Add agglomerative clustering

main.py

@@ -2,13 +2,58 @@
 import os
 import sys
 
+import numpy
+import pandas as pd
+# import scipy.cluster.hierarchy as sc
+from matplotlib import pyplot as plt
+from pandas import DataFrame
+from sklearn.cluster import AgglomerativeClustering
+from sklearn.decomposition import PCA
+
 from src.main.df_loader import DfLoader
 
 
+def __clustering(data: DataFrame) -> None:
+    # clusters = round(math.sqrt(len(data) / 2))
+    # plt.figure(figsize=(20, 7))
+    # plt.title("Dendrograms")
+    # # Create dendrogram
+    # sc.dendrogram(sc.linkage(data.to_numpy(), method='ward'))
+    # plt.title('Dendrogram')
+    # plt.xlabel('Sample index')
+    # plt.ylabel('Euclidean distance')
+
+    clusters = 3
+    model = AgglomerativeClustering(n_clusters=clusters, metric='euclidean', linkage='ward')
+    model.fit(data)
+    labels = model.labels_
+
+    data_norm = (data - data.min()) / (data.max() - data.min())
+
+    pca = PCA(n_components=2)  # 2-dimensional PCA
+    transformed = pd.DataFrame(pca.fit_transform(data_norm))
+    # plt.scatter(x=transformed[:, 0], y=transformed[:, 1], c=labels, cmap='rainbow')
+    for i in range(clusters):
+        series = transformed.iloc[numpy.where(labels[:] == i)]
+        plt.scatter(series[0], series[1], label=f'Cluster {i + 1}')
+    plt.legend()
+    plt.show()
+
+    # fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))
+    # sns.scatterplot(ax=axes[0], data=data, x='location-la,location-lo', y='age,sex').set_title('Without clustering')
+    # sns.scatterplot(ax=axes[1], data=data, x='location-la,location-lo', y='age,sex', hue=labels) \
+    #     .set_title('With clustering')
+    # plt.show()
+
+    # s = numpy.where(labels[:] == 34)
+    # print(labels)
+
+
 def __main(json_file):
     df_loader: DfLoader = DfLoader(json_file)
-    df = df_loader.get_data_frame()
+    data = df_loader.get_clustering_data()
-    print('done')
+    print(data)
+    __clustering(data)
 
 
 if __name__ == '__main__':

requirements.txt

@@ -1,3 +1,7 @@
 pandas==2.0.1
 geopy==2.3.0
-numpy==1.24.3
+numpy==1.24.3
+scikit-learn==1.2.2
+matplotlib==3.7.1
+seaborn==0.12.2
+scipy==1.10.1