Source code for mindspore.nn.probability.toolbox.anomaly_detection

# 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.
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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 =, 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: float, 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)))