In [1]:
import h2o
import numpy as np
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from h2o.estimators.glrm import H2OGeneralizedLowRankEstimator
In [2]:
h2o.init()
H2O cluster uptime:
7 seconds 288 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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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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57.86759000000001 157.32503000000003 62.790000000000006 159.97461 242.90858000000003 33.52863 102.08940000000001 76.37951000000001 58.7473 -74.0361 211.85968000000003 69.02702000000001 -69.30095 185.39088 48.22646 -140.09074 254.42706 36.65646 -88.18266000000001 627.3817700000001 1342.8541300000002 -35.207950000000004 511.80032000000006 1427.4450700000002 164.48206000000002 551.72424 1453.5893600000002 -249.89265000000003 485.94559000000004 1227.90454 216.05853000000002 531.12134 1217.6442900000002 -266.60974000000004 424.32767 1129.09949 242.29697000000002 460.03223 1123.8029800000002 -304.79358 535.8743900000001 889.81964 262.1105 486.99316000000005 876.5388200000001 304.71069 554.3527200000001 895.9242600000001 -240.59163 502.82971000000003 886.58942 -300.87775000000005 179.7708 33.33099 181.24631000000002 218.04131 36.963620000000006 71.79319000000001 199.80693000000002 36.02512 -153.13719 228.04898000000003 42.970600000000005 -54.889030000000005 675.97009 1562.28418 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921.9683218543047 -274.01243317880784 601.509835033112 61.89592642384106 194.6828558278145 619.8241700000001 68.4889279470199 86.61932410596025 618.3381468874173 65.92961562913905 -132.6487337748344 638.6451865562915 72.31719158940398 -31.812834701986745 705.7277435761589 1598.0397398675493 94.05463324503312 718.7310321854308 1603.0323291390728 -44.31930543046361 698.879092384106 1772.9682127152323 33.555429536423844 maxs 2.5 641.12048 1091.43665 180.07411000000002 661.46918 1078.02356 -80.37424 451.5914 1077.82214 55.369930000000004 692.8951400000001 788.1136500000001 217.07512000000003 766.4453100000001 750.7237500000001 136.25021 728.6724200000001 672.4224200000001 209.89946 674.53168 783.71576 -138.11635 773.9770500000001 780.8144500000001 -66.30186 713.1610700000001 681.3862300000001 -123.18440000000001 719.6970200000001 531.05676 200.56787000000003 799.4701500000001 457.5513 154.56749000000002 751.88885 458.64368 202.59061000000003 764.30676 494.35605000000004 -116.46951000000001 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15.560029141241564 14.911113572072345 151.594655659586 25.435023343449412 13.275024941597394 237.43944206830935 86.80343780253493 9.298073657103735 244.6407179572301 37.91043095679009 7.582862433872318 251.0617538001127 44.41348608713427 6.393457760711201 267.4568938007459 36.23264239834704 13.613927648644092 241.49943110684558 83.28595104733681 9.622767350801517 252.60488599087827 30.581903744070164 12.50947521438336 255.3742520395276 36.29889281644612 9.253300952802068 271.41405247323576 34.54637819120151 13.739176761986469 12.057560570460472 14.748920262780642 26.994429532604197 15.75877414903572 16.42509134119633 32.91664846673709 16.795593925312957 15.43515813813026 31.807591147525685 32.68904808090951 16.857366037548523 29.30191109925967 36.888188302031175 15.249894444342027 23.3207475248551 45.85588288351911 18.356038206581573 31.267462014418022 50.64618869740734 17.77634010039676 23.133037855069336 78.9687004520418 29.96213905357626 13.839689939145192 88.31690351648665 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In [5]:
# Basic GLRM using quadratic loss and no regularization (PCA)
model = H2OGeneralizedLowRankEstimator(k=10, loss="Quadratic", regularization_x="None", regularization_y="None", max_iterations=1000)
model.train(x=list(range(1,gait.ncol)), training_frame=gait)
model.show()
glrm Model Build Progress: [##################################################] 100%
Model Details
=============
H2OGeneralizedLowRankEstimator : Beta - Generalized Low Rank Model
Model Key: GLRM_model_python_1449616108681_4
Model Summary:
number_of_iterations
final_step_size
final_objective_value 15.0
0.0000336
348858.1320175
ModelMetricsGLRM: glrm
** Reported on train data. **
MSE: NaN
Sum of Squared Error (Numeric): 348858.1373928058
Misclassification Error (Categorical): 0.0
Scoring History:
timestamp
duration
iteration
step_size
objective 2015-12-08 15:08:53
0.567 sec
0.0
1.05
355953.0495218 2015-12-08 15:08:53
0.654 sec
1.0
0.7000000
355953.0495218 2015-12-08 15:08:53
0.696 sec
2.0
0.4666667
355953.0495218 2015-12-08 15:08:53
0.738 sec
3.0
0.3111111
355953.0495218 2015-12-08 15:08:53
0.751 sec
4.0
0.1555556
355953.0495218 2015-12-08 15:08:53
0.764 sec
5.0
0.0518519
355953.0495218 2015-12-08 15:08:53
0.777 sec
6.0
0.0129630
355953.0495218 2015-12-08 15:08:53
0.790 sec
7.0
0.0136111
348858.1320175 2015-12-08 15:08:53
0.804 sec
8.0
0.0090741
348858.1320175 2015-12-08 15:08:53
0.819 sec
9.0
0.0060494
348858.1320175 2015-12-08 15:08:53
0.832 sec
10.0
0.0040329
348858.1320175 2015-12-08 15:08:53
0.845 sec
11.0
0.0020165
348858.1320175 2015-12-08 15:08:53
0.857 sec
12.0
0.0006722
348858.1320175 2015-12-08 15:08:53
0.870 sec
13.0
0.0001680
348858.1320175 2015-12-08 15:08:53
0.883 sec
14.0
0.0000336
348858.1320175
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")
print(model_score)
plt.plot(model_score["iteration"], model_score["objective"])
plt.show()
timestamp duration iteration step_size objective
0 2015-12-08 15:08:53 0.567 sec 0 1.050000 355953.049522
1 2015-12-08 15:08:53 0.654 sec 1 0.700000 355953.049522
2 2015-12-08 15:08:53 0.696 sec 2 0.466667 355953.049522
3 2015-12-08 15:08:53 0.738 sec 3 0.311111 355953.049522
4 2015-12-08 15:08:53 0.751 sec 4 0.155556 355953.049522
5 2015-12-08 15:08:53 0.764 sec 5 0.051852 355953.049522
6 2015-12-08 15:08:53 0.777 sec 6 0.012963 355953.049522
7 2015-12-08 15:08:53 0.790 sec 7 0.013611 348858.132018
8 2015-12-08 15:08:53 0.804 sec 8 0.009074 348858.132018
9 2015-12-08 15:08:53 0.819 sec 9 0.006049 348858.132018
10 2015-12-08 15:08:53 0.832 sec 10 0.004033 348858.132018
11 2015-12-08 15:08:53 0.845 sec 11 0.002016 348858.132018
12 2015-12-08 15:08:53 0.857 sec 12 0.000672 348858.132018
13 2015-12-08 15:08:53 0.870 sec 13 0.000168 348858.132018
