StandardScaler with Pipelines and GridSearchCV
up vote
1
down vote
favorite
I've put standardScaler on the pipeline, and
the results of CV_mlpregressor.predict(x_test), are weird. I think i must have to bring the values back from the standardScaler, but still can't figure how.
pipe_MLPRegressor = Pipeline([('scaler', StandardScaler()),
('MLPRegressor', MLPRegressor(random_state = 42))])
grid_params_MLPRegressor = [{
'MLPRegressor__solver': ['lbfgs'],
'MLPRegressor__max_iter': [100,200,300,500],
'MLPRegressor__activation' : ['relu','logistic','tanh'],
'MLPRegressor__hidden_layer_sizes':[(2,), (4,),(2,2),(4,4),(4,2),(10,10),(2,2,2)],
}]
CV_mlpregressor = GridSearchCV (estimator = pipe_MLPRegressor,
param_grid = grid_params_MLPRegressor,
cv = 5,return_train_score=True, verbose=0)
CV_mlpregressor.fit(x_train, y_train)
CV_mlpregressor.predict(x_test)
The Results:
array([ 2.67564153e+04, 1.90010572e+04, 9.62702942e+04, 3.98791931e+04,
1.48889808e+03, 7.08980726e+03, 3.86311279e+02, 7.05602301e+04,
4.06858486e+03, 4.29186303e+04, 3.86701735e+03, 6.30228075e+04,
6.78276925e+04, -5.91956287e+02, -7.37680434e+02, 3.07485001e+04,
4.81417953e+03, 5.18697686e+03, 1.61221952e+04, 1.33794944e+04,
-1.48375101e+03, 1.80891807e+04, 1.39740243e+04, 6.57156849e+04,
3.32962481e+04, 5.71332087e+05, 1.79130092e+03, 5.25642370e+04,
2.08111172e+04, 4.31060127e+04])
Thanks in advance.
python scikit-learn regression analysis
asked Nov 11 at 19:03
Lain Iwakura
254
add a comment |
up vote
1
down vote
favorite
I've put standardScaler on the pipeline, and
the results of CV_mlpregressor.predict(x_test), are weird. I think i must have to bring the values back from the standardScaler, but still can't figure how.
pipe_MLPRegressor = Pipeline([('scaler', StandardScaler()),
('MLPRegressor', MLPRegressor(random_state = 42))])
grid_params_MLPRegressor = [{
'MLPRegressor__solver': ['lbfgs'],
'MLPRegressor__max_iter': [100,200,300,500],
'MLPRegressor__activation' : ['relu','logistic','tanh'],
'MLPRegressor__hidden_layer_sizes':[(2,), (4,),(2,2),(4,4),(4,2),(10,10),(2,2,2)],
}]
CV_mlpregressor = GridSearchCV (estimator = pipe_MLPRegressor,
param_grid = grid_params_MLPRegressor,
cv = 5,return_train_score=True, verbose=0)
CV_mlpregressor.fit(x_train, y_train)
CV_mlpregressor.predict(x_test)
The Results:
array([ 2.67564153e+04, 1.90010572e+04, 9.62702942e+04, 3.98791931e+04,
1.48889808e+03, 7.08980726e+03, 3.86311279e+02, 7.05602301e+04,
4.06858486e+03, 4.29186303e+04, 3.86701735e+03, 6.30228075e+04,
6.78276925e+04, -5.91956287e+02, -7.37680434e+02, 3.07485001e+04,
4.81417953e+03, 5.18697686e+03, 1.61221952e+04, 1.33794944e+04,
-1.48375101e+03, 1.80891807e+04, 1.39740243e+04, 6.57156849e+04,
3.32962481e+04, 5.71332087e+05, 1.79130092e+03, 5.25642370e+04,
2.08111172e+04, 4.31060127e+04])
Thanks in advance.
python scikit-learn regression analysis
asked Nov 11 at 19:03
Lain Iwakura
254
add a comment |
up vote
1
down vote
favorite
up vote
1
down vote
favorite
I've put standardScaler on the pipeline, and
the results of CV_mlpregressor.predict(x_test), are weird. I think i must have to bring the values back from the standardScaler, but still can't figure how.
