Fixed the features dependent on time duration.
nikunjgoel95
parent
4746d7ab6c
commit
0bbf15f52e
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@ -45,7 +45,6 @@ def doryab_features(sensor_data_files, time_segment, provider, filter_data_by_se
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if cluster_on == "PARTICIPANT_DATASET":
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location_data = cluster_and_label(location_data,clustering_algorithm,threshold_static,**hyperparameters)
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location_data = filter_data_by_segment(location_data, time_segment)
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elif cluster_on == "TIME_SEGMENT":
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location_data = filter_data_by_segment(location_data, time_segment)
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location_data = cluster_and_label(location_data,clustering_algorithm,threshold_static,**hyperparameters)
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@ -73,6 +72,7 @@ def doryab_features(sensor_data_files, time_segment, provider, filter_data_by_se
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location_features = location_features.reset_index(drop=True)
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return location_features
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location_data['timeInSeconds'] = (location_data.timestamp.diff(-1)* -1)/1000
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if "locationvariance" in features_to_compute:
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location_features["locationvariance"] = location_data.groupby(['local_segment'])['double_latitude'].var() + location_data.groupby(['local_segment'])['double_longitude'].var()
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@ -118,49 +118,52 @@ def doryab_features(sensor_data_files, time_segment, provider, filter_data_by_se
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for localDate in stationaryLocations['local_segment'].unique():
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location_features.loc[localDate,"radiusgyration"] = radius_of_gyration(stationaryLocations[stationaryLocations['local_segment']==localDate],sampling_frequency)
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preComputedTimeArray = pd.DataFrame()
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for localDate in stationaryLocations["local_segment"].unique():
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top1,top2,top3,smax,smin,sstd,smean = len_stay_timeattopn(stationaryLocations[stationaryLocations["local_segment"]==localDate])
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preComputedTimeArray.loc[localDate,"timeattop1"] = top1
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preComputedTimeArray.loc[localDate,"timeattop2"] = top2
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preComputedTimeArray.loc[localDate,"timeattop3"] = top3
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preComputedTimeArray.loc[localDate,"maxlengthstayatclusters"] = smax
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preComputedTimeArray.loc[localDate,"minlengthstayatclusters"] = smin
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preComputedTimeArray.loc[localDate,"stdlengthstayatclusters"] = sstd
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preComputedTimeArray.loc[localDate,"meanlengthstayatclusters"] = smean
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if "timeattop1location" in features_to_compute:
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for localDate in stationaryLocations['local_segment'].unique():
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location_features.loc[localDate,"timeattop1"] = time_at_topn_clusters_in_group(stationaryLocations[stationaryLocations['local_segment']==localDate],1,sampling_frequency)
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location_features.loc[localDate,"timeattop1"] = preComputedTimeArray.loc[localDate,"timeattop1"]
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if "timeattop2location" in features_to_compute:
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for localDate in stationaryLocations['local_segment'].unique():
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location_features.loc[localDate,"timeattop2"] = time_at_topn_clusters_in_group(stationaryLocations[stationaryLocations['local_segment']==localDate],2,sampling_frequency)
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location_features.loc[localDate,"timeattop2"] = preComputedTimeArray.loc[localDate,"timeattop2"]
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if "timeattop3location" in features_to_compute:
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for localDate in stationaryLocations['local_segment'].unique():
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location_features.loc[localDate,"timeattop3"] = time_at_topn_clusters_in_group(stationaryLocations[stationaryLocations['local_segment']==localDate],3,sampling_frequency)
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location_features.loc[localDate,"timeattop3"] = preComputedTimeArray.loc[localDate,"timeattop3"]
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if "movingtostaticratio" in features_to_compute:
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for localDate in stationaryLocations['local_segment'].unique():
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location_features.loc[localDate,"movingtostaticratio"] = (stationaryLocations[stationaryLocations['local_segment']==localDate].shape[0]*sampling_frequency) / (location_data[location_data['local_segment']==localDate].shape[0] * sampling_frequency)
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location_features.loc[localDate,"movingtostaticratio"] = (stationaryLocations[stationaryLocations['local_segment']==localDate]['timeInSeconds'].sum()) / (location_data[location_data['local_segment']==localDate]['timeInSeconds'].sum())
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if "outlierstimepercent" in features_to_compute:
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for localDate in stationaryLocations['local_segment'].unique():
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location_features.loc[localDate,"outlierstimepercent"] = outliers_time_percent(stationaryLocations[stationaryLocations['local_segment']==localDate],sampling_frequency)
