AIM-PIbd-31-Bakalskaya-E-D/lab_5/lab5.ipynb

Бизнес-цель: кластеризация пациентов для выявления групп с схожими характеристиками здоровья и рисками инсульта. Что, к примеру, может использоваться для следующего:¶

• определение, люди каких групп могут иметь бОльшую предрасположенность к возникновению инсульта
• помощь в медицине на основе полученных данных в разработке медицинских показаний людям с повышенным риском возникновения инсульта

In [141]:

import pandas as pd
import numpy as np
from sklearn import cluster
from scipy.cluster import hierarchy

df1 = pd.read_csv("./csv/option4.csv", index_col='id')
df1.info
df = df1.head(2500)
df

Out[141]:

	gender	age	hypertension	heart_disease	ever_married	work_type	Residence_type	avg_glucose_level	bmi	smoking_status	stroke
id
9046	Male	67.0	0	1	Yes	Private	Urban	228.69	36.6	formerly smoked	1
51676	Female	61.0	0	0	Yes	Self-employed	Rural	202.21	NaN	never smoked	1
31112	Male	80.0	0	1	Yes	Private	Rural	105.92	32.5	never smoked	1
60182	Female	49.0	0	0	Yes	Private	Urban	171.23	34.4	smokes	1
1665	Female	79.0	1	0	Yes	Self-employed	Rural	174.12	24.0	never smoked	1
...	...	...	...	...	...	...	...	...	...	...	...
34383	Male	46.0	0	0	Yes	Private	Urban	88.23	25.8	Unknown	0
8646	Female	54.0	0	0	Yes	Private	Rural	97.47	26.7	never smoked	0
46653	Female	81.0	1	1	Yes	Private	Rural	59.28	28.1	never smoked	0
1099	Female	15.0	0	0	No	children	Rural	101.15	22.2	Unknown	0
61676	Male	77.0	0	0	Yes	Self-employed	Urban	68.38	25.1	Unknown	0

2500 rows × 11 columns

уберем пустые значения, подготовим данные:

In [142]:

print(df.isnull().sum())
print()

print(df.isnull().any())
print()

gender                 0
age                    0
hypertension           0
heart_disease          0
ever_married           0
work_type              0
Residence_type         0
avg_glucose_level      0
bmi                  119
smoking_status         0
stroke                 0
dtype: int64

gender               False
age                  False
hypertension         False
heart_disease        False
ever_married         False
work_type            False
Residence_type       False
avg_glucose_level    False
bmi                   True
smoking_status       False
stroke               False
dtype: bool

In [143]:

df['bmi'] = df['bmi'].fillna(df['bmi'].median())
print("\nНаличие пропущенных значений:")
print(df.isnull().sum())
df.describe()

Наличие пропущенных значений:
gender               0
age                  0
hypertension         0
heart_disease        0
ever_married         0
work_type            0
Residence_type       0
avg_glucose_level    0
bmi                  0
smoking_status       0
stroke               0
dtype: int64

C:\Users\elena\AppData\Local\Temp\ipykernel_68948\1629916119.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df['bmi'] = df['bmi'].fillna(df['bmi'].median())

Out[143]:

	age	hypertension	heart_disease	avg_glucose_level	bmi	stroke
count	2500.000000	2500.000000	2500.000000	2500.000000	2500.000000	2500.000000
mean	44.605296	0.108000	0.062400	108.630440	29.102840	0.099600
std	22.817713	0.310443	0.241929	47.124712	7.804786	0.299526
min	0.080000	0.000000	0.000000	55.220000	10.300000	0.000000
25%	26.000000	0.000000	0.000000	77.902500	23.975000	0.000000
50%	47.000000	0.000000	0.000000	93.200000	28.200000	0.000000
75%	63.000000	0.000000	0.000000	117.510000	33.000000	0.000000
max	82.000000	1.000000	1.000000	271.740000	97.600000	1.000000

In [144]:

import matplotlib.pyplot as plt
import seaborn as sns
from typing import List, Any

def draw_data_2d(
    df: pd.DataFrame,
    col1: int,
    col2: int,
    y: List | None = None,
    classes: List | None = None,
    subplot: Any | None = None,
):
    ax = None
    if subplot is None:
        _, ax = plt.subplots()
    else:
        ax = subplot
    scatter = ax.scatter(df[df.columns[col1]], df[df.columns[col2]], c=y)
    ax.set(xlabel=df.columns[col1], ylabel=df.columns[col2])
    if classes is not None:
        ax.legend(
            scatter.legend_elements()[0], classes, loc="lower right", title="Classes"
        )

columns = ['age', 'avg_glucose_level', 'bmi', 'hypertension']
df_temp = df[columns]

