Randomstate and kmeans issues The Next CEO of Stack Overflow2019 Community Moderator ElectionCouple PCA plot and clusters to labelsKmeans: Between class intertiaQuick start using python and sklearn kmeans?Predicting contract churn/cancellation: Great model results does not work in the real worldConfused by kmeans resultshow to compare different sets of time series dataBinary classification, precision-recall curve and thresholdsHow to get data back into two separate audio files after successfully applying kmeans clustering on an audio file?Accuracy for Kmeans clusteringKmeans large dataset
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Randomstate and kmeans issues
The Next CEO of Stack Overflow2019 Community Moderator ElectionCouple PCA plot and clusters to labelsKmeans: Between class intertiaQuick start using python and sklearn kmeans?Predicting contract churn/cancellation: Great model results does not work in the real worldConfused by kmeans resultshow to compare different sets of time series dataBinary classification, precision-recall curve and thresholdsHow to get data back into two separate audio files after successfully applying kmeans clustering on an audio file?Accuracy for Kmeans clusteringKmeans large dataset
2
$begingroup$
I try to cluster a dataframe of 227 rows in 5 clusters using kmeans algorithm. Each time I run my code I got different labels and different clusters which make my analysis afterwards a bit tricky.
Someone told me to use the parameter: randomstate to have a reproductility in my results. I did. I have the same clusters but still not the same label. Is it normal? Is there a way to get the same labels ?
below my code:
Test sur 5 clusters
# Data
X = df.iloc[:,1:]
myseed = 10
# Modèle kmeans à 5 clusters
km = KMeans(n_clusters=5, random_state=myseed, n_init=30)
# Fitting du modèle aux points
km = km.fit(X)
y_km = km.predict(X)
python k-means
asked Mar 23 at 11:57
Aurelie GiraudAurelie Giraud
112
$endgroup$
add a comment |
2
$begingroup$
I try to cluster a dataframe of 227 rows in 5 clusters using kmeans algorithm. Each time I run my code I got different labels and different clusters which make my analysis afterwards a bit tricky.
Someone told me to use the parameter: randomstate to have a reproductility in my results. I did. I have the same clusters but still not the same label. Is it normal? Is there a way to get the same labels ?
below my code:
Test sur 5 clusters
# Data
X = df.iloc[:,1:]
myseed = 10
# Modèle kmeans à 5 clusters
km = KMeans(n_clusters=5, random_state=myseed, n_init=30)
# Fitting du modèle aux points
km = km.fit(X)
y_km = km.predict(X)
python k-means
asked Mar 23 at 11:57
Aurelie GiraudAurelie Giraud
112
$endgroup$
add a comment |
2
2
2
$begingroup$
I try to cluster a dataframe of 227 rows in 5 clusters using kmeans algorithm. Each time I run my code I got different labels and different clusters which make my analysis afterwards a bit tricky.
Someone told me to use the parameter: randomstate to have a reproductility in my results. I did. I have the same clusters but still not the same label. Is it normal? Is there a way to get the same labels ?
below my code:
Test sur 5 clusters
# Data
X = df.iloc[:,1:]
myseed = 10
# Modèle kmeans à 5 clusters
km = KMeans(n_clusters=5, random_state=myseed, n_init=30)
# Fitting du modèle aux points
km = km.fit(X)
y_km = km.predict(X)
python k-means
asked Mar 23 at 11:57
Aurelie GiraudAurelie Giraud
112
$endgroup$
I try to cluster a dataframe of 227 rows in 5 clusters using kmeans algorithm. Each time I run my code I got different labels and different clusters which make my analysis afterwards a bit tricky.
Someone told me to use the parameter: randomstate to have a reproductility in my results. I did. I have the same clusters but still not the same label. Is it normal? Is there a way to get the same labels ?
below my code:
Test sur 5 clusters
# Data
X = df.iloc[:,1:]
myseed = 10
# Modèle kmeans à 5 clusters
km = KMeans(n_clusters=5, random_state=myseed, n_init=30)
# Fitting du modèle aux points
km = km.fit(X)
y_km = km.predict(X)
python k-means
python k-means
asked Mar 23 at 11:57
Aurelie GiraudAurelie Giraud
112
asked Mar 23 at 11:57
Aurelie GiraudAurelie Giraud
112
asked Mar 23 at 11:57
Aurelie GiraudAurelie Giraud
112
asked Mar 23 at 11:57
Aurelie GiraudAurelie Giraud
112
asked Mar 23 at 11:57
Aurelie GiraudAurelie Giraud
112
112
add a comment |
add a comment |
1 Answer
1
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oldest
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0
$begingroup$
The code below produces the same labels and centers on multiple runs. Is the order and value of rows exactly the same?
