What's the best way to test whether an sklearn model has been fitted?

What's the most elegant way to check whether an sklearn model has been fitted? i.e. whether its fit() function has been called after it was instantiated, or not.

edited Jul 13 '17 at 6:27

Thierry Lathuille

7,80582730

asked Oct 5 '16 at 21:30

user3436624

3101316

i'm not sure if I understand what you want, but a for loop will do the job!!!

– Juggernaut
Oct 5 '16 at 21:49

1

Just to clarify, the person below answered perfectly.

– user3436624
Nov 2 '16 at 17:32

2

I've edited the question so that it is clear, can someone please unclose it? It's a good question and I have an alternate answer that I cannot add since it is still closed.

– Moot
Jul 13 '17 at 7:05

2

The correct answer is to use scikit's check_is_fitted

– sapo_cosmico
Nov 9 '17 at 16:57

1

@sapo_cosmico, thanks for bringing up check_is_fitted. I updated the answer with more details.

– elyase
Apr 3 '18 at 11:11

add a comment |

What's the most elegant way to check whether an sklearn model has been fitted? i.e. whether its fit() function has been called after it was instantiated, or not.

edited Jul 13 '17 at 6:27

Thierry Lathuille

7,80582730

asked Oct 5 '16 at 21:30

user3436624

3101316

i'm not sure if I understand what you want, but a for loop will do the job!!!

– Juggernaut
Oct 5 '16 at 21:49

1

Just to clarify, the person below answered perfectly.

– user3436624
Nov 2 '16 at 17:32

2

I've edited the question so that it is clear, can someone please unclose it? It's a good question and I have an alternate answer that I cannot add since it is still closed.

– Moot
Jul 13 '17 at 7:05

2

The correct answer is to use scikit's check_is_fitted

– sapo_cosmico
Nov 9 '17 at 16:57

1

@sapo_cosmico, thanks for bringing up check_is_fitted. I updated the answer with more details.

– elyase
Apr 3 '18 at 11:11

add a comment |

What's the most elegant way to check whether an sklearn model has been fitted? i.e. whether its fit() function has been called after it was instantiated, or not.

edited Jul 13 '17 at 6:27

Thierry Lathuille

7,80582730

asked Oct 5 '16 at 21:30

user3436624

3101316

What's the most elegant way to check whether an sklearn model has been fitted? i.e. whether its fit() function has been called after it was instantiated, or not.

python scikit-learn

edited Jul 13 '17 at 6:27

Thierry Lathuille

7,80582730

asked Oct 5 '16 at 21:30

user3436624

3101316

edited Jul 13 '17 at 6:27

Thierry Lathuille

7,80582730

asked Oct 5 '16 at 21:30

user3436624

3101316

edited Jul 13 '17 at 6:27

Thierry Lathuille

7,80582730

edited Jul 13 '17 at 6:27

Thierry Lathuille

7,80582730

edited Jul 13 '17 at 6:27

Thierry Lathuille

7,80582730

asked Oct 5 '16 at 21:30

user3436624

3101316

asked Oct 5 '16 at 21:30

user3436624

3101316

asked Oct 5 '16 at 21:30

user3436624

3101316

i'm not sure if I understand what you want, but a for loop will do the job!!!

– Juggernaut
Oct 5 '16 at 21:49

1

Just to clarify, the person below answered perfectly.

– user3436624
Nov 2 '16 at 17:32

2

I've edited the question so that it is clear, can someone please unclose it? It's a good question and I have an alternate answer that I cannot add since it is still closed.

– Moot
Jul 13 '17 at 7:05

2

The correct answer is to use scikit's check_is_fitted

– sapo_cosmico
Nov 9 '17 at 16:57

1

@sapo_cosmico, thanks for bringing up check_is_fitted. I updated the answer with more details.

– elyase
Apr 3 '18 at 11:11

add a comment |

i'm not sure if I understand what you want, but a for loop will do the job!!!

