我希望使用 k 均值聚類來(lái)繪制并返回每個(gè)聚類質(zhì)心的位置。下面將兩組 xy 散點(diǎn)分為 6 個(gè)簇。使用下面的 df，將A和B中C的坐標(biāo)D繪制為散點(diǎn)圖。我希望繪制并返回每個(gè)簇的質(zhì)心。import pandas as pdimport matplotlib.pyplot as pltimport numpy as npfrom sklearn.cluster import KMeansdf = pd.DataFrame(np.random.randint(-50,50,size=(100, 4)), columns=list('ABCD'))fig, ax = plt.subplots()Y_sklearn = df[['A','B','C','D']].values model = KMeans(n_clusters = 4)model.fit(Y_sklearn)plt.scatter(Y_sklearn[:,0],Y_sklearn[:,1], c = model.labels_); plt.scatter(Y_sklearn[:,2],Y_sklearn[:,3], c = model.labels_); plt.show()
2 回答
開(kāi)心每一天1111
TA貢獻(xiàn)1836條經(jīng)驗(yàn) 獲得超13個(gè)贊
部分列表
A. 使用KMeans方法識(shí)別數(shù)據(jù)中的簇
B. 導(dǎo)入庫(kù)
C. 虛擬數(shù)據(jù)
D. 自定義函數(shù)
E. 計(jì)算
True聚類中心KMeansF. 使用模型?定義、擬合和預(yù)測(cè)F.1。預(yù)測(cè)
y_train使用X_trainF.2。預(yù)測(cè)
y_test使用X_test
G. 用
train,test和prediction數(shù)據(jù)制作圖形參考
A. 使用KMeans方法識(shí)別數(shù)據(jù)中的簇
我們將用它sklearn.cluster.KMeans來(lái)識(shí)別集群。該屬性model.cluster_centers_將為我們提供預(yù)測(cè)的聚類中心。比如說(shuō)，我們想找出5訓(xùn)練數(shù)據(jù)中X_train形狀為：的簇(n_samples, n_features)和y_train形狀為標(biāo)簽的簇：(n_samples,)。以下代碼塊將模型擬合到數(shù)據(jù) (?X_train)，然后進(jìn)行預(yù)測(cè)y并將預(yù)測(cè)結(jié)果保存在y_pred_train變量中。
# Define model
model = KMeans(n_clusters = 5)
# Fit model to training data
model.fit(X_train)
# Make prediction on training data
y_pred_train = model.predict(X_train)
# Get predicted cluster centers
model.cluster_centers_ # shape: (n_cluster, n_features)
## Displaying cluster centers on a plot?
# if you just want to add cluster centers?
# to your existing scatter-plot,?
# just do this --->>
cluster_centers = model.cluster_centers_
plt.scatter(cluster_centers[:, 0], cluster_centers[:, 1],?
? ? ? ? ? ? marker='s', color='orange', s = 100,?
? ? ? ? ? ? alpha=0.5, label='pred')
這就是結(jié)果??? 跳轉(zhuǎn)到部分G查看用于制作繪圖的代碼。
B. 導(dǎo)入庫(kù)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
import pprint
%matplotlib inline?
%config InlineBackend.figure_format = 'svg' # 'svg', 'retina'?
plt.style.use('seaborn-white')
C. 虛擬數(shù)據(jù)
我們將使用以下代碼塊中生成的數(shù)據(jù)。根據(jù)設(shè)計(jì)，我們創(chuàng)建一個(gè)包含5集群和以下規(guī)范的數(shù)據(jù)集。然后使用將數(shù)據(jù)分為train和塊。testsklearn.model_selection.train_test_split
## Creating data with?
#? n_samples = 2500
#? n_features = 4
#? Expected clusters = 5
#? ? ?centers = 5
#? ? ?cluster_std = [1.0, 2.5, 0.5, 1.5, 2.0]
NUM_SAMPLES = 2500
RANDOM_STATE = 42
NUM_FEATURES = 4
NUM_CLUSTERS = 5
CLUSTER_STD = [1.0, 2.5, 0.5, 1.5, 2.0]
TEST_SIZE = 0.20
def dummy_data():? ? ?
? ? ## Creating data with?
? ? #? n_samples = 2500
? ? #? n_features = 4
? ? #? Expected clusters = 5
? ? #? ? ?centers = 5
? ? #? ? ?cluster_std = [1.0, 2.5, 0.5, 1.5, 2.0]
? ? X, y = make_blobs(
? ? ? ? n_samples = NUM_SAMPLES,?
? ? ? ? random_state = RANDOM_STATE,?
? ? ? ? n_features = NUM_FEATURES,?
? ? ? ? centers = NUM_CLUSTERS,?
