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Add new bluetooth doryab features and deprecate rapids provider

pull/107/head
JulioV 2020-12-12 17:01:46 -05:00
parent 266dd28d02
commit 5bd1bfe856
3 changed files with 163 additions and 63 deletions
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17
config.yaml
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@ -128,11 +128,20 @@ PHONE_BLUETOOTH:
SRC_FOLDER: "rapids" # inside src/features/phone_bluetooth
SRC_LANGUAGE: "r"
DORYAB:
COMPUTE: False
COMPUTE: FALSE
FEATURES:
ALL: ["countscans", "uniquedevices", "countscansmostuniquedevice", "countscansleastuniquedevice", "meanscans", "stdscans"]
OWN: ["countscans", "uniquedevices", "countscansmostuniquedevice", "countscansleastuniquedevice", "meanscans", "stdscans"]
OTHERS: ["countscans", "uniquedevices", "countscansmostuniquedevice", "countscansleastuniquedevice", "meanscans", "stdscans"]
ALL:
DEVICES: ["countscans", "uniquedevices", "meanscans", "stdscans"]
SCANS_MOST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"]
SCANS_LEAST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"]
OWN:
DEVICES: ["countscans", "uniquedevices", "meanscans", "stdscans"]
SCANS_MOST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"]
SCANS_LEAST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"]
OTHERS:
DEVICES: ["countscans", "uniquedevices", "meanscans", "stdscans"]
SCANS_MOST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"]
SCANS_LEAST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"]
SRC_FOLDER: "doryab" # inside src/features/phone_bluetooth
SRC_LANGUAGE: "python"

135
docs/features/phone-bluetooth.md
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@ -8,6 +8,9 @@ Sensor parameters description for `[PHONE_BLUETOOTH]`:
## RAPIDS provider
!!! warning
The features of this provider are deprecated in favor of `DORYAB` provider (see below).
!!! info "Available time segments and platforms"
- Available for all time segments
- Available for Android only
@ -33,14 +36,15 @@ Features description for `[PHONE_BLUETOOTH][PROVIDERS][RAPIDS]`:
|Feature |Units |Description|
|-------------------------- |---------- |---------------------------|
| countscans | devices | Number of scanned devices during a `time_segment`, a device can be detected multiple times over time and these appearances are counted separately |
| uniquedevices | devices | Number of unique devices during a `time_segment` as identified by their hardware (`bt_address`) address |
| countscansmostuniquedevice | scans | Number of scans of the most scanned device during a `time_segment` across the whole monitoring period |
| {--countscans--} | devices | Number of scanned devices during a time segment, a device can be detected multiple times over time and these appearances are counted separately |
| {--uniquedevices--} | devices | Number of unique devices during a time segment as identified by their hardware (`bt_address`) address |
| {--countscansmostuniquedevice--} | scans | Number of scans of the most sensed device within each time segment instance |
!!! note "Assumptions/Observations"
NA
- From `v0.2.0` `countscans`, `uniquedevices`, `countscansmostuniquedevice` were deprecated because they overlap with the respective features for `ALL` devices of the `PHONE_BLUETOOTH` `DORYAB` provider
## DORYAB provider
This provider is adapted from the work by [Doryab et al](../../citation#doryab-bluetooth).
