main.py

@@ -1,14 +1,12 @@
 #!/usr/bin/env python3
 import os
 import sys
-from tkinter import Tk, Frame, Scrollbar, RIGHT, Y
 from typing import List
 
 import numpy
 import numpy as np
 import pandas as pd
 import scipy.cluster.hierarchy as sc
-from anytree import RenderTree
 from matplotlib import pyplot as plt
 from numpy import ndarray
 from pandas import Series
@@ -17,35 +15,22 @@ from sklearn.decomposition import PCA
 
 from src.main.df_loader import DfLoader
 from src.main.georeverse import Georeverse
-from src.main.tree_node import TreeNode
-from src.main.tree_view import TreeView
-
-MAX_LEVEL: int = 5
-GENDERS: dict = {
-    0: 'не указан',
-    1: 'женский',
-    2: 'мужской'
-}
-georeverse: Georeverse = Georeverse()
 
 is_plots: bool = False
+default_clusters: int = 3
+georeverse: Georeverse = Georeverse()
 
 
-def __plots(data: ndarray, labels: ndarray, level: int) -> None:
+def __plots(data: ndarray, labels: ndarray) -> None:
-    print(level)
+    plt.figure(figsize=(12, 6))
-    if data.shape[1] > 1:
+    plt.subplot(1, 2, 1)
-        plt.figure(figsize=(12, 6))
-        plt.subplot(1, 2, 1)
-    else:
-        plt.figure(figsize=(6, 6))
     sc.dendrogram(sc.linkage(data, method='ward'), p=4, truncate_mode='level')
     plt.title('Dendrogram')
-    if data.shape[1] > 1:
+    pca = PCA(n_components=2)
-        pca = PCA(n_components=2)
+    transformed = pd.DataFrame(pca.fit_transform(data)).to_numpy()
-        transformed = pd.DataFrame(pca.fit_transform(data)).to_numpy()
+    plt.subplot(1, 2, 2)
-        plt.subplot(1, 2, 2)
+    plt.scatter(x=transformed[:, 0], y=transformed[:, 1], c=labels, cmap='rainbow')
-        plt.scatter(x=transformed[:, 0], y=transformed[:, 1], c=labels, cmap='rainbow')
+    plt.title('Clustering')
-        plt.title('Clustering')
     plt.show()
 
 
@@ -57,92 +42,32 @@ def __get_cluster_centers(data: ndarray, labels: ndarray) -> ndarray:
     return np.array(centers)
 
 
-def __format_center(center: ndarray, level: int) -> str:
+def __print_center(center: ndarray) -> None:
-    if level == 1:
+    location: str = georeverse.get_city(center[0], center[1])
-        location: str = str(georeverse.get_city(center[0], center[1]))
+    sex = round(center[2])
-        return location
+    age = round(center[3])
-    if level == 3:
+    is_university = bool(round(center[4]))
-        age = round(center[0])
+    is_work = bool(round(center[5]))
-        return str(age)
+    is_student = bool(round(center[6]))
-    if level == 2:
+    is_schoolboy = bool(round(center[7]))
-        sex = round(center[0])
+    print(f'location: {location}, sex: {sex}, age: {age},'
-        return GENDERS[sex]
+          f' univer: {is_university}, work: {is_work}, student: {is_student}, school: {is_schoolboy}')
-    # if level == 4:
-    #     is_university = bool(round(center[0]))
-    #     is_work = bool(round(center[1]))
-    #     is_student = bool(round(center[2]))
-    #     is_schoolboy = bool(round(center[3]))
-    #     return f'univer: {is_university}, work: {is_work}, student: {is_student}, school: {is_schoolboy}'
-    raise Exception(f'Unknown level {level}')
 
