It´s possible to apply cross_val_score() form sklearn to neupy NN that has an addon like Weigth Elimination?
up vote
0
down vote
favorite
I´m trying to apply cross_val_score() to the following algorithm:
cgnet = algorithms.LevenbergMarquardt(
connection=[
layers.Input(XTrain.shape[1]),
layers.Linear(6),
layers.Linear(1)],
mu_update_factor=2,
mu=0.1,
shuffle_data=True,
verbose=True,
decay_rate=0.1,
addons=[algorithms.WeightElimination])
kfold = KFold(n_splits=5, shuffle=True, random_state=7)
scores=cross_val_score(cgnet, XTrainScaled,yTrainScaled,scoring='neg_mean_absolute_error',cv=kfold,verbose=10)
print scores
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
And this is the error message I get:
TypeError: Cannot create a consistent method resolution
order (MRO) for bases LevenbergMarquardtWeightElimination, WeightElimination
Without WeightElimination or any other addon, cross_val_score(), works fine...Is there another way to do this? Thank you
scikit-learn cross-validation levenberg-marquardt neupy
asked Nov 20 at 13:27
Manuel Almeida
9216
add a comment |
up vote
0
down vote
favorite
I´m trying to apply cross_val_score() to the following algorithm:
cgnet = algorithms.LevenbergMarquardt(
connection=[
layers.Input(XTrain.shape[1]),
layers.Linear(6),
layers.Linear(1)],
mu_update_factor=2,
mu=0.1,
shuffle_data=True,
verbose=True,
decay_rate=0.1,
addons=[algorithms.WeightElimination])
kfold = KFold(n_splits=5, shuffle=True, random_state=7)
scores=cross_val_score(cgnet, XTrainScaled,yTrainScaled,scoring='neg_mean_absolute_error',cv=kfold,verbose=10)
print scores
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
And this is the error message I get:
TypeError: Cannot create a consistent method resolution
order (MRO) for bases LevenbergMarquardtWeightElimination, WeightElimination
Without WeightElimination or any other addon, cross_val_score(), works fine...Is there another way to do this? Thank you
scikit-learn cross-validation levenberg-marquardt neupy
asked Nov 20 at 13:27
Manuel Almeida
9216
What neupy and python versions do you use?
– itdxer
Nov 21 at 14:06
neupy 0.6.5 and numpy 1.15.1
– Manuel Almeida
Nov 21 at 16:07
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I´m trying to apply cross_val_score() to the following algorithm:
cgnet = algorithms.LevenbergMarquardt(
connection=[
layers.Input(XTrain.shape[1]),
layers.Linear(6),
layers.Linear(1)],
mu_update_factor=2,
mu=0.1,
shuffle_data=True,
verbose=True,
decay_rate=0.1,
addons=[algorithms.WeightElimination])
kfold = KFold(n_splits=5, shuffle=True, random_state=7)
scores=cross_val_score(cgnet, XTrainScaled,yTrainScaled,scoring='neg_mean_absolute_error',cv=kfold,verbose=10)
print scores
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
And this is the error message I get:
TypeError: Cannot create a consistent method resolution
order (MRO) for bases LevenbergMarquardtWeightElimination, WeightElimination
Without WeightElimination or any other addon, cross_val_score(), works fine...Is there another way to do this? Thank you
scikit-learn cross-validation levenberg-marquardt neupy
asked Nov 20 at 13:27
Manuel Almeida
9216
I´m trying to apply cross_val_score() to the following algorithm:
cgnet = algorithms.LevenbergMarquardt(
connection=[
layers.Input(XTrain.shape[1]),
layers.Linear(6),
layers.Linear(1)],
mu_update_factor=2,
mu=0.1,
shuffle_data=True,
verbose=True,
decay_rate=0.1,
addons=[algorithms.WeightElimination])
kfold = KFold(n_splits=5, shuffle=True, random_state=7)
scores=cross_val_score(cgnet, XTrainScaled,yTrainScaled,scoring='neg_mean_absolute_error',cv=kfold,verbose=10)
print scores
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
And this is the error message I get:
TypeError: Cannot create a consistent method resolution
order (MRO) for bases LevenbergMarquardtWeightElimination, WeightElimination
Without WeightElimination or any other addon, cross_val_score(), works fine...Is there another way to do this? Thank you
scikit-learn cross-validation levenberg-marquardt neupy
scikit-learn cross-validation levenberg-marquardt neupy
asked Nov 20 at 13:27
Manuel Almeida
9216
asked Nov 20 at 13:27
Manuel Almeida
9216
asked Nov 20 at 13:27
Manuel Almeida
9216
asked Nov 20 at 13:27
Manuel Almeida
9216
asked Nov 20 at 13:27
Manuel Almeida
9216
9216
What neupy and python versions do you use?
– itdxer
Nov 21 at 14:06
neupy 0.6.5 and numpy 1.15.1
– Manuel Almeida
Nov 21 at 16:07
add a comment |
What neupy and python versions do you use?
– itdxer
Nov 21 at 14:06
neupy 0.6.5 and numpy 1.15.1
– Manuel Almeida
Nov 21 at 16:07
What neupy and python versions do you use?
