Checkpoints in SklearnXgboost predict probabilitieskeras validation mean squared error always similar to 1Learning rate in logistic regression with sklearnmigrating to python from R: specific questionsPython sklearn - average classification reportsOutlier detection with sklearnPass 2 different kinds of X training data to ML model simultaneouslyTensorflow and SklearnUsing deep learning to classify similar imagesKerasRegressor serialize/save a model as a .h5df
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Checkpoints in Sklearn
Xgboost predict probabilitieskeras validation mean squared error always similar to 1Learning rate in logistic regression with sklearnmigrating to python from R: specific questionsPython sklearn - average classification reportsOutlier detection with sklearnPass 2 different kinds of X training data to ML model simultaneouslyTensorflow and SklearnUsing deep learning to classify similar imagesKerasRegressor serialize/save a model as a .h5df
$begingroup$
Is there a way to save the current state of your experiment so that you can pick up from where you left off in Sklearn similar like checkpoints in Keras ?
machine-learning python deep-learning keras scikit-learn
$endgroup$
add a comment |
$begingroup$
Is there a way to save the current state of your experiment so that you can pick up from where you left off in Sklearn similar like checkpoints in Keras ?
machine-learning python deep-learning keras scikit-learn
$endgroup$
add a comment |
$begingroup$
Is there a way to save the current state of your experiment so that you can pick up from where you left off in Sklearn similar like checkpoints in Keras ?
machine-learning python deep-learning keras scikit-learn
$endgroup$
Is there a way to save the current state of your experiment so that you can pick up from where you left off in Sklearn similar like checkpoints in Keras ?
machine-learning python deep-learning keras scikit-learn
machine-learning python deep-learning keras scikit-learn
asked Apr 10 at 5:55
Sreejithc321Sreejithc321
1,12711128
1,12711128
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
I think the closest you can get is with either the warm_start
parameter or the partial_fit
call. They are available in some models and allow you to train a model several times without losing progress.
From sklearn docs:
warm_start : bool, optional, default False
When set to True, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution.
and:
partial_fit(X, y, classes=None, sample_weight=None)[source]
Perform one epoch of stochastic gradient descent on given samples.
Internally, this method uses max_iter = 1. Therefore, it is not guaranteed that a minimum of the cost function is reached after
calling it once. Matters such as objective convergence and early
stopping should be handled by the user.
Which one to use depends on what model you are using and when you want to make your checkpoints. But if you for example use a RandomForestClassifier
which has warm_start
you could do the following:
# set warm_start so model to avoid erasing model between fits
clf = RandomForestClassifier(warm_start=True)
number_of_checkpoints = 10
for checkpoint in range(number_of_checkpoints):
# Load only a subset of the data and train on it
X, y = load_data_batch(batches=number_of_checkpoints, current_batch=checkpoint)
clf.fit(X, y)
# Save model checkpoint for each fit
with open('path/to/models/random_forest_ckp_.p'.format(checkpoint), 'wb') as f:
pickle.dump(clf, f)
$endgroup$
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
$begingroup$
I think the closest you can get is with either the warm_start
parameter or the partial_fit
call. They are available in some models and allow you to train a model several times without losing progress.
From sklearn docs:
warm_start : bool, optional, default False
When set to True, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution.
and:
partial_fit(X, y, classes=None, sample_weight=None)[source]
Perform one epoch of stochastic gradient descent on given samples.
Internally, this method uses max_iter = 1. Therefore, it is not guaranteed that a minimum of the cost function is reached after
calling it once. Matters such as objective convergence and early
stopping should be handled by the user.
Which one to use depends on what model you are using and when you want to make your checkpoints. But if you for example use a RandomForestClassifier
which has warm_start
you could do the following:
# set warm_start so model to avoid erasing model between fits
clf = RandomForestClassifier(warm_start=True)
number_of_checkpoints = 10
for checkpoint in range(number_of_checkpoints):
# Load only a subset of the data and train on it
X, y = load_data_batch(batches=number_of_checkpoints, current_batch=checkpoint)
clf.fit(X, y)
# Save model checkpoint for each fit
with open('path/to/models/random_forest_ckp_.p'.format(checkpoint), 'wb') as f:
pickle.dump(clf, f)
$endgroup$
add a comment |
$begingroup$
I think the closest you can get is with either the warm_start
parameter or the partial_fit
call. They are available in some models and allow you to train a model several times without losing progress.
