VisualAI

class datarobot.models.visualai.Image(image_id, project_id, height=0, width=0)

An image stored in a project’s dataset.

Attributes:

idstr

Image ID for this image.

image_typestr

Image media type. Accessing this may require a server request and an associated delay in returning.

image_bytesbytes

Raw bytes of this image. Accessing this may require a server request and an associated delay in returning.

heightint

Height of the image in pixels.

widthint

Width of the image in pixels.

classmethod get(project_id, image_id)

Get a single image object from project.

Parameters:

project_idstr

Id of the project that contains the images.

image_idstr

ID of image to load from the project.

Return type:

Image

class datarobot.models.visualai.SampleImage(project_id, image_id, height, width, target_value=None)

A sample image in a project’s dataset.

If Project.stage is datarobot.enums.PROJECT_STAGE.EDA2 then the target_* attributes of this class will have values, otherwise the values will all be None.

Attributes:

imageImage

Image object.

target_valueTargetValue

Value associated with the feature_name.

project_idstr

Id of the project that contains the images.

classmethod list(project_id, feature_name, target_value=None, target_bin_start=None, target_bin_end=None, offset=None, limit=None)

Get sample images from a project.

Parameters:

project_idstr

Project that contains the images.

feature_namestr

Name of feature column that contains images.

target_valueTargetValue

For classification projects - target value to filter images. Please note that you can only use this parameter when the project has finished the EDA2 stage.

target_bin_startOptional[Union[int, float]]

For regression projects - only images corresponding to the target values above (inclusive) this value will be returned. Must be specified together with target_bin_end. Please note that you can only use this parameter when the project has finished the EDA2 stage.

target_bin_endOptional[Union[int, float]]

For regression projects - only images corresponding to the target values below (exclusive) this value will be returned. Must be specified together with target_bin_start. Please note that you can only use this parameter when the project has finished the EDA2 stage.

offsetOptional[int]

Number of images to be skipped.

limitOptional[int]

Number of images to be returned.

Return type:

List[SampleImage]

class datarobot.models.visualai.DuplicateImage(image_id, row_count, project_id)

An image that was duplicated in the project dataset.

Attributes:

imageImage

Image object.

countint

Number of times the image was duplicated.

classmethod list(project_id, feature_name, offset=None, limit=None)

Get all duplicate images in a project.

Parameters:

project_idstr

Project that contains the images.

feature_namestr

Name of feature column that contains images.

offsetOptional[int]

Number of images to be skipped.

limitOptional[int]

Number of images to be returned.

Return type:

List[DuplicateImage]

class datarobot.models.visualai.ImageEmbedding(feature_name, position_x, position_y, image_id, project_id, model_id, actual_target_value=None, target_values=None, target_bins=None)

Vector representation of an image in an embedding space.

A vector in an embedding space will allow linear computations to be carried out between images: for example computing the Euclidean distance of the images.

Attributes:

imageImage

Image object used to create this map.

feature_namestr

Name of the feature column this embedding is associated with.

position_xint

X coordinate of the image in the embedding space.

position_yint

Y coordinate of the image in the embedding space.

actual_target_valueobject

Actual target value of the dataset row.

target_valuesOptional[List[str]]

For classification projects, a list of target values of this project.

target_binsOptional[List[Dict[str, float]]]

For regression projects, a list of target bins of this project.

project_idstr

Id of the project this Image Embedding belongs to.

model_idstr

Id of the model this Image Embedding belongs to.

classmethod compute(project_id, model_id)

Start the computation of image embeddings for the model.

Parameters:

project_idstr

Project to start creation in.

model_idstr

Project’s model to start creation in.

Returns:

str

URL to check for image embeddings progress.

Raises:

datarobot.errors.ClientError

Server rejected creation due to client error. Most likely cause is bad project_id or model_id.

Return type:

str

classmethod models(project_id)

For a given project_id, list all model_id - feature_name pairs with available Image Embeddings.

