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Simple implementation of DBSCAN in python with scikit-learn
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import numpy as np | |
import matplotlib.pyplot as plt | |
from sklearn.neighbors import NearestNeighbors | |
def get_eps_nei(neigh, X, i): | |
""" Wrap the unnecessary output from sklearn. | |
Returns the indices of the k nearest neigbhors of a point, | |
excluding the query point. | |
""" | |
e = list(neigh.radius_neighbors(X[i].reshape(1, -1), | |
return_distance=False)[0]) | |
e_filter = [j for j in e if j !=i] | |
return e_filter | |
def my_dbscan(X, eps=0.3, min_pts=4): | |
""" My implementation of DBSCAN. | |
Pseudocode: | |
* For each unseen point, get its density | |
* If too sparse, mark as noise | |
* If dense enough start a new cluster | |
* Mark all its neighbourhood with cluster. | |
* For each point in neighbourhood, add neighbors if they are core | |
* Stop if no new unvisited points | |
Arguments | |
----------- | |
X: np Nxd array | |
eps: radius of neighbourhood | |
min_pts: minimum neighbourhood size | |
Returns | |
-------- | |
list: | |
List of n elements with cluster assignments for each point in X. -1 for noise, 1,2..,k for clusters. | |
""" | |
n_pts = X.shape[0] | |
neigh = NearestNeighbors(radius=eps) | |
neigh.fit(X) | |
# 0 is undefined | |
# -1 is noise | |
# >=1 is cluster ID | |
clusters = [0] * n_pts | |
cluster_id = 1 | |
for i in range(n_pts): | |
N = get_eps_nei(neigh, X, i) | |
# we already saw this point | |
if clusters[i] != 0: | |
continue | |
# noise | |
if len(N) < min_pts: | |
clusters[i] = -1 | |
continue | |
# we are in a dense region | |
if len(N) >= min_pts: | |
clusters[i] = cluster_id | |
while len(N): | |
n = N.pop() | |
# this was a noise point but now it is border | |
if clusters[n] == -1: | |
clusters[n] = cluster_id | |
continue | |
# already clustered | |
if clusters[n] > 0: | |
continue | |
# check unprocessed or noise points | |
clusters[n] = cluster_id | |
Q = get_eps_nei(neigh, X, n) | |
if len(Q) >= min_pts: | |
N.extend(Q) | |
cluster_id += 1 | |
return clusters | |
def draw(X, clusts): | |
colors = np.array(['tab:blue','tab:orange','tab:green','tab:red','tab:purple','tab:brown','tab:pink','tab:gray','tab:olive','tab:cyan','black']) | |
# matplotlib tableau palette, plus black for outliers | |
colors = np.hstack([colors] * 20) | |
fig = plt.figure(figsize=(10,10)) | |
ax = plt.gca() | |
ax.scatter(X[:, 0], X[:, 1], c=colors[clusts].tolist(), s=10) | |
plt.show() | |
############ | |
X = np.loadtxt('smile.txt') | |
my_clusts = my_dbscan(X, eps=0.1) | |
draw(X, my_clusts) | |
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matplotlib | |
numpy | |
scikit-learn |
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