#
# Copyright (c) 2021, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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import dask.dataframe as dd
import numpy as np
from dask.delayed import Delayed
from merlin.core.dispatch import (
DataFrameType,
arange,
concat_columns,
random_state,
read_parquet_dispatch,
)
from merlin.dag import Node
from merlin.schema import Schema, Tags
from . import categorify as nvt_cat
from .moments import _custom_moments
from .operator import ColumnSelector, Operator
from .stat_operator import StatOperator
[docs]class TargetEncoding(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.
"""
def __init__(
self,
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,
):
super().__init__()
target = Node.construct_from(target)
self.dependency = target
self.target = target
self.target_mean = target_mean
self.kfold = kfold or 3
self.fold_seed = fold_seed
self.p_smooth = p_smooth
self.out_col = [out_col] if isinstance(out_col, str) else out_col
self.out_dtype = out_dtype
self.tree_width = tree_width
self.out_path = out_path or "./"
self.on_host = on_host
self.cat_cache = cat_cache
self.name_sep = name_sep
self.drop_folds = drop_folds
self.fold_name = "__fold__"
self.stats = {}
self.means = {} # TODO: just update target_mean?
[docs] def fit(self, col_selector: ColumnSelector, ddf: dd.DataFrame):
moments = None
if self.target_mean is None:
# calculate the mean if we don't have it already
moments = _custom_moments(ddf[self.target_columns])
col_groups = col_selector.grouped_names
if self.kfold > 1:
# Add new fold column if necessary
if self.fold_name not in ddf.columns:
ddf[self.fold_name] = ddf.index.map_partitions(
_add_fold,
self.kfold,
self.fold_seed,
meta=_add_fold(ddf._meta.index, self.kfold, self.fold_seed),
)
# Add new col_groups with fold
for group in col_selector.grouped_names:
if isinstance(group, tuple):
group = list(group)
if isinstance(group, list):
col_groups.append([self.fold_name] + group)
else:
col_groups.append([self.fold_name, group])
dsk, key = nvt_cat._category_stats(
ddf,
nvt_cat.FitOptions(
col_groups,
self.target_columns,
["count", "sum"],
self.out_path,
0,
self.tree_width,
self.on_host,
concat_groups=False,
name_sep=self.name_sep,
),
)
return Delayed(key, dsk), moments
[docs] def fit_finalize(self, dask_stats):
for col, value in dask_stats[0].items():
self.stats[col] = value
for col in dask_stats[1].index:
self.means[col] = float(dask_stats[1]["mean"].loc[col])
@property
def dependencies(self):
return self.dependency
[docs] def compute_selector(
self,
input_schema: Schema,
selector: ColumnSelector,
parents_selector: ColumnSelector,
dependencies_selector: ColumnSelector,
) -> ColumnSelector:
self._validate_matching_cols(input_schema, parents_selector, "computing input selector")
return parents_selector
[docs] def column_mapping(self, col_selector):
column_mapping = {}
for group in col_selector.grouped_names:
group = ColumnSelector(group)
tag = nvt_cat._make_name(*group.names, sep=self.name_sep)
for target_name in self.target_columns:
result_name = f"TE_{tag}_{target_name}"
column_mapping[result_name] = [target_name, *group.names]
if self.kfold > 1 and not self.drop_folds:
column_mapping[self.fold_name] = []
return column_mapping
def _compute_dtype(self, col_schema, input_schema):
if input_schema.column_schemas:
new_schema = super()._compute_dtype(col_schema, input_schema)
else:
# fold only, setting the np.int
new_schema = col_schema.with_dtype(np.uint8)
return new_schema
def _compute_tags(self, col_schema, input_schema):
if input_schema.column_schemas:
source_col_name = input_schema.column_names[0]
return col_schema.with_tags(input_schema[source_col_name].tags + self.output_tags)
return col_schema
@property
def output_dtype(self):
return self.out_dtype or np.float32
@property
def output_tags(self):
return [Tags.CATEGORICAL]
@property
def target_columns(self):
if self.target.output_schema is not None:
return self.target.output_schema.column_names
elif self.target.selector is not None:
return self.target.selector.names
else:
return []
def _compute_properties(self, col_schema, input_schema):
if input_schema.column_schemas:
source_col_name = input_schema.column_names[0]
return col_schema.with_properties(input_schema[source_col_name].properties)
return col_schema
[docs] def set_storage_path(self, new_path, copy=False):
self.stats = nvt_cat._copy_storage(self.stats, self.out_path, new_path, copy)
self.out_path = new_path
[docs] def clear(self):
self.stats = {}
self.means = {}
def _make_te_name(self, cat_group, target_columns, name_sep):
tag = nvt_cat._make_name(*cat_group, sep=name_sep)
return [f"TE_{tag}_{x}" for x in target_columns]
def _op_group_logic(self, cat_group, df, y_mean, fit_folds, group_ind):
# Define name of new TE column
if isinstance(self.out_col, list):
if group_ind >= len(self.out_col):
raise ValueError("out_col and cat_groups are different sizes.")