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preComputedmaxminCluster = pd.DataFrame()
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for localDate in stationaryLocations['local_segment'].unique():
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smax, smin, sstd,smean = len_stay_at_clusters_in_minutes(stationaryLocations[stationaryLocations['local_segment']==localDate],sampling_frequency)
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preComputedmaxminCluster.loc[localDate,"maxlengthstayatclusters"] = smax
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preComputedmaxminCluster.loc[localDate,"minlengthstayatclusters"] = smin
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preComputedmaxminCluster.loc[localDate,"stdlengthstayatclusters"] = sstd
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preComputedmaxminCluster.loc[localDate,"meanlengthstayatclusters"] = smean
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location_features.loc[localDate,"outlierstimepercent"] = outlier_time_percent_new(stationaryLocations[stationaryLocations['local_segment']==localDate])
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if "maxlengthstayatclusters" in features_to_compute:
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for localDate in stationaryLocations['local_segment'].unique():
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location_features.loc[localDate,"maxlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"maxlengthstayatclusters"]
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location_features.loc[localDate,"maxlengthstayatclusters"] = preComputedTimeArray.loc[localDate,"maxlengthstayatclusters"]
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if "minlengthstayatclusters" in features_to_compute:
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for localDate in stationaryLocations['local_segment'].unique():
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location_features.loc[localDate,"minlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"minlengthstayatclusters"]
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location_features.loc[localDate,"minlengthstayatclusters"] = preComputedTimeArray.loc[localDate,"minlengthstayatclusters"]
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if "stdlengthstayatclusters" in features_to_compute:
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for localDate in stationaryLocations['local_segment'].unique():
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location_features.loc[localDate,"stdlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"stdlengthstayatclusters"]
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location_features.loc[localDate,"stdlengthstayatclusters"] = preComputedTimeArray.loc[localDate,"stdlengthstayatclusters"]
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if "meanlengthstayatclusters" in features_to_compute:
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for localDate in stationaryLocations['local_segment'].unique():
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location_features.loc[localDate,"meanlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"meanlengthstayatclusters"]
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location_features.loc[localDate,"meanlengthstayatclusters"] = preComputedTimeArray.loc[localDate,"meanlengthstayatclusters"]
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if "locationentropy" in features_to_compute:
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for localDate in stationaryLocations['local_segment'].unique():
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@ -174,6 +177,21 @@ def doryab_features(sensor_data_files, time_segment, provider, filter_data_by_se
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return location_features
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def len_stay_timeattopn(locationData):
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if locationData is None or len(locationData) == 0:
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return (None, None, None,None, None, None, None)
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calculationDf = locationData[locationData["location_label"] >= 1][['location_label','timeInSeconds']]
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calculationDf[calculationDf['timeInSeconds'] >= 300]['timeInSeconds'] = 60
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timeArray = calculationDf.groupby('location_label')['timeInSeconds'].sum().reset_index()['timeInSeconds'].sort_values(ascending=False)/60
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if len(timeArray) > 2:
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return (timeArray[0],timeArray[1],timeArray[2],timeArray.max(),timeArray.min(),timeArray.std(),timeArray.mean())
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elif len(timeArray)==2:
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return (timeArray[0],timeArray[1],None,timeArray.max(),timeArray.min(),timeArray.std(),timeArray.mean())
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else:
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return (timeArray[0],None,None,timeArray.max(),timeArray.min(),timeArray.std(),timeArray.mean())
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def getMinutesData(locationData):
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@ -188,7 +206,6 @@ def distance_to_degrees(d):
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def get_all_travel_distances_meters_speed(locationData,threshold,maximum_gap_allowed):
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locationData['timeInSeconds'] = (locationData.timestamp.diff(-1)* -1)/1000
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lat_lon_temp = locationData[locationData['timeInSeconds'] <= maximum_gap_allowed][['double_latitude','double_longitude','timeInSeconds']]
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if lat_lon_temp.empty:
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@ -314,7 +331,6 @@ def rank_count_map(clusters):
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labels, counts = tuple(np.unique(clusters, return_counts = True))
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sorted_by_count = [x for (y,x) in sorted(zip(counts, labels), reverse = True)]
