Complete main algorithm, add GUI

main.py

@@ -1,12 +1,14 @@
 #!/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
@@ -15,22 +17,35 @@ 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
 
-is_plots: bool = False
+MAX_LEVEL: int = 5
-default_clusters: int = 3
+GENDERS: dict = {
+    0: 'не указан',
+    1: 'женский',
+    2: 'мужской'
+}
 georeverse: Georeverse = Georeverse()
 
+is_plots: bool = False
 
-def __plots(data: ndarray, labels: ndarray) -> None:
+
-    plt.figure(figsize=(12, 6))
+def __plots(data: ndarray, labels: ndarray, level: int) -> None:
-    plt.subplot(1, 2, 1)
+    print(level)
+    if data.shape[1] > 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')
-    pca = PCA(n_components=2)
+    if data.shape[1] > 1:
-    transformed = pd.DataFrame(pca.fit_transform(data)).to_numpy()
+        pca = PCA(n_components=2)
-    plt.subplot(1, 2, 2)
+        transformed = pd.DataFrame(pca.fit_transform(data)).to_numpy()
-    plt.scatter(x=transformed[:, 0], y=transformed[:, 1], c=labels, cmap='rainbow')
+        plt.subplot(1, 2, 2)
-    plt.title('Clustering')
+        plt.scatter(x=transformed[:, 0], y=transformed[:, 1], c=labels, cmap='rainbow')
+        plt.title('Clustering')
     plt.show()
 
 
@@ -42,32 +57,92 @@ def __get_cluster_centers(data: ndarray, labels: ndarray) -> ndarray:
     return np.array(centers)
 
 
-def __print_center(center: ndarray) -> None:
+def __format_center(center: ndarray, level: int) -> str:
-    location: str = georeverse.get_city(center[0], center[1])
+    if level == 1:
-    sex = round(center[2])
+        location: str = str(georeverse.get_city(center[0], center[1]))
-    age = round(center[3])
+        return location
-    is_university = bool(round(center[4]))
+    if level == 3:
-    is_work = bool(round(center[5]))
+        age = round(center[0])
-    is_student = bool(round(center[6]))
+        return str(age)
-    is_schoolboy = bool(round(center[7]))
+    if level == 2:
-    print(f'location: {location}, sex: {sex}, age: {age},'
+        sex = round(center[0])
-          f' univer: {is_university}, work: {is_work}, student: {is_student}, school: {is_schoolboy}')
+        return GENDERS[sex]
+    # 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, n_clusters: int = 3, plots: bool = False) -> None:
+def __clustering(data: ndarray, root_node: TreeNode, plots: bool = False, level: int = 1) -> None:
-    model = AgglomerativeClustering(n_clusters=n_clusters, metric='euclidean', linkage='ward')
+    if level == MAX_LEVEL:
-    model.fit(data)
+        return
-    labels = model.labels_
+    cl_data = None
+    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(data, labels)
+        __plots(cl_data, labels, level)
-    centers = __get_cluster_centers(data, labels)
+    centers = __get_cluster_centers(cl_data, labels)
-    for center in centers:
+    nodes: dict = {}
-        __print_center(center)
+    for index, center in enumerate(centers):
+        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()
-    __clustering(data, default_clusters, is_plots)
+    tree_root: TreeNode = TreeNode(0, f'ROOT', len(data))
+    __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__':

src/main/tree_node.py

@@ -0,0 +1,18 @@
+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

@@ -0,0 +1,26 @@
+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