Refactor activity recognition features
Meng Li
parent
ff1bc33c34
commit
aa1baaf948
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@ -81,7 +81,7 @@ BLUETOOTH:
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ACTIVITY_RECOGNITION:
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DAY_SEGMENTS: *day_segments
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FEATURES: ['count','mostcommonactivity','countuniqueactivities','activitychangecount','sumstationary','summobile','sumvehicle']
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FEATURES: ["count","mostcommonactivity","countuniqueactivities","activitychangecount","sumstationary","summobile","sumvehicle"]
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BATTERY:
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DAY_SEGMENTS: *day_segments
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@ -1,63 +1,15 @@
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import pandas as pd
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import numpy as np
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import scipy.stats as stats
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from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
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from ar.ar_base import base_ar_features
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day_segment = snakemake.params["segment"]
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features = snakemake.params["features"]
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#Read csv into a pandas dataframe
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data = pd.read_csv(snakemake.input[0],parse_dates=["local_date_time"])
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ar_data = pd.read_csv(snakemake.input[0],parse_dates=["local_date_time"])
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ar_deltas = pd.read_csv(snakemake.input[1],parse_dates=["local_start_date_time", "local_end_date_time", "local_start_date", "local_end_date"])
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columns = list("ar_" + str(day_segment) + "_" + column for column in features)
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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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ar_deltas = splitMultiSegmentEpisodes(ar_deltas, day_segment, [])
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data.local_date_time = pd.to_datetime(data.local_date_time)
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resampledData = data.set_index(data.local_date_time)
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resampledData.drop(columns=['local_date_time'],inplace=True)
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if(day_segment!='daily'):
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resampledData = resampledData.loc[resampledData['local_day_segment'] == str(day_segment)]
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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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#Finding the count of samples of the day
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if("count" in features):
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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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if("mostcommonactivity" in features):
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finalDataset["ar_" + str(day_segment) + "_mostcommonactivity"] = resampledData['activity_type'].resample('D').apply(lambda x: stats.mode(x)[0] if len(stats.mode(x)[0]) != 0 else None)
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#finding different number of activities during a day
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if("countuniqueactivities" in features):
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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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if("activitychangecount" in features):
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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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day_segment = snakemake.params["segment"]
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requested_features = snakemake.params["features"]
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ar_features = pd.DataFrame(columns=["local_date"])
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deltas_features = {'sumstationary':['still','tilting'],
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'summobile':['on_foot','walking','running','on_bicycle'],
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'sumvehicle':['in_vehicle']}
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for column, activity_labels in deltas_features.items():
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if column in features:
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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.index.names = ['local_date']
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finalDataset.to_csv(snakemake.output[0])
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ar_features = ar_features.merge(base_ar_features(ar_data, ar_deltas, day_segment, requested_features), on="local_date", how="outer")
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assert len(requested_features) + 1 == ar_features.shape[1], "The number of features in the output dataframe (=" + str(ar_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your activity recognition feature extraction functions"
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ar_features.to_csv(snakemake.output[0], index=False)
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@ -0,0 +1,61 @@
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import pandas as pd
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import numpy as np
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import scipy.stats as stats
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from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
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def base_ar_features(ar_data, ar_deltas, day_segment, requested_features):
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# name of the features this function can compute
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base_features_names = ["count","mostcommonactivity","countuniqueactivities","activitychangecount","sumstationary","summobile","sumvehicle"]
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# the subset of requested features this function can compute
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features_to_compute = list(set(requested_features) & set(base_features_names))
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if ar_data.empty:
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ar_features = pd.DataFrame(columns = ["local_date"] + ["ar_" + day_segment + "_" + x for x in features_to_compute])
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else:
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ar_features = pd.DataFrame()
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ar_deltas = splitOvernightEpisodes(ar_deltas, [],["activity"])
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if day_segment != "daily":
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ar_deltas = splitMultiSegmentEpisodes(ar_deltas, day_segment, [])
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ar_data.local_date_time = pd.to_datetime(ar_data.local_date_time)
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resampledData = ar_data.set_index(ar_data.local_date_time)
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resampledData.drop(columns=["local_date_time"], inplace=True)
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if(day_segment!="daily"):
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resampledData = resampledData.loc[resampledData["local_day_segment"] == day_segment]
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if resampledData.empty:
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ar_features = pd.DataFrame(columns = ["ar_" + day_segment + "_" + x for x in features_to_compute])
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else:
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#Finding the count of samples of the day
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if "count" in features_to_compute:
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ar_features["ar_" + day_segment + "_count"] = resampledData["activity_type"].resample("D").count()
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#Finding most common activity of the day
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if "mostcommonactivity" in features_to_compute:
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ar_features["ar_" + day_segment + "_mostcommonactivity"] = resampledData["activity_type"].resample("D").apply(lambda x: stats.mode(x)[0] if len(stats.mode(x)[0]) != 0 else None)
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#finding different number of activities during a day
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if "countuniqueactivities" in features_to_compute:
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ar_features["ar_" + day_segment + "_countuniqueactivities"] = resampledData["activity_type"].resample("D").nunique()
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#finding Number of times activity changed
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if "activitychangecount" in features_to_compute:
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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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ar_features["ar_" + day_segment + "_activitychangecount"] = resampledData["different_activity"].resample("D").sum()
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deltas_features = {"sumstationary":["still","tilting"],
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"summobile":["on_foot","walking","running","on_bicycle"],
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"sumvehicle":["in_vehicle"]}
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for column, activity_labels in deltas_features.items():
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if column in features_to_compute:
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ar_features["ar_" + day_segment + "_" + column] = ar_deltas[ar_deltas["activity"].isin(pd.Series(activity_labels))].groupby(["local_start_date"])["time_diff"].sum()
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ar_features.index.names = ["local_date"]
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ar_features = ar_features.reset_index()
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return ar_features
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