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Final Update of DBSCAN
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tkgowtham
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machine_learning/dbscan.py
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190
machine_learning/dbscan.py
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'''
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Author : Gowtham Kamalasekar
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LinkedIn : https://www.linkedin.com/in/gowtham-kamalasekar/
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'''
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import math
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import matplotlib.pyplot as plt
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import pandas as pd
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from typing import dict, list
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class DbScan:
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'''
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DBSCAN Algorithm :
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Density-Based Spatial Clustering Of Applications With Noise
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Refer this website for more details : https://en.wikipedia.org/wiki/DBSCAN
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Functions:
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----------
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__init__() : Constructor that sets minPts, radius and file
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perform_dbscan() : Invoked by constructor and calculates the core
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and noise points and returns a dictionary.
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print_dbscan() : Prints the core and noise points along
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with stating if the noise are border points or not.
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plot_dbscan() : Plots the points to show the core and noise point.
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To create a object
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------------------
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import dbscan
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obj = dbscan.DbScan(minpts, radius, file)
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obj.print_dbscan()
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obj.plot_dbscan()
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'''
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def __init__(self, minpts : int, radius : int, file : str =
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({'x': 3, 'y': 7}, {'x': 4, 'y': 6}, {'x': 5, 'y': 5},
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{'x': 6, 'y': 4},{'x': 7, 'y': 3}, {'x': 6, 'y': 2},
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{'x': 7, 'y': 2}, {'x': 8, 'y': 4},{'x': 3, 'y': 3},
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{'x': 2, 'y': 6}, {'x': 3, 'y': 5}, {'x': 2, 'y': 4})
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) -> None:
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'''
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Constructor
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Args:
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-----------
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minpts (int) : Minimum number of points needed to be
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within the radius to considered as core
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radius (int) : The radius from a given core point where
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other core points can be considered as core
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file (csv) : CSV file location. Should contain x and y
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coordinate value for each point.
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Example :
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minPts = 4
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radius = 1.9
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file = 'data_dbscan.csv'
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File Structure of CSV Data:
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---------------------------
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_____
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x | y
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-----
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3 | 7
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4 | 6
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5 | 5
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6 | 4
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7 | 3
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-----
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'''
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self.minpts = minpts
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self.radius = radius
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self.file = file
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self.dict1 = self.perform_dbscan()
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def perform_dbscan(self) -> dict[int, list[int]]:
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'''
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Args:
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-----------
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None
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Return:
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--------
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Dictionary with points and the list
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of points that lie in its radius
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>>> result = DbScan(4, 1.9).perform_dbscan()
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>>> for key in sorted(result):
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... print(key, sorted(result[key]))
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1 [1, 2, 10]
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2 [1, 2, 3, 11]
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3 [2, 3, 4]
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4 [3, 4, 5]
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5 [4, 5, 6, 7, 8]
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6 [5, 6, 7]
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7 [5, 6, 7]
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8 [5, 8]
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9 [9, 12]
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10 [1, 10, 11]
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11 [2, 10, 11, 12]
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12 [9, 11, 12]
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'''
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if type(self.file) is str:
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data = pd.read_csv(self.file)
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else:
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data = pd.DataFrame(list(self.file))
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e = self.radius
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dict1 = {}
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for i in range(len(data)):
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for j in range(len(data)):
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dist = math.sqrt(pow(data['x'][j] - data['x'][i],2)
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+ pow(data['y'][j] - data['y'][i],2))
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if dist < e:
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if i+1 in dict1:
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dict1[i+1].append(j+1)
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else:
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dict1[i+1] = [j+1,]
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return dict1
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def print_dbscan(self) -> None:
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'''
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Outputs:
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--------
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Prints each point and if it is a core or a noise (w/ border)
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>>> DbScan(4,1.9).print_dbscan()
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1 [1, 2, 10] ---> Noise ---> Border
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2 [1, 2, 3, 11] ---> Core
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3 [2, 3, 4] ---> Noise ---> Border
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4 [3, 4, 5] ---> Noise ---> Border
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5 [4, 5, 6, 7, 8] ---> Core
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6 [5, 6, 7] ---> Noise ---> Border
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7 [5, 6, 7] ---> Noise ---> Border
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8 [5, 8] ---> Noise ---> Border
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9 [9, 12] ---> Noise
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10 [1, 10, 11] ---> Noise ---> Border
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11 [2, 10, 11, 12] ---> Core
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12 [9, 11, 12] ---> Noise ---> Border
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'''
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for i in self.dict1:
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print(i," ",self.dict1[i], end=' ---> ')
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if len(self.dict1[i]) >= self.minpts:
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print("Core")
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else:
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for j in self.dict1:
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if (
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i != j
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and len(self.dict1[j]) >= self.minpts
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and i in self.dict1[j]
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):
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print("Noise ---> Border")
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break
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else:
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print("Noise")
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def plot_dbscan(self) -> None:
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'''
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Output:
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-------
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A matplotlib plot that show points as core and noise along
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with the circle that lie within it.
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>>> DbScan(4,1.9).plot_dbscan()
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Plotted Successfully
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'''
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if type(self.file) is str:
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data = pd.read_csv(self.file)
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else:
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data = pd.DataFrame(list(self.file))
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e = self.radius
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for i in self.dict1:
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if len(self.dict1[i]) >= self.minpts:
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plt.scatter(data['x'][i-1], data['y'][i-1], color='red')
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circle = plt.Circle((data['x'][i-1], data['y'][i-1]),
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e, color='blue', fill=False)
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plt.gca().add_artist(circle)
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plt.text(data['x'][i-1], data['y'][i-1],
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'P'+str(i), ha='center', va='bottom')
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else:
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plt.scatter(data['x'][i-1], data['y'][i-1], color='green')
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plt.text(data['x'][i-1], data['y'][i-1],
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'P'+str(i), ha='center', va='bottom')
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plt.xlabel('X')
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plt.ylabel('Y')
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plt.title('DBSCAN Clustering')
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plt.legend(['Core','Noise'])
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plt.show()
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print("Plotted Successfully")
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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