In [1]:
import h2o
import numpy as np
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
In [2]:
h2o.init()
H2O cluster uptime:
8 seconds 762 milliseconds H2O cluster version:
3.7.0.99999 H2O cluster name:
spIdea H2O cluster total nodes:
1 H2O cluster total free memory:
12.44 GB H2O cluster total cores:
8 H2O cluster allowed cores:
8 H2O cluster healthy:
True H2O Connection ip:
127.0.0.1 H2O Connection port:
54321 H2O Connection proxy:
None Python Version:
3.5.0
In [3]:
from h2o.utils.shared_utils import _locate # private function. used to find files within h2o git project directory.
# Import walking gait data
gait = h2o.upload_file(path=_locate("smalldata/glrm_test/subject01_walk1.csv"))
gait.describe()
Parse Progress: [##################################################] 100%
Rows:151 Cols:124
Chunk compression summary:
chunk_type
chunk_name
count
count_percentage
size
size_percentage C2S
2-Byte Fractions
1
0.8064516
386 B
0.4540642 C4S
4-Byte Fractions
123
99.19355
82.6 KB
99.54594
Frame distribution summary:
size
number_of_rows
number_of_chunks_per_column
number_of_chunks 172.16.2.84:54321
83.0 KB
151.0
1.0
124.0 mean
83.0 KB
151.0
1.0
124.0 min
83.0 KB
151.0
1.0
124.0 max
83.0 KB
151.0
1.0
124.0 stddev
0 B
0.0
0.0
0.0 total
83.0 KB
151.0
1.0
124.0
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In [4]:
# Plot first row of data on x- vs. y-coordinate features
gait_row = gait[1,:].drop("Time")
gait_row_np = np.array(h2o.as_list(gait_row))
x_coords = range(0, gait_row_np.shape[1], 3)
y_coords = range(1, gait_row_np.shape[1], 3)
x_pts = gait_row_np[0,x_coords]
y_pts = gait_row_np[0,y_coords]
plt.plot(x_pts, y_pts, 'bo')
# Add feature labels to each point
feat_names = [nam[:-2] for nam in gait_row.col_names[1::3]]
for i in range(len(feat_names)):
plt.annotate(feat_names[i], xy = [x_pts[i], y_pts[i]])
plt.title("Location of Body Parts at Time 0")
plt.xlabel("X-Coordinate Weight")
plt.ylabel("Y-Coordinate Weight")
plt.show()
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/__init__.py:7: DeprecationWarning: bad escape \s
from pandas import hashtable, tslib, lib
In [5]:
# Basic GLRM using quadratic loss and no regularization (PCA)
model = h2o.glrm(x=gait[1:], k=10, loss="Quadratic", regularization_x="None", regularization_y="None", max_iterations=1000)
model.show()
glrm Model Build Progress: [##################################################] 100%
Model Details
=============
H2OEstimator : Beta - Generalized Low Rank Model
Model Key: GLRM_model_python_1449615858397_2
Model Summary:
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:2: DeprecationWarning: `h2o.glrm` is deprecated. Use the estimators sub module to build an H2OGeneralizedLowRankEstimator.
from ipykernel import kernelapp as app
number_of_iterations
final_step_size
final_objective_value 15.0
0.0000336
317138.1639954
ModelMetricsGLRM: glrm
** Reported on train data. **
MSE: NaN
Sum of Squared Error (Numeric): 317138.1650737164
Misclassification Error (Categorical): 0.0
Scoring History:
timestamp
duration
iteration
step_size
objective 2015-12-08 15:04:41
0.514 sec
0.0
1.05
325634.9123606 2015-12-08 15:04:41
0.605 sec
1.0
0.7000000
325634.9123606 2015-12-08 15:04:41
0.618 sec
2.0
0.4666667
325634.9123606 2015-12-08 15:04:41
0.632 sec
3.0
0.3111111
325634.9123606 2015-12-08 15:04:41
0.646 sec
4.0
0.1555556
325634.9123606 2015-12-08 15:04:41
0.660 sec
5.0
0.0518519
325634.9123606 2015-12-08 15:04:41
0.674 sec
6.0
0.0129630
325634.9123606 2015-12-08 15:04:41
0.688 sec
7.0
0.0025926
325634.9123606 2015-12-08 15:04:41
0.703 sec
8.0
0.0027222
317138.1639954 2015-12-08 15:04:41
0.748 sec
9.0
0.0018148
317138.1639954 2015-12-08 15:04:41
0.762 sec
10.0
0.0012099
317138.1639954 2015-12-08 15:04:41
0.774 sec
11.0
0.0008066
317138.1639954 2015-12-08 15:04:41
0.788 sec
12.0
0.0004033
317138.1639954 2015-12-08 15:04:41
0.801 sec
13.0
0.0001344
317138.1639954 2015-12-08 15:04:41
0.815 sec
14.0
0.0000336
317138.1639954
In [6]:
# Plot objective function value each iteration
model_score = model.score_history()
plt.xlabel("Iteration")
plt.ylabel("Objective")