import numpy as np
import sklearn
from sklearn.cluster import KMeans
feature1 = np.array([0.06899715, 0.06241017, 0.05136961, 0.08888344, 0.02369817, 0.05132511
, 0.07644885, 0.05571872, 0.1181635, 0.11287314, 0.15657083, 0.02658089
, 0.09810791, 0.16733219, 0.0374563, 0.08576906, 0.09522029, 0.04036745
, 0.1771768, 0.02325055, 0.13287777, 0.17448146, 0.07643926, 0.11694316
, 0.05478085, 0.17871513, 0.12706873, 0.13088636, 0.04807535, 0.15287181
, 0.05939004, 0.11667131, 0.15096193, 0.08683943, 0.02983505, 0.16516065
, 0.13741847, 0.08085856])
feature2 = np.array([0.10912874,0.18179051,0.06677442,0.11514302,0.13528425,0.05294313
,0.104772,0.12043084,0.08678998,0.13244747,0.11542028,0.18976266
,0.09423382,0.1131851,0.08747229,0.11630518,0.13750788,0.16403124
,0.16001422,0.15831517,0.16077575,0.12676131,0.08902124,0.16560226
,0.12596398,0.10481269,0.07881513,0.07465646,0.06645936,0.15950977
,0.13438658,0.18380235,0.07926124,0.18421547,0.05638499,0.11649947
,0.18400138,0.15033764])
feature3 = np.array([0.14816871, 0.1242456, 0.05020879, 0.12977452, 0.11865668, 0.1240002
, 0.16643243, 0.14401847, 0.17220796, 0.1708265, 0.04874987, 0.13442849
, 0.1375112, 0.15013606, 0.16671397, 0.13733997, 0.0516441, 0.16258701
, 0.13466661, 0.05516904, 0.14082673, 0.10032826, 0.13947572, 0.16405601
, 0.04752982, 0.15857467, 0.11730741, 0.15302504, 0.0404311, 0.03593672
, 0.07661769, 0.07276992, 0.08319156, 0.14247431, 0.1514434, 0.08060953
, 0.06952104, 0.17438457])
X = np.vstack([feature1, feature2, feature3]).T
kmeans = KMeans(n_clusters=10, random_state=0).fit(X)
print('sklearn version', sklearn.__version__)
print('labelsn', kmeans.labels_)
print('centersn', kmeans.cluster_centers_)
exit()
Output:
sklearn version 0.19.1
labels
[1 3 9 1 3 7 1 1 0 6 4 3 1 8 7 1 2 3 8 2 6 4 1 6 2 8 0 0 9 5 2 5 4 6 7 4 5
6]
centers
[[0.12537286 0.08008719 0.14751347]
[0.07862348 0.10700498 0.14324586]
[0.05816043 0.1390434 0.05774016]
[0.03826417 0.16771717 0.13497945]
[0.16179372 0.10948558 0.07821981]
[0.13565386 0.17577117 0.05940923]
[0.10607841 0.15867572 0.15851362]
[0.03953882 0.06560014 0.14738586]
[0.17440804 0.126004 0.14779245]
[0.04972248 0.06661689 0.04531995]]
answered Mar 23 at 12:16
EsmailianEsmailian
2,232218
$endgroup$
add a comment |
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1 Answer
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1 Answer
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active
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active
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0
$begingroup$
The code below produces the same labels and centers on multiple runs. Is the order and value of rows exactly the same?