sns.set_theme(style="whitegrid")
print("ЗАВИСИМОСТЬ ЗНАЧЕНИЙ ДРУГ ОТ ДРУГА")
plt.figure(figsize=(16,12))
draw_data_2d(df_temp, 0, 1, subplot=plt.subplot(2, 2, 1))  # age vs avg_glucose_level
plt.figure(figsize=(16,12))
draw_data_2d(df_temp, 0, 2, subplot=plt.subplot(2, 2, 2))  # age vs bmi
plt.figure(figsize=(16,12))
draw_data_2d(df_temp, 0, 3, subplot=plt.subplot(2, 2, 3))  # age vs hypertension
plt.figure(figsize=(16,12))
draw_data_2d(df_temp, 1, 2, subplot=plt.subplot(2, 2, 4))  # avg_glucose_level vs bmi

ЗАВИСИМОСТЬ ЗНАЧЕНИЙ ДРУГ ОТ ДРУГА

видно, что индекс массы тела в зависимости от возраста в основном держится поменьше в раннем возрасте (до полового созревания, грубо говоря), а потом уже распределяется от адекватного до 40+ (в общем, вплоть до ожирения, с выбросами-то)

потом гипертония встречается все таки после 20 лет чаще

ну и чем ниже индекс массы тела, тем ниже и адекватнее уровень глюкозы (ну тут ясно, почему. люди с избыточным весом и болеют диабетом чаще)

продолжим приводить данные к нормальному виду, и теперь их стандартизуем:

In [145]:

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()

columns_to_scale = df_temp.drop(columns=["hypertension"]).columns
columns_to_keep = ["hypertension"]
data_scaled = scaler.fit_transform(df_temp[columns_to_scale])
df_scaled = pd.DataFrame(data_scaled, columns=columns_to_scale, index=df_temp.index)
df_scaled[columns_to_keep] = df_temp[columns_to_keep]

df_scaled

Out[145]:

	age	avg_glucose_level	bmi	hypertension
id
9046	0.981658	2.548208	0.960777	0
51676	0.718652	1.986183	-0.115701	0
31112	1.551505	-0.057528	0.435353	0
60182	0.192639	1.328647	0.678842	0
1665	1.507670	1.389985	-0.653940	1
...	...	...	...	...
34383	0.061136	-0.432990	-0.423266	0
8646	0.411811	-0.236875	-0.307929	0
46653	1.595339	-1.047440	-0.128516	1
1099	-1.297729	-0.158769	-0.884614	0
61676	1.420001	-0.854297	-0.512973	0

2500 rows × 4 columns

самое время применить иерархический алгоритм кластеризации (когда мы создаем дерево кластеров, где каждый уровень - это объединение более мелких кластеров)

In [146]:

# linkage_matrix = linkage(data_scaled, method='ward')
# plt.figure(figsize=(10,10))
# dendrogram(linkage_matrix)
# plt.title('Дендрограмма')
# plt.ylabel('')
# plt.xlabel('')
# plt.show()


import numpy as np
from sklearn import cluster
from scipy.cluster import hierarchy

def run_agglomerative(
    df: pd.DataFrame, num_clusters: int | None = 2
) -> cluster.AgglomerativeClustering:
    agglomerative = cluster.AgglomerativeClustering(
        n_clusters=num_clusters,
        compute_distances=True,
    )
    return agglomerative.fit(df)


def get_linkage_matrix(model: cluster.AgglomerativeClustering) -> np.ndarray:
    counts = np.zeros(model.children_.shape[0])  # type: ignore
    n_samples = len(model.labels_)
    for i, merge in enumerate(model.children_):  # type: ignore
        current_count = 0
        for child_idx in merge:
            if child_idx < n_samples:
                current_count += 1
            else:
                current_count += counts[child_idx - n_samples]
        counts[i] = current_count

    return np.column_stack([model.children_, model.distances_, counts]).astype(float)

def draw_dendrogram(linkage_matrix: np.ndarray):
    hierarchy.dendrogram(linkage_matrix, truncate_mode="level", p=3)
    plt.xticks(fontsize=10, rotation=45)
    plt.tight_layout()

In [147]:

tree = run_agglomerative(df_scaled)
linkage_matrix = get_linkage_matrix(tree)
draw_dendrogram(linkage_matrix)

In [149]:

result = hierarchy.fcluster(linkage_matrix, 40, criterion="distance")
y_names = ['0', '1', '2']

plt.figure(figsize=(12, 10))


draw_data_2d(df_temp, 0, 1, result, y_names, subplot=plt.subplot(2, 2, 1))  
draw_data_2d(df_temp, 0, 2, result, y_names, subplot=plt.subplot(2, 2, 2))  
draw_data_2d(df_temp, 0, 3, result, y_names, subplot=plt.subplot(2, 2, 3))  
draw_data_2d(df_temp, 1, 2, result, y_names, subplot=plt.subplot(2, 2, 4))

