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Replace dask_ml.wrappers.ParallelPostFit with custom ParallelPostFit class #832

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merged 17 commits into from
Oct 24, 2022
Merged

Replace dask_ml.wrappers.ParallelPostFit with custom ParallelPostFit class #832

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7f27005
create ParallelPostFit class
sarahyurick Sep 30, 2022
a95741f
_timer
sarahyurick Sep 30, 2022
c8d901d
update create_experiment
sarahyurick Sep 30, 2022
272501b
update comment
sarahyurick Oct 4, 2022
47c9a76
migrate changes from 799
sarahyurick Oct 5, 2022
e9c3ce7
Merge branch 'main' into parallelpostfit
sarahyurick Oct 5, 2022
2195275
predict_proba_meta
sarahyurick Oct 5, 2022
0634534
fix gpu?
sarahyurick Oct 5, 2022
c75fab1
fix TypeError?
sarahyurick Oct 5, 2022
68de808
trying again
sarahyurick Oct 5, 2022
b2ca477
meta to output_meta
sarahyurick Oct 5, 2022
a44ea19
remove _timer
sarahyurick Oct 11, 2022
cc2169a
Merge branch 'main' into parallelpostfit
sarahyurick Oct 13, 2022
c26c5f4
try import sklearn
sarahyurick Oct 18, 2022
b31c355
style fix
sarahyurick Oct 18, 2022
009fd28
Update wrappers.py
sarahyurick Oct 24, 2022
ef087db
use ImportError
sarahyurick Oct 24, 2022
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14 changes: 2 additions & 12 deletions dask_sql/physical/rel/custom/create_experiment.py
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Original file line number Diff line number Diff line change
Expand Up @@ -168,12 +168,7 @@ def convert(self, rel: "LogicalPlan", context: "dask_sql.Context") -> DataContai
f"Can not import tuner {experiment_class}. Make sure you spelled it correctly and have installed all packages."
)

try:
from dask_ml.wrappers import ParallelPostFit
except ImportError: # pragma: no cover
raise ValueError(
"dask_ml must be installed to use automl and tune hyperparameters"
)
from dask_sql.physical.rel.custom.wrappers import ParallelPostFit

model = ModelClass()

Expand All @@ -199,12 +194,7 @@ def convert(self, rel: "LogicalPlan", context: "dask_sql.Context") -> DataContai
f"Can not import automl model {automl_class}. Make sure you spelled it correctly and have installed all packages."
)

try:
from dask_ml.wrappers import ParallelPostFit
except ImportError: # pragma: no cover
raise ValueError(
"dask_ml must be installed to use automl and tune hyperparameters"
)
from dask_sql.physical.rel.custom.wrappers import ParallelPostFit

automl = AutoMLClass(**automl_kwargs)
# should be avoided if data doesn't fit in memory
Expand Down
19 changes: 13 additions & 6 deletions dask_sql/physical/rel/custom/create_model.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
import logging
from typing import TYPE_CHECKING

import numpy as np
from dask import delayed

from dask_sql.datacontainer import DataContainer
Expand Down Expand Up @@ -43,7 +44,7 @@ class CreateModelPlugin(BaseRelPlugin):
unsupervised algorithms). This means, you typically
want to set this parameter.
* wrap_predict: Boolean flag, whether to wrap the selected
model with a :class:`dask_ml.wrappers.ParallelPostFit`.
model with a :class:`dask_sql.physical.rel.custom.wrappers.ParallelPostFit`.
Have a look into the
[dask-ml docu](https://ml.dask.org/meta-estimators.html#parallel-prediction-and-transformation)
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Remove this documentation link too and your own documentation in dask_sql. I would like us to move away from wrap_fit functionality to auto-detecting if its a sklearn/single-GPU cuML/xgboost model vs a dask model.

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Will be resolved by #855

to learn more about it. Defaults to false. Typically you set
Expand Down Expand Up @@ -165,10 +166,7 @@ def convert(self, rel: "LogicalPlan", context: "dask_sql.Context") -> DataContai
model = Incremental(estimator=model)

if wrap_predict:
try:
from dask_ml.wrappers import ParallelPostFit
except ImportError: # pragma: no cover
raise ValueError("Wrapping requires dask-ml to be installed.")
from dask_sql.physical.rel.custom.wrappers import ParallelPostFit

# When `wrap_predict` is set to True we train on single partition frames
# because this is only useful for non dask distributed models
Expand All @@ -183,7 +181,16 @@ def convert(self, rel: "LogicalPlan", context: "dask_sql.Context") -> DataContai

delayed_model = [delayed(model.fit)(x_p, y_p) for x_p, y_p in zip(X_d, y_d)]
model = delayed_model[0].compute()
model = ParallelPostFit(estimator=model)
if "sklearn" in model_class:
output_meta = np.array([])
model = ParallelPostFit(
estimator=model,
predict_meta=output_meta,
predict_proba_meta=output_meta,
transform_meta=output_meta,
)
else:
model = ParallelPostFit(estimator=model)

else:
model.fit(X, y, **fit_kwargs)
Expand Down
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