Implementation of Density-based spatial clustering of applications with noise (DBSCAN) ML Algorithm

machine_learning/dbscan.py

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+"""
+
+Author : Gowtham Kamalasekar
+LinkedIn : https://www.linkedin.com/in/gowtham-kamalasekar/
+
+"""
+
+import math
+
+import matplotlib.patches as mpatches
+import matplotlib.pyplot as plt
+import pandas as pd
+
+
+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()
+    """
+
+    def __init__(
+        self,
+        minpts: int,
+        radius: int,
+        file: str = "None",
+    ) -> 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
+            if file != "None"
+            else (
+                {"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},
+            )
+        )
+        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]
+
+        >>> result = DbScan(3, 2.5).perform_dbscan()
+        >>> for key in sorted(result):
+        ...     print(key, sorted(result[key]))
+        1 [1, 2, 10, 11]
+        2 [1, 2, 3, 10, 11]
+        3 [2, 3, 4, 11]
+        4 [3, 4, 5, 6, 7, 8]
+        5 [4, 5, 6, 7, 8]
+        6 [4, 5, 6, 7]
+        7 [4, 5, 6, 7, 8]
+        8 [4, 5, 7, 8]
+        9 [9, 11, 12]
+        10 [1, 2, 10, 11, 12]
+        11 [1, 2, 3, 9, 10, 11, 12]
+        12 [9, 10, 11, 12]
+
+        >>> result = DbScan(5, 2.5).perform_dbscan()
+        >>> for key in sorted(result):
+        ...     print(key, sorted(result[key]))
+        1 [1, 2, 10, 11]
+        2 [1, 2, 3, 10, 11]
+        3 [2, 3, 4, 11]
+        4 [3, 4, 5, 6, 7, 8]
+        5 [4, 5, 6, 7, 8]
+        6 [4, 5, 6, 7]
+        7 [4, 5, 6, 7, 8]
+        8 [4, 5, 7, 8]
+        9 [9, 11, 12]
+        10 [1, 2, 10, 11, 12]
+        11 [1, 2, 3, 9, 10, 11, 12]
+        12 [9, 10, 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: dict[int, list[int]] = {}
+        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
+
+        >>> DbScan(5,2.5).print_dbscan()
+        1   [1, 2, 10, 11] ---> Noise ---> Border
+        2   [1, 2, 3, 10, 11] ---> Core
+        3   [2, 3, 4, 11] ---> Noise ---> Border
+        4   [3, 4, 5, 6, 7, 8] ---> Core
+        5   [4, 5, 6, 7, 8] ---> Core
+        6   [4, 5, 6, 7] ---> Noise ---> Border
+        7   [4, 5, 6, 7, 8] ---> Core
+        8   [4, 5, 7, 8] ---> Noise ---> Border
+        9   [9, 11, 12] ---> Noise ---> Border
+        10   [1, 2, 10, 11, 12] ---> Core
+        11   [1, 2, 3, 9, 10, 11, 12] ---> Core
+        12   [9, 10, 11, 12] ---> Noise ---> Border
+
+        >>> DbScan(2,0.5).print_dbscan()
+        1   [1] ---> Noise
+        2   [2] ---> Noise
+        3   [3] ---> Noise
+        4   [4] ---> Noise
+        5   [5] ---> Noise
+        6   [6] ---> Noise
+        7   [7] ---> Noise
+        8   [8] ---> Noise
+        9   [9] ---> Noise
+        10   [10] ---> Noise
+        11   [11] ---> Noise
+        12   [12] ---> Noise
+
+        """
+        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
+
+        >>> DbScan(5,2.5).plot_dbscan()
+        Plotted Successfully
+
+        >>> DbScan(5,2.5).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",
+                )
+        core_legend = mpatches.Patch(color="red", label="Core")
+        noise_legend = mpatches.Patch(color="green", label="Noise")
+        plt.xlabel("X")
+        plt.ylabel("Y")
+        plt.title("DBSCAN Clustering")
+        plt.legend(handles=[core_legend, noise_legend])
+        plt.show()
+        print("Plotted Successfully")
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()