– Juggernaut
Oct 5 '16 at 21:49

1

Just to clarify, the person below answered perfectly.

– user3436624
Nov 2 '16 at 17:32

2

I've edited the question so that it is clear, can someone please unclose it? It's a good question and I have an alternate answer that I cannot add since it is still closed.

– Moot
Jul 13 '17 at 7:05

2

The correct answer is to use scikit's check_is_fitted

– sapo_cosmico
Nov 9 '17 at 16:57

1

@sapo_cosmico, thanks for bringing up check_is_fitted. I updated the answer with more details.

– elyase
Apr 3 '18 at 11:11

i'm not sure if I understand what you want, but a for loop will do the job!!!

– Juggernaut
Oct 5 '16 at 21:49

Just to clarify, the person below answered perfectly.

– user3436624
Nov 2 '16 at 17:32

I've edited the question so that it is clear, can someone please unclose it? It's a good question and I have an alternate answer that I cannot add since it is still closed.

– Moot
Jul 13 '17 at 7:05

The correct answer is to use scikit's check_is_fitted

– sapo_cosmico
Nov 9 '17 at 16:57

@sapo_cosmico, thanks for bringing up check_is_fitted. I updated the answer with more details.

– elyase
Apr 3 '18 at 11:11

add a comment |

3 Answers
3

active

oldest

votes

You can do something like:

from sklearn.exceptions import NotFittedError



for model in models:

    try:

        model.predict(some_test_data)

    except NotFittedError as e:

        print(repr(e))

Ideally you would check the results of model.predict against expected results but if all you want to know if wether the model is fitted or not that should suffice.

Update:

Some commenters have suggested using check_is_fitted. I consider check_is_fitted an internal method. Most algorithms will call check_is_fitted inside their predict method which in turn might raise NotFittedError if needed. The problem with using check_is_fitted directly is that it is model specific, i.e. you need to know which members to check depending on your algorithm. For example:

╔════════════════╦════════════════════════════════════════════╗

║ Tree models    ║ check_is_fitted(self, 'tree_')             ║

║ Linear models  ║ check_is_fitted(self, 'coefs_')            ║

║ KMeans         ║ check_is_fitted(self, 'cluster_centers_')  ║

║ SVM            ║ check_is_fitted(self, 'support_')          ║

╚════════════════╩════════════════════════════════════════════╝

and so on. So in general I would recommend calling model.predict() and letting the specific algorithm handle the best way to check whether it is already fitted or not.

edited Apr 3 '18 at 14:38

answered Oct 6 '16 at 16:13

elyase

24k45784

Thank you, this is perfect.

– user3436624
Nov 2 '16 at 17:32

1

@elyase is right for sklearn. There are others: from xgboost.core import XGBoostError and from lightgbm.sklearn import LightGBMError, for instance

– George Fisher
May 1 '17 at 19:37

@GeorgeFisher: True, but the OP did specify sklearn specifically.

– Moot
Jul 12 '17 at 23:20

1

This is not a good solution, as your try might fail because of a problem with some_test_data. For instance, if some_test_data = "duck", the model might be completely fine, but will fail. You will then report the wrong error. Use check_is_fitted instead

– sapo_cosmico
Nov 9 '17 at 16:53

Along the same lines with @elyase, see scikit-learn.org/stable/modules/generated/…

– Zouzias
Feb 3 '18 at 19:58

|
show 1 more comment

I do this for classifiers:

def check_fitted(clf): 

    return hasattr(clf, "classes_")

answered Jul 5 '18 at 22:46

O.rka

7,27530107172

add a comment |

This is sort of a greedy approach, but it should be fine for most if not all models. The only time this might not work is for models that set an attribute ending in an underscore prior to being fit, which I'm pretty sure would violate scikit-learn convention so this should be fine.

import inspect



def is_fitted(model):

