agentMET4FOF - Metrological Agent-based system¶
agentMET4FOF is a Python library developed at the Institute for Manufacturing of the University of Cambridge (UK) as part of the European joint Research Project EMPIR 17IND12 Met4FoF.
For the agentMET4FOF homepage go to GitHub.
agentMET4FOF is written in Python 3.
# Multi-Agent System for Metrology for Factory of the Future (Met4FoF) Code This is supported by European Metrology Programme for Innovation and Research (EMPIR) under the project Metrology for the Factory of the Future (Met4FoF), project number 17IND12. (https://www.ptb.de/empir2018/met4fof/home/)
About — - How can metrological input be incorporated into an agent-based system for addressing uncertainty of machine learning in future manufacturing? - Includes agent-based simulation and implementation
Updates — - Implemented Sensor Agent, Aggregator Agent, Predictor Agent, DecisionMaker Agent, Sensor Network Agent - Able to handle multiple Sensor Agents & Predictor Agents (each equipped with a different model) - Implemented with ZEMA condition monitoring of hydraulic system data set as use case [](https://doi.org/10.5281/zenodo.1323611) - Implemented web visualization with user interface
](https://doi.org/10.5281/zenodo.1323611){kind=link}
To run — - Run Code 01-03 to prepare the ML models, and run Code 04 to start and run the agents. While Code 04 is running, run Code 05 in separate terminal to visualize them.
## Screenshot of web visualization 
Note
—
- In the event of agents not terminating cleanly, run `taskkill /f /im python.exe /t` in Windows Command Prompt to terminate all background python processes.
Download data¶
Firstly we download data for condition monitoring from zenodo at https://doi.org/10.5281/zenodo.1323611
[1]:
data_url='https://zenodo.org/record/1323611/files/data.zip?download=1'
[2]:
import os, requests, zipfile, io
def download_and_extract(url, destination, force=False):
response = requests.get(url)
zipDocument = zipfile.ZipFile(io.BytesIO(response.content))
# Attempt to see if we are going to overwrite anything
if not force:
abort = False
for file in zipDocument.filelist:
if os.path.isfile(os.path.join(destination, file.filename)):
print(file.filename, 'already exists. If you want to overwrite the file call the method with force=True')
abort = True
if abort:
print('Zip file was not extracted')
return
zipDocument.extractall(destination)
[3]:
download_and_extract(data_url, 'Dataset/ZEMA_Hydraulic/')
[ ]:
Data Preprocessing¶
We preprocess and resample the raw data .txt files we downloaded earlier into numpy.
Resample 10Hz and 100Hz data to 1Hz¶
[1]:
import numpy as np
data_path = "Dataset/ZEMA_Hydraulic/"
filenames_input_data_10Hz = ["fs1","fs2"]
filenames_input_data_10Hz = [file + ".txt" for file in filenames_input_data_10Hz]
filenames_input_data_100Hz = ["ps1","ps2","ps3","ps4","ps5","ps6","eps1"]
filenames_input_data_100Hz = [file + ".txt" for file in filenames_input_data_100Hz]
data_input_data_10Hz = np.zeros((2205,600,len(filenames_input_data_10Hz)))
data_input_data_100Hz = np.zeros((2205,6000,len(filenames_input_data_100Hz)))
for id_,file_name in enumerate(filenames_input_data_10Hz):
input_data = np.loadtxt(data_path + file_name, delimiter = "\t")
data_input_data_10Hz[:,:,id_] = input_data.copy()
for id_,file_name in enumerate(filenames_input_data_100Hz):
input_data = np.loadtxt(data_path + file_name, delimiter = "\t")
data_input_data_100Hz[:,:,id_] = input_data.copy()
filenames_input_data_10Hz_resampled = ["res_"+file for file in filenames_input_data_10Hz]
filenames_input_data_100Hz_resampled = ["res_"+file for file in filenames_input_data_100Hz]
#resample 10Hz
resample = np.linspace(0,600-1, num =60,dtype="int")
data_resampled_10Hz=data_input_data_10Hz[:,resample,:]
#resample 100Hz
resample = np.linspace(0,5999, num =60,dtype="int")
data_resampled_100Hz=data_input_data_100Hz[:,resample,:]
#save file
for id_,file_name in enumerate(filenames_input_data_10Hz_resampled):
np.savetxt(data_path+file_name,data_resampled_10Hz[:,:,id_],delimiter='\t')
for id_,file_name in enumerate(filenames_input_data_100Hz_resampled):
np.savetxt(data_path+file_name,data_resampled_100Hz[:,:,id_],delimiter='\t')
Load all the 1Hz data¶
Load all data including the resampled sensors into numpy arrays
[2]:
#save data
datarows = 2205
seq_length = 60
#deal with inputs data
filenames_input_data_1Hz = ["ts1","ts2","ts3","ts4","vs1","se","res_fs1","res_fs2","res_ps1","res_ps2","res_ps3","res_ps4","res_ps5","res_ps6","res_eps1","ce","cp"]
filenames_input_data_1Hz = [file + ".txt" for file in filenames_input_data_1Hz]
filename_target_data = "profile.txt"
data_input_data_1Hz = np.zeros((datarows,seq_length,len(filenames_input_data_1Hz)))
for id_,file_name in enumerate(filenames_input_data_1Hz):
input_data = np.loadtxt(data_path + file_name, delimiter = "\t")
data_input_data_1Hz[:,:,id_] = input_data.copy()
Load the target multi-target, multi-class output data¶
We load them and preprocess into one hot vector
[3]:
#deal with output data now
targets_data = np.loadtxt(data_path+filename_target_data, delimiter = "\t")
#conversion of outputs to one hot
def makeOneHotVectorMap(length):
map_toOneHot ={}
for i in range(length):
oneHot = np.zeros(length)
oneHot[i] = 1
map_toOneHot[i] = oneHot
return map_toOneHot
id2x_dictionaries = []
x2id_dictionaries = []
id2onehot_dictionaries = []
for label in range(targets_data.shape[1]):
label_column = list(set(targets_data[:,label]))
label_column.sort(reverse=True)
id2x_dictionary = {}
x2id_dictionary = {}
id2onehot_dictionary = makeOneHotVectorMap(len(label_column))
for i in range(len(label_column)):
id2x_dictionary[i] = label_column[i]
x2id_dictionary[label_column[i]] = i
id2x_dictionaries+=[id2x_dictionary]
x2id_dictionaries+=[x2id_dictionary]
id2onehot_dictionaries+=[id2onehot_dictionary]
#convert a row into one-hot coded multi-class multi-label
onehot_tensor_output = []
id_output =[]
for row in range(targets_data.shape[0]):
row_output_data= targets_data[row]
onehots_row =[]
id_row =[]
for label in range(row_output_data.shape[0]):
id_ = x2id_dictionaries[label][row_output_data[label]]
onehot= id2onehot_dictionaries[label][id_]
onehots_row =np.append(onehots_row,onehot)
id_row = np.append(id_row,id_)
id_output+=[id_row]
onehot_tensor_output += [onehots_row]
onehot_tensor_output = np.array(onehot_tensor_output)
id_tensor_output = np.array(id_output)
tensor_output = id_tensor_output
all_tensor_output = id_tensor_output
Pickle data¶
[5]:
import os
import pickle
pickle_folder= "pickles"
if os.path.exists(pickle_folder) == False:
os.mkdir(pickle_folder)
#Pickle them
pickle.dump(data_input_data_1Hz, open( pickle_folder+"/data_input_data_1Hz_full.p", "wb" ) )
pickle.dump(data_input_data_10Hz, open( pickle_folder+"/data_input_data_10Hz.p", "wb" ) )
pickle.dump(data_input_data_100Hz, open( pickle_folder+"/data_input_data_100Hz.p", "wb" ) )
pickle.dump(id2onehot_dictionaries, open( pickle_folder+"/id2onehot_dictionaries.p", "wb" ) )
pickle.dump(all_tensor_output, open( pickle_folder+"/zema_outputs.p", "wb" ) )
[ ]:
Train model Bayesian Neural Network¶
We train and evaluate the BNN on the dataset which we loaded earlier.
Credits to Felix Laumann (https://github.com/felix-laumann/Bayesian_CNN) for the method of training BNN with Bayes by Backprop and the model code.