import numpy as np
import sklearn
from sklearn.cluster import KMeans
feature1 = np.array([0.06899715, 0.06241017, 0.05136961, 0.08888344, 0.02369817, 0.05132511
, 0.07644885, 0.05571872, 0.1181635, 0.11287314, 0.15657083, 0.02658089
, 0.09810791, 0.16733219, 0.0374563, 0.08576906, 0.09522029, 0.04036745
, 0.1771768, 0.02325055, 0.13287777, 0.17448146, 0.07643926, 0.11694316
, 0.05478085, 0.17871513, 0.12706873, 0.13088636, 0.04807535, 0.15287181
, 0.05939004, 0.11667131, 0.15096193, 0.08683943, 0.02983505, 0.16516065
, 0.13741847, 0.08085856])
feature2 = np.array([0.10912874,0.18179051,0.06677442,0.11514302,0.13528425,0.05294313
,0.104772,0.12043084,0.08678998,0.13244747,0.11542028,0.18976266
,0.09423382,0.1131851,0.08747229,0.11630518,0.13750788,0.16403124
,0.16001422,0.15831517,0.16077575,0.12676131,0.08902124,0.16560226
,0.12596398,0.10481269,0.07881513,0.07465646,0.06645936,0.15950977
,0.13438658,0.18380235,0.07926124,0.18421547,0.05638499,0.11649947
,0.18400138,0.15033764])
feature3 = np.array([0.14816871, 0.1242456, 0.05020879, 0.12977452, 0.11865668, 0.1240002
, 0.16643243, 0.14401847, 0.17220796, 0.1708265, 0.04874987, 0.13442849
, 0.1375112, 0.15013606, 0.16671397, 0.13733997, 0.0516441, 0.16258701
, 0.13466661, 0.05516904, 0.14082673, 0.10032826, 0.13947572, 0.16405601
, 0.04752982, 0.15857467, 0.11730741, 0.15302504, 0.0404311, 0.03593672
, 0.07661769, 0.07276992, 0.08319156, 0.14247431, 0.1514434, 0.08060953
, 0.06952104, 0.17438457])
X = np.vstack([feature1, feature2, feature3]).T
kmeans = KMeans(n_clusters=10, random_state=0).fit(X)
print('sklearn version', sklearn.__version__)
print('labelsn', kmeans.labels_)
print('centersn', kmeans.cluster_centers_)
exit()
Output:
sklearn version 0.19.1
labels
[1 3 9 1 3 7 1 1 0 6 4 3 1 8 7 1 2 3 8 2 6 4 1 6 2 8 0 0 9 5 2 5 4 6 7 4 5
6]
centers
[[0.12537286 0.08008719 0.14751347]
[0.07862348 0.10700498 0.14324586]
[0.05816043 0.1390434 0.05774016]
[0.03826417 0.16771717 0.13497945]
[0.16179372 0.10948558 0.07821981]
[0.13565386 0.17577117 0.05940923]
[0.10607841 0.15867572 0.15851362]
[0.03953882 0.06560014 0.14738586]
[0.17440804 0.126004 0.14779245]
[0.04972248 0.06661689 0.04531995]]
answered Mar 23 at 12:16
EsmailianEsmailian
2,232218
$endgroup$
add a comment |
0
$begingroup$
The code below produces the same labels and centers on multiple runs. Is the order and value of rows exactly the same?
import numpy as np
import sklearn
from sklearn.cluster import KMeans
feature1 = np.array([0.06899715, 0.06241017, 0.05136961, 0.08888344, 0.02369817, 0.05132511
, 0.07644885, 0.05571872, 0.1181635, 0.11287314, 0.15657083, 0.02658089
, 0.09810791, 0.16733219, 0.0374563, 0.08576906, 0.09522029, 0.04036745
, 0.1771768, 0.02325055, 0.13287777, 0.17448146, 0.07643926, 0.11694316
, 0.05478085, 0.17871513, 0.12706873, 0.13088636, 0.04807535, 0.15287181
, 0.05939004, 0.11667131, 0.15096193, 0.08683943, 0.02983505, 0.16516065
, 0.13741847, 0.08085856])
feature2 = np.array([0.10912874,0.18179051,0.06677442,0.11514302,0.13528425,0.05294313
,0.104772,0.12043084,0.08678998,0.13244747,0.11542028,0.18976266
,0.09423382,0.1131851,0.08747229,0.11630518,0.13750788,0.16403124
,0.16001422,0.15831517,0.16077575,0.12676131,0.08902124,0.16560226
,0.12596398,0.10481269,0.07881513,0.07465646,0.06645936,0.15950977
,0.13438658,0.18380235,0.07926124,0.18421547,0.05638499,0.11649947
,0.18400138,0.15033764])
feature3 = np.array([0.14816871, 0.1242456, 0.05020879, 0.12977452, 0.11865668, 0.1240002
, 0.16643243, 0.14401847, 0.17220796, 0.1708265, 0.04874987, 0.13442849
