ExplanationClient Class

Defines the client that uploads and downloads explanations.

Create the client used to interact with explanations and run history.

Inheritance

builtins.object

ExplanationClient

Constructor

ExplanationClient(service_context, experiment_name, run_id, _run=None, datastore_name=None)

Parameters

Name	Description
service_context Required	<xref:ServiceContext> Holder for service information.
run_id Required	str A GUID that represents a run.
_run	Run A run. If passed in, other args will be ignored. default value: None
service_context Required	<xref:ServiceContext> Holder for service information.
run_id Required	str A GUID that represents a run.
_run Required	Run A run. If passed in, run_id will be ignored.
datastore_name	str Name of the datastore to use for uploads (defaults to workspace store) default value: None
experiment_name Required

Methods

download_model_explanation	Download a model explanation that has been stored in run history.
from_run	Create the client with factory method given a run.
from_run_id	Create the client with factory method given a run ID.
list_model_explanations	Return a dictionary of metadata for all model explanations available.
upload_model_explanation	Upload the model explanation information to run history.

download_model_explanation

Download a model explanation that has been stored in run history.

download_model_explanation(explanation_id=None, top_k=None, comment=None, raw=None, engineered=None)

Parameters

Name	Description
explanation_id	str If specified, tries to download the asset from the run with the given explanation ID. If unspecified, returns the most recently uploaded explanation. default value: None
top_k	int If specified, limit the ordered data returned to the most important features and values. If this is the case, global_importance_values and per_class_values will contain the top k sorted values instead of the usual full list of unsorted values. default value: None
comment	str A string used to filter explanations based on the strings they were uploaded with. Requires an exact match. If multiple explanations share this string, the most recent will be returned. default value: None
raw	bool or None If True or False, explanations will be filtered based on whether they are raw or not. If nothing is specified, this filter will not be applied. default value: None
engineered	bool or None If True or False, explanations will be filtered based on whether they are engineered or not. If nothing is specified, this filter will not be applied. default value: None

Returns

Type	Description
<xref:interpret_community.explanation.explanation.BaseExplanation>	The explanation as it was uploaded to run history

from_run

Create the client with factory method given a run.

from_run(run, datastore_name=None)

Parameters

Name	Description
cls Required	ExplanationClient The ExplanationClient class.
run Required	Run The run explanations will be attached to.
datastore_name	str Name of the datastore to use for uploads (defaults to workspace store) default value: None

Returns

Type	Description
ExplanationClient	An instance of the ExplanationClient.

from_run_id

Create the client with factory method given a run ID.

from_run_id(workspace, experiment_name, run_id, datastore_name=None)

Parameters

Name	Description
cls Required	ExplanationClient The ExplanationClient class.
workspace Required	Workspace An object that represents a workspace.
experiment_name Required	str The name of an experiment.
run_id Required	str A GUID that represents a run.
datastore_name	str Name of the datastore to use for uploads (defaults to workspace store) default value: None

Returns

Type	Description
ExplanationClient	An instance of the ExplanationClient.

list_model_explanations

Return a dictionary of metadata for all model explanations available.

list_model_explanations(comment=None, raw=None, engineered=None)

Parameters

Name	Description
comment	str A string used to filter explanations based on the strings they were uploaded with. Requires an exact match. default value: None
raw	bool or None If True or False, explanations will be filtered based on whether they are raw or not. If nothing is specified, this filter will not be applied. default value: None
engineered	bool or None If True or False, explanations will be filtered based on whether they are engineered or not. If nothing is specified, this filter will not be applied. default value: None

Returns

Type	Description
dict	A dictionary of explanation metadata such as id, data type, explanation method, model type, and upload time, sorted by upload time

upload_model_explanation

Upload the model explanation information to run history.

upload_model_explanation(explanation, max_num_blocks=None, block_size=None, top_k=None, comment=None, init_dataset_id=None, eval_dataset_id=None, ys_pred_dataset_id=None, ys_pred_proba_dataset_id=None, upload_datasets=False, model_id=None, true_ys=None, visualization_points=5000) -> None

Parameters

Name	Description
explanation Required	<xref:interpret_community.explanation.explanation.BaseExplanation> The explanation information to save.
max_num_blocks	int The maximum number of blocks to store. default value: None
block_size	int The size of each block for the summary stored in artifacts storage. default value: None
top_k	int Number of important features stored in the explanation. If specified, only the names and values corresponding to the top K most important features will be returned/stored. If this is the case, global_importance_values and per_class_values will contain the top k sorted values instead of the usual full list of unsorted values. default value: None
comment	str An optional string to identify the explanation. The string is displayed when listing explanations, which allows identification of uploaded explanations. default value: None
init_dataset_id	str The ID of the initialization (background) dataset in the Dataset service, if available. Used to link the explanation to the Dataset. default value: None
eval_dataset_id	str The ID of the evaluation dataset in the Dataset service, if available. Used to link the explanation to the Dataset. default value: None
ys_pred_dataset_id	str The ID of the predicted values dataset in the Dataset service, if available. default value: None
ys_pred_proba_dataset_id	str The ID of the predicted probability values dataset in the Dataset service, if available. default value: None
upload_datasets	bool If set to True and no dataset IDs are passed in, the evaluation dataset will be uploaded to Azure storage as a Dataset object. This will allow the explanation to be linked to the Dataset in the web view. default value: False
model_id	str The MMS model ID. default value: None
true_ys	list | <xref:pandas.Dataframe> | ndarray The true labels for the evaluation examples. default value: None
visualization_points	int or list[int] If set to an integer, this is the upper bound on the number of points that will be available for visualization in the web UI. If set to a list of integers, these integers will be used as indices for selecting a sample of points (original data and explanations) to be visualized in the web UI. If not planning to view the explanation in the web UI, this parameter can be set to 0 and no extra computation or storage will take place. The upper limit for either the integer or the length of the list is currently 20000 (twenty thousand). In the case that a larger integer or longer list is passed in, the function will fail. The intention is to limit the amount of data entering the web UI for performance reasons. With more evaluation, this limit may be raised. default value: 5000

Attributes

run

Get the run from the explanation client.

Returns

Type	Description
Run	The run object.