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ETL with NVTabular

This notebook is created using the latest stable merlin-pytorch container.

Launch the docker container

docker run -it --gpus device=0 -p 8000:8000 -p 8001:8001 -p 8002:8002 -p 8888:8888 -v <path_to_data>:/workspace/data/  nvcr.io/nvidia/merlin/merlin-pytorch:22.XX

This script will mount your local data folder that includes your data files to /workspace/data directory in the merlin-pytorch docker container.

Overview

This notebook demonstrates how to use NVTabular to perform the feature engineering that is needed to model the YOOCHOOSE dataset which contains a collection of sessions from a retailer. Each session encapsulates the click events that the user performed in that session.

The dataset is available on Kaggle. You need to download it and copy to the DATA_FOLDER path. Note that we are only using the yoochoose-clicks.dat file.

First, let’s start by importing several libraries:

import os
import glob
import numpy as np
import gc

import cudf
import cupy
import nvtabular as nvt
from merlin.dag import ColumnSelector
from merlin.schema import Schema, Tags

Define Data Input and Output Paths

DATA_FOLDER = "/workspace/data/"
FILENAME_PATTERN = 'yoochoose-clicks.dat'
DATA_PATH = os.path.join(DATA_FOLDER, FILENAME_PATTERN)

OUTPUT_FOLDER = "./yoochoose_transformed"
OVERWRITE = False

Load and clean raw data

interactions_df = cudf.read_csv(DATA_PATH, sep=',', 
                                names=['session_id','timestamp', 'item_id', 'category'], 
                                dtype=['int', 'datetime64[s]', 'int', 'int'])

Remove repeated interactions within the same session

print("Count with in-session repeated interactions: {}".format(len(interactions_df)))
# Sorts the dataframe by session and timestamp, to remove consecutive repetitions
interactions_df.timestamp = interactions_df.timestamp.astype(int)
interactions_df = interactions_df.sort_values(['session_id', 'timestamp'])
past_ids = interactions_df['item_id'].shift(1).fillna()
session_past_ids = interactions_df['session_id'].shift(1).fillna()
# Keeping only no consecutive repeated in session interactions
interactions_df = interactions_df[~((interactions_df['session_id'] == session_past_ids) & (interactions_df['item_id'] == past_ids))]
print("Count after removed in-session repeated interactions: {}".format(len(interactions_df)))

Count with in-session repeated interactions: 33003944
Count after removed in-session repeated interactions: 28971543

Create new feature with the timestamp when the item was first seen

items_first_ts_df = interactions_df.groupby('item_id').agg({'timestamp': 'min'}).reset_index().rename(columns={'timestamp': 'itemid_ts_first'})
interactions_merged_df = interactions_df.merge(items_first_ts_df, on=['item_id'], how='left')
interactions_merged_df.head()

	session_id	timestamp	item_id	itemid_ts_first
0	4993	1396727816	214835285	1396332436
1	4993	1396727863	214530703	1396339114
2	4993	1396727898	214530705	1396330224
3	4993	1396728063	214835713	1396327474
4	4993	1396730097	214512611	1396328044

Let’s save the interactions_merged_df to disk to be able to use in the inference step.

interactions_merged_df.to_parquet(os.path.join(DATA_FOLDER, 'interactions_merged_df.parquet'))

# free gpu memory
del interactions_df, session_past_ids, items_first_ts_df
gc.collect()

Define a preprocessing workflow with NVTabular

NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems. It provides a high level abstraction to simplify code and accelerates computation on the GPU using the RAPIDS cuDF library.

NVTabular supports different feature engineering transformations required by deep learning (DL) models such as Categorical encoding and numerical feature normalization. It also supports feature engineering and generating sequential features.

More information about the supported features can be found here.

Feature engineering: Create and Transform items features

In this cell, we are defining three transformations ops:

1. Encoding categorical variables using Categorify() op. We set start_index to 1, so that encoded null values start from 1 instead of 0 because we reserve 0 for padding the sequence features.
1. Deriving temporal features from timestamp and computing their cyclical representation using a custom lambda function.
1. Computing the item recency in days using a custom Op. Note that item recency is defined as the difference between the first occurrence of the item in dataset and the actual date of item interaction.

For more ETL workflow examples, visit NVTabular example notebooks.

