[sdk] SlicedClassificationMetrics Artifact load_confusion_matrix has incorrect call

[hollyhutson]

Environment

• KFP version: N/A
• KFP SDK version: 1.8.13

Steps to reproduce

Use a SlicedClassificationMetrics Artifact and try to call load_confusion_matrix. Internally this calls log_confusion_matrix_cell on the ClassificationMetrics object instead of log_confusion_matrix, which results in a type error: TypeError: log_confusion_matrix_cell() missing 1 required positional argument: 'value'

Expected result

Calling load_confusion_matrix on SlicedClassificationMetrics doesn't break, and correctly adds the confusion matrix to the right slice in the metadata.

Materials and Reference

Full reproducible sample:

from kfp.v2.dsl import component, Output, SlicedClassificationMetrics
from kfp.v2 import compiler
import kfp

@component()
def simple_metric_op(sliced_eval_metrics: Output[SlicedClassificationMetrics]):
    sliced_eval_metrics._sliced_metrics = {}
    sliced_eval_metrics.load_confusion_matrix('a slice', 
                                                categories=['cat1', 'cat2'],
                                                matrix=[[1,0],[2,4]]
                                                )

@kfp.dsl.pipeline(name="test-sliced-metric")
def metric_test_pipeline():
    m_op = simple_metric_op()

compiler.Compiler().compile(
    pipeline_func=metric_test_pipeline,
    package_path="evaluation_pipeline.json",
)

See the incorrect line of code here: https://github.com/kubeflow/pipelines/blob/061905b6df397c40fbcc4ffafa24d7b3b9daf439/sdk/python/kfp/components/types/artifact_types.py#L452-L464 Where self._sliced_metrics[slice].log_confusion_matrix_cell( categories, matrix) Should be self._sliced_metrics[slice].log_confusion_matrix( categories, matrix) Since self._sliced_metrics[slice] is of type ClassificationMetrics after the call to self._upsert_classification_metrics_for_slice(slice)

As an aside - _sliced_metrics is never initialised in the class, leaving it up to the user of the Artifact class, which isn't very intuitive.

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