        """Checks if model object has any attributes ending with an underscore"""

        return 0 < len( [k for k,v in inspect.getmembers(model) if k.endswith('_') and not k.startswith('__')] )

answered Jan 1 '18 at 1:42

David Marx

5,06512447

interesting solution! Goes along the lines of scikit-learn.org/dev/glossary.html#term-fitted. +1

– elyase
Apr 3 '18 at 14:39

add a comment |

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3 Answers
3

active

oldest

votes

3 Answers
3

active

oldest

votes

You can do something like:

from sklearn.exceptions import NotFittedError



for model in models:

    try:

        model.predict(some_test_data)

    except NotFittedError as e:

        print(repr(e))

Ideally you would check the results of model.predict against expected results but if all you want to know if wether the model is fitted or not that should suffice.

Update:

╔════════════════╦════════════════════════════════════════════╗

║ Tree models    ║ check_is_fitted(self, 'tree_')             ║

║ Linear models  ║ check_is_fitted(self, 'coefs_')            ║

║ KMeans         ║ check_is_fitted(self, 'cluster_centers_')  ║

║ SVM            ║ check_is_fitted(self, 'support_')          ║

╚════════════════╩════════════════════════════════════════════╝

and so on. So in general I would recommend calling model.predict() and letting the specific algorithm handle the best way to check whether it is already fitted or not.

edited Apr 3 '18 at 14:38

answered Oct 6 '16 at 16:13

elyase

24k45784

Thank you, this is perfect.

– user3436624
Nov 2 '16 at 17:32

1

@elyase is right for sklearn. There are others: from xgboost.core import XGBoostError and from lightgbm.sklearn import LightGBMError, for instance

– George Fisher
May 1 '17 at 19:37

@GeorgeFisher: True, but the OP did specify sklearn specifically.

– Moot
Jul 12 '17 at 23:20

1

This is not a good solution, as your try might fail because of a problem with some_test_data. For instance, if some_test_data = "duck", the model might be completely fine, but will fail. You will then report the wrong error. Use check_is_fitted instead

– sapo_cosmico
Nov 9 '17 at 16:53

Along the same lines with @elyase, see scikit-learn.org/stable/modules/generated/…

– Zouzias
Feb 3 '18 at 19:58

|
show 1 more comment

You can do something like:

from sklearn.exceptions import NotFittedError



for model in models:

    try:

        model.predict(some_test_data)

    except NotFittedError as e:

        print(repr(e))

Ideally you would check the results of model.predict against expected results but if all you want to know if wether the model is fitted or not that should suffice.

Update:

╔════════════════╦════════════════════════════════════════════╗

║ Tree models    ║ check_is_fitted(self, 'tree_')             ║

║ Linear models  ║ check_is_fitted(self, 'coefs_')            ║

║ KMeans         ║ check_is_fitted(self, 'cluster_centers_')  ║

║ SVM            ║ check_is_fitted(self, 'support_')          ║

╚════════════════╩════════════════════════════════════════════╝

and so on. So in general I would recommend calling model.predict() and letting the specific algorithm handle the best way to check whether it is already fitted or not.

edited Apr 3 '18 at 14:38

answered Oct 6 '16 at 16:13

elyase

24k45784

Thank you, this is perfect.

– user3436624
Nov 2 '16 at 17:32

1

@elyase is right for sklearn. There are others: from xgboost.core import XGBoostError and from lightgbm.sklearn import LightGBMError, for instance

– George Fisher
May 1 '17 at 19:37

@GeorgeFisher: True, but the OP did specify sklearn specifically.