емае теперь переходим к НЕиерархической кластеризации будем использовать метод К-средних (K-means), мы выбираем количество кластеров и флгоритм пытается распределить данные так, чтобы минимизировать расстояние между объектами и центром их кластера

In [152]:

from typing import Tuple

def print_cluster_result(
    df: pd.DataFrame, clusters_num: int, labels: np.ndarray, separator: str = ", "
):
    for cluster_id in range(clusters_num):
        cluster_indices = np.where(labels == cluster_id)[0]
        print(f"Cluster {cluster_id + 1} ({len(cluster_indices)}):")
        rules = [str(df.index[idx]) for idx in cluster_indices]
        print(separator.join(rules))
        print("")
        print("--------")


def run_kmeans(
    df: pd.DataFrame, num_clusters: int, random_state: int
) -> Tuple[np.ndarray, np.ndarray]:
    kmeans = cluster.KMeans(n_clusters=num_clusters, random_state=random_state)
    labels = kmeans.fit_predict(df)
    return labels, kmeans.cluster_centers_

In [153]:

random_state = 9

labels, centers = run_kmeans(df_scaled, 3, random_state) 
print_cluster_result(df_scaled, 3, labels)
display(centers)

Cluster 1 (1314):
31112, 53882, 10434, 27419, 60491, 12109, 12095, 12175, 27458, 4219, 70822, 38047, 33879, 14248, 712, 24977, 47306, 62602, 4651, 1261, 61960, 1845, 37937, 47472, 35626, 36338, 18587, 15102, 59190, 47167, 38829, 55927, 65842, 19557, 7356, 17013, 72366, 6118, 7371, 70676, 27169, 19773, 66159, 71673, 42117, 57419, 26015, 26727, 66638, 70042, 32399, 3253, 71796, 14499, 49130, 51169, 66315, 37726, 54385, 35512, 8154, 4639, 12363, 4712, 33175, 2346, 42072, 30456, 59125, 56546, 48405, 36706, 71639, 60744, 7547, 5563, 68798, 72918, 50522, 3352, 70943, 48796, 16817, 31563, 55824, 20439, 45965, 8045, 37651, 41241, 62861, 72081, 32503, 12482, 56939, 43054, 34567, 50931, 16590, 3512, 42899, 43364, 44993, 210, 28939, 60739, 67432, 2182, 40899, 62466, 36841, 33486, 54567, 66204, 8003, 28378, 41081, 16077, 30184, 66071, 36255, 23410, 35684, 18937, 491, 8580, 28484, 62019, 51314, 37060, 35578, 54921, 33454, 62439, 2548, 2390, 68023, 54724, 8899, 39105, 31154, 69959, 10552, 12917, 68356, 28493, 1836, 32221, 10548, 17739, 27153, 34060, 43424, 30468, 56543, 15266, 10460, 64908, 67855, 25774, 49589, 17986, 29217, 72911, 47175, 48588, 66767, 29908, 45222, 40311, 71750, 42203, 71379, 58261, 67318, 8831, 65199, 43454, 7282, 49003, 16371, 40181, 66174, 6319, 55232, 41940, 72214, 37089, 52134, 54918, 1703, 16934, 32689, 18051, 38805, 61837, 49713, 17608, 62608, 4630, 66333, 49916, 71038, 58617, 69064, 9404, 28286, 10159, 58282, 64489, 64553, 69936, 46527, 13547, 9608, 10504, 37090, 60148, 52173, 23462, 11091, 70374, 31143, 66972, 55810, 37031, 34608, 36007, 48298, 44749, 46468, 51983, 20351, 55351, 67431, 20546, 52342, 59906, 53144, 11999, 38119, 3355, 67177, 58600, 65946, 48368, 36471, 15689, 8233, 23221, 31830, 15296, 17718, 18498, 56735, 621, 5835, 11838, 38165, 71585, 3009, 32361, 53910, 67548, 33404, 50965, 21077, 66570, 29158, 34299, 7696, 34668, 68483, 6072, 51112, 69673, 71238, 63958, 34511, 24892, 29496, 19939, 27832, 27757, 25099, 33123, 21713, 6726, 17242, 16380, 9729, 56974, 65574, 17019, 41800, 6855, 26028, 14712, 23094, 40622, 46438, 65144, 34641, 13129, 37629, 62936, 59829, 55424, 61697, 55138, 39399, 721, 40448, 58007, 11960, 24592, 67744, 8328, 32437, 55420, 53660, 56553, 30480, 31988, 45585, 