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How to plot multiple learning curve from different model on the same graph?

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1

I have few models that I have trained, and wanted to plot the learning curve of each model on a single graph

I tried this, and worked. But it felt redundant.

train_sizes, train_scores, test_scores = learning_curve(model, 
 train_dummies, 
 y,
 cv=5,
 scoring='neg_mean_squared_error')

Because I need to repeat the train_scores and test_scores for each model.

I tried it using for loop.

First , I saved the models in an array.

arr = [m1,m2,m3]

But when I started the for loop, it only produced a single line on the graph.

for i in arr:
 train_sizes, train_scores, test_scores = learning_curve(i, 
 train_dummies, 
 y,
 cv=5,
 scoring='neg_mean_squared_error')
 train_mean = np.mean(train_scores, axis=1)
 train_std = np.std(train_scores, axis=1)

 test_mean = np.mean(test_scores, axis=1)
 test_std = np.std(test_scores, axis=1)


 plt.plot(train_sizes, test_mean, label="Cross-validation score")

Here is the desired output

Will someone show me what am I lacking ? Your time is deeply appreciated.

python matplotlib machine-learning graph

share|improve this question

edited Mar 28 at 9:33

Iqlaas Ismail

asked Mar 28 at 6:33

Iqlaas IsmailIqlaas Ismail

23544 silver badges1515 bronze badges

• 
  
  
  

  
  2
  

  
  
  
  
  
  

  
  
  are you aware that test_mean is not defined within the for loop?
  
  – SamProell
  Mar 28 at 7:16
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @SamProell thanks for pointing. left it out on purpose. :)
  
  – Iqlaas Ismail
  Mar 28 at 9:34
  

  
  
  
  


• 
  
  
  

  
  2
  

  
  
  
  
  
  

  
  
  i can't spot anything wrong with what you are doing.. This works for me: gist.github.com/SamProell/59d1d4fac1e319b3d19e98335058fc3f
  
  – SamProell
  Mar 28 at 10:01
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @SamProell, thanks. My mistake was, iterating the variable instead of the modelling itself. I saved model_1 earlier, and iterate using model_1. I guess the coding could not pickup what is model_1 and thats why it plotted the graph separately. If you put your gist here, I would gladly star it.
  
  – Iqlaas Ismail
  Mar 31 at 1:33
  
  

  
  
  
  

add a comment | 

1

I have few models that I have trained, and wanted to plot the learning curve of each model on a single graph

I tried this, and worked. But it felt redundant.

train_sizes, train_scores, test_scores = learning_curve(model, 
 train_dummies, 
 y,
 cv=5,
 scoring='neg_mean_squared_error')

Because I need to repeat the train_scores and test_scores for each model.

I tried it using for loop.

First , I saved the models in an array.

arr = [m1,m2,m3]

But when I started the for loop, it only produced a single line on the graph.

for i in arr:
 train_sizes, train_scores, test_scores = learning_curve(i, 
 train_dummies, 
 y,
 cv=5,
 scoring='neg_mean_squared_error')
 train_mean = np.mean(train_scores, axis=1)
 train_std = np.std(train_scores, axis=1)

 test_mean = np.mean(test_scores, axis=1)
 test_std = np.std(test_scores, axis=1)


 plt.plot(train_sizes, test_mean, label="Cross-validation score")

Here is the desired output

Will someone show me what am I lacking ? Your time is deeply appreciated.

python matplotlib machine-learning graph

share|improve this question

edited Mar 28 at 9:33

Iqlaas Ismail

asked Mar 28 at 6:33

Iqlaas IsmailIqlaas Ismail

23544 silver badges1515 bronze badges

• 
  
  
  

  
  2
  

  
  
  
  
  
  

  
  
  are you aware that test_mean is not defined within the for loop?
  
  – SamProell
  Mar 28 at 7:16
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @SamProell thanks for pointing. left it out on purpose. :)
  
  – Iqlaas Ismail
  Mar 28 at 9:34
  

  
  
  
  


• 
  
  
  

  
  2
  

  
  
  
  
  
  

  
  
  i can't spot anything wrong with what you are doing.. This works for me: gist.github.com/SamProell/59d1d4fac1e319b3d19e98335058fc3f
  
  – SamProell
  Mar 28 at 10:01
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @SamProell, thanks. My mistake was, iterating the variable instead of the modelling itself. I saved model_1 earlier, and iterate using model_1. I guess the coding could not pickup what is model_1 and thats why it plotted the graph separately. If you put your gist here, I would gladly star it.
  
  – Iqlaas Ismail
  Mar 31 at 1:33
  
  

  
  
  
  

add a comment | 

I have few models that I have trained, and wanted to plot the learning curve of each model on a single graph

I tried this, and worked. But it felt redundant.

train_sizes, train_scores, test_scores = learning_curve(model, 
 train_dummies, 
 y,
 cv=5,
 scoring='neg_mean_squared_error')

Because I need to repeat the train_scores and test_scores for each model.

I tried it using for loop.

