Transforming clinical data

Transformation blocks take raw data from your organizational datasets and convert the data into a different dataset or files that can be loaded in an Edge Impulse project. You can use transformation blocks to only include certain parts of individual data files, calculate long-running features like a running mean or derivatives, or efficiently generate features with different window lengths. Transformation blocks can be written in any language, and run on the Edge Impulse infrastructure.

In this tutorial we build a Python-based transformation block that loads Parquet files, calculates features from the Parquet file, and then writes a new file back to your dataset. If you haven't done so, go through synchronizing clinical data with a bucket first.

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1. Prerequisites

You'll need:

• The Edge Impulse CLI.

  • If you receive any warnings that's fine. Run edge-impulse-blocks afterwards to verify that the CLI was installed correctly.

• The gestures.parquet file which you can use to test the transformation block. This contains some data from the Continuous gestures dataset in Parquet format.

Transformation blocks use Docker containers, a virtualization technique that lets developers package up an application with all dependencies in a single package. If you want to test your blocks locally you'll also need (this is not a requirement):

• Docker desktop installed on your machine.

1.1 - Parquet schema

This is the Parquet schema for the gestures.parquet file which we'll transform:

message root {
  required binary sampleName (UTF8);
  required int64 timestamp (TIMESTAMP_MILLIS);
  required int64 added (TIMESTAMP_MILLIS);
  required boolean signatureValid;
  required binary device (UTF8);
  required binary label (UTF8);
  required float accX;
  required float accY;
  required float accZ;
}

2. Building your first transformation block

To build a transformation block open a command prompt or terminal window, create a new folder, and run:

$ edge-impulse-blocks init

This will prompt you to log in, and enter the details for your block. E.g.:

Edge Impulse Blocks v1.9.0
? What is your user name or e-mail address (edgeimpulse.com)? [e-mail]
? What is your password? [hidden]
Attaching block to organization 'Demo org Inc.'
? Choose a type of block Transformation block
? Choose an option Create a new block
? Enter the name of your block Demo dataset transformation
? Enter the description of your block Reads a Parquet file, extracts features, and writes the block back to the dataset
Creating block with config: {
  name: 'Demo dataset transformation',
  type: 'transform',
  description: 'Reads a Parquet file and splits it up in labeled data',
  organizationId: 34
}
Your new block 'Demo dataset transformation' has been created in '~/repos/tutorial-processing-block'.
When you have finished building your transformation block, run "edge-impulse-blocks push" to update the block in Edge Impulse.

Then, create the following files in this directory:

2.1 - Dockerfile

We're building a Python based transformation block. The Dockerfile describes our base image (Python 3.7.5), our dependencies (in requirements.txt) and which script to run (transform.py).

FROM python:3.7.5-stretch

WORKDIR /app

# Python dependencies
COPY requirements.txt ./
RUN pip3 --no-cache-dir install -r requirements.txt

COPY . ./

ENTRYPOINT [ "python3",  "transform.py" ]

Note: Do not use a WORKDIR under /home! The /home path will be mounted in by Edge Impulse, making your files inaccessible.

ENTRYPOINT vs RUN / CMD

If you use a different programming language, make sure to use ENTRYPOINT to specify the application to execute, rather than RUN or CMD.

2.2 - requirements.txt

This file describes the dependencies for the block. We'll be using pandas and pyarrow to parse the Parquet file, and numpy to do some calculations.

numpy==1.16.4
pandas==0.23.4
pyarrow==0.16.0

2.3 - transform.py

This file includes the actual application. Transformation blocks are invoked with three parameters (as command line arguments):

• --in-file or --in-directory - A file (if the block operates on a file), or a directory (if the block operates on a data item) from the organizational dataset. In this case the gestures.parquet file.

• --out-directory - Directory to write files to.

• --hmac-key - You can use this HMAC key to sign the output files. This is not used in this tutorial.