[1]:
import torch
import pickle
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler, MinMaxScaler,RobustScaler
import random
from torch.autograd import Variable
import numpy as np
from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels
import matplotlib.pyplot as plt
import pandas as pd
from copy import copy
from ML_models.BBBlayers import BBBLinearFactorial
from ML_models.BBBlayers import GaussianVariationalInference
from ML_models.BNN_Wrapper import BNN_Wrapper
from ML_models.BNN_Wrapper import BNN_Full
import seaborn as sns
from scipy.stats import skew
from scipy.stats import kurtosis
from scipy.stats import sem
from scipy.fftpack import fft
from sklearn.metrics import f1_score
Load data and label outputs¶
[2]:
pickle_path="pickles/"
data_input = pickle.load( open( pickle_path+"data_input_data_1Hz_full.p", "rb" ) )
data_output = pickle.load( open( pickle_path+"zema_outputs.p", "rb" ) )
output_labels = [{0: "Optimal", 1: "Reduced", 2: "Nearly Fail"},
{0: "Optimal", 1: "Small lag", 2: "Severe lag", 3: "Nearly Fail"},
{0: "No Leakage", 1: "Weak Leakage", 2: "Severe Leakage"},
{0: "Optimal", 1: "Slightly Reduced", 2: "Severely Reduced", 3: "Nearly Fail"},
{0: "Stable", 1: "Unstable"}]
output_sizes = [3,4,3,4,2]
Initialization¶
Setup parameters such as learning rate, number of Monte Carlo samples during predictions, training epoch, certainty threshold
[3]:
learning_rate = 0.005
num_samples = 50
num_epochs = 300
certainty_threshold = 80
X_data = data_input
Y_data = data_output
Shuffle data¶
- Shuffle data randomly to have a random distribution
- Split the time series into segments if necessary (n=1 means no segmentation occurs)
[4]:
randomShuffling= True
#randomShuffling
if(randomShuffling == True):
index_list = np.arange(X_data.shape[0])
random.shuffle(index_list)
Y_data=Y_data[index_list,:]
X_data=X_data[index_list,:,:]
def split_segment(X_data,split_n =1):
X_data_split=np.split(X_data,split_n,axis=1)
X_data_split=np.moveaxis(X_data_split, 0, -2)
X_data_split=X_data_split.reshape((X_data_split.shape[0],X_data_split.shape[1],-1))
return X_data_split
X_data=split_segment(X_data)
Train and evaluate data in k-fold validation¶
- Setup empty arrays for keeping results from each k-fold validation iteration
- Training consists of two loops:
- Outer loop: Train a model for each prediction task (from valve, pump, accumulator, etc)
- Inner loop: k-fold iteration
- Results are stored in arrays
[5]:
simulate_broken_sensor = False #experimental and not fully implemented yet
y_pred_kfold =[]
y_actuals_kfold=[]
mse_kfold=[]
certainties_kfold=[]
confidences_kfold=[]
DL_model_kfold =[]
final_results =[]
for target in range(len(output_sizes)):
selected_output = target
output_size =output_sizes[selected_output]
bnn_wrapper = BNN_Wrapper(BNN_Full,output_size=output_size)
kfold_times = 0
kfold_limit = 5
#kfold validation training
kf = KFold(n_splits=5)
f1_score_model_kfold=[]
p_accurate_certain_kfold=[]
p_accurate_uncertain_kfold=[]
for train, test in kf.split(X_data,y=Y_data):
if(kfold_times >kfold_limit):
break
print("%s %s" % (train, test))
x_train=X_data[train]
y_train=Y_data[train,selected_output]
x_test=X_data[test]
y_test=Y_data[test,selected_output]
#simulate broken sensors on test set
if simulate_broken_sensor == True:
x_test_brokenSensor = Variable(torch.from_numpy(x_test).float()).repeat(x_train.shape[-1]+1,1,1,1)
for corrupted_sensor in range(x_train.shape[-1]):
x_test_brokenSensor[corrupted_sensor,:,:,corrupted_sensor] =torch.randn_like(x_test_brokenSensor[corrupted_sensor,:,:,corrupted_sensor])
x_test_brokenSensor = x_test_brokenSensor.view(-1,x_test_brokenSensor.shape[2],x_test_brokenSensor.shape[3])
x_test_brokenSensor = x_test_brokenSensor.cpu().detach().numpy()
x_test = copy(x_test_brokenSensor)
y_test_tensor = Variable(torch.from_numpy(y_test).long())
if simulate_broken_sensor == True:
y_test_tensor_repeated = y_test_tensor.repeat(x_train.shape[-1]+1,1)
y_test_tensor_repeated = y_test_tensor_repeated.view(-1)
elif simulate_broken_sensor == False:
y_test_tensor_repeated = y_test_tensor
#perform feature extraction on train and test
df_feats_train = bnn_wrapper.extract_features(x_train)
x_train = df_feats_train.values
df_feats_test = bnn_wrapper.extract_features(x_test)
x_test = df_feats_test.values
#train model
bnn_wrapper.train_model(x_train,y_train,learning_rate= learning_rate,num_epochs = num_epochs)
#predict using model
y_pred_test,certainties,confidences = bnn_wrapper.predict_model_wUnc(x_test,num_samples=num_samples)
#now form all the kfolds
DL_model_kfold.append(bnn_wrapper.trained_model)
y_pred_kfold.append(y_pred_test)
y_actuals_kfold.append(y_test_tensor_repeated.cpu().detach().numpy())
certainties_kfold.append(np.array(certainties))
confidences_kfold.append(np.array(confidences))
kfold_times = kfold_times+1
#evaluate model
y_true_np=np.array(y_actuals_kfold).reshape(-1)
y_pred_np=np.array(y_pred_kfold).reshape(-1)
certainties_np = np.array(certainties_kfold).reshape(-1)
f1_score_model, p_accurate_certain, p_accurate_uncertain = bnn_wrapper.evaluate_model(y_true_np,y_pred_np,certainties_np,certainty_threshold=certainty_threshold)
f1_score_model_kfold.append(f1_score_model)
p_accurate_certain_kfold.append(p_accurate_certain)
p_accurate_uncertain_kfold.append(p_accurate_uncertain)
print("Target output: ", selected_output)
print("F1 SCORE: ",np.mean(f1_score_model_kfold),"+-",sem(f1_score_model_kfold), f1_score_model_kfold)
print("P(Acc | Certain): ",np.mean(p_accurate_certain_kfold),"+-",sem(p_accurate_certain_kfold),p_accurate_certain_kfold)
print("P(Acc | Uncertain): ",np.mean(p_accurate_uncertain_kfold),"+-",sem(p_accurate_uncertain_kfold),p_accurate_uncertain_kfold)
final_results.append([f1_score_model_kfold, p_accurate_certain_kfold, p_accurate_uncertain_kfold])
pickle.dump(bnn_wrapper, open( pickle_path+"bnn_wrapper_"+str(selected_output)+".p", "wb" ) )
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1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700
1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714
1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742
1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756
1757 1758 1759 1760 1761 1762 1763]
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
[ 0 1 2 ... 1761 1762 1763] [1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777
1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791
1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805
1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819
1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833
1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847
1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861
1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875
1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889
1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903
1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917
1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931
1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945
1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959
1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973
1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987
1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029
2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043
2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057
2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071
2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085
2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099
2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113
2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127
2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141
2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155
2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169
2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183
2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197
2198 2199 2200 2201 2202 2203 2204]
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
Target output: 0
F1 SCORE: 0.9945351473922903 +- 0.0004707605326990252 [0.9931972789115646, 0.9954648526077098, 0.9954648526077098, 0.9948979591836735, 0.9936507936507937]
P(Acc | Certain): 0.9980861983060005 +- 0.00024045557469217787 [0.997716894977169, 0.9988465974625144, 0.9984662576687117, 0.9977011494252873, 0.9977000919963201]
P(Acc | Uncertain): 0.6848302207130731 +- 0.08938562630975022 [0.3333333333333333, 0.8, 0.7894736842105263, 0.7916666666666666, 0.7096774193548387]
[ 441 442 443 ... 2202 2203 2204] [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53
54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89
90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107
108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125
126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143
144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161
162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179
180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197
198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215
216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233
234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251
252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269
270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287
288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305
306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323
324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341
342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359
360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377
378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395
396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413
414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431
432 433 434 435 436 437 438 439 440]
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
[ 0 1 2 ... 2202 2203 2204] [441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458
459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476
477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494
495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530
531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548
549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566
567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584
585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602
603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620
621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638
639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674
675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692
693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710
711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728
729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746
747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764
765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782
783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818
819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836
837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854
855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872
873 874 875 876 877 878 879 880 881]
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
[ 0 1 2 ... 2202 2203 2204] [ 882 883 884 885 886 887 888 889 890 891 892 893 894 895
896 897 898 899 900 901 902 903 904 905 906 907 908 909
910 911 912 913 914 915 916 917 918 919 920 921 922 923
924 925 926 927 928 929 930 931 932 933 934 935 936 937
938 939 940 941 942 943 944 945 946 947 948 949 950 951
952 953 954 955 956 957 958 959 960 961 962 963 964 965
966 967 968 969 970 971 972 973 974 975 976 977 978 979
980 981 982 983 984 985 986 987 988 989 990 991 992 993
994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007
1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021
1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035
1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049
1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063
1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077
1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091
1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105
1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119
1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133
1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147
1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161
1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175
1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189
1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203
1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217
1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231
1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245
1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259
1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273
1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287
1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301
1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315
1316 1317 1318 1319 1320 1321 1322]
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
[ 0 1 2 ... 2202 2203 2204] [1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336
1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350
1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364
1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378
1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406
1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420
1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434
1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448
1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462
1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476
1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490
1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504
1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518
1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532
1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546
1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560
1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574
1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588
1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602
1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616
1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630
1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644
1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658
1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672
1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686
1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700
1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714
1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742
1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756
1757 1758 1759 1760 1761 1762 1763]
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
[ 0 1 2 ... 1761 1762 1763] [1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777
1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791
1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805
1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819
1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833
1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847
1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861
1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875
1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889
1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903
1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917
