.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "checks_gallery/vision/distribution/plot_image_property_drift.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_checks_gallery_vision_distribution_plot_image_property_drift.py: Image Property Drift Check ************************** This notebooks provides an overview for using and understanding the image property drift check. **Structure:** * `How Does the ImagePropertyDrift Check Work? <#how-does-the-imagepropertydrift-check-work>`__ * `Which Image Properties Are Used? <#which-image-properties-are-used>`__ * `Prepare data <#prepare-data>`__ * `Run the check <#run-the-check>`__ * `Define a condition <#define-a-condition>`__ * `Check Parameters <#check-parameters>`__ How Does the ImagePropertyDrift Check Work? ================================= Data drift is simply a change in the distribution of data over time. It is also one of the top reasons that a machine learning model performance degrades over time. In the context of machine learning, drift between the training set and the test set will likely make the model prone to errors. In other words, if the model was trained on data that is different from the current test data, it will probably make more mistakes predicting the target variable. The Image Property Drift check calculates a drift score for each image property in the test dataset, by comparing its distribution to the train dataset. For this, we use the Earth Movers Distance (Wasserstein distance). Which Image Properties Are Used? ================================= ============================== ========== Property name What is it ============================== ========== Aspect Ratio Ratio between height and width of image (height / width) Area Area of image in pixels (height * width) Brightness Average intensity of image pixels. Color channels have different weights according to RGB-to-Grayscale formula RMS Contrast Contrast of image, calculated by standard deviation of pixels Mean Red Relative Intensity Mean over all pixels of the red channel, scaled to their relative intensity in comparison to the other channels [r / (r + g + b)]. Mean Green Relative Intensity Mean over all pixels of the green channel, scaled to their relative intensity in comparison to the other channels [g / (r + g + b)]. Mean Blue Relative Intensity Mean over all pixels of the blue channel, scaled to their relative intensity in comparison to the other channels [b / (r + g + b)]. ============================== ========== Imports ------- .. GENERATED FROM PYTHON SOURCE LINES 54-57 .. code-block:: default from deepchecks.vision.checks.distribution import ImagePropertyDrift from deepchecks.vision.datasets.detection import coco .. GENERATED FROM PYTHON SOURCE LINES 58-60 Prepare data ------------ .. GENERATED FROM PYTHON SOURCE LINES 60-65 .. code-block:: default from deepchecks.vision.utils import image_properties train_dataset = coco.load_dataset(train=True, object_type='VisionData') test_dataset = coco.load_dataset(train=False, object_type='VisionData') .. GENERATED FROM PYTHON SOURCE LINES 66-68 Run the check ------------- .. GENERATED FROM PYTHON SOURCE LINES 68-72 .. code-block:: default check_result = ImagePropertyDrift().run(train_dataset, test_dataset) check_result .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Validating Input: 0%| | 0/1 [00:00

Image Property Drift

Calculate drift between train dataset and test dataset per image property, using statistical measures.

Additional Outputs
The Drift score is a measure for the difference between two distributions. In this check, drift is measured for the distribution of the following image properties: ['Area', 'Aspect Ratio', 'Brightness', 'Mean Blue Relative Intensity', 'Mean Green Relative Intensity', 'Mean Red Relative Intensity', 'RMS Contrast'].


.. GENERATED FROM PYTHON SOURCE LINES 73-76 Observe the check’s output -------------------------- The result value is a pandas DataFrame that contains drift score for each image property. .. GENERATED FROM PYTHON SOURCE LINES 76-79 .. code-block:: default check_result.value .. rst-class:: sphx-glr-script-out Out: .. code-block:: none {'Aspect Ratio': 0.06673251751083462, 'Area': 0.05020210991879349, 'Brightness': 0.07114552630082199, 'RMS Contrast': 0.021987071714629717, 'Mean Red Relative Intensity': 0.03433695520859712, 'Mean Green Relative Intensity': 0.06265872287013945, 'Mean Blue Relative Intensity': 0.04916001505220028} .. GENERATED FROM PYTHON SOURCE LINES 80-84 We can also pass the check a list of classes we wish to inspect, and the check will calculate the properties only for images either belonging to the classes or containing annotations belonging to the classes. (We'll lower the min_samples to 5 to tell the check to calculate drift despite having only a few images left after the class filtration) .. GENERATED FROM PYTHON SOURCE LINES 84-90 .. code-block:: default check_result = ImagePropertyDrift(classes_to_display=['bicycle', 'bench', 'bus', 'truck'], min_samples=5 ).run(train_dataset, test_dataset) check_result .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Validating Input: 0%| | 0/1 [00:00

Image Property Drift

Calculate drift between train dataset and test dataset per image property, using statistical measures.

Additional Outputs
The Drift score is a measure for the difference between two distributions. In this check, drift is measured for the distribution of the following image properties: ['Area', 'Aspect Ratio', 'Brightness', 'Mean Blue Relative Intensity', 'Mean Green Relative Intensity', 'Mean Red Relative Intensity', 'RMS Contrast'].


.. GENERATED FROM PYTHON SOURCE LINES 91-95 Define a condition ================== We can define a condition that make sure that image properties drift scores do not exceed allowed threshold. .. GENERATED FROM PYTHON SOURCE LINES 95-103 .. code-block:: default check_result = ( ImagePropertyDrift() .add_condition_drift_score_not_greater_than(0.001) .run(train_dataset, test_dataset) ) check_result.show(show_additional_outputs=False) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Validating Input: 0%| | 0/1 [00:00 Image Property Drift

.. GENERATED FROM PYTHON SOURCE LINES 104-119 Check Parameters ---------------- Image Property Drift Check accepts two parameters that allows us to control the look of the output: * `image_properties` - list of image properties that we are interested in * `max_num_categories` - Maximal number of categories to use for the calculation of drift using PSI (Population Stability Index) Only next string values are allowed for the `image_properties` parameter: * `aspect_ratio` * `area` * `brightness` * `mean_red_relative_intensity` * `mean_green_relative_intensity` * `mean_blue_relative_intensity` .. GENERATED FROM PYTHON SOURCE LINES 119-145 .. code-block:: default from typing import List import numpy as np def area(images: List[np.ndarray]) -> List[int]: # Return list of integers of image areas (height multiplied by width) return [img.shape[0] * img.shape[1] for img in images] def aspect_ratio(images: List[np.ndarray]) -> List[float]: # Return list of floats of image height to width ratio return [img.shape[0] / img.shape[1] for img in images] properties = [ {'name': 'Area', 'method': area, 'output_type': 'continuous'}, {'name': 'Aspect Ratio', 'method': aspect_ratio, 'output_type': 'continuous'} ] check_result = ImagePropertyDrift( image_properties=properties, max_num_categories_for_drift=20 ).run(train_dataset, test_dataset) check_result .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Validating Input: 0%| | 0/1 [00:00

Image Property Drift

Calculate drift between train dataset and test dataset per image property, using statistical measures.

Additional Outputs
The Drift score is a measure for the difference between two distributions. In this check, drift is measured for the distribution of the following image properties: ['Area', 'Aspect Ratio'].


.. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 13.137 seconds) .. _sphx_glr_download_checks_gallery_vision_distribution_plot_image_property_drift.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_image_property_drift.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_image_property_drift.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_