154 lines
6.8 KiB
Markdown
154 lines
6.8 KiB
Markdown
Minimal Working Example
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=======================
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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.
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1. Install RAPIDS and make sure your `conda` environment is active (see [Installation](../../setup/installation))
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3. Download this [CSV file](../img/calls.csv) and save it as `data/external/aware_csv/calls.csv`
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2. Make the changes listed below for the corresponding [Configuration](../../setup/configuration) step (we provide an example of what the relevant sections in your `config.yml` will look like after you are done)
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??? info "Required configuration changes (*click to expand*)"
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1. **Supported [data streams](../../setup/configuration#supported-data-streams).**
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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.
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3. **Create your [participants file](../../setup/configuration#participant-files).**
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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)
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1. Add `p01` to `[PIDS]` in `config.yaml`
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1. Create a file in `data/external/participant_files/p01.yaml` with the following content:
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```yaml
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PHONE:
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DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524] # the participant's AWARE device id
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PLATFORMS: [android] # or ios
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LABEL: MyTestP01 # any string
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START_DATE: 2020-01-01 # this can also be empty
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END_DATE: 2021-01-01 # this can also be empty
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```
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4. **Select what [time segments](../../setup/configuration#time-segments) you want to extract features on.**
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1. Set `[TIME_SEGMENTS][FILE]` to `data/external/timesegments_periodic.csv`
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1. Create a file in `data/external/timesegments_periodic.csv` with the following content
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```csv
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label,start_time,length,repeats_on,repeats_value
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daily,00:00:00,23H 59M 59S,every_day,0
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night,00:00:00,5H 59M 59S,every_day,0
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```
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2. **Choose the [timezone of your study](../../setup/configuration#timezone-of-your-study).**
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We will use the default time zone settings since this example is processing data collected on the US East Coast (`America/New_York`)
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```yaml
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TIMEZONE:
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TYPE: SINGLE
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SINGLE:
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TZCODE: America/New_York
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```
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5. **Modify your [device data stream configuration](../../setup/configuration#data-stream-configuration)**
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1. Set `[PHONE_DATA_STREAMS][USE]` to `aware_csv`.
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2. We will use the default value for `[PHONE_DATA_STREAMS][aware_csv][FOLDER]` since we already stored the test calls CSV file there.
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6. **Select what [sensors and features](../../setup/configuration#sensor-and-features-to-process) you want to process.**
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1. Set `[PHONE_CALLS][CONTAINER]` to `calls.csv` in the `config.yaml` file.
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1. Set `[PHONE_CALLS][PROVIDERS][RAPIDS][COMPUTE]` to `True` in the `config.yaml` file.
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!!! example "Example of the `config.yaml` sections after the changes outlined above"
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This will be your `config.yaml` after following the instructions above. Click on the numbered markers to know more.
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``` { .yaml .annotate }
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PIDS: [p01] # (1)
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TIMEZONE:
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TYPE: SINGLE # (2)
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SINGLE:
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TZCODE: America/New_York
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# ... other irrelevant sections
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TIME_SEGMENTS: &time_segments
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TYPE: PERIODIC # (3)
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FILE: "data/external/timesegments_periodic.csv" # (4)
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INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE
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PHONE_DATA_STREAMS:
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USE: aware_csv # (5)
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aware_csv:
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FOLDER: data/external/aware_csv # (6)
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# ... other irrelevant sections
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############## PHONE ###########################################################
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################################################################################
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# ... other irrelevant sections
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# Communication call features config, TYPES and FEATURES keys need to match
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PHONE_CALLS:
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CONTAINER: calls.csv # (7)
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PROVIDERS:
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RAPIDS:
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COMPUTE: True # (8)
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CALL_TYPES: ...
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```
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1. We added `p01` to PIDS after creating the participant file:
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```bash
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data/external/participant_files/p01.yaml
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```
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With the following content:
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```yaml
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PHONE:
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DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524] # the participant's AWARE device id
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PLATFORMS: [android] # or ios
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LABEL: MyTestP01 # any string
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START_DATE: 2020-01-01 # this can also be empty
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END_DATE: 2021-01-01 # this can also be empty
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```
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2. We use the default `SINGLE` time zone.
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3. We use the default `PERIODIC` time segment `[TYPE]`
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4. We created this time segments file with these lines:
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```csv
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label,start_time,length,repeats_on,repeats_value
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daily,00:00:00,23H 59M 59S,every_day,0
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night,001:00:00,5H 59M 59S,every_day,0
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```
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5. We set `[USE]` to `aware_device` to tell RAPIDS to process sensor data collected with the AWARE Framework stored in CSV files.
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6. We used the default `[FOLDER]` for `awre_csv` since we already stored our test `calls.csv` file there
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7. We changed `[CONTAINER]` to `calls.csv` to process our test call data.
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8. We flipped `[COMPUTE]` to `True` to extract call behavioral features using the `RAPIDS` feature provider.
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3. Run RAPIDS
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```bash
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./rapids -j1
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```
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4. The call features for daily and morning time segments will be in
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```
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data/processed/features/all_participants/all_sensor_features.csv
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```
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