1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931
1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945
1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959
1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973
1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987
1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029
2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043
2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057
2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071
2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085
2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099
2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113
2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127
2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141
2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155
2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169
2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183
2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197
2198 2199 2200 2201 2202 2203 2204]
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
Target output: 1
F1 SCORE: 0.9244593816362524 +- 0.012535227234323546 [0.9622071050642479, 0.9416909620991255, 0.9220521541950113, 0.9022423784328546, 0.8941043083900226]
P(Acc | Certain): 0.9909499081179456 +- 0.001979188876666969 [0.9966144731273805, 0.9934665641813989, 0.9911253106141285, 0.9883488681757656, 0.9851943244910549]
P(Acc | Uncertain): 0.6525351521609853 +- 0.0074524186694632615 [0.6749116607773852, 0.6639175257731958, 0.6483825597749648, 0.6341968911917099, 0.6412671232876712]
[ 441 442 443 ... 2202 2203 2204] [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53
54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89
90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107
108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125
126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143
144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161
162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179
180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197
198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215
216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233
234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251
252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269
270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287
288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305
306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323
324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341
342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359
360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377
378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395
396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413
414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431
432 433 434 435 436 437 438 439 440]
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
[ 0 1 2 ... 2202 2203 2204] [441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458
459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476
477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494
495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530
531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548
549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566
567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584
585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602
603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620
621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638
639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674
675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692
693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710
711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728
729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746
747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764
765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782
783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818
819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836
837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854
855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872
873 874 875 876 877 878 879 880 881]
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
[ 0 1 2 ... 2202 2203 2204] [ 882 883 884 885 886 887 888 889 890 891 892 893 894 895
896 897 898 899 900 901 902 903 904 905 906 907 908 909
910 911 912 913 914 915 916 917 918 919 920 921 922 923
924 925 926 927 928 929 930 931 932 933 934 935 936 937
938 939 940 941 942 943 944 945 946 947 948 949 950 951
952 953 954 955 956 957 958 959 960 961 962 963 964 965
966 967 968 969 970 971 972 973 974 975 976 977 978 979
980 981 982 983 984 985 986 987 988 989 990 991 992 993
994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007
1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021
1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035
1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049
1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063
1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077
1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091
1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105
1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119
1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133
1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147
1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161
1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175
1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189
1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203
1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217
1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231
1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245
1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259
1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273
1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287
1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301
1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315
1316 1317 1318 1319 1320 1321 1322]
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
[ 0 1 2 ... 2202 2203 2204] [1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336
1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350
1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364
1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378
1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406
1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420
1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434
1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448
1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462
1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476
1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490
1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504
1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518
1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532
1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546
1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560
1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574
1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588
1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602
1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616
1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630
1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644
1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658
1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672
1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686
1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700
1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714
1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742
1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756
1757 1758 1759 1760 1761 1762 1763]
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
[ 0 1 2 ... 1761 1762 1763] [1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777
1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791
1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805
1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819
1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833
1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847
1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861
1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875
1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889
1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903
1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917
1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931
1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945
1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959
1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973
1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987
1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029
2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043
2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057
2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071
2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085
2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099
2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113
2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127
2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141
2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155
2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169
2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183
2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197
2198 2199 2200 2201 2202 2203 2204]
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
Target output: 2
F1 SCORE: 0.8956939054898239 +- 0.0004668869383694529 [0.8954854669140383, 0.8945578231292517, 0.8958660387231816, 0.8952057013281504, 0.8973544973544973]
P(Acc | Certain): 0.9802237531820188 +- 0.0008447896849073807 [0.983127109111361, 0.9806651198762568, 0.98018147086915, 0.9788841964881084, 0.9782608695652174]
P(Acc | Uncertain): 0.6658386804182905 +- 0.004254498716107378 [0.6548262548262548, 0.6581740976645435, 0.6673139158576051, 0.6704477611940298, 0.6784313725490196]
[ 441 442 443 ... 2202 2203 2204] [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53
54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89
90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107
108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125
126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143
144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161
162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179
180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197
198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215
216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233
234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251
252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269
270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287
288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305
306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323
324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341
342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359
360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377
378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395
396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413
414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431
432 433 434 435 436 437 438 439 440]
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
[ 0 1 2 ... 2202 2203 2204] [441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458
459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476
477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494
495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530
531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548
549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566
567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584
585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602
603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620
621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638
639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674
675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692
693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710
711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728
729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746
747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764
765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782
783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818
819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836
837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854
855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872
873 874 875 876 877 878 879 880 881]
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
[ 0 1 2 ... 2202 2203 2204] [ 882 883 884 885 886 887 888 889 890 891 892 893 894 895
896 897 898 899 900 901 902 903 904 905 906 907 908 909
910 911 912 913 914 915 916 917 918 919 920 921 922 923
924 925 926 927 928 929 930 931 932 933 934 935 936 937
938 939 940 941 942 943 944 945 946 947 948 949 950 951
952 953 954 955 956 957 958 959 960 961 962 963 964 965
966 967 968 969 970 971 972 973 974 975 976 977 978 979
980 981 982 983 984 985 986 987 988 989 990 991 992 993
994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007
1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021
1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035
1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049
1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063
1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077
1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091
1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105
1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119
1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133
1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147
1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161
1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175
1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189
1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203
1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217
1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231
1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245
1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259
1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273
1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287
1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301
1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315
1316 1317 1318 1319 1320 1321 1322]
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
[ 0 1 2 ... 2202 2203 2204] [1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336
1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350
1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364
1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378
1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406
1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420
1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434
1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448
1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462
1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476
1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490
