Reference
The Comet MPM class is used to upload a model's input and output features to MPM
CometMPM.init¶
__init__(api_key: Optional[str] = None,
workspace_name: Optional[str] = None,
model_name: Optional[str] = None,
model_version: Optional[str] = None,
disabled: Optional[bool] = None, asyncio: bool = False,
max_batch_size: Optional[int] = None,
max_batch_time: Optional[int] = None)
Creates the Comet MPM Event logger object.
Args:
- api_key: The Comet API Key
- workspace_name: The Comet Workspace Name of the model
- model_name: The Comet Model Name of the model
- model_version: The Comet Model Version of the model
- disabled: If set to True, CometMPM will not send anything to the backend.
- asyncio: Set to True if you are using an Asyncio-based framework like FastAPI.
- max_batch_size: Maximum number of MPM events sent in a batch, can also be configured using the environment variable MPM_MAX_BATCH_SIZE.
- max_batch_time: Maximum time before a batch of events is submitted to MPM, can also be configured using the environment variable MPM_MAX_BATCH_SIZE.
CometMPM.connect¶
connect() -> None
When using CometMPM in asyncio mode, this coroutine needs to be awaited at the server start.
CometMPM.join¶
join(timeout: Optional[int] = None) -> Optional[Awaitable[None]]
When using CometMPM in asyncio mode, this coroutine needs to be awaited at the server stop.
CometMPM.log_dataframe¶
log_dataframe(dataframe, prediction_id_column: str,
feature_columns: Optional[List[str]] = None,
output_value_column: Optional[str] = None, output_probability_column: Optional[str] = None) -> LogEventsResult
Log a pandas DataFrame to MPM. Every row in the dataframe will be interpreted as an event to be logged.
Events are structured as described in .log_event() method, please refer to it to have the full context.
Args:
- dataframe: the pandas DataFrame to be logged.
- prediction_id_column: this column should have the prediction_id values for the events.
- feature_columns: if provided, this column will be used as the input_features parameter for the events.
- output_value_column: if provided, this column will be used as the output_value for the events.
- output_probability_column: if provided, this column will be used as the output_probability for the events.
CometMPM.log_event¶
log_event(prediction_id: str, input_features: Optional[Dict[str,
Any]] = None, output_value: Optional[Any] = None, output_probability: Optional[Any] = None) -> Optional[Awaitable[None]]
Log a single event asynchronously to MPM. Events are identified by the mandatory prediction_id parameter. You can send multiple events with the same prediction_id, events will be merged on the backend side automatically.
Args:
- prediction_id: The unique prediction ID, could be provided by the framework, you or a random unique value could be provided like str(uuid4())
- input_features: If provided must be a flat Dictionary where the keys are the feature name and the value are native Python scalars, int, floats, booleans or strings. For example:
{“age”: 42, “income”: 42894.89}
- output_value: The prediction as a native Python scalar, int, float, boolean or string.
- output_probability: If provided, must be a float between 0 and 1 indicating the confidence of the model in the prediction
Mar. 21, 2023