pipe_MLPRegressor = Pipeline([('scaler', StandardScaler()),
('MLPRegressor', MLPRegressor(random_state = 42))])
grid_params_MLPRegressor = [{
'MLPRegressor__solver': ['lbfgs'],
'MLPRegressor__max_iter': [100,200,300,500],
'MLPRegressor__activation' : ['relu','logistic','tanh'],
'MLPRegressor__hidden_layer_sizes':[(2,), (4,),(2,2),(4,4),(4,2),(10,10),(2,2,2)],
}]
CV_mlpregressor = GridSearchCV (estimator = pipe_MLPRegressor,
param_grid = grid_params_MLPRegressor,
cv = 5,return_train_score=True, verbose=0)
CV_mlpregressor.fit(x_train, y_train)
CV_mlpregressor.predict(x_test)
The Results:
array([ 2.67564153e+04, 1.90010572e+04, 9.62702942e+04, 3.98791931e+04,
1.48889808e+03, 7.08980726e+03, 3.86311279e+02, 7.05602301e+04,
4.06858486e+03, 4.29186303e+04, 3.86701735e+03, 6.30228075e+04,
6.78276925e+04, -5.91956287e+02, -7.37680434e+02, 3.07485001e+04,
4.81417953e+03, 5.18697686e+03, 1.61221952e+04, 1.33794944e+04,
-1.48375101e+03, 1.80891807e+04, 1.39740243e+04, 6.57156849e+04,
3.32962481e+04, 5.71332087e+05, 1.79130092e+03, 5.25642370e+04,
2.08111172e+04, 4.31060127e+04])
Thanks in advance.
python scikit-learn regression analysis
asked Nov 11 at 19:03
Lain Iwakura
254
I've put standardScaler on the pipeline, and
the results of CV_mlpregressor.predict(x_test), are weird. I think i must have to bring the values back from the standardScaler, but still can't figure how.
pipe_MLPRegressor = Pipeline([('scaler', StandardScaler()),
('MLPRegressor', MLPRegressor(random_state = 42))])
grid_params_MLPRegressor = [{
'MLPRegressor__solver': ['lbfgs'],
'MLPRegressor__max_iter': [100,200,300,500],
'MLPRegressor__activation' : ['relu','logistic','tanh'],
'MLPRegressor__hidden_layer_sizes':[(2,), (4,),(2,2),(4,4),(4,2),(10,10),(2,2,2)],
}]
CV_mlpregressor = GridSearchCV (estimator = pipe_MLPRegressor,
param_grid = grid_params_MLPRegressor,
cv = 5,return_train_score=True, verbose=0)
CV_mlpregressor.fit(x_train, y_train)
CV_mlpregressor.predict(x_test)
The Results:
array([ 2.67564153e+04, 1.90010572e+04, 9.62702942e+04, 3.98791931e+04,
1.48889808e+03, 7.08980726e+03, 3.86311279e+02, 7.05602301e+04,
4.06858486e+03, 4.29186303e+04, 3.86701735e+03, 6.30228075e+04,
6.78276925e+04, -5.91956287e+02, -7.37680434e+02, 3.07485001e+04,
4.81417953e+03, 5.18697686e+03, 1.61221952e+04, 1.33794944e+04,
-1.48375101e+03, 1.80891807e+04, 1.39740243e+04, 6.57156849e+04,
3.32962481e+04, 5.71332087e+05, 1.79130092e+03, 5.25642370e+04,
2.08111172e+04, 4.31060127e+04])
Thanks in advance.
python scikit-learn regression analysis
python scikit-learn regression analysis
asked Nov 11 at 19:03
Lain Iwakura
254
asked Nov 11 at 19:03
Lain Iwakura
254
edited Nov 11 at 19:16
asked Nov 11 at 19:03
Lain Iwakura
254
asked Nov 11 at 19:03
Lain Iwakura
254
asked Nov 11 at 19:03
Lain Iwakura
254
254
add a comment |
add a comment |
1 Answer
1
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oldest
votes
up vote
1
down vote
accepted
@Lian, I think you are doing everything in the correct way. Please check your data. I did an experiment with sklearn dataset and this works as expected.
