ValueError: Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6 (SMOTE)
0
$begingroup$
I am designing a multi class classifier for 11 labels. I am using SMOTE to do the sampling. However, I face the following error:
SMOTE Error:
from imblearn.over_sampling import SMOTE
sm = SMOTE(random_state=42)
X_res, Y_res = sm.fit_sample(X_f, Y_f)
Error:
~/.local/lib/python3.6/site-packages/sklearn/neighbors/base.py in kneighbors(self, X, n_neighbors, return_distance)
414 "Expected n_neighbors <= n_samples, "
415 " but n_samples = %d, n_neighbors = %d" %
--> 416 (train_size, n_neighbors)
417 )
418 n_samples, _ = X.shape
ValueError: Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6
Why does it say that I have only 1 n_samples?
When I tried the same code for a much smaller dataset (100k rows and 4 labels), it ran just fine.
Details about my input:
Input parameters:
X_f
array([[1.43347000e+05, 1.00000000e+00, 2.03869492e+03, ...,
1.00000000e+00, 1.00000000e+00, 1.35233019e+03],
[5.09050000e+04, 0.00000000e+00, 0.00000000e+00, ...,
5.09050000e+04, 0.00000000e+00, 5.09050000e+04],
[1.43899000e+05, 2.00000000e+00, 2.11447368e+03, ...,
1.00000000e+00, 2.00000000e+00, 1.39707767e+03],
...,
[8.50000000e+01, 0.00000000e+00, 0.00000000e+00, ...,
8.50000000e+01, 0.00000000e+00, 8.50000000e+01],
[2.33000000e+02, 4.00000000e+00, 4.90000000e+01, ...,
4.00000000e+00, 4.00000000e+00, 7.76666667e+01],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])
Y_f
array([[1., 0., 0., ..., 0., 0., 0.],
[1., 0., 0., ..., 0., 0., 0.],
[1., 0., 0., ..., 0., 0., 0.],
...,
[1., 0., 0., ..., 0., 0., 0.],
[1., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 1.]])
Dimensions of input parameters:
print(X_f.shape, Y_f.shape)
(2087620, 31) (2087620, 11)
What I have tried to do so far:
Use other techniques of imblearn package:
- Using
SVMSMOTE:- Takes too long to compute (over 2days), and PC crashes. - Using
RandomOverSampler:- Model gives poor accuracy, of 45% - Using different
sampling_strategy:- works forminorityonly. - Also of the suggestions provided here and here., unsuccessfully. I could not understand them, honestly.
Despite trying, I do not understand much of it. I am a newbie at sampling. Can you help me fix this problem, please?
python data-mining sampling smote
edited yesterday
Ethan
701625
asked yesterday
venom8914venom8914
1312
$endgroup$
add a comment |
0
$begingroup$
I am designing a multi class classifier for 11 labels. I am using SMOTE to do the sampling. However, I face the following error:
SMOTE Error:
from imblearn.over_sampling import SMOTE
sm = SMOTE(random_state=42)
X_res, Y_res = sm.fit_sample(X_f, Y_f)
Error:
~/.local/lib/python3.6/site-packages/sklearn/neighbors/base.py in kneighbors(self, X, n_neighbors, return_distance)
414 "Expected n_neighbors <= n_samples, "
415 " but n_samples = %d, n_neighbors = %d" %
--> 416 (train_size, n_neighbors)
417 )
418 n_samples, _ = X.shape
ValueError: Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6
Why does it say that I have only 1 n_samples?
When I tried the same code for a much smaller dataset (100k rows and 4 labels), it ran just fine.
Details about my input:
Input parameters:
X_f
array([[1.43347000e+05, 1.00000000e+00, 2.03869492e+03, ...,
1.00000000e+00, 1.00000000e+00, 1.35233019e+03],
[5.09050000e+04, 0.00000000e+00, 0.00000000e+00, ...,
5.09050000e+04, 0.00000000e+00, 5.09050000e+04],
[1.43899000e+05, 2.00000000e+00, 2.11447368e+03, ...,
1.00000000e+00, 2.00000000e+00, 1.39707767e+03],
...,
[8.50000000e+01, 0.00000000e+00, 0.00000000e+00, ...,
8.50000000e+01, 0.00000000e+00, 8.50000000e+01],
[2.33000000e+02, 4.00000000e+00, 4.90000000e+01, ...,
4.00000000e+00, 4.00000000e+00, 7.76666667e+01],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])
Y_f
array([[1., 0., 0., ..., 0., 0., 0.],
[1., 0., 0., ..., 0., 0., 0.],
[1., 0., 0., ..., 0., 0., 0.],
...,
[1., 0., 0., ..., 0., 0., 0.],
[1., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 1.]])
Dimensions of input parameters:
print(X_f.shape, Y_f.shape)
(2087620, 31) (2087620, 11)
What I have tried to do so far:
Use other techniques of imblearn package:
- Using
SVMSMOTE:- Takes too long to compute (over 2days), and PC crashes. - Using
RandomOverSampler:- Model gives poor accuracy, of 45% - Using different
sampling_strategy:- works forminorityonly. - Also of the suggestions provided here and here., unsuccessfully. I could not understand them, honestly.