– itdxer
Nov 21 at 14:06
What neupy and python versions do you use?
– itdxer
Nov 21 at 14:06
neupy 0.6.5 and numpy 1.15.1
– Manuel Almeida
Nov 21 at 16:07
neupy 0.6.5 and numpy 1.15.1
– Manuel Almeida
Nov 21 at 16:07
add a comment |
1 Answer
1
active
oldest
votes
up vote
2
down vote
accepted
It looks like function cross_val_score won't work in neupy, but you can run the same code in slightly different way.
import numpy as np
from neupy import algorithms, layers
from sklearn.model_selection import *
from sklearn import metrics
XTrainScaled = XTrain = np.random.random((10, 2))
yTrainScaled = np.random.random((10, 1))
kfold = KFold(n_splits=5, shuffle=True, random_state=7)
scores =
for train, test in kfold.split(XTrainScaled):
x_train, x_test = XTrainScaled[train], XTrainScaled[test]
y_train, y_test = yTrainScaled[train], yTrainScaled[test]
cgnet = algorithms.LevenbergMarquardt(
connection=[
layers.Input(XTrain.shape[1]),
layers.Linear(6),
layers.Linear(1)
],
mu_update_factor=2,
mu=0.1,
shuffle_data=True,
verbose=True,
decay_rate=0.1,
addons=[algorithms.WeightElimination]
)
cgnet.train(x_train, y_train, epochs=5)
y_predicted = cgnet.predict(x_test)
score = metrics.mean_absolute_error(y_test, y_predicted)
scores.append(score)
print(scores)
scores = np.array(scores)
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
answered Nov 21 at 14:39
itdxer
8541030
Thank you, best regards
– Manuel Almeida
Nov 21 at 16:38
1
@ManuelAlmeida The issue seems to be in the fact that sklearn wants to clone the supplied estimators during thecross_val_scorebutneupyhas something weird going on in its code to initialize the models from clone. You should file this as an issue onneupy's github page
– Vivek Kumar
Nov 22 at 7:07
Thank you, I will..
– Manuel Almeida
Nov 22 at 10:42
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
2
down vote
accepted
It looks like function cross_val_score won't work in neupy, but you can run the same code in slightly different way.
import numpy as np
from neupy import algorithms, layers
from sklearn.model_selection import *
from sklearn import metrics
XTrainScaled = XTrain = np.random.random((10, 2))
yTrainScaled = np.random.random((10, 1))
kfold = KFold(n_splits=5, shuffle=True, random_state=7)
scores =
for train, test in kfold.split(XTrainScaled):
x_train, x_test = XTrainScaled[train], XTrainScaled[test]
y_train, y_test = yTrainScaled[train], yTrainScaled[test]
cgnet = algorithms.LevenbergMarquardt(
connection=[
layers.Input(XTrain.shape[1]),
layers.Linear(6),
layers.Linear(1)
],
mu_update_factor=2,
mu=0.1,
shuffle_data=True,
verbose=True,
decay_rate=0.1,
addons=[algorithms.WeightElimination]
)
cgnet.train(x_train, y_train, epochs=5)
y_predicted = cgnet.predict(x_test)
score = metrics.mean_absolute_error(y_test, y_predicted)
scores.append(score)
print(scores)
scores = np.array(scores)
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
answered Nov 21 at 14:39
itdxer
8541030
Thank you, best regards
– Manuel Almeida
Nov 21 at 16:38
1
@ManuelAlmeida The issue seems to be in the fact that sklearn wants to clone the supplied estimators during thecross_val_scorebutneupyhas something weird going on in its code to initialize the models from clone. You should file this as an issue onneupy's github page
– Vivek Kumar
Nov 22 at 7:07
Thank you, I will..
– Manuel Almeida
Nov 22 at 10:42
add a comment |
up vote
2
down vote
accepted
It looks like function cross_val_score won't work in neupy, but you can run the same code in slightly different way.
import numpy as np
from neupy import algorithms, layers
from sklearn.model_selection import *
from sklearn import metrics
XTrainScaled = XTrain = np.random.random((10, 2))
yTrainScaled = np.random.random((10, 1))
kfold = KFold(n_splits=5, shuffle=True, random_state=7)
scores =
for train, test in kfold.split(XTrainScaled):
x_train, x_test = XTrainScaled[train], XTrainScaled[test]
y_train, y_test = yTrainScaled[train], yTrainScaled[test]
cgnet = algorithms.LevenbergMarquardt(
connection=[
layers.Input(XTrain.shape[1]),
layers.Linear(6),
layers.Linear(1)
],
mu_update_factor=2,
mu=0.1,
shuffle_data=True,
verbose=True,
decay_rate=0.1,
addons=[algorithms.WeightElimination]
)
cgnet.train(x_train, y_train, epochs=5)
y_predicted = cgnet.predict(x_test)
score = metrics.mean_absolute_error(y_test, y_predicted)
scores.append(score)
print(scores)
scores = np.array(scores)
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
answered Nov 21 at 14:39
itdxer
8541030
Thank you, best regards
– Manuel Almeida
Nov 21 at 16:38
1
@ManuelAlmeida The issue seems to be in the fact that sklearn wants to clone the supplied estimators during thecross_val_scorebutneupyhas something weird going on in its code to initialize the models from clone. You should file this as an issue onneupy's github page
– Vivek Kumar
Nov 22 at 7:07
Thank you, I will..