!!! info "Available time segments and platforms"
- Available for all time segments
@ -65,51 +69,92 @@ Parameters description for `[PHONE_BLUETOOTH][PROVIDERS][DORYAB]`:
Features description for `[PHONE_BLUETOOTH][PROVIDERS][DORYAB]`:
|Feature |Units |Description|
|Feature                                                                                   |Units |Description|
|-------------------------- |---------- |---------------------------|
| countscans | scans | Number of scans (rows) from the devices sensed during a time segment instance. The more scans a bluetooth device has the longer it remained within range of the participant's phone |
| uniquedevices | devices | Number of unique bluetooth devices sensed during a time segment instance as identified by their hardware addresses (`bt_address`) |
| countscansmostuniquedevice | scans | Number of scans of the most sensed device within each time segment instance|
| countscansleastuniquedevice| scans| Number of scans of the least sensed device within each time segment instance |
| meanscans | scans| Mean of the scans of every sensed device within each time segment instance|
| stdscans | scans| Standard deviation of the scans of every sensed device within each time segment instance|
| countscans{==most==}frequentdevice{==within==}segments | scans | Number of scans of the **most** sensed device **within** each time segment instance|
| countscans{==least==}frequentdevice{==within==}segments| scans| Number of scans of the **least** sensed device **within** each time segment instance |
| countscans{==most==}frequentdevice{==across==}segments | scans | Number of scans of the **most** sensed device **across** time segment instances of the same type|
| countscans{==least==}frequentdevice{==across==}segments| scans| Number of scans of the **least** sensed device **across** time segment instances of the same type per device|
| countscans{==most==}frequentdevice{==acrossdataset==} | scans | Number of scans of the **most** sensed device **across** the entire dataset of every participant|
| countscans{==least==}frequentdevice{==acrossdataset==}| scans| Number of scans of the **least** sensed device **across** the entire dataset of every participant |
!!! note "Assumptions/Observations"
- This provider is adapted from the work by [Doryab et al](../../citation#doryab-bluetooth). Devices are clasified as belonging to the participant (`own`) or to other people (`others`) using k-means based on the number of times and the number of days each device was detected across each participant's dataset.
- If ownership cannot be computed because all devices were detected on only one day, they are all considered as `other`. Thus `all` and `other` features will be equal.
- These features are computed for devices detected within each time segment instance. For example, let's say that we logged the following devices on three different time segment instances (days) for `p01`:
```csv
local_date bt_address
2016-11-29 55C836F5-487E-405F-8E28-21DBD40FA4FF
2016-11-29 55C836F5-487E-405F-8E28-21DBD40FA4FF
2016-11-29 55C836F5-487E-405F-8E28-21DBD40FA4FF
2016-11-29 48872A52-68DE-420D-98DA-73339A1C4685
2016-11-29 48872A52-68DE-420D-98DA-73339A1C4685
2016-11-30 55C836F5-487E-405F-8E28-21DBD40FA4FF
2016-11-30 55C836F5-487E-405F-8E28-21DBD40FA4FF
2016-11-30 48872A52-68DE-420D-98DA-73339A1C4685
2017-05-07 5C5A9C41-2F68-4CEB-96D0-77DE3729B729
2017-05-07 25262DC7-780C-4AD5-AD3A-D9776AEF7FC1
