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Gowtham Kamalasekar
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machine_learning/dbscan.py
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299
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.patches as mpatches
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import matplotlib.pyplot as plt
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import pandas as pd
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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__(
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self,
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minpts: int,
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radius: int,
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file: str = "None",
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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 = (
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file
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if file != "None"
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else (
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{"x": 3, "y": 7},
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{"x": 4, "y": 6},
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{"x": 5, "y": 5},
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{"x": 6, "y": 4},
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{"x": 7, "y": 3},
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{"x": 6, "y": 2},
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{"x": 7, "y": 2},
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{"x": 8, "y": 4},
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{"x": 3, "y": 3},
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{"x": 2, "y": 6},
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{"x": 3, "y": 5},
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{"x": 2, "y": 4},
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)
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)
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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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>>> result = DbScan(3, 2.5).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, 11]
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2 [1, 2, 3, 10, 11]
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3 [2, 3, 4, 11]
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4 [3, 4, 5, 6, 7, 8]
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5 [4, 5, 6, 7, 8]
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6 [4, 5, 6, 7]
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7 [4, 5, 6, 7, 8]
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8 [4, 5, 7, 8]
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9 [9, 11, 12]
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10 [1, 2, 10, 11, 12]
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11 [1, 2, 3, 9, 10, 11, 12]
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12 [9, 10, 11, 12]
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>>> result = DbScan(5, 2.5).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, 11]
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2 [1, 2, 3, 10, 11]
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3 [2, 3, 4, 11]
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4 [3, 4, 5, 6, 7, 8]
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5 [4, 5, 6, 7, 8]
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6 [4, 5, 6, 7]
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7 [4, 5, 6, 7, 8]
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8 [4, 5, 7, 8]
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9 [9, 11, 12]
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10 [1, 2, 10, 11, 12]
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11 [1, 2, 3, 9, 10, 11, 12]
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12 [9, 10, 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: dict[int, list[int]] = {}
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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(
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pow(data["x"][j] - data["x"][i], 2)
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+ pow(data["y"][j] - data["y"][i], 2)
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)
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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] = [
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j + 1,
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]
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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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>>> DbScan(5,2.5).print_dbscan()
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1 [1, 2, 10, 11] ---> Noise ---> Border
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2 [1, 2, 3, 10, 11] ---> Core
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3 [2, 3, 4, 11] ---> Noise ---> Border
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4 [3, 4, 5, 6, 7, 8] ---> Core
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5 [4, 5, 6, 7, 8] ---> Core
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6 [4, 5, 6, 7] ---> Noise ---> Border
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7 [4, 5, 6, 7, 8] ---> Core
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8 [4, 5, 7, 8] ---> Noise ---> Border
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9 [9, 11, 12] ---> Noise ---> Border
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10 [1, 2, 10, 11, 12] ---> Core
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11 [1, 2, 3, 9, 10, 11, 12] ---> Core
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12 [9, 10, 11, 12] ---> Noise ---> Border
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>>> DbScan(2,0.5).print_dbscan()
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1 [1] ---> Noise
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2 [2] ---> Noise
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3 [3] ---> Noise
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4 [4] ---> Noise
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5 [5] ---> Noise
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6 [6] ---> Noise
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7 [7] ---> Noise
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8 [8] ---> Noise
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9 [9] ---> Noise
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10 [10] ---> Noise
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11 [11] ---> Noise
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12 [12] ---> Noise
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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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>>> DbScan(5,2.5).plot_dbscan()
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Plotted Successfully
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>>> DbScan(5,2.5).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(
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(data["x"][i - 1], data["y"][i - 1]), e, color="blue", fill=False
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)
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plt.gca().add_artist(circle)
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plt.text(
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data["x"][i - 1],
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data["y"][i - 1],
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"P" + str(i),
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ha="center",
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va="bottom",
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)
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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(
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data["x"][i - 1],
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data["y"][i - 1],
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"P" + str(i),
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ha="center",
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va="bottom",
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)
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core_legend = mpatches.Patch(color="red", label="Core")
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noise_legend = mpatches.Patch(color="green", label="Noise")
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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(handles=[core_legend, noise_legend])
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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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