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPRegressor
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
import numpy as np
x,y = load_boston(return_X_y=True)
xtrain, xtest, ytrain, ytest = train_test_split(x,y, random_state=6784)
pipe_MLPRegressor = Pipeline([('scaler', StandardScaler()),
('MLPRegressor', MLPRegressor(random_state = 42))])
grid_params_MLPRegressor = [{
'MLPRegressor__solver': ['lbfgs'],
'MLPRegressor__max_iter': [100,200,300,500],
'MLPRegressor__activation' : ['relu','logistic','tanh'],
'MLPRegressor__hidden_layer_sizes':[(2,), (4,),(2,2),(4,4),(4,2),(10,10),(2,
2,2)],}]
CV_mlpregressor = GridSearchCV (estimator = pipe_MLPRegressor,
param_grid = grid_params_MLPRegressor,
cv = 5,return_train_score=True, verbose=0)
CV_mlpregressor.fit(xtrain, ytrain)
ypred=CV_mlpregressor.predict(xtest)
print np.c_[ytest, ypred]
This prints
array([[ 29.9 , 30.79749986],
[ 22.5 , 24.52180656],
[ 22.6 , 18.9567779 ],
[ 28.7 , 22.17189123],
[ 13.8 , 19.16797811],
[ 21.2 , 24.63527335],
[ 11.3 , 13.58962076],
[ 23. , 18.33693455],
[ 12.7 , 15.52294714],
[ 23.3 , 26.65083451],
[ 25.3 , 24.04219813],
[ 22.6 , 19.81454969],
[ 36.2 , 22.16994764],
[ 17.9 , 11.1221789 ],
[ 18.5 , 17.84162452],
[ 16.8 , 22.99832673],
[ 20.3 , 20.22598426],
[ 23.9 , 26.80997945],
[ 17.6 , 16.08188321],
[ 23.2 , 18.5995955 ],
[ 48.3 , 43.37911488],
[ 19.1 , 22.36379857],
answered Nov 12 at 14:09
sukhbinder
31223
Thanks for your reply, I'll check my database!
– Lain Iwakura
Nov 12 at 23:16
add a comment |
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1 Answer
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1 Answer
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active
oldest
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active
oldest
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active
oldest
votes
up vote
1
down vote
accepted
@Lian, I think you are doing everything in the correct way. Please check your data. I did an experiment with sklearn dataset and this works as expected.
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPRegressor
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
import numpy as np
x,y = load_boston(return_X_y=True)
xtrain, xtest, ytrain, ytest = train_test_split(x,y, random_state=6784)
pipe_MLPRegressor = Pipeline([('scaler', StandardScaler()),
('MLPRegressor', MLPRegressor(random_state = 42))])
grid_params_MLPRegressor = [{
'MLPRegressor__solver': ['lbfgs'],
'MLPRegressor__max_iter': [100,200,300,500],
'MLPRegressor__activation' : ['relu','logistic','tanh'],
'MLPRegressor__hidden_layer_sizes':[(2,), (4,),(2,2),(4,4),(4,2),(10,10),(2,
2,2)],}]
CV_mlpregressor = GridSearchCV (estimator = pipe_MLPRegressor,
param_grid = grid_params_MLPRegressor,
cv = 5,return_train_score=True, verbose=0)
CV_mlpregressor.fit(xtrain, ytrain)
ypred=CV_mlpregressor.predict(xtest)
print np.c_[ytest, ypred]
This prints
array([[ 29.9 , 30.79749986],
[ 22.5 , 24.52180656],
[ 22.6 , 18.9567779 ],
[ 28.7 , 22.17189123],
[ 13.8 , 19.16797811],
[ 21.2 , 24.63527335],
[ 11.3 , 13.58962076],
[ 23. , 18.33693455],
[ 12.7 , 15.52294714],
[ 23.3 , 26.65083451],
[ 25.3 , 24.04219813],
[ 22.6 , 19.81454969],
[ 36.2 , 22.16994764],
[ 17.9 , 11.1221789 ],
[ 18.5 , 17.84162452],
[ 16.8 , 22.99832673],
[ 20.3 , 20.22598426],
[ 23.9 , 26.80997945],
[ 17.6 , 16.08188321],
[ 23.2 , 18.5995955 ],
[ 48.3 , 43.37911488],
[ 19.1 , 22.36379857],
answered Nov 12 at 14:09
sukhbinder
31223
Thanks for your reply, I'll check my database!
