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add AnomalyTransformer

This commit is contained in:
xuhongzuo 2023-09-04 10:39:04 +08:00
parent 71cac7ed3b
commit da7b6e7d39
9 changed files with 93 additions and 1168 deletions
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56
deepod/models/time_series/anomalytransformer.py
View File

@ -4,9 +4,11 @@ import torch.nn.functional as F
import numpy as np
from torch.utils.data import DataLoader
import math
import time
from deepod.utils.utility import get_sub_seqs
from deepod.core.base_model import BaseDeepAD
def my_kl_loss(p, q):
res = p * (torch.log(p + 0.0001) - torch.log(q + 0.0001))
return torch.mean(torch.sum(res, dim=-1), dim=1)
@ -15,7 +17,7 @@ def my_kl_loss(p, q):
class AnomalyTransformer(BaseDeepAD):
def __init__(self, seq_len=100, stride=1, lr=0.0001, epochs=10, batch_size=32,
epoch_steps=20, prt_steps=1, device='cuda',
k=3, input_c=25, output_c=25, anomaly_ratio=1,
k=3, anomaly_ratio=1,
verbose=2, random_state=42):
super(AnomalyTransformer, self).__init__(
model_name='AnomalyTransformer', data_type='ts', epochs=epochs, batch_size=batch_size, lr=lr,
@ -24,31 +26,35 @@ class AnomalyTransformer(BaseDeepAD):
verbose=verbose, random_state=random_state
)
self.k = k
self.input_c = input_c
self.output_c = output_c
self.anomaly_ratio = anomaly_ratio
def fit(self, X, y=None):
self.n_features = X.shape[1]
train_seqs = get_sub_seqs(X, seq_len=self.seq_len, stride=self.stride)
self.model = AnomalyTransformerModel(
self.net = AnomalyTransformerModel(
win_size=self.seq_len,
enc_in=self.input_c,
c_out=self.output_c,
e_layers=3).to(self.device)
enc_in=self.n_features,
c_out=self.n_features,
e_layers=3,
device=self.device
).to(self.device)
dataloader = DataLoader(train_seqs, batch_size=self.batch_size,
shuffle=True, pin_memory=True)
self.optimizer = torch.optim.AdamW(self.model.parameters(), lr=self.lr, weight_decay=1e-5)
self.optimizer = torch.optim.AdamW(self.net.parameters(), lr=self.lr, weight_decay=1e-5)
self.scheduler = torch.optim.lr_scheduler.StepLR(self.optimizer, step_size=5, gamma=0.5)
self.model.train()
self.net.train()
for e in range(self.epochs):
t1 = time.time()
loss = self.training(dataloader)
print(f'Epoch {e + 1},\t L1 = {loss}')
if self.verbose >= 1 and (e == 0 or (e + 1) % self.prt_steps == 0):
print(f'epoch{e + 1:3d}, '
f'training loss: {loss:.6f}, '
f'time: {time.time() - t1:.1f}s')
self.decision_scores_ = self.decision_function(X)
self.labels_ = self._process_decision_scores() # in base model
return
@ -58,7 +64,7 @@ class AnomalyTransformer(BaseDeepAD):
dataloader = DataLoader(seqs, batch_size=self.batch_size,
shuffle=False, drop_last=False)
self.model.eval()
self.net.eval()
loss, _ = self.inference(dataloader) # (n,d)
loss_final = np.mean(loss, axis=1) # (n,)
@ -76,7 +82,7 @@ class AnomalyTransformer(BaseDeepAD):
self.optimizer.zero_grad()
input = batch_x.float().to(self.device)
output, series, prior, _ = self.model(input)
output, series, prior, _ = self.net(input)
# calculate Association discrepancy
series_loss = 0.0
@ -118,14 +124,14 @@ class AnomalyTransformer(BaseDeepAD):
return np.average(loss_list)
def inference(self, dataloader):
criterion = nn.MSELoss(reduce=False)
criterion = nn.MSELoss(reduction='none')
temperature = 50
attens_energy = []
preds = []
for input_data in dataloader: # test_set
input = input_data.float().to(self.device)
output, series, prior, _ = self.model(input)
output, series, prior, _ = self.net(input)
loss = torch.mean(criterion(input, output), dim=-1)
@ -175,10 +181,9 @@ class AnomalyTransformer(BaseDeepAD):
return
# Proposed Model
class AnomalyTransformerModel(nn.Module):
def __init__(self, win_size, enc_in, c_out, d_model=512, n_heads=8, e_layers=3, d_ff=512,
dropout=0.0, activation='gelu', output_attention=True):
dropout=0.0, activation='gelu', output_attention=True, device='cuda'):
super(AnomalyTransformerModel, self).__init__()
self.output_attention = output_attention
@ -190,7 +195,10 @@ class AnomalyTransformerModel(nn.Module):
[
EncoderLayer(
AttentionLayer(
AnomalyAttention(win_size, False, attention_dropout=dropout, output_attention=output_attention),
AnomalyAttention(win_size, False,
attention_dropout=dropout, output_attention=output_attention,
device=device
),
d_model, n_heads),
d_model,
d_ff,
@ -323,16 +331,17 @@ class TriangularCausalMask():
class AnomalyAttention(nn.Module):
def __init__(self, win_size, mask_flag=True, scale=None, attention_dropout=0.0, output_attention=False):
def __init__(self, win_size, mask_flag=True, scale=None,
attention_dropout=0.0, output_attention=False, device='cuda'):
super(AnomalyAttention, self).__init__()
self.scale = scale
self.mask_flag = mask_flag
self.output_attention = output_attention
self.dropout = nn.Dropout(attention_dropout)
window_size = win_size
self.distances = torch.zeros((window_size, window_size)).cuda()
for i in range(window_size):
for j in range(window_size):
self.device = device
self.distances = torch.zeros((win_size, win_size)).to(device)
for i in range(win_size):
for j in range(win_size):
self.distances[i][j] = abs(i - j)
def forward(self, queries, keys, values, sigma, attn_mask):
@ -352,9 +361,8 @@ class AnomalyAttention(nn.Module):
sigma = torch.sigmoid(sigma * 5) + 1e-5
sigma = torch.pow(3, sigma) - 1
sigma = sigma.unsqueeze(-1).repeat(1, 1, 1, window_size) # B H L L
prior = self.distances.unsqueeze(0).unsqueeze(0).repeat(sigma.shape[0], sigma.shape[1], 1, 1).cuda()
prior = self.distances.unsqueeze(0).unsqueeze(0).repeat(sigma.shape[0], sigma.shape[1], 1, 1)
prior = 1.0 / (math.sqrt(2 * math.pi) * sigma) * torch.exp(-prior ** 2 / 2 / (sigma ** 2))
series = self.dropout(torch.softmax(attn, dim=-1))
V = torch.einsum("bhls,bshd->blhd", series, values)

1
deepod/utils/utility.py
View File

@ -27,6 +27,7 @@ def get_sub_seqs(x_arr, seq_len=100, stride=1):
return x_seqs
def get_sub_seqs_label(y, seq_len=100, stride=1):
"""

554
examples/data/feature.csv
View File

@ -1,554 +0,0 @@
Data,ClassEntropy,ClassProbabilityMax,ClassProbabilityMean,ClassProbabilityMin,ClassProbabilitySTD,DatasetRatio,InverseDatasetRatio,KurtosisMax,KurtosisMean,KurtosisMin,KurtosisSTD,Landmark1NN,LandmarkDecisionNodeLearner,LandmarkDecisionTree,LandmarkLDA,LandmarkNaiveBayes,LandmarkRandomNodeLearner,LogDatasetRatio,LogInverseDatasetRatio,LogNumberOfFeatures,LogNumberOfInstances,NumberOfCategoricalFeatures,NumberOfClasses,NumberOfFeatures,NumberOfFeaturesWithMissingValues,NumberOfInstances,NumberOfInstancesWithMissingValues,NumberOfMissingValues,NumberOfNumericFeatures,PCAFractionOfComponentsFor95PercentVariance,PCAKurtosisFirstPC,PCASkewnessFirstPC,PercentageOfFeaturesWithMissingValues,PercentageOfInstancesWithMissingValues,PercentageOfMissingValues,RatioNominalToNumerical,RatioNumericalToNominal,SkewnessMax,SkewnessMean,SkewnessMin,SkewnessSTD,SymbolsMax,SymbolsMean,SymbolsMin,SymbolsSTD,SymbolsSum
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1046,0.913483831,0.671405597,0.5,0.328594403,0.171405597,0.000321647,3109,53.57331848,9.971884337,-1.924848574,21.81149523,0.881634514,0.934448793,0.934127001,0.806882859,0.690192628,0.671405632,-8.04205641,8.04205641,1.609437912,9.651494322,0,2,5,0,15545,0,0,5,0.8,-0.136389256,0.919326961,0,0,0,0,0,5.892052174,1.471329857,-0.274138033,2.269157022,0,0,0,0,0
