Correlation algorithm
Distance and correlation
I now have two sets of data. I want to know how they are related. What should I do?
For example, the following two sets of data.
a = [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ]
b = [ 1, 1, 2, 4, 5, 6, 8, 8, 9, 9 ]Euclidean distance
The simplest thing that can be thought of is distance. If the distance between the two data is very close, then the two data are very close, if the distance between the two data is very far, then the two data are far apart.
import matplotlib.pyplot as plt
a = [1, 2]
b = [5, 4]
plt.plot( [ a[0], b[0] ],
[ a[1], b[1] ] )
plt.scatter( [ a[0], b[0] ],
[ a[1], b[1] ] )
plt.xlim(0, 6)
plt.ylim(0, 5)
plt.xlabel('x')
plt.ylabel('y')
plt.title('Euclidean distance')
plt.grid()
plt.show()
The length of the line segment in the figure is the Euclidean distance.
a = [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ]
b = [ 1, 1, 2, 4, 5, 6, 8, 8, 9, 9 ]
dist = 0
for i in range(len(a)):
dist += (a[i] - b[i]) ** 2
dist = dist ** 0.5
print(dist)The above code is the calculation method of Euclidean distance, and the results obtained are as follows:
2.0
>>> We can use numpy's own function to calculate Euclidean distance.
import numpy as np
a = np.array( [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] )
b = np.array( [ 1, 1, 2, 4, 5, 6, 8, 8, 9, 9 ] )
dist = np.linalg.norm( a - b )
print(dist)Can get the same result
2.0
>>> In particular, linalg is a linear algebra library, we will often use it in subsequent programs.
Cosine distance
The cosine distance can measure the angle between two vectors. If the angle is small, the two vectors are relatively close. If the angle is large, the distance between the two vectors is large.
import matplotlib.pyplot as plt
a = [1, 2]
b = [5, 4]
plt.plot( [ a[0], 0 ],
[ a[1], 0 ] )
plt.plot( [ 0, b[0] ],
[ 0, b[1] ], '#1f77b4' ) # color: matplotlib blue
plt.scatter( [ a[0], b[0] ],
[ a[1], b[1] ] )
plt.xlim(0, 6)
plt.ylim(0, 5)
plt.xlabel('x')
plt.ylabel('y')
plt.title('Cosine distance')
plt.grid()
plt.show()
The angle between the two vectors is the cosine distance.
The formula of the cosine distance is very complicated. Fortunately, scipy has created a library for us to calculate the cosine distance, which only needs to be called:
import numpy as np
a = np.array( [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] )
b = np.array( [ 1, 1, 2, 4, 5, 6, 8, 8, 9, 9 ] )
from scipy.spatial import distance
dist = distance.cosine( a, b )
print(dist)
# The smaller the distance, the higher the similarity
similarity = 1 - dist
print(similarity)It shows that the directionality of these two data is quite consistent.
0.005152370287896502
0.9948476297121035
>>> If the cosine similarity between two vectors is positive, it means that the two vectors are in the same direction (positive correlation). If it is negative, then it means that the two vectors are in opposite directions (negative correlation). The absolute value of the cosine similarity of the two vectors The larger the value, the closer the angle of the two vectors, and the closer the cosine similarity of the two vectors is to 0, indicating that the two vectors are closer to unrelated (perpendicular to each other).
Pearson Correlation Coefficient
Pearson's correlation coefficient is a linear correlation coefficient.
At the end, I will explain the comparison of these types of correlation coefficients with you.
In scipy, there is a Pearson correlation coefficient library, which can be easily used by just calling it.
import numpy as np
a = np.array( [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] )
b = np.array( [ 1, 1, 2, 4, 5, 6, 8, 8, 9, 9 ] )
from scipy.stats import pearsonr
pearsonr = pearsonr(a, b)
print(pearsonr)The output obtained is as follows:
(0.9808651973571281, 5.731479081203351e-07)
>>> The first of these is the Pearson correlation coefficient
(0.9808651973571281, 5.731479081203351e-07)
>>> pearsonr[0]
0.9808651973571281
>>> Pearson correlation coefficient can be extracted through index
If the correlation coefficient is closer to 1, it means that the two vectors have a stronger positive correlation.
If the correlation coefficient is close to 0, it means that the two vectors are orthogonal (irrelevant).
If the correlation coefficient is closer to -1, it means that the two vectors are more strongly negative Related.