Parameters:

project_idstr

Id of the project to list model_id - feature_name pairs with available Image Embeddings for.

Returns:

list( tuple(model_id, feature_name) )

List of model and feature name pairs.

Return type:

List[Tuple[str, str]]

classmethod list(project_id, model_id, feature_name)

Return a list of ImageEmbedding objects.

Parameters:

project_id: str

Id of the project the model belongs to.

model_id: str

Id of the model to list Image Embeddings for.

feature_name: str

Name of feature column to list Image Embeddings for.

Return type:

List[ImageEmbedding]

class datarobot.models.visualai.ImageActivationMap(feature_name, activation_values, image_width, image_height, image_id, overlay_image_id, project_id, model_id, actual_target_value=None, predicted_target_value=None, target_values=None, target_bins=None)

Mark areas of image with weight of impact on training.

This is a technique to display how various areas of the region were used in training, and their effect on predictions. Larger values in activation_values indicates a larger impact.

Attributes:

imageImage

Image object used to create this map.

overlay_imageImage

Image object containing the original image overlaid by the activation heatmap.

feature_namestr

Name of the feature column that contains the value this map is based on.

activation_valuesList[List[int]]

A row-column matrix that contains the activation strengths for image regions. Values are integers in the range [0, 255].

actual_target_valueTargetValue

Actual target value of the dataset row.

predicted_target_valueTargetValue

Predicted target value of the dataset row that contains this image.

target_valuesOptional[List[str]]

For classification projects a list of target values of this project.

target_binsOptional[List[Dict[str, float]]]

For regression projects a list of target bins.

project_idstr

Id of the project this Activation Map belongs to.

model_idstr

Id of the model this Activation Map belongs to.

classmethod compute(project_id, model_id)

Start the computation of activation maps for the given model.

Parameters:

project_idstr

Project to start creation in.

model_idstr

Project’s model to start creation in.

Returns:

str

URL to check for image embeddings progress.

Raises:

datarobot.errors.ClientError

Server rejected creation due to client error. Most likely cause is bad project_id or model_id.

Return type:

str

classmethod models(project_id)

For a given project_id, list all model_id - feature_name pairs with available Image Activation Maps.

Parameters:

project_idstr

Id of the project to list model_id - feature_name pairs with available Image Activation Maps for.

Returns:

list( tuple(model_id, feature_name) )

List of model and feature name pairs.

Return type:

List[Tuple[str, str]]

classmethod list(project_id, model_id, feature_name, offset=None, limit=None)

Return a list of ImageActivationMap objects.

Parameters:

project_idstr

Project that contains the images.

model_idstr

Model that contains the images.

feature_namestr

Name of feature column that contains images.

offsetOptional[int]

Number of images to be skipped.

limitOptional[int]

Number of images to be returned.

Return type:

List[ImageActivationMap]

class datarobot.models.visualai.ImageAugmentationOptions(id, name, project_id, min_transformation_probability, current_transformation_probability, max_transformation_probability, min_number_of_new_images, current_number_of_new_images, max_number_of_new_images, transformations=None)

A List of all supported Image Augmentation Transformations for a project. Includes additional information about minimum, maximum, and default values for a transformation.

Attributes:

name: string

The name of the augmentation list

project_id: string

The project containing the image data to be augmented

min_transformation_probability: float

The minimum allowed value for transformation probability.

current_transformation_probability: float

Default setting for probability that each transformation will be applied to an image.

max_transformation_probability: float

The maximum allowed value for transformation probability.

min_number_of_new_images: int

The minimum allowed number of new rows to add for each existing row

current_number_of_new_images: int

The default number of new rows to add for each existing row

max_number_of_new_images: int

The maximum allowed number of new rows to add for each existing row

transformations: list[dict]

List of transformations to possibly apply to each image

classmethod get(project_id)

Returns a list of all supported transformations for the given project

Parameters:

project_id (str) – sting The id of the project for which to return the list of supported transformations.