plt.title("Objective Function Value per Iteration")
plt.plot(model_score["iteration"], model_score["objective"])
plt.show()
In [7]:
# Archetype to feature mapping (Y)
gait_y = model._model_json["output"]["archetypes"]
print(gait_y)
gait_y_np = np.array(model.archetypes())
x_coords = range(0, gait_y_np.shape[1], 3)
y_coords = range(1, gait_y_np.shape[1], 3)
# Plot archetypes on x- vs. y-coordinate features
for k in range(gait_y_np.shape[0]):
x_pts = gait_y_np[k, x_coords]
y_pts = gait_y_np[k, y_coords]
plt.plot(x_pts, y_pts, 'bo')
# Add feature labels to each point
feat_names = [nam[:-1] for nam in gait_y.col_header[1::3]]
for i in range(len(feat_names)):
plt.annotate(feat_names[i], xy = [x_pts[i], y_pts[i]])
plt.title("Feature Weights of Archetype " + str(k+1))
plt.xlabel("X-Coordinate Weight")
plt.ylabel("Y-Coordinate Weight")
plt.show()
Archetypes:
rasisx
rasisy
rasisz
lasisx
lasisy
lasisz
vsacralx
vsacraly
vsacralz
rthighupperx
rthighuppery
rthighupperz
rthighfrontx
rthighfronty
rthighfrontz
rthighrearx
rthighreary
rthighrearz
lthighupperx
lthighuppery
lthighupperz
lthighfrontx
lthighfronty
lthighfrontz
lthighrearx
lthighreary
lthighrearz
rshankupperx
rshankuppery
rshankupperz
rshankfrontx
rshankfronty
rshankfrontz
rshankrearx
rshankreary
rshankrearz
lshankupperx
lshankuppery
lshankupperz
lshankfrontx
lshankfronty
lshankfrontz
lshankrearx
lshankreary
lshankrearz
rheelx
rheely
rheelz
rmidfootsupx
rmidfootsupy
rmidfootsupz
rmidfootlatx
rmidfootlaty
rmidfootlatz
rtoetipx
rtoetipy
rtoetipz
lheelx
lheely
lheelz
lmidfootsupx
lmidfootsupy
lmidfootsupz
lmidfootlatx
lmidfootlaty
lmidfootlatz
ltoetipx
ltoetipy
ltoetipz
sternumx
sternumy
sternumz
racromiumx
racromiumy
racromiumz
lacromiumx
lacromiumy
lacromiumz
rbicepx
rbicepy
rbicepz
lbicepx
lbicepy
lbicepz
relbowx
relbowy
relbowz
lelbowx
lelbowy
lelbowz
rwristmedx
rwristmedy
rwristmedz
rwristlatx
rwristlaty
rwristlatz
lwristmedx
lwristmedy
lwristmedz
lwristlatx
lwristlaty
lwristlatz
rtoelatx
rtoelaty
rtoelatz
rtoemedx
rtoemedy
rtoemedz
ltoelatx
ltoelaty
ltoelatz
ltoemedx
ltoemedy
ltoemedz
rtemplex
rtempley
rtemplez
ltemplex
ltempley
ltemplez
topheadx
topheady
topheadz Arch1
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99.5371742
-136.1607949
300.9240526
95.1670973
-36.3059206
690.0353850
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129.3794559
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In [8]:
# Projection into archetype space (X)
x_key = model._model_json["output"]["representation_name"]
gait_x = h2o.get_frame(x_key)
gait_x.show()
time_np = np.array(h2o.as_list(gait["Time"]))
gait_x_np = np.array(h2o.as_list(gait_x))
# Plot archetypes over time
lines = []
for i in range(gait_x_np.shape[1]):
lines += plt.plot(time_np, gait_x_np[:,i], '-')
plt.title("Archetypes over Time")
plt.xlabel("Time")
plt.ylabel("Archetypal Projection")
plt.legend(lines, gait_x.col_names)
plt.show()
Arch1 Arch2 Arch3 Arch4 Arch5 Arch6 Arch7 Arch8 Arch9 Arch10 1.11967 -0.00968171 1.02436 -0.189199 0.540833 -0.0566785 0.256513 -1.11069 -0.401164 -0.165113 0.967947 0.207059 0.793362 -0.167029 0.404331 -0.0531109 0.164881 -0.98099 -0.236759 -0.0915042 0.812497 0.428022 0.553478 -0.145476 0.252684 -0.0274794 0.0652164 -0.838934 -0.0701454 -0.0217387 0.661712 0.62232 0.367628 -0.118589 0.122266 -0.00302047 -0.0269913 -0.723645 0.0624622 0.043565 0.513527 0.793111 0.231479 -0.0926915 0.0187138 0.0044018 -0.106714 -0.631752 0.16702 0.110099 0.382003 0.928375 0.1318 -0.0731224 -0.0751936 0.0248629 -0.15789 -0.540257 0.239901 0.146461 0.24767 1.02724 0.114882 -0.0373691 -0.133111 0.0285745 -0.194912 -0.494005 0.271898 0.175124 0.136832 1.08719 0.146316 -0.0214329 -0.1573 0.0260325 -0.205432 -0.458855 0.262836 0.188859 0.0428783 1.12132 0.226008 -0.0207053 -0.123338 0.0214259 -0.186868 -0.452016 0.197306 0.178313 -0.0300847 1.11504 0.338336 -0.0254833 -0.0765898 0.0255396 -0.154818 -0.443672 0.104869 0.150329
In [9]:
# Reconstruct data from X and Y
pred = model.predict(gait)
pred.head()
