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ETL with NVTabular

You can execute these tutorial notebooks 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:23.XX

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

1. Introduction

In this notebook, we will create a preprocessing and feature engineering pipeline with Rapids cuDF and Merlin NVTabular libraries to prepare our dataset for session-based recommendation model training.

NVTabular is a feature engineering and preprocessing library for tabular data that is designed to easily manipulate terabyte scale datasets and train deep learning (DL) based recommender systems. It provides high-level abstraction to simplify code and accelerates computation on the GPU using the RAPIDS Dask-cuDF library, and is designed to be interoperable with both PyTorch and TensorFlow using dataloaders that have been developed as extensions of native framework code.

Our main goal is to create sequential features. In order to do that, we are going to perform the following:

  • Categorify categorical features with Categorify() op

  • Create temporal features with a user-defined custom op and Lambda op

  • Transform continuous features using Log and Normalize ops

  • Group all these features together at the session level sorting the interactions by time with Groupby

  • Finally export the preprocessed datasets to parquet files by hive-partitioning.

1.1. Dataset

In our hands-on exercise notebooks we are going to use a subset of the publicly available eCommerce dataset. The eCommerce behavior data contains 7 months of data (from October 2019 to April 2020) from a large multi-category online store. Each row in the file represents an event. All events are related to products and users. Each event is like many-to-many relation between products and users.

Data collected by Open CDP project and the source of the dataset is REES46 Marketing Platform.

2. Import Libraries

import os

import numpy as np 
import cupy as cp
import glob

import cudf
import nvtabular as nvt

from nvtabular.ops import Operator
from merlin.dag import ColumnSelector
from merlin.schema import Schema, Tags

/usr/local/lib/python3.8/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
/usr/local/lib/python3.8/dist-packages/merlin/dtypes/mappings/tf.py:52: UserWarning: Tensorflow dtype mappings did not load successfully due to an error: No module named 'tensorflow'
  warn(f"Tensorflow dtype mappings did not load successfully due to an error: {exc.msg}")

# avoid numba warnings
from numba import config
config.CUDA_LOW_OCCUPANCY_WARNINGS = 0

3. Set up Input and Output Data Paths

# define data path about where to get our data
INPUT_DATA_DIR = os.environ.get("INPUT_DATA_DIR", "/workspace/data/")

4. Read the Input Parquet file

Even though the original dataset contains 7 months data files, we are going to use the first seven days of the Oct-2019.csv ecommerce dataset. We already performed certain preprocessing steps on the first month (Oct-2019) of the raw dataset in the 01-preprocess notebook:

  • we created event_time_ts column from event_time column which shows the time when event happened at (in UTC).

  • we created prod_first_event_time_ts column which indicates the timestamp that an item was seen first time.

  • we removed the rows where the user_session is Null. As a result, 2 rows were removed.

  • we categorified the user_session column, so that it now has only integer values.

  • we removed consequetively repeated (user, item) interactions. For example, an original session with [1, 2, 4, 1, 2, 2, 3, 3, 3] product interactions has become [1, 2, 4, 1, 2, 3] after removing the repeated interactions on the same item within the same session.

Below, we start by reading in Oct-2019.parquetwith cuDF. In order to create and save Oct-2019.parquet file, please run 01-preprocess.ipynb notebook first.

%%time
df = cudf.read_parquet(os.path.join(INPUT_DATA_DIR, 'Oct-2019.parquet'))  
print(df.head(5))

   user_session event_type  product_id          category_id category_code  \
0            43       view     5300797  2053013563173241677          <NA>   
1            43       view     5300798  2053013563173241677          <NA>   
2            43       view     5300284  2053013563173241677          <NA>   
3            43       view     5300382  2053013563173241677          <NA>   
4            43       view     5300366  2053013563173241677          <NA>   

       brand  price    user_id  event_time_ts  prod_first_event_time_ts  
0  panasonic  39.90  513903572     1570460611                1569948287  
1  panasonic  32.18  513903572     1570460616                1569934097  
2    rowenta  30.86  513903572     1570460621                1569927253  
3  remington  28.22  513903572     1570460636                1570026747  
4    polaris  26.46  513903572     1570460650                1570097085  
CPU times: user 1.9 s, sys: 596 ms, total: 2.5 s
Wall time: 2.52 s

df.shape

(6390928, 10)

Let’s check if there is any column with nulls.

df.isnull().any()

user_session                False
event_type                  False
product_id                  False
category_id                 False
category_code                True
brand                        True
price                       False
user_id                     False
event_time_ts               False
prod_first_event_time_ts    False
dtype: bool

We see that 'category_code' and 'brand' columns have null values, and in the following cell we are going to fill these nulls with via categorify op, and then all categorical columns will be encoded to continuous integers. Note that we add start_index=1 in the Categorify op for the categorical columns, the reason for that we want the encoded null values to start from 1 instead of 0 because we reserve 0 for padding the sequence features.

