AutoMM for Text + Tabular - Quick Start

In many applications, text data may be mixed with numeric/categorical data. AutoGluon’s MultiModalPredictor can train a single neural network that jointly operates on multiple feature types, including text, categorical, and numerical columns. The general idea is to embed the text, categorical and numeric fields separately and fuse these features across modalities. This tutorial demonstrates such an application.

import numpy as np
import pandas as pd
import warnings
import os

warnings.filterwarnings('ignore')
np.random.seed(123)

!python3 -m pip install openpyxl

Collecting openpyxl
  Downloading openpyxl-3.0.10-py2.py3-none-any.whl (242 kB)
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[?25hCollecting et-xmlfile
  Downloading et_xmlfile-1.1.0-py3-none-any.whl (4.7 kB)
Installing collected packages: et-xmlfile, openpyxl
Successfully installed et-xmlfile-1.1.0 openpyxl-3.0.10

Book Price Prediction Data

For demonstration, we use the book price prediction dataset from the MachineHack Book Price Prediction Hackathon. Our goal is to predict a book’s price given various features like its author, the abstract, the book’s rating, etc.

!mkdir -p price_of_books
!wget https://automl-mm-bench.s3.amazonaws.com/machine_hack_competitions/predict_the_price_of_books/Data.zip -O price_of_books/Data.zip
!cd price_of_books && unzip -o Data.zip
!ls price_of_books/Participants_Data

--2022-07-28 21:25:52--  https://automl-mm-bench.s3.amazonaws.com/machine_hack_competitions/predict_the_price_of_books/Data.zip
Resolving automl-mm-bench.s3.amazonaws.com (automl-mm-bench.s3.amazonaws.com)... 52.217.69.12
Connecting to automl-mm-bench.s3.amazonaws.com (automl-mm-bench.s3.amazonaws.com)|52.217.69.12|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 3521673 (3.4M) [application/zip]
Saving to: ‘price_of_books/Data.zip’

price_of_books/Data 100%[===================>]   3.36M  --.-KB/s    in 0.06s

2022-07-28 21:25:52 (53.4 MB/s) - ‘price_of_books/Data.zip’ saved [3521673/3521673]

Archive:  Data.zip
  inflating: Participants_Data/Data_Test.xlsx
  inflating: Participants_Data/Data_Train.xlsx
  inflating: Participants_Data/Sample_Submission.xlsx
Data_Test.xlsx      Data_Train.xlsx  Sample_Submission.xlsx

train_df = pd.read_excel(os.path.join('price_of_books', 'Participants_Data', 'Data_Train.xlsx'), engine='openpyxl')
train_df.head()

	Title	Author	Edition	Reviews	Ratings	Synopsis	Genre	BookCategory	Price
0	The Prisoner's Gold (The Hunters 3)	Chris Kuzneski	Paperback,– 10 Mar 2016	4.0 out of 5 stars	8 customer reviews	THE HUNTERS return in their third brilliant no...	Action & Adventure (Books)	Action & Adventure	220.00
1	Guru Dutt: A Tragedy in Three Acts	Arun Khopkar	Paperback,– 7 Nov 2012	3.9 out of 5 stars	14 customer reviews	A layered portrait of a troubled genius for wh...	Cinema & Broadcast (Books)	Biographies, Diaries & True Accounts	202.93
2	Leviathan (Penguin Classics)	Thomas Hobbes	Paperback,– 25 Feb 1982	4.8 out of 5 stars	6 customer reviews	"During the time men live without a common Pow...	International Relations	Humour	299.00
3	A Pocket Full of Rye (Miss Marple)	Agatha Christie	Paperback,– 5 Oct 2017	4.1 out of 5 stars	13 customer reviews	A handful of grain is found in the pocket of a...	Contemporary Fiction (Books)	Crime, Thriller & Mystery	180.00
4	LIFE 70 Years of Extraordinary Photography	Editors of Life	Hardcover,– 10 Oct 2006	5.0 out of 5 stars	1 customer review	For seven decades, "Life" has been thrilling t...	Photography Textbooks	Arts, Film & Photography	965.62

We do some basic preprocessing to convert Reviews and Ratings in the data table to numeric values, and we transform prices to a log-scale.

def preprocess(df):
    df = df.copy(deep=True)
    df.loc[:, 'Reviews'] = pd.to_numeric(df['Reviews'].apply(lambda ele: ele[:-len(' out of 5 stars')]))
    df.loc[:, 'Ratings'] = pd.to_numeric(df['Ratings'].apply(lambda ele: ele.replace(',', '')[:-len(' customer reviews')]))
    df.loc[:, 'Price'] = np.log(df['Price'] + 1)
    return df

train_subsample_size = 1500  # subsample for faster demo, you can try setting to larger values
test_subsample_size = 5
train_df = preprocess(train_df)
train_data = train_df.iloc[100:].sample(train_subsample_size, random_state=123)
test_data = train_df.iloc[:100].sample(test_subsample_size, random_state=245)
train_data.head()