? ? ? ? cluster_std = CLUSTER_STD
? ? )
? ? return X, y
def test_dummy_data(X, y):
? ? assert X.shape == (NUM_SAMPLES, NUM_FEATURES), "Shape mismatch for X"
? ? assert set(y) == set(np.arange(NUM_CLUSTERS)), "NUM_CLUSTER mismatch for y"
## D. Create Dummy Data
X, y = dummy_data()
test_dummy_data(X, y)
## Create train-test-split
X_train, X_test, y_train, y_test = train_test_split(
? ? X, y, test_size=TEST_SIZE, random_state=RANDOM_STATE)
D. 自定義函數(shù)
我們將使用以下3自定義函數(shù)：
get_cluster_centers()scatterplot()add_cluster_centers()
def get_cluster_centers(X, y, num_clusters=None):
? ? """Returns the cluster-centers as numpy.array of?
? ? shape: (num_cluster, num_features).
? ? """
? ? num_clusters = NUM_CLUSTERS if (num_clusters is None) else num_clusters
? ? return np.stack([X[y==i].mean(axis=0) for i in range(NUM_CLUSTERS)])
def scatterplot(X, y,?
? ? ? ? ? ? ? ? cluster_centers=None,?
? ? ? ? ? ? ? ? alpha=0.5,?
? ? ? ? ? ? ? ? cmap='viridis',?
? ? ? ? ? ? ? ? legend_title="Classes",?
? ? ? ? ? ? ? ? legend_loc="upper left",?
? ? ? ? ? ? ? ? ax=None):
? ? if ax is not None:
? ? ? ? plt.sca(ax)
? ? scatter = plt.scatter(X[:, 0], X[:, 1],?
? ? ? ? ? ? ? ? ? ? ? ? ? s=None, c=y, alpha=alpha, cmap=cmap)
? ? legend = ax.legend(*scatter.legend_elements(),
? ? ? ? ? ? ? ? ? ? ? ? loc=legend_loc, title=legend_title)
? ? ax.add_artist(legend)
? ? if cluster_centers is not None:
? ? ? ?plt.scatter(cluster_centers[:, 0], cluster_centers[:, 1],?
? ? ? ? ? ? ? ? ? ?marker='o', color='red', alpha=1.0)
? ? ax = plt.gca()
? ? return ax
def add_cluster_centers(true_cluster_centers=None,?
? ? ? ? ? ? ? ? ? ? ? ? pred_cluster_centers=None,?
? ? ? ? ? ? ? ? ? ? ? ? markers=('o', 's'),?
? ? ? ? ? ? ? ? ? ? ? ? colors=('red, ''orange'),?
? ? ? ? ? ? ? ? ? ? ? ? s = (None, 200),?
? ? ? ? ? ? ? ? ? ? ? ? alphas = (1.0, 0.5),?
? ? ? ? ? ? ? ? ? ? ? ? center_labels = ('true', 'pred'),?
? ? ? ? ? ? ? ? ? ? ? ? legend_title = "Cluster Centers",?
? ? ? ? ? ? ? ? ? ? ? ? legend_loc = "upper right",?
? ? ? ? ? ? ? ? ? ? ? ? ax = None):
? ? if ax is not None:
? ? ? ? plt.sca(ax)
? ? for idx, cluster_centers in enumerate([true_cluster_centers,?
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?pred_cluster_centers]):? ? ? ??
? ? ? ? if cluster_centers is not None:
? ? ? ? ? ? scatter = plt.scatter(
? ? ? ? ? ? ? ? cluster_centers[:, 0], cluster_centers[:, 1],?
? ? ? ? ? ? ? ? marker = markers[idx],?
? ? ? ? ? ? ? ? color = colors[idx],?
? ? ? ? ? ? ? ? s = s[idx],?
? ? ? ? ? ? ? ? alpha = alphas[idx],
? ? ? ? ? ? ? ? label = center_labels[idx]
? ? ? ? ? ? )
? ? legend = ax.legend(loc=legend_loc, title=legend_title)
? ? ax.add_artist(legend)
? ? return ax
E. 計(jì)算True聚類中心
我們將計(jì)算和數(shù)據(jù)集true的聚類中心并將結(jié)果保存到: 。traintestdicttrue_cluster_centers
true_cluster_centers = {
? ? 'train': get_cluster_centers(X = X_train, y = y_train, num_clusters = NUM_CLUSTERS),?