reconstr_R.ASIS.X reconstr_R.ASIS.Y reconstr_R.ASIS.Z reconstr_L.ASIS.X reconstr_L.ASIS.Y reconstr_L.ASIS.Z reconstr_V.Sacral.X reconstr_V.Sacral.Y reconstr_V.Sacral.Z reconstr_R.Thigh.Upper.X reconstr_R.Thigh.Upper.Y reconstr_R.Thigh.Upper.Z reconstr_R.Thigh.Front.X reconstr_R.Thigh.Front.Y reconstr_R.Thigh.Front.Z reconstr_R.Thigh.Rear.X reconstr_R.Thigh.Rear.Y reconstr_R.Thigh.Rear.Z reconstr_L.Thigh.Upper.X reconstr_L.Thigh.Upper.Y reconstr_L.Thigh.Upper.Z reconstr_L.Thigh.Front.X reconstr_L.Thigh.Front.Y reconstr_L.Thigh.Front.Z reconstr_L.Thigh.Rear.X reconstr_L.Thigh.Rear.Y reconstr_L.Thigh.Rear.Z reconstr_R.Shank.Upper.X reconstr_R.Shank.Upper.Y reconstr_R.Shank.Upper.Z reconstr_R.Shank.Front.X reconstr_R.Shank.Front.Y reconstr_R.Shank.Front.Z reconstr_R.Shank.Rear.X reconstr_R.Shank.Rear.Y reconstr_R.Shank.Rear.Z reconstr_L.Shank.Upper.X reconstr_L.Shank.Upper.Y reconstr_L.Shank.Upper.Z reconstr_L.Shank.Front.X reconstr_L.Shank.Front.Y reconstr_L.Shank.Front.Z reconstr_L.Shank.Rear.X reconstr_L.Shank.Rear.Y reconstr_L.Shank.Rear.Z reconstr_R.Heel.X reconstr_R.Heel.Y reconstr_R.Heel.Z reconstr_R.Midfoot.Sup.X reconstr_R.Midfoot.Sup.Y reconstr_R.Midfoot.Sup.Z reconstr_R.Midfoot.Lat.X reconstr_R.Midfoot.Lat.Y reconstr_R.Midfoot.Lat.Z reconstr_R.Toe.Tip.X reconstr_R.Toe.Tip.Y reconstr_R.Toe.Tip.Z reconstr_L.Heel.X reconstr_L.Heel.Y reconstr_L.Heel.Z reconstr_L.Midfoot.Sup.X reconstr_L.Midfoot.Sup.Y reconstr_L.Midfoot.Sup.Z reconstr_L.Midfoot.Lat.X reconstr_L.Midfoot.Lat.Y reconstr_L.Midfoot.Lat.Z reconstr_L.Toe.Tip.X reconstr_L.Toe.Tip.Y reconstr_L.Toe.Tip.Z reconstr_Sternum.X reconstr_Sternum.Y reconstr_Sternum.Z reconstr_R.Acromium.X reconstr_R.Acromium.Y reconstr_R.Acromium.Z reconstr_L.Acromium.X reconstr_L.Acromium.Y reconstr_L.Acromium.Z reconstr_R.Bicep.X reconstr_R.Bicep.Y reconstr_R.Bicep.Z reconstr_L.Bicep.X reconstr_L.Bicep.Y reconstr_L.Bicep.Z reconstr_R.Elbow.X reconstr_R.Elbow.Y reconstr_R.Elbow.Z reconstr_L.Elbow.X reconstr_L.Elbow.Y reconstr_L.Elbow.Z reconstr_R.Wrist.Med.X reconstr_R.Wrist.Med.Y reconstr_R.Wrist.Med.Z reconstr_R.Wrist.Lat.X reconstr_R.Wrist.Lat.Y reconstr_R.Wrist.Lat.Z reconstr_L.Wrist.Med.X reconstr_L.Wrist.Med.Y reconstr_L.Wrist.Med.Z reconstr_L.Wrist.Lat.X reconstr_L.Wrist.Lat.Y reconstr_L.Wrist.Lat.Z reconstr_R.Toe.Lat.X reconstr_R.Toe.Lat.Y reconstr_R.Toe.Lat.Z reconstr_R.Toe.Med.X reconstr_R.Toe.Med.Y reconstr_R.Toe.Med.Z reconstr_L.Toe.Lat.X reconstr_L.Toe.Lat.Y reconstr_L.Toe.Lat.Z reconstr_L.Toe.Med.X reconstr_L.Toe.Med.Y reconstr_L.Toe.Med.Z reconstr_R.Temple.X reconstr_R.Temple.Y reconstr_R.Temple.Z reconstr_L.Temple.X reconstr_L.Temple.Y reconstr_L.Temple.Z reconstr_Top.Head.X reconstr_Top.Head.Y reconstr_Top.Head.Z 618.745 1057.85 169.044 638.078 1050.44 -94.6512 428.505 1059.79 27.6865 509.172 737.329 206.084 558.528 659.271 115.706 468.184 619.95 193.454 647.136 748.63 -150.191 739.133 735.113 -78.813 678.694 644.023 -138.576 352.381 469.599 179.659 409.775 413.54 127.01 337.491 388.974 182.552 735.344 445.976 -133.019 803.455 418.153 -85.196 731.477 398.611 -141.118 174.186 123.946 93.5964 306.426 105.595 61.4908 295.797 94.2187 170.762 427.561 67.5874 106.644 812.685 65.0551 -55.8285 967.281 131.836 -30.8167 945.912 117.762 -129.913 1074.26 150.26 -66.6741 661.927 1363.44 17.6093 570.522 1459.12 230.858 591.655 1477.69 -191.232 601.054 1256 279.196 573.375 1238.62 -222.795 575.777 1148.53 315.014 505.62 1150.8 -256.094 777.657 976.301 267.596 754.188 947.574 322.535 600.949 911.204 -220.6 549.413 903.864 -277.628 368.263 62.7096 187.76 377.58 63.4161 76.5535 1014.02 116.422 -141.342 1032.43 131.755 -40.0784 733.419 1590.93 102.366 743.376 1593.33 -37.8817 735.806 1765.44 40.2475 619.591 1055.62 167.686 640.264 1049.18 -95.5492 430.06 1057.34 25.2817 508.524 735.033 205.9 559.196 656.087 117.225 467.561 617.401 194.369 651.095 748.173 -150.665 744.151 736.097 -80.4928 683.884 644.333 -138.495 350.176 468.516 181.303 405.802 410.773 129.097 332.655 388.999 184.424 739.24 445.623 -133.043 805.391 415.685 -83.968 734.112 398.526 -141.332 163.058 133.242 95.6064 291.81 107.896 63.5739 279.545 95.7233 172.269 406.594 64.8207 107.473 800.345 65.5223 -55.4538 957.011 124.366 -29.3289 937.232 111.335 -128.105 1065.51 138.904 -63.4489 662.478 1361.33 13.77 570.371 1457.72 226.216 591.991 1473.71 -195.677 600 1255.18 275.429 575.203 1234.54 -226.694 573.817 1148.04 311.658 508.375 1146.17 -260.008 773.559 972.092 268.336 749.027 944.4 323.24 604.32 908.143 -223.251 553.081 900.297 -280.085 347.931 60.0291 188.795 358.839 61.7493 77.5984 1004.7 107.281 -138.781 1022.29 121.82 -37.2204 733.044 1588.8 97.9674 743.447 1591.01 -42.1063 735.411 1763.15 35.5977 620.87 1053.73 166.166 642.875 1048.26 -96.7632 431.95 1055.18 22.7485 508.996 733.12 205.677 561.225 653.447 118.703 468.408 615.205 195.239 654.428 748.073 -151.343 748.46 737.071 -82.3744 688.002 644.981 -138.536 349.63 467.829 182.973 403.426 408.229 131.124 329.33 389.403 186.325 741.462 445.303 -133.18 805.687 413.099 -82.7986 735.038 398.382 -141.7 152.754 143.322 97.6184 278.033 110.312 65.6218 264.005 97.3942 173.78 386.286 61.6721 108.21 785.623 65.1235 -55.3129 944.932 116.114 -27.8091 926.837 103.906 -126.348 1055.31 126.993 -59.991 663.433 1359.71 9.75627 570.375 1456.85 221.44 592.814 1470.23 -200.471 598.828 1254.82 271.574 577.773 1230.83 -230.937 571.555 1148 308.184 511.994 1141.78 -264.356 768.99 967.944 269.266 743.268 941.343 324.11 609.023 905.331 -226.105 558.107 896.853 -282.808 328.14 57.0896 189.822 340.77 59.8036 78.5725 993.756 97.1841 -136.118 1010.55 111.004 -34.1751 733.063 1587.31 93.3962 743.955 1589.31 -46.5805 735.447 1761.61 30.6974 622.427 1052.22 164.459 645.479 1047.58 -98.1051 434.054 1053.29 20.3114 510.992 731.517 205.304 564.852 651.449 119.93 471.194 613.285 195.898 657.965 748.092 -152.237 752.873 738.041 -84.4071 692.213 645.621 -138.852 351.25 467.773 184.33 402.905 406.017 132.813 327.808 390.473 187.805 743.629 445.07 -133.62 805.825 410.614 -82.0615 735.814 398.418 -142.323 143.027 154.798 98.9358 264.661 113.474 67.1368 248.843 100.325 174.695 366.205 58.5264 108.622 770.666 65.4708 -55.3561 932.313 108.689 -26.5526 915.559 97.1861 -124.984 1044.11 115.502 -57.1735 664.513 1358.39 5.98333 570.52 1456.11 216.888 593.809 1467.16 -204.904 597.857 1254.54 267.784 580.6 1227.55 -234.715 569.525 1148.05 304.645 515.823 1137.89 -268.158 764.183 963.842 270.008 737.243 938.353 324.712 613.39 902.423 -228.814 562.68 893.544 -285.408 308.568 55.1572 190.315 322.878 58.4105 79.1512 981.805 87.6434 -133.937 998.036 100.814 -31.7253 733.13 1586.04 89.0554 744.445 1587.84 -50.7871 735.423 1760.21 26.0586 624.387 1051.58 162.405 648.253 1047.55 -99.7863 436.451 1052.1 17.7121 514.858 730.565 204.802 570.512 650.424 120.969 476.39 611.944 196.479 661.913 748.379 -153.533 757.692 739.192 -86.7618 696.75 646.316 -139.576 355.553 468.627 185.503 404.773 404.4 134.397 328.572 392.506 189.036 745.943 444.963 -134.465 805.872 408.271 -81.7601 736.6 398.743 -143.271 134.46 167.998 99.6537 252.207 117.755 68.366 234.482 104.816 175.256 346.711 55.7253 109.085 755.28 67.2335 -55.4617 918.639 102.307 -25.4738 902.936 91.593 -123.9 1031.22 104.339 -54.8934 665.831 1357.88 2.14308 570.863 1456.12 212.252 595.161 1465.01 -209.384 597.003 1254.92 263.821 584.128 1225.15 -238.337 567.512 1148.78 300.817 520.441 1134.83 -271.724 758.574 959.959 270.614 730.285 935.711 325.103 618.111 899.944 -231.51 567.571 890.855 -288.054 289.585 54.547 190.583 305.631 57.9528 79.6358 968.323 78.9594 -132.17 984.087 91.4685 -29.7616 733.4 1585.5 84.6394 745.12 1587.13 -55.062 735.614 1759.55 21.3645 626.527 1051.8 160.114 650.901 1048.14 -101.833 438.843 1051.68 15.1814 520.582 730.547 204.298 578.32 650.798 121.924 484.132 611.547 197.016 664.556 749.054 -155.149 760.958 740.131 -89.2451 699.313 647.171 -140.587 362.71 470.695 186.534 409.377 403.577 135.861 331.831 395.634 190.082 745.837 444.825 -135.54 803.698 406.062 -81.8314 735.06 398.98 -144.407 126.891 182.495 99.8197 241.031 122.591 69.366 221.186 110.286 175.583 328.639 52.6407 109.728 738.808 68.6573 -55.7277 903.865 96.3585 -24.5679 888.965 86.3273 -123.129 1017.08 94.1708 -53.0216 667.212 1358.1 -1.48384 571.252 1456.84 207.903 596.832 1463.91 -213.643 595.887 1255.93 260.129 588.123 1223.75 -241.704 