, 0.1375112, 0.15013606, 0.16671397, 0.13733997, 0.0516441, 0.16258701
, 0.13466661, 0.05516904, 0.14082673, 0.10032826, 0.13947572, 0.16405601
, 0.04752982, 0.15857467, 0.11730741, 0.15302504, 0.0404311, 0.03593672
, 0.07661769, 0.07276992, 0.08319156, 0.14247431, 0.1514434, 0.08060953
, 0.06952104, 0.17438457])
X = np.vstack([feature1, feature2, feature3]).T
kmeans = KMeans(n_clusters=10, random_state=0).fit(X)
print('sklearn version', sklearn.__version__)
print('labelsn', kmeans.labels_)
print('centersn', kmeans.cluster_centers_)
exit()
Output:
sklearn version 0.19.1
labels
[1 3 9 1 3 7 1 1 0 6 4 3 1 8 7 1 2 3 8 2 6 4 1 6 2 8 0 0 9 5 2 5 4 6 7 4 5
6]
centers
[[0.12537286 0.08008719 0.14751347]
[0.07862348 0.10700498 0.14324586]
[0.05816043 0.1390434 0.05774016]
[0.03826417 0.16771717 0.13497945]
[0.16179372 0.10948558 0.07821981]
[0.13565386 0.17577117 0.05940923]
[0.10607841 0.15867572 0.15851362]
[0.03953882 0.06560014 0.14738586]
[0.17440804 0.126004 0.14779245]
[0.04972248 0.06661689 0.04531995]]
answered Mar 23 at 12:16
EsmailianEsmailian
2,232218
$endgroup$
add a comment |
0
0
0
$begingroup$
The code below produces the same labels and centers on multiple runs. Is the order and value of rows exactly the same?
import numpy as np
import sklearn
from sklearn.cluster import KMeans
feature1 = np.array([0.06899715, 0.06241017, 0.05136961, 0.08888344, 0.02369817, 0.05132511
, 0.07644885, 0.05571872, 0.1181635, 0.11287314, 0.15657083, 0.02658089
, 0.09810791, 0.16733219, 0.0374563, 0.08576906, 0.09522029, 0.04036745
, 0.1771768, 0.02325055, 0.13287777, 0.17448146, 0.07643926, 0.11694316
, 0.05478085, 0.17871513, 0.12706873, 0.13088636, 0.04807535, 0.15287181
, 0.05939004, 0.11667131, 0.15096193, 0.08683943, 0.02983505, 0.16516065
, 0.13741847, 0.08085856])
feature2 = np.array([0.10912874,0.18179051,0.06677442,0.11514302,0.13528425,0.05294313
,0.104772,0.12043084,0.08678998,0.13244747,0.11542028,0.18976266
,0.09423382,0.1131851,0.08747229,0.11630518,0.13750788,0.16403124
,0.16001422,0.15831517,0.16077575,0.12676131,0.08902124,0.16560226
,0.12596398,0.10481269,0.07881513,0.07465646,0.06645936,0.15950977
,0.13438658,0.18380235,0.07926124,0.18421547,0.05638499,0.11649947
,0.18400138,0.15033764])
feature3 = np.array([0.14816871, 0.1242456, 0.05020879, 0.12977452, 0.11865668, 0.1240002
, 0.16643243, 0.14401847, 0.17220796, 0.1708265, 0.04874987, 0.13442849
, 0.1375112, 0.15013606, 0.16671397, 0.13733997, 0.0516441, 0.16258701
, 0.13466661, 0.05516904, 0.14082673, 0.10032826, 0.13947572, 0.16405601
, 0.04752982, 0.15857467, 0.11730741, 0.15302504, 0.0404311, 0.03593672
, 0.07661769, 0.07276992, 0.08319156, 0.14247431, 0.1514434, 0.08060953
, 0.06952104, 0.17438457])
X = np.vstack([feature1, feature2, feature3]).T
kmeans = KMeans(n_clusters=10, random_state=0).fit(X)
print('sklearn version', sklearn.__version__)
print('labelsn', kmeans.labels_)
print('centersn', kmeans.cluster_centers_)
exit()
Output:
sklearn version 0.19.1
labels
[1 3 9 1 3 7 1 1 0 6 4 3 1 8 7 1 2 3 8 2 6 4 1 6 2 8 0 0 9 5 2 5 4 6 7 4 5
6]
centers
[[0.12537286 0.08008719 0.14751347]
[0.07862348 0.10700498 0.14324586]
[0.05816043 0.1390434 0.05774016]
[0.03826417 0.16771717 0.13497945]
[0.16179372 0.10948558 0.07821981]
[0.13565386 0.17577117 0.05940923]
[0.10607841 0.15867572 0.15851362]
[0.03953882 0.06560014 0.14738586]
[0.17440804 0.126004 0.14779245]
[0.04972248 0.06661689 0.04531995]]
answered Mar 23 at 12:16
EsmailianEsmailian
2,232218
$endgroup$
The code below produces the same labels and centers on multiple runs. Is the order and value of rows exactly the same?