Return type:

ImageAugmentationOptions

Returns:

ImageAugmentationOptions

A list containing all the supported transformations for the project.

class datarobot.models.visualai.ImageAugmentationList(id, name, project_id, feature_name=None, in_use=False, initial_list=False, transformation_probability=0.0, number_of_new_images=1, transformations=None, samples_id=None)

A List of Image Augmentation Transformations

Attributes:

name: string

The name of the augmentation list

project_id: string

The project containing the image data to be augmented

feature_name: string (optional)

name of the feature that the augmentation list is associated with

in_use: boolean

Whether this is the list that will passed in to every blueprint during blueprint generation before autopilot

initial_list: boolean

True if this is the list to be used during training to produce augmentations

transformation_probability: float

Probability that each transformation will be applied to an image. Value should be between 0.01 - 1.0.

number_of_new_images: int

Number of new rows to add for each existing row

transformations: array

List of transformations to possibly apply to each image

samples_id: str

Id of last image augmentation sample generated for image augmentation list.

classmethod create(name, project_id, feature_name=None, initial_list=False, transformation_probability=0.0, number_of_new_images=1, transformations=None, samples_id=None)

create a new image augmentation list

Return type:

ImageAugmentationList

classmethod list(project_id, feature_name=None)

List Image Augmentation Lists present in a project.

Parameters:

project_idstr

Project Id to retrieve augmentation lists for.

feature_nameOptional[str]

If passed, the response will only include Image Augmentation Lists active for the provided feature name.

Returns:

list[ImageAugmentationList]

Return type:

List[ImageAugmentationList]

update(name=None, feature_name=None, initial_list=None, transformation_probability=None, number_of_new_images=None, transformations=None)

Update one or multiple attributes of the Image Augmentation List in the DataRobot backend as well on this object.

Parameters:

nameOptional[str]

New name of the feature list.

feature_nameOptional[str]

The new feature name for which the Image Augmentation List is effective.

initial_listOptional[bool]

New flag that indicates whether this list will be used during Autopilot to perform image augmentation.

transformation_probabilityOptional[float]

New probability that each enabled transformation will be applied to an image. This does not apply to Horizontal or Vertical Flip, which are always set to 50%.

number_of_new_imagesOptional[int]

New number of new rows to add for each existing row, updating the existing augmentation list.

transformationsOptional[list]

New list of Transformations to possibly apply to each image.

Returns:

ImageAugmentationList

Reference to self. The passed values will be updated in place.

Return type:

ImageAugmentationList

retrieve_samples()

Lists already computed image augmentation sample for image augmentation list. Returns samples only if they have been already computed. It does not initialize computation.

Returns:

List of class ImageAugmentationSample

Return type:

List[ImageAugmentationSample]

compute_samples(max_wait=600)

Initializes computation and retrieves list of image augmentation samples for image augmentation list. If samples exited prior to this call method, this will compute fresh samples and return latest version of samples.

Returns:

List of class ImageAugmentationSample

Return type:

List[ImageAugmentationSample]

class datarobot.models.visualai.ImageAugmentationSample(image_id, project_id, height, width, original_image_id=None, sample_id=None)

A preview of the type of images that augmentations will create during training.

Attributes:

sample_idObjectId

The id of the augmentation sample, used to group related images together

image_idObjectId

A reference to the Image which can be used to retrieve the image binary

project_idObjectId

A reference to the project containing the image

original_image_idObjectId

A reference to the original image that generated this image in the case of an augmented image. If this is None it signifies this is an original image

heightint

Image height in pixels

widthint

Image width in pixels

classmethod list(auglist_id=None)

Return a list of ImageAugmentationSample objects.

Parameters:

auglist_id: str

ID for augmentation list to retrieve samples for

Returns:

List of class ImageAugmentationSample

Return type:

List[ImageAugmentationSample]