Fixed 2 Issues: Feature Names, Implementation of metrics in Config File
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4aec2c4032
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@ -1,5 +1,5 @@
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# Valid database table names
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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]
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SENSORS: [applications_crashes, applications_foreground, applications_notifications, battery, bluetooth, calls, locations, messages, plugin_ambient_noise, plugin_device_usage, plugin_google_activity_recognition, screen]
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FITBIT_TABLE: [fitbit_data]
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FITBIT_SENSORS: [heartrate, steps, sleep]
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@ -71,7 +71,8 @@ BLUETOOTH:
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GOOGLE_ACTIVITY_RECOGNITION:
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DAY_SEGMENTS: *day_segments
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METRICS: ['count','most_common_activity','number_unique_activities','activity_change_count']
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METRICS: ['count','mostcommonactivity','countuniqueactivities','activitychangecount','sumstationary','summobile','sumvehicle']
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BATTERY:
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DAY_SEGMENTS: *day_segments
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@ -4,18 +4,17 @@ import scipy.stats as stats
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from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
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day_segment = snakemake.params["segment"]
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metrics = snakemake.params["metrics"]
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#Read csv into a pandas dataframe
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data = pd.read_csv(snakemake.input['gar_events'],parse_dates=['local_date_time'])
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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"])
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columns = ['count','most_common_activity','count_unique_activities','activity_change_count','sumstationary','summobile','sumvehicle']
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columns = list("ar_" + str(day_segment) + "_" + column for column in columns)
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columns = list("ar_" + str(day_segment) + "_" + column for column in metrics)
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if data.empty:
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finalDataset = pd.DataFrame(columns = columns)
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else:
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finalDataset = pd.DataFrame()
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ar_deltas = splitOvernightEpisodes(ar_deltas, [],['activity'])
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if day_segment != "daily":
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@ -31,31 +30,34 @@ else:
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if resampledData.empty:
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finalDataset = pd.DataFrame(columns = columns)
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else:
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count = resampledData['activity_type'].resample('D').count()
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#Finding the count of samples of the day
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if("count" in metrics):
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finalDataset["ar_" + str(day_segment) + "_count"] = resampledData['activity_type'].resample('D').count()
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#Finding most common activity of the day
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mostCommonActivity = resampledData['activity_type'].resample('D').apply(lambda x:stats.mode(x)[0])
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if("mostcommonactivity" in metrics):
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finalDataset["ar_" + str(day_segment) + "_mostcommonactivity"] = resampledData['activity_type'].resample('D').apply(lambda x:stats.mode(x)[0])
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#finding different number of activities during a day
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uniqueActivities = resampledData['activity_type'].resample('D').nunique()
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if("countuniqueactivities" in metrics):
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finalDataset["ar_" + str(day_segment) + "_countuniqueactivities"] = resampledData['activity_type'].resample('D').nunique()
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#finding Number of times activity changed
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resampledData['activity_type_shift'] = resampledData['activity_type'].shift().fillna(resampledData['activity_type'].head(1),inplace=True)
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resampledData['different_activity'] = np.where(resampledData['activity_type']!=resampledData['activity_type_shift'],1,0)
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countChanges = resampledData['different_activity'].resample('D').sum()
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finalDataset = pd.concat([count, mostCommonActivity, uniqueActivities, countChanges],axis=1)
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if("activitychangecount" in metrics):
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resampledData['activity_type_shift'] = resampledData['activity_type'].shift().fillna(resampledData['activity_type'].head(1))
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resampledData['different_activity'] = np.where(resampledData['activity_type']!=resampledData['activity_type_shift'],1,0)
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finalDataset["ar_" + str(day_segment) + "_activitychangecount"] = resampledData['different_activity'].resample('D').sum()
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deltas_metrics = {'sumstationary':['still','tilting'],
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'summobile':['on_foot','running','on_bicycle'],
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'sumvehicle':['in_vehicle']}
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for column, activity_labels in deltas_metrics.items():
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metric = (ar_deltas[ar_deltas['activity'].isin(pd.Series(activity_labels))]
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.groupby(['local_start_date'])['time_diff']
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.agg({"ar_" + str(day_segment) + "_" + str(column) :'sum'}))
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finalDataset = finalDataset.merge(metric,how='outer',left_index=True,right_index=True)
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if column in metrics:
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finalDataset["ar_" + str(day_segment) + "_"+str(column)] = (ar_deltas[ar_deltas['activity'].isin(pd.Series(activity_labels))]
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.groupby(['local_start_date'])['time_diff']
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.agg({"ar_" + str(day_segment) + "_" + str(column) :'sum'}))
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finalDataset.fillna(0,inplace=True)
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finalDataset.index.names = ['local_date']
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finalDataset.columns=columns
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finalDataset.to_csv(snakemake.output[0])
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