Deteksi Anomali Python dengan Autoencoder di Python

Deteksi anomali adalah tugas pembelajaran mesin yang menarik. Tidak ada cara khusus untuk menyelesaikannya, karena setiap kumpulan data memiliki karakteristiknya sendiri. Tetapi pada saat yang sama, ada beberapa pendekatan yang membantu untuk berhasil. Saya ingin berbicara tentang salah satu pendekatan ini - autoencoder.

Dataset mana yang harus dipilih?

Masalah paling mendesak dalam kehidupan ilmuwan data mana pun. Untuk menyederhanakan cerita, saya akan menggunakan dataset sederhana dalam struktur, yang akan kami hasilkan di sini.

#  
import os
import numpy as np
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize

#        
def gen_normal_distribution(mu, sigma, size, range=(0, 1), max_val=1):
  bins = np.linspace(*range, size)
  result = 1 / (sigma * np.sqrt(2*np.pi)) * np.exp(-(bins - mu)**2 / (2*sigma**2))

  cur_max_val = result.max()
  k = max_val / cur_max_val

  result *= k

  return result

Pertimbangkan contoh fungsi. Buat distribusi normal dengan μ = 0,3 dan σ = 0,05:

dist = gen_normal_distribution(0.3, 0.05, 256, max_val=1)
print(dist.max())
>>> 1.0
plt.plot(np.linspace(0, 1, 256), dist)

Nyatakan parameter dataset kami:

in_distribution_size = 2000
out_distribution_size = 200
val_size = 100
sample_size = 256

random_generator = np.random.RandomState(seed=42) #    seed

Dan fungsi untuk menghasilkan contoh normal dan tidak normal. Distribusi dengan satu maksimum akan dianggap normal, abnormal - dengan dua:

def generate_in_samples(size, sample_size):
  global random_generator

  in_samples = np.zeros((size, sample_size))

  in_mus = random_generator.uniform(0.1, 0.9, size)
  in_sigmas = random_generator.uniform(0.05, 0.5, size)

  for i in range(size):
    in_samples[i] = gen_normal_distribution(in_mus[i], in_sigmas[i], sample_size, max_val=1)

  return in_samples

def generate_out_samples(size, sample_size):
  global random_generator

  #     
  out_samples = generate_in_samples(size, sample_size)

  #        
  out_additional_mus = random_generator.uniform(0.1, 0.9, size)
  out_additional_sigmas = random_generator.uniform(0.01, 0.05, size)

  for i in range(size):
    anomaly = gen_normal_distribution(out_additional_mus[i], out_additional_sigmas[i], sample_size, max_val=0.12)
    out_samples[i] += anomaly

  return out_samples

Ini adalah contoh yang normal:

in_samples = generate_in_samples(in_distribution_size, sample_size)
plt.plot(np.linspace(0, 1, sample_size), in_samples[42])

Dan abnormal - seperti ini:

out_samples = generate_out_samples(out_distribution_size, sample_size)
plt.plot(np.linspace(0, 1, sample_size), out_samples[42])

Buat array dengan tag dan tag:

x = np.concatenate((in_samples, out_samples))
#     0,  -- 1
y = np.concatenate((np.zeros(in_distribution_size), np.ones(out_distribution_size)))

#   / 
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, shuffle=True, random_state=42)

. , , 2 1 (). , ( ), :

#     100   100  
x_val_out = generate_out_samples(val_size, sample_size)
x_val_in = generate_in_samples(val_size, sample_size)

x_val = np.concatenate((x_val_out, x_val_in))
y_val = np.concatenate((np.ones(val_size), np.zeros(val_size)))

, , Sklearn: SVM . , , .

#     
from sklearn.metrics import classification_report
from sklearn.metrics import f1_score

One class SVM

from sklearn.svm import OneClassSVM

OneClassSVM nu — .

out_dist_part = out_distribution_size / (out_distribution_size + in_distribution_size)
svm = OneClassSVM(nu=out_dist_part)
svm.fit(x_train, y_train)
>>> OneClassSVM(cache_size=200, coef0=0.0, degree=3, gamma='scale', kernel='rbf',
            max_iter=-1, nu=0.09090909090909091, shrinking=True, tol=0.001,
            verbose=False)

:

svm_prediction = svm.predict(x_val)
svm_prediction[svm_prediction == 1] = 0
svm_prediction[svm_prediction == -1] = 1

sklearn — classification_report, Anomaly detection , precision recall, :

print(classification_report(y_val, svm_prediction))

>>>           precision    recall  f1-score   support

         0.0       0.57      0.93      0.70       100
         1.0       0.81      0.29      0.43       100

    accuracy                           0.61       200
   macro avg       0.69      0.61      0.57       200
weighted avg       0.69      0.61      0.57       200

, . f1-score , .

