SciKit: Taking SciPy One Step Further

SciPy and NumPy are great tools and provide us with most of the functionality that we need. Sometimes, though we need more advanced tools, and that’s where the scikits come in. These are a set of packages that are complementary to SciPy. There are currently more than 20 scikit packages available; a list can be found at SciKits. Here we will go over two well-maintained and popular packages: Scikitimage, a more beefed-up image module than scipy.ndimage, is aimed to be an imaging processing toolkit for SciPy. Scikit-learn is a machine learning package that can be used for a range of scientific and engineering purposes.

4.1 Scikit-Image

SciPy’s ndimage class contains many useful tools for processing multi-dimensional data, such as basic filtering (e.g., Gaussian smoothing), Fourier transform, morphology (e.g., binary erosion), interpolation, and measurements. From those functions we can write programs to execute more complex operations. Scikit-image has fortunately taken on the task of going a step further to provide more advanced functions that we may need for scientific research. These advanced and high-level modules include color space conversion, image intensity adjustment algorithms, feature detections, filters for sharpening and denoising, read/write capabilities, and more.

4.1.1 Dynamic Threshold

A common application in imaging science is segmenting image components from one another, which is referred to as thresholding. The classic thresholding technique works well when the background of the image is flat. Unfortunately, this situation is not the norm; instead, the background visually will be changing throughout the image. Hence, adaptive thresholding techniques have been developed, and we can easily utilize them in scikit-image. In the following example, we generate an image with a non-uniform background that has randomly placed fuzzy dots throughout (see Figure 4-1). Then we run a basic and adaptive threshold function on the image to see how well we can segment the fuzzy dots from the background.

Illustration of thresholding. The original synthetic image is on the left, with classic and dynamic threshold algorithms at work from middle to right, respectively
Figure 4-1. Illustration of thresholding. The original synthetic image is on the left, with classic and dynamic threshold algorithms at work from middle to right, respectively
# Source code available: scikits_411_ex1.py
import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage as ndimage
import skimage.filter as skif

# Generating data points with a non-uniform background
x = np.random.uniform(low=0, high=100, size=20).astype(int)
y = np.random.uniform(low=0, high=100, size=20).astype(int)

# Creating image with non-uniform background
func = lambda x, y: x ** 2 + y ** 2
grid_x, grid_y = np.mgrid[-1:1:100j, -2:2:100j]
bkg = func(grid_x, grid_y)
bkg = bkg / np.max(bkg)

# Creating points
clean = np.zeros((100, 100))
clean[(x, y)] += 5
clean = ndimage.gaussian_filter(clean, 3)
clean = clean / np.max(clean)

# Combining both the non-uniform background
# and points
fimg = bkg + clean
fimg = fimg / np.max(fimg)

# Defining minimum neighboring size of objects
block_size = 3

# Adaptive threshold function which returns image
# map of structures that are different relative to
# background
adaptive_cut = skif.threshold_adaptive(fimg, block_size, offset=0)

# Global threshold
global_thresh = skif.threshold_otsu(fimg)
global_cut = fimg > global_thresh

# Creating figure to highlight difference between
# adaptive and global threshold methods
fig = plt.figure(figsize=(8, 4))
fig.subplots_adjust(hspace=0.05, wspace=0.05)

ax1 = fig.add_subplot(131)
ax1.imshow(fimg)
ax1.xaxis.set_visible(False)
ax1.yaxis.set_visible(False)

ax2 = fig.add_subplot(132)
ax2.imshow(global_cut)
ax2.xaxis.set_visible(False)
ax2.yaxis.set_visible(False)

ax3 = fig.add_subplot(133)
ax3.imshow(adaptive_cut)
ax3.xaxis.set_visible(False)
ax3.yaxis.set_visible(False)

fig.savefig('scikits_411_ex1.pdf', bbox_inches='tight')

In this case, as shown in Figure 4-1, the adaptive thresholding technique (right panel) obviously works far better than the basic one (middle panel). Most of the code above is for generating the image and plotting the output for context. The actual code for adaptively thresholding the image took only two lines.