2017-05-07 5B1E6981-2E50-4D9A-99D8-67AED430C5A8
2017-05-07 6C444841-FE64-4375-BC3F-FA410CDC0AC7
2017-05-07 5B1E6981-2E50-4D9A-99D8-67AED430C5A8
2017-05-07 4DC7A22D-9F1F-4DEF-8576-086910AABCB5
```
- For each device we compute `days_scanned` (the number of days on which each device was detected), `scans` (the number of times each device was detected), `scans_per_day` that's equal to `scans/days_scanned`, and whether a devices is labelled as `own` or `other` (note the last device is labelled as a `own` device because it was detected 6 times over two time segment instances):
```csv
bt_address days_scanned scans scans_per_day own_device
25262DC7-780C-4AD5-AD3A-D9776AEF7FC1 1 1 1.0 0
4DC7A22D-9F1F-4DEF-8576-086910AABCB5 1 1 1.0 0
5C5A9C41-2F68-4CEB-96D0-77DE3729B729 1 1 1.0 0
6C444841-FE64-4375-BC3F-FA410CDC0AC7 1 1 1.0 0
5B1E6981-2E50-4D9A-99D8-67AED430C5A8 1 2 2.0 0
48872A52-68DE-420D-98DA-73339A1C4685 2 3 1.5 0
55C836F5-487E-405F-8E28-21DBD40FA4FF 2 5 2.5 1
```
- These are the metrics for each time instance (day) for `own` and `other` devices (we ignore `all` for brevity). The only `own` device (`55C836F5-487E-405F-8E28-21DBD40FA4FF`) was detected on the first two days, 3 and 2 times respectively, the `other` devices where detected on all three days. On the last day (`2017-05-07`) there were 6 scans from 5 unique devices, the most frequent device for that day was `5B1E6981-2E50-4D9A-99D8-67AED430C5A8` with 2 scans, and the mean number of scans among all devices was 1.2 (`[1 + 1 + 1 + 1 + 2] / 5`)
```csv
local_segment countscansown uniquedevicesown countscansmostuniquedeviceown countscansleastuniquedeviceown meanscansown stdscansown countscansothers uniquedevicesothers countscansmostuniquedeviceothers countscansleastuniquedeviceothers meanscansothers stdscansothers
2016-11-29 3.0 1.0 3.0 3.0 3.0 NaN 2 1 2 2 2.0 NaN
2016-11-30 2.0 1.0 2.0 2.0 2.0 NaN 1 1 1 1 1.0 NaN
2017-05-07 NaN NaN NaN NaN NaN NaN 6 5 2 1 1.2 0.447214
```
- Devices are classified as belonging to the participant (`own`) or to other people (`others`) using k-means based on the number of times and the number of days each device was detected across each participant's dataset. See [Doryab et al](../../citation#doryab-bluetooth) for more details.
- If ownership cannot be computed because all devices were detected on only one day, they are all considered as `other`. Thus `all` and `other` features will be equal. The likelihood of this scenario decreases the more days of data you have.
- The most and least frequent devices will be the same across time segment instances and across the entire dataset when every time segment instance covers every hour of a dataset. For example, daily segments (00:00 to 23:59) fall in this category but morning segments (06:00am to 11:59am) or periodic 30-minute segments don't.
??? info "Example"
??? example "Simplified raw bluetooth data"
The following is a simplified example with bluetooth data from three days and two time segments: morning and afternoon. There are two `own` devices: `5C836F5-487E-405F-8E28-21DBD40FA4FF` detected seven times across two days and `499A1EAF-DDF1-4657-986C-EA5032104448` detected eight times on a single day.
```csv
local_date segment bt_address own_device
2016-11-29 morning 55C836F5-487E-405F-8E28-21DBD40FA4FF 1
2016-11-29 morning 55C836F5-487E-405F-8E28-21DBD40FA4FF 1
2016-11-29 morning 55C836F5-487E-405F-8E28-21DBD40FA4FF 1
2016-11-29 morning 55C836F5-487E-405F-8E28-21DBD40FA4FF 1
