# Appendix A: 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

Date Polar Transformer

The Date Polar Transformer expands the date using polar coordinates. The Date Transformer will only expand the date into different units, for example month. This does not capture the similarity between the months December and January (12 and 1) or the hours 23 and 0. The polar coordinates capture the similarities between these cases by representing the unit of the date as a point in a cycle. For example, the polar coordinates of: get minute in hour, would be the minute hand position on a clock.

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.

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.

## 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.

### Date Polar Transformer¶

- get hour in day, get minute in hour, get day in month, get day in year, get quarter in year, get month in year, get week in year, get week day in week

Date Built | Square Footage | Num Beds | Num Baths | State | Price | DateBuilt_MonthInYear_x | DateBuilt_MonthInYear_y |
---|---|---|---|---|---|---|---|

01/01/1920 | 1700 | 3 | 2 | NY | 700,000 | 0.5 | 1 |

The polar coordinates of the month January in year is (0.5, 1). This allows the model to catch the similarities between January and December. This information was not captured in the simple Date Transformer.

### 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.