I want to add a "spheres" to my data cluster

Question

I want to add a kind of "spheres" to my data cluster.

My data cluster is this, which does not have ""spheres".

And this is my code

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
style.use('ggplot')
import pandas as pd
from sklearn.cluster import KMeans

MY_FILE='total_watt.csv'
date = []
consumption = []

df = pd.read_csv(MY_FILE, parse_dates=[0], index_col=[0])
df = df.resample('1D', how='sum')
df = df.dropna()

date = df.index.tolist()
date = [x.strftime('%Y-%m-%d') for x in date]
from sklearn.preprocessing import LabelEncoder

encoder = LabelEncoder()
date_numeric = encoder.fit_transform(date)
consumption = df[df.columns[0]].values

X = np.array([date_numeric, consumption]).T

kmeans = KMeans(n_clusters=3)
kmeans.fit(X)

centroids = kmeans.cluster_centers_
labels = kmeans.labels_

print(centroids)
print(labels)

fig, ax = plt.subplots(figsize=(10,8))
rect = fig.patch
rect.set_facecolor('#2D2B2B')



colors = ["b.","r.","g."]

for i in range(len(X)):
    print("coordinate:",encoder.inverse_transform(X[i,0].astype(int)), X[i,1], "label:", labels[i])
    ax.plot(X[i][0], X[i][1], colors[labels[i]], markersize = 10)
ax.scatter(centroids[:, 0],centroids[:, 1], marker = "x", s=150, linewidths = 5, zorder = 10)
a = np.arange(0, len(X), 5)
ax.set_xticks(a)
ax.set_xticklabels(encoder.inverse_transform(a.astype(int)))
ax.tick_params(axis='x', colors='lightseagreen')
ax.tick_params(axis='y', colors='lightseagreen')
plt.scatter(centroids[:, 0],centroids[:, 1], marker = "x", s=100, c="black", linewidths = 5, zorder = 10)
ax.set_title('Energy consumptions Clusters (high/medium/low)', color='gold')
ax.set_xlabel('time', color='gold')
ax.set_ylabel('date(year 2011)', color='gold')


plt.show()

"Spheres" is area which surroundings plot(cluster), as this picture.

I tried to google it.

But when I type "matplotlib spheres", I could not get any result..

Answer 1

The sample graph in your post looks like resulting from Generalized Gaussian Mixture where each sphere is a Gaussian 2-d density.

I'll write up a sample code shortly to demonstrate how to use GMM on your dataset and do this kind of plotting.

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
style.use('ggplot')
import pandas as pd
# code changes here
# ===========================================
from sklearn.mixture import GMM
# ===========================================
from sklearn.preprocessing import LabelEncoder

# replace it with you file path
MY_FILE='/home/Jian/Downloads/total_watt.csv'

df = pd.read_csv(MY_FILE, parse_dates=[0], index_col=[0])
df = df.resample('1D', how='sum')
df = df.dropna()

date = df.index.tolist()
date = [x.strftime('%Y-%m-%d') for x in date]

encoder = LabelEncoder()
date_numeric = encoder.fit_transform(date)
consumption = df[df.columns[0]].values

X = np.array([date_numeric, consumption]).T


# code changes here
# ===========================================
gmm = GMM(n_components=3, random_state=0)
gmm.fit(X)
y_pred = gmm.predict(X)

# the center is given by mean
gmm.means_

# ===========================================

import matplotlib as mpl
fig, ax = plt.subplots(figsize=(10,8))

for i, color in enumerate('rgb'):
    # sphere background
    width, height = 2 * 1.96 * np.sqrt(np.diagonal(gmm._get_covars()[i]))
    ell = mpl.patches.Ellipse(gmm.means_[i], width, height, color=color)
    ell.set_alpha(0.1)
    ax.add_artist(ell)
    # data points
    X_data = X[y_pred == i]
    ax.scatter(X_data[:,0], X_data[:,1], color=color)
    # center
    ax.scatter(gmm.means_[i][0], gmm.means_[i][1], marker='x', s=100, c=color)


ax.set_title('Energy consumptions Clusters (high/medium/low)', color='gold')
ax.set_xlabel('time', color='gold')
ax.set_ylabel('date(year 2011)', color='gold')
a = np.arange(0, len(X), 5)
ax.set_xticks(a)
ax.set_xticklabels(encoder.inverse_transform(a.astype(int)))
ax.tick_params(axis='x', colors='lightseagreen')
ax.tick_params(axis='y', colors='lightseagreen')