# Encodes categorical features as contiguous integers
cat_feats = ColumnSelector(['session_id', 'category', 'item_id']) >> nvt.ops.Categorify(start_index=1)

# create time features
session_ts = ColumnSelector(['timestamp'])
session_time = (
    session_ts >> 
    nvt.ops.LambdaOp(lambda col: cudf.to_datetime(col, unit='s')) >> 
    nvt.ops.Rename(name = 'event_time_dt')
)
sessiontime_weekday = (
    session_time >> 
    nvt.ops.LambdaOp(lambda col: col.dt.weekday) >> 
    nvt.ops.Rename(name ='et_dayofweek')
)

# Derive cyclical features: Defines a custom lambda function 
def get_cycled_feature_value_sin(col, max_value):
    value_scaled = (col + 0.000001) / max_value
    value_sin = np.sin(2*np.pi*value_scaled)
    return value_sin

weekday_sin = sessiontime_weekday >> (lambda col: get_cycled_feature_value_sin(col+1, 7)) >> nvt.ops.Rename(name = 'et_dayofweek_sin')

# Compute Item recency: Define a custom Op 
class ItemRecency(nvt.ops.Operator):
    def transform(self, columns, gdf):
        for column in columns.names:
            col = gdf[column]
            item_first_timestamp = gdf['itemid_ts_first']
            delta_days = (col - item_first_timestamp) / (60*60*24)
            gdf[column + "_age_days"] = delta_days * (delta_days >=0)
        return gdf

    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

    def column_mapping(self, col_selector):
        column_mapping = {}
        for col_name in col_selector.names:
            column_mapping[col_name + "_age_days"] = [col_name]
        return column_mapping

    @property
    def dependencies(self):
        return ["itemid_ts_first"]

    @property
    def output_dtype(self):
        return np.float64
    
recency_features = session_ts >> ItemRecency() 
# Apply standardization to this continuous feature
recency_features_norm = recency_features >> nvt.ops.LogOp() >> nvt.ops.Normalize(out_dtype=np.float32) >> nvt.ops.Rename(name='product_recency_days_log_norm')

time_features = (
    session_time +
    sessiontime_weekday +
    weekday_sin + 
    recency_features_norm
)

features = ColumnSelector(['timestamp', 'session_id']) + cat_feats + time_features 

Define the preprocessing of sequential features

Once the item features are generated, the objective of this cell is grouping interactions at the session level, sorting the interactions by time. We additionally truncate all sessions to first 20 interactions and filter out sessions with less than 2 interactions.

# Define Groupby Operator
groupby_features = features >> nvt.ops.Groupby(
    groupby_cols=["session_id"], 
    sort_cols=["timestamp"],
    aggs={
        'item_id': ["list", "count"],
        'category': ["list"],  
        'timestamp': ["first"],
        'event_time_dt': ["first"],
        'et_dayofweek_sin': ["list"],
        'product_recency_days_log_norm': ["list"]
        },
    name_sep="-") >> nvt.ops.AddMetadata(tags=[Tags.CATEGORICAL])


# Truncate sequence features to first interacted 20 items 
SESSIONS_MAX_LENGTH = 20 

groupby_features_list = groupby_features['item_id-list', 'category-list', 'et_dayofweek_sin-list', 'product_recency_days_log_norm-list']
groupby_features_truncated = groupby_features_list >> nvt.ops.ListSlice(0, SESSIONS_MAX_LENGTH, pad=True) >> nvt.ops.Rename(postfix = '_seq')

# Calculate session day index based on 'event_time_dt-first' column
day_index = ((groupby_features['event_time_dt-first'])  >> 
    nvt.ops.LambdaOp(lambda col: (col - col.min()).dt.days +1) >> 
    nvt.ops.Rename(f = lambda col: "day_index")
)

# Select features for training 
selected_features = groupby_features['session_id', 'item_id-count'] + groupby_features_truncated + day_index

# Filter out sessions with less than 2 interactions 
MINIMUM_SESSION_LENGTH = 2
filtered_sessions = selected_features >> nvt.ops.Filter(f=lambda df: df["item_id-count"] >= MINIMUM_SESSION_LENGTH) 