import numpy as np
import sklearn
from sklearn.cluster import KMeans
feature1 = np.array([0.06899715, 0.06241017, 0.05136961, 0.08888344, 0.02369817, 0.05132511
, 0.07644885, 0.05571872, 0.1181635, 0.11287314, 0.15657083, 0.02658089
, 0.09810791, 0.16733219, 0.0374563, 0.08576906, 0.09522029, 0.04036745
, 0.1771768, 0.02325055, 0.13287777, 0.17448146, 0.07643926, 0.11694316
, 0.05478085, 0.17871513, 0.12706873, 0.13088636, 0.04807535, 0.15287181
, 0.05939004, 0.11667131, 0.15096193, 0.08683943, 0.02983505, 0.16516065
, 0.13741847, 0.08085856])
feature2 = np.array([0.10912874,0.18179051,0.06677442,0.11514302,0.13528425,0.05294313
,0.104772,0.12043084,0.08678998,0.13244747,0.11542028,0.18976266
,0.09423382,0.1131851,0.08747229,0.11630518,0.13750788,0.16403124
,0.16001422,0.15831517,0.16077575,0.12676131,0.08902124,0.16560226
,0.12596398,0.10481269,0.07881513,0.07465646,0.06645936,0.15950977
,0.13438658,0.18380235,0.07926124,0.18421547,0.05638499,0.11649947
,0.18400138,0.15033764])
feature3 = np.array([0.14816871, 0.1242456, 0.05020879, 0.12977452, 0.11865668, 0.1240002
, 0.16643243, 0.14401847, 0.17220796, 0.1708265, 0.04874987, 0.13442849
, 0.1375112, 0.15013606, 0.16671397, 0.13733997, 0.0516441, 0.16258701
, 0.13466661, 0.05516904, 0.14082673, 0.10032826, 0.13947572, 0.16405601
, 0.04752982, 0.15857467, 0.11730741, 0.15302504, 0.0404311, 0.03593672
, 0.07661769, 0.07276992, 0.08319156, 0.14247431, 0.1514434, 0.08060953
, 0.06952104, 0.17438457])
X = np.vstack([feature1, feature2, feature3]).T
kmeans = KMeans(n_clusters=10, random_state=0).fit(X)
print('sklearn version', sklearn.__version__)
print('labelsn', kmeans.labels_)
print('centersn', kmeans.cluster_centers_)
exit()
Output:
sklearn version 0.19.1
labels
[1 3 9 1 3 7 1 1 0 6 4 3 1 8 7 1 2 3 8 2 6 4 1 6 2 8 0 0 9 5 2 5 4 6 7 4 5
6]
centers
[[0.12537286 0.08008719 0.14751347]
[0.07862348 0.10700498 0.14324586]
[0.05816043 0.1390434 0.05774016]
[0.03826417 0.16771717 0.13497945]
[0.16179372 0.10948558 0.07821981]
[0.13565386 0.17577117 0.05940923]
[0.10607841 0.15867572 0.15851362]
[0.03953882 0.06560014 0.14738586]
[0.17440804 0.126004 0.14779245]
[0.04972248 0.06661689 0.04531995]]
answered Mar 23 at 12:16
EsmailianEsmailian
2,232218
edited Mar 23 at 13:49
answered Mar 23 at 12:16
EsmailianEsmailian
2,232218
answered Mar 23 at 12:16
EsmailianEsmailian
2,232218
answered Mar 23 at 12:16
EsmailianEsmailian
2,232218
2,232218
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