我想添加一个 "spheres" 到我的数据集群

Question

我想在我的数据集群中添加一种"spheres"。

我的数据集群是这样的，没有""spheres".

这是我的代码

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"是周围环境plot(cluster)的区域，如图。

我试过google了。

但是当我输入"matplotlib spheres"时，我无法得到任何结果..

Answer 1

您 post 中的示例图看起来像是 Generalized Gaussian Mixture 的结果，其中每个球体都是高斯二维密度。

我将很快编写一个示例代码来演示如何在您的数据集上使用 GMM 并进行这种绘图。

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')

我想添加一个 "spheres" 到我的数据集群

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

matplotlib

k-means

pandas

scikit-learn