From sklearn docs:
warm_start : bool, optional, default False
When set to True, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution.
and:
partial_fit(X, y, classes=None, sample_weight=None)[source]
Perform one epoch of stochastic gradient descent on given samples.
Internally, this method uses max_iter = 1. Therefore, it is not guaranteed that a minimum of the cost function is reached after
calling it once. Matters such as objective convergence and early
stopping should be handled by the user.
Which one to use depends on what model you are using and when you want to make your checkpoints. But if you for example use a RandomForestClassifier
which has warm_start
you could do the following:
# set warm_start so model to avoid erasing model between fits
clf = RandomForestClassifier(warm_start=True)
number_of_checkpoints = 10
for checkpoint in range(number_of_checkpoints):
# Load only a subset of the data and train on it
X, y = load_data_batch(batches=number_of_checkpoints, current_batch=checkpoint)
clf.fit(X, y)
# Save model checkpoint for each fit
with open('path/to/models/random_forest_ckp_.p'.format(checkpoint), 'wb') as f:
pickle.dump(clf, f)
$endgroup$
add a comment |
$begingroup$
I think the closest you can get is with either the warm_start
parameter or the partial_fit
call. They are available in some models and allow you to train a model several times without losing progress.
From sklearn docs:
warm_start : bool, optional, default False
When set to True, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution.
and:
partial_fit(X, y, classes=None, sample_weight=None)[source]
Perform one epoch of stochastic gradient descent on given samples.
Internally, this method uses max_iter = 1. Therefore, it is not guaranteed that a minimum of the cost function is reached after
calling it once. Matters such as objective convergence and early
stopping should be handled by the user.
Which one to use depends on what model you are using and when you want to make your checkpoints. But if you for example use a RandomForestClassifier
which has warm_start
you could do the following:
# set warm_start so model to avoid erasing model between fits
clf = RandomForestClassifier(warm_start=True)
number_of_checkpoints = 10
for checkpoint in range(number_of_checkpoints):
# Load only a subset of the data and train on it
X, y = load_data_batch(batches=number_of_checkpoints, current_batch=checkpoint)
clf.fit(X, y)
# Save model checkpoint for each fit
with open('path/to/models/random_forest_ckp_.p'.format(checkpoint), 'wb') as f:
pickle.dump(clf, f)
$endgroup$
I think the closest you can get is with either the warm_start
parameter or the partial_fit
call. They are available in some models and allow you to train a model several times without losing progress.
From sklearn docs:
warm_start : bool, optional, default False
When set to True, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution.
and:
partial_fit(X, y, classes=None, sample_weight=None)[source]
Perform one epoch of stochastic gradient descent on given samples.
Internally, this method uses max_iter = 1. Therefore, it is not guaranteed that a minimum of the cost function is reached after
calling it once. Matters such as objective convergence and early
stopping should be handled by the user.
Which one to use depends on what model you are using and when you want to make your checkpoints. But if you for example use a RandomForestClassifier
which has warm_start
you could do the following:
# set warm_start so model to avoid erasing model between fits
clf = RandomForestClassifier(warm_start=True)
number_of_checkpoints = 10
for checkpoint in range(number_of_checkpoints):
# Load only a subset of the data and train on it
X, y = load_data_batch(batches=number_of_checkpoints, current_batch=checkpoint)
clf.fit(X, y)
# Save model checkpoint for each fit
with open('path/to/models/random_forest_ckp_.p'.format(checkpoint), 'wb') as f:
pickle.dump(clf, f)
edited Apr 10 at 7:22
answered Apr 10 at 7:06
![](https://i.stack.imgur.com/0dFXn.png?s=32&g=1)
![](https://i.stack.imgur.com/0dFXn.png?s=32&g=1)
Simon LarssonSimon Larsson
1,195217
1,195217
add a comment |
add a comment |
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