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Merge branch 'feature/doryab_bluetooth' into develop

pull/107/head v0.2.0
JulioV 2020-12-12 17:14:32 -05:00
parent 9ff75b10ba 1359acf5fd
commit a17326ad95
7 changed files with 309 additions and 6 deletions
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17
config.yaml
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@ -127,6 +127,23 @@ PHONE_BLUETOOTH:
FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
SRC_FOLDER: "rapids" # inside src/features/phone_bluetooth
SRC_LANGUAGE: "r"
DORYAB:
COMPUTE: FALSE
FEATURES:
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"
# See https://www.rapids.science/latest/features/phone-calls/
PHONE_CALLS:

5
docs/change-log.md
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@ -1,6 +1,9 @@
# Change Log
## Release in progress
## v0.2.0
- Add new `PHONE_BLUETOOTH` `DORYAB` provider
- Deprecate `PHONE_BLUETOOTH` `RAPIDS` provider
- Fix bug in `filter_data_by_segment` for Python when dataset was empty
- Minor doc updates
- New FAQ item

7
docs/citation.md
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@ -28,6 +28,13 @@ If you computed applications foreground features using the app category (genre)
!!! cite "Stachl et al. citation"
Clemens Stachl, Quay Au, Ramona Schoedel, Samuel D. Gosling, Gabriella M. Harari, Daniel Buschek, Sarah Theres Völkel, Tobias Schuwerk, Michelle Oldemeier, Theresa Ullmann, Heinrich Hussmann, Bernd Bischl, Markus Bühner. Proceedings of the National Academy of Sciences Jul 2020, 117 (30) 17680-17687; DOI: 10.1073/pnas.1920484117
## Doryab (bluetooth)
If you computed bluetooth features using the provider `[PHONE_BLUETOOTH][DORYAB]` cite [this paper](https://arxiv.org/abs/1812.10394) in addition to RAPIDS.
!!! cite "Doryab et al. citation"
Doryab, A., Chikarsel, P., Liu, X., & Dey, A. K. (2019). Extraction of Behavioral Features from Smartphone and Wearable Data. ArXiv:1812.10394 [Cs, Stat]. http://arxiv.org/abs/1812.10394
## Barnett (locations)
If you computed locations features using the provider `[PHONE_LOCATIONS][BARNETT]` cite [this paper](https://doi.org/10.1093/biostatistics/kxy059) and [this paper](https://doi.org/10.1145/2750858.2805845) in addition to RAPIDS.

1
docs/developers/git-flow.md
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@ -29,6 +29,7 @@ git commit -m "Add my new feature" # use a concise description
```bash
git checkout feature/feature1
git pull origin develop
git rebase -i develop
git checkout develop
git merge --no-ff feature/feature1 # (use the default merge message)

127
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
@ -33,9 +36,125 @@ 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
- Available for Android only
!!! info "File Sequence"
```bash
- data/raw/{pid}/phone_bluetooth_raw.csv
- data/raw/{pid}/phone_bluetooth_with_datetime.csv
- data/interim/{pid}/phone_bluetooth_features/phone_bluetooth_{language}_{provider_key}.csv
- data/processed/features/{pid}/phone_bluetooth.csv"
```
Parameters description for `[PHONE_BLUETOOTH][PROVIDERS][DORYAB]`:
|Key                              | Description |
|----------------|-----------------------------------------------------------------------------------------------------------------------------------
|`[COMPUTE]`| Set to `True` to extract `PHONE_BLUETOOTH` features from the `DORYAB` provider|
|`[FEATURES]` | Features to be computed, see table below. These features are computed for three device categories: `all` devices, `own` devices and `other` devices.
Features description for `[PHONE_BLUETOOTH][PROVIDERS][DORYAB]`:
|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`) |
| 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"
- 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
```

