.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "vision/auto_checks/model_evaluation/plot_prediction_drift.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_vision_auto_checks_model_evaluation_plot_prediction_drift.py: .. _vision__prediction_drift: Prediction Drift *************************** This notebooks provides an overview for using and understanding the vision prediction drift check. **Structure:** * `What Is Prediction Drift? <#what-is-prediction-drift>`__ * `Which Prediction Properties Are Used? <#which-prediction-properties-are-used>`__ * `Run Check on a Classification Task <#run-the-check-on-a-classification-task-mnist>`__ * `Run Check on an Object Detection Task <#run-the-check-on-an-object-detection-task-coco>`__ What Is Prediction Drift? ========================= Drift is simply a change in the distribution of data over time, and it is also one of the top reasons why machine learning model's performance degrades over time. Prediction drift is when drift occurs in the prediction itself. Calculating prediction drift is especially useful in cases in which labels are not available for the test dataset, and so a drift in the predictions is a direct indication that a change that happened in the data has affected the model's predictions. If labels are available, it's also recommended to run the :ref:`Label Drift check `. For more information on drift, please visit our :ref:`Drift Guide ` How Deepchecks Detects Prediction Drift --------------------------------------- This check detects prediction drift by using :ref:`univariate measures ` on the prediction properties. Using Prediction Properties to Detect Prediction Drift ------------------------------------------------------ In computer vision specifically, our predictions may be complex, and measuring their drift is not a straightforward task. Therefore, we calculate drift on different :ref:`properties of the prediction `, on which we can directly measure drift. Which Prediction Properties Are Used? ===================================== ================ =================================== ========== Task Type Property name What is it ================ =================================== ========== Classification Samples Per Class Number of images per class Object Detection Samples Per Class Number of bounding boxes per class Object Detection Bounding Box Area Area of bounding box (height * width) Object Detection Number of Bounding Boxes Per Image Number of bounding box objects in each image ================ =================================== ========== Run the Check on a Classification Task (MNIST) ============================================== .. GENERATED FROM PYTHON SOURCE LINES 64-72 Imports ------- .. note:: In this example, we use the pytorch version of the mnist dataset and model. In order to run this example using tensorflow, please change the import statements to:: from deepchecks.vision.datasets.classification.mnist_tensorflow import load_dataset .. GENERATED FROM PYTHON SOURCE LINES 72-76 .. code-block:: default from deepchecks.vision.checks import PredictionDrift from deepchecks.vision.datasets.classification.mnist_torch import load_dataset .. GENERATED FROM PYTHON SOURCE LINES 77-79 Load Dataset ------------ .. GENERATED FROM PYTHON SOURCE LINES 79-85 .. code-block:: default train_ds = load_dataset(train=True, batch_size=64, object_type='VisionData') test_ds = load_dataset(train=False, batch_size=64, object_type='VisionData') .. GENERATED FROM PYTHON SOURCE LINES 86-88 Running PredictionDrift on classification -------------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 88-93 .. code-block:: default check = PredictionDrift() result = check.run(train_ds, test_ds) result .. rst-class:: sphx-glr-script-out .. code-block:: none Processing Train Batches: | | 0/1 [Time: 00:00] Processing Train Batches: |█████| 1/1 [Time: 00:01] Processing Train Batches: |█████| 1/1 [Time: 00:01] Processing Test Batches: | | 0/1 [Time: 00:00] Processing Test Batches: |█████| 1/1 [Time: 00:07] Processing Test Batches: |█████| 1/1 [Time: 00:07] Computing Check: | | 0/1 [Time: 00:00] Computing Check: |█████| 1/1 [Time: 00:00] .. raw:: html
Prediction Drift