4.1.2 Local Maxima

Approaching a slightly different problem, but with a similar setup as before, how can we identify points on a non-uniform background to obtain their pixel coordinates? Here we can use skimage.morphology.is_local_maximum, which only needs the image as a default input. The function works surprisingly well; see Figure 4-2, where the identified maxima are circled in blue:

# Source code available: scikits_412_ex1.py
import numpy as np
import scipy.ndimage as ndimage
import skimage.morphology as morph
import matplotlib.pyplot as plt

# Generating data points with a non-uniform background
x = np.random.uniform(low=0, high=200, size=20).astype(int)
y = np.random.uniform(low=0, high=400, size=20).astype(int)

# Creating image with non-uniform background
func = lambda x, y: np.cos(x) + np.sin(y)
grid_x, grid_y = np.mgrid[0:12:200j, 0:24:400j]
bkg = func(grid_x, grid_y)
bkg = bkg / np.max(bkg)

# Creating points
clean = np.zeros((200, 400))
clean[(x, y)] += 5
clean = ndimage.gaussian_filter(clean, 3)
clean = clean / np.max(clean)

# Combining both the non-uniform background
# and points
fimg = bkg + clean
fimg = fimg / np.max(fimg)

# Calculating local maxima
lm1 = morph.is_local_maximum(fimg)
x1, y1 = np.where(lm1.T == True)

# Creating figure to show local maximum detection
# rate success
fig = plt.figure(figsize=(8, 4))

ax = fig.add_subplot(111)
ax.imshow(fimg)
ax.scatter(x1, y1, s=100, facecolor='none', edgecolor='#009999')
ax.set_xlim(0, 400)
ax.set_ylim(0, 200)
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)

fig.savefig('scikits_412_ex1.pdf', bbox_inches='tight')

If you look closely at the figure, you will notice that there are identified maxima that do not point to fuzzy sources but instead to the background peaks. These peaks are a problem, but by definition this is what skimage.morphology.is_local_maximum will find. How can we filter out these “false positives”? Since we have the coordinates of the local maxima, we can look for properties that will differentiate the sources from the rest. The background is relatively smooth compared to the sources, so we could differentiate them easily by standard deviation from the peaks to their local neighboring pixels.

Identified local maxima (blue circles)
Figure 4-2. Identified local maxima (blue circles)

How does scikit-image fare with real-world research problems? Quite well, in fact. In astronomy, the flux per unit area received from stars can be measured in images by quantifying intensity levels at their locations—a process called photometry. Photometry has been done for quite some time in multiple programming languages, but there is no de facto package for Python yet. The first step in photometry is identifying the stars. In the following example, we will use is_local_maximum to identify sources (hopefully stars) in a stellar cluster called NGC 3603 that was observed with the Hubble Space Telescope. Note that one additional package, PyFITS, is used here. It is a standard astronomical package for loading binary data stored in FITS format:

# Source code available: scikits_412_ex1.py
import numpy as np
import pyfits
import skimage.morphology as morph
import skimage.exposure as skie
import matplotlib.pyplot as plt

# Loading astronomy image from an infrared space telescope
img = pyfits.getdata('stellar_cluster.fits')[500:1500, 500:1500]

# Prep file scikit-image environment and plotting
limg = np.arcsinh(img)
limg = limg / limg.max()
low = np.percentile(limg, 0.25)
high = np.percentile(limg, 99.5)
opt_img = skie.exposure.rescale_intensity(limg, in_range=(low, high))

# Calculating local maxima and filtering out noise
lm = morph.is_local_maximum(limg)
x1, y1 = np.where(lm.T == True)
v = limg[(y1, x1)]
lim = 0.5
x2, y2 = x1[v > lim], y1[v > lim]

# Creating figure to show local maximum detection
# rate success
fig = plt.figure(figsize=(8, 4))
fig.subplots_adjust(hspace=0.05, wspace=0.05)

ax1 = fig.add_subplot(121)
ax1.imshow(opt_img)
ax1.set_xlim(0, img.shape[1])
ax1.set_ylim(0, img.shape[0])
ax1.xaxis.set_visible(False)
ax1.yaxis.set_visible(False)

ax2 = fig.add_subplot(122)
ax2.imshow(opt_img)
ax2.scatter(x2, y2, s=80, facecolor='none', edgecolor='#FF7400')
ax2.set_xlim(0, img.shape[1])
ax2.set_ylim(0, img.shape[0])
ax2.xaxis.set_visible(False)
ax2.yaxis.set_visible(False)

fig.savefig('scikits_412_ex2.pdf', bbox_inches='tight')

The skimage.morphology.is_local_maximum function returns over 30,000 local maxima in the image, and many of the detections are false positives. We apply a simple threshold value to get rid of any maxima peaks that have a pixel value below 0.5 (from the normalized image) to bring that number down to roughly 200. There are much better ways to filter out non-stellar maxima (e.g., noise), but we will still stick with the current method for simplicity. In Figure 4-3 we can see that the detections are good overall. Once we know where the stars are, we can apply flux measurement algorithms, but that goes beyond the scope of this chapter.

