TargetEncoding

class nvtabular.ops.TargetEncoding(target, target_mean=None, kfold=None, fold_seed=42, p_smooth=20, out_col=None, out_dtype=None, tree_width=None, cat_cache='host', out_path=None, on_host=True, name_sep='_', drop_folds=True)

Bases: nvtabular.ops.stat_operator.StatOperator

Target encoding is a common feature-engineering technique for categorical columns in tabular datasets. For each categorical group, the mean of a continuous target column is calculated, and the group-specific mean of each row is used to create a new feature (column). To prevent overfitting, the following additional logic is applied:

1. Cross Validation: To prevent overfitting in training data, a cross-validation strategy is used - The data is split into k random “folds”, and the mean values within the i-th fold are calculated with data from all other folds. The cross-validation strategy is only employed when the dataset is used to update recorded statistics. For transformation-only workflow execution, global-mean statistics are used instead.

2. Smoothing: To prevent overfitting for low cardinality categories, the means are smoothed with the overall mean of the target variable.

Target Encoding Function:

TE = ((mean_cat*count_cat)+(mean_global*p_smooth)) / (count_cat+p_smooth)

count_cat := count of the categorical value
mean_cat := mean target value of the categorical value
mean_global := mean target value of the whole dataset
p_smooth := smoothing factor

Example usage:

# First, we can transform the label columns to binary targets
LABEL_COLUMNS = ['label1', 'label2']
labels = ColumnSelector(LABEL_COLUMNS) >> (lambda col: (col>0).astype('int8'))
# We target encode cat1, cat2 and the cross columns cat1 x cat2
target_encode = (
    ['cat1', 'cat2', ['cat2','cat3']] >>
    nvt.ops.TargetEncoding(
        labels,
        kfold=5,
        p_smooth=20,
        out_dtype="float32",
        )
)
processor = nvt.Workflow(target_encode)

Parameters

• target (str) – Continuous target column to use for the encoding of cat_groups. The same continuous target will be used for all cat_groups.

• target_mean (float) – Global mean of the target column to use for encoding. Supplying this value up-front will improve performance.

• kfold (int, default 3) – Number of cross-validation folds to use while gathering statistics.

• fold_seed (int, default 42) – Random seed to use for numpy-based fold assignment.

• p_smooth (int, default 20) – Smoothing factor.

• out_col (str or list of str, default is problem-specific) – Name of output target-encoding column. If cat_groups includes multiple elements, this should be a list of the same length (and elements must be unique).

• out_dtype (str, default is problem-specific) – dtype of output target-encoding columns.

• tree_width (dict or int, optional) – Tree width of the hash-based groupby reduction for each categorical column. High-cardinality columns may require a large tree_width, while low-cardinality columns can likely use tree_width=1. If passing a dict, each key and value should correspond to the column name and width, respectively. The default value is 8 for all columns.

• cat_cache ({"device", "host", "disk"} or dict) – Location to cache the list of unique categories for each categorical column. If passing a dict, each key and value should correspond to the column name and location, respectively. Default is “host” for all columns.

• out_path (str, optional) – Root directory where category statistics will be written out in parquet format.

• on_host (bool, default True) – Whether to convert cudf data to pandas between tasks in the hash-based groupby reduction. The extra host <-> device data movement can reduce performance. However, using on_host=True typically improves stability (by avoiding device-level memory pressure).

• name_sep (str, default "_") – String separator to use between concatenated column names for multi-column groups.

• drop_folds (bool, default True) – Whether to drop the “__fold__” column created. This is really only useful for unittests.

fit(col_selector: nvtabular.columns.selector.ColumnSelector, ddf: dask.dataframe.core.DataFrame)

Calculate statistics for this operator, and return a dask future to these statistics, which will be computed by the workflow.

fit_finalize(dask_stats)

Finalize statistics calculation - the workflow calls this function with the computed statistics from the ‘fit’ object’

dependencies()

output_column_names(columns)

set_storage_path(new_path, copy=False)

clear()

transform(col_selector: nvtabular.columns.selector.ColumnSelector, df: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame

Transform the dataframe by applying this operator to the set of input columns

Parameters

• columns (list of str or list of list of str) – The columns to apply this operator to

• df (Dataframe) – A pandas or cudf dataframe that this operator will work on

Returns

Returns a transformed dataframe for this operator

Return type

DataFrame