565.099 1150.15 297.19 525.555 1132.77 -275.092 752.2 956.585 271.421 722.511 933.612 325.649 623.274 897.939 -234.114 572.893 888.75 -290.745 271.665 54.5254 190.821 289.652 57.686 80.1602 953.616 70.8946 -130.748 969.029 82.8347 -28.1537 734.272 1585.23 80.5086 746.429 1586.65 -59.0929 737.559 1759.39 16.9808 628.992 1052.77 157.36 653.453 1049.33 -104.218 441.467 1051.91 12.5386 528.517 731.042 203.453 588.483 652.149 122.422 494.758 611.568 197.226 667.801 750.191 -157.293 764.746 741.507 -92.1896 702.465 648.308 -142.186 373.262 473.622 187.054 417.033 403.359 136.934 338.112 399.736 190.507 746.031 445.087 -137.205 801.654 404.148 -82.7112 733.558 399.769 -146.025 121.495 198.927 98.9339 231.613 128.842 69.7467 209.578 117.865 175.137 311.966 50.1642 110.202 721.622 71.3512 -56.1626 888.072 91.4543 -24.212 873.417 82.1265 -123.045 1001.3 84.0771 -52.3984 668.754 1358.91 -5.07061 571.964 1457.99 203.552 598.712 1463.56 -217.645 595.115 1257.34 256.241 592.442 1223.17 -244.618 563.018 1151.99 293.203 530.939 1131.61 -277.824 745.315 953.485 271.839 714.145 931.857 325.676 628.017 896.253 -236.604 577.656 887.24 -293.308 255.198 56.4943 190.449 275.102 58.8289 80.334 937.184 63.4473 -130.26 952.61 74.862 -27.6745 735.387 1585.18 76.4028 747.9 1586.39 -63.0208 739.964 1759.35 12.7117 631.405 1054.44 154.094 655.752 1050.82 -107.15 443.951 1052.62 9.6564 538.371 732.339 202.418 600.793 654.799 122.66 508.082 612.457 197.292 669.531 751.199 -159.794 766.714 742.122 -95.1723 703.338 649.024 -144.041 386.971 477.807 187.343 427.645 404.066 137.878 347.22 405.088 190.676 743.723 444.812 -139.002 797.265 402.125 -83.7627 729.778 400.003 -147.724 117.824 216.952 97.4668 223.979 136.218 69.9447 199.604 127.001 174.469 297.049 48.1956 110.969 704.385 73.8697 -56.6154 871.649 87.5816 -23.7823 857.093 78.8131 -122.975 984.549 75.5319 -51.875 670.115 1360.22 -8.72454 572.3 1459.61 199.034 600.843 1463.9 -221.757 593.559 1259.18 252.148 597.624 1223.28 -247.373 559.911 1154.33 288.871 537.476 1131.04 -280.412 736.372 950.405 272.068 703.597 930.294 325.435 634.028 895.105 -238.79 583.805 886.297 -295.708 240.316 59.8528 190.087 262.278 60.948 80.6293 920.084 57.3416 -129.836 935.445 68.361 -27.297 736.499 1585.46 72.1976 749.421 1586.45 -67.0496 742.821 1759.76 8.33337 633.798 1056.6 150.544 657.853 1052.63 -110.489 446.303 1053.79 6.73311 549.524 734.021 201.193 614.448 658.125 122.545 523.214 613.737 197.202 669.666 752.244 -162.538 766.686 742.09 -98.0152 701.741 649.549 -145.997 403.293 482.604 187.473 440.748 405.563 138.684 359.101 411.244 190.679 738.575 444.119 -140.748 790.291 400.052 -84.785 723.365 399.725 -149.365 117.825 236.077 95.8028 219.669 144.997 70.1185 193.067 137.915 173.799 285.337 47.9114 111.991 686.637 75.4314 -56.9558 854.63 84.4193 -23.162 840.029 75.8315 -122.821 967.17 68.3013 -51.2751 671.364 1362.02 -12.2266 572.296 1461.66 194.636 603.236 1464.99 -225.804 591.305 1261.33 248.081 603.606 1224.09 -249.844 555.865 1157.04 284.422 545.099 1131.05 -282.734 725.469 947.061 272.145 690.996 928.637 325.025 641.505 894.801 -240.405 591.553 886.108 -297.643 228.786 65.3741 189.839 252.734 64.8817 81.0727 902.486 52.0621 -129.343 917.802 62.9395 -26.8783 737.665 1586.13 68.2084 751.07 1586.94 -70.8961 746.099 1760.78 4.1576 635.781 1059.13 146.681 659.32 1054.52 -114.143 448.181 1055.21 3.81922 562.014 736.37 199.804 629.619 662.642 122.137 540.389 615.789 196.889 667.931 753.284 -165.438 764.455 741.48 -100.762 697.587 649.912 -148.064 422.165 488.274 187.27 456.506 407.813 139.208 373.524 418.119 190.33 730.559 443.113 -142.486 780.789 397.969 -85.9524 714.298 399.002 -150.967 120.021 255.318 93.5462 218.022 153.986 70.002 189.123 149.313 172.811 276.854 47.6763 113.114 668.399 75.9171 -57.3369 837.148 81.6727 -22.6105 822.381 73.0119 -122.774 949.402 62.3189 -50.9005 672.153 1364 -15.5626 571.723 1463.8 190.334 605.451 1466.44 -229.635 588.179 1263.61 244.058 609.724 1225.36 -252.005 550.823 1159.95 279.898 553.026 1131.49 -284.783 713.159 944.014 272.076 676.975 927.296 324.381 649.671 895.027 -241.587 600.086 886.378 -299.226 220.173 71.425 189.482 246.179 69.0875 81.5026 884.543 47.4899 -128.996 899.853 58.3724 -26.6558 738.507 1586.89 64.3611 752.394 1587.5 -74.5861 749.266 1761.91 0.127725
