Importing packages & modules

import sys
# Add parent directory to sys.path to import tsad modules
sys.path.insert(1, '../')

# Import custom modules
from tsad.base.pipeline import Pipeline

Data loading

# # Import function for loading the dataset
from tsad.base.datasets import load_skab

# Load the skab dataset in memory
dataset = load_skab()

# Get the feature and target names from the dataset
columns = dataset.feature_names
targets = dataset.target_names

# Get the dataframe from the dataset
df = dataset.frame

# Get the 0 level of the index of the dataframe
df.index.levels[0]

Index(['air/0', 'air/1', 'fluid/0', 'fluid/1', 'fluid/2', 'fluid/3', 'fluid/4',
       'fluid/5', 'rotor/0', 'rotor/1', 'rotor/2', 'rotor/3', 'rotor/4',
       'temperature/0', 'valve1/0', 'valve1/1', 'valve1/10', 'valve1/11',
       'valve1/12', 'valve1/13', 'valve1/14', 'valve1/15', 'valve1/2',
       'valve1/3', 'valve1/4', 'valve1/5', 'valve1/6', 'valve1/7', 'valve1/8',
       'valve1/9', 'valve2/0', 'valve2/1', 'valve2/2', 'valve2/3'],
      dtype='object', name='experiment')

Train Test Split

# Split the dataset into train and test sets (and we need
# to remove the labeling of the dataset for anomaly detection)
test_datasets = ['valve1/0', 'valve2/0', 'fluid/0']
train_raw = df.drop(test_datasets,level=0).droplevel(level=0).drop(columns=targets)
test_raw = df.loc[test_datasets].droplevel(level=0).drop(columns=targets)

train_raw.head(3)

	Accelerometer1RMS	Accelerometer2RMS	Current	Pressure	Temperature	Thermocouple	Voltage	Volume Flow RateRMS
datetime
2020-03-09 12:14:36	0.027429	0.040353	0.77031	0.382638	71.2129	25.0827	219.789	32.0000
2020-03-09 12:14:37	0.027269	0.040226	1.09696	0.710565	71.4284	25.0863	233.117	32.0104
2020-03-09 12:14:38	0.027040	0.039773	1.14015	0.054711	71.3468	25.0874	234.745	32.0000

Making and fit pipeline

# Create a pipeline for multivariate time series deep learning forecasting with task set
from tsad.pipelines import ResidualAnomalyDetectionTaskSet
simple_pipeline = Pipeline(ResidualAnomalyDetectionTaskSet)

# Fit the pipeline on the train set
y_pred_train = simple_pipeline.fit_predict(train_raw)

Epoch: 05
        Train Loss: 0.499
         Val. Loss: 0.462

Epoch: 04
        Train Loss: 0.539
         Val. Loss: 0.501

Epoch: 03
        Train Loss: 0.677
         Val. Loss: 0.542

Epoch: 02
        Train Loss: 0.884
         Val. Loss: 0.715

Epoch: 01
        Train Loss: 1.028
         Val. Loss: 0.949


After choosing the best model:
Test Loss: 1.295
asdasdas 4
(11707, 1, 8) (11707, 1, 8)

Inference of pipeline

y_pred_test = simple_pipeline.predict(test_raw)

y_pred = y_pred_test.set_index(0)
y_pred['fact'] = 1
y_pred = y_pred['fact']

Skipped 5 datasets because the number of samples is too small in the dataset. (len_seq + points_ahead + gap -1 <= len(df))
(1348, 1, 8) (1348, 1, 8)

Getting true labelling

true  = df.loc[test_datasets].droplevel(level=0)['changepoint']
true = true.reset_index().drop_duplicates().set_index('datetime')['changepoint'].sort_index()

Comparing results

from tsad.utils.evaluating.evaluating import evaluating

evaluating(true = true,
           prediction=y_pred,
           metric='nab',
           window_width='30min',
           anomaly_window_destination = 'center',
       )

Standard  -  9.49
LowFP  -  9.44
LowFN  -  9.66

{'Standard': 9.49, 'LowFP': 9.44, 'LowFN': 9.66}