52063, 27377, 7446, 65130, 30753, 22853, 12465, 64849, 24183, 39601, 46891, 38987, 21886, 5353, 44300, 46218, 39745, 13517, 36355, 22678, 52512, 3579, 3130, 5545, 63693, 33528, 11068, 62233, 7291, 36814, 48265, 10139, 12662, 43174, 72823, 30567, 21117, 50491, 61013, 71010, 23551, 12738, 57772, 16615, 68995, 53010, 967, 31145, 54338, 13223, 67932, 10255, 27012, 20541, 5892, 66883, 43196, 51514, 38184, 13997, 27796, 18390, 63409, 72882, 40866, 63561, 51422, 3590, 60665, 18430, 13365, 60983, 14615, 50277, 50811, 1246, 30712, 31308, 3325, 52808, 36109, 53336, 56831, 55592, 44583, 58227, 60810, 34612, 25595, 30550, 13367, 38609, 22159, 37413, 4169, 18888, 42503, 23645, 62382, 59521, 55386, 72547, 26973, 41033, 71442, 49624, 10572, 28910, 10381, 31956, 24665, 13683, 7387, 57011, 50053, 51125, 29077, 4970, 58291, 99, 64633, 23016, 18412, 67412, 37545, 14491, 66220, 25458, 69645, 53695, 26692, 33400, 67078, 22540, 26999, 30102, 49521, 29134, 68281, 40350, 39375, 12106, 25283, 49949, 28681, 62332, 25488, 14807, 16110, 40970, 28933, 11709, 16809, 13907, 54071, 28024, 11730, 17245, 70852, 60957, 19742, 4808, 49928, 52688, 65698, 820, 55721, 72310, 24115, 7122, 48755, 33551, 62716, 68438, 41148, 14924, 47950, 9262, 71896, 38623, 26503, 5475, 15525, 48748, 71591, 67864, 34857, 34995, 22952, 57879, 36638, 41097, 54620, 19681, 6988, 25287, 5934, 58999, 28261, 35222, 44105, 65256, 20044, 54769, 12686, 48830, 47924, 59336, 5684, 3673, 31867, 23633, 52549, 37349, 2751, 64464, 66494, 42786, 33401, 24174, 61715, 60663, 46875, 69091, 1821, 44978, 10762, 84, 45824, 61838, 57212, 62668, 33142, 17437, 38303, 36484, 60047, 16542, 18805, 17869, 6793, 49265, 6606, 23031, 22902, 4807, 9641, 10313, 12097, 45323, 39120, 68344, 66752, 5077, 49279, 42856, 10752, 42133, 4842, 58138, 65053, 24168, 61973, 18687, 72642, 54782, 24437, 65429, 3509, 15220, 4813, 31166, 9051, 28669, 59894, 18684, 35866, 51907, 7250, 37888, 42329, 27323, 72836, 66321, 53817, 32240, 28127, 40824, 29380, 65453, 9415, 65258, 62756, 49042, 66362, 10370, 156, 11105, 65667, 35231, 1893, 34728, 30352, 45497, 50371, 32687, 35295, 15746, 43268, 54540, 20973, 3532, 52550, 47414, 38804, 72861, 30944, 26191, 69312, 20162, 48989, 30411, 8598, 57347, 32645, 46643, 13602, 28326, 16906, 16837, 47585, 37404, 70678, 38649, 53266, 1191, 36820, 10133, 22566, 7055, 69177, 44764, 61983, 72268, 35927, 8727, 7516, 20425, 59878, 5858, 37053, 48073, 27145, 30328, 739, 28414, 70610, 62284, 22295, 27583, 9696, 1164, 48781, 50947, 47844, 49412, 43088, 16355, 43172, 65339, 60399, 22488, 16114, 35293, 26172, 67814, 12618, 39123, 53967, 34772, 43124, 68003, 59157, 54383, 6928, 321, 33526, 38350, 6040, 17639, 1678, 57668, 27966, 38123, 47345, 17222, 7195, 52457, 52306, 132, 8951, 64752, 51285, 14349, 66310, 58101, 28904, 6563, 55315, 65849, 31125, 5103, 67309, 2275, 15757, 38523, 65388, 60816, 67350, 40124, 20370, 35188, 28716, 56166, 47357, 33167, 21042, 71062, 49646, 24256, 62340, 39927, 17398, 20938, 21850, 43905, 62272, 62767, 3184, 69239, 42703, 39258, 48648, 71444, 35372, 31849, 2772, 11148, 50775, 69259, 12414, 21381, 68650, 57124, 19382, 24674, 72361, 36523, 1577, 32459, 7725, 67217, 49976, 42201, 11232, 14709, 57137, 51124, 42191, 35332, 36960, 12992, 62460, 41402, 63577, 52559, 66893, 742, 42902, 61512, 10943, 11447, 30746, 16949, 45297, 26452, 16378, 4559, 45257, 34386, 9076, 72824, 64132, 17827, 29378, 