Hi Nicolas,
I tried your Code and my script also crashed.
However, I think you might have forgotten to scale your input before
using the SVR.
Try this instead:
from sklearn.svm import SVR
from sklearn.preprocessing import StandardScaler
import numpy as np
X=np.array([[ 40.8 ],
[ 21327.5900838],
[ 28781.2890295],
[ 29978.2941176],
[ 30732.562406 ],
[ 40.8 ],
[ 21327.5900838],
[ 28781.2890295],
[ 29978.2941176],
[ 30732.562406 ],
[ 40.8 ],
[ 21327.5900838],
[ 28781.2890295],
[ 29978.2941176],
[ 30732.562406 ],
[ 40.8 ],
[ 21327.5900838],
[ 28781.2890295],
[ 29978.2941176],
[ 30732.562406 ],
[ 40.8 ],
[ 21327.5900838],
[ 28781.2890295],
[ 29978.2941176],
[ 30732.562406 ],
[ 40.8 ],
[ 21327.5900838],
[ 28781.2890295],
[ 29978.2941176],
[ 30732.562406 ],
[ 40.8 ],
[ 21327.5900838],
[ 28781.2890295],
[ 29978.2941176],
[ 30732.562406 ],
[ 40.8 ],
[ 21327.5900838],
[ 28781.2890295],
[ 29978.2941176],
[ 30732.562406 ],
[ 40.8 ],
[ 21327.5900838],
[ 28781.2890295],
[ 29978.2941176],
[ 30732.562406 ]])
y=np.array([ 0.1, 0.1, 0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.2,
0.3,
0.3, 0.3, 0.3, 0.3, 0.4, 0.4, 0.4, 0.4, 0.4, 0.5, 0.5,
0.5, 0.5, 0.5, 0.6, 0.6, 0.6, 0.6, 0.6, 0.7, 0.7, 0.7,
0.7, 0.7, 0.8, 0.8, 0.8, 0.8, 0.8, 0.9, 0.9, 0.9, 0.9,
0.9])
weights=np.array([ 1. , 0.75 , 1. , 0.88867188,
0.66650391,
1. , 0.75 , 1. , 0.88867188, 0.66650391,
1. , 0.75 , 1. , 0.88867188, 0.66650391,
1. , 0.75 , 1. , 0.88867188, 0.66650391,
1. , 0.75 , 1. , 0.88867188, 0.66650391,
1. , 0.75 , 1. , 0.88867188, 0.66650391,
1. , 0.75 , 1. , 0.88867188, 0.66650391,
1. , 0.75 , 1. , 0.88867188, 0.66650391,
1. , 0.75 , 1. , 0.88867188, 0.66650391],
dtype=np.float16)
X =StandardScaler().fit_transform(X)
svr_poly = SVR(kernel='poly', C=1e3, degree=2, verbose=True)
fit = svr_poly.fit(X, y, weights)
This is working for me.
Don't forget to fit your StandardScaler to your training set and using
the training mean and scale on your test/validation set.
Greets,
Piotr
On 21.04.2016 13:32, Nicolas Cedilnik wrote:
> Hi all,
>
> I'm trying to use scikit-learn to do SV regression and this small data
> set causes it to crash every time. I can't even stop the process with
> CTRL+C and have to kill the process some other way. I've tested it on
> python 3.5 and 2.7.
>
> Am I doing something wrong or should I report a bug?
>
> Here's some copy-pastable code to reproduce the issue:
>
> from sklearn.svm import SVR
> import numpy as np
>
> X=np.array([[ 40.8 ],
> [ 21327.5900838],
> [ 28781.2890295],
> [ 29978.2941176],
> [ 30732.562406 ],
> [ 40.8 ],
> [ 21327.5900838],
> [ 28781.2890295],
> [ 29978.2941176],
> [ 30732.562406 ],
> [ 40.8 ],
> [ 21327.5900838],
> [ 28781.2890295],
> [ 29978.2941176],
> [ 30732.562406 ],
> [ 40.8 ],
> [ 21327.5900838],
> [ 28781.2890295],
> [ 29978.2941176],
> [ 30732.562406 ],
> [ 40.8 ],
> [ 21327.5900838],
> [ 28781.2890295],
> [ 29978.2941176],
> [ 30732.562406 ],
> [ 40.8 ],
> [ 21327.5900838],
> [ 28781.2890295],
> [ 29978.2941176],
> [ 30732.562406 ],
> [ 40.8 ],
> [ 21327.5900838],
> [ 28781.2890295],
> [ 29978.2941176],
> [ 30732.562406 ],
> [ 40.8 ],
> [ 21327.5900838],
> [ 28781.2890295],
> [ 29978.2941176],
> [ 30732.562406 ],
> [ 40.8 ],
> [ 21327.5900838],
> [ 28781.2890295],
> [ 29978.2941176],
> [ 30732.562406 ]])
> y=np.array([ 0.1, 0.1, 0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.2,
> 0.3,
> 0.3, 0.3, 0.3, 0.3, 0.4, 0.4, 0.4, 0.4, 0.4, 0.5, 0.5,
> 0.5, 0.5, 0.5, 0.6, 0.6, 0.6, 0.6, 0.6, 0.7, 0.7, 0.7,
> 0.7, 0.7, 0.8, 0.8, 0.8, 0.8, 0.8, 0.9, 0.9, 0.9, 0.9,
> 0.9])
> weights=np.array([ 1. , 0.75 , 1. , 0.88867188,
> 0.66650391,
> 1. , 0.75 , 1. , 0.88867188, 0.66650391,
> 1. , 0.75 , 1. , 0.88867188, 0.66650391,
> 1. , 0.75 , 1. , 0.88867188, 0.66650391,
> 1. , 0.75 , 1. , 0.88867188, 0.66650391,
> 1. , 0.75 , 1. , 0.88867188, 0.66650391,
> 1. , 0.75 , 1. , 0.88867188, 0.66650391,
> 1. , 0.75 , 1. , 0.88867188, 0.66650391,
> 1. , 0.75 , 1. , 0.88867188, 0.66650391],
> dtype=np.float16)
>
> svr_poly = SVR(kernel='poly', C=1e3, degree=2)
> fit = svr_poly.fit(X, y, weights)
>
> -- Nicolas Cedilnik
>
> PS: this is not the 'real' data I need the regression on.
>
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