Note
Go to the end to download the full example code
Weak Segments Performance#
This notebook provides an overview for using and understanding the weak segment performance check.
Structure:
What is the purpose of the check?#
The check is designed to help you easily identify the model’s weakest segments in the data provided. In addition, it enables to provide a sublist of the Dataset’s features, thus limiting the check to search in interesting subspaces.
Automatically detecting weak segments#
The check contains several steps:
We calculate loss for each sample in the dataset using the provided model via either log-loss or MSE according to the task type.
Select a subset of features for the weak segment search. This is done by selecting the features with the highest feature importance to the model provided (within the features selected for check, if limited).
We train multiple simple tree based models, each one is trained using exactly two features (out of the ones selected above) to predict the per sample error calculated before.
We extract the corresponding data samples for each of the leaves in each of the trees (data segments) and calculate the model performance on them. For the weakest data segments detected we also calculate the model’s performance on data segments surrounding them.
Generate data & model#
from deepchecks.tabular.datasets.classification.lending_club import (
load_data, load_fitted_model)
_, test_ds = load_data()
model = load_fitted_model()
Run the check#
The check has several key parameters (that are all optional) that affect the behavior of the check and especially its output.
columns / ignore_columns: Controls which columns should be searched for weak segments. By default,
a heuristic is used to determine which columns to use based solely on their feature importance.
alternative_scorer: Determines the metric to be used as the performance measurement of the model on different
segments. It is important to select a metric that is relevant to the data domain and task you are performing.
By default, the check uses Neg RMSE for regression tasks and Accuracy for classification tasks.
For additional information on scorers and how to use them see
Metrics Guide.
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 feature to be a segmenting feature in multiple
segments, otherwise each feature can appear in one segment at most. True by default.
see API reference for more details.
from deepchecks.tabular.checks import WeakSegmentsPerformance
from sklearn.metrics import make_scorer, f1_score
scorer = {'f1': make_scorer(f1_score, average='micro')}
check = WeakSegmentsPerformance()
result = check.run(test_ds, model)
result.show()
/home/runner/work/deepchecks/deepchecks/venv/lib/python3.9/site-packages/joblib/externals/loky/process_executor.py:752: UserWarning:
A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.
/home/runner/work/deepchecks/deepchecks/venv/lib/python3.9/site-packages/joblib/externals/loky/process_executor.py:752: UserWarning:
A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.
Observe the check’s output#
We see in the results that the check indeed found several segments on which the model performance is below average.
In the heatmap display we can see model performance on the weakest segments and their environment with respect to the
two features that are relevant to the segment. We can switch between several such segments using the tabs on the top.
In order to get the full list of weak 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 model’s performance on the weakest segment detected is above a certain threshold. A scenario where this can be useful is when we want to make sure that the model is not under performing on a subset of the data that is of interest to us, for example specific age or gender groups.
# Let's add a condition and re-run the check:
check = WeakSegmentsPerformance(alternative_scorer=scorer, segment_minimum_size_ratio=0.03)
check.add_condition_segments_relative_performance_greater_than(0.1)
result = check.run(test_ds, model)
result.show(show_additional_outputs=False)
Total running time of the script: (1 minutes 29.518 seconds)