Out[9]:
In [10]:
# Plot original and reconstructed L.Acromium.X over time
lacro_np = np.array(h2o.as_list(gait["L.Acromium.X"]))
lacro_pred_np = np.array(h2o.as_list(pred["reconstr_L.Acromium.X"]))
line_orig = plt.plot(time_np, lacro_np, '-')
line_imp = plt.plot(time_np, lacro_pred_np, '-')
plt.title("Position of Left Acromium over Time")
plt.xlabel("Time")
plt.ylabel("X-Coordinate of Left Acromium")
blue_patch = mpatches.Patch(color = 'blue', label = 'Original')
green_patch = mpatches.Patch(color = 'green', label='Imputed')
plt.legend([blue_patch, green_patch], ["Original", "Imputed"])
plt.show()
In [11]:
# Import walking gait data with missing values
gait_miss = h2o.upload_file(_locate("smalldata/glrm_test/subject01_walk1_miss15.csv"))
gait_miss.describe()
Parse Progress: [##################################################] 100%
Rows:151 Cols:124
Chunk compression summary:
chunk_type
chunk_name
count
count_percentage
size
size_percentage C2S
2-Byte Fractions
1
0.8064516
386 B
0.4540642 C4S
4-Byte Fractions
123
99.19355
82.6 KB
99.54594
Frame distribution summary:
size
number_of_rows
number_of_chunks_per_column
number_of_chunks 172.16.2.84:54321
83.0 KB
151.0
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124.0 mean
83.0 KB
151.0
1.0
124.0 min
83.0 KB
151.0
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83.0 KB
151.0
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83.0 KB
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Time R.ASIS.X R.ASIS.Y R.ASIS.Z L.ASIS.X L.ASIS.Y L.ASIS.Z V.Sacral.X V.Sacral.Y V.Sacral.Z R.Thigh.Upper.X R.Thigh.Upper.Y R.Thigh.Upper.Z R.Thigh.Front.X R.Thigh.Front.Y R.Thigh.Front.Z R.Thigh.Rear.X R.Thigh.Rear.Y R.Thigh.Rear.Z L.Thigh.Upper.X L.Thigh.Upper.Y L.Thigh.Upper.Z L.Thigh.Front.X L.Thigh.Front.Y L.Thigh.Front.Z L.Thigh.Rear.X L.Thigh.Rear.Y L.Thigh.Rear.Z R.Shank.Upper.X R.Shank.Upper.Y R.Shank.Upper.Z R.Shank.Front.X R.Shank.Front.Y R.Shank.Front.Z R.Shank.Rear.X R.Shank.Rear.Y R.Shank.Rear.Z L.Shank.Upper.X L.Shank.Upper.Y L.Shank.Upper.Z L.Shank.Front.X L.Shank.Front.Y L.Shank.Front.Z L.Shank.Rear.X L.Shank.Rear.Y L.Shank.Rear.Z R.Heel.X R.Heel.Y R.Heel.Z R.Midfoot.Sup.X R.Midfoot.Sup.Y R.Midfoot.Sup.Z R.Midfoot.Lat.X R.Midfoot.Lat.Y R.Midfoot.Lat.Z R.Toe.Tip.X R.Toe.Tip.Y R.Toe.Tip.Z L.Heel.X L.Heel.Y L.Heel.Z L.Midfoot.Sup.X L.Midfoot.Sup.Y L.Midfoot.Sup.Z L.Midfoot.Lat.X L.Midfoot.Lat.Y L.Midfoot.Lat.Z L.Toe.Tip.X L.Toe.Tip.Y L.Toe.Tip.Z Sternum.X Sternum.Y Sternum.Z R.Acromium.X R.Acromium.Y R.Acromium.Z L.Acromium.X L.Acromium.Y L.Acromium.Z R.Bicep.X R.Bicep.Y R.Bicep.Z L.Bicep.X L.Bicep.Y L.Bicep.Z R.Elbow.X R.Elbow.Y R.Elbow.Z L.Elbow.X L.Elbow.Y L.Elbow.Z R.Wrist.Med.X R.Wrist.Med.Y R.Wrist.Med.Z R.Wrist.Lat.X R.Wrist.Lat.Y R.Wrist.Lat.Z L.Wrist.Med.X L.Wrist.Med.Y L.Wrist.Med.Z L.Wrist.Lat.X L.Wrist.Lat.Y L.Wrist.Lat.Z R.Toe.Lat.X R.Toe.Lat.Y R.Toe.Lat.Z R.Toe.Med.X R.Toe.Med.Y R.Toe.Med.Z L.Toe.Lat.X L.Toe.Lat.Y L.Toe.Lat.Z L.Toe.Med.X L.Toe.Med.Y L.Toe.Med.Z R.Temple.X R.Temple.Y R.Temple.Z L.Temple.X L.Temple.Y L.Temple.Z Top.Head.X Top.Head.Y Top.Head.Z type real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real real 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In [12]:
# Basic GLRM using quadratic loss and no regularization
model2 = h2o.glrm(x=gait_miss[1:], validation_x=gait[1:], k=10, init="SVD", svd_method = "GramSVD", loss="Quadratic", regularization_x="None", regularization_y="None", max_iterations=2000, min_step_size=1e-6)
model2.show()
glrm Model Build Progress: [##################################################] 100%
Model Details
=============
H2OEstimator : Beta - Generalized Low Rank Model
Model Key: GLRM_model_python_1449615858397_5
Model Summary:
/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/ipykernel/__main__.py:2: DeprecationWarning: `h2o.glrm` is deprecated. Use the estimators sub module to build an H2OGeneralizedLowRankEstimator.