? ? 'test': get_cluster_centers(X = X_test, y = y_test, num_clusters = NUM_CLUSTERS)
}
# Show result
pprint.pprint(true_cluster_centers, indent=2)
輸出：
{ 'test': array([[-2.44425795,? 9.06004013,? 4.7765817 ,? 2.02559904],
? ? ? ?[-6.68967507, -7.09292101, -8.90860337,? 7.16545582],
? ? ? ?[ 1.99527271,? 4.11374524, -9.62610383,? 9.32625443],
? ? ? ?[ 6.46362854, -5.90122349, -6.2972843 , -6.04963714],
? ? ? ?[-4.07799392,? 0.61599582, -1.82653858, -4.34758032]]),
? 'train': array([[-2.49685525,? 9.08826? ?,? 4.64928719,? 2.01326914],
? ? ? ?[-6.82913109, -6.86790673, -8.99780554,? 7.39449295],
? ? ? ?[ 2.04443863,? 4.12623661, -9.64146529,? 9.39444917],
? ? ? ?[ 6.74707792, -5.83405806, -6.3480674 , -6.37184345],
? ? ? ?[-3.98420601,? 0.45335025, -1.23919526, -3.98642807]])}
KMeansF. 使用模型定義、擬合和預(yù)測(cè)
model = KMeans(n_clusters = NUM_CLUSTERS, random_state = RANDOM_STATE)
model.fit(X_train)
## Output
# KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
#? ? ? ? n_clusters=5, n_init=10, n_jobs=None, precompute_distances='auto',
#? ? ? ? random_state=42, tol=0.0001, verbose=0)
F.1。預(yù)測(cè)y_train使用X_train
## Process Prediction: train data
y_pred_train = model.predict(X_train)
# get model predicted cluster-centers
pred_train_cluster_centers = model.cluster_centers_ # shape: (n_cluster, n_features)
# sanity check
assert all([
? ? y_pred_train.shape == (NUM_SAMPLES * (1 - TEST_SIZE),),?
? ? ?set(y_pred_train) == set(y_train)
])
F.2。預(yù)測(cè)y_test使用X_test
## Process Prediction: test data
y_pred_test = model.predict(X_test)
# get model predicted cluster-centers
pred_test_cluster_centers = model.cluster_centers_ # shape: (n_cluster, n_features)
# sanity check
assert all([
? ? y_pred_test.shape == (NUM_SAMPLES * TEST_SIZE,),?
? ? ?set(y_pred_test) == set(y_test)
])
G. 用train,test和prediction數(shù)據(jù)制作圖形
fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(15, 6))
FONTSIZE = {'title': 16, 'suptitle': 20}
TITLE = {
? ? 'train': 'Train Data Clusters',?
? ? 'test': 'Test Data Clusters',?
? ? 'suptitle': 'Cluster Identification using KMeans Method',?
}
CENTER_LEGEND_LABELS = ('true', 'pred')
LAGEND_PARAMS = {
? ? 'data': {'title': "Classes", 'loc': "upper left"},?
? ? 'cluster_centers': {'title': "Cluster Centers", 'loc': "upper right"}
}
SCATTER_ALPHA = 0.4?
CMAP = 'viridis'
CLUSTER_CENTER_PLOT_PARAMS = dict(
? ? markers = ('o', 's'),?
? ? colors = ('red', 'orange'),?
? ? s = (None, 200),?
? ? alphas = (1.0, 0.5),?
? ? center_labels = CENTER_LEGEND_LABELS,? ? ??
? ? legend_title = LAGEND_PARAMS['cluster_centers']['title'],?
? ? legend_loc = LAGEND_PARAMS['cluster_centers']['loc']
)
SCATTER_PLOT_PARAMS = dict(
? ? alpha = SCATTER_ALPHA,?
? ? cmap = CMAP,?
? ? legend_title = LAGEND_PARAMS['data']['title'],?
? ? legend_loc = LAGEND_PARAMS['data']['loc'],
)
## plot train data
data_label = 'train'
ax = axs[0]
plt.sca(ax)
ax = scatterplot(X = X_train, y = y_train,?
? ? ? ? ? ? ? ? ?cluster_centers = None,? ? ? ? ? ? ? ? ? ?
? ? ? ? ? ? ? ? ?ax = ax, **SCATTER_PLOT_PARAMS)
ax = add_cluster_centers(
? ? true_cluster_centers = true_cluster_centers[data_label],
? ? pred_cluster_centers = pred_train_cluster_centers,? ? ?
? ? ax = ax, **CLUSTER_CENTER_PLOT_PARAMS)
plt.title(TITLE[data_label], fontsize = FONTSIZE['title'])
## plot test data
data_label = 'test'
ax = axs[1]
plt.sca(ax)
ax = scatterplot(X = X_test, y = y_test,?
? ? ? ? ? ? ? ? ?cluster_centers = None,?