48609, 72474, 32094, 66818, 17860, 17078, 23836, 5296, 48184, 9511, 25559, 8096, 19735, 15310, 10245, 49023, 51020, 29095, 68994, 25935, 24567, 36679, 37759, 47558, 54264, 47893, 3178, 18141, 354, 5777, 46210, 36620, 21720, 6304, 18887, 57468, 24218, 69792, 6372, 34664, 30116, 35584, 2898, 10603, 1231, 25107, 32766, 9011, 14832, 49789, 33525, 12318, 54553, 45976, 12915, 4542, 60973, 68739, 3154, 22623, 47037, 48614, 15969, 17752, 50889, 56459, 34163, 14222, 31461, 47947, 51149, 17079, 53610, 48210, 8960, 63491, 51883, 20460, 47181, 1842, 34720, 9489, 28725, 13723, 60104, 48722, 14481, 67963, 14711, 36722, 55235, 20468, 44171, 8470, 13949, 61096, 19239, 70447, 37451, 29104, 26862, 61365, 35143, 5043, 26826, 16953, 51660, 27135, 16028, 49645, 71016, 62681, 41007, 33462, 33906, 43510, 21202, 7222, 13561, 29179, 5511, 20825, 67144, 69524, 28443, 50978, 45573, 64412, 3591, 25138, 29221, 6802, 8644, 54579, 41935, 17926, 64523, 11024, 11238, 19805, 34719, 48769, 38242, 42465, 24638, 58587, 36545, 19467, 16868, 23543, 65396, 23561, 57533, 59370, 57285, 23988, 31811, 26830, 25883, 61299, 12600, 35117, 28913, 42229, 23459, 2209, 38673, 42184, 59250, 29525, 42713, 29232, 70122, 66110, 69461, 60050, 21608, 32150, 24066, 39242, 57618, 27479, 10238, 67063, 33298, 48739, 40878, 12376, 26154, 47972, 43806, 39849, 1741, 20129, 1151, 6672, 11134, 25199, 66490, 33144, 16783, 727, 44177, 2421, 48693, 5723, 28309, 31806, 68750, 32840, 49797, 13503, 62238, 4627, 47208, 6844, 60001, 44503, 40371, 1460, 30285, 21472, 6537, 41827, 49775, 25777, 2070, 15752, 45915, 65697, 56889, 21724, 29863, 31999, 44202, 18704, 36518, 21521, 27163, 2447, 44391, 12741, 15418, 69482, 45399, 26247, 25818, 62817, 25919, 71978, 38316, 15647, 67689, 31689, 23240, 30850, 66080, 57263, 60126, 64393, 59642, 12064, 59737, 19352, 61903, 48453, 8031, 39139, 4838, 14889, 31746, 1499, 34396, 68816, 16129, 51845, 30692, 42161, 16113, 65109, 61242, 65252, 14089, 53399, 36946, 69069, 11539, 16847, 15873, 15415, 22891, 7394, 54997, 6199, 4635, 46261, 42624, 66680, 34248, 60210, 67890, 49254, 8345, 8770, 22370, 25930, 33478, 23009, 24885, 56420, 55220, 7384, 59745, 9644, 50837, 67941, 66051, 70783, 10716, 2868, 56324, 42047, 64980, 65962, 41501, 53032, 37320, 3842, 38143, 54296, 66677, 17950, 42830, 34721, 5157, 26084, 69623, 40378, 4671, 65419, 14406, 924, 71339, 394, 63362, 59928, 18680, 19439, 14099, 57774, 64033, 48472, 67956, 35602, 13818, 64895, 38320, 42040, 31857, 34764, 64817, 72450, 13988, 64371, 50295, 58645, 42599, 68370, 57813, 1329, 32974, 47600, 48246, 58086, 53957, 40137, 70500, 43872, 62951, 43244, 31198, 4591, 37150, 65632, 21826, 21036, 46923, 2265, 2860, 15964, 46483, 32166, 34188, 58359, 59347, 12849, 29694, 30806, 39956, 58543, 57549, 24289, 6206, 28227, 35229, 3045, 22386, 1077, 57903, 55982, 55465, 38432, 49666, 32365, 15351, 17466, 57569, 170, 10649, 28258, 22269, 56312, 26993, 58599, 61764, 13620, 39308, 34336, 1505, 60258, 63450, 43903, 15579, 43090, 12469, 58820, 45259, 9986, 46488, 54172, 21804, 36317, 3724, 69668, 59274, 29676, 37655, 16980, 40213, 47831, 39129, 40837, 59000, 44510, 3793, 32215, 35296, 27664, 49555, 68141, 30432, 6480, 22136, 2244, 61010, 12336, 3668, 68725, 464, 42225, 51254, 70429, 47937, 39593, 35378, 69835, 4709, 20393, 45864, 68685, 44962, 7892, 11744, 20740, 559, 68524, 34896, 44325, 3761, 65324, 34383, 8646, 46653, 61676