Avoid Numba low occupancy warnings

from numba import config
config.CUDA_LOW_OCCUPANCY_WARNINGS = 0

Execute NVTabular workflow

Once we have defined the general workflow (filtered_sessions), we provide our cudf dataset to nvt.Dataset class which is optimized to split data into chunks that can fit in device memory and to handle the calculation of complex global statistics. Then, we execute the pipeline that fits and transforms data to get the desired output features.

dataset = nvt.Dataset(interactions_merged_df)
workflow = nvt.Workflow(filtered_sessions)
# Learns features statistics necessary of the preprocessing workflow
workflow.fit(dataset)
# Apply the preprocessing workflow in the dataset and converts the resulting Dask cudf dataframe to a cudf dataframe
sessions_gdf = workflow.transform(dataset).compute()

/usr/local/lib/python3.8/dist-packages/cudf/core/frame.py:384: UserWarning: The deep parameter is ignored and is only included for pandas compatibility.
  warnings.warn(

Let’s print the head of our preprocessed dataset. You can notice that now each example (row) is a session and the sequential features with respect to user interactions were converted to lists with matching length.

sessions_gdf.head()

	session_id	item_id-count	item_id-list_seq	category-list_seq	et_dayofweek_sin-list_seq	product_recency_days_log_norm-list_seq	day_index
0	2	200	[2223, 2125, 1800, 123, 3030, 1861, 1076, 1285...	[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ...	[1.1285199e-06, 1.1285199e-06, 1.1285199e-06, ...	[-1.1126351356506348, -0.9665398597717285, -0....	27
1	3	200	[35137, 19260, 46449, 29027, 39096, 27266, 326...	[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ...	[0.43388295, 0.43388295, 0.43388295, 0.4338829...	[0.393317312002182, 0.541846752166748, -3.0278...	58
2	4	200	[23212, 30448, 16468, 2052, 22490, 31097, 6243...	[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ...	[0.9749277, 0.9749277, 0.9749277, 0.9749277, 0...	[0.6801633834838867, 0.7174698114395142, 0.718...	71
3	5	200	[230, 451, 732, 1268, 2014, 567, 497, 439, 338...	[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, ...	[0.43388295, 0.43388295, 0.43388295, 0.4338829...	[1.3680896759033203, -0.6530488133430481, -0.6...	149
4	6	200	[23, 70, 160, 70, 90, 742, 851, 359, 734, 878,...	[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ...	[0.43388295, 0.43388295, 0.43388295, 0.4338829...	[1.3714832067489624, 1.371589183807373, 1.3715...	149

Save the preprocessing workflow

workflow.save('workflow_etl')

Export pre-processed data by day

In this example we are going to split the preprocessed parquet files by days, to allow for temporal training and evaluation. There will be a folder for each day and three parquet files within each day: train.parquet, validation.parquet and test.parquet

P.s. It is worthwhile a note that the dataset have a single categorical feature (category), but it is inconsistent over time in the dataset. All interactions before day 84 (2014-06-23) have the same value for that feature, whereas many other categories are introduced afterwards. Thus for this example, we save only the last five days.

sessions_gdf = sessions_gdf[sessions_gdf.day_index>=178]

from transformers4rec.data.preprocessing import save_time_based_splits
save_time_based_splits(data=nvt.Dataset(sessions_gdf),
                       output_dir= "./preproc_sessions_by_day",
                       partition_col='day_index',
                       timestamp_col='session_id', 
                      )

Creating time-based splits: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  4.73it/s]

def list_files(startpath):
    """
    Util function to print the nested structure of a directory
    """
    for root, dirs, files in os.walk(startpath):
        level = root.replace(startpath, "").count(os.sep)
        indent = " " * 4 * (level)
        print("{}{}/".format(indent, os.path.basename(root)))
        subindent = " " * 4 * (level + 1)
        for f in files:
            print("{}{}".format(subindent, f))

list_files('./preproc_sessions_by_day')

preproc_sessions_by_day/
    182/
        test.parquet
        valid.parquet
        train.parquet
    179/
        test.parquet
        valid.parquet
        train.parquet
    180/
        test.parquet
        valid.parquet
        train.parquet
    178/
        test.parquet
        valid.parquet
        train.parquet
    181/
        test.parquet
        valid.parquet
        train.parquet

# free gpu memory
del  sessions_gdf
gc.collect()

That’s it! We created our sequential features, now we can go to next notebook to train a PyTorch session-based model.