– Lain Iwakura
Nov 12 at 23:16
add a comment |
up vote
1
down vote
accepted
@Lian, I think you are doing everything in the correct way. Please check your data. I did an experiment with sklearn dataset and this works as expected.
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPRegressor
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
import numpy as np
x,y = load_boston(return_X_y=True)
xtrain, xtest, ytrain, ytest = train_test_split(x,y, random_state=6784)
pipe_MLPRegressor = Pipeline([('scaler', StandardScaler()),
('MLPRegressor', MLPRegressor(random_state = 42))])
grid_params_MLPRegressor = [{
'MLPRegressor__solver': ['lbfgs'],
'MLPRegressor__max_iter': [100,200,300,500],
'MLPRegressor__activation' : ['relu','logistic','tanh'],
'MLPRegressor__hidden_layer_sizes':[(2,), (4,),(2,2),(4,4),(4,2),(10,10),(2,
2,2)],}]
CV_mlpregressor = GridSearchCV (estimator = pipe_MLPRegressor,
param_grid = grid_params_MLPRegressor,
cv = 5,return_train_score=True, verbose=0)
CV_mlpregressor.fit(xtrain, ytrain)
ypred=CV_mlpregressor.predict(xtest)
print np.c_[ytest, ypred]
This prints
array([[ 29.9 , 30.79749986],
[ 22.5 , 24.52180656],
[ 22.6 , 18.9567779 ],
[ 28.7 , 22.17189123],
[ 13.8 , 19.16797811],
[ 21.2 , 24.63527335],
[ 11.3 , 13.58962076],
[ 23. , 18.33693455],
[ 12.7 , 15.52294714],
[ 23.3 , 26.65083451],
[ 25.3 , 24.04219813],
[ 22.6 , 19.81454969],
[ 36.2 , 22.16994764],
[ 17.9 , 11.1221789 ],
[ 18.5 , 17.84162452],
[ 16.8 , 22.99832673],
[ 20.3 , 20.22598426],
[ 23.9 , 26.80997945],
[ 17.6 , 16.08188321],
[ 23.2 , 18.5995955 ],
[ 48.3 , 43.37911488],
[ 19.1 , 22.36379857],
answered Nov 12 at 14:09
sukhbinder
31223
Thanks for your reply, I'll check my database!
– Lain Iwakura
Nov 12 at 23:16
add a comment |
up vote
1
down vote
accepted
up vote
1
down vote
accepted
@Lian, I think you are doing everything in the correct way. Please check your data. I did an experiment with sklearn dataset and this works as expected.