Isolation forest

, , - ?

from sklearn.ensemble import IsolationForest

, . :

out_dist_part = out_distribution_size / (out_distribution_size + in_distribution_size)

iso_forest = IsolationForest(n_estimators=100, contamination=out_dist_part, max_features=100, n_jobs=-1)
iso_forest.fit(x_train)
>>> IsolationForest(behaviour='deprecated', bootstrap=False,
                contamination=0.09090909090909091, max_features=100,
                max_samples='auto', n_estimators=100, n_jobs=-1,
                random_state=None, verbose=0, warm_start=False)

Classification report? — Classification report!

iso_forest_prediction = iso_forest.predict(x_val)
iso_forest_prediction[iso_forest_prediction == 1] = 0
iso_forest_prediction[iso_forest_prediction == -1] = 1

print(classification_report(y_val, iso_forest_prediction))
>>>            precision    recall  f1-score   support

         0.0       0.50      0.91      0.65       100
         1.0       0.53      0.10      0.17       100

    accuracy                           0.51       200
   macro avg       0.51      0.51      0.41       200
weighted avg       0.51      0.51      0.41       200

RandomForestClassifier

, - " ?" , :

from sklearn.ensemble import RandomForestClassifier

random_forest = RandomForestClassifier(n_estimators=100, max_features=100, n_jobs=-1)
random_forest.fit(x_train, y_train)
>>> RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features=100,
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=-1, oob_score=False, random_state=None, verbose=0,
                       warm_start=False)

random_forest_prediction = random_forest.predict(x_val)
print(classification_report(y_val, random_forest_prediction))
>>>            precision    recall  f1-score   support

         0.0       0.57      0.99      0.72       100
         1.0       0.96      0.25      0.40       100

    accuracy                           0.62       200
   macro avg       0.77      0.62      0.56       200
weighted avg       0.77      0.62      0.56       200

Autoencoder

, : . .

, "" , . 2 : Encoder' Decoder', .

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.

PyTorch, :

import torch
from torch import nn
from torch.utils.data import TensorDataset, DataLoader
from torch.optim import Adam

:

batch_size = 32
lr = 1e-3

. , , "" . , ( , learning rate, ) , , .

#      x_train,    
train_in_distribution = x_train[y_train == 0]
train_in_distribution = torch.tensor(train_in_distribution.astype(np.float32))

train_in_dataset = TensorDataset(train_in_distribution)
train_in_loader = DataLoader(train_in_dataset, batch_size=batch_size, shuffle=True)

#       ,           
test_dataset = TensorDataset(
    torch.tensor(x_test.astype(np.float32)),
    torch.tensor(y_test.astype(np.long))
)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

val_dataset = TensorDataset(torch.tensor(x_val.astype(np.float32)))
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)

. 4 , ( 2 : μ, — σ, ; 4 , ).

class Autoencoder(nn.Module):
  def __init__(self, input_size):
    super(Autoencoder, self).__init__()
    self.encoder = nn.Sequential(
      nn.Linear(input_size, 128),
      nn.LeakyReLU(0.2),
      nn.Linear(128, 64),
      nn.LeakyReLU(0.2),
      nn.Linear(64, 16),
      nn.LeakyReLU(0.2),
      nn.Linear(16, 4),
      nn.LeakyReLU(0.2),
    )
    self.decoder = nn.Sequential(
      nn.Linear(4, 16),
      nn.LeakyReLU(0.2),
      nn.Linear(16, 64),
      nn.LeakyReLU(0.2),
      nn.Linear(64, 128),
      nn.LeakyReLU(0.2),
      nn.Linear(128, 256),
      nn.LeakyReLU(0.2),
    )

  def forward(self, x):
    x = self.encoder(x)
    x = self.decoder(x)
    return x

model = Autoencoder(sample_size).cuda()
criterion = nn.MSELoss()
per_sample_criterion = nn.MSELoss(reduction="none") # loss   ,   
#   reduction="none" pytorch  loss'   
optimizer = Adam(model.parameters(), lr=lr, weight_decay=1e-5)

- loss' , , boxplot' :

def save_score_distribution(model, data_loader, criterion, save_to, figsize=(8, 6)):
  losses = [] #    loss    
  labels = [] #  --  
  for (x_batch, y_batch) in data_loader:
    x_batch = x_batch.cuda()

    output = model(x_batch)
    loss = criterion(output, x_batch)

    loss = torch.mean(loss, dim=1) #  loss    (        )
    loss = loss.detach().cpu().numpy().flatten()
    losses.append(loss)

    labels.append(y_batch.detach().cpu().numpy().flatten())

  losses = np.concatenate(losses)
  labels = np.concatenate(labels)

  losses_0 = losses[labels == 0] #  
  losses_1 = losses[labels == 1] #  

  fig, ax = plt.subplots(1, figsize=figsize)

  ax.boxplot([losses_0, losses_1])
  ax.set_xticklabels(['normal', 'anomaly'])

  plt.savefig(save_to)
  plt.close(fig)

:

:

experiment_path = "ood_detection" #    
!rm -rf $experiment_path
os.makedirs(experiment_path, exist_ok=True)

epochs = 100

for epoch in range(epochs):
  running_loss = 0
  for (x_batch, ) in train_in_loader:
    x_batch = x_batch.cuda()

    output = model(x_batch)
    loss = criterion(output, x_batch)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    running_loss += loss.item()

  print("epoch [{}/{}], train loss:{:.4f}".format(epoch+1, epochs, running_loss))

