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tfdf.tuner.RandomSearch
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Tuner using random hyperparameter values.
Inherits From: Tuner
tfdf.tuner.RandomSearch(
num_trials: int = 100,
use_predefined_hps: bool = False,
trial_num_threads: int = 1,
trial_maximum_training_duration_seconds: Optional[float] = None
)
Used in the notebooks
The candidate hyper-parameter can be evaluated independently and in parallel.
Attributes |
|---|
num_trials
|
Number of random hyperparameter values to evaluate.
|
use_predefined_hps
|
If true, automatically configure the the space of
hyper-parameters explored by the tuner. In this case, configuring the
hyper-parameters manually (e.g. calling "choice(...)" on the tuner) is not
necessary.
|
trial_num_threads
|
Number of threads used to train the models in each trial.
This parameter is different from the num_threads parameter of the model
constructor that indicates how many threads to use for the overal
training+possibly tuning. For example trial_num_threads=2 and
num_threads=5, 5 models will be training in parallel during tuning, and
each of those models will be trained with 2 threads. In reverse, if you
want to run at most 100 threads globally, make sure that
trial_num_threads*num_threads = 100.
|
trial_maximum_training_duration_seconds
|
Maximum training duration of an
individual trial expressed in seconds. This parameter is different from
the maximum_training_duration_seconds parameter of the model constructor
that define the maximum training+tuning duration.
|
Methods
choice
View source
choice(
key: str,
values: Union[List[int], List[float], List[str], List[bool]],
merge: bool = False
) -> SearchSpace
Adds a hyperparameter with a list of possible values.
| Args |
|---|
key
|
Name of the hyper-parameter.
|
values
|
List of possible value for the hyperparameter.
|
merge
|
If false (default), raises an error if the hyper-parameter already
exist. If true, adds values to the parameter if it already exist.
|
| Returns |
|---|
|
The conditional SearchSpace corresponding to the values in "values".
|
set_base_learner
View source
set_base_learner(
learner: str
) -> None
Sets the base learner key.
train_config
View source
train_config() -> TrainConfig
YDF training configuration for the Hyperparameter optimizer.
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Last updated 2024-04-26 UTC.
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tfdf.tuner.RandomSearch\n\n\u003cbr /\u003e\n\n|--------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/decision-forests/blob/main/tensorflow_decision_forests/component/tuner/tuner.py#L218-L257) |\n\nTuner using random hyperparameter values.\n\nInherits From: [`Tuner`](../../tfdf/tuner/Tuner) \n\n tfdf.tuner.RandomSearch(\n num_trials: int = 100,\n use_predefined_hps: bool = False,\n trial_num_threads: int = 1,\n trial_maximum_training_duration_seconds: Optional[float] = None\n )\n\n### Used in the notebooks\n\n| Used in the tutorials |\n|--------------------------------------------------------------------------------------------------------------------|\n| - [Automated hyper-parameter tuning](https://www.tensorflow.org/decision_forests/tutorials/automatic_tuning_colab) |\n\nThe candidate hyper-parameter can be evaluated independently and in parallel.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Attributes ---------- ||\n|-------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `num_trials` | Number of random hyperparameter values to evaluate. |\n| `use_predefined_hps` | If true, automatically configure the the space of hyper-parameters explored by the tuner. In this case, configuring the hyper-parameters manually (e.g. calling \"choice(...)\" on the tuner) is not necessary. |\n| `trial_num_threads` | Number of threads used to train the models in each trial. This parameter is different from the `num_threads` parameter of the model constructor that indicates how many threads to use for the overal training+possibly tuning. For example trial_num_threads=2 and num_threads=5, 5 models will be training in parallel during tuning, and each of those models will be trained with 2 threads. In reverse, if you want to run at most 100 threads globally, make sure that trial_num_threads\\*num_threads = 100. |\n| `trial_maximum_training_duration_seconds` | Maximum training duration of an individual trial expressed in seconds. This parameter is different from the maximum_training_duration_seconds parameter of the model constructor that define the maximum training+tuning duration. |\n\n\u003cbr /\u003e\n\nMethods\n-------\n\n### `choice`\n\n[View source](https://github.com/tensorflow/decision-forests/blob/main/tensorflow_decision_forests/component/tuner/tuner.py#L194-L211) \n\n choice(\n key: str,\n values: Union[List[int], List[float], List[str], List[bool]],\n merge: bool = False\n ) -\u003e SearchSpace\n\nAdds a hyperparameter with a list of possible values.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ||\n|----------|--------------------------------------------------------------------------------------------------------------------------------------|\n| `key` | Name of the hyper-parameter. |\n| `values` | List of possible value for the hyperparameter. |\n| `merge` | If false (default), raises an error if the hyper-parameter already exist. If true, adds values to the parameter if it already exist. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---|---|\n| The conditional SearchSpace corresponding to the values in \"values\". ||\n\n\u003cbr /\u003e\n\n### `set_base_learner`\n\n[View source](https://github.com/tensorflow/decision-forests/blob/main/tensorflow_decision_forests/component/tuner/tuner.py#L189-L192) \n\n set_base_learner(\n learner: str\n ) -\u003e None\n\nSets the base learner key.\n\n### `train_config`\n\n[View source](https://github.com/tensorflow/decision-forests/blob/main/tensorflow_decision_forests/component/tuner/tuner.py#L184-L187) \n\n train_config() -\u003e TrainConfig\n\nYDF training configuration for the Hyperparameter optimizer."]]
View source on GitHub