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"""
Author : Gowtham Kamalasekar
LinkedIn : https://www.linkedin.com/in/gowtham-kamalasekar/
"""
class DbScan:
"""
DBSCAN Algorithm :
Density-Based Spatial Clustering Of Applications With Noise
Refer this website for more details : https://en.wikipedia.org/wiki/DBSCAN
Functions:
----------
__init__() : Constructor that sets minPts, radius and file
perform_dbscan() : Invoked by constructor and calculates the core
and noise points and returns a dictionary.
print_dbscan() : Prints the core and noise points along
with stating if the noise are border points or not.
plot_dbscan() : Plots the points to show the core and noise point.
To create a object
------------------
import dbscan
obj = dbscan.DbScan(minpts, radius, file)
obj.print_dbscan()
obj.plot_dbscan()
"""
import math
import matplotlib.pyplot as plt
import pandas as pd
from typing import dict, list
def __init__(
self,
minpts: int,
radius: int,
file: str = (
{"x": 3, "y": 7},
{"x": 4, "y": 6},
{"x": 5, "y": 5},
{"x": 6, "y": 4},
{"x": 7, "y": 3},
{"x": 6, "y": 2},
{"x": 7, "y": 2},
{"x": 8, "y": 4},
{"x": 3, "y": 3},
{"x": 2, "y": 6},
{"x": 3, "y": 5},
{"x": 2, "y": 4},
),
) -> None:
"""
Constructor
Args:
-----------
minpts (int) : Minimum number of points needed to be
within the radius to considered as core
radius (int) : The radius from a given core point where
other core points can be considered as core
file (csv) : CSV file location. Should contain x and y
coordinate value for each point.
Example :
minPts = 4
radius = 1.9
file = 'data_dbscan.csv'
File Structure of CSV Data:
---------------------------
_____
x | y
-----
3 | 7
4 | 6
5 | 5
6 | 4
7 | 3
-----
"""
self.minpts = minpts
self.radius = radius
self.file = file
self.dict1 = self.perform_dbscan()
def perform_dbscan(self) -> dict[int, list[int]]:
"""
Args:
-----------
None
Return:
--------
Dictionary with points and the list
of points that lie in its radius
>>> result = DbScan(4, 1.9).perform_dbscan()
>>> for key in sorted(result):
... print(key, sorted(result[key]))
1 [1, 2, 10]
2 [1, 2, 3, 11]
3 [2, 3, 4]
4 [3, 4, 5]
5 [4, 5, 6, 7, 8]
6 [5, 6, 7]
7 [5, 6, 7]
8 [5, 8]
9 [9, 12]
10 [1, 10, 11]
11 [2, 10, 11, 12]
12 [9, 11, 12]
"""
if type(self.file) is str:
data = pd.read_csv(self.file)
else:
data = pd.DataFrame(list(self.file))
e = self.radius
dict1 = {}
for i in range(len(data)):
for j in range(len(data)):
dist = math.sqrt(
pow(data["x"][j] - data["x"][i], 2)
+ pow(data["y"][j] - data["y"][i], 2)
)
if dist < e:
if i + 1 in dict1:
dict1[i + 1].append(j + 1)
else:
dict1[i + 1] = [
j + 1,
]
return dict1
def print_dbscan(self) -> None:
"""
Outputs:
--------
Prints each point and if it is a core or a noise (w/ border)
>>> DbScan(4,1.9).print_dbscan()
1 [1, 2, 10] ---> Noise ---> Border
2 [1, 2, 3, 11] ---> Core
3 [2, 3, 4] ---> Noise ---> Border
4 [3, 4, 5] ---> Noise ---> Border
5 [4, 5, 6, 7, 8] ---> Core
6 [5, 6, 7] ---> Noise ---> Border
7 [5, 6, 7] ---> Noise ---> Border
8 [5, 8] ---> Noise ---> Border
9 [9, 12] ---> Noise
10 [1, 10, 11] ---> Noise ---> Border
11 [2, 10, 11, 12] ---> Core
12 [9, 11, 12] ---> Noise ---> Border
"""
for i in self.dict1:
print(i, " ", self.dict1[i], end=" ---> ")
if len(self.dict1[i]) >= self.minpts:
print("Core")
else:
for j in self.dict1:
if (
i != j
and len(self.dict1[j]) >= self.minpts
and i in self.dict1[j]
):
print("Noise ---> Border")
break
else:
print("Noise")
def plot_dbscan(self) -> None:
"""
Output:
-------
A matplotlib plot that show points as core and noise along
with the circle that lie within it.
>>> DbScan(4,1.9).plot_dbscan()
Plotted Successfully
"""
if type(self.file) is str:
data = pd.read_csv(self.file)
else:
data = pd.DataFrame(list(self.file))
e = self.radius
for i in self.dict1:
if len(self.dict1[i]) >= self.minpts:
plt.scatter(data["x"][i - 1], data["y"][i - 1], color="red")
circle = plt.Circle(
(data["x"][i - 1], data["y"][i - 1]), e, color="blue", fill=False
)
plt.gca().add_artist(circle)
plt.text(
data["x"][i - 1],
data["y"][i - 1],
"P" + str(i),
ha="center",
va="bottom",
)
else:
plt.scatter(data["x"][i - 1], data["y"][i - 1], color="green")
plt.text(
data["x"][i - 1],
data["y"][i - 1],
"P" + str(i),
ha="center",
va="bottom",
)
plt.xlabel("X")
plt.ylabel("Y")
plt.title("DBSCAN Clustering")
plt.legend(["Core", "Noise"])
plt.show()
print("Plotted Successfully")
if __name__ == "__main__":
import doctest
doctest.testmod()