Fix the bug on ClusterAndLabel when recieving empty data.
nikunjgoel95
parent
47ab0a2ce5
commit
4f64f7eeef
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@ -9,7 +9,7 @@ def base_location_features(location_data, day_segment, requested_features, dbsca
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base_features_names = ["locationvariance","loglocationvariance","totaldistance","averagespeed","varspeed","circadianmovement","numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","meanlengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy","minutesdataused"]
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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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dataEmptyFlag = 0
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if location_data.empty:
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location_features = pd.DataFrame(columns=["local_date"] + ["location_" + day_segment + "_" + x for x in features_to_compute])
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@ -33,6 +33,9 @@ def base_location_features(location_data, day_segment, requested_features, dbsca
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location_data = location_data[(location_data['double_latitude']!=0.0) & (location_data['double_longitude']!=0.0)]
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if location_data.empty:
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dataEmptyFlag = 1
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if "locationvariance" in features_to_compute:
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location_features["location_" + day_segment + "_locationvariance"] = location_data.groupby(['local_date'])['double_latitude'].var() + location_data.groupby(['local_date'])['double_longitude'].var()
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@ -47,88 +50,126 @@ def base_location_features(location_data, day_segment, requested_features, dbsca
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preComputedDistanceandSpeed.loc[localDate,"avgspeed"] = speeddf[speeddf['speedTag'] == 'Moving']['speed'].mean()
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preComputedDistanceandSpeed.loc[localDate,"varspeed"] = speeddf[speeddf['speedTag'] == 'Moving']['speed'].var()
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if "totaldistance" in features_to_compute:
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if "totaldistance" in features_to_compute and dataEmptyFlag==0:
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for localDate in location_data['local_date'].unique():
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location_features.loc[localDate,"location_" + day_segment + "_totaldistance"] = preComputedDistanceandSpeed.loc[localDate,"distance"]
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else:
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location_features["location_" + day_segment + "_totaldistance"] = 0
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if "averagespeed" in features_to_compute:
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if "averagespeed" in features_to_compute and dataEmptyFlag==0:
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for localDate in location_data['local_date'].unique():
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location_features.loc[localDate,"location_" + day_segment + "_averagespeed"] = preComputedDistanceandSpeed.loc[localDate,"avgspeed"]
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else:
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location_features["location_" + day_segment + "_averagespeed"] = 0
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if "varspeed" in features_to_compute:
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if "varspeed" in features_to_compute and dataEmptyFlag==0:
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for localDate in location_data['local_date'].unique():
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location_features.loc[localDate,"location_" + day_segment + "_varspeed"] = preComputedDistanceandSpeed.loc[localDate,"varspeed"]
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else:
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location_features["location_" + day_segment + "_varspeed"] = 0
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if "circadianmovement" in features_to_compute:
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if "circadianmovement" in features_to_compute and dataEmptyFlag==0:
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for localDate in location_data['local_date'].unique():
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location_features.loc[localDate,"location_" + day_segment + "_circadianmovement"] = circadian_movement(location_data[location_data['local_date']==localDate])
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else:
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location_features["location_" + day_segment + "_circadianmovement"] = 0
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newLocationData = cluster_and_label(location_data, eps= distance_to_degrees(dbscan_eps), min_samples=dbscan_minsamples)
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if dataEmptyFlag==0:
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newLocationData = cluster_and_label(location_data, eps= distance_to_degrees(dbscan_eps), min_samples=dbscan_minsamples)
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if "numberofsignificantplaces" in features_to_compute:
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if "numberofsignificantplaces" in features_to_compute and dataEmptyFlag==0:
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for localDate in newLocationData['local_date'].unique():
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location_features.loc[localDate,"location_" + day_segment + "_numberofsignificantplaces"] = number_of_significant_places(newLocationData[newLocationData['local_date']==localDate])
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else:
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location_features["location_" + day_segment + "_numberofsignificantplaces"] = 0
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if "numberlocationtransitions" in features_to_compute:
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if "numberlocationtransitions" in features_to_compute and dataEmptyFlag==0:
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for localDate in newLocationData['local_date'].unique():
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location_features.loc[localDate,"location_" + day_segment + "_numberlocationtransitions"] = number_location_transitions(newLocationData[newLocationData['local_date']==localDate])
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if "radiusgyration" in features_to_compute:
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else:
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location_features["location_" + day_segment + "_numberlocationtransitions"] = 0
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if "radiusgyration" in features_to_compute and dataEmptyFlag==0:
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for localDate in newLocationData['local_date'].unique():
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location_features.loc[localDate,"location_" + day_segment + "_radiusgyration"] = radius_of_gyration(newLocationData[newLocationData['local_date']==localDate],sampling_frequency)
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else:
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location_features["location_" + day_segment + "_radiusgyration"] = 0
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if "timeattop1location" in features_to_compute:
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if "timeattop1location" in features_to_compute and dataEmptyFlag==0:
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for localDate in newLocationData['local_date'].unique():
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location_features.loc[localDate,"location_" + day_segment + "_timeattop1location"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_date']==localDate],1,sampling_frequency)
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else:
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location_features["location_" + day_segment + "_timeattop1location"] = 0
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if "timeattop2location" in features_to_compute:
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if "timeattop2location" in features_to_compute and dataEmptyFlag==0:
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for localDate in newLocationData['local_date'].unique():
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location_features.loc[localDate,"location_" + day_segment + "_timeattop2location"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_date']==localDate],2,sampling_frequency)
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if "timeattop3location" in features_to_compute:
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else:
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location_features["location_" + day_segment + "_timeattop2location"] = 0
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if "timeattop3location" in features_to_compute and dataEmptyFlag==0:
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for localDate in newLocationData['local_date'].unique():