14 2015-12-08 15:08:53 0.883 sec 14 0.000034 348858.132018
/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 [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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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 -0.362422 -0.570615 0.366496 0.160492 -0.496611 0.545266 0.150324 0.685559 0.492391 0.0320439 -0.268436 -0.375445 0.323127 0.129955 -0.391362 0.353488 0.0492167 0.8056 0.332023 0.0441441 -0.157574 -0.19305 0.264606 0.088763 -0.273069 0.173273 -0.0631878 0.929926 0.169373 0.0625199 -0.0566838 -0.0254627 0.206427 0.0448357 -0.171361 0.0163901 -0.139129 1.02823 0.0227587 0.0743581 0.0149536 0.126614 0.143023 0.00511055 -0.0848319 -0.120307 -0.188121 1.1071 -0.0968295 0.0928464 0.0835833 0.21865 0.0791339 -0.0338994 0.00405319 -0.207306 -0.202243 1.1465 -0.177828 0.0886814 0.12449 0.296898 0.00377988 -0.0662024 0.0645689 -0.258014 -0.1609 1.15533 -0.235635 0.0740767 0.139783 0.322037 -0.0557214 -0.0879325 0.11846 -0.275535 -0.0836994 1.11837 -0.249453 0.0517003 0.129183 0.297413 -0.109574 -0.0887692 0.176341 -0.255597 0.0155158 1.04285 -0.226476 0.0170711 0.115742 0.232302 -0.144512 -0.0850974 0.224998 -0.202661 0.153602 0.919357 -0.181238 -0.0343747
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 614.319 1058.33 165.338 634.543 1046.85 -95.9465 425.62 1058.65 25.512 510.068 745.564 207.56 562.164 669.528 118.953 472.788 630.211 195.927 648.33 745.119 -149.839 743.375 736.364 -80.4767 684.858 641.294 -138.733 351.392 475.557 182.272 414.037 415.506 131.856 334.607 388.644 186.8 745.015 442.756 -132.549 813.202 414.092 -84.1604 741.45 394.801 -140.147 153.771 103.965 98.3823 300.246 86.1461 68.621 287.346 67.1792 176.56 434.31 43.896 114.23 819.256 65.1668 -51.6123 970.945 117.287 -35.7846 951.751 110.729 -129.757 1078.42 134.944 -70.8595 658.824 1359.23 12.9543 568.317 1455.85 224.119 589.271 1473.17 -194.185 594.207 1255.99 274.101 571.387 1237.15 -226.891 567.151 1150.28 310.604 505.589 1149.49 -261.403 768.98 980.024 267.093 745.371 951.478 321.244 607.943 919.576 -220.662 558.265 909.472 -276.74 368.121 30.6619 194.941 380.568 38.262 83.5026 1020.03 109.772 -143.857 1035.41 120.307 -42.7285 734.56 1580.23 97.4022 745.063 1582.72 -41.4815 745.96 1755 36.6798 616.588 1056.4 164.401 637.999 1046.08 -96.662 428.478 1056.27 23.4562 510.699 743.432 207.229 563.502 666.369 119.854 472.82 627.898 196.347 654.222 745.371 -150.247 749.995 737.916 -81.5209 691.767 642.32 -138.17 349.818 475.212 183.648 409.963 413.286 133.192 330.199 389.645 188.25 750.213 443.092 -131.922 816.031 412.016 -82.0848 745.266 395.543 -139.974 141.366 114.992 101.065 284.371 89.6121 69.9525 270.856 70.1892 177.661 412.645 42.6499 113.233 805.187 65.7636 -51.4019 960.305 111.47 -32.2049 943.06 104.222 -126.833 1070.01 124.371 -65.1291 660.436 1357.51 10.0369 568.799 1454.63 220.409 591.132 1470.06 -198.001 594.005 1254.9 270.858 574.988 1233.68 -229.984 566.042 1149.32 307.56 510.209 1145.42 -264.523 764.781 973.97 267.405 740.028 946.581 321.722 613.092 916.302 -222.537 563.626 905.785 -278.609 347.817 29.5616 195.051 361.004 37.5831 83.1468 1010.7 100.125 -139.375 1025.35 