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label_to_rank = {label : rank + 1 for (label, rank) in [(sorted_by_count[i],i) for i in range(len(sorted_by_count))]}
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return lambda x: label_to_rank.get(x, -1)
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@ -367,78 +383,15 @@ def radius_of_gyration(locationData,sampling_frequency):
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return np.sqrt(final_rog)
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def time_at_topn_clusters_in_group(locationData,n,sampling_frequency): # relevant only for global location features since, top3_clusters = top3_clusters_in_group for local
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if locationData is None or len(locationData) == 0:
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def outlier_time_percent_new(locationData):
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if locationData is None or len(locationData)==0:
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return None
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locationData = locationData[locationData["location_label"] >= 1] # remove outliers/ cluster noise
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valcounts = locationData["location_label"].value_counts().to_dict()
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sorted_valcounts = sorted(valcounts.items(), key=lambda kv: (-kv[1], kv[0]))
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if len(sorted_valcounts) >= n:
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topn = sorted_valcounts[n-1]
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topn_time = topn[1] * sampling_frequency
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else:
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topn_time = None
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return topn_time
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def outliers_time_percent(locationData,sampling_frequency):
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if locationData is None or len(locationData) == 0:
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return None
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clusters = locationData["location_label"]
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numoutliers = clusters[(clusters == -1)].sum() * sampling_frequency
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numtotal = len(clusters) * sampling_frequency
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return (float(-1*numoutliers) / numtotal)
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def moving_time_percent(locationData):
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if locationData is None or len(locationData) == 0:
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return None
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lbls = locationData["location_label"]
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nummoving = lbls.isnull().sum()
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numtotal = len(lbls)
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return (float(nummoving) / numtotal)
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def len_stay_at_clusters_in_minutes(locationData,sampling_frequency):
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if locationData is None or len(locationData) == 0:
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return (None, None, None,None)
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lenstays = []
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count = 0
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prev_loc_label = None
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for row in locationData.iterrows():
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cur_loc_label = row[1]["location_label"]
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if np.isnan(cur_loc_label):
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continue
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elif prev_loc_label == None:
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prev_loc_label = int(cur_loc_label)
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count += 1
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else:
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if prev_loc_label == int(cur_loc_label):
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count += 1
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else:
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lenstays.append(count)
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prev_loc_label = int(cur_loc_label)
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count = 0 + 1
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if count > 0: # in case of no transition
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lenstays.append(count)
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lenstays = np.array(lenstays) * sampling_frequency
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if len(lenstays) > 0:
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smax = np.max(lenstays)
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smin = np.min(lenstays)
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sstd = np.std(lenstays)
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smean = np.mean(lenstays)
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else:
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smax = None
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smin = None
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sstd = None
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smean = None
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return (smax, smin, sstd, smean)
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clustersDf = locationData[["location_label","timeInSeconds"]]
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numoutliers = clustersDf[clustersDf["location_label"]== -1]["timeInSeconds"].sum()
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numtotal = clustersDf.timeInSeconds.sum()
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return numoutliers/numtotal
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def location_entropy(locationData):
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if locationData is None or len(locationData) == 0:
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