? ? ? ? ? ? ? ? ?ax = ax, **SCATTER_PLOT_PARAMS)
ax = add_cluster_centers(
? ? true_cluster_centers = true_cluster_centers[data_label],
? ? pred_cluster_centers = pred_test_cluster_centers,?
? ? ax = ax, **CLUSTER_CENTER_PLOT_PARAMS)
plt.title(TITLE[data_label], fontsize = FONTSIZE['title'])
plt.suptitle(TITLE['suptitle'],?
? ? ? ? ? ? ?fontsize = FONTSIZE['suptitle'])
plt.show()
# save figure
fig.savefig("kmeans_fit_result.png", dpi=300)
結(jié)果：
繁星點(diǎn)點(diǎn)滴滴
TA貢獻(xiàn)1803條經(jīng)驗(yàn) 獲得超3個(gè)贊
根據(jù)你制作散點(diǎn)圖的方式，我猜測(cè)A和B對(duì)應(yīng)于第一組點(diǎn)的xy坐標(biāo)，而C和D對(duì)應(yīng)于第二組點(diǎn)的xy坐標(biāo)。如果是這樣，則無(wú)法Kmeans直接應(yīng)用于數(shù)據(jù)框，因?yàn)橹挥袃蓚€(gè)特征，即 x 和 y 坐標(biāo)。找到質(zhì)心實(shí)際上很簡(jiǎn)單，你所需要的就是model_zero.cluster_centers_。
我們首先構(gòu)建一個(gè)更適合可視化的數(shù)據(jù)框
import numpy as np
# set the seed for reproducible datasets
np.random.seed(365)
# cov matrix of a 2d gaussian
stds = np.eye(2)
# four cluster means
means_zero = np.random.randint(10,20,(4,2))
sizes_zero = np.array([20,30,15,35])
# four cluster means
means_one = np.random.randint(0,10,(4,2))
sizes_one = np.array([20,20,25,35])
points_zero = np.vstack([np.random.multivariate_normal(mean,stds,size=(size)) for mean,size in zip(means_zero,sizes_zero)])
points_one = np.vstack([np.random.multivariate_normal(mean,stds,size=(size)) for mean,size in zip(means_one,sizes_one)])
all_points = np.hstack((points_zero,points_one))
正如您所看到的，這四個(gè)簇是由具有不同均值的四個(gè)高斯分布的采樣點(diǎn)構(gòu)建的。使用此數(shù)據(jù)框，您可以按照以下方式繪制它
import matplotlib.patheffects as PathEffects
from sklearn.cluster import KMeans
df = pd.DataFrame(all_points, columns=list('ABCD'))
fig, ax = plt.subplots(figsize=(10,8))
scatter_zero = df[['A','B']].values
scatter_one = df[['C','D']].values
model_zero = KMeans(n_clusters=4)
model_zero.fit(scatter_zero)
model_one = KMeans(n_clusters=4)
model_one.fit(scatter_one)
plt.scatter(scatter_zero[:,0],scatter_zero[:,1],c=model_zero.labels_,cmap='bwr');
plt.scatter(scatter_one[:,0],scatter_one[:,1],c=model_one.labels_,cmap='bwr');
# plot the cluster centers
txts = []
for ind,pos in enumerate(model_zero.cluster_centers_):
txt = ax.text(pos[0],pos[1],
'cluster %i \n (%.1f,%.1f)' % (ind,pos[0],pos[1]),
fontsize=12,zorder=100)
txt.set_path_effects([PathEffects.Stroke(linewidth=5, foreground="aquamarine"),PathEffects.Normal()])
txts.append(txt)
for ind,pos in enumerate(model_one.cluster_centers_):
txt = ax.text(pos[0],pos[1],
'cluster %i \n (%.1f,%.1f)' % (ind,pos[0],pos[1]),
fontsize=12,zorder=100)
txt.set_path_effects([PathEffects.Stroke(linewidth=5, foreground="lime"),PathEffects.Normal()])
txts.append(txt)
zero_mean = np.mean(model_zero.cluster_centers_,axis=0)
one_mean = np.mean(model_one.cluster_centers_,axis=0)
txt = ax.text(zero_mean[0],zero_mean[1],
'point set zero',
fontsize=15)
txt.set_path_effects([PathEffects.Stroke(linewidth=5, foreground="violet"),PathEffects.Normal()])
txts.append(txt)
txt = ax.text(one_mean[0],one_mean[1],
'point set one',
fontsize=15)
txt.set_path_effects([PathEffects.Stroke(linewidth=5, foreground="violet"),PathEffects.Normal()])
txts.append(txt)
plt.show()
運(yùn)行這段代碼，你會(huì)得到
添加回答
舉報(bào)
0/150
提交
取消