1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504
1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518
1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532
1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546
1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560
1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574
1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588
1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602
1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616
1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630
1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644
1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658
1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672
1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686
1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700
1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714
1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742
1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756
1757 1758 1759 1760 1761 1762 1763]
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
[ 0 1 2 ... 1761 1762 1763] [1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777
1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791
1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805
1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819
1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833
1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847
1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861
1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875
1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889
1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903
1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917
1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931
1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945
1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959
1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973
1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987
1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029
2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043
2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057
2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071
2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085
2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099
2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113
2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127
2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141
2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155
2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169
2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183
2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197
2198 2199 2200 2201 2202 2203 2204]
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
Target output: 3
F1 SCORE: 0.8721943923768338 +- 0.004728381107143069 [0.8864795918367347, 0.8782179538482061, 0.871504157218443, 0.8650196920873613, 0.859750566893424]
P(Acc | Certain): 0.9782688014829797 +- 8.581726290369855e-05 [0.9781036560867818, 0.9780241151302995, 0.9784090909090909, 0.9784490698102781, 0.9783580754784476]
P(Acc | Uncertain): 0.6594160576349053 +- 0.0021611149247641815 [0.6669874879692012, 0.6602972399150743, 0.6591422121896162, 0.656271186440678, 0.6543821616599567]
[ 441 442 443 ... 2202 2203 2204] [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53
54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89
90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107
108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125
126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143
144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161
162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179
180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197
198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215
216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233
234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251
252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269
270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287
288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305
306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323
324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341
342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359
360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377
378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395
396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413
414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431
432 433 434 435 436 437 438 439 440]
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
[ 0 1 2 ... 2202 2203 2204] [441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458
459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476
477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494
495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530
531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548
549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566
567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584
585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602
603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620
621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638
639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674
675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692
693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710
711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728
729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746
747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764
765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782
783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818
819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836
837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854
855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872
873 874 875 876 877 878 879 880 881]
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
[ 0 1 2 ... 2202 2203 2204] [ 882 883 884 885 886 887 888 889 890 891 892 893 894 895
896 897 898 899 900 901 902 903 904 905 906 907 908 909
910 911 912 913 914 915 916 917 918 919 920 921 922 923
924 925 926 927 928 929 930 931 932 933 934 935 936 937
938 939 940 941 942 943 944 945 946 947 948 949 950 951
952 953 954 955 956 957 958 959 960 961 962 963 964 965
966 967 968 969 970 971 972 973 974 975 976 977 978 979
980 981 982 983 984 985 986 987 988 989 990 991 992 993
994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007
1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021
1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035
1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049
1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063
1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077
1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091
1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105
1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119
1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133
1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147
1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161
1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175
1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189
1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203
1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217
1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231
1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245
1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259
1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273
1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287
1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301
1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315
1316 1317 1318 1319 1320 1321 1322]
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[ 0 1 2 ... 2202 2203 2204] [1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336
1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350
1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364
1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378
1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406
1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420
1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434
1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448
1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462
1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476
1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490
1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504
1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518
1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532
1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546
1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560
1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574
1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588
1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602
1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616
1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630
1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644
1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658
1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672
1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686
1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700
1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714
1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742
1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756
1757 1758 1759 1760 1761 1762 1763]
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[ 0 1 2 ... 1761 1762 1763] [1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777
1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791
1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805
1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819
1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833
1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847
1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861
1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875
1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889
1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903
1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917
1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931
1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945
1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959
1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973
1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987
1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029
2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043
2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057
2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071
2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085
2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099
2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113
2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127
2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141
2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155
2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169
2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183
2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197
2198 2199 2200 2201 2202 2203 2204]
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Target output: 4
F1 SCORE: 0.8668427457060999 +- 0.0014700490292759586 [0.8624338624338624, 0.8646670789527933, 0.8673962338558612, 0.8693310657596371, 0.8703854875283448]
P(Acc | Certain): 0.974729743470298 +- 0.0005165493723274688 [0.9765494137353434, 0.9749290444654684, 0.9745838287752675, 0.9741173160782107, 0.9734691142971995]
P(Acc | Uncertain): 0.6555131429772774 +- 0.00039401996474623446 [0.6554238833181404, 0.656547619047619, 0.6562225475841874, 0.6549640287769785, 0.654407636159461]
<Figure size 432x288 with 0 Axes>
[6]:
# print results
task_names = ["Cooler Condition", "Valve Condition", "Internal Pump Leakage", "Accumulator", "Stable Flag"]
for i in range(len(final_results)):
f1_score_model_kfold = final_results[i][0]
p_accurate_certain_kfold = final_results[i][1]
p_accurate_uncertain_kfold = final_results[i][2]
print("Target output: ", task_names[i])
print("F1 SCORE: ",np.mean(f1_score_model_kfold),"+-",sem(f1_score_model_kfold), f1_score_model_kfold)
print("P(Acc | Certain): ",np.mean(p_accurate_certain_kfold),"+-",sem(p_accurate_certain_kfold),p_accurate_certain_kfold)
print("P(Acc | Uncertain): ",np.mean(p_accurate_uncertain_kfold),"+-",sem(p_accurate_uncertain_kfold),p_accurate_uncertain_kfold)
print("\n")
Target output: Cooler Condition
F1 SCORE: 0.9945351473922903 +- 0.0004707605326990252 [0.9931972789115646, 0.9954648526077098, 0.9954648526077098, 0.9948979591836735, 0.9936507936507937]
P(Acc | Certain): 0.9980861983060005 +- 0.00024045557469217787 [0.997716894977169, 0.9988465974625144, 0.9984662576687117, 0.9977011494252873, 0.9977000919963201]
P(Acc | Uncertain): 0.6848302207130731 +- 0.08938562630975022 [0.3333333333333333, 0.8, 0.7894736842105263, 0.7916666666666666, 0.7096774193548387]
Target output: Valve Condition
F1 SCORE: 0.9244593816362524 +- 0.012535227234323546 [0.9622071050642479, 0.9416909620991255, 0.9220521541950113, 0.9022423784328546, 0.8941043083900226]
P(Acc | Certain): 0.9909499081179456 +- 0.001979188876666969 [0.9966144731273805, 0.9934665641813989, 0.9911253106141285, 0.9883488681757656, 0.9851943244910549]
P(Acc | Uncertain): 0.6525351521609853 +- 0.0074524186694632615 [0.6749116607773852, 0.6639175257731958, 0.6483825597749648, 0.6341968911917099, 0.6412671232876712]
Target output: Internal Pump Leakage
F1 SCORE: 0.8956939054898239 +- 0.0004668869383694529 [0.8954854669140383, 0.8945578231292517, 0.8958660387231816, 0.8952057013281504, 0.8973544973544973]
P(Acc | Certain): 0.9802237531820188 +- 0.0008447896849073807 [0.983127109111361, 0.9806651198762568, 0.98018147086915, 0.9788841964881084, 0.9782608695652174]
P(Acc | Uncertain): 0.6658386804182905 +- 0.004254498716107378 [0.6548262548262548, 0.6581740976645435, 0.6673139158576051, 0.6704477611940298, 0.6784313725490196]
Target output: Accumulator
F1 SCORE: 0.8721943923768338 +- 0.004728381107143069 [0.8864795918367347, 0.8782179538482061, 0.871504157218443, 0.8650196920873613, 0.859750566893424]
P(Acc | Certain): 0.9782688014829797 +- 8.581726290369855e-05 [0.9781036560867818, 0.9780241151302995, 0.9784090909090909, 0.9784490698102781, 0.9783580754784476]
P(Acc | Uncertain): 0.6594160576349053 +- 0.0021611149247641815 [0.6669874879692012, 0.6602972399150743, 0.6591422121896162, 0.656271186440678, 0.6543821616599567]
Target output: Stable Flag
F1 SCORE: 0.8668427457060999 +- 0.0014700490292759586 [0.8624338624338624, 0.8646670789527933, 0.8673962338558612, 0.8693310657596371, 0.8703854875283448]
P(Acc | Certain): 0.974729743470298 +- 0.0005165493723274688 [0.9765494137353434, 0.9749290444654684, 0.9745838287752675, 0.9741173160782107, 0.9734691142971995]
P(Acc | Uncertain): 0.6555131429772774 +- 0.00039401996474623446 [0.6554238833181404, 0.656547619047619, 0.6562225475841874, 0.6549640287769785, 0.654407636159461]
Run Multi Agent System¶
We initialize the data source and create the agent network, once the agents are fully up and running, run the dashboard code in a separate terminal to visualize the agents.