Despite trying, I do not understand much of it. I am a newbie at sampling. Can you help me fix this problem, please?
python data-mining sampling smote
edited yesterday
Ethan
701625
asked yesterday
venom8914venom8914
1312
$endgroup$
add a comment |
0
0
0
$begingroup$
I am designing a multi class classifier for 11 labels. I am using SMOTE to do the sampling. However, I face the following error:
SMOTE Error:
from imblearn.over_sampling import SMOTE
sm = SMOTE(random_state=42)
X_res, Y_res = sm.fit_sample(X_f, Y_f)
Error:
~/.local/lib/python3.6/site-packages/sklearn/neighbors/base.py in kneighbors(self, X, n_neighbors, return_distance)
414 "Expected n_neighbors <= n_samples, "
415 " but n_samples = %d, n_neighbors = %d" %
--> 416 (train_size, n_neighbors)
417 )
418 n_samples, _ = X.shape
ValueError: Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6
Why does it say that I have only 1 n_samples?
When I tried the same code for a much smaller dataset (100k rows and 4 labels), it ran just fine.
Details about my input:
Input parameters:
X_f
array([[1.43347000e+05, 1.00000000e+00, 2.03869492e+03, ...,
1.00000000e+00, 1.00000000e+00, 1.35233019e+03],
[5.09050000e+04, 0.00000000e+00, 0.00000000e+00, ...,
5.09050000e+04, 0.00000000e+00, 5.09050000e+04],
[1.43899000e+05, 2.00000000e+00, 2.11447368e+03, ...,
1.00000000e+00, 2.00000000e+00, 1.39707767e+03],
...,
[8.50000000e+01, 0.00000000e+00, 0.00000000e+00, ...,
8.50000000e+01, 0.00000000e+00, 8.50000000e+01],
[2.33000000e+02, 4.00000000e+00, 4.90000000e+01, ...,
4.00000000e+00, 4.00000000e+00, 7.76666667e+01],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])
Y_f
array([[1., 0., 0., ..., 0., 0., 0.],
[1., 0., 0., ..., 0., 0., 0.],
[1., 0., 0., ..., 0., 0., 0.],
...,
[1., 0., 0., ..., 0., 0., 0.],
[1., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 1.]])
Dimensions of input parameters:
print(X_f.shape, Y_f.shape)
(2087620, 31) (2087620, 11)
What I have tried to do so far:
Use other techniques of imblearn package:
- Using
SVMSMOTE:- Takes too long to compute (over 2days), and PC crashes. - Using
RandomOverSampler:- Model gives poor accuracy, of 45% - Using different
sampling_strategy:- works forminorityonly. - Also of the suggestions provided here and here., unsuccessfully. I could not understand them, honestly.
Despite trying, I do not understand much of it. I am a newbie at sampling. Can you help me fix this problem, please?
python data-mining sampling smote
edited yesterday
Ethan
701625
asked yesterday
venom8914venom8914
1312
$endgroup$
I am designing a multi class classifier for 11 labels. I am using SMOTE to do the sampling. However, I face the following error:
SMOTE Error:
from imblearn.over_sampling import SMOTE
sm = SMOTE(random_state=42)
X_res, Y_res = sm.fit_sample(X_f, Y_f)
Error:
~/.local/lib/python3.6/site-packages/sklearn/neighbors/base.py in kneighbors(self, X, n_neighbors, return_distance)
414 "Expected n_neighbors <= n_samples, "
415 " but n_samples = %d, n_neighbors = %d" %
--> 416 (train_size, n_neighbors)
417 )
418 n_samples, _ = X.shape
ValueError: Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6
Why does it say that I have only 1 n_samples?
When I tried the same code for a much smaller dataset (100k rows and 4 labels), it ran just fine.
Details about my input:
Input parameters:
X_f
array([[1.43347000e+05, 1.00000000e+00, 2.03869492e+03, ...,
1.00000000e+00, 1.00000000e+00, 1.35233019e+03],
[5.09050000e+04, 0.00000000e+00, 0.00000000e+00, ...,
5.09050000e+04, 0.00000000e+00, 5.09050000e+04],
[1.43899000e+05, 2.00000000e+00, 2.11447368e+03, ...,
1.00000000e+00, 2.00000000e+00, 1.39707767e+03],
...,
[8.50000000e+01, 0.00000000e+00, 0.00000000e+00, ...,
8.50000000e+01, 0.00000000e+00, 8.50000000e+01],
[2.33000000e+02, 4.00000000e+00, 4.90000000e+01, ...,
4.00000000e+00, 4.00000000e+00, 7.76666667e+01],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])
Y_f
array([[1., 0., 0., ..., 0., 0., 0.],
[1., 0., 0., ..., 0., 0., 0.],
[1., 0., 0., ..., 0., 0., 0.],
...,
[1., 0., 0., ..., 0., 0., 0.],
[1., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 1.]])
Dimensions of input parameters:
print(X_f.shape, Y_f.shape)
(2087620, 31) (2087620, 11)
What I have tried to do so far:
Use other techniques of imblearn package:
- Using
SVMSMOTE:- Takes too long to compute (over 2days), and PC crashes. - Using
RandomOverSampler:- Model gives poor accuracy, of 45% - Using different
sampling_strategy:- works forminorityonly. - Also of the suggestions provided here and here., unsuccessfully. I could not understand them, honestly.
Despite trying, I do not understand much of it. I am a newbie at sampling. Can you help me fix this problem, please?
python data-mining sampling smote
python data-mining sampling smote
edited yesterday
Ethan
701625
asked yesterday
venom8914venom8914
1312
edited yesterday
Ethan
701625
asked yesterday
venom8914venom8914
1312
edited yesterday
Ethan
701625
edited yesterday
Ethan
701625
edited yesterday
Ethan
701625
701625
asked yesterday
venom8914venom8914
1312
asked yesterday
venom8914venom8914
1312
asked yesterday
venom8914venom8914
1312
1312
add a comment |
add a comment |
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