Source code for nvtabular.ops.groupby

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
import re

import numpy
from dask.dataframe.utils import meta_nonempty

from merlin.core.dispatch import DataFrameType, annotate
from merlin.dtypes.shape import DefaultShapes
from merlin.schema import Schema
from nvtabular.ops.operator import ColumnSelector, Operator

[docs]class Groupby(Operator): """Groupby Transformation Locally transform each partition of a Dataset with one or more groupby aggregations. WARNING: This transformation does NOT move data between partitions. Please make sure that the target Dataset object is already shuffled by ``groupby_cols``, otherwise the output may be incorrect. See: ``Dataset.shuffle_by_keys``. Example usage:: groupby_cols = ['user_id', 'session_id'] dataset = dataset.shuffle_by_keys(keys=groupby_cols) groupby_features = [ 'user_id', 'session_id', 'month', 'prod_id', ] >> ops.Groupby( groupby_cols=groupby_cols, sort_cols=['month'], aggs={ 'prod_id': 'list', 'month': ['first', 'last'], }, ) processor = nvtabular.Workflow(groupby_features) dataset_transformed = workflow.transform(dataset) Parameters ----------- groupby_cols : str or list of str The column names to be used as groupby keys. WARNING: Ensure the dataset was partitioned by those groupby keys (see above for an example). sort_cols : str or list of str Columns to be used to sort each partition before groupby aggregation is performed. If this argument is not specified, the results will not be sorted. aggs : dict, list or str Groupby aggregations to perform. Supported list-based aggregations include "list", "first" & "last". Most conventional aggregations supported by Pandas/cuDF are also allowed (e.g. "sum", "count", "max", "mean", etc.). name_sep : str String separator to use for new column names. """
[docs] def __init__( self, groupby_cols=None, sort_cols=None, aggs="list", name_sep="_", ascending=True ): self.groupby_cols = groupby_cols self.sort_cols = sort_cols or [] if isinstance(self.groupby_cols, str): self.groupby_cols = [self.groupby_cols] if isinstance(self.sort_cols, str): self.sort_cols = [self.sort_cols] self.ascending = ascending # Split aggregations into "conventional" aggregations # and "list-based" aggregations. After this block, # we will have a dictionary for each of these cases. # We use the "__all__" key to specify aggregations # that will be performed on all (non-key) columns. self.list_aggs, self.conv_aggs = {}, {} if isinstance(aggs, str): aggs = {"__all__": [aggs]} elif isinstance(aggs, list): aggs = {"__all__": aggs} for col, v in aggs.items(): _aggs = v if isinstance(v, list) else [v] _conv_aggs, _list_aggs = set(), set() for _agg in _aggs: if is_list_agg(_agg): _list_aggs.add("list" if _agg == list else _agg) _conv_aggs.add(list) else: _conv_aggs.add(_agg) if _conv_aggs: self.conv_aggs[col] = list(_conv_aggs) if _list_aggs: self.list_aggs[col] = list(_list_aggs) self.name_sep = name_sep super().__init__()
[docs] @annotate("Groupby_op", color="darkgreen", domain="nvt_python") def transform(self, col_selector: ColumnSelector, df: DataFrameType) -> DataFrameType: # Sort if necessary if self.sort_cols: df = df.sort_values(self.sort_cols, ascending=self.ascending, ignore_index=True) # List aggregations do not work with empty data. # Use synthetic metadata to predict output columns. empty_df = not len(df) _df = meta_nonempty(df) if empty_df else df # Get "complete" aggregation dicts _list_aggs, _conv_aggs = _get_agg_dicts( self.groupby_cols, self.list_aggs, self.conv_aggs, col_selector ) # Apply aggregations new_df = _apply_aggs( _df, self.groupby_cols, _list_aggs, _conv_aggs, name_sep=self.name_sep, ascending=self.ascending, ) if empty_df: return new_df.iloc[:0] return new_df
transform.__doc__ = Operator.transform.__doc__
[docs] def compute_output_schema( self, input_schema: Schema, col_selector: ColumnSelector, prev_output_schema: Schema = None ) -> Schema: if not col_selector and hasattr(self, "target"): col_selector = ( ColumnSelector( if isinstance(, list) else ) return super().compute_output_schema(input_schema, col_selector, prev_output_schema)
