39 lines
1.6 KiB
Python
39 lines
1.6 KiB
Python
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import pandas as pd
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import matplotlib.pyplot as plt
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from scipy.cluster.hierarchy import dendrogram, linkage
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from sklearn.cluster import AgglomerativeClustering
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from sklearn.metrics import silhouette_score
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# Загрузка данных
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data = pd.read_csv("smoking_drinking_dataset.csv")
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# Уменьшаем размер данных
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data = data.sample(frac=0.01, random_state=42)
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# Выбор подмножества признаков для кластеризации
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features = ['age', 'height', 'weight', 'waistline', 'sight_left', 'sight_right', 'hear_left', 'hear_right', 'SBP', 'DBP', 'BLDS', 'tot_chole', 'HDL_chole', 'LDL_chole', 'triglyceride', 'hemoglobin', 'urine_protein', 'serum_creatinine', 'SGOT_AST', 'SGOT_ALT', 'gamma_GTP']
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# Выбор данных для кластеризации
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cluster_data = data[features]
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# Нормализация данных
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normalized_data = (cluster_data - cluster_data.mean()) / cluster_data.std()
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# Расчет матрицы связи
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linkage_matrix = linkage(normalized_data, method='ward')
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# Построение dendrogram
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plt.figure(figsize=(16, 8))
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dendrogram(linkage_matrix, orientation='top', labels=data.index, distance_sort='descending', show_leaf_counts=True)
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plt.title('Дендрограма')
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plt.ylabel('Дистанция')
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#plt.show()
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plt.savefig("dendr.png")
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# Оценка метода
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n_clusters = 4
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cluster_model = AgglomerativeClustering(n_clusters=n_clusters, metric='euclidean', linkage='ward')
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labels = cluster_model.fit_predict(normalized_data)
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data['cluster'] = labels
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silhouette_avg = silhouette_score(normalized_data, labels)
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print(f"Silhouette Score: {silhouette_avg}")
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