.. GENERATED FROM PYTHON SOURCE LINES 94-95 To display the results in an IDE like PyCharm, you can use the following code: .. GENERATED FROM PYTHON SOURCE LINES 95-97 .. code-block:: default # result.show_in_window() .. GENERATED FROM PYTHON SOURCE LINES 98-99 The result will be displayed in a new window. .. GENERATED FROM PYTHON SOURCE LINES 101-107 Understanding the results ------------------------- We can see there is almost no drift between the train & test predictions. This means the split to train and test was good (as it is balanced and random). Let's check the performance of a simple model trained on MNIST. .. GENERATED FROM PYTHON SOURCE LINES 107-112 .. code-block:: default from deepchecks.vision.checks import ClassPerformance ClassPerformance().run(train_ds, test_ds) .. rst-class:: sphx-glr-script-out .. code-block:: none Processing Train Batches: | | 0/1 [Time: 00:00] Processing Train Batches: |█████| 1/1 [Time: 00:01] Processing Train Batches: |█████| 1/1 [Time: 00:01] Processing Test Batches: | | 0/1 [Time: 00:00] Processing Test Batches: |█████| 1/1 [Time: 00:01] Processing Test Batches: |█████| 1/1 [Time: 00:01] Computing Check: | | 0/1 [Time: 00:00] Computing Check: |█████| 1/1 [Time: 00:00] Computing Check: |█████| 1/1 [Time: 00:00] .. raw:: html
Class Performance


.. GENERATED FROM PYTHON SOURCE LINES 113-120 MNIST with prediction drift =========================== Now, let's try to separate the MNIST dataset in a different manner that will result in a prediction drift, and see how it affects the performance. We are going to create a custom `collate_fn`` in the test dataset, that will select a few of the samples with class 0 and change their most of their predicted classes to 1. .. GENERATED FROM PYTHON SOURCE LINES 122-124 Inserting drift to the test set ------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 124-151 .. code-block:: default import numpy as np import torch np.random.seed(42) def generate_collate_fn_with_label_drift(collate_fn): def collate_fn_with_label_drift(batch): batch_dict = collate_fn(batch) images = batch_dict['images'] labels = batch_dict['labels'] for i in range(len(images)): image, label = images[i], labels[i] if label == 0: if np.random.randint(5) != 0: batch_dict['labels'][i] = 1 # In 9/10 cases, the prediction vector will change to match the label if np.random.randint(10) != 0: batch_dict['predictions'][i] = torch.tensor([0, 1, 0, 0, 0, 0, 0, 0, 0, 0]) return batch_dict return collate_fn_with_label_drift mod_test_ds = load_dataset(train=False, batch_size=1000, object_type='VisionData') mod_test_ds._batch_loader.collate_fn = generate_collate_fn_with_label_drift(mod_test_ds._batch_loader.collate_fn) .. GENERATED FROM PYTHON SOURCE LINES 152-154 Run the check ------------- .. GENERATED FROM PYTHON SOURCE LINES 154-159 .. code-block:: default check = PredictionDrift() result = check.run(train_ds, mod_test_ds) result .. rst-class:: sphx-glr-script-out .. code-block:: none Processing Train Batches: | | 0/1 [Time: 00:00] Processing Train Batches: |█████| 1/1 [Time: 00:01] Processing Train Batches: |█████| 1/1 [Time: 00:01] Processing Test Batches: | | 0/1 [Time: 00:00] Processing Test Batches: |█████| 1/1 [Time: 00:06] Processing Test Batches: |█████| 1/1 [Time: 00:06] Computing Check: | | 0/1 [Time: 00:00] Computing Check: |█████| 1/1 [Time: 00:00] .. raw:: html
Prediction Drift


.. GENERATED FROM PYTHON SOURCE LINES 160-164 Add a condition --------------- We could also add a condition to the check to alert us about changes in the prediction distribution, such as the one that occurred here. .. GENERATED FROM PYTHON SOURCE LINES 164-169 .. code-block:: default check = PredictionDrift().add_condition_drift_score_less_than() result = check.run(train_ds, mod_test_ds) result .. rst-class:: sphx-glr-script-out .. code-block:: none Processing Train Batches: | | 0/1 [Time: 00:00] Processing Train Batches: |█████| 1/1 [Time: 00:01] Processing Train Batches: |█████| 1/1 [Time: 00:01] Processing Test Batches: | | 0/1 [Time: 00:00] Processing Test Batches: |█████| 1/1 [Time: 00:01] Processing Test Batches: |█████| 1/1 [Time: 00:01] Computing Check: | | 0/1 [Time: 00:00] Computing Check: |█████| 1/1 [Time: 00:00] .. raw:: html
Prediction Drift