  #   
  plot_path = os.path.join(experiment_path, "{}.jpg".format(epoch+1))
  save_score_distribution(model, test_loader, per_sample_criterion, plot_path)

>>> 
epoch [1/100], train loss:8.5728
epoch [2/100], train loss:4.2405
epoch [3/100], train loss:4.0852
epoch [4/100], train loss:1.7578
epoch [5/100], train loss:0.8543
...
epoch [96/100], train loss:0.0147
epoch [97/100], train loss:0.0154
epoch [98/100], train loss:0.0117
epoch [99/100], train loss:0.0105
epoch [100/100], train loss:0.0097

50

100

, . , :

# ,    
def get_prediction(model, x):
  global batch_size

  dataset = TensorDataset(torch.tensor(x.astype(np.float32)))
  data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=False)

  predictions = []
  for batch in data_loader:
    x_batch = batch[0].cuda()
    pred = model(x_batch) # x -> encoder -> decoder -> x_pred
    predictions.append(pred.detach().cpu().numpy())

  predictions = np.concatenate(predictions)
  return predictions

#      (  )
def compare_data(xs, sample_num, data_range=(0, 1), labels=None):
  fig, axes = plt.subplots(len(xs))
  sample_size = len(xs[0][sample_num])

  for i in range(len(xs)):
    axes[i].plot(np.linspace(*data_range, sample_size), xs[i][sample_num])

  if labels:
    for i, label in enumerate(labels):
      axes[i].set_ylabel(label)

:

x_test_pred = get_prediction(model, x_test)
compare_data([x_test[y_test == 0], x_test_pred[y_test == 0]], 10, labels=["real", "encoded"])

:

X? .

Difference score

— . , , , . .

def get_difference_score(model, x):
  global batch_size

  dataset = TensorDataset(torch.tensor(x.astype(np.float32)))
  data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=False)

  predictions = []
  for (x_batch, ) in data_loader:
    x_batch = x_batch.cuda()
    preds = model(x_batch)
    predictions.append(preds.detach().cpu().numpy())

  predictions = np.concatenate(predictions)

  #     
  return (x - predictions)

from sklearn.ensemble import RandomForestClassifier

test_score = get_difference_score(model, x_test)

score_forest = RandomForestClassifier(max_features=100)
score_forest.fit(test_score, y_test)
>>> RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features=100,
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False)

, : 2 — . , 2 : , — difference_score, — . , , .

? . difference_score , ( ) , , . , , difference_score , ( ). .

:

val_score = get_difference_score(model, x_val)
prediction = score_forest.predict(val_score)
print(classification_report(y_val, prediction))
>>>            precision    recall  f1-score   support

         0.0       0.76      1.00      0.87       100
         1.0       1.00      0.69      0.82       100

    accuracy                           0.84       200
   macro avg       0.88      0.84      0.84       200
weighted avg       0.88      0.84      0.84       200

. :

indices = np.arange(len(prediction))
#       ,     
wrong_indices = indices[(prediction == 0) & (y_val == 1)]

x_val_pred = get_prediction(model, x_val)
compare_data([x_val, x_val_pred], wrong_indices[0])

? :

plt.imshow(val_score[wrong_indices], norm=Normalize(0, 1, clip=True))

:

plt.imshow(val_score[(prediction == 1) & (y_val == 1)], norm=Normalize(0, 1, clip=True))

:

plt.imshow(val_score[(prediction == 0) & (y_val == 0)], norm=Normalize(0, 1, clip=True))

, : .

Difference histograms

. . — , "", .

, difference score

print("test score: [{}; {}]".format(test_score.min(), test_score.max()))
>>> test score: [-0.2260764424351479; 0.26339245919832344]

, :

def score_to_histograms(scores, bins=10, data_range=(-0.3, 0.3)):
  result_histograms = np.zeros((len(scores), bins))

  for i in range(len(scores)):
    hist, bins = np.histogram(scores[i], bins=bins, range=data_range)
    result_histograms[i] = hist

  return result_histograms

test_histogram = score_to_histograms(test_score, bins=10, data_range=(-0.3, 0.3))
val_histogram = score_to_histograms(val_score, bins=10, data_range=(-0.3, 0.3))

plt.title("normal histogram")
plt.bar(np.linspace(-0.3, 0.3, 10), test_histogram[y_test == 0][0])

plt.title("anomaly histogram")
plt.bar(np.linspace(-0.3, 0.3, 10), test_histogram[y_test == 1][0])

, "", .

histogram_forest = RandomForestClassifier(n_estimators=10)
histogram_forest.fit(test_histogram, y_test)
>>> RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=10,
                       n_jobs=None, oob_score=False, random_state=None,
                       verbose=0, warm_start=False)

val_prediction = histogram_forest.predict(val_histogram)
print(classification_report(y_val, val_prediction))
>>>            precision    recall  f1-score   support

         0.0       0.83      0.99      0.90       100
         1.0       0.99      0.80      0.88       100

    accuracy                           0.90       200
   macro avg       0.91      0.90      0.89       200
weighted avg       0.91      0.90      0.89       200

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c:

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