from ipykernel import kernelapp as app
number_of_iterations
final_step_size
final_objective_value 1000.0
0.0013404
417370.6542389
ModelMetricsGLRM: glrm
** Reported on train data. **
MSE: NaN
Sum of Squared Error (Numeric): 417370.65961611824
Misclassification Error (Categorical): 0.0
ModelMetricsGLRM: glrm
** Reported on validation data. **
MSE: NaN
Sum of Squared Error (Numeric): 860945.6344740451
Misclassification Error (Categorical): 0.0
Scoring History:
timestamp
duration
iteration
step_size
objective 2015-12-08 15:05:32
0.741 sec
0.0
0.6666667
4709528.8513359 2015-12-08 15:05:32
0.750 sec
1.0
0.4444444
4709528.8513359 2015-12-08 15:05:32
0.760 sec
2.0
0.2222222
4709528.8513359 2015-12-08 15:05:32
0.769 sec
3.0
0.0740741
4709528.8513359 2015-12-08 15:05:32
0.779 sec
4.0
0.0185185
4709528.8513359 ---
---
---
---
---
--- 2015-12-08 15:05:45
13.888 sec
995.0
0.0017368
418507.6754296 2015-12-08 15:05:45
13.907 sec
996.0
0.0018236
417934.4281432 2015-12-08 15:05:45
13.926 sec
997.0
0.0019148
417473.7912993 2015-12-08 15:05:45
13.943 sec
998.0
0.0020105
417370.6542389 2015-12-08 15:05:45
13.960 sec
999.0
0.0013404
417370.6542389
In [13]:
# Plot objective function value each iteration
model2_score = model2.score_history()
plt.xlabel("Iteration")
plt.ylabel("Objective")
plt.title("Objective Function Value per Iteration")
plt.plot(model2_score["iteration"], model2_score["objective"])
plt.show()
In [14]:
# Impute missing data from X and Y
pred2 = model2.predict(gait_miss)
pred2.head()
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750.665 444.954 -136.844 808.628 405.349 -85.1711 739.292 398.862 -145.325 113.17 177.755 97.65 236.042 113.224 68.2548 214.294 99.1359 172.779 332.062 36.8681 110.709 733.86 67.8694 -55.4051 904.764 89.8697 -28.9355 910.628 82.3937 -125.284 1017.68 88.6806 -59.5434 665.146 1353.31 -2.4333 571.285 1451.46 206.028 597.56 1460.08 -212.391 594.463 1252.82 258.353 586.405 1222.7 -240.542 564.328 1148.18 295.143 522.797 1132.75 -273.631 749.295 958.648 268.373 721.209 932.959 322.361 623.414 898.884 -233.844 571.181 889.612 -289.927 270.82 39.1103 190.369 289.874 42.713 80.6396 956.314 67.3024 -133.688 969.797 77.1494 -32.441 737.426 1572.53 78.4398 749.247 1574.61 -58.8066 750.245 1746.76 16.8909 628.708 1052.29 154.515 652.158 1046.73 -105.558 442.902 1050.17 11.5271 536.184 736.63 202.429 597.639 659.947 121.351 503.624 618.455 194.952 671.61 750.11 -158.3 767.619 742.564 -93.0836 708.114 648.749 -142.816 380.163 480.515 184.564 424.961 405.973 135.369 342.584 401.724 188.646 752.614 444.67 -137.835 809.068 403.165 -85.2165 739.501 398.948 -146.57 105.626 194.903 97.5663 224.057 119.899 68.2116 200.741 106.76 172.413 312.806 35.5188 109.897 718.824 68.633 -55.9269 892.614 84.8522 -27.6775 912.487 76.794 -124.349 1006.38 79.2012 -57.1989 668.326 1354.87 -5.82511 572.562 1453.37 202.022 601.068 1460.35 -216.625 594.714 1254.43 254.845 592.635 1222.16 -243.897 563.31 1150.18 291.433 530.27 1131.39 -276.902 743.388 954.533 268.566 713.815 930.462 322.187 630.663 897.009 -236.313 578.456 887.617 -292.652 252.397 41.5592 189.669 272.704 44.1503 79.7287 940.435 59.2992 -132.132 953.203 69.338 -30.2605 738.799 1575.16 74.2595 751.044 1577.01 -62.8588 751.039 1749.32 12.336 629.66 1054.18 151.52 652.419 1048.14 -107.89 443.812 1051.25 9.37437 545.266 737.565 201.858 609.06 662.461 122.379 516.499 618.97 195.843 671.681 750.244 -160.732 767.504 742.147 -96.2162 707.123 648.133 -145.203 393.919 484.71 185.369 436.086 406.782 137.221 351.775 407.196 189.359 748.179 444.341 -140.281 801.596 401.626 -87.0782 733.961 399.76 -148.543 104.44 212.581 95.3332 218.513 128.452 69.1832 192.369 117.451 172.126 299.493 34.9597 112.99 702.883 77.585 -55.7632 872.07 85.2997 -27.5806 878.389 79.7422 -124.78 982.814 74.8541 -57.8961 667.965 1355.43 -8.40202 572.552 1454.2 198.55 601.654 1460.76 -219.035 592.725 1255.7 251.651 595.579 1222.68 -245.011 559.876 1151.96 288.266 534.425 1131.76 -277.787 734.064 951.557 269.634 702.929 928.916 323.001 631.582 895.768 -238.016 580.031 887.22 -294.835 238.793 47.4399 190.435 261.496 48.4732 81.837 922.559 59.4147 -132.728 935.42 67.7954 -31.142 740.317 1572.32 71.6171 752.971 1573.72 -65.4065 757.96 1746.91 9.68498 632.887 1056.96 147.665 655.762 1050.67 -111.877 446.888 1053.17 5.60209 556.364 739.168 200.613 622.967 665.292 122.623 531.43 620.024 196.11 671.388 751.576 -164.025 767.076 741.89 -99.7132 704.421 648.756 -147.617 410.24 489.399 186.058 449.196 408.311 138.624 363.599 413.566 190.07 741.25 443.535 -142.481 792.781 399.458 -88.2712 725.893 399.394 -150.671 106.096 233.285 94.0924 214.846 138.836 70.1094 186.129 129.627 172.277 286.915 36.4198 115.077 684.146 79.5919 -56.3313 852.654 81.8992 -26.6655 858.034 76.8904 -124.494 963.129 67.779 -56.4657 670.302 1358.16 -12.9493 573.444 1457.72 193.24 605.234 1462.77 -224.564 590.928 1259.09 246.969 603.09 1223.96 -248.945 555.795 1155.95 283.403 543.926 1131.99 -281.67 722.998 948.25 270.14 689.788 927.59 323.176 641.112 896.109 -240.681 590.363 887.314 -298.081 226.683 54.419 191.207 251.602 54.0922 83.1826 902.502 54.6889 -131.954 917.624 62.5262 -30.0411 743.047 1574.02 66.5511 756.373 1574.97 -70.581 763.961 1749.2 4.133 636.729 1063.8 145.457 659.754 1056.98 -115.186 449.774 1058.73 3.25221 568.597 743.134 200.711 637.709 670.543 123.433 547.561 623.012 197.489 671.77 754.938 -166.764 767.495 743.481 -101.967 702.372 650.905 -149.668 428.337 495.202 187.617 464.454 411.606 140.419 377.854 421.003 191.455 735.764 444.037 -144.251 785.574 399.119 -88.5592 719.708 400.205 -152.243 112.901 252.845 92.9059 216.56 150.167 70.768 185.852 143.172 172.756 280.731 40.2268 117.035 670.584 82.5269 -56.3484 837.97 81.9302 -24.7279 841.261 77.231 -123.592 948.139 64.6959 -54.1548 672.992 1366.69 -15.997 573.535 1466.79 190.117 608.491 1470.52 -229.469 589.191 1267.15 244.561 611.505 1230.06 -252.28 551.978 1164.06 280.738 554.467 1136.61 -285.165 713.049 949.295 272.15 677.827 930.481 324.889 652.427 900.473 -242.45 602.644 891.34 -300.757 221.212 63.9514 192.229 247.901 62.0311 84.3735 888.914 53.7524 -130.764 902.444 61.3355 -28.202 743.58 1585.41 63.153 757.383 1586.24 -74.8574 761.444 1761.16 -0.187107
Out[14]:
In [16]:
# Plot original and imputed L.Acromium.X over time
lacro_pred_np2 = np.array(h2o.as_list(pred2["reconstr_L.Acromium.X"]))
plt.plot(time_np, lacro_np, 'b-')
plt.plot(time_np, lacro_pred_np2, 'g-')
# Mark points where training data contains missing values
idx_miss = list(zip(*gait_miss["L.Acromium.X"].isna().which().as_data_frame(True).values.tolist()))
plt.plot(time_np[idx_miss], lacro_np[idx_miss], "o", marker = "x", ms = 8, mew = 1.5, mec = "r")
plt.title("Position of Left Acromium over Time")
plt.xlabel("Time")
plt.ylabel("X-Coordinate of Left Acromium")
blue_patch = mpatches.Patch(color = 'blue', label = 'Original')
green_patch = mpatches.Patch(color = 'green', label = 'Imputed')
red_patch = mpatches.Patch(color = 'red', label = "Missing")
plt.legend([blue_patch, green_patch, red_patch], ["Original", "Imputed", "Missing"])
plt.show()
Content source: nilbody/h2o-3
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