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Fix bugs with empty dataframes in screen and gar metrics

replace/cb67d183ce858faab452a2747db7be3eda789434
JulioV 2019-12-04 12:04:20 -05:00
parent 0bd946b53c
commit 4d52e2d980
3 changed files with 36 additions and 27 deletions
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2
Snakefile
View File

@ -9,7 +9,7 @@ rule all:
expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SENSORS"]),
expand("data/processed/{pid}/battery_deltas.csv", pid=config["PIDS"]),
expand("data/processed/{pid}/screen_deltas.csv", pid=config["PIDS"]),
expand("data/processed/{pid}/google_activity_recognition_deltas.csv", pid=config["PIDS"]),
expand("data/processed/{pid}/plugin_google_activity_recognition_deltas.csv", pid=config["PIDS"]),
expand("data/interim/{pid}/phone_valid_sensed_days.csv", pid=config["PIDS"]),
expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"]),
expand("data/processed/{pid}/sms_{sms_type}_{day_segment}.csv",

49
src/features/google_activity_recognition.py
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@ -27,30 +27,33 @@ else:
if(day_segment!='daily'):
resampledData = resampledData.loc[resampledData['local_day_segment'] == str(day_segment)]
if resampledData.empty:
finalDataset = pd.DataFrame(columns = columns)
else:
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])
#finding different number of activities during a day
uniqueActivities = 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)
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)
deltas_metrics = {'sumstationary':['still','tilting'],
'summobile':['on_foot','running','on_bicycle'],
'sumvehicle':['in_vehicle']}
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])
#finding different number of activities during a day
uniqueActivities = 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)
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)
deltas_metrics = {'sumstationary':['still','tilting'],
'summobile':['on_foot','running','on_bicycle'],
'sumvehicle':['on_vehicle']}
for column, activity_labels in deltas_metrics.items():
metric = (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)
for column, activity_labels in deltas_metrics.items():
metric = (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']

12
src/features/screen_metrics.py
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@ -1,6 +1,7 @@
import pandas as pd
import numpy as np
import datetime
import itertools
from datetime import datetime, timedelta, time
from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
@ -73,9 +74,14 @@ else:
# extract features for events and episodes
event_features = getEventFeatures(screen_data, metrics_event)
duration_features = pd.DataFrame()
for episode in episodes:
duration_features = pd.concat([duration_features, getEpisodeDurationFeatures(screen_deltas, episode, metrics_deltas)], axis=1)
if screen_deltas.empty:
metrics_deltas_name = ["".join(metric) for metric in itertools.product(metrics_deltas,episodes)]
duration_features = pd.DataFrame(columns=["screen_" + day_segment + "_" + x for x in metrics_deltas_name])
else:
duration_features = pd.DataFrame()
for episode in episodes:
duration_features = pd.concat([duration_features, getEpisodeDurationFeatures(screen_deltas, episode, metrics_deltas)], axis=1)
screen_features = pd.concat([event_features, duration_features], axis = 1).fillna(0)
screen_features = screen_features.rename_axis("local_date").reset_index()