ValueError: Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6 (SMOTE) The 2019 Stack Overflow Developer Survey Results Are InCan SMOTE be applied over sequence of words (sentences)?ValueError when doing validation with random forestsSMOTE and multi class oversamplingLogic behind SMOTE-NC?ValueError: Error when checking target: expected dense_1 to have shape (7,) but got array with shape (1,)SmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?solving multi-class imbalance classification using smote and OSSUsing SMOTE for Synthetic Data generation to improve performance on unbalanced dataproblem of entry format for a simple model in KerasSVM SMOTE fit_resample() function runs forever with no result
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ValueError: Expected n_neighbors
The 2019 Stack Overflow Developer Survey Results Are InCan SMOTE be applied over sequence of words (sentences)?ValueError when doing validation with random forestsSMOTE and multi class oversamplingLogic behind SMOTE-NC?ValueError: Error when checking target: expected dense_1 to have shape (7,) but got array with shape (1,)SmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?solving multi-class imbalance classification using smote and OSSUsing SMOTE for Synthetic Data generation to improve performance on unbalanced dataproblem of entry format for a simple model in KerasSVM SMOTE fit_resample() function runs forever with no result
$begingroup$
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 6 hours ago
Ethan
703525
asked 9 hours ago
venom8914venom8914
1312
$endgroup$
add a comment |
$begingroup$
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 6 hours ago
Ethan
703525
asked 9 hours ago
venom8914venom8914
1312
$endgroup$
add a comment |
$begingroup$
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 6 hours ago
Ethan
703525
asked 9 hours ago
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 6 hours ago
Ethan
703525
asked 9 hours ago
venom8914venom8914
1312
edited 6 hours ago
Ethan
703525
asked 9 hours ago
venom8914venom8914
1312
edited 6 hours ago
Ethan
703525
edited 6 hours ago
Ethan
703525
edited 6 hours ago
Ethan
703525
703525
asked 9 hours ago
venom8914venom8914
1312
asked 9 hours ago
venom8914venom8914
1312
asked 9 hours ago
venom8914venom8914
1312
1312
add a comment |
add a comment |
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