# Euclidean vs. Cosine Distance¶

https://cmry.github.io/notes/euclidean-v-cosine

In [1]:
import numpy as np

X = np.array([[6.6, 6.2, 1],
[9.7, 9.9, 2],
[8.0, 8.3, 2],
[6.3, 5.4, 1],
[1.3, 2.7, 0],
[2.3, 3.1, 0],
[6.6, 6.0, 1],
[6.5, 6.4, 1],
[6.3, 5.8, 1],
[9.5, 9.9, 2],
[8.9, 8.9, 2],
[8.7, 9.5, 2],
[2.5, 3.8, 0],
[2.0, 3.1, 0],
[1.3, 1.3, 0]])

In [2]:
import pandas as pd

df = pd.DataFrame(X, columns=['weight', 'length', 'label'])
df

Out[2]:
weight length label
0 6.6 6.2 1.0
1 9.7 9.9 2.0
2 8.0 8.3 2.0
3 6.3 5.4 1.0
4 1.3 2.7 0.0
5 2.3 3.1 0.0
6 6.6 6.0 1.0
7 6.5 6.4 1.0
8 6.3 5.8 1.0
9 9.5 9.9 2.0
10 8.9 8.9 2.0
11 8.7 9.5 2.0
12 2.5 3.8 0.0
13 2.0 3.1 0.0
14 1.3 1.3 0.0
In [3]:
%matplotlib inline

ax = df[df['label'] == 0].plot.scatter(x='weight', y='length', c='blue', label='young')
ax = df[df['label'] == 1].plot.scatter(x='weight', y='length', c='orange', label='mid', ax=ax)
ax = df[df['label'] == 2].plot.scatter(x='weight', y='length', c='red', label='adult', ax=ax)
ax

Out[3]:
<matplotlib.axes._subplots.AxesSubplot at 0x108e750b8>
In [4]:
df2 = pd.DataFrame([df.iloc[0], df.iloc[1], df.iloc[4]], columns=['weight', 'length', 'label'])
df3 = pd.DataFrame([df.iloc[14]], columns=['weight', 'length', 'label'])

ax = df2[df2['label'] == 0].plot.scatter(x='weight', y='length', c='blue', label='x4(young)')
ax = df2[df2['label'] == 1].plot.scatter(x='weight', y='length', c='orange', label='x0(mid)', ax=ax)
ax = df2[df2['label'] == 2].plot.scatter(x='weight', y='length', c='red', label='x1(adult)', ax=ax)
ax = df3.plot.scatter(x='weight', y='length', c='gray', label='x14(?)', ax=ax)
ax

Out[4]:
<matplotlib.axes._subplots.AxesSubplot at 0x108f4e0b8>
In [5]:
def euclidean_distance(x, y):
return np.sqrt(np.sum((x - y) ** 2))


$\sqrt{\sum^n_{i=1} (x_i - y_i)^2}$

In [6]:
x0 = X[0][:-1]
x1 = X[1][:-1]
x4 = X[4][:-1]
x14 = X[14][:-1]
print(" x0:", x0, "\n x1:", x1, "\n x4:", x4, "\nx14:", x14)

 x0: [6.6 6.2]
x1: [9.7 9.9]
x4: [1.3 2.7]
x14: [1.3 1.3]

In [7]:
print(" x14 and x0:", euclidean_distance(x14, x0), "\n",
"x14 and x1:", euclidean_distance(x14, x1), "\n",
"x14 and x4:", euclidean_distance(x14, x4))

 x14 and x0: 7.218032973047436
x14 and x1: 12.021647141718974
x14 and x4: 1.4000000000000001

In [8]:
def cosine_similarity(x, y):
return np.dot(x, y) / (np.sqrt(np.dot(x, x)) * np.sqrt(np.dot(y, y)))


$\frac{x \bullet y}{ \sqrt{x \bullet x} \sqrt{y \bullet y}}$

In [9]:
print(" x14 and x0:", cosine_similarity(x14, x0), "\n",
"x14 and x1:", cosine_similarity(x14, x1), "\n",
"x14 and x4:", cosine_similarity(x14, x4))

 x14 and x0: 0.9995120760870786
x14 and x1: 0.9999479424242859
x14 and x4: 0.9438583563660174


While cosine looks at the angle between vectors (thus not taking into regard their weight or magnitude), euclidean distance is similar to using a ruler to actually measure the distance.

# Cosine Similarity in Action¶

In [10]:
import wikipedia

q1 = wikipedia.page('Machine Learning')
q2 = wikipedia.page('Artifical Intelligence')
q3 = wikipedia.page('Soccer')
q4 = wikipedia.page('Tennis')

In [11]:
from sklearn.feature_extraction.text import CountVectorizer

cv = CountVectorizer()
X = np.array(cv.fit_transform([q1.content, q2.content, q3.content, q4.content]).todense())

In [12]:
print("ML \t", len(q1.content.split()), "\n"
"AI \t", len(q2.content.split()), "\n"
"soccer \t", len(q3.content.split()), "\n"
"tennis \t", len(q4.content.split()))

ML 	 4048
AI 	 13742
soccer 	 6470
tennis 	 9736

In [13]:
q1.content[:100]

Out[13]:
'Machine learning is a field of computer science that often uses statistical techniques to give compu'
In [14]:
q1.content.split()[:10]

Out[14]:
['Machine',
'learning',
'is',
'a',
'field',
'of',
'computer',
'science',
'that',
'often']
In [15]:
X[0][:20]

Out[15]:
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
dtype=int64)
In [16]:
X[0].shape