1048,0.991927046,0.552845528,0.5,0.447154472,0.052845528,0.021680217,46.125,89.71899414,38.19136469,-1.329630256,36.55225484,0.650169907,0.5293425,0.615591908,0.620859017,0.52568753,0.599506085,-3.831355102,3.831355102,2.079441542,5.910796644,0,2,8,0,369,0,0,8,0.625,34.4363327,-4.817249775,0,0,0,0,0,8.434929848,4.384561405,-0.375310063,3.290135021,0,0,0,0,0
1049,0.535324722,0.877914952,0.5,0.122085048,0.377914952,0.025377229,39.40540541,768.9856558,104.7230005,-1.509888887,148.7757364,0.88,0.877921587,0.885436939,0.907392537,0.876542277,0.877921587,-3.673903,3.673903,3.610917913,7.284820913,0,2,37,0,1458,0,0,37,0.405405405,17.02150345,-3.223246574,0,0,0,0,0,25.45763397,6.443644042,-0.463453293,5.10214431,0,0,0,0,0
1050,0.476454971,0.897632758,0.5,0.102367242,0.397632758,0.023672425,42.24324324,1036.012451,238.1864817,-1.481336002,268.3187769,0.872717622,0.89763188,0.851571942,0.890613261,0.46518455,0.89763188,-3.743444418,3.743444418,3.610917913,7.35436233,0,2,37,0,1563,0,0,37,0.351351351,282.1505432,12.88049603,0,0,0,0,0,30.45972824,10.31441462,-0.58893913,8.275274351,0,0,0,0,0
1054,0.907594165,0.677018634,0.5,0.322981366,0.177018634,0.242236025,4.128205128,20.33580987,8.856117276,-1.147039533,5.719254268,0.697965686,0.707843137,0.682794118,0.683946078,0.733210784,0.695710784,-1.417842719,1.417842719,3.663561646,5.081404365,0,2,39,0,161,0,0,39,0.282051282,9.1891222,2.762178659,0,0,0,0,0,4.357198238,2.470189744,-1.332507491,1.316884112,0,0,0,0,0
1055,0.768691303,0.775280899,0.5,0.224719101,0.275280899,0.08988764,11.125,39.52298851,10.13595047,2.017206478,12.59815668,0.752777778,0.775,0.752777778,0.686111111,0.608333333,0.775,-2.409194828,2.409194828,2.079441542,4.48863637,1,2,8,0,89,0,0,7,0.6,2.327927311,1.693061355,0,0,0,0.142857143,7,6.44383337,2.970467037,1.726373266,1.553276508,3,3,3,0,3
1056,0.061481433,0.992816396,0.5,0.007183604,0.492816396,0.004014367,249.1052632,4582.773087,826.0889537,-1.956662893,1146.675699,0.993448987,0.992816748,0.994295324,0.975810775,0.936720806,0.992816748,-5.517875551,5.517875551,3.63758616,9.15546171,0,2,38,0,9466,0,0,38,0.368421053,717.5955811,-19.05817795,0,0,0,0,0,67.24964905,17.76724944,-1.97544384,16.87187413,0,0,0,0,0
1059,0.382060954,0.925619835,0.5,0.074380165,0.425619835,0.239669421,4.172413793,45.41973064,7.690276956,-1.134144219,9.646351914,0.925641026,0.926282051,0.875524476,0.902564103,0.698135198,0.926282051,-1.428494716,1.428494716,3.36729583,4.795790546,0,2,29,0,121,0,0,29,0.310344828,4.873206615,-1.864887118,0,0,0,0,0,6.405302525,2.220180553,0.003909946,1.323342959,0,0,0,0,0
1060,0.549108745,0.873015873,0.5,0.126984127,0.373015873,0.46031746,2.172413793,30.1835443,8.013814485,-0.64332544,7.17437708,0.90952381,0.918095238,0.858571429,0.860952381,0.852857143,0.898095238,-0.775838896,0.775838896,3.36729583,4.143134726,0,2,29,0,63,0,0,29,0.275862069,5.155508995,-2.105021238,0,0,0,0,0,5.036952019,2.459793226,-0.140535831,1.042743055,0,0,0,0,0
1061,0.694974967,0.813084112,0.5,0.186915888,0.313084112,0.271028037,3.689655172,44.18273544,17.47393789,-0.185591265,13.20836078,0.785454545,0.814545455,0.768181818,0.852727273,0.861818182,0.861818182,-1.305533004,1.305533004,3.36729583,4.672828834,0,2,29,0,107,0,0,29,0.275862069,20.59246635,3.869280815,0,0,0,0,0,6.685526371,3.310167533,-0.443244547,1.796762588,0,0,0,0,0
1062,0.764204507,0.777777778,0.5,0.222222222,0.277777778,0.805555556,1.24137931,14.9146051,5.638101595,-0.541855097,3.272833024,0.8,0.925,0.9,0.725,0.85,0.8,-0.216223108,0.216223108,3.36729583,3.583518938,0,2,29,0,36,0,0,29,0.24137931,5.109066963,-2.006716013,0,0,0,0,0,3.650631666,2.103565815,0.212810367,0.691641251,0,0,0,0,0
1063,0.731778619,0.795019157,0.5,0.204980843,0.295019157,0.040229885,24.85714286,318.1755371,128.8540488,1.961354733,86.67786048,0.769937249,0.80848274,0.812591423,0.827871453,0.829610262,0.79494721,-3.21314515,3.21314515,3.044522438,6.257667588,0,2,21,0,522,0,0,21,0.238095238,131.4053345,9.519731522,0,0,0,0,0,16.7404213,8.927345395,1.043470621,4.239857771,0,0,0,0,0
1064,0.606111207,0.851485149,0.5,0.148514851,0.351485149,0.287128713,3.482758621,46.29821365,11.83942265,-0.394074628,17.14529093,0.856666667,0.812222222,0.786060606,0.860808081,0.841515152,0.854646465,-1.247824687,1.247824687,3.36729583,4.615120517,0,2,29,0,101,0,0,29,0.24137931,1.364985466,1.351101756,0,0,0,0,0,6.935419083,2.738885019,0.257636636,2.145369391,0,0,0,0,0
1065,0.449310976,0.906113537,0.5,0.093886463,0.406113537,0.085152838,11.74358974,134.1347525,18.71357316,-1.495996832,24.80624354,0.890788365,0.906249358,0.880059616,0.899586802,0.851267345,0.906249358,-2.463307538,2.463307538,3.663561646,6.126869184,0,2,39,0,458,0,0,39,0.256410256,12.73615265,3.028117895,0,0,0,0,0,9.998589516,3.090972795,-2.011446714,2.192713537,0,0,0,0,0
1066,0.978449329,0.586206897,0.5,0.413793103,0.086206897,0.648275862,1.542553191,67.51398779,11.33179608,-3,14.63148544,0.723333333,0.719047619,0.674761905,0.680952381,0.688571429,0.585714286,-0.43343896,0.43343896,4.543294782,4.976733742,0,2,94,0,145,0,0,94,0.180851064,3.552359104,1.705561757,0,0,0,0,0,8.337506294,2.469140812,-1.095258236,1.8306461,0,0,0,0,0
1067,0.621173342,0.845424372,0.5,0.154575628,0.345424372,0.009957326,100.4285714,102.8319855,31.77507882,1.235610485,28.47021124,0.822204622,0.845430075,0.830263985,0.858210656,0.824100335,0.845430075,-4.609446743,4.609446743,3.044522438,7.65396918,0,2,21,0,2109,0,0,21,0.333333333,17.99316025,3.448182106,0,0,0,0,0,8.782782555,4.200187916,1.139954329,1.967587427,0,0,0,0,0
1068,0.363800105,0.930568079,0.5,0.069431921,0.430568079,0.018935978,52.80952381,408.6346741,115.5173427,13.66287804,104.4476353,0.923363315,0.930587341,0.907965953,0.923347081,0.890028519,0.928752779,-3.96669155,3.96669155,3.044522438,7.011213987,0,2,21,0,1109,0,0,21,0.380952381,88.38054657,-7.153527737,0,0,0,0,0,18.5251236,7.997669549,2.169271469,3.980557693,0,0,0,0,0
1069,0.038537181,0.995884774,0.5,0.004115226,0.495884774,0.006441224,155.25,2753.649489,681.437129,-1.02311635,715.1770512,0.992306711,0.99588582,0.991057356,0.990160978,0.971023774,0.99588582,-5.045036721,5.045036721,3.583518938,8.628555659,0,2,36,0,5589,0,0,36,0.277777778,719.2261353,20.13447571,0,0,0,0,0,45.830513,17.14622742,-2.76834631,13.03315109,0,0,0,0,0
1071,0.391243564,0.923076923,0.5,0.076923077,0.423076923,0.091811414,10.89189189,56.43342046,11.49242474,-1.523055258,13.11797572,0.878699187,0.905781069,0.850844948,0.918162021,0.833463995,0.923159117,-2.388018649,2.388018649,3.610917913,5.998936562,0,2,37,0,403,0,0,37,0.297297297,4.041664124,1.846059442,0,0,0,0,0,7.269940376,2.509911745,-1.267155886,1.525081866,0,0,0,0,0
1073,0.999654076,0.510948905,0.5,0.489051095,0.010948905,0.02919708,34.25,99.94854736,37.61563632,-1.377103925,37.68727239,0.675396825,0.704232804,0.697751323,0.69047619,0.682804233,0.643121693,-3.533686565,3.533686565,2.079441542,5.613128106,0,2,8,0,274,0,0,8,0.625,30.82920074,4.655107498,0,0,0,0,0,9.016893387,4.405748308,-0.259121537,3.381945841,0,0,0,0,0
1075,0.41823657,0.915384615,0.5,0.084615385,0.415384615,0.061538462,16.25,6.576421362,3.312418793,-1.976190442,2.673705334,0.900457875,0.908150183,0.88507326,0.88507326,0.785622711,0.915842491,-2.788092909,2.788092909,2.079441542,4.86753445,0,2,8,0,130,0,0,8,0.5,2.975593567,1.538241506,0,0,0,0,0,2.033634901,1.511539444,0.154303432,0.570087243,0,0,0,0,0
