certifai.model.sdk.simple_wrapper module

class certifai.model.sdk.simple_wrapper.PredictResponse(predictions: numpy.ndarray, scores: Optional[numpy.ndarray], labels: Optional[list], threshold: Optional[float])

Representation of model prediction response, allowing for optional soft scoring information

Create new instance of PredictResponse(predictions, scores, labels, threshold)

property predictions

Alias for field number 0

property scores

Alias for field number 1

property labels

Alias for field number 2

property threshold

Alias for field number 3

class certifai.model.sdk.simple_wrapper.SimpleModelWrapper(endpoint_url: str = '/predict', port: int = 8551, host: str = '127.0.0.1', model: Optional[certifai.common.hosted_model.IBaseModel] = None, encoder: Optional[Callable[Sequence, Sequence]] = None, decoder: Optional[Callable[Sequence, Sequence]] = None, supports_soft_scores: bool = False, score_labels: Optional[list] = None, threshold: Optional[float] = None, model_type: Optional[str] = None, model_path: Optional[str] = None)

SimpleModelWrapper provides a Flask app that runs a single model. It is optimized for models that accept a numpy array of instances from the dataset, and returns a numpy array of the predictions. For a model matching that pattern, simply invoke the predict endpoint with numpy array of instances to get json encoded ordered list of predictions as response.

Parameters

  • model (IBaseModel) – any predictor object that has a predict method which takes a sequence of data vectors as a numpy array and returns a sequence of corresponding predicted values. To override default predict behaviour see SimpleModelWrapper.predict().

  • endpoint_url (Optional[str]) – valid url route string to create POST endpoint for model invoke e.g. /api/model/predict. defaults to /predict.

  • port (Optional[int]) – the port of the webserver. Defaults to 8551

  • host (Optional[str]) – the hostname to listen on. Set this to ‘0.0.0.0’ to have the server available externally as well. Defaults to ‘127.0.0.1’.

  • encoder (Optional[Callable[[Sequence],Sequence]]) – optional function used to transform the model’s input (e.g. - to perform one-hot encoding and so on).

  • decoder (Optional[Callable[[Sequence],Sequence]]) – optional function used to transform the model’s output (e.g. - to binarize with some threshold).

  • supports_soft_scores (Optional[bool]) – True, if model supports soft scores. default is False

  • score_labels (Optional[list]) – ordered list of class labels corresponding to each predicted score array in-case of soft scoring model

  • threshold (Optional[float]) – value at which prediction to be considered positive; only used in binary-classification when model returns simple list of scores for the positive class

  • model_type (Optional[ModelTypesEnum]) – type of third-party model to import. currently supported ‘h2o_mojo’

  • model_path (Optional[str]) – disk path of third-party model to import. currently supported ‘h2o_mojo’

predict_raw(instances: List) → certifai.model.sdk.simple_wrapper.PredictResponse

Override this method if the model doesnt use numpy arrays for predict input/output.

Parameters

instances (List) – {array-like, list} of data instances of shape (n_samples, n_features)

Returns

NamedTuple (PredictResponse) of model predictions, scores, labels and threshold

Return type

PredictResponse NamedTuple( predictions: np.ndarray scores: Optional[np.ndarray] labels: Optional[list] threshold: Optional[float] )

set_global_imports()

Override this method to perform external imports in-case prediction requires additional dependencies sets the third-party helper modules to be used throughout. Note: Imports must be marked global e.g. usage

` global dt import datatable as dt `

Returns

None

predict(npinstances: numpy.ndarray) → numpy.ndarray

Override this method to change the way the model is called. The default implementation calls model.predict(npinstances)

Parameters

npinstances (np.ndarray) – numpy array of shape (n_samples, n_features) to predict on

Returns

numpy array of model predictions of shape (n_samples,)

Return type

np.ndarray

soft_predict(npinstances: numpy.ndarray) → numpy.ndarray
“Override this method to change to compute soft scores. The default implementation calls model.predict_proba(npinstances).

computes soft scores along with ordered list of score labels if supports_soft_scores is enabled

Parameters

npinstances (np.ndarray) – numpy array of shape (n_samples, n_features) to predict on

Returns

model predict scores np.ndarray of shape (n_samples,n_classes)

Return type

np.ndarray(n_samples,n_classes)

run(production: Optional[bool] = False, worker_class: Optional[certifai.model.utils.gunicorn_conf.WorkerTypeEnum] = WorkerTypeEnum.gevent, log_level: Optional[str] = 'info', num_workers: Optional[int] = 3, timeout_secs: Optional[int] = 20)

start model server

Parameters

  • production (Optional[bool]) – start gunicorn server if True else run native Flask app. default is False

  • worker_class (Optional[str]) – type of gunicorn worker. default is gevent. supported type (gthread,gevent,sync)

  • log_level (Optional[str]) – logging level. default is info.

  • num_workers (Optional[int]) – number of gunicorn worker processes to start. default is 3

  • timeout_secs (Optional[int]) – gunicorn worker timeout in secs. default is 20

Returns

None