Issue
I have a dataframe like below. The shape is (24,7)
Name x1 x2 x3 x4 x5 x6
Harry 102 204 0.43 0.21 1.02 0.39
James 242 500 0.31 0.11 0.03 0.73
.
.
.
Mike 3555 4002 0.12 0.03 0.52. 0.11
Henry 532 643 0.01 0.02 0.33 0.10
I want to run Scikit-learn's Different Clustering Algorithms Script on the above dataframe. However, the input data looks quite confusing, not too sure how to input my dataframe
Solution
There are two main differences between your scenario and the scikit-learn example you link to:
- You only have one dataset, not several different ones to compare.
- You have six features, not just two.
Point one allows you to simplify the example code by deleting the loops over the different datasets and related calculations. Point two implies that you cannot easily plot your results. Instead, you could just add the predicted class labels found by each algorithm to your dataset.
So you could modify the example code like this:
import time
import warnings
import numpy as np
import pandas as pd
from sklearn import cluster, datasets, mixture
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler
from itertools import cycle, islice
np.random.seed(0)
# ============
# Introduce your dataset
# ============
my_df = # Insert your data here, as a pandas dataframe.
features = [f'x{i}' for i in range(1, 7)]
X = my_df[features].values
# ============
# Set up cluster parameters
# ============
params = {
"quantile": 0.3,
"eps": 0.3,
"damping": 0.9,
"preference": -200,
"n_neighbors": 3,
"n_clusters": 3,
"min_samples": 7,
"xi": 0.05,
"min_cluster_size": 0.1,
}
# normalize dataset for easier parameter selection
X = StandardScaler().fit_transform(X)
# estimate bandwidth for mean shift
bandwidth = max(cluster.estimate_bandwidth(X, quantile=params["quantile"]),
0.001) # arbitrary correction to avoid 0
# connectivity matrix for structured Ward
connectivity = kneighbors_graph(
X, n_neighbors=params["n_neighbors"], include_self=False
)
# make connectivity symmetric
connectivity = 0.5 * (connectivity + connectivity.T)
# ============
# Create cluster objects
# ============
ms = cluster.MeanShift(bandwidth=bandwidth, bin_seeding=True)
two_means = cluster.MiniBatchKMeans(n_clusters=params["n_clusters"])
ward = cluster.AgglomerativeClustering(
n_clusters=params["n_clusters"], linkage="ward", connectivity=connectivity
)
spectral = cluster.SpectralClustering(
n_clusters=params["n_clusters"],
eigen_solver="arpack",
affinity="nearest_neighbors",
)
dbscan = cluster.DBSCAN(eps=params["eps"])
optics = cluster.OPTICS(
min_samples=params["min_samples"],
xi=params["xi"],
min_cluster_size=params["min_cluster_size"],
)
affinity_propagation = cluster.AffinityPropagation(
damping=params["damping"], preference=params["preference"], random_state=0
)
average_linkage = cluster.AgglomerativeClustering(
linkage="average",
affinity="cityblock",
n_clusters=params["n_clusters"],
connectivity=connectivity,
)
birch = cluster.Birch(n_clusters=params["n_clusters"])
gmm = mixture.GaussianMixture(
n_components=params["n_clusters"], covariance_type="full"
)
clustering_algorithms = (
("MiniBatch\nKMeans", two_means),
("Affinity\nPropagation", affinity_propagation),
("MeanShift", ms),
("Spectral\nClustering", spectral),
("Ward", ward),
("Agglomerative\nClustering", average_linkage),
("DBSCAN", dbscan),
("OPTICS", optics),
("BIRCH", birch),
("Gaussian\nMixture", gmm),
)
for name, algorithm in clustering_algorithms:
t0 = time.time()
# catch warnings related to kneighbors_graph
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
message="the number of connected components of the "
+ "connectivity matrix is [0-9]{1,2}"
+ " > 1. Completing it to avoid stopping the tree early.",
category=UserWarning,
)
warnings.filterwarnings(
"ignore",
message="Graph is not fully connected, spectral embedding"
+ " may not work as expected.",
category=UserWarning,
)
algorithm.fit(X)
t1 = time.time()
if hasattr(algorithm, "labels_"):
y_pred = algorithm.labels_.astype(int)
else:
y_pred = algorithm.predict(X)
# Add cluster labels to the dataset
my_df[name] = y_pred
Answered By - Arne
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