110.601 -37.9462 735.149 1578.52 94.1773 746.252 1580.91 -44.6406 746.786 1753.54 33.1807 618.786 1054.16 163.224 641.311 1045.02 -97.5901 431.269 1053.57 21.3481 512.112 741.235 206.799 565.855 663.378 120.728 474.07 625.558 196.696 658.952 745.472 -150.732 755.297 738.952 -82.6534 697.044 643.201 -137.61 349.763 474.968 185.044 407.306 411.026 134.517 327.172 390.778 189.721 753.204 443.079 -131.282 816.706 409.516 -80.015 746.861 395.868 -139.817 129.793 126.764 103.787 269.185 93.1382 71.3792 254.896 73.3345 178.794 391.35 40.8871 112.296 788.576 65.4568 -51.2487 947.43 104.793 -28.7006 932.118 96.6464 -123.927 1059.49 113.103 -59.3131 661.889 1355.4 7.06987 568.98 1452.97 216.559 592.987 1466.57 -201.86 593.184 1253.43 267.47 578.822 1229.9 -233.082 564.144 1148.06 304.31 515.219 1140.98 -267.698 759.397 967.317 267.824 733.398 941.172 322.24 618.903 912.762 -224.35 569.688 901.76 -280.429 327.763 28.1748 195.189 341.862 36.5867 82.8653 999.153 89.4814 -134.825 1013.16 99.9716 -33.1107 735.686 1576.2 90.9035 747.446 1578.47 -47.8186 748.022 1751.53 29.638 620.889 1052.16 161.801 644.274 1044.04 -98.6465 433.953 1051.04 19.3017 514.701 739.212 206.214 569.575 660.935 121.449 477.015 623.352 196.886 663.38 745.553 -151.425 760.205 739.837 -84.0224 701.899 643.951 -137.405 351.67 475.194 186.098 406.419 408.988 135.591 325.78 392.419 190.76 755.628 443.077 -131.045 816.767 407.086 -78.5148 747.814 396.272 -139.958 119.073 139.78 105.522 254.614 97.3562 72.315 239.308 77.713 179.305 370.3 39.0459 111.376 771.709 65.8187 -51.2678 933.858 98.7445 -25.7703 920.138 89.8788 -121.552 1047.8 102.367 -54.4818 663.162 1353.4 4.19273 569.109 1451.31 212.773 594.623 1463.21 -205.437 592.35 1252 264.071 582.441 1226.33 -235.844 562.284 1146.87 300.968 519.937 1136.84 -270.462 753.785 961.016 268.138 726.541 936.111 322.543 623.874 909.062 -226.136 574.832 897.794 -282.227 307.82 27.7994 194.92 322.975 36.1729 82.4246 986.48 79.6338 -131.006 1000.02 90.0744 -29.1152 735.99 1573.91 87.6459 748.303 1576.05 -50.9202 748.837 1749.38 26.1403 623.386 1051.75 160.018 647.426 1044.37 -100.155 436.835 1049.96 17.0314 519.35 738.454 205.659 575.728 660.135 122.186 482.756 622.274 197.225 668.185 746.371 -152.66 765.589 741.404 -85.8934 707.108 645.161 -137.806 356.579 476.675 187.104 408.484 407.838 136.816 326.997 395.207 191.728 758.282 443.482 -131.399 816.943 405.081 -77.6554 748.868 397.186 -140.557 109.925 154.177 106.515 241.626 102.46 73.1601 224.933 83.3369 179.64 350.623 37.1645 111.036 754.891 67.5882 -51.2973 919.697 93.521 -23.382 907.268 84.3567 -119.684 1034.97 91.9559 -50.6466 664.767 1353.05 1.06204 569.599 1451.34 208.833 596.612 1461.6 -209.297 591.71 1252.16 260.558 586.691 1224.4 -238.755 560.484 1147.31 297.469 525.33 1134.27 -273.328 747.704 956.024 268.591 719.074 932.451 322.899 629.254 906.615 -228.143 580.38 895.218 -284.32 288.96 28.3685 194.776 305.466 36.4359 82.2808 972.828 70.8649 -127.987 985.957 81.0853 -25.9313 736.79 1573.16 84.144 749.633 1575.17 -54.3151 750.398 1748.84 22.4076 626.015 1051.9 158.125 650.395 1045.15 -101.928 439.654 1049.5 15.0081 525.536 737.993 205.128 