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location_features.loc[localDate,"location_" + day_segment + "_timeattop3location"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_date']==localDate],3,sampling_frequency)
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else:
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location_features["location_" + day_segment + "_timeattop3location"] = 0
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if "movingtostaticratio" in features_to_compute:
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if "movingtostaticratio" in features_to_compute and dataEmptyFlag==0:
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for localDate in newLocationData['local_date'].unique():
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location_features.loc[localDate,"location_" + day_segment + "_movingtostaticratio"] = (newLocationData[newLocationData['local_date']==localDate].shape[0]*sampling_frequency) / (location_data[location_data['local_date']==localDate].shape[0] * sampling_frequency)
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if "outlierstimepercent" in features_to_compute:
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else:
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location_features["location_" + day_segment + "_movingtostaticratio"] = 0
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if "outlierstimepercent" in features_to_compute and dataEmptyFlag==0:
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for localDate in newLocationData['local_date'].unique():
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location_features.loc[localDate,"location_" + day_segment + "_outlierstimepercent"] = outliers_time_percent(newLocationData[newLocationData['local_date']==localDate],sampling_frequency)
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else:
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location_features["location_" + day_segment + "_outlierstimepercent"] = 0
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preComputedmaxminCluster = pd.DataFrame()
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for localDate in newLocationData['local_date'].unique():
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smax, smin, sstd,smean = len_stay_at_clusters_in_minutes(newLocationData[newLocationData['local_date']==localDate],sampling_frequency)
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preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_maxlengthstayatclusters"] = smax
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preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_minlengthstayatclusters"] = smin
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preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_stdlengthstayatclusters"] = sstd
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preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_meanlengthstayatclusters"] = smean
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if dataEmptyFlag==0:
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preComputedmaxminCluster = pd.DataFrame()
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for localDate in newLocationData['local_date'].unique():
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smax, smin, sstd,smean = len_stay_at_clusters_in_minutes(newLocationData[newLocationData['local_date']==localDate],sampling_frequency)
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preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_maxlengthstayatclusters"] = smax
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preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_minlengthstayatclusters"] = smin
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preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_stdlengthstayatclusters"] = sstd
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preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_meanlengthstayatclusters"] = smean
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if "maxlengthstayatclusters" in features_to_compute:
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if "maxlengthstayatclusters" in features_to_compute and dataEmptyFlag==0:
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for localDate in newLocationData['local_date'].unique():
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location_features.loc[localDate,"location_" + day_segment + "_maxlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_maxlengthstayatclusters"]
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if "minlengthstayatclusters" in features_to_compute:
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else:
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location_features["location_" + day_segment + "_maxlengthstayatclusters"] = 0
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if "minlengthstayatclusters" in features_to_compute and dataEmptyFlag==0:
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for localDate in newLocationData['local_date'].unique():
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location_features.loc[localDate,"location_" + day_segment + "_minlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_minlengthstayatclusters"]
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else:
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location_features["location_" + day_segment + "_minlengthstayatclusters"] = 0
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if "stdlengthstayatclusters" in features_to_compute:
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if "stdlengthstayatclusters" in features_to_compute and dataEmptyFlag==0:
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for localDate in newLocationData['local_date'].unique():
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location_features.loc[localDate,"location_" + day_segment + "_stdlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_stdlengthstayatclusters"]
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else:
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location_features["location_" + day_segment + "_stdlengthstayatclusters"] = 0
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if "meanlengthstayatclusters" in features_to_compute:
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if "meanlengthstayatclusters" in features_to_compute and dataEmptyFlag==0:
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for localDate in newLocationData['local_date'].unique():
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location_features.loc[localDate,"location_" + day_segment + "_meanlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_meanlengthstayatclusters"]
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else:
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location_features["location_" + day_segment + "_meanlengthstayatclusters"] = 0
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if "locationentropy" in features_to_compute:
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if "locationentropy" in features_to_compute and dataEmptyFlag==0:
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for localDate in newLocationData['local_date'].unique():
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location_features.loc[localDate,"location_" + day_segment + "_locationentropy"] = location_entropy(newLocationData[newLocationData['local_date']==localDate])
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else:
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location_features["location_" + day_segment + "_locationentropy"] = 0
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if "normalizedlocationentropy" in features_to_compute:
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if "normalizedlocationentropy" in features_to_compute and dataEmptyFlag==0:
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for localDate in newLocationData['local_date'].unique():
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location_features.loc[localDate,"location_" + day_segment + "_normalizedlocationentropy"] = location_entropy_normalized(newLocationData[newLocationData['local_date']==localDate])
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else:
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location_features["location_" + day_segment + "_normalizedlocationentropy"] = 0
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location_features = location_features.reset_index()
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return location_features
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@ -17,7 +17,10 @@ if(minutes_data_used):
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base_features = base_location_features(location_data, day_segment, requested_features, dbscan_eps, dbscan_minsamples,threshold_static,maximum_gap_allowed,sampling_frequency)
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location_features = location_features.merge(base_features, on="local_date", how="outer")
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if base_features.empty:
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location_features = base_features
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else:
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location_features = location_features.merge(base_features, on="local_date", how="outer")
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assert len(requested_features) + 1 == location_features.shape[1], "The number of features in the output dataframe (=" + str(location_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your location feature extraction functions"
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