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Skip transform (#665)

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* Skip transform

* Fix logic and docstring, add test

* Add period ending to skip_transform doc

* Add skip_transform to retrain_from_log method

* Update test/automl/test_classification.py

Co-authored-by: Xueqing Liu <liususan091219@users.noreply.github.com>

Co-authored-by: Xueqing Liu <liususan091219@users.noreply.github.com>
This commit is contained in:
jmrichardson
2022-08-11 19:41:23 -04:00
committed by GitHub
parent b6e8b9ccca
commit 25ad397d55
2 changed files with 29 additions and 1 deletions
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11
flaml/automl.py
View File

@@ -678,6 +678,7 @@ class AutoML(BaseEstimator):
}
}
```
skip_transform: boolean, default=False | Whether to pre-process data prior to modeling.
fit_kwargs_by_estimator: dict, default=None | The user specified keywords arguments, grouped by estimator name.
e.g.,
@@ -732,6 +733,7 @@ class AutoML(BaseEstimator):
"fit_kwargs_by_estimator", {}
)
settings["custom_hp"] = settings.get("custom_hp", {})
settings["skip_transform"] = settings.get("skip_transform", False)
self._estimator_type = (
"classifier" if settings["task"] in CLASSIFICATION else "regressor"
@@ -1119,7 +1121,7 @@ class AutoML(BaseEstimator):
"or all columns of X are integer ids (tokenized)"
)
if issparse(X_train_all):
if issparse(X_train_all) or self._skip_transform:
self._transformer = self._label_transformer = False
self._X_train_all, self._y_train_all = X, y
else:
@@ -1540,6 +1542,7 @@ class AutoML(BaseEstimator):
record_id=-1,
auto_augment=None,
custom_hp=None,
skip_transform=None,
fit_kwargs_by_estimator=None,
**fit_kwargs,
):
@@ -1649,6 +1652,7 @@ class AutoML(BaseEstimator):
self._state.fit_kwargs = fit_kwargs
self._state.custom_hp = custom_hp or self._settings.get("custom_hp")
self._skip_transform = self._settings.get("skip_transform") if skip_transform is None else skip_transform
self._state.fit_kwargs_by_estimator = (
fit_kwargs_by_estimator or self._settings.get("fit_kwargs_by_estimator")
)
@@ -2070,6 +2074,7 @@ class AutoML(BaseEstimator):
use_ray=None,
metric_constraints=None,
custom_hp=None,
skip_transform=None,
fit_kwargs_by_estimator=None,
**fit_kwargs,
):
@@ -2274,6 +2279,8 @@ class AutoML(BaseEstimator):
Each key is the estimator name, each value is a dict of the custom search space for that estimator. Notice the
domain of the custom search space can either be a value of a sample.Domain object.
```python
custom_hp = {
"transformer_ms": {
@@ -2287,6 +2294,7 @@ class AutoML(BaseEstimator):
}
```
skip_transform: boolean, default=False | Whether to pre-process data prior to modeling.
fit_kwargs_by_estimator: dict, default=None | The user specified keywords arguments, grouped by estimator name.
For TransformersEstimator, available fit_kwargs can be found from
[TrainingArgumentsForAuto](nlp/huggingface/training_args).
@@ -2418,6 +2426,7 @@ class AutoML(BaseEstimator):
self._state.fit_kwargs = fit_kwargs
custom_hp = custom_hp or self._settings.get("custom_hp")
self._skip_transform = self._settings.get("skip_transform") if skip_transform is None else skip_transform
fit_kwargs_by_estimator = fit_kwargs_by_estimator or self._settings.get(
"fit_kwargs_by_estimator"
)

19
test/automl/test_classification.py
View File

@@ -155,6 +155,25 @@ class TestClassification(unittest.TestCase):
# "verbose": 4,
"ensemble": True,
}
automl_settings["keep_search_state"] = True
automl.fit(X, y, **automl_settings)
X, y = automl._X_train_all, automl._y_train_all
del automl
automl = AutoML()
automl_settings = {
"time_budget": 3,
"task": "classification",
"n_jobs": 1,
"estimator_list": ["kneighbor"],
"eval_method": "cv",
"n_splits": 3,
"metric": "accuracy",
"log_training_metric": True,
# "verbose": 4,
"ensemble": True,
"skip_transform": True,
}
automl.fit(X, y, **automl_settings)
del automl