1077,1.263850223,0.552380952,0.333333333,0.066666667,0.201130591,212.2190476,0.004712112,88.92982774,1.93445346,-1.160598636,3.92534983,0.58020202,0.598383838,0.473434343,0.535050505,0.556969697,0.496565657,5.357618985,-5.357618985,10.01157933,4.65396035,0,3,22283,0,105,0,0,22283,0.003679935,-0.062419415,-0.193107694,0,0,0,0,0,9.270062447,0.385437408,-8.061332703,0.924041744,0,0,0,0,0
1078,1.263850223,0.552380952,0.333333333,0.066666667,0.201130591,212.2190476,0.004712112,88.92982774,1.93445346,-1.160598636,3.92534983,0.58020202,0.598383838,0.473434343,0.535050505,0.556969697,0.496565657,5.357618985,-5.357618985,10.01157933,4.65396035,0,3,22283,0,105,0,0,22283,0.003679935,-0.062419415,-0.193107694,0,0,0,0,0,9.270062447,0.385437408,-8.061332703,0.924041744,0,0,0,0,0
1079,2.181322431,0.284210526,0.2,0.052631579,0.078489903,234.4947368,0.004264488,28.36915413,0.486509946,-1.538751116,1.903481411,0.757828283,0.448181818,0.750808081,0.753535354,0.819368687,0.369848485,5.457433143,-5.457433143,10.01131004,4.553876892,0,5,22277,0,95,0,0,22277,0.003860484,-0.328139305,-0.664667904,0,0,0,0,0,4.483501434,-0.265832096,-4.604210854,0.784045331,0,0,0,0,0
1080,1.976231283,0.451327434,0.2,0.088495575,0.144288501,483.8495575,0.002066758,102.3933083,1.576248125,-1.868216546,3.732811724,0.399350649,0.520779221,0.357142857,0.502597403,0.408441558,0.451948052,6.181774027,-6.181774027,10.90916185,4.727387819,0,5,54675,0,113,0,0,54675,0.001646091,1.083429813,0.835655153,0,0,0,0,0,10.08588409,0.794710804,-2.70801878,0.685656126,0,0,0,0,0
1081,1.790483686,0.483146067,0.25,0.112359551,0.140421346,613.6292135,0.001629649,66.08068727,0.807379688,-1.859960148,2.199125317,0.663333333,0.575555556,0.611388889,0.681111111,0.671388889,0.435833333,6.419390859,-6.419390859,10.90802723,4.48863637,0,4,54613,0,89,0,0,54613,0.001409921,-0.535614014,-0.706085145,0,0,0,0,0,6.404469967,-0.233389667,-7.776824951,0.740483668,0,0,0,0,0
1082,1.80356772,0.576086957,0.2,0.076086957,0.190214286,641.3478261,0.001559216,83.09010342,0.963609613,-1.41625011,5.030018532,0.624689755,0.578023088,0.442554113,0.734430014,0.731255411,0.579047619,6.46357194,-6.46357194,10.98536052,4.521788577,0,5,59004,0,92,0,0,59004,0.001237204,-0.475459576,0.076877207,0,0,0,0,0,9.121827126,0.379596215,-1.862823606,0.755752055,0,0,0,0,0
1084,1.398208844,0.581818182,0.333333333,0.2,0.175862038,101.2863636,0.009872997,162.053929,2.584491787,-1.717501692,10.07806383,0.807114625,0.754802372,0.785889328,0.90027668,0.805039526,0.582450593,4.617951789,-4.617951789,10.01157933,5.393627546,0,3,22283,0,220,0,0,22283,0.007763766,-0.516903162,0.05983711,0,0,0,0,0,11.93551445,0.527758889,-4.101751328,1.094938978,0,0,0,0,0
11,1.318116246,0.4608,0.333333333,0.0784,0.180265089,0.0064,156.25,-1.299999785,-1.299999812,-1.299999821,1.55E-08,0.768004163,0.579462659,0.780856102,0.864266198,0.907306792,0.63541504,-5.051457289,5.051457289,1.386294361,6.43775165,0,3,4,0,625,0,0,4,1,-1.300000429,-4.58E-09,0,0,0,0,0,7.63E-09,1.14E-09,-6.10E-09,4.99E-09,0,0,0,0,0
1100,1.465322403,0.516736402,0.333333333,0.188284519,0.136805084,0.020920502,47.8,-0.294103133,-0.900127049,-1.33593574,0.374161726,0.478859097,0.497873245,0.489491967,0.514456687,0.485105478,0.497873245,-3.867025639,3.867025639,2.302585093,6.169610732,4,3,10,0,478,0,0,6,0.454545455,-1.026086144,0.106657926,0,0,0,0.666666667,1.5,0.47966761,-0.068362737,-0.901466456,0.464828589,9,4,2,2.915475947,16
1104,0.931562768,0.652777778,0.5,0.347222222,0.152777778,99.01388889,0.010099593,66.97145701,3.428219706,-1.262147427,7.157283381,0.861309524,0.79047619,0.801190476,0.919047619,1,0.617857143,4.595260132,-4.595260132,8.871926251,4.276666119,0,2,7129,0,72,0,0,7129,0.008416328,-0.022660494,0.582350194,0,0,0,0,0,8.303570747,0.717762395,-8.191485405,1.416478107,0,0,0,0,0
1106,3.697010342,0.157894737,0.071428571,0.052631579,0.030606214,84.54210526,0.011828426,183.1636353,14.94130711,-1.656277735,22.36904709,0.601754386,0.21920078,0.600487329,0.733235867,0.689376218,0.209649123,4.437249697,-4.437249697,9.68427377,5.247024072,0,14,16063,0,190,0,0,16063,0.007221565,4.274822712,1.658394456,0,0,0,0,0,13.57456779,2.044837821,-13.42353725,2.564797386,0,0,0,0,0
1107,0.934068055,0.65,0.5,0.35,0.15,118.8166667,0.008416328,54.40029526,4.217960436,-1.097160947,6.210260651,0.572857143,0.618095238,0.583809524,0.614761905,0.668095238,0.65047619,4.777581689,-4.777581689,8.871926251,4.094344562,0,2,7129,0,60,0,0,7129,0.006312246,1.393968105,1.03484571,0,0,0,0,0,7.487051487,0.691900301,-7.317475319,1.542925792,0,0,0,0,0
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1489,0.873182258,0.706513694,0.5,0.293486306,0.206513694,0.000925241,1080.8,1.762372607,0.338323904,-0.857600723,1.095199546,0.905620463,0.75426442,0.869165969,0.758516824,0.760740424,0.75426442,-6.985456787,6.985456787,1.609437912,8.594894699,0,2,5,0,5404,0,0,5,1,-1.245508075,0.077530302,0,0,0,0,0,1.481981277,0.779248771,0.2094367,0.492757696,0,0,0,0,0
1496,0.999931711,0.504864865,0.5,0.495135135,0.004864865,0.002702703,370,1.712436846,1.503945374,1.313849374,0.113544053,0.751482106,0.602300917,0.878651214,0.7624288,0.97973244,0.588782814,-5.913503006,5.913503006,2.995732274,8.909235279,0,2,20,0,7400,0,0,20,0.95,0.502646208,0.696339548,0,0,0,0,0,-0.12180046,-0.233763503,-0.311524302,0.042830417,0,0,0,0,0
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1503,3.32191346,0.100647279,0.1,0.099234205,0.000450299,5.32E-05,18804,1.648521423,-0.074283441,-1.999875784,0.75276403,0.246919411,0.103253012,0.133398595,0.106759184,0.106352754,0.102292046,-9.841824892,9.841824892,2.63905733,12.48088222,0,10,14,0,263256,0,0,14,0.857142857,-0.439258099,0.609737039,0,0,0,0,0,0.212598443,-0.152899303,-0.745547473,0.260555421,0,0,0,0,0
1505,4.245430966,0.063476115,0.05,0.030514842,0.015768661,6.55E-05,15260.33333,-1.199957493,-1.200070449,-1.200183405,0.000112956,1,0.126930357,1,0.265666324,1,0.06349823,-9.633012148,9.633012148,1.098612289,10.73162444,1,20,3,0,45781,0,0,2,0.848484848,-0.605402472,0.000696314,0,0,0,0.5,2,-0.001229058,-0.004706757,-0.008184457,0.003477699,31,31,31,0,31
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1509,3.991583886,0.147262476,0.045454545,0.006100501,0.039585912,2.68E-05,37333,2.14864881,1.466742244,0.291643312,0.702921267,0.566885026,0.148347779,0.588098984,0.278138967,0.350106107,0.148347779,-10.52763293,10.52763293,1.386294361,11.91392729,0,22,4,0,149332,0,0,4,1,2.163201332,-0.858695686,0,0,0,0,0,1.219953299,0.402580373,-0.386417061,0.760459434,0,0,0,0,0
151,0.983509391,0.575454626,0.5,0.424545374,0.075454626,0.000176554,5664,7048.170114,1029.286902,-1.314318935,2457.813688,0.832869637,0.757393212,0.890911406,0.730931617,0.728394062,0.575454628,-8.641885635,8.641885635,2.079441542,10.72132718,1,2,8,0,45312,0,0,7,0.714285714,7.624698758,-0.384731962,0,0,0,0.142857143,7,78.69416057,12.62156266,-0.167695995,27.15246219,7,7,7,0,7
1510,0.952635122,0.62741652,0.5,0.37258348,0.12741652,0.052724077,18.96666667,48.76718813,7.735705864,-0.541366945,12.54773008,0.943758102,0.88369847,0.938527353,0.956072293,0.93486302,0.706599473,-2.942683052,2.942683052,3.401197382,6.343880434,0,2,30,0,569,0,0,30,0.333333333,0.895444393,-1.05363667,0,0,0,0,0,5.432815075,1.735815129,0.414330006,1.253919536,0,0,0,0,0
