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PCA-Based Anomaly Detection component

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This article describes how to use the PCA-Based Anomaly Detection component in Azure Machine Learning designer, to create an anomaly detection model based on principal component analysis (PCA).

This component helps you build a model in scenarios where it's easy to get training data from one class, such as valid transactions, but difficult to get sufficient samples of the targeted anomalies.

For example, to detect fraudulent transactions, you often don't have enough examples of fraud to train on. But you might have many examples of good transactions. The PCA-Based Anomaly Detection component solves the problem by analyzing available features to determine what constitutes a "normal" class. The component then applies distance metrics to identify cases that represent anomalies. This approach lets you train a model by using existing imbalanced data.

More about principal component analysis

PCA is an established technique in machine learning. It's frequently used in exploratory data analysis because it reveals the inner structure of the data and explains the variance in the data.

PCA works by analyzing data that contains multiple variables. It looks for correlations among the variables and determines the combination of values that best captures differences in outcomes. These combined feature values are used to create a more compact feature space called the principal components.

For anomaly detection, each new input is analyzed. The anomaly detection algorithm computes its projection on the eigenvectors, together with a normalized reconstruction error. The normalized error is used as the anomaly score. The higher the error, the more anomalous the instance is.

For more information about how PCA works, and about the implementation for anomaly detection, see these papers:

How to configure PCA-Based Anomaly Detection

  1. Add the PCA-Based Anomaly Detection component to your pipeline in the designer. You can find this component in the Anomaly Detection category.

  2. In the right panel of the component, select the Training mode option. Indicate whether you want to train the model by using a specific set of parameters, or use a parameter sweep to find the best parameters.

    If you know how you want to configure the model, select the Single Parameter option, and provide a specific set of values as arguments.

  3. For Number of components to use in PCA, specify the number of output features or components that you want.

    The decision of how many components to include is an important part of experiment design that uses PCA. General guidance is that you should not include the same number of PCA components as there are variables. Instead, you should start with a smaller number of components and increase them until some criterion is met.

    The best results are obtained when the number of output components is less than the number of feature columns available in the dataset.

  4. Specify the amount of oversampling to perform during randomized PCA training. In anomaly detection problems, imbalanced data makes it difficult to apply standard PCA techniques. By specifying some amount of oversampling, you can increase the number of target instances.

    If you specify 1, no oversampling is performed. If you specify any value higher than 1, additional samples are generated to use in training the model.

    There are two options, depending on whether you're using a parameter sweep or not:

    • Oversampling parameter for randomized PCA: Type a single whole number that represents the ratio of oversampling of the minority class over the normal class. (This option is available when you're using the Single parameter training method.)

    Note

    You can't view the oversampled data set. For more information on how oversampling is used with PCA, see Technical notes.

  5. Select the Enable input feature mean normalization option to normalize all input features to a mean of zero. Normalization or scaling to zero is generally recommended for PCA, because the goal of PCA is to maximize variance among variables.

    This option is selected by default. Deselect it if values have already been normalized through a different method or scale.

  6. Connect a tagged training dataset and one of the training components.

    If you set the Create trainer mode option to Single Parameter, use the Train Anomaly Detection Model component.

  7. Submit the pipeline.

Results

When training is complete, you can save the trained model. Or you can connect it to the Score Model component to predict anomaly scores.

To evaluate the results of an anomaly detection model:

  1. Ensure that a score column is available in both datasets.

    If you try to evaluate an anomaly detection model and get the error "There is no score column in scored dataset to compare," you're using a typical evaluation dataset that contains a label column but no probability scores. Choose a dataset that matches the schema output for anomaly detection models, which includes Scored Labels and Scored Probabilities columns.

  2. Ensure that label columns are marked.

    Sometimes the metadata associated with the label column is removed in the pipeline graph. If this happens, when you use the Evaluate Model component to compare the results of two anomaly detection models, you might get the error "There is no label column in scored dataset." Or you might get the error "There is no label column in scored dataset to compare."

    You can avoid these errors by adding the Edit Metadata component before the Evaluate Model component. Use the column selector to choose the class column, and in the Fields list, select Label.

  3. Use the Execute Python Script component to adjust label column categories as 1(positive, normal) and 0(negative, abnormal).

    label_column_name = 'XXX'
anomaly_label_category = YY
dataframe1[label_column_name] = dataframe1[label_column_name].apply(lambda x: 0 if x == anomaly_label_category else 1)

    ​

Technical notes

This algorithm uses PCA to approximate the subspace that contains the normal class. The subspace is spanned by eigenvectors associated with the top eigenvalues of the data covariance matrix.

For each new input, the anomaly detector first computes its projection on the eigenvectors, and then computes the normalized reconstruction error. This error is the anomaly score. The higher the error, the more anomalous the instance. For details on how the normal space is computed, see Wikipedia: Principal component analysis.

Next steps

See the set of components available to Azure Machine Learning.

See Exceptions and error codes for the designer for a list of errors specific to the designer components.