Source code for zoo.chronos.model.anomaly.ae_detector

# Copyright 2018 Analytics Zoo Authors.
# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from zoo.chronos.model.anomaly.abstract import AnomalyDetector
from zoo.chronos.model.anomaly.util import roll_arr, scale_arr
import numpy as np

def create_tf_model(compress_rate,
    from tensorflow.keras.layers import Input, Dense
    from tensorflow.keras.models import Model
    from tensorflow.keras import backend

    inp = Input(shape=(input_dim,))
    encoded = Dense(int(compress_rate * input_dim), activation='relu')(inp)
    decoded = Dense(input_dim, activation='sigmoid')(encoded)
    autoencoder = Model(inp, decoded)
    autoencoder.compile(optimizer=optimizer, loss=loss)
    backend.set_value(autoencoder.optimizer.learning_rate, lr)
    return autoencoder

def create_torch_model(compress_rate, input_dim):
    import torch.nn as nn
    autoencoder = nn.Sequential(nn.Linear(input_dim, int(compress_rate * input_dim)),
                                nn.Linear(int(compress_rate * input_dim), input_dim),
    return autoencoder

[docs]class AEDetector(AnomalyDetector): """ Example: >>> #The dataset to detect is y >>> y = numpy.array(...) >>> ad = AEDetector(roll_len=24) >>> >>> anomaly_scores = ad.score() >>> anomaly_indexes = ad.anomaly_indexes() """ def __init__(self, roll_len=24, ratio=0.1, compress_rate=0.8, batch_size=100, epochs=200, verbose=0, sub_scalef=1, backend="keras", lr=0.001): """ Initialize an AEDetector. AEDetector supports two modes to detect anomalies in input time series. 1. direct-mode: It trains an autoencoder network directly on the input times series and calculate anomaly scores based on reconstruction error. For each sample in the input, the larger the reconstruction error, the higher the anomaly score. 2. window mode: It first rolls the input series into a batch of subsequences, each with length = `roll_len`. Then it trains an autoencoder network on the batch of subsequences and calculate the reconstruction error. The anomaly score for each sample is a linear combinition of two parts: 1) the reconstruction error of the sample in a subsequence. 2) the reconstruction error of the entire subsequence as a vector. You can use `sub_scalef` to control the weights of the 2nd part. Note that one sample may belong to several subsequences as subsequences overlap because of rolling, and we only keep the largest anomaly score as the final score. :param roll_len: the length of window when rolling the input data. If roll_len=0, direct mode is used. If roll_len >0, window mode is used. When setting roll_len, we suggest use a number that is probably a full or half a cycle in your data. e.g. half a day, one day, etc. Note that roll_len must be smaller than the total length of the input time series. :param ratio: (estimated) ratio of anomalies :param compress_rate: the compression rate of the autoencoder, changing this value will have impact on the reconstruction error it calculated. :param batch_size: batch size for autoencoder training :param epochs: num of epochs fro autoencoder training :param verbose: verbose option for autoencoder training :param sub_scalef: scale factor for the subsequence distance when calculating anomaly score :param backend: the backend type, can be "keras" or "torch" :param lr: the learning rate of model's optimizer """ self.ratio = ratio self.compress_rate = compress_rate self.roll_len = roll_len self.batch_size = batch_size self.epochs = epochs self.verbose = verbose self.sub_scalef = sub_scalef self.recon_err = None self.recon_err_subseq = None self.anomaly_scores_ = None self.backend = backend = lr def check_rolled(self, arr): if arr.size == 0: raise ValueError("rolled array is empty, ", "please check if roll_len is larger than the total series length") def check_data(self, arr): if len(arr.shape) > 1: raise ValueError("Only univariate time series is supported")
[docs] def fit(self, y): """ Fit the model. :param y: the input time series. y must be 1-D numpy array. """ self.check_data(y) self.anomaly_scores_ = np.zeros_like(y) if self.roll_len != 0: # roll the time series to create sub sequences y = roll_arr(y, self.roll_len) self.check_rolled(y) else: y = y.reshape(-1, 1) y = scale_arr(y) if self.backend == "keras": ae_model = create_tf_model(self.compress_rate, len(y[0]),, y, batch_size=self.batch_size, epochs=self.epochs, verbose=self.verbose) y_pred = ae_model.predict(y) elif self.backend == "torch": import torch.optim as optim import torch.nn as nn import torch from import TensorDataset, DataLoader ae_model = create_torch_model(self.compress_rate, len(y[0])) optimizer = optim.Adadelta(ae_model.parameters(), criterion = nn.BCELoss() y = torch.from_numpy(y).float() if y.ndim == 1: y = y.reshape(-1, 1) train_loader = DataLoader(TensorDataset(y, y), batch_size=int(self.batch_size), shuffle=True) for epochs in range(self.epochs): for x_batch, y_batch in train_loader: optimizer.zero_grad() yhat = ae_model(x_batch) loss = criterion(yhat, y_batch) loss.backward() optimizer.step() y_pred_list = [] for x_batch, y_batch in train_loader: y_pred_list.append(ae_model(x_batch).detach().numpy()) y_pred = np.concatenate(y_pred_list, axis=0) else: raise ValueError("backend type can only be \"keras\" or \"torch\"") # calculate the recon err for each data point in rolled array self.recon_err = abs(y - y_pred) # calculate the (aggregated) recon err for each sub sequence if self.roll_len != 0: self.recon_err_subseq = np.linalg.norm(self.recon_err, axis=1)
[docs] def score(self): """ Gets the anomaly scores for each sample. All anomaly scores are positive numbers. Samples with larger scores are more likely the anomalies. If rolled, the anomaly score is calculated by aggregating the reconstruction errors of each point and subsequence. :return: the anomaly scores, in an array format with the same size as input """ if self.anomaly_scores_ is None: raise RuntimeError("please call fit before calling score") # if input is rolled if self.recon_err_subseq is not None: # recon_err = MinMaxScaler().fit_transform(self.recon_err) # recon_err_subseq = MinMaxscaler().fit_transform(self.recon_err_subseq.reshape(-1, 1)) for index, e in np.ndenumerate(self.recon_err): agg_err = e + self.sub_scalef * self.recon_err_subseq[index[0]] y_index = index[0] + index[1] # only keep the largest err score for each ts sample if agg_err > self.anomaly_scores_[y_index]: self.anomaly_scores_[y_index] = agg_err else: self.anomaly_scores_ = self.recon_err self.anomaly_scores_ = scale_arr(self.anomaly_scores_.reshape(-1, 1)).squeeze() return self.anomaly_scores_
[docs] def anomaly_indexes(self): """ Gets the indexes of N samples with the largest anomaly scores in y (N = size of input y * AEDetector.ratio) :return: the indexes of N samples """ if self.anomaly_scores_ is None: self.score() num_anomalies = int(len(self.anomaly_scores_) * self.ratio) return self.anomaly_scores_.argsort()[-num_anomalies:]