1512,2.174547125,0.28,0.2,0.05,0.080062476,0.065,15.38461538,95.01008248,9.141557392,-1.471705317,25.96742435,0.342914351,0.269219244,0.30578457,0.343026588,0.119020377,0.294820748,-2.733368009,2.733368009,2.564949357,5.298317367,0,5,13,0,200,0,0,13,0.923076923,-1.080070376,0.517071068,0,0,0,0,0,9.849369049,0.501973324,-5.51037693,3.190060451,0,0,0,0,0
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1517,2.113338068,0.425531915,0.2,0.106382979,0.116225534,1.914893617,0.522222222,36.10629739,4.22289878,-0.8466811,6.95346687,0.623333333,0.54,0.696666667,0.448333333,0.755,0.543333333,0.649662069,-0.649662069,4.49980967,3.850147602,0,5,90,0,47,0,0,90,0.266666667,2.682838917,1.44725585,0,0,0,0,0,5.92502594,0.070979898,-5.68070507,1.599360947,0,0,0,0,0
1518,1.760415802,0.425531915,0.25,0.063829787,0.135716498,1.914893617,0.522222222,36.10629739,4.22289878,-0.8466811,6.95346687,0.603333333,0.416666667,0.615,0.52,0.75,0.483333333,0.649662069,-0.649662069,4.49980967,3.850147602,0,4,90,0,47,0,0,90,0.266666667,2.682838917,1.44725585,0,0,0,0,0,5.92502594,0.070979898,-5.68070507,1.599360947,0,0,0,0,0
1519,1.344618002,0.615384615,0.333333333,0.179487179,0.199714897,0.769230769,1.3,58.37981984,24.91756592,3.923469415,12.19526457,0.803338328,0.608075258,0.756052281,0.675732601,0.876398601,0.617166167,-0.262364264,0.262364264,4.49980967,4.762173935,0,3,90,0,117,0,0,90,0.222222222,17.23736,-3.903002977,0,0,0,0,0,6.567877769,-1.235846779,-6.538087845,3.549449955,0,0,0,0,0
1520,2.248148355,0.286585366,0.2,0.12804878,0.064461343,0.548780488,1.822222222,78.36369945,32.15225674,3.513165828,15.59159763,0.59520861,0.371239864,0.587957148,0.409857978,0.609245663,0.383745147,-0.600056757,0.600056757,4.49980967,5.099866428,0,5,90,0,164,0,0,90,0.266666667,26.37688065,-4.740766525,0,0,0,0,0,7.386831284,-1.436914546,-7.395630836,3.896323551,0,0,0,0,0
1527,0.626548429,0.907749077,0.2,0.017835178,0.353891319,0.000922509,1084,2.490040779,0.015634031,-1.23705298,1.749715464,0.992022513,0.907771221,0.994468996,0.909922271,0.910233768,0.907771221,-6.988413182,6.988413182,1.098612289,8.087025471,0,5,3,0,3252,0,0,3,1,0.29199481,0.608585715,0,0,0,0,0,1.606671453,0.549755057,0.014198839,0.747375221,0,0,0,0,0
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1543,0.583762598,0.911111111,0.2,0.007407407,0.355807685,0.002777778,360,0.802020073,-0.516550422,-1.20300591,0.932634024,0.871397282,0.911226191,0.872237899,0.897465711,0.898417448,0.911226191,-5.886104031,5.886104031,1.098612289,6.98471632,0,5,3,0,1080,0,0,3,1,-0.344025135,-0.148455158,0,0,0,0,0,1.142785668,0.361371882,-0.139471486,0.559812869,0,0,0,0,0
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155,1.416953339,0.501116135,0.1,2.41E-06,0.182330477,1.21E-05,82920.1,1.315022637,0.246186195,-0.65023983,0.868479349,0.708822092,0.533268766,0.99623855,0.566193207,0.323869578,0.501116135,-11.32563277,11.32563277,2.302585093,13.62821787,5,10,10,0,829201,0,0,5,0.72,-0.237356917,-0.003567547,0,0,0,1,1,1.243067654,0.002590367,-1.235110007,0.834834278,4,4,4,0,20
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1557,1.583963615,0.346420876,0.333333333,0.316734498,0.012371126,0.00191525,522.125,76.70122497,11.12824687,-0.046857232,26.77114314,0.569288999,0.573845327,0.573106374,0.637792532,0.570265416,0.552299738,-6.257907023,6.257907023,2.079441542,8.337348564,1,3,8,0,4177,0,0,7,0.4,-0.438298552,-0.0431529,0,0,0,0.142857143,7,3.165983879,0.625616004,-0.639643443,1.170924024,3,3,3,0,3
1561,0.993650712,0.546875,0.5,0.453125,0.046875,58.140625,0.017199678,0,0,0,0,0.657142857,0.85952381,0.808571429,0.842857143,0.876190476,0.536190476,4.062864645,-4.062864645,8.221747728,4.158883083,3721,2,3721,0,64,0,0,0,0.006987369,1.898510324,1.384546633,0,0,0,0,0,0,0,0,0,2,2,2,0,7442
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1567,1.416985463,0.501167307,0.1,7.80E-06,0.1823283,9.76E-06,102500.9,-1.21394303,-1.287482781,-1.362077507,0.072850101,0.505950656,0.501167307,0.64566263,0.501167307,0.501167307,0.501167307,-11.53762686,11.53762686,2.302585093,13.84021195,0,10,10,0,1025009,0,0,10,1,-0.262187243,-0.000886612,0,0,0,0,0,0.003977951,-0.000236739,-0.002335814,0.001674295,0,0,0,0,0
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164,1,0.5,0.5,0.5,0,0.537735849,1.859649123,0,0,0,0,0.811666667,0.713333333,0.773333333,0.766666667,0.878333333,0.355,-0.620387826,0.620387826,4.043051268,4.663439094,57,2,57,0,106,0,0,0,0.333333333,1.220580669,1.109858911,0,0,0,0,0,0,0,0,0,4,4,4,0,228
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948,0.991223253,0.555096419,0.5,0.444903581,0.055096419,0.00137741,726,10.76923752,3.048028151,-0.816184998,5.459720297,0.51656473,0.544087666,0.505067108,0.558753823,0.553682467,0.544087666,-6.587550015,6.587550015,1.098612289,7.686162303,0,2,3,0,2178,0,0,3,1,0.833331585,0.839292943,0,0,0,0,0,3.273695707,0.820768575,-0.857283056,1.773240051,0,0,0,0,0
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950,0.21401924,0.966010733,0.5,0.033989267,0.466010733,0.007155635,139.75,105.838133,34.53412077,-1.214285714,50.419612,0.982077922,0.987467532,0.978441558,0.989253247,0.903311688,0.966038961,-4.939855112,4.939855112,1.386294361,6.326149473,1,2,4,0,559,0,0,3,0.760869565,38.36501778,4.484362808,0,0,0,0.333333333,3,9.651290781,3.356858648,6.36E-18,4.454125946,43,43,43,0,43
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958,0.591672779,0.857142857,0.5,0.142857143,0.357142857,0.008225108,121.5789474,338.4846089,36.25067792,-3,87.96566332,0.996969697,0.883116883,0.993506494,0.980952381,0.827705628,0.857142857,-4.800563824,4.800563824,2.944438979,7.745002804,0,2,19,0,2310,0,0,19,0.526315789,-0.384955406,0.864197373,0,0,0,0,0,16.88995552,3.144128094,-0.884785771,4.646399689,0,0,0,0,0
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962,0.468995594,0.9,0.5,0.1,0.4,0.003,333.3333333,0.076297063,-0.537895987,-1.038760534,0.377207056,0.998,0.949,0.997,0.998,0.9985,0.9,-5.80914299,5.80914299,1.791759469,7.60090246,0,2,6,0,2000,0,0,6,0.5,-1.156086445,0.01698835,0,0,0,0,0,1.007726192,0.531168324,-0.062789634,0.334673702,0,0,0,0,0
964,0.918295834,0.666666667,0.5,0.333333333,0.166666667,0.611111111,1.636363636,4.855055494,-0.116089321,-3,1.420130464,0.728333333,0.83,0.813333333,0.728333333,0.926666667,0.67,-0.492476485,0.492476485,3.091042453,3.583518938,1,2,22,0,36,0,0,21,0.52,-0.962381457,-0.56485293,0,0,0,0.047619048,21,2.129628724,0.466678933,-0.482002251,0.598846351,4,4,4,0,4
965,0.974319721,0.594059406,0.5,0.405940594,0.094059406,0.158415842,6.3125,1.067462789,1.067462789,1.067462789,0,1,1,1,0.96,0.99,0.544545455,-1.842531795,1.842531795,2.772588722,4.615120517,15,2,16,0,101,0,0,1,0.322580645,-1.663408004,-0.291221511,0,0,0,15,0.066666667,0.715141739,0.715141739,0.715141739,0,2,2,2,0,30
969,0.918295834,0.666666667,0.5,0.333333333,0.166666667,0.026666667,37.5,0.241442919,-0.76568265,-1.395359804,0.665602139,1,1,1,1,1,0.92,-3.624340933,3.624340933,1.386294361,5.010635294,0,2,4,0,150,0,0,4,0.5,-1.367664337,-0.26016587,0,0,0,0,0,0.330702811,0.066700071,-0.271711767,0.261433681,0,0,0,0,0