Stars (orange circles) in a Hubble Space Telescope image of a stellar cluster, identified using the is_local_maximum function
Figure 4-3. Stars (orange circles) in a Hubble Space Telescope image of a stellar cluster, identified using the is_local_maximum function

Hopefully, with this brief overview of what is available in the scikit-image package, you already have a good idea of how it can be used for your objectives.

4.2 Scikit-Learn

Possibly the most extensive scikit is scikit-learn. It is an easy-to-use machine learning bundle that contains a collection of tools associated with supervised and unsupervised learning. Some of you may be asking, “So what can machine learning help me do that I could not do before?” One word: predictions.

Let us assume that we are given a problem where there is a good sample of empirical data at hand: can predictions be made about it? To figure this out, we would try to create an analytical model to describe the data, though that does not always work due to complex dependencies. But what if you could feed that data to a machine, teach the machine what is good and bad about the data, and then let it provide its own predictions? That is what machine learning is. If used right, it can be very powerful.

Not only is the scikit-learn package impressive, but its documentation is generous and well organized. Rather than reinventing the wheel to show what scikit-learn is, I’m going to take several examples that we did in prior sections and see if scikit-learn could provide better and more elegant solutions. This method of implementing scikit-learn is aimed to inspire you as to how the package could be applied to your own research.

4.2.1 Linear Regression

In Chapter 3 we fitted a line to a dataset, which is a linear regression problem. If we are dealing with data that has a higher number of dimensions, how do we go about a linear regression solution? Scikit-learn has a large number of tools to do this, such as Lasso and ridge regression. For now we will stick with the ordinary least squares regression function, which solves mathematical problems of the form

4.2.1 Linear Regression

where w is the set of coefficients. The number of coefficients depends on the number of dimensions in the data, N(coeff) = MD – 1, where M > 1 and is an integer. In the example below we are computing the linear regression of a plane in 3D space, so there are two coefficients to solve for. Here we show how to use LinearRegression to train the model with data, approximate a best fit, give a prediction from the data, and test other data (test) to see how well it fits the model. A visual output of the linear regression is shown in Figure 4-4:

# Source code available: scikits_421_ex1.py
import numpy as np
from sklearn import linear_model
from sklearn.datasets.samples_generator import make_regression
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# Generating synthetic data for training and testing
X, y = make_regression(n_samples=100, n_features=2, n_informative=1,
                         random_state=0, noise=50)

# X and y are values for 3D space. We first need to train
# the machine, so we split X and y into X_train, X_test,
# y_train, and y_test. The *_train data will be given to the
# model to train it.
X_train, X_test = X[:80], X[-20:]
y_train, y_test = y[:80], y[-20:]

# Creating instance of model
regr = linear_model.LinearRegression()

# Training the model
regr.fit(X_train, y_train)

# Printing the coefficients
print(regr.coef_)
# [-10.25691752  90.5463984 ]

# Predicting y-value based on training
X1 = np.array([1.2, 4])
print(regr.predict(X1))
# 350.860363861

# With the *_test data we can see how the result matches
# the data the model was trained with.
# It should be a good match as the *_train and *_test
# data come from the same sample. Output: 1 is perfect
# prediction and anything lower is worse.
print(regr.score(X_test, y_test))
# 0.949827492261

fig = plt.figure(figsize=(8, 5))
ax = fig.add_subplot(111, projection='3d')

# Data
ax.scatter(X_train[:,0], X_train[:,1], y_train, facecolor='#00CC00')
ax.scatter(X_test[:,0], X_test[:,1], y_test, facecolor='#FF7800')

# Function with coefficient variables
coef = regr.coef_
line = lambda x1, x2: coef[0] * x1 + coef[1] * x2

grid_x1, grid_x2 = np.mgrid[-2:2:10j, -2:2:10j]
ax.plot_surface(grid_x1, grid_x2, line(grid_x1, grid_x2),
                alpha=0.1, color='k')
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
ax.zaxis.set_visible(False)
fig.savefig('scikits_421_ex1.pdf', bbox='tight')
A scikit-learn linear regression in 3D space
Figure 4-4. A scikit-learn linear regression in 3D space

This LinearRegression function can work with much higher dimensions, so dealing with a larger number of inputs in a model is straightforward. It is advisable to look at the other linear regression models as well, as they may be more appropriate for your data.

