API Cheetsheet

Model

• IsolationForest

  • realseries.models.iforest.IForest.fit(): Fit Isolation Forest. y is ignored.

  • realseries.models.iforest.IForest.detect(): Predict the score of a sample being anomaly by the detector. The anomaly score is returned.

• LSTM_dynamic

  • realseries.models.lstm_dynamic.LSTM_dynamic.fit(): Fit LSTM model. y is ignored.

  • realseries.models.lstm_dynamic.LSTM_dynamic.detect(): Predict the score of a sample being anomaly by the dynamic method. The anomaly sequence and score is returned.

• Luminol

  • realseries.models.lumino.Lumino.detect(): Predict the score of a sample being anomaly by the detector. The anomaly score is returned.

• Random cut forest

  • realseries.models.rcforest.RCForest.detect(): Predict the score of a sample being anomaly by the detector. The anomaly score is returned.

• LSTM encoder decoder

  • realseries.models.rnn.LSTMED.fit(): Fit LSTM. y is ignored.

  • realseries.models.rnn.LSTMED.detect(): Predict the score of a sample being anomaly by the LSTM. The anomaly score is returned.

• SeqVL

  • realseries.models.seqvl.SeqVL.fit(): Fit detector. y is ignored in unsupervised methods.

  • realseries.models.seqvl.SeqVL.detect(): Predict the score of a sample being anomaly by the detector. The anomaly score is returned.

• SR_CNN

  • realseries.models.srcnn.SR_CNN.fit(): Fit CNN model.

  • realseries.models.srcnn.SR_CNN.detect(): Predict the score of a sample being anomaly by the CNN. The anomaly score is returned.

• VAE_AD

  • realseries.models.vae_ad.VAE_AD.fit(): Fit detector. y is ignored in unsupervised methods.

  • realseries.models.vae_ad.VAE_AD.detect(): Predict the score of a sample being anomaly by the detector. The anomaly score is returned.

• STL

  • realseries.models.stl.STL.fit(): Fit STL model. y is ignored in unsupervised methods.

  • realseries.models.stl.STL.forecast(): Forecast the later value of a sequence. The array is returned.

• Granger Causality

  • realseries.models.GC.GC.detect(): Granger Causality detector, which is channel-level.

  • realseries.models.DWGC.DWGC.detect(): Dynamic Window-level Granger Causality detector.

See base class definition in realseries.models.base.

Data

The following functions are used for raw data loading easily.

• realseries.utils.data.load_NAB(): Load data in the NAB_data diary. Train DataFrame and Test DataFrame with labels are returned.

• realseries.utils.data.load_Yahoo(): Load data in the Yahoo_data diary. Train DataFrame and Test DataFrame with labels are returned.

• realseries.utils.data.load_split_NASA(): Load data in the NASA diary. Train DataFrame and Test DataFrame with labels are returned.

Visualize

The following functions are used plotting raw data and predicted result.

• realseries.utils.visualize.plot_anom(): The parameters mainly include pd_data_label, pred_anom and pred_score. pd_data_label is the pandas.DataFrame() with data and label, pred_anom is the array with predicted label, and pred_score is the corresponding anomaly score.

• realseries.utils.visualize.plot_mne(): The parameters mainly include X, scalings, ch_types, color. If X is the array and last column as label. We set label column to different ch_type, so it will show different color in the figure.