Mixing
Early stopping with mmlspark LightGBMClassifier
I have successfully been able to train an xgboost model using early stopping against an "eval_set" in Python. I am now trying to do the same but with LightGBM in pyspark.
This works in Python:
model = xgb.XGBClassifier(learning_rate = 0.05, n_estimators=2000)
eval_set = [(X_test, Y_test)]
model.fit(X_train, Y_train, eval_set=eval_set, eval_metric="auc", early_stopping_rounds=50, verbose = True)
In pyspark (Databricks), I created a dataset that contains a features column and a labels column that are required in the mmlspark library. I got this to work:
from mmlspark import LightGBMClassifier model =
LightGBMClassifier(featuresCol = 'features', labelCol = 'label',
learningRate = 0.05, numIterations = 100) model.fit(train)
Can one get early stopping to work in the LightGBMClassifier library against an evaluation test set?
pyspark lightgbm
asked Nov 21 '18 at 14:59
GivenXGivenX
122112
add a comment |
I have successfully been able to train an xgboost model using early stopping against an "eval_set" in Python. I am now trying to do the same but with LightGBM in pyspark.
This works in Python:
model = xgb.XGBClassifier(learning_rate = 0.05, n_estimators=2000)
eval_set = [(X_test, Y_test)]
model.fit(X_train, Y_train, eval_set=eval_set, eval_metric="auc", early_stopping_rounds=50, verbose = True)
In pyspark (Databricks), I created a dataset that contains a features column and a labels column that are required in the mmlspark library. I got this to work:
from mmlspark import LightGBMClassifier model =
LightGBMClassifier(featuresCol = 'features', labelCol = 'label',
learningRate = 0.05, numIterations = 100) model.fit(train)
Can one get early stopping to work in the LightGBMClassifier library against an evaluation test set?
pyspark lightgbm
asked Nov 21 '18 at 14:59
GivenXGivenX
122112
I've just deleted my answer, it seems that I slightly misunderstood your question; that is why I've presented earlyStoppingRound=50 to add only. Of course, you need an eval set for early stopping... I just went searching for an answer but it seems LightGBM version of pyspark is currently uses a subset of features of original LightGBM, it is being updated part by part. There are discussions on that on GitHub and other forums; but I could not find a solution for that. Good luck!
– Ugur MULUK
Nov 23 '18 at 11:14
add a comment |
I have successfully been able to train an xgboost model using early stopping against an "eval_set" in Python. I am now trying to do the same but with LightGBM in pyspark.
This works in Python:
model = xgb.XGBClassifier(learning_rate = 0.05, n_estimators=2000)
eval_set = [(X_test, Y_test)]
model.fit(X_train, Y_train, eval_set=eval_set, eval_metric="auc", early_stopping_rounds=50, verbose = True)
In pyspark (Databricks), I created a dataset that contains a features column and a labels column that are required in the mmlspark library. I got this to work:
from mmlspark import LightGBMClassifier model =
LightGBMClassifier(featuresCol = 'features', labelCol = 'label',
learningRate = 0.05, numIterations = 100) model.fit(train)
Can one get early stopping to work in the LightGBMClassifier library against an evaluation test set?
pyspark lightgbm
asked Nov 21 '18 at 14:59
GivenXGivenX
122112
I have successfully been able to train an xgboost model using early stopping against an "eval_set" in Python. I am now trying to do the same but with LightGBM in pyspark.
This works in Python:
model = xgb.XGBClassifier(learning_rate = 0.05, n_estimators=2000)
eval_set = [(X_test, Y_test)]
model.fit(X_train, Y_train, eval_set=eval_set, eval_metric="auc", early_stopping_rounds=50, verbose = True)
In pyspark (Databricks), I created a dataset that contains a features column and a labels column that are required in the mmlspark library. I got this to work:
from mmlspark import LightGBMClassifier model =
LightGBMClassifier(featuresCol = 'features', labelCol = 'label',
learningRate = 0.05, numIterations = 100) model.fit(train)
Can one get early stopping to work in the LightGBMClassifier library against an evaluation test set?
pyspark lightgbm
pyspark lightgbm
asked Nov 21 '18 at 14:59
GivenXGivenX
122112
asked Nov 21 '18 at 14:59
GivenXGivenX
122112
asked Nov 21 '18 at 14:59
GivenXGivenX
122112
asked Nov 21 '18 at 14:59
GivenXGivenX
122112
asked Nov 21 '18 at 14:59
GivenXGivenX
122112
122112
I've just deleted my answer, it seems that I slightly misunderstood your question; that is why I've presented earlyStoppingRound=50 to add only. Of course, you need an eval set for early stopping... I just went searching for an answer but it seems LightGBM version of pyspark is currently uses a subset of features of original LightGBM, it is being updated part by part. There are discussions on that on GitHub and other forums; but I could not find a solution for that. Good luck!
– Ugur MULUK
Nov 23 '18 at 11:14
add a comment |
I've just deleted my answer, it seems that I slightly misunderstood your question; that is why I've presented earlyStoppingRound=50 to add only. Of course, you need an eval set for early stopping... I just went searching for an answer but it seems LightGBM version of pyspark is currently uses a subset of features of original LightGBM, it is being updated part by part. There are discussions on that on GitHub and other forums; but I could not find a solution for that. Good luck!
– Ugur MULUK
Nov 23 '18 at 11:14
I've just deleted my answer, it seems that I slightly misunderstood your question; that is why I've presented earlyStoppingRound=50 to add only. Of course, you need an eval set for early stopping... I just went searching for an answer but it seems LightGBM version of pyspark is currently uses a subset of features of original LightGBM, it is being updated part by part. There are discussions on that on GitHub and other forums; but I could not find a solution for that. Good luck!
– Ugur MULUK
Nov 23 '18 at 11:14
I've just deleted my answer, it seems that I slightly misunderstood your question; that is why I've presented earlyStoppingRound=50 to add only. Of course, you need an eval set for early stopping... I just went searching for an answer but it seems LightGBM version of pyspark is currently uses a subset of features of original LightGBM, it is being updated part by part. There are discussions on that on GitHub and other forums; but I could not find a solution for that. Good luck!
– Ugur MULUK
Nov 23 '18 at 11:14
add a comment |
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I've just deleted my answer, it seems that I slightly misunderstood your question; that is why I've presented earlyStoppingRound=50 to add only. Of course, you need an eval set for early stopping... I just went searching for an answer but it seems LightGBM version of pyspark is currently uses a subset of features of original LightGBM, it is being updated part by part. There are discussions on that on GitHub and other forums; but I could not find a solution for that. Good luck!
– Ugur MULUK
Nov 23 '18 at 11:14