[docs] def column_mapping(self, col_selector): column_mapping = {} for groupby_col in self.groupby_cols: if groupby_col in col_selector.names: column_mapping[groupby_col] = [groupby_col] _list_aggs, _conv_aggs = _get_agg_dicts( self.groupby_cols, self.list_aggs, self.conv_aggs, col_selector ) for input_col_name, aggs in _list_aggs.items(): output_col_names = _columns_out_from_aggs( {input_col_name: aggs}, name_sep=self.name_sep ) for output_col_name in output_col_names: column_mapping[output_col_name] = [input_col_name] for input_col_name, aggs in _conv_aggs.items(): output_col_names = _columns_out_from_aggs( {input_col_name: aggs}, name_sep=self.name_sep ) for output_col_name in output_col_names: column_mapping[output_col_name] = [input_col_name] return column_mapping
@property def dependencies(self): return self.groupby_cols def _compute_dtype(self, col_schema, input_schema): col_schema = super()._compute_dtype(col_schema, input_schema) agg_dtypes = { "count": numpy.int32, "nunique": numpy.int32, "mean": numpy.float32, "var": numpy.float32, "std": numpy.float32, "median": numpy.float32, "sum": numpy.float32, } agg = self._find_agg(col_schema, input_schema) dtype = agg_dtypes.get(agg, col_schema.dtype) return col_schema.with_dtype(dtype) def _compute_shape(self, col_schema, input_schema): agg_is_lists = {"list": True} agg = self._find_agg(col_schema, input_schema) is_list = agg_is_lists.get(agg, col_schema.is_list) shape = DefaultShapes.LIST if is_list else DefaultShapes.SCALAR return col_schema.with_shape(shape) def _find_agg(self, col_schema, input_schema): input_selector = ColumnSelector(input_schema.column_names) column_mapping = self.column_mapping(input_selector) input_column_name = column_mapping[][0] agg =, "").lstrip(self.name_sep) return agg
def _aggs_for_column(col_name, agg_dict): return agg_dict.get(col_name, []) + agg_dict.get("__all__", []) def _columns_out_from_aggs(aggs, name_sep="_"): # Helper function for `output_column_names` _agg_cols = [] for k, v in aggs.items(): for _v in v: if isinstance(_v, str): _agg_cols.append(name_sep.join([k, _v])) return _agg_cols def _apply_aggs(_df, groupby_cols, _list_aggs, _conv_aggs, name_sep="_", ascending=True): # Apply conventional aggs _columns = list(set(groupby_cols) | set(_conv_aggs) | set(_list_aggs)) df = _df[_columns].groupby(groupby_cols).agg(_conv_aggs).reset_index() df.columns = [ name_sep.join([n for n in name if n != ""]) for name in df.columns.to_flat_index() ] # Handle custom aggs (e.g. "first" and "last") for col, aggs in _list_aggs.items(): for _agg in aggs: if is_list_agg(_agg, custom=True): df[f"{col}{name_sep}{_agg}"] = _first_or_last( df[f"{col}{name_sep}list"], _agg, ascending=ascending ) if "list" not in aggs: df.drop(columns=[col + f"{name_sep}list"], inplace=True) for col in df.columns: if"{name_sep}(count|nunique)$", col): df[col] = df[col].astype(numpy.int32) elif"{name_sep}(mean|median|std|var|sum)$", col): df[col] = df[col].astype(numpy.float32) return df def _get_agg_dicts(groupby_cols, list_aggs, conv_aggs, columns): # Get updated aggregation dicts. This should map "__all__" # to specific columns, and remove elements that are not # in `columns`. _allowed_cols = [c for c in columns.names if c not in groupby_cols] _list_aggs = _ensure_agg_dict(list_aggs, _allowed_cols) _conv_aggs = _ensure_agg_dict(conv_aggs, _allowed_cols) return _list_aggs, _conv_aggs def _ensure_agg_dict(_aggs, _allowed_cols): # Make sure aggregation dict has legal keys if "__all__" in _aggs: return {col: _aggs["__all__"] for col in _allowed_cols} else: return {k: v for k, v in _aggs.items() if k in _allowed_cols} def is_list_agg(agg, custom=False): # check if `agg` is a supported list aggregation if custom: return agg in ("first", "last") else: return agg in ("list", list, "first", "last") def _first_or_last(x, kind, ascending=True): # Redirect to _first or _last if kind == "first" and ascending: return _first(x) elif kind == "last" and not ascending: return _first(x) else: return _last(x) def _first(x): # Convert each element of a list column to be the first # item in the list if hasattr(x, "list"): # cuDF-specific behavior return x.list.get(0) else: # cpu/pandas return x.apply(lambda y: y[0]) def _last(x): # Convert each element of a list column to be the last # item in the list if hasattr(x, "list"): # cuDF-specific behavior return x.list.get(-1) else: # cpu/pandas return x.apply(lambda y: y[-1])