Spearman correlation coefficient
Spearman's correlation coefficient can still be implemented easily through the scipy library:
import numpy as np
a = np.array( [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] )
b = np.array( [ 1, 1, 2, 4, 5, 6, 8, 8, 9, 9 ] )
from scipy.stats import spearmanr
spearmanr = spearmanr(a, b)
print(spearmanr)
print(spearmanr[0])The results are as follows:
SpearmanrResult(correlation=0.9908673886137245, pvalue=3.010166323417494e-08)
0.9908673886137245
>>> If the correlation coefficient is closer to 1, it means that the two vectors have a stronger positive correlation.
If the correlation coefficient is close to 0, it means that the two vectors are orthogonal (irrelevant).
If the correlation coefficient is closer to -1, it means that the two vectors are more strongly negative Related.
Kendall rank correlation coefficient
Kendall rank correlation coefficient also has a library in scipy, which is very simple to implement:
import numpy as np
a = np.array( [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] )
b = np.array( [ 1, 1, 2, 4, 5, 6, 8, 8, 9, 9 ] )
from scipy.stats import kendalltau
kendalltau = kendalltau(a, b)
print(kendalltau)
print(kendalltau[0])Same as the previous output and usage method, this is the result:
KendalltauResult(correlation=0.9660917830792959, pvalue=0.00014324249514425838)
0.9660917830792959
>>> Kendall rank correlation coefficient is different from the previous correlation coefficient usage scenarios!
Kendall rank correlation coefficient is a category correlation coefficient.
For example: each judge has a different taste, some judges like sweeter food, and some judges like salty food.
There are two judges who gave comments on the five dishes. We want to know whether the tastes of the two judges are similar. Kendall rank correlation coefficient is suitable for solving such problems.
If the Kendall rank correlation coefficient is high, it means that the two judges have similar preferences. If the Kendall rank correlation coefficient is close to zero, it means that the two judges prefer a low correlation. If the Kendall rank correlation coefficient is negative, it means that the two judges have opposite preferences.
If the correlation coefficient is closer to 1, it means that the two vectors have a stronger positive correlation.
If the correlation coefficient is close to 0, it means that the two vectors are orthogonal (irrelevant).
If the correlation coefficient is closer to -1, it means that the two vectors are more strongly negative Related.
Comparison of several methods
Distance and average distance are a good way to measure the similarity between samples.
When the samples are really close, the distance is very useful, but if there is a proportional relationship between the samples, then the non-normalization method of distance will not work.
The cosine distance is the correlation. The Pearson correlation coefficient is an improved and normalized version of the cosine distance. Its essence is the same as the cosine distance, except that the cosine distance is directly calculated using the sample. The Pearson correlation coefficient subtracts the sample average from the sample (the calculation is the sample difference between).
Spearman's correlation coefficient is another improvement of Pearson's correlation coefficient. Pearson's correlation coefficient is linear, which means that if two variables are positively correlated, but not linear, the result of Pearson's correlation coefficient will be relatively low (not correlated) ), and the result of Spearman's correlation coefficient will be higher (correlation).
Let's look at the following example:
import numpy as np
mu = 0
sigma = 0.6
# Normally distributed data
x = np.random.normal(mu, sigma, 100)
y = (x - mu) ** 3
import matplotlib.pyplot as plt
plt.plot(x, x, '#ff7f0e')
plt.scatter(x, y)
plt.grid()
plt.title('data visualization')
plt.show()Let's visualize the data first: we found that the data we generated (blue) and the original data (orange) are positively correlated. But the data we generated is a 100-point Gaussian distribution, so the distribution is not uniform.
Now let's try the correlation coefficient:
>>> from scipy.spatial import distance
>>> 1 - distance.cosine( x, y )
0.8338464893428985
>>> from scipy.stats import pearsonr
>>> pearsonr( x, y )[0]
0.8325135614884113
>>> from scipy.stats import spearmanr
>>> spearmanr( x, y )[0]
0.9999999999999999
>>> Cosine distance is the correlation coefficient without normalization and centering. Because the data is normally distributed (higher symmetry), the effect of cosine distance is not bad.
The Pearson correlation coefficient is a normalized correlation coefficient. It can be found that the two sets of data are positively correlated, but because the two sets of data are not linearly positively correlated, the Pearson correlation coefficient is not 1.
Spearman's correlation coefficient can be used to process non-linear data, so very high results are obtained.
In actual data mining:
We rarely use cosine distance, because cosine distance does not perform well on arbitrary data.
Pearson's correlation coefficient is often used to measure linear data. This method handles noise and nonlinear data poorly. Notice that: Pearson's correlation coefficient may make you misjudge the data. There are some data with poor correlation, and the Pearson's correlation coefficient may not be bad.
Spearman correlation coefficient is a recommended algorithm, because this method has wide applicability, has better recognition ability for nonlinear data, and can eliminate the influence of noise.
Statistics
Start time of this page: December 21, 2021
Completion time of this page: December 21, 2021
Last updated