583.731 660.03 122.914 490.713 621.385 197.586 671.155 747.262 -154.151 768.823 742.242 -87.919 709.615 646.171 -138.603 364.349 478.706 187.997 413.173 407.104 137.914 330.876 398.709 192.495 757.761 443.547 -132.212 814.228 403.073 -77.458 746.892 397.698 -141.524 103.3 170.456 106.63 230.571 108.786 73.6988 212.16 90.925 179.631 332.124 35.7603 111.057 737.511 69.0233 -51.618 904.416 88.8417 -21.7493 892.756 79.4371 -118.547 1020.44 82.9563 -47.9208 666.41 1353.36 -1.70964 570.122 1451.95 205.415 598.727 1460.88 -212.761 590.951 1252.8 257.538 591.143 1223.25 -241.267 558.509 1148.21 294.383 530.855 1132.51 -275.83 741.262 951.661 269.423 711.242 929.409 323.555 634.312 904.228 -230.156 585.526 892.945 -286.533 271.136 30.7363 194.552 289.224 37.9025 82.2709 957.599 63.1873 -125.889 970.564 73.2575 -23.6846 737.895 1572.99 81.0318 751.208 1574.84 -57.3811 752.67 1748.98 19.036 628.646 1052.77 155.739 652.995 1046.47 -104.025 442.435 1049.72 12.8889 533.608 737.986 204.297 593.914 660.887 123.327 501.355 620.831 197.72 674.268 748.52 -156.158 772.153 743.43 -90.5188 712.268 647.384 -140.12 375.34 481.433 188.379 420.922 406.888 138.743 337.667 402.994 192.663 757.142 443.979 -133.778 811.322 401.383 -78.2651 744.589 398.686 -143.065 99.3073 188.364 105.358 221.701 116.393 73.6724 201.26 100.509 178.836 315.296 34.8261 111.336 719.682 71.6365 -52.102 888.224 84.9511 -21.1399 876.783 75.7378 -118.338 1004.39 74.2169 -46.9807 668.001 1354.22 -4.59534 570.874 1453.01 201.865 600.701 1460.76 -216.054 590.415 1253.97 254.295 595.446 1222.83 -243.511 556.771 1149.72 290.973 536.131 1131.59 -277.913 734.53 948.144 270.009 703.082 927.238 323.799 638.486 902.31 -232.202 589.7 891.382 -288.745 254.868 35.0289 193.89 274.717 40.7694 82.2014 940.843 56.5039 -125.134 953.923 66.2637 -22.954 739.043 1573.08 77.7303 752.691 1574.75 -60.5467 755 1749.15 15.5557 631.087 1054.66 152.885 655.233 1048.45 -106.716 444.867 1050.85 10.4978 543.362 738.788 203.368 606.168 663.028 123.648 514.56 621.032 197.881 675.428 749.845 -158.615 773.261 743.953 -93.3422 712.128 648.314 -142.113 389.508 485.238 188.589 431.757 407.566 139.594 347.409 408.432 192.641 753.508 443.959 -135.757 805.714 399.757 -79.5918 739.524 399.156 -144.895 98.1658 207.997 103.117 215.383 125.436 73.4955 192.484 112.099 177.822 300.495 34.7226 112.357 702.305 74.0318 -52.6935 871.652 82.0195 -20.9759 860.118 73.2162 -118.532 987.456 67.3147 -46.8204 669.374 1356.1 -7.69123 571.305 1455.15 198.141 602.704 1461.76 -219.625 589.216 1256.15 250.96 600.281 1223.43 -245.822 554.169 1152.31 287.401 542.115 1131.58 -280.068 726.327 945.574 270.694 693.349 926.121 324.042 643.351 901.138 -234.275 594.659 890.639 -291.127 240.386 41.2957 193.409 262.342 45.1536 82.5473 923.552 51.5982 -125.013 936.712 61.0702 -22.8478 740.124 1574.26 74.1402 754.095 1575.71 -64.0594 757.415 1750.47 11.7227 632.896 1057.1 149.65 656.717 1050.8 -109.868 446.566 1052.59 7.8886 553.837 739.951 202.306 619.406 665.874 123.825 529.193 621.553 198.065 674.24 751.214 -161.381 771.696 743.842 -96.2375 708.813 649.045 -144.408 405.977 489.466 188.67 445.026 408.946 140.551 359.714 