2016-11-29 morning 48872A52-68DE-420D-98DA-73339A1C4685 0
2016-11-29 afternoon 55C836F5-487E-405F-8E28-21DBD40FA4FF 1
2016-11-29 afternoon 48872A52-68DE-420D-98DA-73339A1C4685 0
2016-11-30 morning 55C836F5-487E-405F-8E28-21DBD40FA4FF 1
2016-11-30 morning 48872A52-68DE-420D-98DA-73339A1C4685 0
2016-11-30 morning 25262DC7-780C-4AD5-AD3A-D9776AEF7FC1 0
2016-11-30 morning 5B1E6981-2E50-4D9A-99D8-67AED430C5A8 0
2016-11-30 morning 5B1E6981-2E50-4D9A-99D8-67AED430C5A8 0
2016-11-30 afternoon 55C836F5-487E-405F-8E28-21DBD40FA4FF 1
2017-05-07 morning 5C5A9C41-2F68-4CEB-96D0-77DE3729B729 0
2017-05-07 morning 25262DC7-780C-4AD5-AD3A-D9776AEF7FC1 0
2017-05-07 morning 5B1E6981-2E50-4D9A-99D8-67AED430C5A8 0
2017-05-07 morning 6C444841-FE64-4375-BC3F-FA410CDC0AC7 0
2017-05-07 morning 4DC7A22D-9F1F-4DEF-8576-086910AABCB5 0
2017-05-07 afternoon 5B1E6981-2E50-4D9A-99D8-67AED430C5A8 0
2017-05-07 afternoon 499A1EAF-DDF1-4657-986C-EA5032104448 1
2017-05-07 afternoon 499A1EAF-DDF1-4657-986C-EA5032104448 1
2017-05-07 afternoon 499A1EAF-DDF1-4657-986C-EA5032104448 1
2017-05-07 afternoon 499A1EAF-DDF1-4657-986C-EA5032104448 1
2017-05-07 afternoon 499A1EAF-DDF1-4657-986C-EA5032104448 1
2017-05-07 afternoon 499A1EAF-DDF1-4657-986C-EA5032104448 1
2017-05-07 afternoon 499A1EAF-DDF1-4657-986C-EA5032104448 1
2017-05-07 afternoon 499A1EAF-DDF1-4657-986C-EA5032104448 1
```
??? example "The most and least frequent `OTHER` devices (`own_device == 0`) during morning segments"
The most and least frequent `ALL`|`OWN`|`OTHER` devices are computed within each time segment instance, across time segment instances of the same type and across the entire dataset of each person. These are the most and least frequent devices for `OTHER` devices during morning segments.
```csv
most frequent device across 2016-11-29 morning: '48872A52-68DE-420D-98DA-73339A1C4685' (this device is the only one in this instance)
least frequent device across 2016-11-29 morning: '48872A52-68DE-420D-98DA-73339A1C4685' (this device is the only one in this instance)
most frequent device across 2016-11-30 morning: '5B1E6981-2E50-4D9A-99D8-67AED430C5A8'
least frequent device across 2016-11-30 morning: '25262DC7-780C-4AD5-AD3A-D9776AEF7FC1' (when tied, the first occurance is chosen)
most frequent device across 2017-05-07 morning: '25262DC7-780C-4AD5-AD3A-D9776AEF7FC1' (when tied, the first occurance is chosen)
least frequent device across 2017-05-07 morning: '25262DC7-780C-4AD5-AD3A-D9776AEF7FC1' (when tied, the first occurance is chosen)
most frequent across morning segments: '5B1E6981-2E50-4D9A-99D8-67AED430C5A8'
least frequent across morning segments: '6C444841-FE64-4375-BC3F-FA410CDC0AC7' (when tied, the first occurance is chosen)
most frequent across dataset: '499A1EAF-DDF1-4657-986C-EA5032104448' (only taking into account "morning" segments)
least frequent across dataset: '4DC7A22D-9F1F-4DEF-8576-086910AABCB5' (when tied, the first occurance is chosen)
```
??? example "Bluetooth features for `OTHER` devices and morning segments"
For brevity we only show the following features for morning segments:
```yaml
OTHER:
DEVICES: ["countscans", "uniquedevices", "meanscans", "stdscans"]
SCANS_MOST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"]
```
Note that `countscansmostfrequentdeviceacrossdatasetothers` is all `0`s because `499A1EAF-DDF1-4657-986C-EA5032104448` is excluded from the count as is labelled as an `own` device (not `other`).