--------
Cluster 2 (813):
31720, 69768, 39912, 33943, 66866, 49669, 30669, 16523, 46136, 52800, 37893, 19584, 24447, 70336, 45801, 67210, 33759, 26325, 65460, 36811, 70970, 55680, 11014, 44338, 34974, 41648, 45538, 68249, 1686, 22284, 39038, 21956, 30171, 65535, 29865, 28799, 40840, 10449, 31091, 9487, 28102, 1506, 40670, 21284, 5973, 42996, 46785, 21408, 7559, 8171, 43232, 34402, 22706, 71539, 28637, 31741, 22537, 50611, 9637, 44862, 5708, 65357, 49465, 14123, 54975, 10913, 27029, 45719, 129, 530, 6107, 3655, 5010, 56681, 56716, 61350, 61465, 18108, 46436, 7351, 31164, 48272, 2893, 34376, 8595, 46474, 69687, 2953, 9179, 63050, 11974, 41523, 50441, 16927, 28265, 54375, 37832, 21058, 31279, 9201, 22622, 29933, 7621, 5374, 31564, 71808, 56998, 43134, 39383, 63606, 36331, 42359, 20751, 68241, 60902, 58439, 29010, 44912, 45238, 61511, 36942, 15095, 44315, 68245, 47732, 55709, 15311, 59807, 40639, 8544, 3361, 61408, 33552, 31364, 9906, 27794, 46809, 35565, 48144, 2532, 34363, 23650, 23046, 41927, 54866, 20364, 10997, 62999, 66184, 22870, 57523, 68131, 29873, 54182, 61300, 15274, 53016, 28848, 7745, 12593, 15553, 45796, 31840, 58767, 14391, 22321, 41673, 49086, 47523, 56870, 40791, 54304, 19234, 52454, 16575, 11176, 9612, 41513, 52580, 33723, 26235, 8320, 49529, 49785, 6886, 40055, 45283, 22685, 46745, 5046, 55847, 42441, 14387, 22384, 24108, 69427, 21688, 60777, 64732, 42710, 46683, 58909, 18616, 55529, 12204, 21397, 10324, 25514, 7663, 71793, 32352, 65218, 54643, 33410, 2543, 45289, 10792, 19153, 47876, 41615, 41537, 45759, 71929, 37154, 70380, 67405, 2549, 10782, 61742, 24099, 13948, 65766, 42482, 8008, 56089, 11745, 17733, 11743, 3606, 32554, 45893, 53538, 17006, 42108, 224, 56679, 27146, 16556, 36698, 57372, 30605, 13622, 39250, 2879, 59684, 56986, 16402, 40889, 4083, 48843, 5694, 44481, 19101, 11973, 59178, 44281, 55599, 45224, 54747, 6090, 46385, 46323, 28122, 50843, 60211, 53279, 37830, 2454, 3437, 6355, 58567, 62187, 72779, 12396, 69622, 58037, 34281, 7990, 57622, 11691, 13319, 53815, 51579, 58203, 6965, 42821, 10367, 66530, 43146, 57497, 16147, 18306, 61769, 26134, 66772, 41861, 954, 23565, 57854, 66678, 56734, 20347, 809, 67052, 69224, 13323, 59940, 46093, 46072, 47848, 71440, 59734, 32733, 61338, 59275, 19996, 31517, 56245, 9225, 45955, 53943, 53276, 39661, 51162, 4683, 782, 63565, 26389, 39518, 542, 26031, 38255, 41565, 39423, 68908, 28418, 33162, 39467, 20282, 51159, 7167, 59147, 18192, 14049, 69355, 2082, 16449, 5447, 44224, 533, 45554, 55744, 25767, 71319, 23604, 46576, 31293, 6044, 45209, 43155, 11882, 45669, 59604, 33187, 44192, 728, 28952, 51916, 21857, 55976, 27572, 54184, 4702, 45048, 30084, 50650, 40571, 13072, 22969, 47537, 26242, 36226, 32723, 35737, 30677, 50453, 14241, 40144, 13504, 51959, 2092, 68235, 3956, 34436, 40513, 48836, 62387, 3807, 51339, 32826, 44179, 14563, 40237, 65970, 51109, 5984, 13062, 47770, 12687, 36858, 50373, 13191, 47330, 64750, 70259, 72132, 11726, 13736, 43913, 41870, 37907, 15987, 57166, 47627, 8723, 937, 51806, 59412, 29233, 17762, 1405, 57953, 40251, 27013, 7586, 45357, 15219, 52987, 29327, 8085, 41820, 49057, 18070, 11770, 25454, 29224, 24219, 52089, 7297, 29910, 59339, 18754, 34312, 57798, 11605, 61924, 58015, 43271, 39714, 21785, 30214, 66419, 40931, 28559, 49815, 1625, 56309, 52034, 17175, 40544, 49152, 43672, 39286, 38043, 71721, 38094, 47350, 43675, 65801, 59953, 43772, 45754, 57485, 6128, 37082, 64541, 4538, 34001, 48964, 40393, 35432, 44010, 71044, 30290, 70752, 26366, 12436, 37698, 14677, 42743, 5686, 4789, 897, 69553, 38036, 36666, 16316, 31835, 4099, 26893, 1486, 5451, 3640, 17835, 37660, 24782, 63416, 42082, 54058, 4861, 33768, 35450, 62793, 66592, 29804, 3753, 27279, 38578, 66502, 9034, 16582, 28500, 70241, 66647, 39450, 57109, 17277, 18861, 38858, 13862, 66065, 71869, 46035, 54946, 29934, 28998, 63668, 24876, 22536, 8760, 53126, 18179, 68708, 12366, 38440, 34621, 16091, 17515, 34958, 30620, 1818, 70654, 49485, 61641, 11566, 72108, 22967, 33692, 25305, 7885, 14599, 38488, 52428, 61171, 2824, 2019, 47751, 34525, 13755, 56019, 67942, 52220, 48226, 10333, 10390, 72497, 51935, 29470, 71590, 16600, 41911, 3390, 72096, 41536, 17441, 737, 44938, 39011, 33412, 16061, 60266, 70965, 56736, 49480, 36704, 8884, 65229, 6596, 46577, 63455, 69249, 20310, 11450, 34133, 72311, 35651, 13749, 8882, 14287, 60139, 42500, 50983, 6493, 67099, 19585, 34261, 64128, 69339, 66546, 56584, 59933, 3429, 38920, 69285, 24428, 47701, 15225, 57236, 22689, 37752, 32320, 13964, 17492, 64006, 65712, 49615, 63511, 16987, 55522, 27954, 62607, 24404, 69666, 24342, 65093, 24735, 1679, 65336, 11573, 9199, 35402, 25996, 63219, 58591, 46363, 15440, 30731, 40471, 24721, 43590, 29014, 7868, 58350, 18927, 31372, 55262, 50238, 51343, 36486, 47582, 49627, 61291, 56791, 72514, 48435, 47607, 16535, 22414, 32252, 20565, 59993, 48851, 23413, 49970, 72414, 21374, 31443, 62289, 59464, 27017, 8277, 37128, 72701, 21969, 64494, 20673, 54985, 6049, 13380, 14404, 19419, 24518, 53494, 15255, 10584, 38675, 61743, 3879, 35085, 38474, 1451, 62834, 33284, 48875, 6104, 58253, 57679, 9197, 6968, 35838, 59200, 22108, 29258, 59904, 77, 54590, 52554, 12557, 2846, 61219, 65321, 23946, 20256, 27849, 33367, 1275, 31887, 35178, 9013, 60158, 8563, 61573, 43827, 31893, 63779, 71250, 55051, 2520, 21206, 60159, 3113, 62126, 51275, 3115, 35974, 44676, 40977, 67620, 63665, 33674, 10886, 67758, 41244, 50309, 27007, 37483, 29855, 66637, 22259, 19088, 6034, 61418, 59164, 54253, 66882, 54012, 28711, 58257, 36547, 13728, 4400, 24096, 65643, 30186, 11904, 20257, 2822, 18072, 53328, 8579, 29229, 48406, 1099

