No description, website, or topics provided.
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
bin
sample-input/geojson
.dockerignore
Dockerfile
README.md
rf-pool-classifier-definition.json

README.md

rf-pool-classifier

A GBDX task that trains a random forest classifier to classify polygons of arbitrary geometry into those that contain swimming pools and those that do not.

Run

Here we run a sample execution of the rf-pool-classifier task. Sample inputs are provided on S3 in the locations specified below.

  1. In a Python terminal create a GBDX interface and specify the task input location:

    from gbdxtools import Interface
    from os.path import join
    import uuid
    
    gbdx = Interface()
    
    input_location = 's3://gbd-customer-data/32cbab7a-4307-40c8-bb31-e2de32f940c2/platform-stories/rf-pool-classifier/'
  2. Create a task instance and set the required inputs:

    rf_task = gbdx.Task('rf-pool-classifier')
    rf_task.inputs.image = join(input_location, 'image')
    rf_task.inputs.geojson = join(input_location, 'geojson')
    rf_task.inputs.n_estimators = "1000"
  3. Create a single-task workflow object and define where the output data should be saved.

    workflow = gbdx.Workflow([rf_task])
    random_str = str(uuid.uuid4())
    output_location = join('platform-stories/trial-runs', random_str)
    
    workflow.savedata(rf_task.outputs.trained_classifier, output_location)
  4. Execute the workflow and monitor its status as follows:

    workflow.execute()
    workflow.status

Input Ports

GBDX input ports can only be of "Directory" or "String" type. Booleans, integers and floats are passed to the task as strings, e.g., "True", "10", "0.001".

Name Type Description Required
image directory Contains the image strip where the polygons are found. True
geojson directory Contains a geojson with labeled polygons. Each polygon has the properties feature_id, image_id, and class_name (either 'No swimming pool' or 'Swimming pool') True
n_estimators string Number of trees to use in the random forest classifier. Defaults to 100. False

Output Ports

Name Type Description
trained_classifier directory Contains the file 'classifier.pkl' which is the trained random forest classifier.

Development

Build the Docker Image

You need to install Docker.

Clone the repository:

git clone https://github.com/platformstories/rf-pool-classifier

Then

cd rf-pool-classifier
docker build -t rf-pool-classifier .

Try out locally

Create a container in interactive mode and mount the sample input under /mnt/work/input/:

docker run --rm -v full/path/to/sample-input:/mnt/work/input -it rf-pool-classifier

Then, within the container:

python /rf-pool-classifier.py

Docker Hub

Login to Docker Hub:

docker login

Tag your image using your username and push it to DockerHub:

docker tag rf-pool-classifier yourusername/rf-pool-classifier
docker push yourusername/rf-pool-classifier

The image name should be the same as the image name under containerDescriptors in rf-pool-classifier.json.

Alternatively, you can link this repository to a Docker automated build. Every time you push a change to the repository, the Docker image gets automatically updated.

Register on GBDX

In a Python terminal:

from gbdxtools import Interface
gbdx=Interface()
gbdx.task_registry.register(json_filename="rf-pool-classifier-definition.json")

Note: If you change the task image, you need to reregister the task with a higher version number in order for the new image to take effect. Keep this in mind especially if you use Docker automated build.