5. Initialize NVTabular Workflow

5.1. Categorical Features Encoding

# categorify features 
item_id = ['product_id'] >> nvt.ops.TagAsItemID()
cat_feats = item_id + ['category_code', 'brand', 'user_id', 'category_id', 'event_type'] >> nvt.ops.Categorify(start_index=1)

5.2. Extract Temporal Features

# create time features
session_ts = ['event_time_ts']

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')
)

Now let’s create cycling features from the sessiontime_weekday column. We would like to use the temporal features (hour, day of week, month, etc.) that have inherently cyclical characteristic. We represent the day of week as a cycling feature (sine and cosine), so that it can be represented in a continuous space. That way, the difference between the representation of two different days is the same, in other words, with cyclical features we can convey closeness between data. You can read more about it here.

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

def get_cycled_feature_value_cos(col, max_value):
    value_scaled = (col + 0.000001) / max_value
    value_cos = np.cos(2*np.pi*value_scaled)
    return value_cos

weekday_sin = (sessiontime_weekday >> 
               (lambda col: get_cycled_feature_value_sin(col+1, 7)) >> 
               nvt.ops.Rename(name = 'et_dayofweek_sin') >>
               nvt.ops.AddMetadata(tags=[Tags.CONTINUOUS])
              )
    
weekday_cos= (sessiontime_weekday >> 
              (lambda col: get_cycled_feature_value_cos(col+1, 7)) >> 
              nvt.ops.Rename(name = 'et_dayofweek_cos') >>
              nvt.ops.AddMetadata(tags=[Tags.CONTINUOUS])
             )

5.2.1 Add Product Recency feature

  • Let’s define a custom op to calculate product recency in days

# 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['prod_first_event_time_ts']
            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 ["prod_first_event_time_ts"]

    @property
    def output_dtype(self):
        return np.float64

recency_features = ['event_time_ts'] >> ItemRecency() 
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 +
    weekday_cos +
    recency_features_norm
)

5.3. Normalize Continuous Features¶

# Smoothing price long-tailed distribution and applying standardization
price_log = ['price'] >> nvt.ops.LogOp() >> nvt.ops.Normalize(out_dtype=np.float32) >> nvt.ops.Rename(name='price_log_norm')

# Relative price to the average price for the category_id
def relative_price_to_avg_categ(col, gdf):
    epsilon = 1e-5
    col = ((gdf['price'] - col) / (col + epsilon)) * (col > 0).astype(int)
    return col
    
avg_category_id_pr = ['category_id'] >> nvt.ops.JoinGroupby(cont_cols =['price'], stats=["mean"]) >> nvt.ops.Rename(name='avg_category_id_price')
relative_price_to_avg_category = (
    avg_category_id_pr >> 
    nvt.ops.LambdaOp(relative_price_to_avg_categ, dependency=['price']) >> 
    nvt.ops.Rename(name="relative_price_to_avg_categ_id") >>
    nvt.ops.AddMetadata(tags=[Tags.CONTINUOUS])
)

5.4. Grouping interactions into sessions

Aggregate by session id and creates the sequential features

groupby_feats = ['event_time_ts', 'user_session'] + cat_feats + time_features + price_log + relative_price_to_avg_category

# Define Groupby Workflow
groupby_features = groupby_feats >> nvt.ops.Groupby(
    groupby_cols=["user_session"], 
    sort_cols=["event_time_ts"],
    aggs={
        'user_id': ['first'],
        'product_id': ["list", "count"],
        'category_code': ["list"],  
        'brand': ["list"], 
        'category_id': ["list"], 
        'event_time_ts': ["first"],
        'event_time_dt': ["first"],
        'et_dayofweek_sin': ["list"],
        'et_dayofweek_cos': ["list"],
        'price_log_norm': ["list"],
        'relative_price_to_avg_categ_id': ["list"],
        'product_recency_days_log_norm': ["list"]
        },
    name_sep="-")