– Moot
Jul 12 '17 at 23:20

1

This is not a good solution, as your try might fail because of a problem with some_test_data. For instance, if some_test_data = "duck", the model might be completely fine, but will fail. You will then report the wrong error. Use check_is_fitted instead

– sapo_cosmico
Nov 9 '17 at 16:53

Along the same lines with @elyase, see scikit-learn.org/stable/modules/generated/…

– Zouzias
Feb 3 '18 at 19:58

|
show 1 more comment

You can do something like:

from sklearn.exceptions import NotFittedError



for model in models:

    try:

        model.predict(some_test_data)

    except NotFittedError as e:

        print(repr(e))

Ideally you would check the results of model.predict against expected results but if all you want to know if wether the model is fitted or not that should suffice.

Update:

╔════════════════╦════════════════════════════════════════════╗

║ Tree models    ║ check_is_fitted(self, 'tree_')             ║

║ Linear models  ║ check_is_fitted(self, 'coefs_')            ║

║ KMeans         ║ check_is_fitted(self, 'cluster_centers_')  ║

║ SVM            ║ check_is_fitted(self, 'support_')          ║

╚════════════════╩════════════════════════════════════════════╝

and so on. So in general I would recommend calling model.predict() and letting the specific algorithm handle the best way to check whether it is already fitted or not.

edited Apr 3 '18 at 14:38

answered Oct 6 '16 at 16:13

elyase

24k45784

You can do something like:

from sklearn.exceptions import NotFittedError



for model in models:

    try:

        model.predict(some_test_data)

    except NotFittedError as e:

        print(repr(e))

Ideally you would check the results of model.predict against expected results but if all you want to know if wether the model is fitted or not that should suffice.

Update:

╔════════════════╦════════════════════════════════════════════╗

║ Tree models    ║ check_is_fitted(self, 'tree_')             ║

║ Linear models  ║ check_is_fitted(self, 'coefs_')            ║

║ KMeans         ║ check_is_fitted(self, 'cluster_centers_')  ║

║ SVM            ║ check_is_fitted(self, 'support_')          ║

╚════════════════╩════════════════════════════════════════════╝

and so on. So in general I would recommend calling model.predict() and letting the specific algorithm handle the best way to check whether it is already fitted or not.

edited Apr 3 '18 at 14:38

answered Oct 6 '16 at 16:13

elyase

24k45784

edited Apr 3 '18 at 14:38

answered Oct 6 '16 at 16:13

elyase

24k45784

answered Oct 6 '16 at 16:13

elyase

24k45784

answered Oct 6 '16 at 16:13

elyase

24k45784

Thank you, this is perfect.

– user3436624
Nov 2 '16 at 17:32

1

@elyase is right for sklearn. There are others: from xgboost.core import XGBoostError and from lightgbm.sklearn import LightGBMError, for instance

– George Fisher
May 1 '17 at 19:37

@GeorgeFisher: True, but the OP did specify sklearn specifically.

– Moot
Jul 12 '17 at 23:20

1

This is not a good solution, as your try might fail because of a problem with some_test_data. For instance, if some_test_data = "duck", the model might be completely fine, but will fail. You will then report the wrong error. Use check_is_fitted instead

– sapo_cosmico
Nov 9 '17 at 16:53

Along the same lines with @elyase, see scikit-learn.org/stable/modules/generated/…

– Zouzias
Feb 3 '18 at 19:58

|
show 1 more comment

Thank you, this is perfect.

– user3436624
Nov 2 '16 at 17:32

1

@elyase is right for sklearn. There are others: from xgboost.core import XGBoostError and from lightgbm.sklearn import LightGBMError, for instance

– George Fisher
May 1 '17 at 19:37

@GeorgeFisher: True, but the OP did specify sklearn specifically.

– Moot
Jul 12 '17 at 23:20

1

This is not a good solution, as your try might fail because of a problem with some_test_data. For instance, if some_test_data = "duck", the model might be completely fine, but will fail. You will then report the wrong error. Use check_is_fitted instead

– sapo_cosmico
Nov 9 '17 at 16:53

Along the same lines with @elyase, see scikit-learn.org/stable/modules/generated/…

– Zouzias
Feb 3 '18 at 19:58

Thank you, this is perfect.