```csv
local_segment countscansothers uniquedevicesothers meanscansothers stdscansothers countscansmostfrequentdevicewithinsegmentsothers countscansmostfrequentdeviceacrosssegmentsothers countscansmostfrequentdeviceacrossdatasetothers
2016-11-29-morning 1 1 1.000000 NaN 1 0.0 0.0
2016-11-30-morning 4 3 1.333333 0.57735 2 2.0 2.0
2017-05-07-morning 5 5 1.000000 0.00000 1 1.0 1.0
```

74
src/features/phone_bluetooth/doryab/main.py
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@ -2,7 +2,7 @@ import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
def deviceFeatures(devices, ownership, features_to_compute, features):
def deviceFeatures(devices, ownership, common_devices, features_to_compute, features):
if devices.shape[0] == 0:
device_value_counts = pd.DataFrame(columns=["local_segment", "bt_address", "scans"], dtype=int)
else:
@ -12,14 +12,29 @@ def deviceFeatures(devices, ownership, features_to_compute, features):
features = features.join(device_value_counts.groupby("local_segment")["scans"].sum().to_frame("countscans" + ownership), how="outer")
if "uniquedevices" in features_to_compute:
features = features.join(device_value_counts.groupby("local_segment")["bt_address"].nunique().to_frame("uniquedevices" + ownership), how="outer")
if "countscansmostuniquedevice" in features_to_compute:
features = features.join(device_value_counts.groupby("local_segment")["scans"].max().to_frame("countscansmostuniquedevice" + ownership), how="outer")
if "countscansleastuniquedevice" in features_to_compute:
features = features.join(device_value_counts.groupby("local_segment")["scans"].min().to_frame("countscansleastuniquedevice" + ownership), how="outer")
if "meanscans" in features_to_compute:
features = features.join(device_value_counts.groupby("local_segment")["scans"].mean().to_frame("meanscans" + ownership), how="outer")
if "stdscans" in features_to_compute:
features = features.join(device_value_counts.groupby("local_segment")["scans"].std().to_frame("stdscans" + ownership), how="outer")
# Most frequent device within segments, across segments, and across dataset
if "countscansmostfrequentdevicewithinsegments" in features_to_compute:
features = features.join(device_value_counts.groupby("local_segment")["scans"].max().to_frame("countscansmostfrequentdevicewithinsegments" + ownership), how="outer")
if "countscansmostfrequentdeviceacrosssegments" in features_to_compute:
common_device = common_devices['most_segments']
features = features.join(device_value_counts.query("bt_address in @common_device").groupby("local_segment")["scans"].max().to_frame("countscansmostfrequentdeviceacrosssegments" + ownership), how="outer")
if "countscansmostfrequentdeviceacrossdataset" in features_to_compute:
common_device = common_devices['most_dataset']
features = features.join(device_value_counts.query("bt_address in @common_device").groupby("local_segment")["scans"].max().to_frame("countscansmostfrequentdeviceacrossdataset" + ownership), how="outer")
# Least frequent device within segments, across segments, and across dataset
if "countscansleastfrequentdevicewithinsegments" in features_to_compute:
features = features.join(device_value_counts.groupby("local_segment")["scans"].min().to_frame("countscansleastfrequentdevicewithinsegments" + ownership), how="outer")
if "countscansleastfrequentdeviceacrosssegments" in features_to_compute:
common_device = common_devices['least_segments']
features = features.join(device_value_counts.query("bt_address in @common_device").groupby("local_segment")["scans"].min().to_frame("countscansleastfrequentdeviceacrosssegments" + ownership), how="outer")
if "countscansleastfrequentdeviceacrossdataset" in features_to_compute:
common_device = common_devices['least_dataset']
features = features.join(device_value_counts.query("bt_address in @common_device").groupby("local_segment")["scans"].min().to_frame("countscansleastfrequentdeviceacrossdataset" + ownership), how="outer")
return(features)
def deviceFrequency(bt_data):
@ -77,30 +92,61 @@ def ownership_based_on_clustering(bt_frequency):
maxcluster = np.where(labels == np.argmax(centers), 1, 0)