--------
Cluster 3 (373):
9046, 51676, 60182, 1665, 56669, 8213, 5317, 58202, 56112, 34120, 25226, 70630, 13861, 68794, 64778, 61843, 54827, 69160, 43717, 39373, 54401, 47269, 7937, 19824, 8752, 25831, 66400, 58631, 5111, 10710, 17004, 2326, 50784, 36236, 45805, 28291, 2458, 56841, 63973, 45277, 12062, 41069, 53401, 13491, 44033, 14164, 37132, 53440, 69551, 20387, 71279, 11762, 29281, 30683, 17308, 67981, 58978, 11933, 46703, 24669, 59437, 66258, 20426, 63453, 14431, 65105, 67895, 66955, 24905, 69112, 64373, 58267, 54695, 68627, 31179, 68025, 29552, 25904, 31421, 20463, 12689, 39186, 32729, 23368, 25974, 1210, 36857, 52282, 45535, 40460, 32257, 41413, 28674, 63884, 4057, 36275, 11577, 20980, 28526, 18518, 42807, 11120, 68614, 4480, 2982, 59368, 65836, 21130, 56357, 45053, 28333, 49421, 54312, 69037, 63732, 2374, 15528, 27213, 22320, 50305, 59729, 12985, 22091, 17291, 66196, 1307, 35846, 28645, 22470, 15649, 12982, 67733, 545, 15791, 67780, 68275, 36561, 47811, 17148, 56179, 25483, 50118, 39639, 31090, 64174, 48993, 39659, 71533, 34558, 42553, 53515, 9752, 49744, 22485, 16685, 25315, 33585, 17813, 64582, 12270, 50826, 14147, 2314, 63058, 13571, 47885, 17351, 25627, 72020, 36618, 37290, 62709, 7273, 10538, 9648, 66922, 8521, 69330, 5824, 8332, 55862, 42550, 14178, 69143, 67603, 34326, 18414, 17708, 38678, 30989, 22363, 41291, 33622, 47735, 55775, 35140, 2750, 62783, 19778, 22440, 32157, 28150, 6419, 39823, 32884, 70031, 5821, 45788, 52150, 37327, 56090, 22001, 16260, 35913, 40624, 45945, 54526, 29869, 47159, 7806, 63984, 5878, 239, 33983, 29375, 62452, 47622, 50098, 46373, 71318, 49341, 4692, 40253, 44950, 42460, 41271, 21491, 43059, 46284, 38493, 5137, 39202, 4833, 19389, 34496, 49709, 31415, 71322, 72337, 7550, 57917, 41424, 54858, 49495, 15166, 16593, 63663, 15988, 35829, 53909, 5799, 16488, 44781, 29385, 48072, 32776, 50841, 26328, 52419, 70344, 6879, 58438, 12512, 2513, 45713, 24272, 54347, 54353, 15515, 48759, 32452, 35333, 15120, 11412, 24630, 24229, 9170, 47608, 63597, 5478, 43657, 71917, 24603, 70857, 16066, 48069, 49014, 61247, 27799, 72435, 46864, 25405, 7344, 61178, 48364, 71182, 48775, 8968, 32016, 32270, 1473, 60963, 66431, 36750, 16938, 29388, 5355, 29915, 3305, 50671, 68333, 44777, 36377, 39531, 14479, 53422, 19550, 62456, 47521, 63938, 45040, 42212, 57270, 63401, 53990, 17295, 55466, 34448, 49672, 59130, 34661, 65680, 5863, 23223, 55566, 63990, 61895, 36589, 28651, 45033, 20316, 7683, 23176, 61000, 9026, 54301, 3099, 46068, 3715, 50402, 69502, 16812, 62629, 63912, 49574, 7411, 6239, 44591, 27626, 12279, 28303, 7658, 35997