[1]:
import osbrain
from osbrain.agent import run_agent
from osbrain.agent import Agent
import pandas as pd
from datetime import datetime
import time
import pickle
import numpy as np
import random
from copy import copy
from .Agent_models.agents import Sensor, Aggregator, Predictor, DecisionMaker, SensorNetwork
# TYPES OF AGENT
# 0 - SENSOR NETWORK
# 1 - SENSOR
# 2 - AGGREGATOR
# 3 - PREDICTOR
# 4 - DECISIONMAKER
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-1-6053d4936c6f> in <module>
13 from copy import copy
14
---> 15 from .Agent_models.agents import Sensor, Aggregator, Predictor, DecisionMaker, SensorNetwork
16
17 # TYPES OF AGENT
ModuleNotFoundError: No module named '__main__.Agent_models'; '__main__' is not a package
[2]:
DemoMode= True
pickle_path = "pickles/"
data_input = pickle.load(open(pickle_path + "data_input_data_1Hz_full.p", "rb"))
data_output = pickle.load(open(pickle_path + "zema_outputs.p", "rb"))
X_data = data_input
Y_data = data_output
randomShuffling = True
if (randomShuffling == True):
index_list = np.arange(X_data.shape[0])
random.shuffle(index_list)
Y_data = Y_data[index_list, :]
X_data = X_data[index_list, :, :]
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-2-8d9935992e57> in <module>
1 DemoMode= True
2 pickle_path = "pickles/"
----> 3 data_input = pickle.load(open(pickle_path + "data_input_data_1Hz_full.p", "rb"))
4 data_output = pickle.load(open(pickle_path + "zema_outputs.p", "rb"))
5
FileNotFoundError: [Errno 2] No such file or directory: 'pickles/data_input_data_1Hz_full.p'
Starting server¶
[3]:
ns = osbrain.nameserver.run_nameserver(addr='127.0.0.1:14065')
Broadcast server running on 0.0.0.0:9091
NS running on 127.0.0.1:14065 (127.0.0.1)
URI = PYRO:Pyro.NameServer@127.0.0.1:14065
Creating Agent¶
We firstly create a SensorNetwork Agent which enable wrapper functions and manages agents
[4]:
sensor_network = run_agent('sensor_network', base=SensorNetwork)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-4-75226932f9c3> in <module>
----> 1 sensor_network = run_agent('sensor_network', base=SensorNetwork)
NameError: name 'SensorNetwork' is not defined
Sensor Agents¶
- Next, we create a Sensor Agent by
sensor_network.addsimsensor(type,unit), and store into a listsensors. - We set the data source of the Sensor Agent :
sensor_new.set_generatorDataSet(dataSet)where dataSet is a 3-dimensional numpy array with: [Row x Sequence Length x Sensor]
[5]:
#add sensors
sensors=[]
sensorTypes = ["Temperature","Temperature","Temperature","Temperature","Vibration","EfficiencyFactor","VolumeFlow","VolumeFlow","Pressure","Pressure","Pressure","Pressure","Pressure","Pressure","MotorPower","CoolingEfficiency","CoolingPower"]
sensorUnits = ["C","C","C","C","mm/s","%","l/min","l/min","bar","bar","bar","bar","bar","bar","W","%","kW"]
for sensor_num in range(X_data.shape[2]):
sensor_new = sensor_network.add_simsensor(type=sensorTypes[sensor_num], unit_v=sensorUnits[sensor_num])
sensor_new.set_generatorDataSet(X_data[:,:,sensor_num])
sensors.append(sensor_new)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-5-ada498f69a90> in <module>
4 sensorUnits = ["C","C","C","C","mm/s","%","l/min","l/min","bar","bar","bar","bar","bar","bar","W","%","kW"]
5
----> 6 for sensor_num in range(X_data.shape[2]):
7 sensor_new = sensor_network.add_simsensor(type=sensorTypes[sensor_num], unit_v=sensorUnits[sensor_num])
8 sensor_new.set_generatorDataSet(X_data[:,:,sensor_num])
NameError: name 'X_data' is not defined
- We can access the Sensor Agents stored in array
sensors. - Alternatively, the SensorNetwork Agent automatically keeps track of sensors added by it, we can access the list by calling
get_attr('sensor_list') - Here, we demonstrate a function of Sensor Agent which is
read_generatorwhich returns a random data row from the loaded dataset
[6]:
#the sensors are loaded into array sensors
sensor1 = sensors[0]
print(len(sensors))
#access sensors by either way
sensor_network.get_attr('sensor_list')[0].read_generator()
sensor1.read_generator()
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-6-fe35f12d5c0e> in <module>
1 #the sensors are loaded into array sensors
----> 2 sensor1 = sensors[0]
3 print(len(sensors))
4
5 #access sensors by either way
IndexError: list index out of range
Aggregator Agents¶
- We add an Aggregator Agent to the
sensor_networkby calling the function.add_aggregator(sensorList)wheresensorListis an optional list of Sensor Agents which automatically binds the aggregator to the Sensor Agents. - Aggregator Agent can bind to Sensor Agent in runtime by calling
.bind_sensors(sensorList).
[7]:
#add aggregators and bind them to sensors
aggregator1 = sensor_network.add_aggregator(sensors)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-7-f1ff1ca81bf3> in <module>
1 #add aggregators and bind them to sensors
----> 2 aggregator1 = sensor_network.add_aggregator(sensors)
NameError: name 'sensor_network' is not defined
Predictor Agents¶
- Similarly, we can add Predictor Agent by
.add_predictor(aggregator)with the optionalaggregatorto be binded to. - For each Predictor Agent, we load the prediction model by
.load_predictor_model(model)wheremodelis a trained ML_Wrapper with signature such as.predict_model_wUnc(x_test,num_samples)wherex_testis the data input andnum_samplesis the number of samples for Monte Carlo sampling. - Here, we load the previously pickled prediction model.
[8]:
#add predictor and bind to aggregator
predictor1 = sensor_network.add_predictor(aggregator=aggregator1)
predictor2 = sensor_network.add_predictor(aggregator=aggregator1)
predictor3 = sensor_network.add_predictor(aggregator=aggregator1)
predictor4 = sensor_network.add_predictor(aggregator=aggregator1)
predictor5 = sensor_network.add_predictor(aggregator=aggregator1)
#load predictor models
predictor1.load_predictor_model(pickle.load(open("pickles/" + "bnn_wrapper_0.p", "rb")))
predictor2.load_predictor_model(pickle.load(open("pickles/" + "bnn_wrapper_1.p", "rb")))
predictor3.load_predictor_model(pickle.load(open("pickles/" + "bnn_wrapper_2.p", "rb")))
predictor4.load_predictor_model(pickle.load(open("pickles/" + "bnn_wrapper_3.p", "rb")))
predictor5.load_predictor_model(pickle.load(open("pickles/" + "bnn_wrapper_4.p", "rb")))
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-8-cfeb5649323c> in <module>
1 #add predictor and bind to aggregator
----> 2 predictor1 = sensor_network.add_predictor(aggregator=aggregator1)
3 predictor2 = sensor_network.add_predictor(aggregator=aggregator1)
4 predictor3 = sensor_network.add_predictor(aggregator=aggregator1)
5 predictor4 = sensor_network.add_predictor(aggregator=aggregator1)
NameError: name 'sensor_network' is not defined
DecisionMaker Agent¶
- We add Decision Maker Agent calling
.add_decisionMaker()on SensorNetwork agent - The DM Agent is binded to every predictor by calling
.bind_predictor(predictor)function
[9]:
decisionMaker = sensor_network.add_decisionMaker()
decisionMaker.bind_predictor(predictor1)
decisionMaker.bind_predictor(predictor2)
decisionMaker.bind_predictor(predictor3)
decisionMaker.bind_predictor(predictor4)
decisionMaker.bind_predictor(predictor5)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-9-180d2cc1fe2f> in <module>
----> 1 decisionMaker = sensor_network.add_decisionMaker()
2 decisionMaker.bind_predictor(predictor1)
3 decisionMaker.bind_predictor(predictor2)
4 decisionMaker.bind_predictor(predictor3)
5 decisionMaker.bind_predictor(predictor4)
NameError: name 'sensor_network' is not defined
Demo¶
- For demo, we run an infinite loop which continuously runs the
.request_sensors_data() - Due to the bindings, the requested data will immediately be propagated to all binded Predictor Agents and to Decision Maker Agent
- While this is running, run the dashboard code in a separate terminal to visualize the multi-agent testbed
[10]:
#send request to aggregator agents for data from sensors
if DemoMode:
for i in range(99999999999):
aggregator1.request_sensors_data()
time.sleep(3)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-10-c6d4a27820dd> in <module>
3 if DemoMode:
4 for i in range(99999999999):
----> 5 aggregator1.request_sensors_data()
6 time.sleep(3)
NameError: name 'aggregator1' is not defined
[ ]:
Agent Dashboard User Interface¶
- Run this code after the Agents are setup and running
- View the web visualization at port 8054 using Internet Browser
[1]:
portNumber = 8054
[2]:
import osbrain
from osbrain.agent import run_agent
from osbrain import NSProxy
# -*- coding: utf-8 -*-
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_cytoscape as cyto
import dash_daq as daq
import plotly.graph_objs as go
import networkx as nx
import numpy as np
import pickle
external_css = ["https://cdnjs.cloudflare.com/ajax/libs/skeleton/2.0.4/skeleton.min.css",
"https://fonts.googleapis.com/css?family=Raleway:400,400i,700,700i",
"https://fonts.googleapis.com/css?family=Product+Sans:400,400i,700,700i"]
app = dash.Dash(__name__, external_stylesheets=external_css)
#===============APP LAYOUT=========
agent_names =['aggregator_1', 'sensor_0', 'sensor_1', 'sensor_2', 'sensor_3', 'sensor_4', 'sensor_5', 'sensor_6', 'sensor_7', 'sensor_8', 'sensor_9', 'sensor_10', 'sensor_11', 'sensor_12', 'sensor_13', 'sensor_14', 'predictor_0', 'decisionMaker_0']
G = nx.Graph()
G.add_nodes_from(agent_names)
myEdges = []
for agent_x in agent_names:
for agent_y in agent_names:
include=False
if 'sensor' in agent_x and 'aggregator' in agent_y:
include = True
elif 'aggregator' in agent_x and 'predictor' in agent_y:
include = True
elif 'predictor' in agent_x and 'decisionMaker' in agent_y:
include = True
if include:
new_edge = (agent_x, agent_y)
myEdges.append(new_edge)
G.add_edges_from(myEdges)
pos=nx.fruchterman_reingold_layout(G)
nodes_ct = [{'data': {'id': k, 'label': k}, 'position': {'x': pos[k][0], 'y': pos[k][1]},'classes': k.split('_')[0]} for k in agent_names]
edges_ct = [{'data': {'source': k[0], 'target': k[1]}} for k in myEdges]
elements = nodes_ct+edges_ct
tab_div_style ={
"padding": "2",
"marginLeft": "5",
"marginRight": "5",
"backgroundColor":"white",
"border": "1px solid #C8D4E3",
"borderRadius": "3px"
}
tab_title = { 'textAlign': 'center',}
output_labels = [{0: "Optimal", 1: "Reduced", 2: "Nearly Fail"},
{0: "Optimal", 1: "Small lag", 2: "Severe lag", 3: "Nearly Fail"},
{0: "No Leakage", 1: "Weak Leakage", 2: "Severe Leakage"},
{0: "Optimal", 1: "Slightly Reduced", 2: "Severely Reduced", 3: "Nearly Fail"},
{0: "Stable", 1: "Unstable"}]
output_category = ["Cooler Condition", "Valve Condition", "Internal Pump", "Accumulator", "Stable Flag"]
def getConditionIndicator(condition_text="Optimal",certain=True):
color = "#00cc96" if certain else "#ff0000"
label = "Certain" if certain else "Uncertain"
return [
html.H5(condition_text, style=tab_title),
daq.Indicator(
value=True,
color=color,
label=label,
style=tab_title
)]
app.layout = html.Div(children=[
html.Div([
html.H3("Multi Agents for Machine Learning under Uncertainty Testbed",style={
'textAlign': 'center',
}),
]),
html.Div([
html.Div([
html.H6("Cooler Condition",style= { 'textAlign': 'center',"border": "1px solid #C8D4E3"}),
html.Div(children=getConditionIndicator("Optimal",certain=True),id='cooler-indicator')
],className="two columns",style=tab_div_style),
html.Div([
html.H6("Valve Condition", style={'textAlign': 'center', "border": "1px solid #C8D4E3"}),
html.Div(children=getConditionIndicator("Optimal", certain=True),id='valve-indicator')
], className="two columns", style=tab_div_style),
html.Div([
html.H6("Internal Pump", style={'textAlign': 'center', "border": "1px solid #C8D4E3"}),
html.Div(children=getConditionIndicator("Optimal", certain=True),id='pump-indicator')
], className="two columns", style=tab_div_style),
html.Div([
html.H6("Accumulator", style={'textAlign': 'center', "border": "1px solid #C8D4E3"}),
html.Div(children=getConditionIndicator("Optimal", certain=True),id='accumulator-indicator')
], className="two columns", style=tab_div_style),
html.Div([
html.H6("Stable Flag", style={'textAlign': 'center', "border": "1px solid #C8D4E3"}),
html.Div(children=getConditionIndicator("Optimal", certain=True),id='stability-indicator')
], className="two columns", style=tab_div_style),
],className="row"),
html.Div([
html.Div([
html.H5('Agent Network Dashboard', style=tab_title
),
cyto.Cytoscape(
id='agents-network',
layout={'name': 'circle'},
style={'width': '100%', 'height': '400px'},
elements=elements,
stylesheet= [
{
'selector': 'node',
'style': {
'label': 'data(id)'
}
},
{
'selector': '.sensor',
'style': {
'background-color': 'green',
'line-color': 'black'
}
},
{
'selector': '.aggregator',
'style': {
'background-color': 'blue',
'line-color': 'black'
}
},
{
'selector': '.predictor',
'style': {
'background-color': 'red',
'line-color': 'black'
}
},
{
'selector': '.decisionMaker',
'style': {
'background-color': 'yellow',
'line-color': 'black'
}
},
]
),], className="six columns",style=tab_div_style),
html.Div([
html.H5(
children='Select Predictor Agent',
style={
'textAlign': 'left',
}
),
html.Div(
id='predictor-dropdown-div',
children=dcc.Dropdown(
id='predictor-dropdown',
options=[],
value='predictor_0',
style={'width': 250},
)),
html.Div([
html.H5("Prediction Graph",style=tab_title)
]),
dcc.Graph(id='prediction-graph'),
], className='six columns ', style=tab_div_style)
],className="row"),
html.Div([
html.Div([
html.Div([
html.H5("Sensor Graph",style=tab_title)
]),
html.H5(
children='Select Sensor Agent',
style={
'textAlign': 'left',
}
),
html.Div(
id='sensor-dropdown-div',
children=dcc.Dropdown(
id='sensor-dropdown',
options=[],
value='sensor_number_0',
style={'width': 250},
)),
dcc.Graph(id='sensor-graph'),
], className='six columns ',style=tab_div_style),
html.Div([
html.Div([
html.H5("Uncertainty Graph",style=tab_title)
]),
dcc.Graph(id='uncertainty-graph'),
], className='six columns ',style=tab_div_style),
], className='row'),
dcc.Interval(
id='interval-component',
interval=3 * 1000, # in milliseconds
n_intervals=0
),
dcc.Interval(
id='interval-component-network-graph',
interval=1000 * 1000, # in milliseconds
n_intervals=0
)
],style={
"padding": "8",
"marginLeft": "45",
"marginRight": "45",
"backgroundColor":"white",
"border": "1px solid #C8D4E3",
"borderRadius": "3px"
})
@app.callback(dash.dependencies.Output('sensor-dropdown', 'value'),
[dash.dependencies.Input('agents-network', 'tapNodeData')])
def displayTapNodeData(data):
if 'sensor' in data['label'] and 'sensor_network' not in data['label']:
return data['label']
@app.callback([dash.dependencies.Output('agents-network', 'elements'),dash.dependencies.Output('sensor-dropdown-div', 'children'),dash.dependencies.Output('predictor-dropdown-div', 'children')],
[dash.dependencies.Input('interval-component-network-graph', 'n_intervals')])
def update_network_graph(n):
ns_temp = NSProxy(nsaddr='127.0.0.1:14065')
agent_names = ns_temp.agents()
print(agent_names)
G = nx.Graph()
G.add_nodes_from(agent_names)
myEdges = []
for agent_x in agent_names:
for agent_y in agent_names:
include = False
if 'sensor' in agent_x and 'aggregator' in agent_y and 'sensor_network' not in agent_x:
include = True
elif 'aggregator' in agent_x and 'predictor' in agent_y:
include = True
elif 'predictor' in agent_x and 'decisionMaker' in agent_y:
include = True
if include:
new_edge = (agent_x, agent_y)
myEdges.append(new_edge)
G.add_edges_from(myEdges)
pos = nx.fruchterman_reingold_layout(G)
nodes_ct = [{'data': {'id': k, 'label': k}, 'position': {'x': pos[k][0], 'y': pos[k][1]},'classes': k.split('_')[0]} for k in agent_names]
edges_ct = [{'data': {'source': k[0], 'target': k[1]}} for k in myEdges]
elements = nodes_ct + edges_ct
sensor_options = [{'label': name, 'value': name} for name in agent_names if 'sensor' in name and 'sensor_network' not in name]
predictor_options = [{'label': name, 'value': name} for name in agent_names if 'predictor' in name ]
sensor_dropdown_component = dcc.Dropdown(
id='sensor-dropdown',
options=sensor_options,
value=sensor_options[0]['value'],
style={'width': 250},
)
predictor_dropdown_component = dcc.Dropdown(
id='predictor-dropdown',
options=predictor_options,
value=predictor_options[0]['value'],
style={'width': 250},
)
print("HELLO")
return [elements, sensor_dropdown_component,predictor_dropdown_component]
@app.callback(dash.dependencies.Output('sensor-graph', 'figure'),
[dash.dependencies.Input('interval-component', 'n_intervals'),dash.dependencies.Input('sensor-dropdown', 'value')])