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPRegressor
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
import numpy as np
x,y = load_boston(return_X_y=True)
xtrain, xtest, ytrain, ytest = train_test_split(x,y, random_state=6784)
pipe_MLPRegressor = Pipeline([('scaler', StandardScaler()),
('MLPRegressor', MLPRegressor(random_state = 42))])
grid_params_MLPRegressor = [{
'MLPRegressor__solver': ['lbfgs'],
'MLPRegressor__max_iter': [100,200,300,500],
'MLPRegressor__activation' : ['relu','logistic','tanh'],
'MLPRegressor__hidden_layer_sizes':[(2,), (4,),(2,2),(4,4),(4,2),(10,10),(2,
2,2)],}]
CV_mlpregressor = GridSearchCV (estimator = pipe_MLPRegressor,
param_grid = grid_params_MLPRegressor,
cv = 5,return_train_score=True, verbose=0)
CV_mlpregressor.fit(xtrain, ytrain)
ypred=CV_mlpregressor.predict(xtest)
print np.c_[ytest, ypred]
This prints
array([[ 29.9 , 30.79749986],
[ 22.5 , 24.52180656],
[ 22.6 , 18.9567779 ],
[ 28.7 , 22.17189123],
[ 13.8 , 19.16797811],
[ 21.2 , 24.63527335],
[ 11.3 , 13.58962076],
[ 23. , 18.33693455],
[ 12.7 , 15.52294714],
[ 23.3 , 26.65083451],
[ 25.3 , 24.04219813],
[ 22.6 , 19.81454969],
[ 36.2 , 22.16994764],
[ 17.9 , 11.1221789 ],
[ 18.5 , 17.84162452],
[ 16.8 , 22.99832673],
[ 20.3 , 20.22598426],
[ 23.9 , 26.80997945],
[ 17.6 , 16.08188321],
[ 23.2 , 18.5995955 ],
[ 48.3 , 43.37911488],
[ 19.1 , 22.36379857],
answered Nov 12 at 14:09
sukhbinder
31223
@Lian, I think you are doing everything in the correct way. Please check your data. I did an experiment with sklearn dataset and this works as expected.
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPRegressor
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
import numpy as np
x,y = load_boston(return_X_y=True)
xtrain, xtest, ytrain, ytest = train_test_split(x,y, random_state=6784)
pipe_MLPRegressor = Pipeline([('scaler', StandardScaler()),
('MLPRegressor', MLPRegressor(random_state = 42))])
grid_params_MLPRegressor = [{
'MLPRegressor__solver': ['lbfgs'],
'MLPRegressor__max_iter': [100,200,300,500],
'MLPRegressor__activation' : ['relu','logistic','tanh'],
'MLPRegressor__hidden_layer_sizes':[(2,), (4,),(2,2),(4,4),(4,2),(10,10),(2,
2,2)],}]
CV_mlpregressor = GridSearchCV (estimator = pipe_MLPRegressor,
param_grid = grid_params_MLPRegressor,
cv = 5,return_train_score=True, verbose=0)
CV_mlpregressor.fit(xtrain, ytrain)
ypred=CV_mlpregressor.predict(xtest)
print np.c_[ytest, ypred]
This prints
array([[ 29.9 , 30.79749986],
[ 22.5 , 24.52180656],
[ 22.6 , 18.9567779 ],
[ 28.7 , 22.17189123],
[ 13.8 , 19.16797811],
[ 21.2 , 24.63527335],
[ 11.3 , 13.58962076],
[ 23. , 18.33693455],
[ 12.7 , 15.52294714],
[ 23.3 , 26.65083451],
[ 25.3 , 24.04219813],
[ 22.6 , 19.81454969],
[ 36.2 , 22.16994764],
[ 17.9 , 11.1221789 ],
[ 18.5 , 17.84162452],
[ 16.8 , 22.99832673],
[ 20.3 , 20.22598426],
[ 23.9 , 26.80997945],
[ 17.6 , 16.08188321],
[ 23.2 , 18.5995955 ],
[ 48.3 , 43.37911488],
[ 19.1 , 22.36379857],
answered Nov 12 at 14:09
sukhbinder
31223
answered Nov 12 at 14:09
sukhbinder
31223
answered Nov 12 at 14:09
sukhbinder
31223
answered Nov 12 at 14:09
sukhbinder
31223
31223
Thanks for your reply, I'll check my database!
– Lain Iwakura
Nov 12 at 23:16
add a comment |
Thanks for your reply, I'll check my database!
– Lain Iwakura
Nov 12 at 23:16
Thanks for your reply, I'll check my database!
– Lain Iwakura
Nov 12 at 23:16
Thanks for your reply, I'll check my database!
– Lain Iwakura
Nov 12 at 23:16
add a comment |
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StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