970,0.955846502,0.623067776,0.5,0.376932224,0.123067776,0.083234245,12.01428571,21.09857557,2.338001055,-0.792696953,3.612692809,0.994046944,0.856149641,0.945317067,0.996470588,0.989298707,0.623077858,-2.486096418,2.486096418,4.248495242,6.73459166,0,2,70,0,841,0,0,70,0.814285714,-1.034485102,-0.266670346,0,0,0,0,0,4.079893589,1.112712952,-0.04063626,0.676603001,0,0,0,0,0
971,0.468995594,0.9,0.5,0.1,0.4,0.038,26.31578947,1.986938712,0.11571058,-1.125465989,0.63272344,0.9985,0.9785,0.9885,0.9985,0.9945,0.9,-3.270169119,3.270169119,4.33073334,7.60090246,0,2,76,0,2000,0,0,76,0.763157895,-0.63745904,0.416411191,0,0,0,0,0,1.029212356,0.544292161,-0.120534241,0.247355552,0,0,0,0,0
973,0.970289538,0.601123596,0.5,0.398876404,0.101123596,0.073033708,13.69230769,2.012806301,-0.026964602,-1.089675426,0.873105889,0.94375645,0.909769522,0.92120743,0.983333333,0.960130719,0.601031992,-2.616834193,2.616834193,2.564949357,5.18178355,0,2,13,0,178,0,0,13,0.769230769,-1.253902197,-0.100623973,0,0,0,0,0,1.088914871,0.347210755,-0.304689825,0.45097868,0,0,0,0,0
974,0.962412735,0.613636364,0.5,0.386363636,0.113636364,0.03030303,33,-0.37650618,-0.657379881,-1.500000119,0.486487022,0.719487179,0.56,0.719487179,0.631282051,0.615897436,0.613846154,-3.496507561,3.496507561,1.386294361,4.882801923,0,2,4,0,132,0,0,4,1,-0.560750008,0.104509547,0,0,0,0,0,0.738133967,0.553600399,-7.22E-09,0.319621343,0,0,0,0,0
976,0.639148479,0.837968075,0.5,0.162031925,0.337968075,0.001405481,711.5,3.792741776,-0.037769336,-0.863969803,1.092217891,0.997992571,0.837968326,0.969983932,0.953318159,0.961448177,0.837968326,-6.567375418,6.567375418,2.63905733,9.206432747,0,2,14,0,9961,0,0,14,0.714285714,-1.117300153,-0.053707302,0,0,0,0,0,1.696630359,0.109820172,-0.377788186,0.504222525,0,0,0,0,0
977,0.245264519,0.95935,0.5,0.04065,0.45935,0.0008,1250,2.074427384,0.697263458,-0.421373982,0.670751004,0.9983499,0.959350069,0.992850124,0.96749997,0.973250196,0.959350069,-7.13089883,7.13089883,2.772588722,9.903487553,0,2,16,0,20000,0,0,16,0.75,-0.388207912,0.033800039,0,0,0,0,0,1.159888148,0.290275264,-0.310055852,0.455196401,0,0,0,0,0
978,0.468995594,0.9,0.5,0.1,0.4,0.108,9.259259259,1.694037437,-0.317399336,-1.388848709,0.538617393,0.9955,0.9795,0.9865,0.995,0.952,0.9,-2.225624052,2.225624052,5.375278408,7.60090246,0,2,216,0,2000,0,0,216,0.138888889,-0.961653471,-0.028900011,0,0,0,0,0,0.862423122,0.027539848,-1.073018312,0.424303618,0,0,0,0,0
979,0.923279379,0.6616,0.5,0.3384,0.1616,0.008,125,0.149257624,-0.197420915,-0.679572582,0.259657243,0.78799527,0.746798002,0.804388499,0.849600549,0.856196969,0.661600258,-4.828313737,4.828313737,3.688879454,8.517193191,0,2,40,0,5000,0,0,40,0.85,-1.07532537,0.028365865,0,0,0,0,0,0.271955431,0.03545762,-0.271949261,0.118019181,0,0,0,0,0
980,0.474610786,0.898220641,0.5,0.101779359,0.398220641,0.0113879,87.8125,2805.001221,163.0465562,-3,475.1536194,0.994306679,0.898221144,0.974021956,0.982920664,0.544655008,0.898221144,-4.47520386,4.47520386,4.158883083,8.634086943,0,2,64,0,5620,0,0,64,0.65625,0.416008472,0.26107654,0,0,0,0,0,52.98114395,5.279426815,-1.30264461,11.07652983,0,0,0,0,0
983,0.984577011,0.572980312,0.5,0.427019688,0.072980312,0.00610998,163.6666667,2.120951534,0.588093715,-0.944764104,1.532857819,0.602855329,0.581169358,0.653770712,0.643584765,0.617136699,0.594071609,-5.097831839,5.097831839,2.197224577,7.295056416,7,2,9,0,1473,0,0,2,0.541666667,-0.039609906,0.576421248,0,0,0,3.5,0.285714286,1.265435193,0.760811587,0.256187982,0.504623606,4,3.142857143,2,0.989743319,22
987,0.634309555,0.84,0.5,0.16,0.34,0.044,22.72727273,30.51748497,3.931372431,-1.2000096,7.711814501,0.928,1,1,1,1,0.84,-3.123565645,3.123565645,3.091042453,6.214608098,2,2,22,0,500,0,0,20,0.5,-0.619427206,-0.618085248,0,0,0,0.1,10,4.321749128,1.171993395,-0.068054657,1.100865027,15,8,1,7,16
988,0.963535984,0.611940299,0.5,0.388059701,0.111940299,0.223880597,4.466666667,56.61039817,17.73168704,2.013016536,15.02330303,0.796428571,0.77797619,0.711309524,0.674404762,0.720238095,0.614880952,-1.496642418,1.496642418,2.708050201,4.204692619,1,2,15,0,67,0,0,14,0.375,5.270490708,-2.404606611,0,0,0,0.071428571,14,7.500130269,3.556698649,1.673759103,1.487907924,2,2,2,0,2
991,0.881007754,0.700231481,0.5,0.299768519,0.200231481,0.003472222,288,0,0,0,0,0.828693373,0.700235247,0.990748084,0.947906305,0.855256083,0.700235247,-5.66296048,5.66296048,1.791759469,7.454719949,6,2,6,0,1728,0,0,0,0.714285714,-1.2231648,0.055820528,0,0,0,0,0,0,0,0,0,4,3.5,3,0.5,21
994,0.82323015,0.742316785,0.5,0.257683215,0.242316785,0.021276596,47,58.02393723,5.115651428,-0.979781866,14.73492739,0.972771084,0.735279943,0.956187101,0.968178597,0.793323884,0.761247342,-3.850147602,3.850147602,2.890371758,6.74051936,0,2,18,0,846,0,0,18,0.388888889,-0.978675127,-0.368951887,0,0,0,0,0,6.766369343,1.03973006,-0.225939855,1.648921893,0,0,0,0,0
995,0.468995594,0.9,0.5,0.1,0.4,0.0235,42.55319149,4.356182422,0.730559926,-0.991933346,1.403124356,0.9925,0.9765,0.98,0.994,0.9245,0.9,-3.750754858,3.750754858,3.850147602,7.60090246,0,2,47,0,2000,0,0,47,0.340425532,-0.053252935,-0.254941016,0,0,0,0,0,1.793253064,0.790236825,0.005487389,0.482084203,0,0,0,0,0
996,0.938575089,0.644859813,0.5,0.355140187,0.144859813,0.042056075,23.77777778,53.39232817,9.650634141,-0.428701367,15.82820127,0.790064935,0.701255411,0.748506494,0.682619048,0.608787879,0.645151515,-3.168751438,3.168751438,2.197224577,5.365976015,0,2,9,0,214,0,0,9,0.666666667,2.539951324,0.432226181,0,0,0,0,0,6.505636692,1.640987876,-1.14446485,2.165862704,0,0,0,0,0
997,0.995561641,0.5392,0.5,0.4608,0.0392,0.0064,156.25,-1.299999785,-1.299999812,-1.299999821,1.55E-08,0.873668925,0.613151268,0.852801071,0.952121578,0.956883483,0.668911637,-5.051457289,5.051457289,1.386294361,6.43775165,0,2,4,0,625,0,0,4,1,-1.300000429,-4.58E-09,0,0,0,0,0,7.63E-09,1.14E-09,-6.10E-09,4.99E-09,0,0,0,0,0
1 Data ClassEntropy ClassProbabilityMax ClassProbabilityMean ClassProbabilityMin ClassProbabilitySTD DatasetRatio InverseDatasetRatio KurtosisMax KurtosisMean KurtosisMin KurtosisSTD Landmark1NN LandmarkDecisionNodeLearner LandmarkDecisionTree LandmarkLDA LandmarkNaiveBayes LandmarkRandomNodeLearner LogDatasetRatio LogInverseDatasetRatio LogNumberOfFeatures LogNumberOfInstances NumberOfCategoricalFeatures NumberOfClasses NumberOfFeatures NumberOfFeaturesWithMissingValues NumberOfInstances NumberOfInstancesWithMissingValues NumberOfMissingValues NumberOfNumericFeatures PCAFractionOfComponentsFor95PercentVariance PCAKurtosisFirstPC PCASkewnessFirstPC PercentageOfFeaturesWithMissingValues PercentageOfInstancesWithMissingValues PercentageOfMissingValues RatioNominalToNumerical RatioNumericalToNominal SkewnessMax SkewnessMean SkewnessMin SkewnessSTD SymbolsMax SymbolsMean SymbolsMin SymbolsSTD SymbolsSum
2 10 1.227677502 0.547297297 0.25 0.013513514 0.234693979 0.121621622 8.222222222 28.71360895 9.512437578 -0.52749456 13.58201658 0.817380952 0.758373016 0.843551587 0.829662698 0.61265873 0.54952381 -2.106840516 2.106840516 2.890371758 4.997212274 15 4 18 0 148 0 0 3 0.446808511 -0.350354369 -0.558601676 0 0 0 5 0.2 5.387046595 2.302843498 0.330402533 2.208985222 8 2.933333333 2 1.569146973 44
3 1004 0.650022422 0.833333333 0.5 0.166666667 0.333333333 0.1 10 0.583361725 -0.562755299 -1.214767456 0.592995673 1 0.975 0.99 1 1 0.833333333 -2.302585093 2.302585093 4.094344562 6.396929655 0 2 60 0 600 0 0 60 0.483333333 -0.873059034 0.024293406 0 0 0 0 0 0.931437373 0.03391965 -0.690259278 0.288004701 0 0 0 0 0