 
-def __clustering(data: ndarray, root_node: TreeNode, plots: bool = False, level: int = 1) -> None:
+def __clustering(data: ndarray, n_clusters: int = 3, plots: bool = False) -> None:
-    if level == MAX_LEVEL:
+    model = AgglomerativeClustering(n_clusters=n_clusters, metric='euclidean', linkage='ward')
-        return
+    model.fit(data)
-    cl_data = None
+    labels = model.labels_
-    clusters = 0
-    if level == 1:
-        cl_data = data[:, 0:2]
-        clusters = 3
-    if level == 3:
-        cl_data = data[:, 3].reshape(-1, 1)
-        clusters = 7
-    if level == 2:
-        cl_data = data[:, 2].reshape(-1, 1)
-        clusters = 2
-    if level == 4:
-        univer: int = len(data[np.where(data[:, 4] == 1)])
-        work: int = len(data[np.where(data[:, 5] == 1)])
-        student: int = len(data[np.where(data[:, 6] == 1)])
-        schoolboy: int = len(data[np.where(data[:, 7] == 1)])
-        TreeNode(1, 'Высшее образование', univer, parent=root_node)
-        TreeNode(2, 'Работает', work, parent=root_node)
-        TreeNode(3, 'Студент', student, parent=root_node)
-        TreeNode(4, 'Школьник', schoolboy, parent=root_node)
-        return
-    if cl_data is None:
-        raise Exception(f'Unknown level {level}')
-    model = AgglomerativeClustering(n_clusters=clusters, metric='euclidean', linkage='ward')
-    if len(cl_data) > 1:
-        model.fit(cl_data)
-        labels = model.labels_
-    else:
-        labels = np.array([0])
     if plots:
-        __plots(cl_data, labels, level)
+        __plots(data, labels)
-    centers = __get_cluster_centers(cl_data, labels)
+    centers = __get_cluster_centers(data, labels)
-    nodes: dict = {}
+    for center in centers:
-    for index, center in enumerate(centers):
+        __print_center(center)
-        size: int = len(cl_data[numpy.where(labels[:] == index)])
-        node: TreeNode = TreeNode(index, __format_center(center, level), size, parent=root_node)
-        nodes[index] = node
-    if len(centers) == 1:
-        return
-    for cluster in range(clusters):
-        __clustering(data[numpy.where(labels[:] == cluster)], nodes[cluster], plots, level + 1)
-
-
-def __tree_sort(items):
-    return sorted(items, key=lambda item: item.data)
 
 
 def __main(json_file):
     data: ndarray = DfLoader(json_file).get_data()
-    tree_root: TreeNode = TreeNode(0, f'ROOT', len(data))
+    __clustering(data, default_clusters, is_plots)
-    __clustering(data, tree_root, is_plots)
-    print(print('\n'.join([f'{pre}{node}' for pre, fill, node in RenderTree(tree_root, childiter=__tree_sort)])))
-    root = Tk(className='Clustering')
-    root.geometry('800x600')
-    frame = Frame(root)
-    frame.grid(column=0, row=0, sticky="nsew")
-    scrollbar = Scrollbar(frame)
-    scrollbar.pack(side=RIGHT, fill=Y)
-    tv = TreeView(frame, tree_root)
-    tv.generate()
-    scrollbar.config(command=tv.get().yview)
-    root.rowconfigure(0, weight=1)
-    root.columnconfigure(0, weight=1)
-    root.mainloop()
 
 
 if __name__ == '__main__':

requirements.txt

@@ -3,5 +3,5 @@ geopy==2.3.0
 numpy==1.24.3
 scikit-learn==1.2.2
 matplotlib==3.7.1
-scipy==1.10.1
+seaborn==0.12.2
-anytree==2.8.0
+scipy==1.10.1

src/main/georeverse.py

@@ -1,13 +1,12 @@
 from functools import partial
 
 from geopy import Nominatim
-from geopy.extra.rate_limiter import RateLimiter
 
 
 class Georeverse:
     def __init__(self) -> None:
         geolocator: Nominatim = Nominatim(user_agent="MyApp")
-        self.__reverse = RateLimiter(partial(geolocator.reverse, language="ru"), min_delay_seconds=1)
+        self.__reverse = partial(geolocator.reverse, language="ru")
 
     def get_city(self, latitude: float, longitude: float) -> str:
         return self.__reverse(f'{latitude}, {longitude}')

src/main/tree_node.py

@@ -1,18 +0,0 @@
-from anytree import NodeMixin
-
-
-class TreeNode(NodeMixin):
-    def __init__(self, index: int, data: str, size: int, parent: NodeMixin = None) -> None:
-        super(TreeNode, self).__init__()
-        self.index = index
-        self.data = data
-        self.size = size
-        self.parent = parent
-
-    def __get_percent(self) -> float:
-        if self.parent is None:
-            return 100
-        return round(self.size / self.parent.size * 100, 2)
-
-    def __repr__(self) -> str:
-        return f'{self.data} [{self.size}/{self.__get_percent()}%]'

src/main/tree_view.py

@@ -1,26 +0,0 @@
-import re
-from tkinter import Frame, ttk, LEFT, BOTH, TRUE
-
-from anytree import LevelOrderGroupIter
-
-
-class TreeView(Frame):
-    def __init__(self, parent, tree):
-        super().__init__()
-        self.tree = tree
-        self.treeview = ttk.Treeview(parent, height=30)
-
-    def get(self):
-        return self.treeview
-
-    @staticmethod
-    def alphanum_key(key):
-        return [int(s) if s.isdigit() else s.lower() for s in re.split("([0-9]+)", key)]
-
-    def generate(self):
-        self.treeview.pack(side=LEFT, fill=BOTH, expand=TRUE)
-        nodes_d = {}
-        for nodes in LevelOrderGroupIter(self.tree):
-            for index, node in enumerate(sorted(nodes, key=lambda item: self.alphanum_key(item.data))):
-                idd = self.treeview.insert('' if node.parent is None else nodes_d[node.parent], index, text=node)
-                nodes_d[node] = idd