def update_sensor_graph(n,chosen_sensor_name):
ns_temp = NSProxy(nsaddr='127.0.0.1:14065')
sensor_type = ns_temp.proxy(chosen_sensor_name).get_attr('type')
sensor_unit = ns_temp.proxy(chosen_sensor_name).get_attr('unit_v')
final_data = ns_temp.proxy(chosen_sensor_name).get_attr('current_data')
final_data = np.array(final_data)
y_data = final_data
N_sequence = y_data.shape[0]
x_data = np.linspace(0, N_sequence - 1, N_sequence)
traces_sensor = go.Scatter(
x=x_data,
y=y_data,
mode='lines',
name='lines'
)
layout = {'title': 'Sensor '+sensor_type+" #"+chosen_sensor_name.split('_')[-1],
'xaxis': {'title': 'Time (s)'},
'yaxis': {'title': sensor_type+" ("+sensor_unit+")"},
}
return {
'data': [traces_sensor],
'layout': layout
}
predictions = []
uncertainties = []
probabilities_accurate = []
def getTimeSeriesGraph(y_data,title='Prediction Certainty vs Time',xaxis='Time (s)',yaxis='Certainty (%) '):
N_sequence = y_data.shape[0]
x_data = np.linspace(0, N_sequence - 1, N_sequence)
traces_sensor = go.Scatter(
x=x_data,
y=y_data,
mode='lines',
name='lines'
)
layout = {'title': title,
'xaxis': {'title': xaxis},
'yaxis': {'title': yaxis},
}
return {
'data': [traces_sensor],
'layout': layout
}
@app.callback([dash.dependencies.Output('prediction-graph', 'figure'), dash.dependencies.Output('uncertainty-graph', 'figure'), dash.dependencies.Output('cooler-indicator', 'children'), dash.dependencies.Output('valve-indicator', 'children'), dash.dependencies.Output('pump-indicator', 'children'), dash.dependencies.Output('accumulator-indicator', 'children'), dash.dependencies.Output('stability-indicator', 'children')],
[dash.dependencies.Input('interval-component', 'n_intervals'),dash.dependencies.Input('predictor-dropdown', 'value')])
def update_prediction_graph(n,selected_predictor):
ns_temp = NSProxy(nsaddr='127.0.0.1:14065')
overall_new_data = ns_temp.proxy('decisionMaker_0').get_attr('current_inference')
column_numbers = [x.split('_')[-1] for x in overall_new_data.columns]
overall_new_data.columns = column_numbers
overall_new_data.sort_index(axis=1, inplace=True)
#condition_indicators = [getConditionIndicator(output_labels[int(predictor['label'].split('_')[-1])][overall_new_data[predictor['label']].pred],certain=overall_new_data[predictor['label']].unc_state) for id_,predictor in enumerate(predictors) ]
print(overall_new_data)
print(overall_new_data.loc['pred'])
new_prediction = overall_new_data.loc['pred']
new_uncertainty = overall_new_data.loc['unc']
new_uncertainty_state = overall_new_data.loc['unc_state']
condition_indicators = [getConditionIndicator(output_labels[_id][new_prediction[_id]],certain=unc_state ) for _id,unc_state in enumerate(new_uncertainty_state)]
predictions.append(new_prediction)
uncertainties.append(new_uncertainty)
selected_predictor_id=int(selected_predictor.split("_")[-1])
# dim 0 = samples, dim 1 = predictor
y_data_pred = np.array(predictions)[:,selected_predictor_id]
y_data_unc = np.array(uncertainties)[:, selected_predictor_id]
#pred
prediction_graph = getTimeSeriesGraph(y_data_pred,'Model Prediction ('+output_category[selected_predictor_id]+') vs Time','Time (s)','Prediction ')
uncertainties_graph = getTimeSeriesGraph(y_data_unc,'Prediction Certainty vs Time','Time (s)','Certainty (%) ')
return [prediction_graph,uncertainties_graph] +condition_indicators
if __name__ == '__main__':
app.run_server(debug=False, port=portNumber)
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-2-3d73c4fa70ad> in <module>()
7 import dash_core_components as dcc
8 import dash_html_components as html
----> 9 import dash_cytoscape as cyto
10 import dash_daq as daq
11 import plotly.graph_objs as go
ModuleNotFoundError: No module named 'dash_cytoscape'
agentMET4FOF agents¶
-
class
agentMET4FOF.agents.AgentMET4FOF(name='', host=None, serializer=None, transport=None, attributes=None)[source]¶ Base class for all agents with specific functions to be overridden/supplied by user.
Behavioural functions for users to provide are init_parameters, agent_loop and on_received_message. Communicative functions are bind_output, unbind_output and send_output.
-
agent_loop()[source]¶ User defined method for the agent to execute for loop_wait seconds specified either in self.loop_wait or explicitly via`init_agent_loop(loop_wait)`
To start a new loop, call init_agent_loop(loop_wait) on the agent Example of usage is to check the current_state of the agent and send data periodically
-
bind_output(output_agent)[source]¶ Forms Output connection with another agent. Any call on send_output will reach this newly binded agent
Adds the agent to its list of Outputs.
Parameters: output_agent (AgentMET4FOF or list) – Agent(s) to be binded to this agent’s output channel
-
convert_to_plotly(matplotlib_fig)[source]¶ Internal method to convert matplotlib figure to plotly figure
Parameters: matplotlib_fig (plt.Figure) – Matplotlib figure to be converted
-
handle_process_data(message)[source]¶ Internal method to handle incoming message before calling user-defined on_received_message method.
If current_state is either Stop or Reset, it will terminate early before entering on_received_message
-
init_agent_loop(loop_wait=1.0)[source]¶ Initiates the agent loop, which iterates every`loop_wait` seconds
Stops every timers and initiate a new loop.
Parameters: loop_wait (int) – The wait between each iteration of the loop
-
log_info(message)[source]¶ Prints logs to be saved into logfile with Logger Agent
Parameters: message (str) – Message to be logged to the internal Logger Agent
-
on_init()[source]¶ Internal initialization to setup the agent: mainly on setting the dictionary of Inputs, Outputs, PubAddr.
Calls user-defined init_parameters() upon finishing.
-
Inputs¶ Dictionary of Agents connected to its input channels. Messages will arrive from agents in this dictionary. Automatically updated when bind_output() function is called
Type: dict
-
Outputs¶ Dictionary of Agents connected to its output channels. Messages will be sent to agents in this dictionary. Automatically updated when bind_output() function is called
Type: dict
-
PubAddr_alias¶ Name of Publish address socket
Type: str
-
PubAddr¶ Publish address socket handle
Type: str
-
AgentType¶ Name of class
Type: str
-
current_state¶ Current state of agent. Can be used to define different states of operation such as “Running”, “Idle, “Stop”, etc.. Users will need to define their own flow of handling each type of self.current_state in the agent_loop
Type: str
-
loop_wait¶ The interval to wait between loop. Call init_agent_loop to restart the timer or set the value of loop_wait in init_parameters when necessary.
Type: int
-
memory_buffer_size¶ The total number of elements to be stored in the agent memory When total elements exceeds this number, the latest elements will be replaced with the incoming data elements
Type: int
-
-
on_received_message(message)[source]¶ User-defined method and is triggered to handle the message passed by Input.
Parameters: message (Dictionary) – The message received is in form {‘from’:agent_name, ‘data’: data, ‘senderType’: agent_class, ‘channel’:channel_name} agent_name is the name of the Input agent which sent the message data is the actual content of the message
-
pack_data(data, channel='default')[source]¶ Internal method to pack the data content into a dictionary before sending out.
Special case : if the data is already a message, then the from and senderType will be altered to this agent, without altering the data and channel within the message this is used for more succinct data processing and passing.
Parameters: - data (argument) – Data content to be packed before sending out to agents.
- channel (str) – Key of dictionary which stores data
Returns: Packed message data
Return type: dict of the form {‘from’:agent_name, ‘data’: data, ‘senderType’: agent_class, ‘channel’:channel_name}.
-
reset()[source]¶ This method will be called on all agents when the global reset_agents is called by the AgentNetwork and when the Reset button is clicked on the dashboard.
Method to reset the agent’s states and parameters. User can override this method to reset the specific parameters.
-
send_output(data, channel='default')[source]¶ Sends message data to all connected agents in self.Outputs.