4 1005 0.938575089 0.644859813 0.5 0.355140187 0.144859813 0.042056075 23.77777778 53.3923358 9.650634316 -0.428701912 15.82820373 0.795064935 0.702186147 0.791709957 0.672467532 0.592445887 0.645151515 -3.168751438 3.168751438 2.197224577 5.365976015 0 2 9 0 214 0 0 9 0.666666667 2.53993988 0.432219446 0 0 0 0 0 6.505636692 1.640987863 -1.144464731 2.165862649 0 0 0 0 0
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533 959 0.918295834 0.666666667 0.5 0.333333333 0.166666667 0.000617284 1620 0 0 0 0 0.923842593 1 1 0.666666667 1 0.666666667 -7.390181428 7.390181428 2.079441542 9.46962297 8 2 8 0 12960 0 0 0 0.666666667 -2 6.98E-18 0 0 0 0 0 0 0 0 0 5 3.375 2 0.856956825 27
534 962 0.468995594 0.9 0.5 0.1 0.4 0.003 333.3333333 0.076297063 -0.537895987 -1.038760534 0.377207056 0.998 0.949 0.997 0.998 0.9985 0.9 -5.80914299 5.80914299 1.791759469 7.60090246 0 2 6 0 2000 0 0 6 0.5 -1.156086445 0.01698835 0 0 0 0 0 1.007726192 0.531168324 -0.062789634 0.334673702 0 0 0 0 0
535 964 0.918295834 0.666666667 0.5 0.333333333 0.166666667 0.611111111 1.636363636 4.855055494 -0.116089321 -3 1.420130464 0.728333333 0.83 0.813333333 0.728333333 0.926666667 0.67 -0.492476485 0.492476485 3.091042453 3.583518938 1 2 22 0 36 0 0 21 0.52 -0.962381457 -0.56485293 0 0 0 0.047619048 21 2.129628724 0.466678933 -0.482002251 0.598846351 4 4 4 0 4
536 965 0.974319721 0.594059406 0.5 0.405940594 0.094059406 0.158415842 6.3125 1.067462789 1.067462789 1.067462789 0 1 1 1 0.96 0.99 0.544545455 -1.842531795 1.842531795 2.772588722 4.615120517 15 2 16 0 101 0 0 1 0.322580645 -1.663408004 -0.291221511 0 0 0 15 0.066666667 0.715141739 0.715141739 0.715141739 0 2 2 2 0 30
537 969 0.918295834 0.666666667 0.5 0.333333333 0.166666667 0.026666667 37.5 0.241442919 -0.76568265 -1.395359804 0.665602139 1 1 1 1 1 0.92 -3.624340933 3.624340933 1.386294361 5.010635294 0 2 4 0 150 0 0 4 0.5 -1.367664337 -0.26016587 0 0 0 0 0 0.330702811 0.066700071 -0.271711767 0.261433681 0 0 0 0 0
538 970 0.955846502 0.623067776 0.5 0.376932224 0.123067776 0.083234245 12.01428571 21.09857557 2.338001055 -0.792696953 3.612692809 0.994046944 0.856149641 0.945317067 0.996470588 0.989298707 0.623077858 -2.486096418 2.486096418 4.248495242 6.73459166 0 2 70 0 841 0 0 70 0.814285714 -1.034485102 -0.266670346 0 0 0 0 0 4.079893589 1.112712952 -0.04063626 0.676603001 0 0 0 0 0
539 971 0.468995594 0.9 0.5 0.1 0.4 0.038 26.31578947 1.986938712 0.11571058 -1.125465989 0.63272344 0.9985 0.9785 0.9885 0.9985 0.9945 0.9 -3.270169119 3.270169119 4.33073334 7.60090246 0 2 76 0 2000 0 0 76 0.763157895 -0.63745904 0.416411191 0 0 0 0 0 1.029212356 0.544292161 -0.120534241 0.247355552 0 0 0 0 0
540 973 0.970289538 0.601123596 0.5 0.398876404 0.101123596 0.073033708 13.69230769 2.012806301 -0.026964602 -1.089675426 0.873105889 0.94375645 0.909769522 0.92120743 0.983333333 0.960130719 0.601031992 -2.616834193 2.616834193 2.564949357 5.18178355 0 2 13 0 178 0 0 13 0.769230769 -1.253902197 -0.100623973 0 0 0 0 0 1.088914871 0.347210755 -0.304689825 0.45097868 0 0 0 0 0
541 974 0.962412735 0.613636364 0.5 0.386363636 0.113636364 0.03030303 33 -0.37650618 -0.657379881 -1.500000119 0.486487022 0.719487179 0.56 0.719487179 0.631282051 0.615897436 0.613846154 -3.496507561 3.496507561 1.386294361 4.882801923 0 2 4 0 132 0 0 4 1 -0.560750008 0.104509547 0 0 0 0 0 0.738133967 0.553600399 -7.22E-09 0.319621343 0 0 0 0 0
542 976 0.639148479 0.837968075 0.5 0.162031925 0.337968075 0.001405481 711.5 3.792741776 -0.037769336 -0.863969803 1.092217891 0.997992571 0.837968326 0.969983932 0.953318159 0.961448177 0.837968326 -6.567375418 6.567375418 2.63905733 9.206432747 0 2 14 0 9961 0 0 14 0.714285714 -1.117300153 -0.053707302 0 0 0 0 0 1.696630359 0.109820172 -0.377788186 0.504222525 0 0 0 0 0
543 977 0.245264519 0.95935 0.5 0.04065 0.45935 0.0008 1250 2.074427384 0.697263458 -0.421373982 0.670751004 0.9983499 0.959350069 0.992850124 0.96749997 0.973250196 0.959350069 -7.13089883 7.13089883 2.772588722 9.903487553 0 2 16 0 20000 0 0 16 0.75 -0.388207912 0.033800039 0 0 0 0 0 1.159888148 0.290275264 -0.310055852 0.455196401 0 0 0 0 0
544 978 0.468995594 0.9 0.5 0.1 0.4 0.108 9.259259259 1.694037437 -0.317399336 -1.388848709 0.538617393 0.9955 0.9795 0.9865 0.995 0.952 0.9 -2.225624052 2.225624052 5.375278408 7.60090246 0 2 216 0 2000 0 0 216 0.138888889 -0.961653471 -0.028900011 0 0 0 0 0 0.862423122 0.027539848 -1.073018312 0.424303618 0 0 0 0 0
545 979 0.923279379 0.6616 0.5 0.3384 0.1616 0.008 125 0.149257624 -0.197420915 -0.679572582 0.259657243 0.78799527 0.746798002 0.804388499 0.849600549 0.856196969 0.661600258 -4.828313737 4.828313737 3.688879454 8.517193191 0 2 40 0 5000 0 0 40 0.85 -1.07532537 0.028365865 0 0 0 0 0 0.271955431 0.03545762 -0.271949261 0.118019181 0 0 0 0 0
546 980 0.474610786 0.898220641 0.5 0.101779359 0.398220641 0.0113879 87.8125 2805.001221 163.0465562 -3 475.1536194 0.994306679 0.898221144 0.974021956 0.982920664 0.544655008 0.898221144 -4.47520386 4.47520386 4.158883083 8.634086943 0 2 64 0 5620 0 0 64 0.65625 0.416008472 0.26107654 0 0 0 0 0 52.98114395 5.279426815 -1.30264461 11.07652983 0 0 0 0 0
547 983 0.984577011 0.572980312 0.5 0.427019688 0.072980312 0.00610998 163.6666667 2.120951534 0.588093715 -0.944764104 1.532857819 0.602855329 0.581169358 0.653770712 0.643584765 0.617136699 0.594071609 -5.097831839 5.097831839 2.197224577 7.295056416 7 2 9 0 1473 0 0 2 0.541666667 -0.039609906 0.576421248 0 0 0 3.5 0.285714286 1.265435193 0.760811587 0.256187982 0.504623606 4 3.142857143 2 0.989743319 22
548 987 0.634309555 0.84 0.5 0.16 0.34 0.044 22.72727273 30.51748497 3.931372431 -1.2000096 7.711814501 0.928 1 1 1 1 0.84 -3.123565645 3.123565645 3.091042453 6.214608098 2 2 22 0 500 0 0 20 0.5 -0.619427206 -0.618085248 0 0 0 0.1 10 4.321749128 1.171993395 -0.068054657 1.100865027 15 8 1 7 16
549 988 0.963535984 0.611940299 0.5 0.388059701 0.111940299 0.223880597 4.466666667 56.61039817 17.73168704 2.013016536 15.02330303 0.796428571 0.77797619 0.711309524 0.674404762 0.720238095 0.614880952 -1.496642418 1.496642418 2.708050201 4.204692619 1 2 15 0 67 0 0 14 0.375 5.270490708 -2.404606611 0 0 0 0.071428571 14 7.500130269 3.556698649 1.673759103 1.487907924 2 2 2 0 2
550 991 0.881007754 0.700231481 0.5 0.299768519 0.200231481 0.003472222 288 0 0 0 0 0.828693373 0.700235247 0.990748084 0.947906305 0.855256083 0.700235247 -5.66296048 5.66296048 1.791759469 7.454719949 6 2 6 0 1728 0 0 0 0.714285714 -1.2231648 0.055820528 0 0 0 0 0 0 0 0 0 4 3.5 3 0.5 21
551 994 0.82323015 0.742316785 0.5 0.257683215 0.242316785 0.021276596 47 58.02393723 5.115651428 -0.979781866 14.73492739 0.972771084 0.735279943 0.956187101 0.968178597 0.793323884 0.761247342 -3.850147602 3.850147602 2.890371758 6.74051936 0 2 18 0 846 0 0 18 0.388888889 -0.978675127 -0.368951887 0 0 0 0 0 6.766369343 1.03973006 -0.225939855 1.648921893 0 0 0 0 0
552 995 0.468995594 0.9 0.5 0.1 0.4 0.0235 42.55319149 4.356182422 0.730559926 -0.991933346 1.403124356 0.9925 0.9765 0.98 0.994 0.9245 0.9 -3.750754858 3.750754858 3.850147602 7.60090246 0 2 47 0 2000 0 0 47 0.340425532 -0.053252935 -0.254941016 0 0 0 0 0 1.793253064 0.790236825 0.005487389 0.482084203 0 0 0 0 0
