StandardScaler with Pipelines and GridSearchCV
1
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 |
1
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 |
1
1
1
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
1
@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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@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 |
1
@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 |
1
1
1
@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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