• Dimensionality Reduction
  • Dataset
  • 2-Dimensional Plot
  • 3-Dimensional Plot

Github Repository

Multidimensional Scaling is a family of statistical methods that focus on creating mappings of items based on distance.

Dimensionality Reduction

Manifold learning is an approach to non-linear dimensionality reduction. Algorithms for this task are based on the idea that the dimensionality of many data sets is only artificially high.

High-dimensional datasets can be very difficult to visualize. While data in two or three dimensions can be plotted to show the inherent structure of the data, equivalent high-dimensional plots are much less intuitive. To aid visualization of the structure of a dataset, the dimension must be reduced in some way.

The simplest way to accomplish this dimensionality reduction is by taking a random projection of the data. Though this allows some degree of visualization of the data structure, the randomness of the choice leaves much to be desired. In a random projection, it is likely that the more interesting structure within the data will be lost.

To address this concern, a number of supervised and unsupervised linear dimensionality reduction frameworks have been designed, such as:

• Principal Component Analysis (PCA)
• Locally Linear Embedding (LLE)
• tStochastic Neighbor Embedding (t-SNE)
• Isometric Mapping (ISOMAP)
• Fisher Linear Discriminant Analysis (LDA)

Dataset

A multivariate study of variation in two species of rock crab of genus Leptograpsus: A multivariate approach has been used to study morphological variation in the blue and orange-form species of rock crab of the genus Leptograpsus. Objective criteria for the identification of the two species are established, based on the following characters:

• SP: Species (Blue or Orange)
• Sex: Male or Female
• FL: Width of the frontal region of the carapace;
• RW: Width of the posterior region of the carapace (rear width);
• CL: Length of the carapace along the midline;
• CW: Maximum width of the carapace;
• BD: and the depth of the body;

The dataset can be downloaded from Github.

(see introduction in: Principal Component Analysis PCA)

raw_data = pd.read_csv('data/A_multivariate_study_of_variation_in_two_species_of_rock_crab_of_genus_Leptograpsus.csv')

data = raw_data.rename(columns={
    'sp': 'Species',
    'sex': 'Sex',
    'index': 'Index',
    'FL': 'Frontal Lobe',
    'RW': 'Rear Width',
    'CL': 'Carapace Midline',
    'CW': 'Maximum Width',
    'BD': 'Body Depth'})

data['Species'] = data['Species'].map({'B':'Blue', 'O':'Orange'})
data['Sex'] = data['Sex'].map({'M':'Male', 'F':'Female'})
data['Class'] = data.Species + data.Sex

data_columns = ['Frontal Lobe',
                'Rear Width',
                'Carapace Midline',
                'Maximum Width',
                'Body Depth']

# generate a class variable for all 4 classes
data['Class'] = data.Species + data.Sex

print(data['Class'].value_counts())
data.head(5)

• BlueMale: 50
• BlueFemale: 50
• OrangeMale: 50
• OrangeFemale: 50

	Species	Sex	Index	Frontal Lobe	Rear Width	Carapace Midline	Maximum Width	Body Depth	Class
0	Blue	Male	1	8.1	6.7	16.1	19.0	7.0	BlueMale
1	Blue	Male	2	8.8	7.7	18.1	20.8	7.4	BlueMale
2	Blue	Male	3	9.2	7.8	19.0	22.4	7.7	BlueMale
3	Blue	Male	4	9.6	7.9	20.1	23.1	8.2	BlueMale
4	Blue	Male	5	9.8	8.0	20.3	23.0	8.2	BlueMale

# normalize data columns
data_norm = data.copy()
data_norm[data_columns] = MinMaxScaler().fit_transform(data[data_columns])

data_norm.describe()

	Index	Frontal Lobe	Rear Width	Carapace Midline	Maximum Width	Body Depth
count	200.000000	200.000000	200.000000	200.000000	200.000000	200.000000
mean	25.500000	0.527233	0.455365	0.529043	0.515053	0.511645
std	14.467083	0.219832	0.187835	0.216382	0.209919	0.220953
min	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	13.000000	0.358491	0.328467	0.382219	0.384000	0.341935
50%	25.500000	0.525157	0.459854	0.528875	0.525333	0.503226
75%	38.000000	0.682390	0.569343	0.684650	0.664000	0.677419
max	50.000000	1.000000	1.000000	1.000000	1.000000	1.000000

2-Dimensional Plot

no_components = 2
n_init = 15
metric = True
n_stress='auto'

mds = MDS(
    n_components=no_components,
    n_init=n_init, metric=metric,
    normalized_stress=n_stress)

data_mds = mds.fit_transform(data_norm[data_columns])
print('MSE: ', mds.stress_)
# MSE:  3.886582480465905
# the more components you add the smaller
# the mean squared error becomes - meaning
# your model starts to fit better

data_norm[['MDS1', 'MDS2']] = data_mds
data_norm.head(1)

	Species	Sex	Index	Frontal Lobe	Rear Width	Carapace Midline	Maximum Width	Body Depth	Class	MDS1	MDS2
0	Blue	Male	1	0.056604	0.014599	0.042553	0.050667	0.058065	BlueMale	-0.482199	-0.917839

fig = plt.figure(figsize=(10, 8))
sns.scatterplot(x='MDS1', y='MDS2', hue='Class', data=data_norm)

3-Dimensional Plot

no_components = 3
n_init = 15
metric = True
n_stress='auto'

mds = MDS(
    n_components=no_components,
    n_init=n_init, metric=metric,
    normalized_stress=n_stress)

data_mds = mds.fit_transform(data_norm[data_columns])
print('MSE: ', mds.stress_)
# MSE:  2.4601741009431457

data_norm[['MDS1', 'MDS2', 'MDS3']] = data_mds
data_norm.head(1)

	Species	Sex	Index	Frontal Lobe	Rear Width	Carapace Midline	Maximum Width	Body Depth	Class	MDS1	MDS2	MDS3
0	Blue	Male	1	0.056604	0.014599	0.042553	0.050667	0.058065	BlueMale	-0.093961	0.80491	0.645809

class_colours = {
    'BlueMale': '#0027c4', #blue
    'BlueFemale': '#f18b0a', #orange
    'OrangeMale': '#0af10a', # green
    'OrangeFemale': '#ff1500', #red
}

colours = data_norm['Class'].apply(lambda x: class_colours[x])

x=data_norm.MDS1
y=data_norm.MDS2
z=data_norm.MDS3

fig = plt.figure(figsize=(10,10))
plt.title('MDS Data Analysis')
ax = fig.add_subplot(projection='3d')

ax.scatter(xs=x, ys=y, zs=z, s=50, c=colours)