How to Import .csv to perform DBSCAN?

JohnS — Sat, 21 Aug 2021 06:13:13 GMT

I'm going to repeat an exercise about sklearn.cluster.DBSCAN. Link:https://scikit-learn.org/stable/auto_examples/cluster/plot_dbscan.html#sphx-glr-auto-examples-cluster-plot-dbscan-py My question's very simple. I'd like to import .csv file that consists of the points for the exercise. My file has 380000 points, the coordinates x and y are separated by a comma and no headings (mean x or y). The first coordinate is x, and the second is y.

print(__doc__) import numpy as np from sklearn.cluster import DBSCAN from sklearn import metrics from sklearn.datasets import make_blobs from sklearn.preprocessing import StandardScaler # ############################################################################# # Generate sample data centers = [[1, 1], [-1, -1], [1, -1]] X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4, random_state=0) X = StandardScaler().fit_transform(X) # ############################################################################# # Compute DBSCAN db = DBSCAN(eps=0.3, min_samples=10).fit(X) core_samples_mask = np.zeros_like(db.labels_, dtype=bool) core_samples_mask[db.core_sample_indices_] = True labels = db.labels_ # Number of clusters in labels, ignoring noise if present. n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0) n_noise_ = list(labels).count(-1) print('Estimated number of clusters: %d' % n_clusters_) print('Estimated number of noise points: %d' % n_noise_) print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels)) print("Completeness: %0.3f" % metrics.completeness_score(labels_true, labels)) print("V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels)) print("Adjusted Rand Index: %0.3f" % metrics.adjusted_rand_score(labels_true, labels)) print("Adjusted Mutual Information: %0.3f" % metrics.adjusted_mutual_info_score(labels_true, labels)) print("Silhouette Coefficient: %0.3f" % metrics.silhouette_score(X, labels)) # ############################################################################# # Plot result import matplotlib.pyplot as plt # Black removed and is used for noise instead. unique_labels = set(labels) colors = [plt.cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))] for k, col in zip(unique_labels, colors): if k == -1: # Black used for noise. col = [0, 0, 0, 1] class_member_mask = (labels == k) xy = X[class_member_mask & core_samples_mask] plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col), markeredgecolor='k', markersize=14) xy = X[class_member_mask & ~core_samples_mask] plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col), markeredgecolor='k', markersize=6) plt.title('Estimated number of clusters: %d' % n_clusters_) plt.show()

Re: How to Import .csv to perform DBSCAN?

DanPatterson — Sat, 21 Aug 2021 09:05:34 GMT

import the csv and make a table in a geodatabase would be a first start recommendation

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How to Import .csv to perform DBSCAN?

Re: How to Import .csv to perform DBSCAN?