# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# ============================================================================
"""Toolbox for anomaly detection by using VAE."""
import numpy as np
from mindspore._checkparam import Validator
from ..dpn import VAE
from ..infer import ELBO, SVI
from ...optim import Adam
from ...wrap.cell_wrapper import WithLossCell
[docs]class VAEAnomalyDetection:
r"""
Toolbox for anomaly detection by using VAE.
Variational Auto-Encoder(VAE) can be used for Unsupervised Anomaly Detection. The anomaly score is the error
between the X and the reconstruction of X. If the score is high, the X is mostly outlier.
Args:
encoder(Cell): The Deep Neural Network (DNN) model defined as encoder.
decoder(Cell): The DNN model defined as decoder.
hidden_size(int): The size of encoder's output tensor.
latent_size(int): The size of the latent space.
Supported Platforms:
``Ascend`` ``GPU``
"""
def __init__(self, encoder, decoder, hidden_size=400, latent_size=20):
self.vae = VAE(encoder, decoder, hidden_size, latent_size)
[docs] def train(self, train_dataset, epochs=5):
"""
Train the VAE model.
Args:
train_dataset (Dataset): A dataset iterator to train model.
epochs (int): Total number of iterations on the data. Default: 5.
Returns:
Cell, the trained model.
"""
net_loss = ELBO()
optimizer = Adam(params=self.vae.trainable_params(), learning_rate=0.001)
net_with_loss = WithLossCell(self.vae, net_loss)
vi = SVI(net_with_loss=net_with_loss, optimizer=optimizer)
self.vae = vi.run(train_dataset, epochs)
return self.vae
[docs] def predict_outlier_score(self, sample_x):
"""
Predict the outlier score.
Args:
sample_x (Tensor): The sample to be predicted, the shape is (N, C, H, W).
Returns:
numpy.dtype, the predicted outlier score of the sample.
"""
reconstructed_sample = self.vae.reconstruct_sample(sample_x)
return self._calculate_euclidean_distance(sample_x.asnumpy(), reconstructed_sample.asnumpy())
[docs] def predict_outlier(self, sample_x, threshold=100.0):
"""
Predict whether the sample is an outlier.
Args:
sample_x (Tensor): The sample to be predicted, the shape is (N, C, H, W).
threshold (float): the threshold of the outlier. Default: 100.0.
Returns:
Bool, whether the sample is an outlier.
"""
threshold = Validator.check_positive_float(threshold)
score = self.predict_outlier_score(sample_x)
return score >= threshold
def _calculate_euclidean_distance(self, sample_x, reconstructed_sample):
"""
Calculate the euclidean distance of the sample_x and reconstructed_sample.
"""
return np.sqrt(np.sum(np.square(sample_x - reconstructed_sample)))