How to show progress of sklearn.multioutput.MultiOutputRegressor and XGBRegressor?
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Is it possible to show the training progress of the MultiOutputRegressor in sklearn? When a huge dataset is processed, my program runs a long time and I have no clue how long it will take. I have shortened my program to a minimal working example below.
import numpy as np
from sklearn.multioutput import MultiOutputRegressor
import xgboost as xgb
df = np.arange(50).reshape(10,5)
train = df[:8]
test = df[8:]
X_train = train[:,0:-2]
X_test = test[:,0:-2]
y_train = train[:,-2:]
y_test = test[:,-2:]
eval_set = [(X_test, y_test)]
multioutputregressor = MultiOutputRegressor(xgb.XGBRegressor(eval_set=eval_set, verbose_eval=True))
multioutputregressor.fit(X_train, y_train)
predictions = multioutputregressor.predict(X_test)
print(predictions)
scikit-learn xgboost
asked Aug 8 '18 at 17:39
DennisDennis
31
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bumped to the homepage by Community♦ yesterday
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
Is it possible to show the training progress of the MultiOutputRegressor in sklearn? When a huge dataset is processed, my program runs a long time and I have no clue how long it will take. I have shortened my program to a minimal working example below.
import numpy as np
from sklearn.multioutput import MultiOutputRegressor
import xgboost as xgb
df = np.arange(50).reshape(10,5)
train = df[:8]
test = df[8:]
X_train = train[:,0:-2]
X_test = test[:,0:-2]
y_train = train[:,-2:]
y_test = test[:,-2:]
eval_set = [(X_test, y_test)]
multioutputregressor = MultiOutputRegressor(xgb.XGBRegressor(eval_set=eval_set, verbose_eval=True))
multioutputregressor.fit(X_train, y_train)
predictions = multioutputregressor.predict(X_test)
print(predictions)
scikit-learn xgboost
asked Aug 8 '18 at 17:39
DennisDennis
31
$endgroup$
bumped to the homepage by Community♦ yesterday
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
$begingroup$
There is no built-in progress bar for scikit learn and most of the ML algorithms. There are might be a solution using tqdm package see :github.com/scikit-learn/scikit-learn/issues/7574, but I am not sure it will work on MultiOutputRegressor or not!!
$endgroup$
– Majid Mortazavi
Aug 9 '18 at 9:34
$begingroup$
Thanks, I tried this but unfortunately it doesn't work in my case, since XGBRegressor doesn't have a partial_fit method.
$endgroup$
– Dennis
Aug 9 '18 at 15:47
add a comment |
$begingroup$
Is it possible to show the training progress of the MultiOutputRegressor in sklearn? When a huge dataset is processed, my program runs a long time and I have no clue how long it will take. I have shortened my program to a minimal working example below.
import numpy as np
from sklearn.multioutput import MultiOutputRegressor
import xgboost as xgb
df = np.arange(50).reshape(10,5)
train = df[:8]
test = df[8:]
X_train = train[:,0:-2]
X_test = test[:,0:-2]
y_train = train[:,-2:]
y_test = test[:,-2:]
eval_set = [(X_test, y_test)]
multioutputregressor = MultiOutputRegressor(xgb.XGBRegressor(eval_set=eval_set, verbose_eval=True))
multioutputregressor.fit(X_train, y_train)
predictions = multioutputregressor.predict(X_test)
print(predictions)
scikit-learn xgboost
asked Aug 8 '18 at 17:39
DennisDennis
31
$endgroup$
Is it possible to show the training progress of the MultiOutputRegressor in sklearn? When a huge dataset is processed, my program runs a long time and I have no clue how long it will take. I have shortened my program to a minimal working example below.
import numpy as np
from sklearn.multioutput import MultiOutputRegressor
import xgboost as xgb
df = np.arange(50).reshape(10,5)
train = df[:8]
test = df[8:]
X_train = train[:,0:-2]
X_test = test[:,0:-2]
y_train = train[:,-2:]
y_test = test[:,-2:]
eval_set = [(X_test, y_test)]
multioutputregressor = MultiOutputRegressor(xgb.XGBRegressor(eval_set=eval_set, verbose_eval=True))
multioutputregressor.fit(X_train, y_train)
predictions = multioutputregressor.predict(X_test)
print(predictions)
scikit-learn xgboost
scikit-learn xgboost
asked Aug 8 '18 at 17:39
DennisDennis
31
asked Aug 8 '18 at 17:39
DennisDennis
31
asked Aug 8 '18 at 17:39
DennisDennis
31
asked Aug 8 '18 at 17:39
DennisDennis
31
asked Aug 8 '18 at 17:39
DennisDennis
31
31
bumped to the homepage by Community♦ yesterday
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ yesterday
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
$begingroup$
There is no built-in progress bar for scikit learn and most of the ML algorithms. There are might be a solution using tqdm package see :github.com/scikit-learn/scikit-learn/issues/7574, but I am not sure it will work on MultiOutputRegressor or not!!
$endgroup$
– Majid Mortazavi
Aug 9 '18 at 9:34
$begingroup$
Thanks, I tried this but unfortunately it doesn't work in my case, since XGBRegressor doesn't have a partial_fit method.
$endgroup$
– Dennis
Aug 9 '18 at 15:47
add a comment |
$begingroup$
There is no built-in progress bar for scikit learn and most of the ML algorithms. There are might be a solution using tqdm package see :github.com/scikit-learn/scikit-learn/issues/7574, but I am not sure it will work on MultiOutputRegressor or not!!