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src/features/phone_bluetooth/doryab/main.py 100644
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@ -0,0 +1,152 @@
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
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:
device_value_counts = devices.groupby(["local_segment"])["bt_address"].value_counts().to_frame("scans").reset_index()
if "countscans" in features_to_compute:
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 "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):
bt_data = bt_data[["local_date", "bt_address"]].dropna(subset=["bt_address"])
bt_data = bt_data.groupby("bt_address").agg({"local_date": pd.Series.nunique, "bt_address" : 'count'})
bt_data = bt_data.rename(columns={"local_date" : "days_scanned", "bt_address" : "scans"})
bt_data["scans_per_day"] = bt_data["scans"] / bt_data["days_scanned"]
return bt_data
def ownership_based_on_clustering(bt_frequency):
bt_frequency = bt_frequency.reset_index()
for col in ["scans_per_day", "days_scanned", "scans"]:
col_zscore = col + '_z'
bt_frequency[col_zscore] = (bt_frequency[col] - bt_frequency[col].mean()) / bt_frequency[col].std(ddof=0)
bt_frequency = bt_frequency.dropna(how='any')
if len(bt_frequency) == 0:
bt_frequency["own_device"] = None
return bt_frequency[["bt_address", "own_device"]]
avgfreq_z = bt_frequency["scans_per_day_z"]
numdays_z = bt_frequency["days_scanned_z"]
score = avgfreq_z + numdays_z
maxscore = np.max(score)
minscore = np.min(score)
midscore = (maxscore + minscore) / 2
initial_k2 = np.array([[maxscore], [minscore]], np.int32)
initial_k3 = np.array([[maxscore], [midscore], [minscore]], np.int32)
X_array = score.values
X = np.reshape(X_array, (len(score), 1))
# K = 2, devices I own VS devices other people own
kmeans_k2 = KMeans(n_clusters=2, init = initial_k2, n_init = 1).fit(X)
labels_k2 = kmeans_k2.labels_
centers_k2 = [c[0] for c in kmeans_k2.cluster_centers_]
diff_k2 = [(X_array[xi] - centers_k2[labels_k2[xi]])**2 for xi in range(0, len(X_array))]
sum_dist_k2 = sum(diff_k2)
# K = 3, devices I own VS devices my partner/roommate owns (can also be other devices I own though) VS devices other people own
kmeans_k3 = KMeans(n_clusters=3, init=initial_k3, n_init = 1).fit(X)
labels_k3 = kmeans_k3.labels_
centers_k3 = [c[0] for c in kmeans_k3.cluster_centers_]
diff_k3 = [(X_array[xi] - centers_k3[labels_k3[xi]])**2 for xi in range(0, len(X_array))]
sum_dist_k3 = sum(diff_k3)
if sum_dist_k2 < sum_dist_k3: # K = 2 is better
labels = labels_k2
centers = centers_k2
numclust = 2
else:
labels = labels_k3
centers = centers_k3
numclust = 3
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"])
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)
dataset_most_common_device, dataset_least_common_device = mostLeastScannedDevices(bt_data)
segment_bt_data = filter_data_by_segment(bt_data, time_segment)
features = pd.DataFrame(columns=['local_segment']).set_index("local_segment")
for ownership in provider["FEATURES"].keys():
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
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

6
src/features/utils/utils.py
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@ -1,13 +1,17 @@
rapids_log_tag = "RAPIDS:"
def filter_data_by_segment(data, time_segment):
if(data.shape[0] == 0): # data is empty
data["local_segment"] = data["timestamps_segment"] = None
return data
datetime_regex = "[0-9]{4}[\-|\/][0-9]{2}[\-|\/][0-9]{2} [0-9]{2}:[0-9]{2}:[0-9]{2}"
timestamps_regex = "[0-9]{13}"
segment_regex = "\[({}#{},{};{},{})\]".format(time_segment, datetime_regex, datetime_regex, timestamps_regex, timestamps_regex)
data["local_segment"] = data["assigned_segments"].str.extract(segment_regex, expand=True)
data = data.drop(columns=["assigned_segments"])
data = data.dropna(subset = ["local_segment"])
if(data.shape[0] == 0): # there are no rows belonging to time_segment
if(data.shape[0] == 0): # there are no rows belonging to time_segment after droping na
data["timestamps_segment"] = None
else:
data[["local_segment","timestamps_segment"]] = data["local_segment"].str.split(pat =";",n=1, expand=True)