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Minimal Working Example

This is a quick guide for creating and running a simple pipeline to extract missing, outgoing, and incoming call features for 24 hr (00:00:00 to 23:59:59) and night (00:00:00 to 05:59:59) time segments of every day of data of one participant that was monitored on the US East coast with an Android smartphone.

Install RAPIDS and make sure your conda environment is active (see Installation)
Download this CSV file and save it as data/external/aware_csv/calls.csv

Make the changes listed below for the corresponding Configuration step (we provide an example of what the relevant sections in your config.yml will look like after you are done)

??? info "Required configuration changes (click to expand)" 1. Supported data streams.

     Based on the docs, we decided to use the `aware_csv` data stream because we are processing aware data saved in a CSV file. We will use this label in a later step; there's no need to type it or save it anywhere yet.

 3. **Create your [participants file](../../setup/configuration#participant-files).**

     Since we are processing data from a single participant, you only need to create a single participant file called `p01.yaml` in `data/external/participant_files`. This participant file only has a `PHONE` section because this hypothetical participant was only monitored with a smartphone. Note that for a real analysis, you can do this [automatically with a CSV file](../../setup/configuration##automatic-creation-of-participant-files)

     1. Add `p01` to `[PIDS]` in `config.yaml`

     1. Create a file in `data/external/participant_files/p01.yaml` with the following content:

         ```yaml
         PHONE:
             DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524] # the participant's AWARE device id
             PLATFORMS: [android] # or ios
             LABEL: MyTestP01 # any string
             START_DATE: 2020-01-01 # this can also be empty
             END_DATE: 2021-01-01 # this can also be empty
         ```

 4. **Select what [time segments](../../setup/configuration#time-segments) you want to extract features on.** 

     1. Set `[TIME_SEGMENTS][FILE]` to `data/external/timesegments_periodic.csv` 

     1. Create a file in `data/external/timesegments_periodic.csv` with the following content

         ```csv
         label,start_time,length,repeats_on,repeats_value
         daily,00:00:00,23H 59M 59S,every_day,0
         night,00:00:00,5H 59M 59S,every_day,0
         ```

 2. **Choose the [timezone of your study](../../setup/configuration#timezone-of-your-study).** 

     We will use the default time zone settings since this example is processing data collected on the US East Coast (`America/New_York`)

     ```yaml
     TIMEZONE: 
         TYPE: SINGLE
         SINGLE:
             TZCODE: America/New_York
     ```

  5. **Modify your [device data stream configuration](../../setup/configuration#data-stream-configuration)**

     1. Set `[PHONE_DATA_STREAMS][USE]` to `aware_csv`. 

     2. We will use the default value for `[PHONE_DATA_STREAMS][aware_csv][FOLDER]` since we already stored the test calls CSV file there.

  6. **Select what [sensors and features](../../setup/configuration#sensor-and-features-to-process) you want to process.** 

     1. Set `[PHONE_CALLS][CONTAINER]` to `calls.csv` in the `config.yaml` file.

     1. Set `[PHONE_CALLS][PROVIDERS][RAPIDS][COMPUTE]` to `True` in the `config.yaml` file.

!!! example "Example of the config.yaml sections after the changes outlined above"

 This will be your `config.yaml` after following the instructions above. Click on the numbered markers to know more.

 ``` { .yaml .annotate } 
 PIDS: [p01] # (1)

 TIMEZONE:
     TYPE: SINGLE # (2)
     SINGLE:
         TZCODE: America/New_York

 # ... other irrelevant sections

 TIME_SEGMENTS: &time_segments
     TYPE: PERIODIC # (3)
     FILE: "data/external/timesegments_periodic.csv" # (4)
     INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE

 PHONE_DATA_STREAMS:
     USE: aware_csv # (5)

     aware_csv:
         FOLDER: data/external/aware_csv # (6)

 # ... other irrelevant sections

 ############## PHONE ###########################################################
 ################################################################################

 # ... other irrelevant sections

 # Communication call features config, TYPES and FEATURES keys need to match
 PHONE_CALLS:
     CONTAINER: calls.csv  # (7) 
     PROVIDERS:
         RAPIDS:
             COMPUTE: True # (8)
             CALL_TYPES: ...
 ```

 1. We added `p01` to PIDS after creating the participant file:
     ```bash
     data/external/participant_files/p01.yaml
     ```

     With the following content:
     ```yaml
     PHONE:
         DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524] # the participant's AWARE device id
         PLATFORMS: [android] # or ios
         LABEL: MyTestP01 # any string
         START_DATE: 2020-01-01 # this can also be empty
         END_DATE: 2021-01-01 # this can also be empty
     ```

 2. We use the default `SINGLE` time zone.

 3. We use the default `PERIODIC` time segment `[TYPE]`

 4. We created this time segments file with these lines:

     ```csv
     label,start_time,length,repeats_on,repeats_value
     daily,00:00:00,23H 59M 59S,every_day,0
     night,001:00:00,5H 59M 59S,every_day,0
     ```

 5. We set `[USE]` to `aware_device` to tell RAPIDS to process sensor data collected with the AWARE Framework stored in CSV files.

 6. We used the default `[FOLDER]` for `awre_csv` since we already stored our test `calls.csv` file there

 7. We changed `[CONTAINER]` to `calls.csv` to process our test call data.

 8. We flipped `[COMPUTE]` to `True` to extract call behavioral features using the `RAPIDS` feature provider.

Run RAPIDS
```
./rapids -j1
```

The call features for daily and morning time segments will be in

data/processed/features/all_participants/all_sensor_features.csv

6.8 KiB Raw Blame History

Minimal Working Example

6.8 KiB

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