Stores information about a model.
Input Parameters | Type | Default | Description |
---|---|---|---|
display_name | str | A display name for the model. | |
input_type | fdl.ModelInputType | A ModelInputType object containing the input type of the model. | |
model_task | fdl.ModelTask | A ModelTask object containing the model task. | |
inputs | list | A list of Column objects corresponding to the inputs (features) of the model. | |
outputs | list | A list of Column objects corresponding to the outputs (predictions) of the model. | |
target_class_order | Optional [list] | None | A list denoting the order of classes in the target. |
metadata | Optional [list] | None | A list of Column objects corresponding to any metadata fields. |
decisions | Optional [list] | None | A list of Column objects corresponding to any decision fields (post-prediction business decisions). |
targets | Optional [list] | None | A list of Column objects corresponding to the targets (ground truth) of the model. |
framework | Optional [str] | None | A string providing information about the software library and version used to train and run this model. |
description | Optional [str] | None | A description of the model. |
datasets | Optional [list] | None | A list of the dataset IDs used by the model. |
mlflow_params | Optional [fdl.MLFlowParams] | None | A MLFlowParams object containing information about MLFlow parameters. |
model_deployment_params | Optional [fdl.ModelDeploymentParams] | None | A ModelDeploymentParams object containing information about model deployment. |
artifact_status | Optional [fdl.ArtifactStatus] | None | An ArtifactStatus object containing information about the model artifact. |
preferred_explanation_method | Optional [fdl.ExplanationMethod] | None | An ExplanationMethod object that specifies the default explanation algorithm to use for the model. |
custom_explanation_names | Optional [list] | [ ] | A list of names that can be passed to the explanation_name _argument of the optional user-defined _explain_custom method of the model object defined in package.py. |
binary_classification_threshold | Optional [float] | .5 | The threshold used for classifying inferences for binary classifiers. |
ranking_top_k | Optional [int] | 50 | Used only for ranking models. Sets the top k results to take into consideration when computing performance metrics like MAP and NDCG. |
group_by | Optional [str] | None | Used only for ranking models. The column by which to group events for certain performance metrics like MAP and NDCG. |
fall_back | Optional [dict] | None | A dictionary mapping a column name to custom missing value encodings for that column. |
**kwargs | Additional arguments to be passed. |
inputs = [
fdl.Column(
name='feature_1',
data_type=fdl.DataType.FLOAT
),
fdl.Column(
name='feature_2',
data_type=fdl.DataType.INTEGER
),
fdl.Column(
name='feature_3',
data_type=fdl.DataType.BOOLEAN
)
]
outputs = [
fdl.Column(
name='output_column',
data_type=fdl.DataType.FLOAT
)
]
targets = [
fdl.Column(
name='target_column',
data_type=fdl.DataType.INTEGER
)
]
model_info = fdl.ModelInfo(
display_name='Example Model',
input_type=fdl.ModelInputType.TABULAR,
model_task=fdl.ModelTask.BINARY_CLASSIFICATION,
inputs=inputs,
outputs=outputs,
targets=targets
)