• --metadata - Key/value pairs containing the metadata for the data item, plus additional metadata about the data item in the dataItemInfo key. E.g.: { "subject": "AAA001", "ei_check": "1", "dataItemInfo": { "id": 101, "dataset": "Human Activity 2022", "bucketName": "edge-impulse-tutorial", "bucketPath": "janjongboom/human_activity/AAA001/", "created": "2022-03-07T09:20:59.772Z", "totalFileCount": 14, "totalFileSize": 6347421 } }

Add the following content. This takes in the Parquet file, groups data by their label, and then calculates the RMS over the X, Y and Z axes of the accelerometer.

import pyarrow.parquet as pq
import numpy as np
import math, os, sys, argparse, json, hmac, hashlib, time
import pandas as pd

# these are the three arguments that we get in
parser = argparse.ArgumentParser(description='Organization transformation block')
parser.add_argument('--in-file', type=str, required=True)
parser.add_argument('--out-directory', type=str, required=True)

args, unknown = parser.parse_known_args()

# verify that the input file exists and create the output directory if needed
if not os.path.exists(args.in_file):
    print('--in-file argument', args.in_file, 'does not exist', flush=True)
    exit(1)

if not os.path.exists(args.out_directory):
    os.makedirs(args.out_directory)

# load and parse the input file
print('Loading parquet file', args.in_file, flush=True)
table = pq.read_table(args.in_file)
data = table.to_pandas()

features = []

# we group by label and then extract some metrics
for label in data.label.unique():
    data_per_label = data[data.label == label]

    # calculate the RMS per axis
    features.append({
        'label': label,
        'rmsX': np.sqrt(np.mean(data_per_label.accX**2)),
        'rmsY': np.sqrt(np.mean(data_per_label.accY**2)),
        'rmsZ': np.sqrt(np.mean(data_per_label.accZ**2))
    })

# and store as new file in the output directory
out_file = os.path.join(args.out_directory, os.path.splitext(os.path.basename(args.in_file))[0] + '_features.parquet')
pd.DataFrame(features).to_parquet(out_file)

print('Written features file', out_file, flush=True)

2.4 - Building and testing the container

On your local machine

To test the transformation block locally, if you have Python and all dependencies installed, just run:

$ python3 transform.py --in-file gestures.parquet --out-directory out/

Docker

You can also build the container locally via Docker, and test the block. The added benefit is that you don't need any dependencies installed on your local computer, and can thus test that you've included everything that's needed for the block. This requires Docker desktop to be installed.

To build the container and test the block, open a command prompt or terminal window and navigate to the source directory. First, build the container:

$ docker build -t test-org-transform-parquet-dataset .

Then, run the container (make sure gestures.parquet is in the same directory):

$ docker run --rm -v $PWD:/data test-org-transform-parquet-dataset --in-file /data/gestures.parquet --out-directory /data/out

Seeing the output

This process has generated a new Parquet file in the out/ directory containing the RMS of the X, Y and Z axes. If you inspect the content of the file (e.g. using parquet-tools) you'll see the output:

$ parquet-tools head -n5 out/gestures_features.parquet 
label = wave
rmsX = 11.424144744873047
rmsY = 4.73303747177124
rmsZ = 2.944265842437744

label = updown
rmsX = 3.899503231048584
rmsY = 3.9587674140930176
rmsZ = 10.34404468536377

label = circle
rmsX = 6.263721942901611
rmsY = 7.0987162590026855
rmsZ = 6.159618854522705

label = idle
rmsX = 3.714001178741455
rmsY = 3.4940428733825684
rmsZ = 8.6710205078125

label = snake
rmsX = 1.282995581626892
rmsY = 1.8830623626708984
rmsZ = 9.597149848937988

Success!

3. Pushing the transformation block to Edge Impulse

With the block ready we can push it to your organization. Open a command prompt or terminal window, navigate to the folder you created earlier, and run:

$ edge-impulse-blocks push

This packages up your folder, sends it to Edge Impulse where it'll be built, and finally is added to your organization.