414.458 192.531 746.44 443.506 -137.917 797.156 398.174 -81.2466 731.289 399.034 -146.807 101.375 228.32 100.265 212.983 135.785 73.431 187.366 125.361 176.841 289.229 36.2001 114.26 685.116 75.2729 -53.1707 854.916 79.4811 -21.2068 843 71.2497 -119.016 970.231 61.8472 -47.3834 670.164 1358.53 -10.9596 571.133 1457.89 194.251 604.351 1463.41 -223.381 587.054 1258.95 247.545 605.148 1224.68 -248.16 550.412 1155.62 283.71 548.325 1132.12 -282.25 716.499 943.72 271.444 681.944 925.8 324.273 648.796 900.894 -236.136 600.353 890.73 -293.369 229.358 49.7299 193.307 253.642 51.4551 83.488 906.145 47.9054 -125.442 919.388 57.155 -23.3061 740.883 1575.91 70.3398 755.193 1577.11 -67.8067 759.844 1752.33 7.65599 634.256 1059.8 146.2 657.53 1053.24 -113.301 447.718 1054.63 5.35582 565.274 741.303 201.051 633.793 669.345 123.708 545.435 622.249 198.025 670.742 752.512 -164.317 767.431 742.909 -99.1179 702.328 649.522 -146.952 424.835 494.064 188.339 460.714 410.84 141.145 374.552 420.963 191.953 735.841 442.678 -140.296 785.492 396.636 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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
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
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124.0 stddev
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0.0
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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 [13]:
# Basic GLRM using quadratic loss and no regularization
model2 = H2OGeneralizedLowRankEstimator(k=10, init="SVD", svd_method = "GramSVD", loss="Quadratic", regularization_x="None", regularization_y="None", max_iterations=2000, min_step_size=1e-6)
model2.train(x=list(range(1,gait_miss.ncol)), training_frame=gait_miss, validation_frame=gait)
model2.show()
glrm Model Build Progress: [##################################################] 100%
Model Details
=============
H2OGeneralizedLowRankEstimator : Beta - Generalized Low Rank Model
Model Key: GLRM_model_python_1449616108681_9
Model Summary:
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 15:09:34
0.724 sec
0.0
0.6666667
4709528.8513359 2015-12-08 15:09:34
0.734 sec
1.0
0.4444444
4709528.8513359 2015-12-08 15:09:34
0.744 sec
2.0
0.2222222
4709528.8513359 2015-12-08 15:09:34
0.754 sec
3.0
0.0740741
4709528.8513359 2015-12-08 15:09:34
0.764 sec
4.0
0.0185185
4709528.8513359 ---
---
---
---
---
--- 2015-12-08 15:09:48
14.574 sec
995.0
0.0017368
418507.6754296 2015-12-08 15:09:48
14.592 sec
996.0
0.0018236
417934.4281432 2015-12-08 15:09:48
14.612 sec
997.0
0.0019148
417473.7912993 2015-12-08 15:09:48
14.630 sec
998.0
0.0020105
417370.6542389 2015-12-08 15:09:48
14.649 sec
999.0
0.0013404
417370.6542389
In [14]:
# 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 [15]:
# Impute missing data from X and Y
pred2 = model2.predict(gait_miss)
pred2.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 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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[15]:
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: jangorecki/h2o-3
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