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Fixed 2 Issues: Feature Names, Implementation of metrics in Config File

replace/5b63663f04ce063d6f3b267d980895ab014e7ecf
Echhit Joshi 2020-01-17 11:39:30 -05:00
parent 4aec2c4032
commit db2f3dc50f
2 changed files with 23 additions and 20 deletions
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5
config.yaml
View File

@ -1,5 +1,5 @@
# Valid database table names
SENSORS: [applications_crashes, applications_foreground, applications_notifications, battery, bluetooth, calls, fitbit_data, locations, messages, plugin_ambient_noise, plugin_device_usage, plugin_google_activity_recognition, screen]
SENSORS: [applications_crashes, applications_foreground, applications_notifications, battery, bluetooth, calls, locations, messages, plugin_ambient_noise, plugin_device_usage, plugin_google_activity_recognition, screen]
FITBIT_TABLE: [fitbit_data]
FITBIT_SENSORS: [heartrate, steps, sleep]
@ -71,7 +71,8 @@ BLUETOOTH:
GOOGLE_ACTIVITY_RECOGNITION:
DAY_SEGMENTS: *day_segments
METRICS: ['count','most_common_activity','number_unique_activities','activity_change_count']
METRICS: ['count','mostcommonactivity','countuniqueactivities','activitychangecount','sumstationary','summobile','sumvehicle']
BATTERY:
DAY_SEGMENTS: *day_segments

30
src/features/google_activity_recognition.py
View File

@ -4,18 +4,17 @@ import scipy.stats as stats
from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
day_segment = snakemake.params["segment"]
metrics = snakemake.params["metrics"]
#Read csv into a pandas dataframe
data = pd.read_csv(snakemake.input['gar_events'],parse_dates=['local_date_time'])
ar_deltas = pd.read_csv(snakemake.input['gar_deltas'],parse_dates=["local_start_date_time", "local_end_date_time", "local_start_date", "local_end_date"])
columns = ['count','most_common_activity','count_unique_activities','activity_change_count','sumstationary','summobile','sumvehicle']
columns = list("ar_" + str(day_segment) + "_" + column for column in columns)
columns = list("ar_" + str(day_segment) + "_" + column for column in metrics)
if data.empty:
finalDataset = pd.DataFrame(columns = columns)
else:
finalDataset = pd.DataFrame()
ar_deltas = splitOvernightEpisodes(ar_deltas, [],['activity'])
if day_segment != "daily":
@ -31,31 +30,34 @@ else:
if resampledData.empty:
finalDataset = pd.DataFrame(columns = columns)
else:
count = resampledData['activity_type'].resample('D').count()
#Finding the count of samples of the day
if("count" in metrics):
finalDataset["ar_" + str(day_segment) + "_count"] = resampledData['activity_type'].resample('D').count()
#Finding most common activity of the day
mostCommonActivity = resampledData['activity_type'].resample('D').apply(lambda x:stats.mode(x)[0])
if("mostcommonactivity" in metrics):
finalDataset["ar_" + str(day_segment) + "_mostcommonactivity"] = resampledData['activity_type'].resample('D').apply(lambda x:stats.mode(x)[0])
#finding different number of activities during a day
uniqueActivities = resampledData['activity_type'].resample('D').nunique()
if("countuniqueactivities" in metrics):
finalDataset["ar_" + str(day_segment) + "_countuniqueactivities"] = resampledData['activity_type'].resample('D').nunique()
#finding Number of times activity changed
resampledData['activity_type_shift'] = resampledData['activity_type'].shift().fillna(resampledData['activity_type'].head(1),inplace=True)
if("activitychangecount" in metrics):
resampledData['activity_type_shift'] = resampledData['activity_type'].shift().fillna(resampledData['activity_type'].head(1))
resampledData['different_activity'] = np.where(resampledData['activity_type']!=resampledData['activity_type_shift'],1,0)
countChanges = resampledData['different_activity'].resample('D').sum()
finalDataset = pd.concat([count, mostCommonActivity, uniqueActivities, countChanges],axis=1)
finalDataset["ar_" + str(day_segment) + "_activitychangecount"] = resampledData['different_activity'].resample('D').sum()
deltas_metrics = {'sumstationary':['still','tilting'],
'summobile':['on_foot','running','on_bicycle'],
'sumvehicle':['in_vehicle']}
for column, activity_labels in deltas_metrics.items():
metric = (ar_deltas[ar_deltas['activity'].isin(pd.Series(activity_labels))]
if column in metrics:
finalDataset["ar_" + str(day_segment) + "_"+str(column)] = (ar_deltas[ar_deltas['activity'].isin(pd.Series(activity_labels))]
.groupby(['local_start_date'])['time_diff']
.agg({"ar_" + str(day_segment) + "_" + str(column) :'sum'}))
finalDataset = finalDataset.merge(metric,how='outer',left_index=True,right_index=True)
finalDataset.fillna(0,inplace=True)
finalDataset.index.names = ['local_date']
finalDataset.columns=columns
finalDataset.to_csv(snakemake.output[0])