out_col = self.out_col[group_ind]
out_col = [out_col] if isinstance(out_col, str) else out_col
# ToDo Test
if len(out_col) != len(self.target_columns):
raise ValueError("out_col and target are different sizes.")
else:
out_col = self._make_te_name(cat_group, self.target_columns, self.name_sep)
# Initialize new data
_read_pq_func = read_parquet_dispatch(df)
tmp = "__tmp__"
if fit_folds:
# Groupby Aggregation for each fold
cols = ["__fold__"] + cat_group
storage_name_folds = nvt_cat._make_name(*cols, sep=self.name_sep)
path_folds = self.stats[storage_name_folds]
agg_each_fold = nvt_cat._read_groupby_stat_df(
path_folds, storage_name_folds, self.cat_cache, _read_pq_func
)
agg_each_fold.columns = cols + ["count_y"] + [x + "_sum_y" for x in self.target_columns]
else:
cols = cat_group
# Groupby Aggregation for all data
storage_name_all = nvt_cat._make_name(*cat_group, sep=self.name_sep)
path_all = self.stats[storage_name_all]
agg_all = nvt_cat._read_groupby_stat_df(
path_all, storage_name_all, self.cat_cache, _read_pq_func
)
agg_all.columns = (
cat_group + ["count_y_all"] + [x + "_sum_y_all" for x in self.target_columns]
)
if fit_folds:
agg_each_fold = agg_each_fold.merge(agg_all, on=cat_group, how="left")
agg_each_fold["count_y_all"] = agg_each_fold["count_y_all"] - agg_each_fold["count_y"]
for i, x in enumerate(self.target_columns):
agg_each_fold[x + "_sum_y_all"] = (
agg_each_fold[x + "_sum_y_all"] - agg_each_fold[x + "_sum_y"]
)
agg_each_fold[out_col[i]] = (
agg_each_fold[x + "_sum_y_all"] + self.p_smooth * y_mean[x]
) / (agg_each_fold["count_y_all"] + self.p_smooth)
agg_each_fold = agg_each_fold.drop(
["count_y_all", "count_y"]
+ [x + "_sum_y" for x in self.target_columns]
+ [x + "_sum_y_all" for x in self.target_columns],
axis=1,
)
tran_df = df[cols + [tmp]].merge(agg_each_fold, on=cols, how="left")
del agg_each_fold
else:
for i, x in enumerate(self.target_columns):
agg_all[out_col[i]] = (agg_all[x + "_sum_y_all"] + self.p_smooth * y_mean[x]) / (
agg_all["count_y_all"] + self.p_smooth
)
agg_all = agg_all.drop(
["count_y_all"] + [x + "_sum_y_all" for x in self.target_columns], axis=1
)
tran_df = df[cols + [tmp]].merge(agg_all, on=cols, how="left")
del agg_all
# TODO: There is no need to perform the `agg_each_fold.merge(agg_all, ...)` merge
# for every partition. We can/should cache the result for better performance.
for i, x in enumerate(self.target_columns):
tran_df[out_col[i]] = tran_df[out_col[i]].fillna(y_mean[x])
if self.out_dtype is not None:
tran_df[out_col] = tran_df[out_col].astype(self.out_dtype)
tran_df = tran_df.sort_values(tmp, ignore_index=True)
tran_df.drop(columns=cols + [tmp], inplace=True)
# Make sure we are preserving the index of df
tran_df.index = df.index
return tran_df
transform.__doc__ = Operator.transform.__doc__
fit.__doc__ = StatOperator.fit.__doc__
fit_finalize.__doc__ = StatOperator.fit_finalize.__doc__
def _add_fold(s, kfold, fold_seed=None):
"""Deterministically computes a '__fold__' column, given an optional
random seed"""
typ = np.min_scalar_type(kfold * 2)
if fold_seed is None:
# If we don't have a specific seed,
# just use a simple modulo-based mapping
fold = arange(len(s), like_df=s, dtype=typ)
np.mod(fold, kfold, out=fold)
return fold
else:
state = random_state(fold_seed, like_df=s)
return state.choice(arange(kfold, like_df=s, dtype=typ), len(s))