--------

array([[ 0.47644409, -0.40065839,  0.33339772,  0.12947449],
       [-1.10132351, -0.3102823 , -0.77003252,  0.004914  ],
       [ 0.72629021,  2.08749131,  0.50685056,  0.25737265]])

In [157]:

def draw_cluster_results(
    df: pd.DataFrame,
    col1: int,
    col2: int,
    labels: np.ndarray,
    cluster_centers: np.ndarray,
    subplot: Any | None = None,
):
    ax = None
    if subplot is None:
        ax = plt
    else:
        ax = subplot

    centroids = cluster_centers
    u_labels = np.unique(labels)

    for i in u_labels:
        ax.scatter(
            df[labels == i][df.columns[col1]],
            df[labels == i][df.columns[col2]],
            label=i,
        )

    ax.scatter(centroids[:, col1], centroids[:, col2], s=80, color="k")
    plt.title('Точка - это кластер, грубо говоря (центр кластера) :)')


plt.figure(figsize=(16, 12))
draw_cluster_results(df_scaled, 0, 1, labels, centers, plt.subplot(2, 2, 1)) # age vs avg_glucose_level
draw_cluster_results(df_scaled, 0, 2, labels, centers, plt.subplot(2, 2, 2)) # age vs bmi
draw_cluster_results(df_scaled, 0, 3, labels, centers, plt.subplot(2, 2, 3)) # age vs hypertension
draw_cluster_results(df_scaled, 1, 2, labels, centers, plt.subplot(2, 2, 4)) # avg_glucose_level vs bmi

In [ ]: