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Edit and run 





In [16]:



    


from itertools import chain
import nltk
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.preprocessing import LabelBinarizer
import sklearn
import pycrfsuite
from bs4 import BeautifulSoup
import pandas as pd
from os import listdir
from os.path import isfile, join
import xml.etree.ElementTree as ET
import codecs
import numpy as np
from dateutil import parser
import scipy as sp



    











In [3]:



    


# Transportation Mode (TM) Data
FOLDER_DATA_TM = 'data/gpx/'
FOLDER_DATA_BUS = 'data/gpx/bus/'
FOLDER_DATA_WALK = 'data/gpx/wk/'
FOLDER_DATA_CAR = 'data/gpx/car/'

Read in data

Read in data from the three folders. Each data file contains a set of trkpt (traking point), and each traking point contains latitude, longitude, elevation and timestamp information.

The sampling rate varied from files to files, and varied within a file as well. Thus the time between every trkpots are not necessarily the same. I found out that for some trace (each file contains a trace), the sampling rate for the end of the trace are sometimes quite slow because sometime people stop moving.





In [4]:



    


"""
READ IN ALL DATA FILE PATHS
"""
files = dict()
files["bus"] = [f for f in listdir(FOLDER_DATA_BUS) if isfile(join(FOLDER_DATA_BUS, f))]
files["walk"] = [f for f in listdir(FOLDER_DATA_WALK) if isfile(join(FOLDER_DATA_WALK, f))]
files["car"] = [f for f in listdir(FOLDER_DATA_CAR) if isfile(join(FOLDER_DATA_CAR, f))]



    











In [4]:



    


# tm = transportation mode
df = pd.DataFrame(columns=("trace_id", "ts", "lat", "lon", "tm"))



    











In [5]:



    


for tm in ["bus", "walk", "car"]:
    if tm == "walk":
        folder_root = FOLDER_DATA_WALK
    elif tm == "car":
        folder_root = FOLDER_DATA_CAR
    elif tm == "bus":
        folder_root = FOLDER_DATA_BUS
        
    for f_name in files[tm]:
        f = codecs.open(folder_root+f_name, 'r', encoding='utf8')
        print f_name
        try:
            # Read in traking points, each contains lat, longitude and timestamp
            soup = BeautifulSoup(f)
            trkpts = soup.find_all('trkpt')
            n, d = df.shape
            for trkpt in trkpts:
                trace_id = f_name.lower().replace(" ", "_")
                df.loc[n-1] = [trace_id, trkpt.time.string, trkpt["lat"], trkpt["lon"], tm]
                n = n+1
        except:
#             print trace_id
            print f_name

        f.close()



    


















    






2011-07-21 0943 bus.gpx
2011-07-28 1119 bus.gpx
2011-07-28 1749 bus.gpx
2011-10-06 0805 bus-1.gpx
2011-10-06 0805 bus-2.gpx
2011-11-23 1004 bus-2.gpx
2011-11-23 1004 bus1.gpx
2011-11-28 1637 Riute 4A.gpx
2011-11-28 1648 Route 15 .gpx
2011-11-30 1108 Route 15.gpx
2011-12-01 1139 Route 4 B.gpx
2011-12-01 1147 Route 15.gpx
2011-12-06 1104 Route 15.gpx
2011-12-07 1118 Route3.gpx
2011-12-08 0812 Route15.gpx
2011-12-08 1146 Route 15.gpx
2011-12-09 0935 Route 15.gpx
2012-01-05 1637 Route 4b.gpx
2012-01-05 1647 15.gpx
2012-01-06 0937 ROUTE 15.gpx
2012-01-06 0942 Route 4.gpx
2012-01-06 1638 Route 3 (1).gpx
2012-01-06 1649 15 (1).gpx
2012-01-10 1108 15 (1).gpx
2012-01-10 1121 3 FromD (1).gpx
2012-01-10 1745 3 Tod (1).gpx
2012-01-12 1108 15.gpx
2012-01-13 1119 Route 3 ToNQ.gpx
2012-02-07 1103 Bus.gpx
April 10, 2012 at 10_49bus.gpx
April 6, 2012 at 9_29 Bus.gpx
April 9, 2012 at 16_52 Bus.gpx
December 12, 2011 at 2_36 PM Route 4A És15.gpx
December 12, 2011 at 2_36 PM Route 4A És15.gpx
December 14, 2011 at 8_40 AM Route 15 2A-2.gpx
December 14, 2011 at 8_40 AM Route 15 2A.gpx
December 6, 2011 at 9_21 AM 4A.gpx
November 30, 2011 at 5_15 PM 15.gpx
2011-06-20 1632 wk.gpx
2011-06-20 1752 wk.gpx
2011-07-19 1801 wk.gpx
2011-07-21 0943 wk.gpx
2011-07-21 1334 wk.gpx
2011-07-21 1453 wk.gpx
2011-07-25 1947 wk.gpx
2011-07-27 1521 wk.gpx
2011-07-27 1727 wk.gpx
2011-07-27 1734 wk.gpx
2011-07-28 1146 wk.gpx
2011-07-28 1749 wk.gpx
2011-08-04 0945 wk.gpx
2011-08-04 1135 wk.gpx
2011-08-04 1920 wk.gpx
2011-08-25 1319 wk.gpx
2011-08-26 1037 wk.gpx
2011-08-26 1114 wk.gpx
2011-08-26 1227 wk.gpx
2011-09-20 0857 walk.gpx
2011-09-28 1534 walk.gpx
2011-09-30 2046 wk.gpx
2012-02-07 1103 wk.gpx
2012-02-07 1429 Walk.gpx
2012-02-07 1741 Walk.gpx
2012-02-07 1752 Wall.gpx
2012-02-21 0935 walk.gpx
2012-03-07 1712 Walk.gpx
2012-03-19 0909 Wk.gpx
2012-03-19 1058 Wk.gpx
2012-03-21 0903 Wk.gpx
2012-03-21 1115 Wk.gpx
2012-03-21 1559 Wk.gpx
2012-03-22 1343 Wk.gpx
2012-03-22 1425 Wk.gpx
2012-03-22 1605 Wk.gpx
2012-03-23 0952 Wk.gpx
2012-03-23 1239 Wk.gpx
2012-03-23 1341 Wk.gpx
2012-03-23 1605 Wk.gpx
2012-03-27 1436 Wk.gpx
2012-03-27 1603 Wk.gpx
2012-03-28 1048 wk.gpx
2012-03-29 1426 wk.gpx
2012-03-29 1557 Wk.gpx
2012-03-30 0956 Wk.gpx
2012-03-31 1700 Wk.gpx
2012-04-03 1434 Wk.gpx
2012-04-03 1530 Wk.gpx
2012-04-03 1959 Wk.gpx
2012-04-05 1436 WK.gpx
2012-04-09 1133 Wk.gpx
April 6, 2012 at 11_44 Walk.gpx
Tk Wu wk.gpx
wk.gpx
2011-06-18 0947 dr.gpx
2011-06-18 1056 dr.gpx
2011-06-19 0959 dr.gpx
2011-06-19 1004 dr.gpx
2011-06-19 1947 dr.gpx
2011-06-20 0913 dr.gpx
2011-06-20 1344 dr.gpx
2011-07-01 1948 dr.gpx
2011-07-01 2333 dr.gpx
2011-07-02 0912 dr.gpx
2011-07-04 1314 dr.gpx
2011-07-05 1849 dr.gpx
2011-07-06 2351 dr.gpx
2011-07-15 1942 dr.gpx
2011-07-19 0029 dr.gpx
2011-07-20 1430 dr.gpx
2011-07-20 1603 dr.gpx
2011-07-20 1948 dr.gpx
2011-07-21 0051 dr.gpx
2011-07-22 1908 dr.gpx
2011-07-22 2120 dr.gpx
2011-07-23 1814 dr.gpx
2011-07-24 0029 dr.gpx
2011-07-24 1214 dr.gpx
2011-07-24 1220 dr.gpx
2011-07-24 1838 dr.gpx
2011-07-24 2103 dr.gpx
2011-07-24 2315 dr.gpx
2011-07-24 2325 dr.gpx
2011-07-26 2048 dr.gpx
2011-07-26 2106 dr.gpx
2011-07-26 2120 dr.gpx
2011-07-27 0006 state toward north Dr.gpx
2011-07-28 2209 dr.gpx
2011-07-29 0930 dr.gpx
2011-07-29 0938 dr.gpx
2011-07-29 0948 dr.gpx
2011-07-29 1203 dr.gpx
2011-07-30 2114 dr.gpx
2011-07-31 1416 dr.gpx
2011-07-31 1801 dr.gpx
2011-08-01 0138 dr.gpx
2011-08-01 1331 dr.gpx
2011-08-01 1654 dr.gpx
2011-08-02 1256 dr.gpx
2011-08-02 2153 dr.gpx
2011-08-03 1257 dr.gpx
2011-08-03 1320 dr.gpx
2011-08-03 1423 dr.gpx
2011-08-04 1141 dr.gpx
2011-08-04 1833 dr.gpx
2011-08-12 1612 dr.gpx
2011-08-23 1915 dr.gpx
2011-08-25 1246 dr.gpx
2011-08-25 1827 dr.gpx
2011-08-26 0943 dr.gpx
2011-08-26 0943 From north  campus to Dairy on main st DR.gpx
2011-08-26 2031 dr.gpx
2011-08-27 1452 dr.gpx
2011-08-27 1958 dr.gpx
2011-08-27 2111 dr.gpx
2011-08-29 1032 dr.gpx
2011-09-08 1449 dr.gpx
2011-09-15 1742 dr.gpx
2011-09-15 2018 dr.gpx
2011-09-17 0928 dr.gpx
2011-10-08 1017 dr.gpx
2011-10-09 1434 dr.gpx
2011-10-30 1610 dr.gpx
2011-11-04 1943 drive.gpx
2011-11-06 1852 dr.gpx
2011-11-11 0807 dr.gpx
2011-11-11 1314 dr.gpx
2011-11-11 1324 dr.gpx
2011-11-11 1743 dr.gpx
2011-11-12 1457 dr.gpx
2011-11-16 1719 dr.gpx
2011-11-16 1732 dr.gpx
2011-11-16 2018 dr.gpx
2011-11-19 1359 dr.gpx
2011-11-19 1445 dr.gpx
2011-11-19 1630 dr.gpx
2011-11-19 2059 dr.gpx
2011-11-21 1018 dr.gpx
2011-11-21 1637 dr.gpx
2011-11-21 1901 dr.gpx
2011-11-22 2053 dr.gpx
2011-11-25 0707 dr.gpx
2011-11-25 1038 dr.gpx
2011-11-26 1354 dr.gpx
2011-11-26 1630 dr.gpx
2011-11-27 0930 dr.gpx
2011-11-27 1254 dr.gpx
2011-11-27 1623 dr.gpx
2011-11-27 1740 dr.gpx
2011-11-27 1843 dr.gpx
2011-12-03 1758 dr.gpx
2011-12-11 1337 dr.gpx
2011-12-21 1927 dr.gpx
2011-12-22 1258 dr.gpx
2011-12-22 1542 dr.gpx
2012-01-14 2011 dr.gpx
2012-01-23 1043 dr.gpx
2012-01-27 1452 dr.gpx
2012-01-28 1423 dr.gpx
2012-01-31 2047 Dr.gpx
2012-01-31 2113 dr.gpx
2012-02-02 1137 Dr.gpx
2012-02-03 1927 Dr.gpx
2012-02-04 1323 Dr.gpx
2012-02-04 1909 Drive.gpx
2012-02-04 2315 Dr.gpx
2012-02-04 2326dr.gpx
2012-02-06 0845 Dr.gpx
2012-02-06 1850 Drive.gpx
2012-02-07 0939 Drive.gpx
2012-02-07 1834 Drive.gpx
2012-02-08 1013 Dribr.gpx
2012-02-27 1833 dr.gpx
2012-02-29 1322chicago.gpx
2012-03-02 2124 Dr.gpx
2012-03-03 1345 Dr.gpx
2012-03-03 1501 Dr.gpx
2012-03-03 1527 Dr.gpx
2012-03-04 1002 Dr.gpx
2012-03-04 1115 Dr.gpx
2012-03-04 1247 Dr.gpx
2012-03-04 1427 Dr.gpx
2012-03-07 1807 Dr.gpx
2012-03-09 1755 Dr.gpx
2012-03-09 1942 Dr.gpx
2012-03-10 1025 Dr.gpx
2012-03-10 1232 Dr_2.gpx
2012-03-10 1915 Dr.gpx
2012-03-13 1640 Dr.gpx
2012-03-16 1829 Dr.gpx
2012-03-17 2245 Dr.gpx
2012-03-18 1323 Dr.gpx
2012-03-19 0857 Dr.gpx
2012-03-19 1029 Dr.gpx
2012-03-20 1145 Dr.gpx
2012-03-21 0856 Dr.gpx
2012-03-21 1106 Dr.gpx
2012-03-22 0233 Dr.gpx
2012-03-22 0245 Dr.gpx
2012-03-23 0945 Dr.gpx
2012-03-23 1608 Dr.gpx
2012-03-23 1623 dr.gpx
2012-03-24 1214 Dr.gpx
2012-03-25 1304 Dr.gpx
2012-03-25 1719 Dr.gpx
2012-03-26 0102 Dr.gpx
2012-03-26 0849 Dr.gpx
2012-03-26 1038 Dr.gpx
2012-03-26 2001 Dr.gpx
2012-03-28 0851 Dr.gpx
2012-03-28 1037 Dr.gpx
2012-03-28 1706 Dr.gpx
2012-03-28 1911 Dr.gpx
2012-03-29 1103 Dr.gpx

Export files for future usage.





In [8]:



    


df.to_csv(FOLDER_DATA_TM+"raw_tm_dataframe~.csv")



    











In [5]:



    


df = pd.read_csv(FOLDER_DATA_TM+"raw_tm_dataframe~.csv")

Number of transportation mode instances





In [6]:



    


df["tm"].value_counts()



    


















    
Out[6]:








car     78739
walk     8675
bus      4742
dtype: int64

Segmentation

There are three major units in this transportation dataset.

  • A trace, which is contained within a file;
  • A segment of the trace;
  • A tracking point.

Trace and traking points were introduced above, thus here I would only talk about segments.

In this dataset, an individual's manually record each trace, and each trace has quite clear start and end, and contains only one type of transportation mode. (Thus it ignores the transition between different transportation modes, which is not a good idea actually.)

There are multiple ways to group the traking points in each trace into segments so to compute the required features. For example, we could group the points by fixed time, or dynamic time given the speed of users, or group by distance or number of points.

The below code segments the data by time, and the duration is set to 120 seconds. The segmentation is done by calculating the time lapse from the beginning of a trace to a specific traking point, and then we can easily get the segment index from that time lapse.





In [7]:



    


"""
CREATE SEGMENTATION
"""
SEGMENTATION_DURATION = 120
def get_time_lapse(group):
    trace_id = group.iloc[0]["trace_id"]
    starttime = parser.parse(group.iloc[0]["ts"])
    group["time_lapse"] = group["ts"].apply(lambda x: (parser.parse(x) - starttime).total_seconds())
    group["seg_name"] = group["time_lapse"].apply(lambda x: trace_id+"__"+str(int(x/SEGMENTATION_DURATION)))
    group["seg_id"] = group["time_lapse"].apply(lambda x: int(x/SEGMENTATION_DURATION))
    
    return group

df = df.groupby("trace_id").apply(get_time_lapse)

Given the GPS information, we want to calculate the distance between segments





In [9]:



    


from math import radians, cos, sin, asin, sqrt

def haversine(lon1, lat1, lon2, lat2):
    """
    Calculate the great circle distance between two points 
    on the earth (specified in decimal degrees)
    """
    # convert decimal degrees to radians 
    lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])

    # haversine formula 
    dlon = lon2 - lon1 
    dlat = lat2 - lat1 
    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
    c = 2 * asin(sqrt(a)) 

    # 6367 km is the radius of the Earth
    km = 6367 * c
    return km



    











In [10]:



    


"""
Get distance and velocity
"""
for index, row in df.iterrows():
    try:
        cur_trace = df.ix[index, "trace_id"]
        next_trace = df.ix[index+1, "trace_id"]
        
#         print lat1, lon1
        
        if cur_trace == next_trace:
            # distance
            lat1, lon1 = df.ix[index, ["lat", "lon"]]
            lat2, lon2 = df.ix[index+1, ["lat", "lon"]]
            dist = haversine(float(lon1), float(lat1), float(lon2), float(lat2)) * 1000
            df.ix[index, "dist_m"] = dist
        
            # duration
            t1 = df.ix[index, "time_lapse"]
            t2 = df.ix[index+1, "time_lapse"]
            dur = t2 - t1
        
            # speed m/s
            df.ix[index, "speed"] = dist/dur
            
        else:
            df.ix[index, "dist_m"] = None
            df.ix[index, "speed"] = None
    except:
        df.ix[index, "dist_m"] = None
        df.ix[index, "speed"] = None

Since to get acceleration we need velocity information of the future data point, we need to separate acceleration extraction from the above code.





In [11]:



    


"""
Get acceleration
"""
for index, row in df.iterrows():
    try:
        cur_trace = df.ix[index, "trace_id"]
        next_trace = df.ix[index+1, "trace_id"]
        
#         print lat1, lon1
        
        if cur_trace == next_trace:
            # acceleration
            v1 = df.ix[index, "speed"]
            v2 = df.ix[index+1, "speed"]
            t1 = df.ix[index, "time_lapse"]
            t2 = df.ix[index+1, "time_lapse"]
            dur = t2 - t1
            acce = (v2 - v1)/dur
            df.ix[index, "acce"] = acce
        else:
            df.ix[index, "dist_m"] = None
    except:
        df.ix[index, "dist_m"] = None



    











In [12]:



    


df.to_csv(FOLDER_DATA_TM+"tm_dataframe~.csv")



    











In [13]:



    


df.dropna(subset=["speed", "dist_m", "acce"], inplace=True)

Pandas treat speed, distance and acceleration as an object, however, we want it to be float.





In [14]:



    


df[["speed", "dist_m", "acce"]] = df[["speed", "dist_m", "acce"]].astype(float)
df_seg = df.groupby(["trace_id", "seg_id"])

Obtain features for segments

Now we have the distance between each tracking points as well as speed and acceleration, we could obtain features for segments (i.e., a group of tracking points.)

These features include maximum speed of a group of tracking points, mean and variation of speed; other features related to acceleration.





In [17]:



    


"""
SPEED RELATED FEATURES
"""
def get_top3_speed(data):
    data.sort(ascending=False)
    return data.iloc[0:3].mean() 

df_seg = df.groupby(["trace_id", "seg_id"])["speed"].agg({"max_speed": np.max,
                                            "avg_speed": np.mean,
                                            "avg_top3_speed": get_top3_speed,
                                            "var_speed": np.var
                                            })

df_seg



    


















    
Out[17]:










  
    

      
      
      avg_speed
      max_speed
      avg_top3_speed
      var_speed
    

    

      trace_id
      seg_id
      
      
      
      
    

  
  
    

      2011-06-18_0947_dr.gpx
      0 
       11.538154
       54.752102
       30.830751
       46.466349
    

    

      1 
        7.706204
       16.443149
       14.541437
        7.974436
    

    

      2 
       15.391269
       28.559408
       25.841018
       15.343339
    

    

      3 
       14.444493
       24.677293
       21.270534
       25.976733
    

    

      4 
        5.307677
        7.018591
        6.559883
        1.374237
    

    

      2011-06-18_1056_dr.gpx
      0 
        4.651058
       12.223762
       10.795077
        9.897790
    

    

      1 
       16.516460
       20.673961
       20.673892
       12.906775
    

    

      2 
       13.723858
       18.223492
       18.189504
       19.199767
    

    

      3 
        7.085528
       17.424587
       15.745808
       41.682044
    

    

      4 
        7.713151
       15.234336
       14.884946
       28.962270
    

    

      5 
        0.030393
        0.030393
        0.030393
             NaN
    

    

      6 
        0.010181
        0.010181
        0.010181
             NaN
    

    

      10
        0.005702
        0.005702
        0.005702
             NaN
    

    

      18
        0.011152
        0.011152
        0.011152
             NaN
    

    

      2011-06-19_0959_dr.gpx
      0 
       14.730080
       21.027324
       20.204101
       20.120307
    

    

      1 
        9.056485
       17.263750
       16.193957
       13.881236
    

    

      2011-06-19_1004_dr.gpx
      0 
       14.542129
       20.532938
       20.503918
       13.108442
    

    

      1 
       14.948355
       21.834748
       21.696186
       26.525654
    

    

      2 
       18.715354
       34.536957
       34.124536
       87.884409
    

    

      3 
       32.769273
       36.432512
       35.979849
        3.957461
    

    

      4 
       34.470555
       37.858140
       37.836353
        5.070147
    

    

      5 
       35.571595
       37.194107
       37.153577
        0.630312
    

    

      6 
       36.899610
       43.136093
       42.494370
        5.425723
    

    

      7 
       35.614048
       38.684000
       37.838210
        0.556182
    

    

      8 
       35.863728
       38.968410
       38.489758
        1.166677
    

    

      9 
       32.397064
       37.471299
       37.324761
       13.994028
    

    

      10
       18.732742
       33.377951
       33.377950
       59.550911
    

    

      11
       35.070955
       37.818208
       37.797401
        5.418521
    

    

      12
       35.881081
       38.845640
       38.264049
        0.791673
    

    

      13
       35.509204
       38.971507
       38.208492
        2.418115
    

    

      ...
      ...
      ...
      ...
      ...
      ...
    

    

      april_6,_2012_at_11_44_walk.gpx
      0 
        5.819242
       13.957784
       13.021371
       24.796552
    

    

      1 
        2.172601
        2.685488
        2.172601
        0.721471
    

    

      april_6,_2012_at_9_29_bus.gpx
      0 
       13.009367
       19.599746
       19.355153
       33.694260
    

    

      1 
       10.666147
       19.613936
       18.320933
       23.856696
    

    

      2 
        6.773855
       11.575950
       10.484743
       14.140069
    

    

      april_9,_2012_at_16_52_bus.gpx
      0 
       10.240259
       19.539810
       17.940452
       26.516630
    

    

      1 
        8.886948
       15.193357
       14.494787
       16.767020
    

    

      december_14,_2011_at_8_40_am_route_15_2a-2.gpx
      0 
        3.659817
        8.844575
        7.389603
        6.247185
    

    

      1 
        5.638129
       17.520835
       12.752477
       18.547910
    

    

      december_14,_2011_at_8_40_am_route_15_2a.gpx
      0 
        6.007853
       10.396240
       10.045129
       11.939613
    

    

      1 
        6.034304
       12.785005
       12.018296
       16.488222
    

    

      2 
        7.316891
       14.930311
       12.463165
        8.174243
    

    

      3 
        5.561768
       10.016671
        9.902695
        9.982387
    

    

      4 
        4.036449
       17.530768
       11.933783
       13.494974
    

    

      5 
        0.844765
        0.844765
        0.844765
             NaN
    

    

      december_6,_2011_at_9_21_am_4a.gpx
      0 
        4.164905
       10.722410
       10.619580
       12.127728
    

    

      1 
        8.786454
       12.233028
       12.131521
       12.890448
    

    

      2 
        6.239269
       13.311453
       11.988907
        9.654239
    

    

      3 
        4.825693
        9.170460
        7.875622
        5.963478
    

    

      november_30,_2011_at_5_15_pm_15.gpx
      0 
        1.564586
        5.628504
        4.217703
        2.270539
    

    

      1 
        6.725017
       52.353157
       26.477631
       91.999173
    

    

      2 
        5.633766
       12.048296
       10.410869
        9.766548
    

    

      tk_wu_wk.gpx
      0 
        1.980868
        5.529358
        5.371810
        1.243029
    

    

      1 
        1.857171
        4.520404
        3.758065
        0.901414
    

    

      wk.gpx
      0 
        1.878076
        4.673194
        4.119896
        0.872023
    

    

      1 
        1.882308
        5.616758
        5.031863
        2.123816
    

    

      2 
        2.281483
       10.646294
        5.079502
        8.165915
    

    

      3 
        1.770278
        4.445005
        4.127848
        0.918916
    

    

      4 
        1.781270
        4.682886
        4.266384
        0.737536
    

    

      5 
        2.183998
        8.516822
        3.416336
       12.693845
    

  



1386 rows × 4 columns



















In [18]:



    


def get_top3_acce(data):
    data.sort(ascending=False)
    return data.iloc[0:3].mean()

df_seg_acce = df.groupby(["trace_id", "seg_id"])["acce"].agg({"avg_acce": np.mean,
                                             "top_acce": np.max,
                                             "avg_top3_acce": get_top3_acce,
                                             "var_acce": np.var})
df_seg_acce



    


















    
Out[18]:










  
    

      
      
      top_acce
      var_acce
      avg_top3_acce
      avg_acce
    

    

      trace_id
      seg_id
      
      
      
      
    

  
  
    

      2011-06-18_0947_dr.gpx
      0 
        5.737922
       22.524166
       3.673204
      -0.803202
    

    

      1 
        4.879425
        2.580380
       3.611650
       0.038851
    

    

      2 
       10.731275
        9.000715
       9.211271
      -0.062543
    

    

      3 
        6.453855
        3.917804
       4.735725
      -0.216421
    

    

      4 
        1.157104
        0.177065
       0.468718
       0.047855
    

    

      2011-06-18_1056_dr.gpx
      0 
        3.844423
        2.804401
       2.380079
      -0.270697
    

    

      1 
        3.842815
        2.034245
       3.082494
       0.106425
    

    

      2 
        2.602753
        1.805781
       2.267103
      -0.283843
    

    

      3 
        0.774225
        0.232625
       0.613609
       0.091476
    

    

      4 
        0.930638
        0.276874
       0.531812
      -0.150022
    

    

      5 
       -0.000114
             NaN
      -0.000114
      -0.000114
    

    

      6 
       -0.000010
             NaN
      -0.000010
      -0.000010
    

    

      10
        0.000005
             NaN
       0.000005
       0.000005
    

    

      18
       -0.000017
             NaN
      -0.000017
      -0.000017
    

    

      2011-06-19_0959_dr.gpx
      0 
        4.215468
        1.545063
       3.624399
       0.094218
    

    

      1 
        2.530010
        2.083043
       2.226849
      -0.388055
    

    

      2011-06-19_1004_dr.gpx
      0 
        5.054952
        2.263633
       4.545702
       0.001071
    

    

      1 
        3.861887
        2.394634
       3.483079
      -0.044449
    

    

      2 
        3.392235
        1.702455
       3.285336
       0.187826
    

    

      3 
        2.216019
        1.161041
       2.189478
      -0.030641
    

    

      4 
        3.284631
        1.316259
       3.031594
       0.069257
    

    

      5 
        2.681952
        1.416252
       2.506367
       0.001439
    

    

      6 
        3.123779
        1.604212
       3.113585
      -0.018504
    

    

      7 
        4.144663
        1.324183
       3.092399
       0.006549
    

    

      8 
        2.597708
        1.425680
       2.519140
      -0.019254
    

    

      9 
        3.359982
        1.858651
       3.087741
      -0.012728
    

    

      10
        4.169359
        2.683024
       3.570229
      -0.115639
    

    

      11
        3.254743
        1.843704
       2.659779
       0.080165
    

    

      12
        2.783200
        1.937480
       2.714856
       0.013593
    

    

      13
        2.221872
        1.242524
       2.209210
      -0.040990
    

    

      ...
      ...
      ...
      ...
      ...
      ...
    

    

      april_6,_2012_at_11_44_walk.gpx
      0 
        1.114246
        1.710861
       0.768834
      -0.336250
    

    

      1 
       -0.004533
        0.000615
      -0.031116
      -0.031116
    

    

      april_6,_2012_at_9_29_bus.gpx
      0 
        3.482772
        2.362665
       1.993037
      -0.257359
    

    

      1 
        2.651707
        5.786086
       2.332451
      -0.573997
    

    

      2 
        1.145005
        2.931728
       0.832442
      -0.564865
    

    

      april_9,_2012_at_16_52_bus.gpx
      0 
        3.135459
        3.236233
       2.107357
      -0.308058
    

    

      1 
        1.211598
        1.187395
       1.016238
      -0.226627
    

    

      december_14,_2011_at_8_40_am_route_15_2a-2.gpx
      0 
        1.290801
        0.548269
       1.017371
      -0.193513
    

    

      1 
        3.266618
        2.137572
       1.567656
      -0.263413
    

    

      december_14,_2011_at_8_40_am_route_15_2a.gpx
      0 
        1.784168
        0.777785
       1.253936
      -0.108496
    

    

      1 
        2.005384
        1.330671
       1.480908
      -0.192867
    

    

      2 
        2.325987
        1.160495
       1.470254
      -0.038940
    

    

      3 
        1.475464
        0.761920
       1.233487
      -0.212175
    

    

      4 
        2.226260
        2.890826
       1.995514
      -0.198894
    

    

      5 
        0.002637
             NaN
       0.002637
       0.002637
    

    

      december_6,_2011_at_9_21_am_4a.gpx
      0 
        0.986310
        0.332616
       0.916140
      -0.009714
    

    

      1 
        1.336505
        0.564952
       0.977997
      -0.087539
    

    

      2 
        1.145576
        0.536762
       0.897760
      -0.122037
    

    

      3 
        1.385260
        1.080696
       1.116700
      -0.056140
    

    

      november_30,_2011_at_5_15_pm_15.gpx
      0 
        0.616260
        0.129100
       0.237585
      -0.146056
    

    

      1 
        8.084065
       16.690987
       3.702041
      -0.487813
    

    

      2 
        1.263224
        0.929744
       1.202770
      -0.256439
    

    

      tk_wu_wk.gpx
      0 
        4.234532
        1.662940
       3.379092
      -0.068441
    

    

      1 
        3.698219
        1.778431
       2.457884
       0.015324
    

    

      wk.gpx
      0 
        2.764669
        1.206307
       2.537047
      -0.070713
    

    

      1 
        2.516915
        1.651628
       2.106118
      -0.254529
    

    

      2 
        3.248170
        2.942989
       1.253711
      -0.344853
    

    

      3 
        2.586934
        1.192527
       2.302683
      -0.068433
    

    

      4 
        2.497953
        1.229380
       2.385920
      -0.072211
    

    

      5 
        1.204856
        2.219767
       0.511072
      -0.250595
    

  



1386 rows × 4 columns



















In [19]:



    


def get_tm(data):
    return data.iloc[0]
df_seg_tm = df.groupby(["trace_id", "seg_id"])["tm"].agg({"tm": get_tm})



    











In [20]:



    


df_seg = df_seg.join(df_seg_acce)
df_seg = df_seg.join(df_seg_tm)



    











In [21]:



    


df_seg.dropna(inplace=True)



    











In [22]:



    


df_seg["tm"].value_counts()



    


















    
Out[22]:








car     1031
walk     222
bus      111
dtype: int64

Discretize the data into N quantiles.





In [71]:



    


N_BINS_MAX_SPEED = 6

for col in df_seg.columns:
    try:
        field_name = "d_" + col
        df_seg[field_name] = pd.qcut(df_seg[col], N_BINS_MAX_SPEED, labels=[x+1 for x in range(N_BINS_MAX_SPEED)])
    except:
        continue



    











In [72]:



    


def get_d_tm(tm):
    tm_to_d = dict()
    tm_to_d = {
        "walk": 1,
        "car": 2,
        "bus": 3
    }
    return tm_to_d[tm]

df_seg["d_tm"] = df_seg["tm"].apply(get_d_tm)



    











In [73]:



    


df_seg



    


















    
Out[73]:










  
    

      
      
      avg_speed
      max_speed
      avg_top3_speed
      var_speed
      top_acce
      var_acce
      avg_top3_acce
      avg_acce
      tm
      d_avg_speed
      ...
      d_avg_acce
      d_tm
      d_d_avg_speed
      d_d_max_speed
      d_d_avg_top3_speed
      d_d_var_speed
      d_d_top_acce
      d_d_var_acce
      d_d_avg_top3_acce
      d_d_avg_acce
    

    

      trace_id
      seg_id
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      2011-06-18_0947_dr.gpx
      0 
       11.538154
       54.752102
       30.830751
       46.466349
        5.737922
       22.524166
       3.673204
      -0.803202
        car
       4
      ...
       1
       2
       4
       6
       6
       6
       6
       6
       5
       1
    

    

      1 
        7.706204
       16.443149
       14.541437
        7.974436
        4.879425
        2.580380
       3.611650
       0.038851
        car
       3
      ...
       6
       2
       3
       3
       3
       3
       5
       5
       5
       6
    

    

      2 
       15.391269
       28.559408
       25.841018
       15.343339
       10.731275
        9.000715
       9.211271
      -0.062543
        car
       5
      ...
       3
       2
       5
       6
       6
       4
       6
       6
       6
       3
    

    

      3 
       14.444493
       24.677293
       21.270534
       25.976733
        6.453855
        3.917804
       4.735725
      -0.216421
        car
       5
      ...
       1
       2
       5
       5
       5
       5
       6
       6
       6
       1
    

    

      4 
        5.307677
        7.018591
        6.559883
        1.374237
        1.157104
        0.177065
       0.468718
       0.047855
        car
       2
      ...
       6
       2
       2
       2
       2
       2
       2
       1
       1
       6
    

    

      2011-06-18_1056_dr.gpx
      0 
        4.651058
       12.223762
       10.795077
        9.897790
        3.844423
        2.804401
       2.380079
      -0.270697
        car
       2
      ...
       1
       2
       2
       2
       2
       3
       4
       5
       3
       1
    

    

      1 
       16.516460
       20.673961
       20.673892
       12.906775
        3.842815
        2.034245
       3.082494
       0.106425
        car
       5
      ...
       6
       2
       5
       5
       5
       4
       4
       4
       4
       6
    

    

      2 
       13.723858
       18.223492
       18.189504
       19.199767
        2.602753
        1.805781
       2.267103
      -0.283843
        car
       5
      ...
       1
       2
       5
       4
       4
       5
       3
       4
       3
       1
    

    

      3 
        7.085528
       17.424587
       15.745808
       41.682044
        0.774225
        0.232625
       0.613609
       0.091476
        car
       3
      ...
       6
       2
       3
       4
       3
       6
       1
       1
       1
       6
    

    

      4 
        7.713151
       15.234336
       14.884946
       28.962270
        0.930638
        0.276874
       0.531812
      -0.150022
        car
       3
      ...
       2
       2
       3
       3
       3
       6
       1
       1
       1
       2
    

    

      2011-06-19_0959_dr.gpx
      0 
       14.730080
       21.027324
       20.204101
       20.120307
        4.215468
        1.545063
       3.624399
       0.094218
        car
       5
      ...
       6
       2
       5
       5
       5
       5
       5
       3
       5
       6
    

    

      1 
        9.056485
       17.263750
       16.193957
       13.881236
        2.530010
        2.083043
       2.226849
      -0.388055
        car
       4
      ...
       1
       2
       4
       4
       3
       4
       3
       4
       3
       1
    

    

      2011-06-19_1004_dr.gpx
      0 
       14.542129
       20.532938
       20.503918
       13.108442
        5.054952
        2.263633
       4.545702
       0.001071
        car
       5
      ...
       5
       2
       5
       5
       5
       4
       5
       4
       6
       5
    

    

      1 
       14.948355
       21.834748
       21.696186
       26.525654
        3.861887
        2.394634
       3.483079
      -0.044449
        car
       5
      ...
       4
       2
       5
       5
       5
       6
       4
       5
       5
       4
    

    

      2 
       18.715354
       34.536957
       34.124536
       87.884409
        3.392235
        1.702455
       3.285336
       0.187826
        car
       6
      ...
       6
       2
       6
       6
       6
       6
       4
       3
       4
       6
    

    

      3 
       32.769273
       36.432512
       35.979849
        3.957461
        2.216019
        1.161041
       2.189478
      -0.030641
        car
       6
      ...
       4
       2
       6
       6
       6
       2
       2
       2
       3
       4
    

    

      4 
       34.470555
       37.858140
       37.836353
        5.070147
        3.284631
        1.316259
       3.031594
       0.069257
        car
       6
      ...
       6
       2
       6
       6
       6
       2
       3
       3
       4
       6
    

    

      5 
       35.571595
       37.194107
       37.153577
        0.630312
        2.681952
        1.416252
       2.506367
       0.001439
        car
       6
      ...
       5
       2
       6
       6
       6
       1
       3
       3
       3
       5
    

    

      6 
       36.899610
       43.136093
       42.494370
        5.425723
        3.123779
        1.604212
       3.113585
      -0.018504
        car
       6
      ...
       4
       2
       6
       6
       6
       2
       3
       3
       4
       4
    

    

      7 
       35.614048
       38.684000
       37.838210
        0.556182
        4.144663
        1.324183
       3.092399
       0.006549
        car
       6
      ...
       5
       2
       6
       6
       6
       1
       5
       3
       4
       5
    

    

      8 
       35.863728
       38.968410
       38.489758
        1.166677
        2.597708
        1.425680
       2.519140
      -0.019254
        car
       6
      ...
       4
       2
       6
       6
       6
       2
       3
       3
       3
       4
    

    

      9 
       32.397064
       37.471299
       37.324761
       13.994028
        3.359982
        1.858651
       3.087741
      -0.012728
        car
       6
      ...
       5
       2
       6
       6
       6
       4
       4
       4
       4
       5
    

    

      10
       18.732742
       33.377951
       33.377950
       59.550911
        4.169359
        2.683024
       3.570229
      -0.115639
        car
       6
      ...
       2
       2
       6
       6
       6
       6
       5
       5
       5
       2
    

    

      11
       35.070955
       37.818208
       37.797401
        5.418521
        3.254743
        1.843704
       2.659779
       0.080165
        car
       6
      ...
       6
       2
       6
       6
       6
       2
       3
       4
       3
       6
    

    

      12
       35.881081
       38.845640
       38.264049
        0.791673
        2.783200
        1.937480
       2.714856
       0.013593
        car
       6
      ...
       5
       2
       6
       6
       6
       1
       3
       4
       3
       5
    

    

      13
       35.509204
       38.971507
       38.208492
        2.418115
        2.221872
        1.242524
       2.209210
      -0.040990
        car
       6
      ...
       4
       2
       6
       6
       6
       2
       2
       3
       3
       4
    

    

      14
       32.687807
       37.929132
       37.545561
        8.975534
        2.895628
        1.615188
       2.864404
       0.032111
        car
       6
      ...
       5
       2
       6
       6
       6
       3
       3
       3
       4
       5
    

    

      15
       35.734783
       37.022590
       37.022393
        0.480017
        2.447435
        1.084308
       2.222972
       0.000699
        car
       6
      ...
       5
       2
       6
       6
       6
       1
       3
       2
       3
       5
    

    

      16
       35.910076
       40.167184
       40.162056
        3.015150
        2.441900
        1.212370
       2.216436
      -0.058016
        car
       6
      ...
       3
       2
       6
       6
       6
       2
       3
       2
       3
       3
    

    

      17
       24.020829
       33.752188
       33.494711
       43.963003
        4.429903
        2.551099
       3.663095
      -0.197392
        car
       6
      ...
       1
       2
       6
       6
       6
       6
       5
       5
       5
       1
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      april_10,_2012_at_10_49bus.gpx
      2 
        7.377441
       11.082855
       10.121430
       11.868290
        0.611137
        1.560010
       0.569477
      -0.312668
        bus
       3
      ...
       1
       3
       3
       2
       2
       3
       1
       3
       1
       1
    

    

      april_6,_2012_at_11_44_walk.gpx
      0 
        5.819242
       13.957784
       13.021371
       24.796552
        1.114246
        1.710861
       0.768834
      -0.336250
       walk
       2
      ...
       1
       1
       2
       3
       3
       5
       2
       3
       2
       1
    

    

      1 
        2.172601
        2.685488
        2.172601
        0.721471
       -0.004533
        0.000615
      -0.031116
      -0.031116
       walk
       2
      ...
       4
       1
       2
       1
       1
       1
       1
       1
       1
       4
    

    

      april_6,_2012_at_9_29_bus.gpx
      0 
       13.009367
       19.599746
       19.355153
       33.694260
        3.482772
        2.362665
       1.993037
      -0.257359
        bus
       5
      ...
       1
       3
       5
       4
       5
       6
       4
       4
       3
       1
    

    

      1 
       10.666147
       19.613936
       18.320933
       23.856696
        2.651707
        5.786086
       2.332451
      -0.573997
        bus
       4
      ...
       1
       3
       4
       4
       4
       5
       3
       6
       3
       1
    

    

      2 
        6.773855
       11.575950
       10.484743
       14.140069
        1.145005
        2.931728
       0.832442
      -0.564865
        bus
       3
      ...
       1
       3
       3
       2
       2
       4
       2
       5
       2
       1
    

    

      april_9,_2012_at_16_52_bus.gpx
      0 
       10.240259
       19.539810
       17.940452
       26.516630
        3.135459
        3.236233
       2.107357
      -0.308058
        bus
       4
      ...
       1
       3
       4
       4
       4
       6
       3
       5
       3
       1
    

    

      1 
        8.886948
       15.193357
       14.494787
       16.767020
        1.211598
        1.187395
       1.016238
      -0.226627
        bus
       3
      ...
       1
       3
       3
       3
       3
       4
       2
       2
       2
       1
    

    

      december_14,_2011_at_8_40_am_route_15_2a-2.gpx
      0 
        3.659817
        8.844575
        7.389603
        6.247185
        1.290801
        0.548269
       1.017371
      -0.193513
        bus
       2
      ...
       1
       3
       2
       2
       2
       3
       2
       2
       2
       1
    

    

      1 
        5.638129
       17.520835
       12.752477
       18.547910
        3.266618
        2.137572
       1.567656
      -0.263413
        bus
       2
      ...
       1
       3
       2
       4
       3
       5
       3
       4
       2
       1
    

    

      december_14,_2011_at_8_40_am_route_15_2a.gpx
      0 
        6.007853
       10.396240
       10.045129
       11.939613
        1.784168
        0.777785
       1.253936
      -0.108496
        bus
       2
      ...
       3
       3
       2
       2
       2
       3
       2
       2
       2
       3
    

    

      1 
        6.034304
       12.785005
       12.018296
       16.488222
        2.005384
        1.330671
       1.480908
      -0.192867
        bus
       2
      ...
       1
       3
       2
       2
       3
       4
       2
       3
       2
       1
    

    

      2 
        7.316891
       14.930311
       12.463165
        8.174243
        2.325987
        1.160495
       1.470254
      -0.038940
        bus
       3
      ...
       4
       3
       3
       3
       3
       3
       2
       2
       2
       4
    

    

      3 
        5.561768
       10.016671
        9.902695
        9.982387
        1.475464
        0.761920
       1.233487
      -0.212175
        bus
       2
      ...
       1
       3
       2
       2
       2
       3
       2
       2
       2
       1
    

    

      4 
        4.036449
       17.530768
       11.933783
       13.494974
        2.226260
        2.890826
       1.995514
      -0.198894
        bus
       2
      ...
       1
       3
       2
       4
       2
       4
       2
       5
       3
       1
    

    

      december_6,_2011_at_9_21_am_4a.gpx
      0 
        4.164905
       10.722410
       10.619580
       12.127728
        0.986310
        0.332616
       0.916140
      -0.009714
        bus
       2
      ...
       5
       3
       2
       2
       2
       3
       2
       1
       2
       5
    

    

      1 
        8.786454
       12.233028
       12.131521
       12.890448
        1.336505
        0.564952
       0.977997
      -0.087539
        bus
       3
      ...
       3
       3
       3
       2
       3
       4
       2
       2
       2
       3
    

    

      2 
        6.239269
       13.311453
       11.988907
        9.654239
        1.145576
        0.536762
       0.897760
      -0.122037
        bus
       3
      ...
       2
       3
       3
       3
       2
       3
       2
       2
       2
       2
    

    

      3 
        4.825693
        9.170460
        7.875622
        5.963478
        1.385260
        1.080696
       1.116700
      -0.056140
        bus
       2
      ...
       3
       3
       2
       2
       2
       3
       2
       2
       2
       3
    

    

      november_30,_2011_at_5_15_pm_15.gpx
      0 
        1.564586
        5.628504
        4.217703
        2.270539
        0.616260
        0.129100
       0.237585
      -0.146056
        bus
       1
      ...
       2
       3
       1
       1
       1
       2
       1
       1
       1
       2
    

    

      1 
        6.725017
       52.353157
       26.477631
       91.999173
        8.084065
       16.690987
       3.702041
      -0.487813
        bus
       3
      ...
       1
       3
       3
       6
       6
       6
       6
       6
       5
       1
    

    

      2 
        5.633766
       12.048296
       10.410869
        9.766548
        1.263224
        0.929744
       1.202770
      -0.256439
        bus
       2
      ...
       1
       3
       2
       2
       2
       3
       2
       2
       2
       1
    

    

      tk_wu_wk.gpx
      0 
        1.980868
        5.529358
        5.371810
        1.243029
        4.234532
        1.662940
       3.379092
      -0.068441
       walk
       1
      ...
       3
       1
       1
       1
       2
       2
       5
       3
       5
       3
    

    

      1 
        1.857171
        4.520404
        3.758065
        0.901414
        3.698219
        1.778431
       2.457884
       0.015324
       walk
       1
      ...
       5
       1
       1
       1
       1
       1
       4
       3
       3
       5
    

    

      wk.gpx
      0 
        1.878076
        4.673194
        4.119896
        0.872023
        2.764669
        1.206307
       2.537047
      -0.070713
       walk
       1
      ...
       3
       1
       1
       1
       1
       1
       3
       2
       3
       3
    

    

      1 
        1.882308
        5.616758
        5.031863
        2.123816
        2.516915
        1.651628
       2.106118
      -0.254529
       walk
       1
      ...
       1
       1
       1
       1
       2
       2
       3
       3
       3
       1
    

    

      2 
        2.281483
       10.646294
        5.079502
        8.165915
        3.248170
        2.942989
       1.253711
      -0.344853
       walk
       2
      ...
       1
       1
       2
       2
       2
       3
       3
       5
       2
       1
    

    

      3 
        1.770278
        4.445005
        4.127848
        0.918916
        2.586934
        1.192527
       2.302683
      -0.068433
       walk
       1
      ...
       3
       1
       1
       1
       1
       1
       3
       2
       3
       3
    

    

      4 
        1.781270
        4.682886
        4.266384
        0.737536
        2.497953
        1.229380
       2.385920
      -0.072211
       walk
       1
      ...
       3
       1
       1
       1
       1
       1
       3
       3
       3
       3
    

    

      5 
        2.183998
        8.516822
        3.416336
       12.693845
        1.204856
        2.219767
       0.511072
      -0.250595
       walk
       2
      ...
       1
       1
       2
       2
       1
       4
       2
       4
       1
       1
    

  



1364 rows × 26 columns



















In [74]:



    


from sklearn_pandas import DataFrameMapper



    











In [75]:



    


mapper = DataFrameMapper([
        ("d_tm", None),
        ("d_avg_speed", None),
        ("d_var_speed", None),
        ('d_max_speed', None),
        ('d_avg_top3_speed', None),
        ('d_top_acce', None),
        ('d_var_acce', None),
        ('d_avg_top3_acce', None),
        ('d_avg_acce', None)
])



    











In [76]:



    


n_feature = len(mapper.features)



    











In [77]:



    


import random



    











In [78]:



    


"""
RANDOM SAMPLING
"""
rows = random.sample(df_seg.index.levels[0].values, len(df_seg.index.levels[0])/2)
df_train = df_seg.ix[rows]

# Originally I used df_seg.drop(rows), however, for some reasons drop will not actually drop 
# the index of rows. 
rows_test = set(df_seg.index.levels[0].values) - set(rows)
df_test = df_seg.ix[rows_test]



    











In [79]:



    


df_train["tm"].value_counts()



    


















    
Out[79]:








car     510
walk    117
bus      50
dtype: int64

















In [80]:



    


df_test["tm"].value_counts()



    


















    
Out[80]:








car     521
walk    105
bus      61
dtype: int64

















In [81]:



    


from scipy import io



    











In [82]:



    


for dataset_type in ["training", "testing"]:
    if dataset_type == "training":
        df_temp = df_train.copy()
    else:
        df_temp = df_test.copy()
        
    output_matrix = np.zeros(shape=(n_feature, 0))
    for index, trace_id in enumerate(df_temp.index.levels[0].values):
        trace_matrix = mapper.fit_transform(df_temp.ix[trace_id])
        
        # I don't know why some index in the training set is still in the testing set
        n, d = trace_matrix.shape
        if n == 0:
            print dataset_type, trace_id
            
        if index == len(df_temp.index.levels[0].values) - 1:
            output_matrix = np.concatenate((output_matrix, trace_matrix.T), axis=1)
        else:
            trace_matrix = np.concatenate((trace_matrix.T, np.zeros(shape=(n_feature, 1))),axis=1)
            output_matrix = np.concatenate((output_matrix, trace_matrix), axis=1)
    
    sp.io.savemat(dataset_type, mdict={'X': output_matrix}, appendmat=True)



    











In [317]:



    


df_temp.ix["2011-06-18_1056_dr.gpx"]



    


















    
Out[317]:










  
    

      
      avg_speed
      max_speed
      avg_top3_speed
      var_speed
      top_acce
      var_acce
      avg_top3_acce
      avg_acce
      tm
      d_avg_speed
      d_max_speed
      d_avg_top3_speed
      d_var_speed
      d_top_acce
      d_var_acce
      d_avg_top3_acce
      d_avg_acce
      d_tm
    

    

      seg_id
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
  




















In [319]:



    


df_seg.ix["2011-06-18_1056_dr.gpx"]



    


















    
Out[319]:










  
    

      
      avg_speed
      max_speed
      avg_top3_speed
      var_speed
      top_acce
      var_acce
      avg_top3_acce
      avg_acce
      tm
      d_avg_speed
      d_max_speed
      d_avg_top3_speed
      d_var_speed
      d_top_acce
      d_var_acce
      d_avg_top3_acce
      d_avg_acce
      d_tm
    

    

      seg_id
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      0
        4.651058
       12.223762
       10.795077
        9.897790
       3.844423
       2.804401
       2.380079
      -0.270697
       car
       1
       1
       1
       2
       3
       4
       2
       0
       2
    

    

      1
       16.516460
       20.673961
       20.673892
       12.906775
       3.842815
       2.034245
       3.082494
       0.106425
       car
       4
       4
       4
       3
       3
       3
       3
       5
       2
    

    

      2
       13.723858
       18.223492
       18.189504
       19.199767
       2.602753
       1.805781
       2.267103
      -0.283843
       car
       4
       3
       3
       4
       2
       3
       2
       0
       2
    

    

      3
        7.085528
       17.424587
       15.745808
       41.682044
       0.774225
       0.232625
       0.613609
       0.091476
       car
       2
       3
       2
       5
       0
       0
       0
       5
       2
    

    

      4
        7.713151
       15.234336
       14.884946
       28.962270
       0.930638
       0.276874
       0.531812
      -0.150022
       car
       2
       2
       2
       5
       0
       0
       0
       1
       2
    

  




















In [307]:



    


output_matrix



    


















    
Out[307]:








array([['2', '2', '2', ..., '1', '1', '0.0'],
       ['3', '2', '4', ..., '0', '0', '0.0'],
       ['5', '2', '3', ..., '1', '0', '0.0'],
       ..., 
       ['5', '4', '5', ..., '2', '2', '0.0'],
       ['4', '4', '5', ..., '4', '2', '0.0'],
       ['0', '5', '2', ..., '2', '4', '0.0']], 
      dtype='|S64')

Process UCI HAR Dataset





In [289]:



    


X_train = sp.io.loadmat("activity_X_train.mat")
y_train = sp.io.loadmat("activity_y_train.mat")

X_test = sp.io.loadmat("activity_X_test.mat")
y_test = sp.io.loadmat("activity_y_test.mat")



    











In [290]:



    


df_X_test = pd.DataFrame(X_test['X_test'])
df_X_train = pd.DataFrame(X_train['X_train'])
df_y_test = pd.DataFrame(y_test['y_test'])
df_y_train = pd.DataFrame(y_train['y_train'])



    











In [291]:



    


print df_X_train.shape
print df_X_test.shape



    


















    






(7352, 561)
(2947, 561)

















In [292]:



    


df_X_test["label"] = df_y_test[0]
df_X_train["label"] = df_y_train[0]



    











In [293]:



    


df = pd.concat((df_X_train, df_X_test), ignore_index=True)



    











In [294]:



    


df["seg_id"] = np.arange(len(df))
df["seg_id"] = np.floor(df["seg_id"]/60)



    











In [295]:



    


N_BINS_MAX_SPEED = 4

for col in df.columns:
    field_name = "d_"+str(col)
    
    if col == "label":
        continue
    elif col == "seg_id":
        continue
    try:
        df[field_name] = pd.qcut(df[col], N_BINS_MAX_SPEED, labels=[x+1 for x in range(N_BINS_MAX_SPEED)])
    except:
        print col
        continue



    


















    






62
63
64
290
291
292
366
367
368
448
449
450
511
523
537

















In [296]:



    


for col in df.columns:
    try:
        if col.startswith("d_"):
            continue
        elif col == "label":
            continue
    except:
        del df[col]



    











In [298]:



    


df_temp = df.copy()

(n, d) = df.shape

output_matrix = np.zeros(shape=(d-1, 0))

for name, group in df_temp.groupby("seg_id"):
#     print name
    (n_data, d) = group.shape
    # Change to matrix, drop the second column as it is seg_id
    seg_matrix = group.as_matrix()
    seg_matrix = np.delete(seg_matrix, 1, 1)
    seg_matrix = seg_matrix.T

    seg_matrix = np.concatenate((seg_matrix, np.zeros(shape=(d-1, 1))),axis=1)
    
    output_matrix = np.concatenate((output_matrix, seg_matrix), axis=1)

sp.io.savemat("activity_discretized", mdict={'data': output_matrix}, appendmat=True)



    











In [303]:



    


output_matrix



    


















    
Out[303]:








array([[ 5.,  5.,  5., ...,  2.,  2.,  0.],
       [ 4.,  3.,  3., ...,  1.,  1.,  0.],
       [ 2.,  3.,  2., ...,  4.,  2.,  0.],
       ..., 
       [ 1.,  1.,  1., ...,  3.,  3.,  0.],
       [ 2.,  2.,  2., ...,  4.,  4.,  0.],
       [ 2.,  2.,  2., ...,  4.,  4.,  0.]])

Reshape the data for Weka's SVM, Random Forest and Naive Bayes.





In [310]:



    


max_seg_id = np.max(df["seg_id"])



    











In [314]:



    


df_train = df[df["seg_id"] <= np.floor(2*max_seg_id/3)+1 ]



    











In [315]:



    


df_test = df[df["seg_id"] > np.floor(2*max_seg_id/3)+1 ]



    











In [316]:



    


df_train.to_csv("weka/activity_train.csv", index=False)
df_test.to_csv("weka/activity_test.csv", index=False)



    











In [372]:



    






    











In [390]:



    


"""
* Group each data by segment id, 
    - obtain a long string which is a combination of the rows in the same segment
    - d_0, d_1, ..., d_n\nd_0, d_1, ..., d_n\n
"""
df_temp = df_train.copy()

N_FEATURES = 50

file_handler = open("weka/activity_hmm.csv", "w")

nn = 1

for name, group in df_temp.groupby("seg_id"):
    f_str = ''
    print name
    for row in group.iterrows():
        index = row[0]
        features = row[1].drop(labels=["label", "seg_id"])

        for index, f in enumerate(features):
            if index >= N_FEATURES:
                break
                
            if index == 0:
                f_str = f_str + str(f)
            elif (index == len(features) - 1) or (index == N_FEATURES -1):
                f_str = f_str + ", " + str(f) + "\\n"
            else:
                f_str = f_str + ", " + str(f)
    file_handler.write("\"" + f_str + "\"\n")
    
    if nn > 20:
        break
    
    nn += 1

file_handler.close()



    


















    






0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0

















In [349]:



    






    


















    






label       1
seg_id    115
d_0         4
d_1         1
d_2         1
d_3         4
d_4         3
d_5         4
d_6         4
d_7         3
d_8         4
d_9         4
d_10        3
d_11        4
d_12        2
...
d_546    3
d_547    4
d_548    4
d_549    4
d_550    2
d_551    1
d_552    3
d_553    3
d_554    1
d_555    4
d_556    3
d_557    3
d_558    1
d_559    2
d_560    2
Name: 6900, Length: 548, dtype: float64
label       1
seg_id    115
d_0         1
d_1         3
d_2         1
d_3         4
d_4         3
d_5         4
d_6         4
d_7         3
d_8         4
d_9         4
d_10        4
d_11        4
d_12        2
...
d_546    3
d_547    3
d_548    3
d_549    3
d_550    3
d_551    2
d_552    4
d_553    3
d_554    4
d_555    4
d_556    3
d_557    3
d_558    1
d_559    2
d_560    2
Name: 6901, Length: 548, dtype: float64
label       1
seg_id    115
d_0         1
d_1         4
d_2         4
d_3         4
d_4         3
d_5         4
d_6         4
d_7         3
d_8         4
d_9         3
d_10        4
d_11        4
d_12        2
...
d_546    3
d_547    3
d_548    3
d_549    3
d_550    2
d_551    3
d_552    3
d_553    3
d_554    4
d_555    1
d_556    4
d_557    2
d_558    1
d_559    2
d_560    2
Name: 6902, Length: 548, dtype: float64
label       1
seg_id    115
d_0         4
d_1         4
d_2         4
d_3         4
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...
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Name: 6939, Length: 548, dtype: float64
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...
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Name: 6940, Length: 548, dtype: float64
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...
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Name: 6941, Length: 548, dtype: float64
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...
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Name: 6942, Length: 548, dtype: float64
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...
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...
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Name: 6944, Length: 548, dtype: float64
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...
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Name: 6945, Length: 548, dtype: float64
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...
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Name: 6946, Length: 548, dtype: float64
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...
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Name: 6947, Length: 548, dtype: float64
label       2
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...
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Name: 6948, Length: 548, dtype: float64
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...
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Name: 6949, Length: 548, dtype: float64
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...
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Name: 6950, Length: 548, dtype: float64
label       2
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...
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Name: 6951, Length: 548, dtype: float64
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...
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...
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...
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...
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Name: 6955, Length: 548, dtype: float64
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...
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...
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Name: 6959, Length: 548, dtype: float64

















In [340]:



    


features = row[1].drop(labels=["label", "seg_id"])



    











In [347]:



    






    











In [359]:



    


print """ %s """ % f_str



    


















    






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In [ ]:

Content source: sprite728/simple-crf

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