– user3436624
Nov 2 '16 at 17:32

@elyase is right for sklearn. There are others: from xgboost.core import XGBoostError and from lightgbm.sklearn import LightGBMError, for instance

– George Fisher
May 1 '17 at 19:37

@GeorgeFisher: True, but the OP did specify sklearn specifically.

– Moot
Jul 12 '17 at 23:20

This is not a good solution, as your try might fail because of a problem with some_test_data. For instance, if some_test_data = "duck", the model might be completely fine, but will fail. You will then report the wrong error. Use check_is_fitted instead

– sapo_cosmico
Nov 9 '17 at 16:53

Along the same lines with @elyase, see scikit-learn.org/stable/modules/generated/…

– Zouzias
Feb 3 '18 at 19:58

|
show 1 more comment

I do this for classifiers:

def check_fitted(clf): 

    return hasattr(clf, "classes_")

answered Jul 5 '18 at 22:46

O.rka

7,27530107172

add a comment |

I do this for classifiers:

def check_fitted(clf): 

    return hasattr(clf, "classes_")

answered Jul 5 '18 at 22:46

O.rka

7,27530107172

add a comment |

I do this for classifiers:

def check_fitted(clf): 

    return hasattr(clf, "classes_")

answered Jul 5 '18 at 22:46

O.rka

7,27530107172

I do this for classifiers:

def check_fitted(clf): 

    return hasattr(clf, "classes_")

answered Jul 5 '18 at 22:46

O.rka

7,27530107172

answered Jul 5 '18 at 22:46

O.rka

7,27530107172

answered Jul 5 '18 at 22:46

O.rka

7,27530107172

answered Jul 5 '18 at 22:46

O.rka

7,27530107172

add a comment |

import inspect



def is_fitted(model):

        """Checks if model object has any attributes ending with an underscore"""

        return 0 < len( [k for k,v in inspect.getmembers(model) if k.endswith('_') and not k.startswith('__')] )

answered Jan 1 '18 at 1:42

David Marx

5,06512447

interesting solution! Goes along the lines of scikit-learn.org/dev/glossary.html#term-fitted. +1

– elyase
Apr 3 '18 at 14:39

add a comment |

import inspect



def is_fitted(model):

        """Checks if model object has any attributes ending with an underscore"""

        return 0 < len( [k for k,v in inspect.getmembers(model) if k.endswith('_') and not k.startswith('__')] )

answered Jan 1 '18 at 1:42

David Marx

5,06512447

interesting solution! Goes along the lines of scikit-learn.org/dev/glossary.html#term-fitted. +1

– elyase
Apr 3 '18 at 14:39

add a comment |

import inspect



def is_fitted(model):

        """Checks if model object has any attributes ending with an underscore"""

        return 0 < len( [k for k,v in inspect.getmembers(model) if k.endswith('_') and not k.startswith('__')] )

answered Jan 1 '18 at 1:42

David Marx

5,06512447

import inspect



def is_fitted(model):

        """Checks if model object has any attributes ending with an underscore"""

        return 0 < len( [k for k,v in inspect.getmembers(model) if k.endswith('_') and not k.startswith('__')] )

answered Jan 1 '18 at 1:42

David Marx

5,06512447

answered Jan 1 '18 at 1:42

David Marx

5,06512447

answered Jan 1 '18 at 1:42

David Marx

5,06512447

answered Jan 1 '18 at 1:42

David Marx

5,06512447

interesting solution! Goes along the lines of scikit-learn.org/dev/glossary.html#term-fitted. +1

– elyase
Apr 3 '18 at 14:39

add a comment |

interesting solution! Goes along the lines of scikit-learn.org/dev/glossary.html#term-fitted. +1

– elyase
Apr 3 '18 at 14:39

interesting solution! Goes along the lines of scikit-learn.org/dev/glossary.html#term-fitted. +1

– elyase
Apr 3 '18 at 14:39

add a comment |

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