Note
Go to the end to download the full example code
Under Annotated Property Segments#
This notebook provides an overview for using and understanding the under annotated property segments check.
Structure:
What is the purpose of the check?#
The Under-Annotated Property Segments check is designed to help you easily identify segments in your data which are under-annotated compared to the rest of your dataset, based on the provided properties. The check could be very useful in identifying a specific data samples (for example less fluent or less formal samples) for which there was a problem in the annotation process. The check can be guided to run only on a specific list of properties, enabling you to focus on properties where you know an issue exists, or on important business segments.
Automatically detecting under annotated segments#
The check contains two main steps:
We train multiple simple tree based models, each one is trained using exactly two properties (out of the ones selected above) to predict whether a sample will have a label.
We extract the corresponding data samples for each of the leaves in each of the trees (data segments) and calculate the annotation ratio in the samples within in. We keep the segments with the lowest annotation ratio.
Generate data & model#
from deepchecks.nlp.utils.test_utils import load_modified_tweet_text_data
text_data = load_modified_tweet_text_data()
text_data.properties.head(3)
Run the check#
The check has several key parameters (that are all optional) that affect the behavior of the check and especially its output.
properties / ignore_properties: Controls which properties should be searched for under
annotated segments. By default, uses all properties.
segment_minimum_size_ratio: Determines the minimum size of segments that are of interest. The check will
return data segments that contain at least this fraction of the total data samples. It is recommended to
try different configurations
of this parameter as larger segments can be of interest even the model performance on them is superior.
categorical_aggregation_threshold: By default the check will combine rare categories into a single category called
“Other”. This parameter determines the frequency threshold for categories to be mapped into to the “other” category.
multiple_segments_per_column: If True, will allow the same property to be a segmenting feature in multiple
segments, otherwise each property can appear in one segment at most. False by default.
see API reference for more details.
from deepchecks.nlp.checks import UnderAnnotatedPropertySegments
check = UnderAnnotatedPropertySegments()
result = check.run(text_data)
result.show()
Observe the check’s output#
We see in the results that the check indeed found several under annotated segments.
In the scatter plot display we can see the under annotated segment as well as the annotation distribution with
respect to the two properties that are relevant to the segment. In order to get the full list
of under annotated segments found we will inspect
the result.value attribute. Shown below are the 3 segments with the worst performance.
result.value['weak_segments_list'].head(3)
Define a condition#
We can add a condition that will validate the annotation ratio in all data segment is above a certain threshold. A scenario where this can be useful is when we want to make sure that we have enough annotations for quality evaluation of the model or drift on a subset of the data that is of interest to us, for example for specific age or gender groups.
# Let's add a condition and re-run the check:
check = UnderAnnotatedPropertySegments()
check.add_condition_segments_annotation_ratio_greater_than(0.7)
result = check.run(text_data)
result.show(show_additional_outputs=False)
Total running time of the script: (0 minutes 9.269 seconds)