  • Select columns which are list

groupby_features_list = groupby_features['product_id-list',
        'category_code-list',  
        'brand-list', 
        'category_id-list', 
        'et_dayofweek_sin-list',
        'et_dayofweek_cos-list',
        'price_log_norm-list',
        'relative_price_to_avg_categ_id-list',
        'product_recency_days_log_norm-list']

SESSIONS_MAX_LENGTH = 20 
MINIMUM_SESSION_LENGTH = 2

We truncate the sequence features in length according to sessions_max_length param, which is set as 20 in our example.

groupby_features_trim = groupby_features_list >> nvt.ops.ListSlice(-SESSIONS_MAX_LENGTH, pad=True)

  • Create a day_index column in order to partition sessions by day when saving the parquet files.

# calculate session day index based on 'timestamp-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") >>
             nvt.ops.AddMetadata(tags=[Tags.CATEGORICAL])
            )

  • Select certain columns to be used in model training

sess_id = groupby_features['user_session'] >> nvt.ops.AddMetadata(tags=[Tags.CATEGORICAL])

selected_features = sess_id + groupby_features['product_id-count'] + groupby_features_trim + day_index

  • Filter out the session that have less than 2 interactions.

filtered_sessions = selected_features >> nvt.ops.Filter(f=lambda df: df["product_id-count"] >= MINIMUM_SESSION_LENGTH)

  • Initialize the NVTabular dataset object and workflow graph.

NVTabular’s preprocessing and feature engineering workflows are directed graphs of operators. When we initialize a Workflow with our pipeline, workflow organizes the input and output columns.

workflow = nvt.Workflow(filtered_sessions)
dataset = nvt.Dataset(df)
# Learn features statistics necessary of the preprocessing workflow
# The following will generate schema.pbtxt file in the provided folder and export the parquet files.
workflow.fit_transform(dataset).to_parquet(os.path.join(INPUT_DATA_DIR, "processed_nvt"))

/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:149: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].
  warnings.warn(
/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:149: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].
  warnings.warn(

workflow.output_schema
name tags dtype is_list is_ragged properties.num_buckets properties.freq_threshold properties.max_size properties.start_index properties.cat_path properties.domain.min properties.domain.max properties.domain.name properties.embedding_sizes.cardinality properties.embedding_sizes.dimension properties.value_count.min properties.value_count.max
0 user_session (Tags.CATEGORICAL) DType(name='int64', element_type=<ElementType.... False False NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 product_id-count (Tags.ITEM_ID, Tags.ITEM, Tags.CATEGORICAL, Ta... DType(name='int32', element_type=<ElementType.... False False NaN 0.0 0.0 1.0 .//categories/unique.product_id.parquet 0.0 118334.0 product_id 118335.0 512.0 NaN NaN
2 product_id-list (Tags.ID, Tags.CATEGORICAL, Tags.LIST, Tags.IT... DType(name='int64', element_type=<ElementType.... True False NaN 0.0 0.0 1.0 .//categories/unique.product_id.parquet 0.0 118334.0 product_id 118335.0 512.0 20.0 20.0
3 category_code-list (Tags.CATEGORICAL, Tags.LIST) DType(name='int64', element_type=<ElementType.... True False NaN 0.0 0.0 1.0 .//categories/unique.category_code.parquet 0.0 124.0 category_code 125.0 24.0 20.0 20.0
4 brand-list (Tags.CATEGORICAL, Tags.LIST) DType(name='int64', element_type=<ElementType.... True False NaN 0.0 0.0 1.0 .//categories/unique.brand.parquet 0.0 2640.0 brand 2641.0 132.0 20.0 20.0
5 category_id-list (Tags.CATEGORICAL, Tags.LIST) DType(name='int64', element_type=<ElementType.... True False NaN 0.0 0.0 1.0 .//categories/unique.category_id.parquet 0.0 566.0 category_id 567.0 56.0 20.0 20.0
6 et_dayofweek_sin-list (Tags.LIST, Tags.CONTINUOUS) DType(name='float32', element_type=<ElementTyp... True False NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 20.0 20.0
7 et_dayofweek_cos-list (Tags.LIST, Tags.CONTINUOUS) DType(name='float32', element_type=<ElementTyp... True False NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 20.0 20.0
8 price_log_norm-list (Tags.LIST, Tags.CONTINUOUS) DType(name='float32', element_type=<ElementTyp... True False NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 20.0 20.0
9 relative_price_to_avg_categ_id-list (Tags.LIST, Tags.CONTINUOUS) DType(name='float64', element_type=<ElementTyp... True False NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 20.0 20.0
10 product_recency_days_log_norm-list (Tags.LIST, Tags.CONTINUOUS) DType(name='float32', element_type=<ElementTyp... True False NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 20.0 20.0
11 day_index (Tags.CATEGORICAL) DType(name='int64', element_type=<ElementType.... False False NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