4.2.2 Clustering

SciPy has two packages for cluster analysis with vector quantization (kmeans) and hierarchy. The kmeans method was the easier of the two for implementing and segmenting data into several components based on their spatial characteristics. Scikit-learn provides a set of tools to do more cluster analysis that goes beyond what SciPy has. For a suitable comparison to the kmeans function in SciPy, the DBSCAN algorithm is used in the following example. DBSCAN works by finding core points that have many data points within a given radius. Once the core is defined, the process is iteratively computed until there are no more core points definable within the maximum radius? This algorithm does exceptionally well compared to kmeans where there is noise present in the data:

# Source code available: scikits_422_ex1.py
import numpy as np
from sklearn.cluster import DBSCAN
import matplotlib.pyplot as plt

# Creating data
c1 = np.random.randn(100, 2) + 5
c2 = np.random.randn(50, 2)

# Creating a uniformly distributed background
u1 = np.random.uniform(low=-10, high=10, size=100)
u2 = np.random.uniform(low=-10, high=10, size=100)
c3 = np.column_stack([u1, u2])

# Pooling all the data into one 150 x 2 array
data = np.vstack([c1, c2, c3])

# Calculating the cluster with DBSCAN function.
# db.labels_ is an array with identifiers to the
# different clusters in the data.
db = DBSCAN().fit(data, eps=0.95, min_samples=10)
labels = db.labels_

# Retrieving coordinates for points in each
# identified core. There are two clusters
# denoted as 0 and 1 and the noise is denoted
# as -1. Here we split the data based on which
# component they belong to.
dbc1 = data[labels == 0]
dbc2 = data[labels == 1]
noise = data[labels == -1]

# Setting up plot details
x1, x2 = -12, 12
y1, y2 = -12, 12

fig = plt.figure()
fig.subplots_adjust(hspace=0.1, wspace=0.1)

ax1 = fig.add_subplot(121, aspect='equal')
ax1.scatter(c1[:,0], c1[:,1], lw=0.5, color='#00CC00')
ax1.scatter(c2[:,0], c2[:,1], lw=0.5, color='#028E9B')
ax1.scatter(c3[:,0], c3[:,1], lw=0.5, color='#FF7800')
ax1.xaxis.set_visible(False)
ax1.yaxis.set_visible(False)
ax1.set_xlim(x1, x2)
ax1.set_ylim(y1, y2)
ax1.text(-11, 10, 'Original')

ax2 = fig.add_subplot(122, aspect='equal')
ax2.scatter(dbc1[:,0], dbc1[:,1], lw=0.5, color='#00CC00')
ax2.scatter(dbc2[:,0], dbc2[:,1], lw=0.5, color='#028E9B')
ax2.scatter(noise[:,0], noise[:,1], lw=0.5, color='#FF7800')
ax2.xaxis.set_visible(False)
ax2.yaxis.set_visible(False)
ax2.set_xlim(x1, x2)
ax2.set_ylim(y1, y2)
ax2.text(-11, 10, 'DBSCAN identified')

fig.savefig('scikits_422_ex1.pdf', bbox_inches='tight')

Nearly all the data points originally defined to be part of the clusters are retained, and the noisy background data points are excluded (see Figure 4-5). This highlights the advantage of DBSCAN over kmeans when data that should not be part of a cluster is present in a sample. This obviously is dependent on the spatial characteristics of the given distributions.

An example of how the DBSCAN algorithm excels over the vector quantization package in SciPy. The uniformly distributed points are not included as cluster members.
Figure 4-5. An example of how the DBSCAN algorithm excels over the vector quantization package in SciPy. The uniformly distributed points are not included as cluster members.

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