Update dbscan.py

machine_learning/dbscan.py

@@ -1,20 +1,21 @@
-import pandas as pd
 import math
 import matplotlib.pyplot as plt
-from typing import dict, list
+import pandas as pd
-
+from typing import dict, list, optional
-
 class DbScan:
-    """
+    '''
     DBSCAN Algorithm :
     Density-Based Spatial Clustering Of Applications With Noise
-    Refer this website for more details : https://en.wikipedia.org/wiki/DBSCAN
+    Reference Website : https://en.wikipedia.org/wiki/DBSCAN
+    Reference YouTube Video : https://youtu.be/-p354tQsKrs?si=t1IxCFhrOB-RAcIU
     
     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.
+        perform_dbscan() : Invoked by constructor and calculates the core
-        print_dbscan()   : Prints the core and noise points along with stating if the noise are border points or not.
+                            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
@@ -23,13 +24,17 @@ class DbScan:
     obj = dbscan.DbScan(minpts, radius, file)
     obj.print_dbscan()
     obj.plot_dbscan()
-    """
+    '''
-
+    def __init__(self, minpts : int, radius : int, file : optional[str] =
-    def __init__(self, minpts: int, radius: int, file: str) -> None:
+                 [{'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
 
-        Attributes:
+        Args:
         -----------
             minpts (int) : Minimum number of points needed to be
                            within the radius to considered as core
@@ -54,97 +59,111 @@ class DbScan:
             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]]:
-        """
+        '''
-        Parameters:
+        Args:
         -----------
             None
 
         Return:
         --------
-        Dictionary with points and the list of points
+            Dictionary with points and the list
-        that lie in its radius
+            of points that lie in its radius
-        """
+
-        data = pd.read_csv(self.file)
+        >>> 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]
+
+        '''
+        data = pd.read_csv(self.file) if type(self.file) == type("str") else pd.DataFrame(self.file)
         e = self.radius
         dict1 = {}
         for i in range(len(data)):
             for j in range(len(data)):
-                dist = math.sqrt(
+                dist = math.sqrt(pow(data['x'][j] - data['x'][i],2)
-                    pow(data["x"][j] - data["x"][i], 2)
+                                 + pow(data['y'][j] - data['y'][i],2))
-                    + pow(data["y"][j] - data["y"][i], 2)
-                )
                 if dist < e:
-                    if i + 1 in dict1:
+                    if i+1 in dict1:
-                        dict1[i + 1].append(j + 1)
+                        dict1[i+1].append(j+1)
                     else:
-                        dict1[i + 1] = [
+                        dict1[i+1] = [j+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=" ---> ")
+            print(i," ",self.dict1[i], end=' ---> ')
             if len(self.dict1[i]) >= self.minpts:
                 print("Core")
             else:
                 for j in self.dict1:
-                    if (
+                    if i != j and len(self.dict1[j]) >= self.minpts:
-                        i != j
+                        if i in self.dict1[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.
-        """
+
-        data = pd.read_csv(self.file)
+        >>> DbScan(4,1.9).plot_dbscan()
+        Plotted Successfully
+        '''
+        data = pd.read_csv(self.file) if type(self.file) == type("str") else pd.DataFrame(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")
+                plt.scatter(data['x'][i-1], data['y'][i-1], color='red')
-                circle = plt.Circle(
+                circle = plt.Circle((data['x'][i-1], data['y'][i-1]),
-                    (data["x"][i - 1], data["y"][i - 1]), e, color="blue", fill=False
+                                    e, color='blue', fill=False)
-                )
                 plt.gca().add_artist(circle)
-                plt.text(
+                plt.text(data['x'][i-1], data['y'][i-1],
-                    data["x"][i - 1],
+                         'P'+str(i), ha='center', va='bottom')
-                    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.scatter(data['x'][i-1], data['y'][i-1], color='green')
-                plt.text(
+                plt.text(data['x'][i-1], data['y'][i-1],
-                    data["x"][i - 1],
+                         'P'+str(i), ha='center', va='bottom')
-                    data["y"][i - 1],
+        plt.xlabel('X')
-                    "P" + str(i),
+        plt.ylabel('Y')
-                    ha="center",
+        plt.title('DBSCAN Clustering')
-                    va="bottom",
+        plt.legend(['Core','Noise'])
-                )
-        plt.xlabel("X")
-        plt.ylabel("Y")
-        plt.title("DBSCAN Clustering")
-        plt.legend(["Core", "Noise"])
         plt.show()
+        print("Plotted Successfully")