AutoTS

AutoTS is an automated machine learning tool for PaddleTS.

It frees the user from selecting hyperparameters for PaddleTS models or PaddleTS pipelines.

1. Installation

pip install paddlets[autots]

2. Quickstart

2.1. Prepare Data

from paddlets.datasets.repository import get_dataset
tsdataset = get_dataset("UNI_WTH")

2.2. Construct and Fitting

With four lines of code, we initialize an AutoTS model with MLPRegressor and perform model fitting. Autots will automatically optimize hyperparameters during the fitting process.

from paddlets.models.forecasting import MLPRegressor
from paddlets.automl.autots import AutoTS
autots_model = AutoTS(MLPRegressor, 96, 2)
autots_model.fit(tsdataset)

3. Search Space

For hyperparameter optimization, you can define a search space, or we also provide built-in recommended search space for the PaddleTS models if you do not define a search space.

You can specify how your hyperparameters are sampled and define valid ranges for hyperparameters.

The following is an example of a autots pipeline which specifying the search space:

from ray.tune import uniform, qrandint, choice
sp = {
    "Fill": {
        "cols": ['WetBulbCelsius'],
        "method": choice(['max', 'min', 'mean', 'median', 'pre', 'next', 'zero']),
        "value": uniform(0.1, 0.9),
        "window_size": qrandint(20, 50, q=1)
    },
    "MLPRegressor": {
        "batch_size": qrandint(16, 64, q=16),
        "use_bn": choice([True, False]),
        "max_epochs": qrandint(10, 50, q=10)
    }
}
autots_model = AutoTS([Fill, MLPRegressor], 25, 2, search_space=sp, sampling_stride=25)
autots_model.fit(tsdataset, n_trails=1)
sp = autots_model.search_space()
predicted = autots_model.predict(tsdataset)

Search space API can refer to: https://docs.ray.io/en/latest/tune/api_docs/search_space.html

4. Search Algorithms

Search Algorithms are wrappers around open-source optimization libraries.

We have built in the following algorithms：

[“Random”, “CMAES”, “TPE”, “CFO”, “BlendSearch”, “Bayes”]

For more details about those optimization libraries, please refer to their documentation.

You can specify different algorithms as follows:

autots_model = AutoTS(MLPRegressor, 96, 2, search_space="CMAES")

If no search algorithm is specified, “TPE” will be used as default.