.. GENERATED FROM PYTHON SOURCE LINES 170-171 As we can see, the condition alerts us to the presence of drift in the predictions. .. GENERATED FROM PYTHON SOURCE LINES 173-180 Results ------- We can see the check successfully detects the (expected) drift in class 0 distribution between the train and test sets. It means the the model correctly predicted 0 for those samples and so we're seeing drift in the predictions as well as the labels. We note that this check enabled us to detect the presence of label drift (in this case) without needing actual labels for the test data. .. GENERATED FROM PYTHON SOURCE LINES 182-184 But how does this affect the performance of the model? ------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 184-188 .. code-block:: default result = ClassPerformance().run(train_ds, mod_test_ds) result .. rst-class:: sphx-glr-script-out .. code-block:: none Processing Train Batches: | | 0/1 [Time: 00:00] Processing Train Batches: |█████| 1/1 [Time: 00:01] Processing Train Batches: |█████| 1/1 [Time: 00:01] Processing Test Batches: | | 0/1 [Time: 00:00] Processing Test Batches: |█████| 1/1 [Time: 00:01] Processing Test Batches: |█████| 1/1 [Time: 00:01] Computing Check: | | 0/1 [Time: 00:00] Computing Check: |█████| 1/1 [Time: 00:00] Computing Check: |█████| 1/1 [Time: 00:00] .. raw:: html
Class Performance


.. GENERATED FROM PYTHON SOURCE LINES 189-193 Inferring the results --------------------- We can see the drop in the precision of class 0, which was caused by the class imbalance indicated earlier by the label drift check. .. GENERATED FROM PYTHON SOURCE LINES 195-203 Run the Check on an Object Detection Task (COCO) ================================================ .. note:: In this example, we use the pytorch version of the coco dataset and model. In order to run this example using tensorflow, please change the import statements to:: from deepchecks.vision.datasets.detection.coco_tensorflow import load_dataset .. GENERATED FROM PYTHON SOURCE LINES 203-209 .. code-block:: default from deepchecks.vision.datasets.detection.coco_torch import load_dataset train_ds = load_dataset(train=True, object_type='VisionData') test_ds = load_dataset(train=False, object_type='VisionData') .. GENERATED FROM PYTHON SOURCE LINES 210-215 .. code-block:: default check = PredictionDrift() result = check.run(train_ds, test_ds) result .. rst-class:: sphx-glr-script-out .. code-block:: none Processing Train Batches: | | 0/1 [Time: 00:00] Processing Train Batches: |█████| 1/1 [Time: 00:00] Processing Train Batches: |█████| 1/1 [Time: 00:00] Processing Test Batches: | | 0/1 [Time: 00:00] Processing Test Batches: |█████| 1/1 [Time: 00:00] Processing Test Batches: |█████| 1/1 [Time: 00:00] Computing Check: | | 0/1 [Time: 00:00] Computing Check: |█████| 1/1 [Time: 00:00] Computing Check: |█████| 1/1 [Time: 00:00] .. raw:: html
Prediction Drift


.. GENERATED FROM PYTHON SOURCE LINES 216-222 Prediction drift is detected! ----------------------------- We can see that the COCO128 contains a drift in the out of the box dataset. In addition to the prediction count per class, the prediction drift check for object detection tasks include drift calculation on certain measurements, like the bounding box area and the number of bboxes per image. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 35.306 seconds) .. _sphx_glr_download_vision_auto_checks_model_evaluation_plot_prediction_drift.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_prediction_drift.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_prediction_drift.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_