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doc update (#352)

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* custom splitter
* NLP
* version number
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Chi Wang 2021-12-22 14:35:13 -08:00 committed by GitHub
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5 changed files with 33 additions and 20 deletions
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33
flaml/automl.py
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@ -502,12 +502,13 @@ class AutoML(BaseEstimator):
'budget' - do best effort to retrain without violating the time
budget.
split_type: str or splitter object, default="auto" | the data split type.
A valid splitter object is an instance of a derived class of scikit-learn KFold
(https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold)
* A valid splitter object is an instance of a derived class of scikit-learn
[KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold)
and have ``split`` and ``get_n_splits`` methods with the same signatures.
Valid str options depend on different tasks.
For classification tasks, valid choices are [
"auto", 'stratified', 'uniform', 'time']. "auto" -> stratified.
Set eval_method to "cv" to use the splitter object.
* Valid str options depend on different tasks.
For classification tasks, valid choices are
["auto", 'stratified', 'uniform', 'time', 'group']. "auto" -> stratified.
For regression tasks, valid choices are ["auto", 'uniform', 'time'].
"auto" -> uniform.
For ts_forecast tasks, must be "auto" or 'time'.
@ -1287,12 +1288,13 @@ class AutoML(BaseEstimator):
split_ratio: A float of the validation data percentage for holdout.
n_splits: An integer of the number of folds for cross-validation.
split_type: str or splitter object, default="auto" | the data split type.
A valid splitter object is an instance of a derived class of scikit-learn KFold
(https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold)
* A valid splitter object is an instance of a derived class of scikit-learn
[KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold)
and have ``split`` and ``get_n_splits`` methods with the same signatures.
Valid str options depend on different tasks.
For classification tasks, valid choices are [
"auto", 'stratified', 'uniform', 'time', 'group']. "auto" -> stratified.
Set eval_method to "cv" to use the splitter object.
* Valid str options depend on different tasks.
For classification tasks, valid choices are
["auto", 'stratified', 'uniform', 'time', 'group']. "auto" -> stratified.
For regression tasks, valid choices are ["auto", 'uniform', 'time'].
"auto" -> uniform.
For ts_forecast tasks, must be "auto" or 'time'.
@ -1806,12 +1808,13 @@ class AutoML(BaseEstimator):
'budget' - do best effort to retrain without violating the time
budget.
split_type: str or splitter object, default="auto" | the data split type.
A valid splitter object is an instance of a derived class of scikit-learn KFold
(https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold)
* A valid splitter object is an instance of a derived class of scikit-learn
[KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold)
and have ``split`` and ``get_n_splits`` methods with the same signatures.
Valid str options depend on different tasks.
For classification tasks, valid choices are [
"auto", 'stratified', 'uniform', 'time']. "auto" -> stratified.
Set eval_method to "cv" to use the splitter object.
* Valid str options depend on different tasks.
For classification tasks, valid choices are
["auto", 'stratified', 'uniform', 'time', 'group']. "auto" -> stratified.
For regression tasks, valid choices are ["auto", 'uniform', 'time'].
"auto" -> uniform.
For ts_forecast tasks, must be "auto" or 'time'.

8
website/docs/Examples/AutoML-NLP.md
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@ -88,7 +88,7 @@ automl.fit(
)
```
### Sample output
#### Sample output
```
[flaml.automl: 12-20 11:47:28] {1965} INFO - task = seq-regression
@ -137,7 +137,7 @@ automl.fit(
X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings
)
```
### Sample Output
#### Sample Output
```
[flaml.automl: 12-20 11:44:03] {1965} INFO - task = summarization
@ -203,4 +203,6 @@ Model config T5Config {
"use_cache": true,
"vocab_size": 32128
}
```
```
For tasks that are not currently supported, use `flaml.tune` for [customized tuning](Tune-HuggingFace).

4
website/docs/Examples/Tune-HuggingFace.md
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@ -2,6 +2,10 @@
This example uses flaml to finetune a transformer model from Huggingface transformers library.
*Note*: `flaml.AutoML` has built-in support for certain finetuning tasks with a
[higher-level API](AutoML-NLP).
It may be easier to use that API unless you have special requirements not handled by that API.
### Requirements
This example requires GPU. Install dependencies:

4
website/docs/Use-Cases/Task-Oriented-AutoML.md
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@ -303,6 +303,10 @@ By default, flaml uses the following method to split the data:
The data split method for classification can be changed into uniform split by setting `split_type="uniform"`. For both classification and regression, time-based split can be enforced if the data are sorted by timestamps, by setting `split_type="time"`.
When `eval_method="cv"`, `split_type` can also be set as a custom splitter. It needs to be an instance of a derived class of scikit-learn
[KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold)
and have ``split`` and ``get_n_splits`` methods with the same signatures.
### Parallel tuning
When you have parallel resources, you can either spend them in training and keep the model search sequential, or perform parallel search. Following scikit-learn, the parameter `n_jobs` specifies how many CPU cores to use for each training job. The number of parallel trials is specified via the parameter `n_concurrent_trials`. By default, `n_jobs=-1, n_concurrent_trials=1`. That is, all the CPU cores (in a single compute node) are used for training a single model and the search is sequential. When you have more resources than what each single training job needs, you can consider increasing `n_concurrent_trials`.

4
website/docs/Use-Cases/Tune-User-Defined-Function.md
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@ -436,12 +436,12 @@ analysis = tune.run(
### Reproducibility
By default, there is randomness in our tuning process (for versions <= 0.9.0). If reproducibility is desired, you could manually set a random seed before calling `tune.run()`. For example, in the following code, we call `np.random.seed(100)` to set the random seed.
By default, there is randomness in our tuning process (for versions <= 0.9.1). If reproducibility is desired, you could manually set a random seed before calling `tune.run()`. For example, in the following code, we call `np.random.seed(100)` to set the random seed.
With this random seed, running the following code multiple times will generate exactly the same search trajectory. The reproducibility can only be guaranteed in sequential tuning.
```python
import numpy as np
np.random.seed(100) # This line is not needed starting from version v0.9.1.
np.random.seed(100) # This line is not needed starting from version v0.9.2.
analysis = tune.run(
simple_obj,
config=config_search_space,