DONUT

Donut is an anomaly detection algorithm for seasonal KPIs.

Citation

@inproceedings{donut,
    Author = {Haowen, Xu and Wenxiao, Chen and Nengwen, Zhao and Zeyan, Li and Jiahao, Bu and Zhihan, Li and Ying, Liu and Youjian, Zhao and Dan, Pei and Yang, Feng and Jie, Chen and Zhaogang, Wang and Honglin, Qiao},
    Booktitle = {Proceedings of 27th World Wide Web conference},
    Organization = {ACM},
    Title = {Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications},
    Year = {2018}
}

Dependencies

TensorFlow >= 1.5

Installation

Checkout this repository and execute:

pip install git+https://github.com/thu-ml/zhusuan.git
pip install git+https://github.com/haowen-xu/tfsnippet.git
pip install .

This will first install ZhuSuan and TFSnippet, the two major dependencies of Donut, then install the Donut package itself.

API Usage

To prepare the data:

import numpy as np
from donut import complete_timestamp, standardize_kpi

# Read the raw data.
timestamp, values, labels = ...
# If there is no label, simply use all zeros.
labels = np.zeros_like(values, dtype=np.int32)

# Complete the timestamp, and obtain the missing point indicators.
timestamp, missing, (values, labels) = \
    complete_timestamp(timestamp, (values, labels))

# Split the training and testing data.
test_portion = 0.3
test_n = int(len(values) * test_portion)
train_values, test_values = values[:-test_n], values[-test_n:]
train_labels, test_labels = labels[:-test_n], labels[-test_n:]
train_missing, test_missing = missing[:-test_n], missing[-test_n:]

# Standardize the training and testing data.
train_values, mean, std = standardize_kpi(
    train_values, excludes=np.logical_or(train_labels, train_missing))
test_values, _, _ = standardize_kpi(test_values, mean=mean, std=std)

To construct a Donut model:

import tensorflow as tf
from donut import Donut
from tensorflow import keras as K
from tfsnippet.modules import Sequential

# We build the entire model within the scope of `model_vs`,
# it should hold exactly all the variables of `model`, including
# the variables created by Keras layers.
with tf.variable_scope('model') as model_vs:
    model = Donut(
        h_for_p_x=Sequential([
            K.layers.Dense(100, kernel_regularizer=K.regularizers.l2(0.001),
                           activation=tf.nn.relu),
            K.layers.Dense(100, kernel_regularizer=K.regularizers.l2(0.001),
                           activation=tf.nn.relu),
        ]),
        h_for_q_z=Sequential([
            K.layers.Dense(100, kernel_regularizer=K.regularizers.l2(0.001),
                           activation=tf.nn.relu),
            K.layers.Dense(100, kernel_regularizer=K.regularizers.l2(0.001),
                           activation=tf.nn.relu),
        ]),
        x_dims=120,
        z_dims=5,
    )

To train the Donut model, and use a trained model for prediction:

from donut import DonutTrainer, DonutPredictor

trainer = DonutTrainer(model=model, model_vs=model_vs)
predictor = DonutPredictor(model)

with tf.Session().as_default():
    trainer.fit(train_values, train_labels, train_missing, mean, std)
    test_score = predictor.get_score(test_values, test_missing)

To save and restore a trained model:

from tfsnippet.utils import get_variables_as_dict, VariableSaver

with tf.Session().as_default():
    # Train the model.
    ...

    # Remember to get the model variables after the birth of a
    # `predictor` or a `trainer`.  The :class:`Donut` instances
    # does not build the graph until :meth:`Donut.get_score` or
    # :meth:`Donut.get_training_loss` is called, which is
    # done in the `predictor` or the `trainer`.
    var_dict = get_variables_as_dict(model_vs)

    # save variables to `save_dir`
    saver = VariableSaver(var_dict, save_dir)
    saver.save()

with tf.Session().as_default():
    # Restore variables from `save_dir`.
    saver = VariableSaver(get_variables_as_dict(model_vs), save_dir)
    saver.restore()

If you need more advanced outputs from the model, you may derive the outputs by using model.vae directly, for example:

from donut import iterative_masked_reconstruct

# Obtain the reconstructed `x`, with MCMC missing data imputation.
# See also:
#   :meth:`donut.Donut.get_score`
#   :func:`donut.iterative_masked_reconstruct`
#   :meth:`tfsnippet.modules.VAE.reconstruct`
input_x = ...  # 2-D `float32` :class:`tf.Tensor`, input `x` windows
input_y = ...  # 2-D `int32` :class:`tf.Tensor`, missing point indicators
               # for the `x` windows
x = model.vae.reconstruct(
    iterative_masked_reconstruct(
        reconstruct=model.vae.reconstruct,
        x=input_x,
        mask=input_y,
        iter_count=mcmc_iteration,
        back_prop=False
    )
)
# `x` is a :class:`tfsnippet.stochastic.StochasticTensor`, from which
# you may derive many useful outputs, for example:
x.tensor  # the `x` samples
x.log_prob(group_ndims=0)  # element-wise log p(x|z) of sampled x
x.distribution.log_prob(input_x)  # the reconstruction probability
x.distribution.mean, x.distribution.std  # mean and std of p(x|z)