Edge Impulse Blocks v1.9.0
Archiving 'tutorial-processing-block'...
Archiving 'tutorial-processing-block' OK (2 KB) /var/folders/3r/fds0qzv914ng4t17nhh5xs5c0000gn/T/ei-transform-block-7812190951a6038c2f442ca02d428c59.tar.gz

Uploading block 'Demo dataset transformation' to organization 'Demo org Inc.'...
Uploading block 'Demo dataset transformation' to organization 'Demo org Inc.' OK

Building transformation block 'Demo dataset transformation'...
Job started
...
Building transformation block 'Demo dataset transformation' OK

Your block has been updated, go to https://studio.edgeimpulse.com/organization/34/data to run a new transformation

The transformation block is now available in Edge Impulse under Data transformation > Transformation blocks.

The transformation block in Edge Impulse

If you make any changes to the block, just re-run edge-impulse-blocks push and the block will be updated.

4. Uploading gestures.parquet to Edge Impulse

Next, upload the gestures.parquet file, by going to Data > Add data... > Add data item, setting name as 'Gestures', dataset to 'Transform tutorial', and selecting the Parquet file.

This makes the gestures.parquet file available from the Data page.

5. Starting the transformation

With the Parquet file in Edge Impulse and the transformation block configured you can now create a new job. Go to Data, and select the Parquet file by setting the filter to dataset = 'Transform tutorial'.

Selecting the transform tutorial dataset

Click the checkbox next to the data item, and select Transform selected (1 file). On the 'Create transformation job' page select 'Import data into Dataset'. Under 'output dataset', select 'Same dataset as source', and under 'Transformation block' select the new transformation block.

Configuring the transformation job

Click Start transformation job to start the job. This pulls the data in, starts a transformation job and finally uploads the data back to your dataset. If you have multiple files selected the transformations will also run in parallel.

Dataset transformation running

You can now find the transformed file back in your dataset:

Dataset transformation successful

6. Next steps

Transformation blocks are a powerful feature which let you set up a data pipeline to turn raw data into actionable machine learning features. It also gives you a reproducible way of transforming many files at once, and is programmable through the Edge Impulse API so you can automatically convert new incoming data. If you're interested in transformation blocks or any of the other enterprise features, let us know!

🚀

Appendix: Advanced features

Updating metadata from a transformation block

You can update the metadata of blocks directly from a transformation block by creating a ei-metadata.json file in the output directory. The metadata is then applied to the new data item automatically when the transform job finishes. The ei-metadata.json file has the following structure:

{
    "version": 1,
    "action": "add",
    "metadata": {
        "some-key": "some-value"
    }
}

Some notes:

• If action is set to add the metadata keys are added to the data item. If action is set to replace all existing metadata keys are removed.

Environmental variables

Transformation blocks get access to the following environmental variables, which let you authenticate with the Edge Impulse API. This way you don't have to inject these credentials into the block. The variables are:

• EI_API_KEY - an API key with 'member' privileges for the organization.

• EI_ORGANIZATION_ID - the organization ID that the block runs in.

• EI_API_ENDPOINT - the API endpoint (default: https://studio.edgeimpulse.com/v1).

Custom parameters

You can specify custom arguments or parameters to your block by adding a parameters.json file in the root of your block directory. This file describes all arguments for your training pipeline, and is used to render custom UI elements for each parameter. For example, this parameters file:

[{
    "name": "Bucket",
    "type": "bucket",
    "param": "bucket-name",
    "value": "",
    "help": "The bucket where you're hosting all data"
},
{
    "name": "Bucket prefix",
    "value": "my-test-prefix/",
    "type": "string",
    "param": "bucket-prefix",
    "help": "The prefix in the bucket, where you're hosting the data"
}]

Renders the following UI when you run the transformation block:

Running a transformation block with custom parameters

And the options are passed in as command line arguments to your block:

--bucket-name "ei-data-dev" --bucket-prefix "my-test-prefix/"

For more information, and all options see Adding parameters to custom blocks.