	Title	Author	Edition	Reviews	Ratings	Synopsis	Genre	BookCategory	Price
949	Furious Hours	Casey Cep	Paperback,– 1 Jun 2019	4.0	NaN	‘It’s been a long time since I picked up a boo...	True Accounts (Books)	Biographies, Diaries & True Accounts	5.743003
5504	REST API Design Rulebook	Mark Masse	Paperback,– 7 Nov 2011	5.0	NaN	In todays market, where rival web services com...	Computing, Internet & Digital Media (Books)	Computing, Internet & Digital Media	5.786897
5856	The Atlantropa Articles: A Novel	Cody Franklin	Paperback,– Import, 1 Nov 2018	4.5	2.0	#1 Amazon Best Seller! Dystopian Alternate His...	Action & Adventure (Books)	Romance	6.893656
4137	Hickory Dickory Dock (Poirot)	Agatha Christie	Paperback,– 5 Oct 2017	4.3	21.0	There’s more than petty theft going on in a Lo...	Action & Adventure (Books)	Crime, Thriller & Mystery	5.192957
3205	The Stanley Kubrick Archives (Bibliotheca Univ...	Alison Castle	Hardcover,– 21 Aug 2016	4.6	3.0	In 1968, when Stanley Kubrick was asked to com...	Cinema & Broadcast (Books)	Humour	6.889591

Training

We can simply create a MultiModalPredictor and call predictor.fit() to train a model that operates on across all types of features. Internally, the neural network will be automatically generated based on the inferred data type of each feature column. To save time, we subsample the data and only train for three minutes.

from autogluon.multimodal import MultiModalPredictor
time_limit = 3 * 60  # set to larger value in your applications
predictor = MultiModalPredictor(label='Price', path='automm_text_book_price_prediction')
predictor.fit(train_data, time_limit=time_limit)

Global seed set to 123
Auto select gpus: [0]
Using 16bit native Automatic Mixed Precision (AMP)
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]

  | Name              | Type                | Params
----------------------------------------------------------
0 | model             | MultimodalFusionMLP | 109 M
1 | validation_metric | MeanSquaredError    | 0
2 | loss_func         | MSELoss             | 0
----------------------------------------------------------
109 M     Trainable params
0         Non-trainable params
109 M     Total params
219.565   Total estimated model params size (MB)
Epoch 0, global step 4: 'val_rmse' reached 1.52574 (best 1.52574), saving model to '/home/ci/autogluon/docs/_build/eval/tutorials/multimodal/automm_text_book_price_prediction/epoch=0-step=4.ckpt' as top 3
Epoch 0, global step 10: 'val_rmse' reached 1.08650 (best 1.08650), saving model to '/home/ci/autogluon/docs/_build/eval/tutorials/multimodal/automm_text_book_price_prediction/epoch=0-step=10.ckpt' as top 3
Epoch 1, global step 14: 'val_rmse' reached 0.88177 (best 0.88177), saving model to '/home/ci/autogluon/docs/_build/eval/tutorials/multimodal/automm_text_book_price_prediction/epoch=1-step=14.ckpt' as top 3
Epoch 1, global step 20: 'val_rmse' reached 0.92199 (best 0.88177), saving model to '/home/ci/autogluon/docs/_build/eval/tutorials/multimodal/automm_text_book_price_prediction/epoch=1-step=20.ckpt' as top 3
Epoch 2, global step 24: 'val_rmse' reached 0.82184 (best 0.82184), saving model to '/home/ci/autogluon/docs/_build/eval/tutorials/multimodal/automm_text_book_price_prediction/epoch=2-step=24.ckpt' as top 3
Epoch 2, global step 30: 'val_rmse' reached 0.88285 (best 0.82184), saving model to '/home/ci/autogluon/docs/_build/eval/tutorials/multimodal/automm_text_book_price_prediction/epoch=2-step=30.ckpt' as top 3
Time limit reached. Elapsed time is 0:03:13. Signaling Trainer to stop.

<autogluon.multimodal.predictor.MultiModalPredictor at 0x7fda1f25e400>

Prediction

We can easily obtain predictions and extract data embeddings using the MultiModalPredictor.

predictions = predictor.predict(test_data)
print('Predictions:')
print('------------')
print(np.exp(predictions) - 1)
print()
print('True Value:')
print('------------')
print(np.exp(test_data['Price']) - 1)

Predictions:
------------
1     537.166748
31    519.463989
19    974.593201
45    569.958374
82    594.307800
Name: Price, dtype: float32

True Value:
------------
1     202.93
31    799.00
19    352.00
45    395.10
82    409.00
Name: Price, dtype: float64

performance = predictor.evaluate(test_data)
print(performance)

{'rmse': 0.6975193605162653}

embeddings = predictor.extract_embedding(test_data)
embeddings.shape

(5, 128)

What’s happening inside?

Internally, we use different networks to encode the text columns, categorical columns, and numerical columns. The features generated by individual networks are aggregated by a late-fusion aggregator. The aggregator can output both the logits or score predictions. The architecture can be illustrated as follows:

Fig. 1 Multimodal Network with Late Fusion

Here, we use the pretrained NLP backbone to extract the text features and then use two other towers to extract the feature from categorical column and the numerical column.

In addition, to deal with multiple text fields, we separate these fields with the [SEP] token and alternate 0s and 1s as the segment IDs, which is shown as follows:

Fig. 2 Preprocessing

How does this compare with TabularPredictor?

Note that TabularPredictor can also handle data tables with text, numeric, and categorical columns, but it uses an ensemble of many types of models and may featurize text. MultiModalPredictor instead directly fuses multiple neural network models directly and handles raw text (which are also capable of handling additional numerical/categorical columns). We generally recommend TabularPredictor if your table contains mainly numeric/categorical columns and MultiModalPredictor if your table contains mainly text columns, but you may easily try both and we encourage this. In fact, TabularPredictor.fit(..., hyperparameters='multimodal') will train a MultiModalPredictor along with many other tabular models and ensemble them together. Refer to the tutorial “Multimodal Data Tables: Combining BERT/Transformers and Classical Tabular Models” for more details.

Other Examples

You may go to AutoMM Examples to explore other examples about AutoMM.

Customization

To learn how to customize AutoMM, please refer to Customize AutoMM.