Driverless AI Transformations

Transformations in Driverless AI are applied to columns in the data. The transformers create the engineered features. Driverless AI provides the following transformers:

  • Filter Transformer

    The Filter Transformer counts each numeric value in the dataset.

  • Frequent Transformer

    The Frequent Transformer counts each categorical value in the dataset. This count can be either the raw count or the normalized count.

  • Bulk Interactions Transformer

    The Bulk Interactions Transformer will add, divide, multiply, and subtract two columns in the data.

  • Truncated SVD Numeric Transformer

    Truncated SVD trains on a selected numeric of columns in the data. The components of the truncated SVD will be new features.

  • Cross Validation Target Encoding

    Cross validation target encoding is done on a categorical column.

  • Cross Validation Categorical to Numeric Encoding

    This transformer converts a categorical column to a numeric column. Cross validation target encoding is done on the categorical column.

  • Dates Transformer

    The Dates Transformer retrieves any date values, including:

    • Year
    • Quarter
    • Month
    • Day
    • Day of year
    • Week
    • Week day
    • Hour
    • Minute
    • Second
  • Text Transformer

    The Text Transform transforms a text column using TFIDF (term frequency-inverse document frequency) or count (count of the word). This may be followed by dimensionality reduction using truncated SVD.

  • Numeric to Categorical Target Encoding Transformer

    This transformer converts a numeric column to categorical by binning. Cross validation target encoding is done on the binned column.

  • Cluster Target Encoding Transformer

    Selected columns in the data are clustered, and target encoding is done on the cluster ID.

  • Cluster Distance Transformer

    Selected columns in the data are clustered, and the distance to a chosen cluster center is calculated.

  • Weight of Evidence

    Creates likelihood type of features using the Weights Of Evidence (WOE) transformation method. The weight of evidence tells the predictive power of an independent variable in relation to the dependent variable, for example, the measurement of good customers in relations to bad customers.

    _images/woe.png

    This only works with a binary target variable. The likelihood needs to be created within a stratified kfold if a fit_transform method is used. More information can be found here: http://ucanalytics.com/blogs/information-value-and-weight-of-evidencebanking-case/.

  • Numeric To Categorical Weight of Evidence Transformer

    This transformer converts a numeric column to categorical by binning and then creates the likelihood type of features using the WOE transformation method.

  • DateTime Label Encoder

    Time series frequency preserving label encoding of date times or relative times in [ns].

  • DateTime Label Normalizer

    Normalization of label encoded time-axis between train/valid or train/test.

  • Lags Transfomer

    Creation of target or feature lags.

  • Lags Interaction Transfomer

    Creation of interactions between target/feature lags (lag2 - lag1, for instance).

  • Lags Aggregates Transformer

    Aggregations of target/feature lags like mean(lag7, lag14, lag21) with support for mean, min, max, median, sum, skew, kurtosis, std.

  • Exponential Lags Smoothing Transformer

    Exponential smoothing of target/feature lags.

  • Linear Lags Regression Transformer

    Linear regression of lags.

Example Transformations

In this section, we will describe some of the available transformations using the example of predicting house prices on the example dataset.

Date Built Square Footage Num Beds Num Baths State Price
01/01/1920 1700 3 2 NY $700K

Frequent Transformer

  • the count of each categorical value in the dataset
  • the count can be either the raw count or the normalized count
Date Built Square Footage Num Beds Num Baths State Price Freq_State
01/01/1920 1700 3 2 NY 700,000 4,500

There are 4,500 properties in this dataset with state = NY.

Bulk Interactions Transformer

  • add, divide, multiply, and subtract two columns in the data
Date Built Square Footage Num Beds Num Baths State Price Interaction_NumBeds#subtract#NumBaths
01/01/1920 1700 3 2 NY 700,000 1

There is one more bedroom than there are number of bathrooms for this property.

Truncated SVD Numeric Transformer

  • truncated SVD trained on selected numeric columns of the data
  • the components of the truncated SVD will be new features
Date Built Square Footage Num Beds Num Baths State Price TruncSVD_Price_NumBeds_NumBaths_1
01/01/1920 1700 3 2 NY 700,000 0.632

The first component of the truncated SVD of the columns Price, Number of Beds, Number of Baths.

Dates Transformer

  • get year, get quarter, get month, get day, get day of year, get week, get week day, get hour, get minute, get second
Date Built Square Footage Num Beds Num Baths State Price DateBuilt_Month
01/01/1920 1700 3 2 NY 700,000 1

The home was built in the month January.

Text Transformer

  • transform text column using methods: TFIDF or count (count of the word)
  • this may be followed by dimensionality reduction using truncated SVD

Categorical Target Encoding Transformer

  • cross validation target encoding done on a categorical column
Date Built Square Footage Num Beds Num Baths State Price CV_TE_State
01/01/1920 1700 3 2 NY 700,000 550,000

The average price of properties in NY state is $550,000*.

*In order to prevent overfitting, Driverless AI calculates this average on out-of-fold data using cross validation.

Numeric to Categorical Target Encoding Transformer

  • numeric column converted to categorical by binning
  • cross validation target encoding done on the binned numeric column
Date Built Square Footage Num Beds Num Baths State Price CV_TE_SquareFootage
01/01/1920 1700 3 2 NY 700,000 345,000

The column Square Footage has been bucketed into 10 equally populated bins. This property lies in the Square Footage bucket 1,572 to 1,749. The average price of properties with this range of square footage is $345,000*.

*In order to prevent overfitting, Driverless AI calculates this average on out-of-fold data using cross validation.

Cluster Target Encoding Transformer

  • selected columns in the data are clustered
  • target encoding is done on the cluster ID
Date Built Square Footage Num Beds Num Baths State Price ClusterTE_4_NumBeds_NumBaths_SquareFootage
01/01/1920 1700 3 2 NY 700,000 450,000

The columns: Num Beds, Num Baths, Square Footage have been segmented into 4 clusters. The average price of properties in the same cluster as the selected property is $450,000*.

*In order to prevent overfitting, Driverless AI calculates this average on out-of-fold data using cross validation.

Cluster Distance Transformer

  • selected columns in the data are clustered
  • the distance to a chosen cluster center is calculated
Date Built Square Footage Num Beds Num Baths State Price ClusterDist_4_NumBeds_NumBaths_SquareFootage_1
01/01/1920 1700 3 2 NY 700,000 0.83

The columns: Num Beds, Num Baths, Square Footage have been segmented into 4 clusters. The difference from this record to Cluster 1 is 0.83.