Output connection can first be formed by calling bind_output. By default calls pack_data(data) before sending out. Can specify specific channel as opposed to ‘default’ channel.
Parameters: - data (argument) – Data content to be sent out
- channel (str) – Key of message dictionary which stores data
Returns: message
Return type: dict of the form {‘from’:agent_name, ‘data’: data, ‘senderType’: agent_class, ‘channel’:channel_name}.
-
send_plot(fig=<Figure size 640x480 with 0 Axes>)[source]¶ Sends plot to agents connected to this agent’s Output channel.
This method is different from send_output which will be sent to through the ‘plot’ channel to be handled.
Parameters: fig (Figure) – Can be either matplotlib figure or plotly figure Returns: The message format is {‘from’ Return type: agent_name, ‘plot’: data, ‘senderType’: agent_class}.
-
stop_agent_loop()[source]¶ Stops agent_loop from running. Note that the agent will still be responding to messages
-
unbind_output(output_agent)[source]¶ Remove existing output connection with another agent. This reverses the bind_output method
Parameters: output_agent (AgentMET4FOF) – Agent binded to this agent’s output channel
-
-
class
agentMET4FOF.agents.AgentNetwork(ip_addr='127.0.0.1', port=3333, connect=False, log_filename='log_file.csv', dashboard_modules=True, dashboard_update_interval=3, dashboard_max_monitors=10, dashboard_port=8050)[source]¶ Object for starting a new Agent Network or connect to an existing Agent Network specified by ip & port
Provides function to add agents, (un)bind agents, query agent network state, set global agent states Interfaces with an internal _AgentController which is hidden from user
-
add_agent(name=' ', agentType=<class 'agentMET4FOF.agents.AgentMET4FOF'>, log_mode=True, memory_buffer_size=1000000, ip_addr=None)[source]¶ Instantiates a new agent in the network.
Parameters: - str (name) – with the same name. Defaults to the agent’s class name.
- AgentMET4FOF (agentType) – network. Defaults to
AgentMET4FOF - bool (log_mode) – Logger Agent. Defaults to True.
Returns: AgentMET4FOF
Return type: Newly instantiated agent
-
agents(filter_agent=None)[source]¶ Returns all agent names connected to Agent Network.
Returns: list Return type: names of all agents
-
bind_agents(source, target)[source]¶ Binds two agents communication channel in a unidirectional manner from source Agent to target Agent
Any subsequent calls of source.send_output() will reach target Agent’s message queue.
Parameters: - source (AgentMET4FOF) – Source agent whose Output channel will be binded to target
- target (AgentMET4FOF) – Target agent whose Input channel will be binded to source
-
connect(ip_addr='127.0.0.1', port=3333, verbose=True)[source]¶ Parameters: - ip_addr (str) – IP Address of server to connect to
- port (int) – Port of server to connect to
-
get_agent(agent_name)[source]¶ Returns a particular agent connected to Agent Network.
Parameters: agent_name (str) – Name of agent to search for in the network
-
set_agents_state(filter_agent=None, state='Idle')[source]¶ Blanket operation on all agents to set their current_state attribute to given state
Can be used to define different states of operation such as “Running”, “Idle, “Stop”, etc.. Users will need to define their own flow of handling each type of self.current_state in the agent_loop
Parameters: - filter_agent (str) – (Optional) Filter name of agents to set the states
- state (str) – State of agents to set
-
set_running_state(filter_agent=None)[source]¶ Blanket operation on all agents to set their current_state attribute to “Running”
Users will need to define their own flow of handling each type of self.current_state in the agent_loop
Parameters: filter_agent (str) – (Optional) Filter name of agents to set the states
-
set_stop_state(filter_agent=None)[source]¶ Blanket operation on all agents to set their current_state attribute to “Stop”
Users will need to define their own flow of handling each type of self.current_state in the agent_loop
Parameters: filter_agent (str) – (Optional) Filter name of agents to set the states
-
start_server(ip_addr='127.0.0.1', port=3333)[source]¶ Parameters: - ip_addr (str) – IP Address of server to start
- port (int) – Port of server to start
-
unbind_agents(source, target)[source]¶ Unbinds two agents communication channel in a unidirectional manner from source Agent to target Agent
This is the reverse of bind_agents()
Parameters: - source (AgentMET4FOF) – Source agent whose Output channel will be unbinded from target
- target (AgentMET4FOF) – Target agent whose Input channel will be unbinded from source
-
-
class
agentMET4FOF.agents.DataStreamAgent(name='', host=None, serializer=None, transport=None, attributes=None)[source]¶ Able to simulate generation of datastream by loading a given DataStreamMET4FOF object.
Can be used in incremental training or batch training mode. To simulate batch training mode, set pretrain_size=-1 , otherwise, set pretrain_size and batch_size for the respective See DataStreamMET4FOF on loading your own data set as a data stream.
-
agent_loop()[source]¶ User defined method for the agent to execute for loop_wait seconds specified either in self.loop_wait or explicitly via`init_agent_loop(loop_wait)`
To start a new loop, call init_agent_loop(loop_wait) on the agent Example of usage is to check the current_state of the agent and send data periodically
-
init_parameters(stream=<agentMET4FOF.streams.DataStreamMET4FOF object>, pretrain_size=None, batch_size=1, loop_wait=1, randomize=False)[source]¶ Parameters: - stream (DataStreamMET4FOF) – A DataStreamMET4FOF object which provides the sample data
- pretrain_size (int) – The number of sample data to send through in the first loop cycle, and subsequently, the batch_size will be used
- batch_size (int) – The number of sample data to send in every loop cycle
- loop_wait (int) – The duration to wait (seconds) at the end of each loop cycle before going into the next cycle
- randomize (bool) – Determines if the dataset should be shuffled before streaming
-
-
class
agentMET4FOF.agents.MonitorAgent(name='', host=None, serializer=None, transport=None, attributes=None)[source]¶ Unique Agent for storing plots and data from messages received from input agents.
The dashboard searches for Monitor Agents’ memory and plots to draw the graphs “plot” channel is used to receive base64 images from agents to plot on dashboard
-
memory¶ Dictionary of format {agent1_name : agent1_data, agent2_name : agent2_data}
Type: dict
-
plots¶ Dictionary of format {agent1_name : agent1_plot, agent2_name : agent2_plot}
Type: dict
-
plot_filter¶ List of keys to filter the ‘data’ upon receiving message to be saved into memory Used to specifically select only a few keys to be plotted
Type: list of str
-
init_parameters(plot_filter=[], custom_plot_function=-1, **kwargs)[source]¶ User provided function to initialize parameters of choice.
-
on_received_message(message)[source]¶ Handles incoming data from ‘default’ and ‘plot’ channels.
Stores ‘default’ data into self.memory and ‘plot’ data into self.plots
Parameters: message (dict) – Acceptable channel values are ‘default’ or ‘plot’
-
reset()[source]¶ This method will be called on all agents when the global reset_agents is called by the AgentNetwork and when the Reset button is clicked on the dashboard.
Method to reset the agent’s states and parameters. User can override this method to reset the specific parameters.
-
update_plot_memory(message)[source]¶ Updates plot figures stored in self.plots with the received message
Parameters: message (dict) – Standard message format specified by AgentMET4FOF class Message[‘data’] needs to be base64 image string and can be nested in dictionary for multiple plots Only the latest plot will be shown kept and does not keep a history of the plots.
-
-
class
agentMET4FOF.agents.TransformerAgent(name='', host=None, serializer=None, transport=None, attributes=None)[source]¶ -
init_parameters(method=None, **kwargs)[source]¶ User provided function to initialize parameters of choice.
-
on_received_message(message)[source]¶ User-defined method and is triggered to handle the message passed by Input.
Parameters: message (Dictionary) – The message received is in form {‘from’:agent_name, ‘data’: data, ‘senderType’: agent_class, ‘channel’:channel_name} agent_name is the name of the Input agent which sent the message data is the actual content of the message
-
agentMET4FOF streams¶
-
class
agentMET4FOF.streams.DataStreamMET4FOF[source]¶ Class for creating finite datastream for ML with x as inputs and y as target Data can be fetched sequentially using next_sample() or all at once all_samples()
For sensors data: The format shape for 2D data stream (num_samples, n_sensors) The format shape for 3D data stream (num_samples, sample_length , n_sensors)
Indices and tables¶
References¶
| [Bang2019] | Bang X. Yong, A. Brintrup Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System, 9th Workshop on Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future |