paddlets.utils.utils

check_model_fitted(model: Trainable, msg: Optional[str] = None)

check if model has fitted, Raise Exception if not fitted

Parameters

• model (Trainable) – model instance.

• msg (str) – str, default=None The default error message is, “This %(name)s instance is not fitted yet. Call ‘fit’ with appropriate arguments before using this estimator.” For custom messages if “%(name)s” is present in the message string, it is substituted for the estimator name. Eg. : “Estimator, %(name)s, must be fitted before sparsifying”.

Returns

None

Raises

ValueError –

get_uuid(prefix: str = '', suffix: str = '')

Get a random string of 16 characters.

Parameters

• prefix (str, optional) – The prefix of the returned string.

• suffix (str, optional) – The suffix of the returned string.

Returns

String of 16 characters.

Return type

str

check_train_valid_continuity(train_data: TSDataset, valid_data: TSDataset) → bool

Check if train and test TSDataset are continous

Parameters

• train_data (TSDataset) – Train dataset.

• test_data (TSDataset) – Test dataset.

Returns

if train and test TSDataset are continous

Return type

bool

split_dataset(dataset: TSDataset, split_point: int) → TSDataset

Split dataset (accroding to the max length)

Parameters

• dataset (TSDataset) – dataset to be splited.

• split_point (int) – split point.

Returns

TSDataset

get_tsdataset_max_len(dataset: TSDataset) → int

Get dataset max length

Parameters

dataset (TSDataset) – dataset use to get length.

Returns

int