553 996 0.938575089 0.644859813 0.5 0.355140187 0.144859813 0.042056075 23.77777778 53.39232817 9.650634141 -0.428701367 15.82820127 0.790064935 0.701255411 0.748506494 0.682619048 0.608787879 0.645151515 -3.168751438 3.168751438 2.197224577 5.365976015 0 2 9 0 214 0 0 9 0.666666667 2.539951324 0.432226181 0 0 0 0 0 6.505636692 1.640987876 -1.14446485 2.165862704 0 0 0 0 0
554 997 0.995561641 0.5392 0.5 0.4608 0.0392 0.0064 156.25 -1.299999785 -1.299999812 -1.299999821 1.55E-08 0.873668925 0.613151268 0.852801071 0.952121578 0.956883483 0.668911637 -5.051457289 5.051457289 1.386294361 6.43775165 0 2 4 0 625 0 0 4 1 -1.300000429 -4.58E-09 0 0 0 0 0 7.63E-09 1.14E-09 -6.10E-09 4.99E-09 0 0 0 0 0

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examples/data/target.csv
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examples/model_selection_fmms_example.py
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from deepod.model_selection.fmms import FMMS
import os
import numpy as np
import pandas as pd
from sklearn.impute import SimpleImputer
def get_data(feature_file, target_file):
df = pd.read_csv(target_file)
Y = df.values[:, 1:].astype(np.float64)
imp = SimpleImputer(missing_values=np.nan, strategy='mean')
Y = imp.fit(Y).transform(Y).T
df = pd.read_csv(feature_file)
df = df.drop(['Data'], axis=1)
df = df.fillna(0)
df = (df - df.min()) / (df.max() - df.min())
df = df.fillna(0)
X = df.values.astype(np.float32)
return Y, X
if __name__ == '__main__':
random_state = 0
feature_f = os.path.join("data/feature.csv")
target_f = os.path.join("data/target.csv")
p_train, f_train = get_data(feature_f, target_f)
f_test = np.array([[1.227677502, 0.547297297, 0.25, 0.013513514, 0.234693979, 0.121621622, 8.222222222, 28.71360895, 9.512437578,
-0.52749456, 13.58201658, 0.817380952, 0.758373016, 0.843551587, 0.829662698, 0.61265873, 0.54952381, -2.106840516,
2.106840516, 2.890371758, 4.997212274, 15, 4, 18, 0, 148, 0, 0, 3, 0.446808511, -0.350354369, -0.558601676,
0, 0, 0, 5, 0.2, 5.387046595, 2.302843498, 0.330402533, 2.208985222, 8, 2.933333333, 2, 1.569146973, 44]])
rfmms = FMMS()
rfmms.fit(f_train, p_train, save_path='./data/fmms.pt')
rfmms.predict(f=f_test, topn=5, load_path='./data/fmms.pt')

256
examples/utils.py
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import os
import sys
import numpy as np
import pandas as pd
from tqdm import tqdm
from sklearn.preprocessing import MinMaxScaler
from sklearn import metrics
from sklearn.model_selection import train_test_split
from scipy.io import arff
from glob import glob
import datetime
def read_data(file, split='50%-normal', normalization='z-score', seed=42):
"""
read data from files, normalization, and perform train-test splitting
Parameters
----------
file: str
file path of dataset
split: str (default='50%-normal', choice=['50%-normal', '60%', 'none'])
training-testing set splitting methods:
- if '50%-normal': use half of the normal data as training set,
and the other half attached with anomalies as testing set,
this splitting method is used in self-supervised studies GOAD [ICLR'20], NeuTraL [ICML'21]
- if 'none': use the whole set as both the training and testing set
This is commonly used in traditional methods.
- if '60%': use 60% data during training and the rest 40% data in testing,
while keeping the original anomaly ratio.
normalization: str (default='z-score', choice=['z-score', 'min-max'])
seed: int (default=42)
random seed
"""
if file.endswith('.npz'):
data = np.load(file, allow_pickle=True)
x, y = data['X'], data['y']
y = np.array(y, dtype=int)
else:
if file.endswith('pkl'):
func = pd.read_pickle
elif file.endswith('csv'):
func = pd.read_csv
elif file.endswith('arff'):
def func(f):
df_ = pd.DataFrame(arff.loadarff(f)[0])
df_ = df_.replace({b'no': 0, b'yes': 1})
df_ = df_.drop('id', axis=1)
return df_
else:
raise NotImplementedError('')
df = func(file)
df.replace([np.inf, -np.inf], np.nan, inplace=True)
df.fillna(method='ffill', inplace=True)
x = df.values[:, :-1]
y = np.array(df.values[:, -1], dtype=int)
# train-test splitting
if split == '50%-normal':
rng = np.random.RandomState(seed)
idx = rng.permutation(np.arange(len(x)))
x, y = x[idx], y[idx]
norm_idx = np.where(y==0)[0]
anom_idx = np.where(y==1)[0]
split = int(0.5 * len(norm_idx))
train_norm_idx, test_norm_idx = norm_idx[:split], norm_idx[split:]
x_train = x[train_norm_idx]
y_train = y[train_norm_idx]
x_test = x[np.hstack([test_norm_idx, anom_idx])]
y_test = y[np.hstack([test_norm_idx, anom_idx])]
print(f'Original size: [{x.shape}], Normal/Anomaly: [{len(norm_idx)}/{len(anom_idx)}] \n'
f'After splitting: training/testing [{len(x_train)}/{len(x_test)}]')
elif split == '60%':
x_train, y_train, x_test, y_test = train_test_split(x, y, shuffle=True, random_state=seed,
test_size=0.4, stratify=y)
else:
x_train, x_test = x.copy(), x.copy()
y_train, y_test = y.copy(), y.copy()
# normalization
if normalization == 'min-max':
minmax_scaler = MinMaxScaler()
minmax_scaler.fit(x_train)
x_train = minmax_scaler.transform(x_train)
x_test = minmax_scaler.transform(x_test)
elif normalization == 'z-score':
mus = np.mean(x_train, axis=0)
sds = np.std(x_train, axis=0)
sds[sds == 0] = 1
x_train = np.array([(xx - mus) / sds for xx in x_train])
x_test = np.array([(xx - mus) / sds for xx in x_test])
elif normalization == 'scale':
x_train = x_train / 255
x_test = x_test / 255
return x_train, y_train, x_test, y_test
def min_max_normalize(x):
filter_lst = []
for k in range(x.shape[1]):
s = np.unique(x[:, k])
if len(s) <= 1:
filter_lst.append(k)
if len(filter_lst) > 0:
print('remove features', filter_lst)
x = np.delete(x, filter_lst, 1)
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit(x)
x = scaler.transform(x)
return x
def evaluate(y_true, scores):
"""calculate evaluation metrics"""
roc_auc = metrics.roc_auc_score(y_true, scores)
ap = metrics.average_precision_score(y_true, scores)
# F1@k, using real percentage to calculate F1-score
ratio = 100.0 * len(np.where(y_true==0)[0]) / len(y_true)
thresh = np.percentile(scores, ratio)
y_pred = (scores >= thresh).astype(int)
y_true = y_true.astype(int)
precision, recall, f_score, support = metrics.precision_recall_fscore_support(y_true, y_pred, average='binary')
return roc_auc, ap, f_score
def get_data_lst(dataset_dir, dataset):
if dataset == 'FULL':
print(os.path.join(dataset_dir, '*.*'))
data_lst = glob(os.path.join(dataset_dir, '*.*'))
else:
name_lst = dataset.split(',')
data_lst = []
for d in name_lst:
data_lst.extend(glob(os.path.join(dataset_dir, d + '.*')))
data_lst = sorted(data_lst)
return data_lst
def add_irrelevant_features(x, ratio, seed=None):
n_samples, n_f = x.shape
size = int(ratio * n_f)
irr_new = np.zeros([n_samples, size])
np.random.seed(seed)
for i in tqdm(range(size)):
irr_new[:, i] = np.random.rand(n_samples)
# irr_new = np.zeros([n_samples, size])
# np.random.seed(seed)
# for i in range(size):
# array = x[:, np.random.choice(x.shape[1], 1)]
# new_array = array[np.random.permutation(n_samples)].flatten()
# irr_new[:, i] = new_array
x_new = np.hstack([x, irr_new])
return x_new
def adjust_contamination(x, y, contamination_r, swap_ratio=0.05, random_state=42):
"""
add/remove anomalies in training data to replicate anomaly contaminated data sets.