Out[16]:
(5484,)
In [17]:
print("ML - AI \t", euclidean_distance(X[0], X[1]), "\n"
"ML - soccer \t", euclidean_distance(X[0], X[2]), "\n"
"ML - tennis \t", euclidean_distance(X[0], X[3]))

ML - AI 	 846.53411035823
ML - soccer 	 479.75827246645787
ML - tennis 	 789.7069076562519

In [18]:
print("ML - AI \t", cosine_similarity(X[0], X[1]), "\n"
"ML - soccer \t", cosine_similarity(X[0], X[2]), "\n"
"ML - tennis \t", cosine_similarity(X[0], X[3]))

ML - AI 	 0.8887965704386804
ML - soccer 	 0.7839297821715802
ML - tennis 	 0.7935675914311315

In [19]:
def l1_normalize(v):
norm = np.sum(v)
return v / norm

def l2_normalize(v):
norm = np.sqrt(np.sum(np.square(v)))
return v / norm

In [20]:
print("ML - AI \t", 1 - euclidean_distance(l1_normalize(X[0]), l1_normalize(X[1])), "\n"
"ML - soccer \t", 1 - euclidean_distance(l1_normalize(X[0]), l1_normalize(X[2])), "\n"
"ML - tennis \t", 1 - euclidean_distance(l1_normalize(X[0]), l1_normalize(X[3])))

ML - AI 	 0.9556356337470292
ML - soccer 	 0.9291904899197152
ML - tennis 	 0.9314819689984162

In [21]:
print("ML - AI \t", 1 - euclidean_distance(l2_normalize(X[0]), l2_normalize(X[1])), "\n"
"ML - soccer \t", 1 - euclidean_distance(l2_normalize(X[0]), l2_normalize(X[2])), "\n"
"ML - tennis \t", 1 - euclidean_distance(l2_normalize(X[0]), l2_normalize(X[3])))

ML - AI 	 0.5283996828641448
ML - soccer 	 0.3426261066509869
ML - tennis 	 0.3574544240773757


# Categorize a Tweet¶

In [22]:
ml_tweet = "New research release: overcoming many of Reinforcement Learning's limitations with Evolution Strategies."
x = np.array(cv.transform([ml_tweet]).todense())[0]

In [23]:
print("tweet - ML \t", euclidean_distance(x, X[0]), "\n"
"tweet - AI \t", euclidean_distance(x, X[1]), "\n"
"tweet - soccer \t", euclidean_distance(x, X[2]), "\n"
"tweet - tennis \t", euclidean_distance(x, X[3]))

tweet - ML 	 373.09114167988497
tweet - AI 	 1160.7269274036853
tweet - soccer 	 712.600168397398
tweet - tennis 	 1052.5796881946753

In [24]:
print("tweet - ML \t", cosine_similarity(x, X[0]), "\n"
"tweet - AI \t", cosine_similarity(x, X[1]), "\n"
"tweet - soccer \t", cosine_similarity(x, X[2]), "\n"
"tweet - tennis \t", cosine_similarity(x, X[3]))

tweet - ML 	 0.2613347291026786
tweet - AI 	 0.19333084671126158
tweet - soccer 	 0.1197543563241326
tweet - tennis 	 0.11622680287651725

In [25]:
print("tweet - ML \t", 1 - euclidean_distance(l2_normalize(x), l2_normalize(X[0])), "\n"
"tweet - AI \t", 1 - euclidean_distance(l2_normalize(x), l2_normalize(X[1])), "\n"
"tweet - soccer \t", 1 - euclidean_distance(l2_normalize(x), l2_normalize(X[2])), "\n"
"tweet - tennis \t", 1 - euclidean_distance(l2_normalize(x), l2_normalize(X[3])))

tweet - ML 	 -0.2154548703241279
tweet - AI 	 -0.2701725499228351
tweet - soccer 	 -0.32683506410998
tweet - tennis 	 -0.3294910282687

In [26]:
so_tweet = "#LegendsDownUnder The Reds are out for the warm up at the @nibStadium. Not long now until kick-off in Perth."
x2 = np.array(cv.transform([so_tweet]).todense())[0]

In [27]:
print("tweet - ML \t", euclidean_distance(x2, X[0]), "\n"
"tweet - AI \t", euclidean_distance(x2, X[1]), "\n"
"tweet - soccer \t", euclidean_distance(x2, X[2]), "\n"
"tweet - tennis \t", euclidean_distance(x2, X[3]))

tweet - ML 	 371.8669116767449
tweet - AI 	 1159.1397672412072
tweet - soccer 	 710.1035135809426
tweet - tennis 	 1050.1485609188826

In [28]:
print("tweet - ML \t", cosine_similarity(x2, X[0]), "\n"
"tweet - AI \t", cosine_similarity(x2, X[1]), "\n"
"tweet - soccer \t", cosine_similarity(x2, X[2]), "\n"
"tweet - tennis \t", cosine_similarity(x2, X[3]))

tweet - ML 	 0.4396242958582417
tweet - AI 	 0.46942065152331963
tweet - soccer 	 0.6136116162795926
tweet - tennis 	 0.5971160690477066

In [29]:
print("tweet - ML \t", 1 - euclidean_distance(l2_normalize(x2), l2_normalize(X[0])), "\n"
"tweet - AI \t", 1 - euclidean_distance(l2_normalize(x2), l2_normalize(X[1])), "\n"
"tweet - soccer \t", 1 - euclidean_distance(l2_normalize(x2), l2_normalize(X[2])), "\n"
"tweet - tennis \t", 1 - euclidean_distance(l2_normalize(x2), l2_normalize(X[3])))

tweet - ML 	 -0.0586554719470902
tweet - AI 	 -0.030125573390623384
tweet - soccer 	 0.12092277504145588
tweet - tennis 	 0.10235426703816686