bt_frequency["own_device"] = maxcluster
return bt_frequency[["bt_address", "own_device"]]
def mostLeastScannedDevices(devices):
device_counts = devices["bt_address"].value_counts()
return ("","") if (len(device_counts) == 0) else (device_counts.idxmax(), device_counts.idxmin())
def validate_requested_features(provider):
base_features = {"DEVICES": set(["countscans", "uniquedevices", "meanscans", "stdscans"]),
"SCANS_MOST_FREQUENT_DEVICE": set(["withinsegments", "acrosssegments", "acrossdataset"]),
"SCANS_LEAST_FREQUENT_DEVICE": set(["withinsegments", "acrosssegments", "acrossdataset"])}
# Check we have three arrays of features
ownership_keys = [x.lower() for x in provider["FEATURES"].keys()]
if set(ownership_keys) != set(["own", "others", "all"]):
raise ValueError("[PHONE_BLUETOOTH][DORYAB][FEATURES] config key must have three types called ALL, OWN and OTHERS, instead you provided {}".format(ownership_keys))
# Check each array contains valid features
for ownership_key in provider["FEATURES"].keys():
for type_key in provider["FEATURES"][ownership_key]:
if len(provider["FEATURES"][ownership_key][type_key]) > 0 and not set(provider["FEATURES"][ownership_key][type_key]) <= base_features[type_key]:
raise ValueError("[PHONE_BLUETOOTH][DORYAB][FEATURES][{}][{}] config key only supports features called [{}], instead you provided [{}]".format(ownership_key, type_key, ",".join(base_features[type_key]), ",".join(set(provider["FEATURES"][ownership_key][type_key]) - base_features[type_key])))
def doryab_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
bt_data = pd.read_csv(sensor_data_files["sensor_data"])
base_features = set(["countscans", "uniquedevices", "countscansmostuniquedevice", "countscansleastuniquedevice", "meanscans", "stdscans"])
ownership_keys = [x.lower() for x in provider["FEATURES"].keys()]
if set(ownership_keys) != set(["own", "others", "all"]):
raise ValueError("[PHONE_BLUETOOTH][DORYAB][FEATURES] config key can only have three lists called ALL, OWN and OTHERS, instead you provided {}".format(ownership_keys))
feature_prefix = {"DEVICES":"", "SCANS_MOST_FREQUENT_DEVICE":"countscansmostfrequentdevice", "SCANS_LEAST_FREQUENT_DEVICE":"countscansleastfrequentdevice"}
validate_requested_features(provider)
device_ownership = ownership_based_on_clustering(deviceFrequency(bt_data)).set_index("bt_address")
bt_data = bt_data.set_index("bt_address").join(device_ownership, how="left").reset_index()
bt_data["own_device"].fillna(0, inplace=True)
segment_bt_data = filter_data_by_segment(bt_data, time_segment)
dataset_most_common_device, dataset_least_common_device = mostLeastScannedDevices(bt_data)
segment_bt_data = filter_data_by_segment(bt_data.head(0), time_segment)
features = pd.DataFrame(columns=['local_segment']).set_index("local_segment")
for ownership in provider["FEATURES"].keys():
features_to_compute = list(set(provider["FEATURES"][ownership]) & base_features)
features_to_compute = []
for type_key in provider["FEATURES"][ownership]:
features_to_compute = features_to_compute + [feature_prefix[type_key] + feature for feature in provider["FEATURES"][ownership][type_key]]
if ownership == "OWN":
owner_segment_bt_data = segment_bt_data.query("own_device == 1")
elif ownership == "OTHERS":
owner_segment_bt_data = segment_bt_data.query("own_device == 0")
else: #ALL
owner_segment_bt_data = segment_bt_data
features = deviceFeatures(owner_segment_bt_data, ownership.lower(), features_to_compute, features)
segment_most_common_device, segment_least_common_device = mostLeastScannedDevices(owner_segment_bt_data)
common_devices = {"most_dataset": dataset_most_common_device, "least_dataset": dataset_least_common_device,
"most_segments": segment_most_common_device, "least_segments": segment_least_common_device}
features = deviceFeatures(owner_segment_bt_data, ownership.lower(), common_devices, features_to_compute, features)
features = features.reset_index()
# Impute all NaN except for std dev
for column in features:
if column not in ["stdscansall", "stdscansown", "stdscansothers"]:
features[column].fillna(0.0, inplace=True)
return features