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Updated G_A_R features with epochs

replace/41f9ffe9539e7fe8707263feab7a633fdb950683
Echhit Joshi 2019-11-18 14:22:08 -05:00
parent f7be15ea9e
commit c5a0c1e0d6
4 changed files with 25 additions and 31 deletions
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3
Snakefile
View File

@ -21,7 +21,8 @@ rule all:
expand("data/processed/{pid}/bluetooth_{segment}.csv",
pid=config["PIDS"],
segment = config["BLUETOOTH"]["DAY_SEGMENTS"]),
expand("data/processed/{pid}/google_activity_recognition.csv",pid=config["PIDS"]),
expand("data/processed/{pid}/google_activity_recognition_{segment}.csv",pid=config["PIDS"],
segment = config["GOOGLE_ACTIVITY_RECOGNITION"]["DAY_SEGMENTS"]),
expand("data/processed/{pid}/battery_daily.csv", pid=config["PIDS"]),
# Reports
expand("reports/figures/{pid}/{sensor}_heatmap_rows.html", pid=config["PIDS"], sensor=config["SENSORS"]),

6
config.yaml
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@ -52,4 +52,8 @@ BARNETT_LOCATION:
BLUETOOTH:
DAY_SEGMENTS: *day_segments
METRICS: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
METRICS: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
GOOGLE_ACTIVITY_RECOGNITION:
DAY_SEGMENTS: *day_segments
METRICS: ['count','most_common_activity','number_unique_activities','activity_change_count']

5
rules/features.snakefile
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@ -55,8 +55,11 @@ rule bluetooth_metrics:
rule activity_metrics:
input:
"data/raw/{pid}/plugin_google_activity_recognition_with_datetime.csv"
params:
segment = "{day_segment}",
metrics = config["GOOGLE_ACTIVITY_RECOGNITION"]["METRICS"]
output:
"data/processed/{pid}/google_activity_recognition.csv"
"data/processed/{pid}/google_activity_recognition_{day_segment}.csv"
script:
"../src/features/google_activity_recognition.py"

42
src/features/google_activity_recognition.py
View File

@ -2,51 +2,37 @@ import pandas as pd
import numpy as np
import scipy.stats as stats
day_segment = snakemake.params["segment"]
#Read csv into a pandas dataframe
data = pd.read_csv(snakemake.input[0])
column = ['local_date_time','count','most_common_activity','number_unique_activities','activity_change_count']
finalDataset = pd.DataFrame(columns=column)
finalDataset.set_index('local_date_time',inplace=True)
columns = ['count','most_common_activity','count_unique_activities','activity_change_count']
columns = list("ar_" + str(day_segment) + "_" + column for column in columns)
if data.empty:
finalDataset.to_csv(snakemake.output[0])
finalDataset = pd.DataFrame(columns = columns)
else:
#Resampling each of the required features as a pandas series
data.local_date_time = pd.to_datetime(data.local_date_time)
resampledData = data.set_index(data.local_date_time)
resampledData = resampledData[~resampledData.index.duplicated()]
resampledData.rename_axis('time',axis='columns',inplace=True)
resampledData.drop(columns=['local_date_time'],inplace=True)
#Finding count grouped by day
count = pd.DataFrame()
if(day_segment!='daily'):
resampledData = resampledData.loc[resampledData['local_day_segment'] == str(day_segment)]
count = resampledData['activity_type'].resample('D').count()
count = count.rename(columns={"activity_type":"count"})
#Finding most common activity of the day
mostCommonActivity = pd.DataFrame()
mostCommonActivity = resampledData['activity_type'].resample('D').apply(lambda x:stats.mode(x)[0])
mostCommonActivity = mostCommonActivity.rename(columns={'activity_type':'most_common_activity'})
#finding different number of activities during a day
uniqueActivities = pd.DataFrame()
# countChanges = resampledData.to_period('D').groupby(resampledData.index)['activity_type'].value_counts()
uniqueActivities = resampledData['activity_type'].resample('D').nunique()
#finding Number of times activity changed
resampledData['activity_type_shift'] = resampledData['activity_type'].shift()
resampledData['activity_type_shift'].fillna(resampledData['activity_type'].head(1),inplace=True)
#resampledData['different_activity'] = resampledData['activity_type'].apply(lambda x: 0 if resampledData['activity_type'] == resampledData['activity_type_shift'] else 1, axis=1)
resampledData['different_activity']=np.where(resampledData['activity_type']!=resampledData['activity_type_shift'],1,0)
countChanges = pd.DataFrame()
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)
#Concatenating all the processed data only, no other sensor data is added here for simplicity
finalDataset = pd.DataFrame()
finalDataset = pd.concat([count,mostCommonActivity,uniqueActivities,countChanges],axis=1)
finalDataset.rename(columns={0:"count",1:'most_common_activity','activity_type':'number_unique_activities','different_activity':'activity_change_count'},inplace = True)
#Export final dataframe with extracted features to respective PID
finalDataset.to_csv(snakemake.output[0])
finalDataset.index.names = ['local_date']
finalDataset.columns=columns
finalDataset.to_csv(snakemake.output[0])