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July 1, 2024 16:21
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| import pandas as pd | |
| import numpy as np | |
| from sklearn.cluster import KMeans | |
| import folium | |
| from math import ceil | |
| # Load your dataset | |
| file_data = "finna-rute-exist.csv" | |
| data = pd.read_csv(file_data) # Adjust this path to your dataset location | |
| jumlah_toko = len(data) | |
| print("Jumlah toko:", jumlah_toko) | |
| jumlah_toko_perhari = 40 | |
| jumlah_cluster = ceil(jumlah_toko / jumlah_toko_perhari) | |
| print("Jumlah cluster awal:", jumlah_cluster) | |
| data['LONG_SHOP'] = data['LONG_SHOP'].astype(str).str.replace(r'[^0-9.-]', '', regex=True).astype(float) | |
| data['LAT_SHOP'] = data['LAT_SHOP'].astype(str).str.replace(r'[^0-9.-]', '', regex=True).astype(float) | |
| # Assuming your data has "longitude" and "latitude" columns | |
| X = data[['LONG_SHOP', 'LAT_SHOP']].values | |
| # Initialize and fit K-means model | |
| kmeans = KMeans(n_clusters=jumlah_cluster, n_init=10) | |
| kmeans.fit(X) | |
| # Get initial cluster labels | |
| labels = kmeans.labels_ | |
| # Define re-cluster function | |
| def recluster_subset(X, num_clusters): | |
| kmeans_subset = KMeans(n_clusters=num_clusters, n_init=10) | |
| kmeans_subset.fit(X) | |
| return kmeans_subset.labels_ | |
| # Define recursive re-clustering function | |
| def recluster_until_fit(X, labels, jumlah_toko_perhari, start_label): | |
| unique_labels = np.unique(labels) | |
| final_labels = labels.copy() | |
| new_label_start = start_label | |
| for cluster_id in unique_labels: | |
| cluster_indices = np.where(labels == cluster_id)[0] | |
| cluster_size = len(cluster_indices) | |
| if cluster_size > jumlah_toko_perhari: | |
| new_cluster_count = ceil(cluster_size / jumlah_toko_perhari) | |
| new_labels = recluster_subset(X[cluster_indices], new_cluster_count) | |
| adjusted_labels = new_labels + new_label_start | |
| final_labels[cluster_indices] = adjusted_labels | |
| new_label_start += new_cluster_count | |
| counts = pd.Series(final_labels).value_counts() | |
| if any(counts > jumlah_toko_perhari): | |
| return recluster_until_fit(X, final_labels, jumlah_toko_perhari, new_label_start) | |
| else: | |
| return final_labels | |
| # Initialize start label for re-clustering | |
| new_label_start = max(labels) + 1 | |
| # Perform recursive re-clustering | |
| final_labels = recluster_until_fit(X, labels, jumlah_toko_perhari, new_label_start) | |
| # Update labels with the final labels | |
| labels = final_labels | |
| # Visualization with Folium | |
| map_center = [np.mean(X[:, 1]), np.mean(X[:, 0])] | |
| mymap = folium.Map(location=map_center, zoom_start=10) | |
| # Define a list of colors for clusters | |
| colors = ['blue', 'green', 'red', 'orange', 'purple', 'pink', 'black', 'gray', 'brown', 'darkblue', 'darkgreen', 'darkred', 'darkpurple', 'darkorange', 'lightblue', 'lightgreen', 'lightred', 'lightgray', 'lightyellow', 'cadetblue', 'beige', 'darkgray', 'darkcyan', 'lightcyan'] | |
| # Add markers for each shop location with cluster color | |
| for i in range(len(X)): | |
| cluster_color = colors[labels[i] % len(colors)] # Cycle through colors for each cluster | |
| folium.CircleMarker(location=[X[i, 1], X[i, 0]], radius=2, color=cluster_color).add_to(mymap) | |
| # Display the map | |
| mymap.save("rute.html") |
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