First , I saved the models in an array.

arr = [m1,m2,m3]

But when I started the for loop, it only produced a single line on the graph.

for i in arr:
 train_sizes, train_scores, test_scores = learning_curve(i, 
 train_dummies, 
 y,
 cv=5,
 scoring='neg_mean_squared_error')
 train_mean = np.mean(train_scores, axis=1)
 train_std = np.std(train_scores, axis=1)

 test_mean = np.mean(test_scores, axis=1)
 test_std = np.std(test_scores, axis=1)


 plt.plot(train_sizes, test_mean, label="Cross-validation score")

Here is the desired output

Will someone show me what am I lacking ? Your time is deeply appreciated.

python matplotlib machine-learning graph

share|improve this question

edited Mar 28 at 9:33

Iqlaas Ismail

asked Mar 28 at 6:33

Iqlaas IsmailIqlaas Ismail

23544 silver badges1515 bronze badges

train_sizes, train_scores, test_scores = learning_curve(model, 
 train_dummies, 
 y,
 cv=5,
 scoring='neg_mean_squared_error')

arr = [m1,m2,m3]

for i in arr:
 train_sizes, train_scores, test_scores = learning_curve(i, 
 train_dummies, 
 y,
 cv=5,
 scoring='neg_mean_squared_error')
 train_mean = np.mean(train_scores, axis=1)
 train_std = np.std(train_scores, axis=1)

 test_mean = np.mean(test_scores, axis=1)
 test_std = np.std(test_scores, axis=1)


 plt.plot(train_sizes, test_mean, label="Cross-validation score")

share|improve this question

edited Mar 28 at 9:33

Iqlaas Ismail

asked Mar 28 at 6:33

Iqlaas IsmailIqlaas Ismail

23544 silver badges1515 bronze badges

share|improve this question

edited Mar 28 at 9:33

Iqlaas Ismail

asked Mar 28 at 6:33

Iqlaas IsmailIqlaas Ismail

23544 silver badges1515 bronze badges

asked Mar 28 at 6:33

Iqlaas IsmailIqlaas Ismail

23544 silver badges1515 bronze badges

asked Mar 28 at 6:33

Iqlaas IsmailIqlaas Ismail

23544 silver badges1515 bronze badges

1 Answer
1

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oldest

votes

1

i can't spot anything wrong with what you are doing.. This works for me (taken in part from here):

import numpy as np
import matplotlib.pyplot as plt
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.datasets import load_digits
from sklearn.model_selection import learning_curve

digits = load_digits()
X, y = digits.data, digits.target
for i in [GaussianNB(), SVC(gamma=0.001)]:
 (train_sizes,
 train_scores,
 test_scores) = learning_curve(i, X, y, cv=5)
 test_mean = np.mean(test_scores, axis=1)
 plt.plot(train_sizes, test_mean, label="Cross-validation score")

plt.legend()
plt.show()

share|improve this answer

answered Apr 1 at 8:49

SamProellSamProell

34611 silver badge1212 bronze badges

add a comment | 

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1

i can't spot anything wrong with what you are doing.. This works for me (taken in part from here):

import numpy as np
import matplotlib.pyplot as plt
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.datasets import load_digits
from sklearn.model_selection import learning_curve

digits = load_digits()
X, y = digits.data, digits.target
for i in [GaussianNB(), SVC(gamma=0.001)]:
 (train_sizes,
 train_scores,
 test_scores) = learning_curve(i, X, y, cv=5)
 test_mean = np.mean(test_scores, axis=1)
 plt.plot(train_sizes, test_mean, label="Cross-validation score")

plt.legend()
plt.show()

share|improve this answer

answered Apr 1 at 8:49

SamProellSamProell

34611 silver badge1212 bronze badges

add a comment | 

1

i can't spot anything wrong with what you are doing.. This works for me (taken in part from here):

import numpy as np
import matplotlib.pyplot as plt
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.datasets import load_digits
from sklearn.model_selection import learning_curve

digits = load_digits()
X, y = digits.data, digits.target
for i in [GaussianNB(), SVC(gamma=0.001)]:
 (train_sizes,
 train_scores,
 test_scores) = learning_curve(i, X, y, cv=5)
 test_mean = np.mean(test_scores, axis=1)
 plt.plot(train_sizes, test_mean, label="Cross-validation score")

plt.legend()
plt.show()

share|improve this answer

answered Apr 1 at 8:49

SamProellSamProell

34611 silver badge1212 bronze badges

add a comment | 

i can't spot anything wrong with what you are doing.. This works for me (taken in part from here):

import numpy as np
import matplotlib.pyplot as plt
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.datasets import load_digits
from sklearn.model_selection import learning_curve

digits = load_digits()
X, y = digits.data, digits.target
for i in [GaussianNB(), SVC(gamma=0.001)]:
 (train_sizes,
 train_scores,
 test_scores) = learning_curve(i, X, y, cv=5)
 test_mean = np.mean(test_scores, axis=1)
 plt.plot(train_sizes, test_mean, label="Cross-validation score")

plt.legend()
plt.show()

share|improve this answer

answered Apr 1 at 8:49

SamProellSamProell

34611 silver badge1212 bronze badges

import numpy as np
import matplotlib.pyplot as plt
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.datasets import load_digits
from sklearn.model_selection import learning_curve

digits = load_digits()
X, y = digits.data, digits.target
for i in [GaussianNB(), SVC(gamma=0.001)]:
 (train_sizes,
 train_scores,
 test_scores) = learning_curve(i, X, y, cv=5)
 test_mean = np.mean(test_scores, axis=1)
 plt.plot(train_sizes, test_mean, label="Cross-validation score")

plt.legend()
plt.show()

share|improve this answer

answered Apr 1 at 8:49

SamProellSamProell

34611 silver badge1212 bronze badges

answered Apr 1 at 8:49

SamProellSamProell

34611 silver badge1212 bronze badges

answered Apr 1 at 8:49

SamProellSamProell

34611 silver badge1212 bronze badges