Above, we created an NVTabular Dataset object using our input dataset. Then, we calculate statistics for this workflow on the input dataset, i.e. on our training set, using the workflow.fit() method so that our Workflow can use these stats to transform any given input.

6. Exporting data

We export dataset to parquet partitioned by the session day_index column.

# define partition column
PARTITION_COL = 'day_index'

# define output_folder to store the partitioned parquet files
OUTPUT_FOLDER = os.environ.get("OUTPUT_FOLDER", INPUT_DATA_DIR + "sessions_by_day")
!mkdir -p $OUTPUT_FOLDER

In this section we are going to create a folder structure as shown below. As we explained above, this is just to structure parquet files so that it would be easier to do incremental training and evaluation.

/sessions_by_day/
|-- 1
|   |-- train.parquet
|   |-- valid.parquet
|   |-- test.parquet

|-- 2
|   |-- train.parquet
|   |-- valid.parquet
|   |-- test.parquet

gpu_preprocessing function converts the process df to a Dataset object and write out hive-partitioned data to disk.

# read in the processed train dataset
sessions_gdf = cudf.read_parquet(os.path.join(INPUT_DATA_DIR, "processed_nvt/part_0.parquet"))

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.

print(sessions_gdf.head(2))

   user_session  product_id-count  \
0             1               779   
1             6               316   

                                     product_id-list  \
0  [7171, 13290, 19757, 20401, 19757, 20401, 2122...   
1  [4093, 3427, 20536, 27530, 11262, 18532, 33985...   

                                  category_code-list  \
0  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   
1  [17, 17, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2...   

                                          brand-list  \
0  [34, 231, 239, 34, 239, 34, 120, 120, 249, 231...   
1  [50, 1, 36, 1, 1, 1, 1, 3, 2, 2, 2, 2, 2, 2, 1...   

                                    category_id-list  \
0  [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, ...   
1  [36, 36, 163, 163, 163, 163, 163, 2, 2, 2, 2, ...   

                               et_dayofweek_sin-list  \
0  [0.7818321, 0.7818321, 0.7818321, 0.7818321, 0...   
1  [0.43388295, 0.43388295, 0.43388295, 0.4338829...   

                               et_dayofweek_cos-list  \
0  [0.6234891, 0.6234891, 0.6234891, 0.6234891, 0...   
1  [-0.90096927, -0.90096927, -0.90096927, -0.900...   

                                 price_log_norm-list  \
0  [-0.543168, -0.5673179, -0.578279, -0.62369585...   
1  [0.23708352, 0.24015874, -0.80294055, -0.66287...   

                 relative_price_to_avg_categ_id-list  \
0  [0.12176345592788261, 0.08783709744899952, 0.0...   
1  [-0.23209574328358615, -0.22911214248041414, -...   

                  product_recency_days_log_norm-list  day_index  
0  [1.1603423, 1.1818986, 1.0499662, 1.0900966, 1...          1  
1  [-0.7081338, -0.69109577, -0.73886937, -0.7074...          2  

from transformers4rec.utils.data_utils import save_time_based_splits
save_time_based_splits(data=nvt.Dataset(sessions_gdf),
                       output_dir= OUTPUT_FOLDER,
                       partition_col=PARTITION_COL,
                       timestamp_col='user_session', 
                      )

Creating time-based splits: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:02<00:00,  2.75it/s]

# check out the OUTPUT_FOLDER
!ls $OUTPUT_FOLDER

1  2  3  4  5  6  7

Save NVTabular workflow to load at the inference step.

You can uncomment the cell below and execute it to see the output schema generated from nvtabular workflow.

workflow_path = os.path.join(INPUT_DATA_DIR, 'workflow_etl')
workflow.save(workflow_path)

7. Wrap Up

That’s it! We finished our first task. We reprocessed our dataset and created new features to train a session-based recommendation model. Please shut down the kernel before moving on to the next notebook.