– Manuel Almeida
Nov 22 at 10:42
add a comment |
up vote
2
down vote
accepted
up vote
2
down vote
accepted
It looks like function cross_val_score won't work in neupy, but you can run the same code in slightly different way.
import numpy as np
from neupy import algorithms, layers
from sklearn.model_selection import *
from sklearn import metrics
XTrainScaled = XTrain = np.random.random((10, 2))
yTrainScaled = np.random.random((10, 1))
kfold = KFold(n_splits=5, shuffle=True, random_state=7)
scores =
for train, test in kfold.split(XTrainScaled):
x_train, x_test = XTrainScaled[train], XTrainScaled[test]
y_train, y_test = yTrainScaled[train], yTrainScaled[test]
cgnet = algorithms.LevenbergMarquardt(
connection=[
layers.Input(XTrain.shape[1]),
layers.Linear(6),
layers.Linear(1)
],
mu_update_factor=2,
mu=0.1,
shuffle_data=True,
verbose=True,
decay_rate=0.1,
addons=[algorithms.WeightElimination]
)
cgnet.train(x_train, y_train, epochs=5)
y_predicted = cgnet.predict(x_test)
score = metrics.mean_absolute_error(y_test, y_predicted)
scores.append(score)
print(scores)
scores = np.array(scores)
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
answered Nov 21 at 14:39
itdxer
8541030
It looks like function cross_val_score won't work in neupy, but you can run the same code in slightly different way.
import numpy as np
from neupy import algorithms, layers
from sklearn.model_selection import *
from sklearn import metrics
XTrainScaled = XTrain = np.random.random((10, 2))
yTrainScaled = np.random.random((10, 1))
kfold = KFold(n_splits=5, shuffle=True, random_state=7)
scores =
for train, test in kfold.split(XTrainScaled):
x_train, x_test = XTrainScaled[train], XTrainScaled[test]
y_train, y_test = yTrainScaled[train], yTrainScaled[test]
cgnet = algorithms.LevenbergMarquardt(
connection=[
layers.Input(XTrain.shape[1]),
layers.Linear(6),
layers.Linear(1)
],
mu_update_factor=2,
mu=0.1,
shuffle_data=True,
verbose=True,
decay_rate=0.1,
addons=[algorithms.WeightElimination]
)
cgnet.train(x_train, y_train, epochs=5)
y_predicted = cgnet.predict(x_test)
score = metrics.mean_absolute_error(y_test, y_predicted)
scores.append(score)
print(scores)
scores = np.array(scores)
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
answered Nov 21 at 14:39
itdxer
8541030
answered Nov 21 at 14:39
itdxer
8541030
answered Nov 21 at 14:39
itdxer
8541030
answered Nov 21 at 14:39
itdxer
8541030
8541030
Thank you, best regards
– Manuel Almeida
Nov 21 at 16:38
1
@ManuelAlmeida The issue seems to be in the fact that sklearn wants to clone the supplied estimators during thecross_val_scorebutneupyhas something weird going on in its code to initialize the models from clone. You should file this as an issue onneupy's github page
– Vivek Kumar
Nov 22 at 7:07
Thank you, I will..
– Manuel Almeida
Nov 22 at 10:42
add a comment |
Thank you, best regards
– Manuel Almeida
Nov 21 at 16:38
1
@ManuelAlmeida The issue seems to be in the fact that sklearn wants to clone the supplied estimators during thecross_val_scorebutneupyhas something weird going on in its code to initialize the models from clone. You should file this as an issue onneupy's github page
– Vivek Kumar
Nov 22 at 7:07
Thank you, I will..
– Manuel Almeida
Nov 22 at 10:42
Thank you, best regards
– Manuel Almeida
Nov 21 at 16:38
Thank you, best regards
– Manuel Almeida
Nov 21 at 16:38
1
1
@ManuelAlmeida The issue seems to be in the fact that sklearn wants to clone the supplied estimators during the
cross_val_score but neupy has something weird going on in its code to initialize the models from clone. You should file this as an issue on neupy's github page– Vivek Kumar
Nov 22 at 7:07
@ManuelAlmeida The issue seems to be in the fact that sklearn wants to clone the supplied estimators during the
cross_val_score but neupy has something weird going on in its code to initialize the models from clone. You should file this as an issue on neupy's github page– Vivek Kumar
Nov 22 at 7:07
Thank you, I will..
– Manuel Almeida
Nov 22 at 10:42
Thank you, I will..
– Manuel Almeida
Nov 22 at 10:42
add a comment |
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What neupy and python versions do you use?
– itdxer
Nov 21 at 14:06
neupy 0.6.5 and numpy 1.15.1
– Manuel Almeida
Nov 21 at 16:07