paddlets.models.classify.base

class BaseClassifier[source]

Bases: ABC

Base class for all classifier.

abstract fit(train_tsdatasets: List[TSDataset], train_labels: ndarray, valid_tsdatasets: Optional[List[TSDataset]] = None, valid_labels: Optional[ndarray] = None)[source]

Fit a BaseClassifier instance.

Any non-abstract classes inherited from this class should implement this method.

Parameters

train_tsdataset (TSDataset) – Train set.
train_labels – (np.ndarray) : The train data class labels
valid_tsdataset (TSDataset|None) – Eval set, used for early stopping.
valid_labels – (np.ndarray) : The valid data class labels

abstract predict(tsdatasets: List[TSDataset]) → ndarray[source]

Predict labels. Results are output as ndarray.

Parameters: tsdataset (List[TSDataset]) – Data to be predicted.
Returns: np.ndarray.

abstract predict_proba(tsdatasets: List[TSDataset]) → ndarray[source]

Find probability estimates for each class for all cases. Results are output as ndarray.

Parameters: tsdataset (List[TSDataset]) – Data to be predicted.
Returns: np.ndarray.

abstract save(path: str) → None[source]

Saves a BaseClassifier instance to a disk file.

Any non-abstract classes inherited from this class should implement this method.

Parameters: path (str) – A path string containing a model file name.

abstract static load(path: str) → BaseClassifier[source]

Loads a BaseClassifier instance from a file.

Any non-abstract classes inherited from this class should implement this method.

Parameters: path (str) – A path string containing a model file name.
Returns: A loaded model.
Return type: BaseClassifier