randomly swap 5% features of two anomalies to avoid duplicate contaminated anomalies.
"""
rng = np.random.RandomState(random_state)
anom_idx = np.where(y == 1)[0]
norm_idx = np.where(y == 0)[0]
n_cur_anom = len(anom_idx)
n_adj_anom = int(len(norm_idx) * contamination_r / (1. - contamination_r))
# x_train = np.delete(x_train, unknown_anom_idx, axis=0)
# y_train = np.delete(y_train, unknown_anom_idx, axis=0)
# noises = inject_noise(true_anoms, n_adj_noise, 42)
# x_train = np.append(x_train, noises, axis=0)
# y_train = np.append(y_train, np.zeros((noises.shape[0], 1)))
# inject noise
if n_cur_anom < n_adj_anom:
n_inj_noise = n_adj_anom - n_cur_anom
print(f'Control Contamination Rate: injecting [{n_inj_noise}] Noisy samples')
seed_anomalies = x[anom_idx]
n_sample, dim = seed_anomalies.shape
n_swap_feat = int(swap_ratio * dim)
inj_noise = np.empty((n_inj_noise, dim))
for i in np.arange(n_inj_noise):
idx = rng.choice(n_sample, 2, replace=False)
o1 = seed_anomalies[idx[0]]
o2 = seed_anomalies[idx[1]]
swap_feats = rng.choice(dim, n_swap_feat, replace=False)
inj_noise[i] = o1.copy()
inj_noise[i, swap_feats] = o2[swap_feats]
x = np.append(x, inj_noise, axis=0)
y = np.append(y, np.ones(n_inj_noise))
# remove noise
elif n_cur_anom > n_adj_anom:
n_remove = n_cur_anom - n_adj_anom
print(f'Control Contamination Rate: Removing [{n_remove}] Noise')
remove_id = anom_idx[rng.choice(n_cur_anom, n_remove, replace=False)]
print(x.shape)
x = np.delete(x, remove_id, 0)
y = np.delete(y, remove_id, 0)
print(x.shape)
return x, y
def make_print_to_file(path='./'):
class Logger(object):
def __init__(self, filename="Default.log", path="./"):
self.terminal = sys.stdout
self.log = open(os.path.join(path, filename), "a", encoding='utf8', )
def write(self, message):
self.terminal.write(message)
self.log.write(message)
def flush(self):
pass
fileName = datetime.datetime.now().strftime('day' + '%Y_%m_%d')
sys.stdout = Logger(fileName + '.log', path=path)
#############################################################
# 这里输出之后的所有的输出的print 内容即将写入日志
#############################################################
print(fileName.center(60, '*'))

89
testbed/configs.py
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from deepod.models.time_series import *
# from deepod.models.time_series.seq_reg_ad import SeqRegAD
def get_model_class(name):
if name == 'dif':
return DeepIsolationForestTS
elif name == 'dsvdd':
return DeepSVDDTS
elif name == 'tranad':
return TranAD
elif name == 'usad':
return USAD
elif name == 'couta':
return COUTA
elif name == 'anomalytransformer':
return AnomalyTransformer
# elif name == 'seqregad':
# return SeqRegAD
def get_additional_configs(model_name, ds_name):
n_dims = {"SMAP": 25, "SMD": 38, "ASD": 19}
config_dict = {
# 'seqregad': {
# 'seq_len_lst': [10, 30, 50],
# 'epochs': 10,
# 'batch_size': 64,
# 'lr': 1e-4,
# 'rep_dim': 128,
# },
'dif': {
'rep_dim': 20,
'hidden_dims': 32,
'n_ensemble': 50,
'n_estimators': 6,
},
'dsvdd': {
'network': 'Transformer',
'rep_dim': 64,
'hidden_dims': '512',
'act': 'GELU',
'lr': 1e-5,
'epochs': 20,
'batch_size': 128,
'epoch_steps': -1,
},
'tranad': {
'lr': 1e-3,
'epochs': 10,
'batch_size': 128,
'epoch_steps': -1,
},
'usad': {
'hidden_dims': 100,
'lr': 1e-3,
'epochs': 10,
'batch_size': 128,
},
'couta': {
'neg_batch_ratio': 0.2,
'alpha': 0.1,
'rep_dim': 16,
'hidden_dims': 16,
'lr': 1e-4,
'epochs': 20,
'batch_size': 64,
},
'anomalytransformer': {
'lr': 1e-4,
'epochs': 10,
'batch_size': 32,
'k': 3,
'input_c': n_dims[ds_name],
'output_c': n_dims[ds_name],
'anomaly_ratio': 1
}
}
try:
return config_dict[model_name]
except KeyError:
return {}

43
testbed/configs.yaml Normal file
View File

@ -0,0 +1,43 @@
DIF:
rep_dim: 20
hidden_dims: 32
n_ensemble: 50
n_estimators: 6
DeepSVDDTS:
network: Transformer
rep_dim: 64
hidden_dims: 512
act: GELU
lr: 0.00001
epochs: 20
batch_size: 128
epoch_steps: -1
TranAD:
lr: 0.001
epochs: 10
batch_size: 128
epoch_steps: -1
USAD:
hidden_dims: 100
lr: 1e-3
epochs: 10
batch_size: 128
COUTA:
neg_batch_ratio: 0.2
alpha: 0.1
rep_dim: 16
hidden_dims: 16
lr: 0.0001
epochs: 20
batch_size: 64
AnomalyTransformer:
lr: 0.0001
epochs: 10
batch_size: 32
k: 3
anomaly_ratio: 1

26
testbed/testbed_unsupervised_tsad.py
View File

@ -7,10 +7,11 @@ testbed of unsupervised time series anomaly detection
import os
import argparse
import getpass
import yaml
import time
import importlib as imp
import numpy as np
from testbed import utils
import configs
import utils
dataset_root = f'/home/{getpass.getuser()}/dataset/5-TSdata/_processed_data/'
@ -21,17 +22,15 @@ parser.add_argument("--runs", type=int, default=5,
help="how many times we repeat the experiments to obtain the average performance")
parser.add_argument("--output_dir", type=str, default='@records/',
help="the output file path")
parser.add_argument("--dataset", type=str,
default='ASD,SMAP,MSL',
)
parser.add_argument("--entities", type=str,
default='FULL',
help='FULL represents all the csv file in the folder, or a list of entity names split by comma'
)
parser.add_argument("--entity_combined", type=int, default=1)
parser.add_argument("--model", type=str, default='anomalytransformer', help="") # anomalytransformer
parser.add_argument("--model", type=str, default='AnomalyTransformer', help="")
parser.add_argument('--silent_header', action='store_true')
parser.add_argument("--flag", type=str, default='')
@ -42,8 +41,17 @@ parser.add_argument('--stride', type=int, default=10)
args = parser.parse_args()
model_class = configs.get_model_class(args.model)
model_configs = configs.get_additional_configs(args.model, args.dataset)
module = imp.import_module('deepod.models.time_series')
model_class = getattr(module, args.model)
path = 'configs.yaml'
with open(path) as f:
d = yaml.safe_load(f)
try:
model_configs = d[args.model]
except KeyError:
print(f'config file does not contain default parameter settings of {args.model}')
model_configs = {}
model_configs['seq_len'] = args.seq_len
model_configs['stride'] = args.stride
@ -91,8 +99,8 @@ for dataset in dataset_name_lst:
t1 = time.time()
clf = model_class(**model_configs, random_state=42+i)
clf.fit(train_data) # 主要改这里
scores = clf.decision_function(test_data) # scores(n,) test_data(n,d)
clf.fit(train_data)
scores = clf.decision_function(test_data)
t = time.time() - t1
eval_metrics = utils.get_metrics(labels, scores)