$endgroup$
– Majid Mortazavi
Aug 9 '18 at 9:34
$begingroup$
Thanks, I tried this but unfortunately it doesn't work in my case, since XGBRegressor doesn't have a partial_fit method.
$endgroup$
– Dennis
Aug 9 '18 at 15:47
$begingroup$
There is no built-in progress bar for scikit learn and most of the ML algorithms. There are might be a solution using tqdm package see :github.com/scikit-learn/scikit-learn/issues/7574, but I am not sure it will work on MultiOutputRegressor or not!!
$endgroup$
– Majid Mortazavi
Aug 9 '18 at 9:34
$begingroup$
There is no built-in progress bar for scikit learn and most of the ML algorithms. There are might be a solution using tqdm package see :github.com/scikit-learn/scikit-learn/issues/7574, but I am not sure it will work on MultiOutputRegressor or not!!
$endgroup$
– Majid Mortazavi
Aug 9 '18 at 9:34
$begingroup$
Thanks, I tried this but unfortunately it doesn't work in my case, since XGBRegressor doesn't have a partial_fit method.
$endgroup$
– Dennis
Aug 9 '18 at 15:47
$begingroup$
Thanks, I tried this but unfortunately it doesn't work in my case, since XGBRegressor doesn't have a partial_fit method.
$endgroup$
– Dennis
Aug 9 '18 at 15:47
add a comment |
1 Answer
1
active
oldest
votes
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You can consider modifying the code of MultiOutputEstimator or XGBModel to introduce some debugging output. These seem to be the classes that implement the fitting logic of the two libraries. The corresponding source files should also be provided in the installations on your local hard drive.
For example, you can print information of when the separate threads are starting and stopping in MultiOutputEstimator.fit (inherited and thus reused in MultiOutputRegressor), lines 167-169. Also, consider the tip in the documentation of the n_jobs parameter:
If 1 is given, no parallel computing code is used at all, which is useful for debugging.
You can use a similar approach to expand the debugging in XGBModel.fit (the basis of XGBRegressor).
answered Aug 10 '18 at 12:32
maptomapto
519212
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add a comment |
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1 Answer
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$begingroup$
You can consider modifying the code of MultiOutputEstimator or XGBModel to introduce some debugging output. These seem to be the classes that implement the fitting logic of the two libraries. The corresponding source files should also be provided in the installations on your local hard drive.
For example, you can print information of when the separate threads are starting and stopping in MultiOutputEstimator.fit (inherited and thus reused in MultiOutputRegressor), lines 167-169. Also, consider the tip in the documentation of the n_jobs parameter:
If 1 is given, no parallel computing code is used at all, which is useful for debugging.
You can use a similar approach to expand the debugging in XGBModel.fit (the basis of XGBRegressor).
answered Aug 10 '18 at 12:32
maptomapto
519212
$endgroup$
add a comment |
$begingroup$
You can consider modifying the code of MultiOutputEstimator or XGBModel to introduce some debugging output. These seem to be the classes that implement the fitting logic of the two libraries. The corresponding source files should also be provided in the installations on your local hard drive.
For example, you can print information of when the separate threads are starting and stopping in MultiOutputEstimator.fit (inherited and thus reused in MultiOutputRegressor), lines 167-169. Also, consider the tip in the documentation of the n_jobs parameter:
If 1 is given, no parallel computing code is used at all, which is useful for debugging.
You can use a similar approach to expand the debugging in XGBModel.fit (the basis of XGBRegressor).
answered Aug 10 '18 at 12:32
maptomapto
519212
$endgroup$
add a comment |
$begingroup$
You can consider modifying the code of MultiOutputEstimator or XGBModel to introduce some debugging output. These seem to be the classes that implement the fitting logic of the two libraries. The corresponding source files should also be provided in the installations on your local hard drive.
For example, you can print information of when the separate threads are starting and stopping in MultiOutputEstimator.fit (inherited and thus reused in MultiOutputRegressor), lines 167-169. Also, consider the tip in the documentation of the n_jobs parameter:
If 1 is given, no parallel computing code is used at all, which is useful for debugging.
You can use a similar approach to expand the debugging in XGBModel.fit (the basis of XGBRegressor).
answered Aug 10 '18 at 12:32
maptomapto
519212
$endgroup$
You can consider modifying the code of MultiOutputEstimator or XGBModel to introduce some debugging output. These seem to be the classes that implement the fitting logic of the two libraries. The corresponding source files should also be provided in the installations on your local hard drive.
For example, you can print information of when the separate threads are starting and stopping in MultiOutputEstimator.fit (inherited and thus reused in MultiOutputRegressor), lines 167-169. Also, consider the tip in the documentation of the n_jobs parameter:
If 1 is given, no parallel computing code is used at all, which is useful for debugging.
You can use a similar approach to expand the debugging in XGBModel.fit (the basis of XGBRegressor).
answered Aug 10 '18 at 12:32
maptomapto
519212
edited Aug 10 '18 at 12:51
answered Aug 10 '18 at 12:32
maptomapto
519212
answered Aug 10 '18 at 12:32
maptomapto
519212
answered Aug 10 '18 at 12:32
maptomapto
519212
519212
add a comment |
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$begingroup$
There is no built-in progress bar for scikit learn and most of the ML algorithms. There are might be a solution using tqdm package see :github.com/scikit-learn/scikit-learn/issues/7574, but I am not sure it will work on MultiOutputRegressor or not!!
$endgroup$
– Majid Mortazavi
Aug 9 '18 at 9:34
$begingroup$
Thanks, I tried this but unfortunately it doesn't work in my case, since XGBRegressor doesn't have a partial_fit method.
$endgroup$
– Dennis
Aug 9 '18 at 15:47