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
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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
--2024-07-16 22:54:07-- 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)... 3.5.12.80, 3.5.25.252, 3.5.22.138, ...
Connecting to automl-mm-bench.s3.amazonaws.com (automl-mm-bench.s3.amazonaws.com)|3.5.12.80|: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 0.04s
2024-07-16 22:54:08 (91.8 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
import uuid
time_limit = 3 * 60 # set to larger value in your applications
model_path = f"./tmp/{uuid.uuid4().hex}-automm_text_book_price_prediction"
predictor = MultiModalPredictor(label='Price', path=model_path)
predictor.fit(train_data, time_limit=time_limit)
=================== System Info ===================
AutoGluon Version: 1.1.1b20240716
Python Version: 3.10.13
Operating System: Linux
Platform Machine: x86_64
Platform Version: #1 SMP Fri May 17 18:07:48 UTC 2024
CPU Count: 8
Pytorch Version: 2.3.1+cu121
CUDA Version: 12.1
Memory Avail: 28.67 GB / 30.95 GB (92.6%)
Disk Space Avail: 185.13 GB / 255.99 GB (72.3%)
===================================================
AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == float and many unique label-values observed).
Label info (max, min, mean, stddev): (9.115699967822062, 3.6109179126442243, 6.02567, 0.7694)
If 'regression' is not the correct problem_type, please manually specify the problem_type parameter during Predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression', 'quantile'])
AutoMM starts to create your model. ✨✨✨
To track the learning progress, you can open a terminal and launch Tensorboard:
```shell
# Assume you have installed tensorboard
tensorboard --logdir /home/ci/autogluon/docs/tutorials/multimodal/multimodal_prediction/tmp/2c19fb5735b94991abc3eb7bce9d8d22-automm_text_book_price_prediction
```
Seed set to 0
GPU Count: 1
GPU Count to be Used: 1
GPU 0 Name: Tesla T4
GPU 0 Memory: 0.42GB/15.0GB (Used/Total)
Using 16bit Automatic Mixed Precision (AMP)
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
HPU available: False, using: 0 HPUs
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
| Name | Type | Params | Mode
------------------------------------------------------------------
0 | model | MultimodalFusionMLP | 110 M | train
1 | validation_metric | MeanSquaredError | 0 | train
2 | loss_func | MSELoss | 0 | train
------------------------------------------------------------------
110 M Trainable params
0 Non-trainable params
110 M Total params
442.755 Total estimated model params size (MB)
Epoch 0, global step 4: 'val_rmse' reached 1.17895 (best 1.17895), saving model to '/home/ci/autogluon/docs/tutorials/multimodal/multimodal_prediction/tmp/2c19fb5735b94991abc3eb7bce9d8d22-automm_text_book_price_prediction/epoch=0-step=4.ckpt' as top 3
Epoch 0, global step 10: 'val_rmse' reached 0.96232 (best 0.96232), saving model to '/home/ci/autogluon/docs/tutorials/multimodal/multimodal_prediction/tmp/2c19fb5735b94991abc3eb7bce9d8d22-automm_text_book_price_prediction/epoch=0-step=10.ckpt' as top 3
Epoch 1, global step 14: 'val_rmse' reached 1.46537 (best 0.96232), saving model to '/home/ci/autogluon/docs/tutorials/multimodal/multimodal_prediction/tmp/2c19fb5735b94991abc3eb7bce9d8d22-automm_text_book_price_prediction/epoch=1-step=14.ckpt' as top 3
Epoch 1, global step 20: 'val_rmse' reached 1.07779 (best 0.96232), saving model to '/home/ci/autogluon/docs/tutorials/multimodal/multimodal_prediction/tmp/2c19fb5735b94991abc3eb7bce9d8d22-automm_text_book_price_prediction/epoch=1-step=20.ckpt' as top 3
Epoch 2, global step 24: 'val_rmse' reached 0.86498 (best 0.86498), saving model to '/home/ci/autogluon/docs/tutorials/multimodal/multimodal_prediction/tmp/2c19fb5735b94991abc3eb7bce9d8d22-automm_text_book_price_prediction/epoch=2-step=24.ckpt' as top 3
Epoch 2, global step 30: 'val_rmse' was not in top 3
Epoch 3, global step 34: 'val_rmse' reached 0.98814 (best 0.86498), saving model to '/home/ci/autogluon/docs/tutorials/multimodal/multimodal_prediction/tmp/2c19fb5735b94991abc3eb7bce9d8d22-automm_text_book_price_prediction/epoch=3-step=34.ckpt' as top 3
Time limit reached. Elapsed time is 0:03:04. Signaling Trainer to stop.
Start to fuse 3 checkpoints via the greedy soup algorithm.
AutoMM has created your model. 🎉🎉🎉
To load the model, use the code below:
```python
from autogluon.multimodal import MultiModalPredictor
predictor = MultiModalPredictor.load("/home/ci/autogluon/docs/tutorials/multimodal/multimodal_prediction/tmp/2c19fb5735b94991abc3eb7bce9d8d22-automm_text_book_price_prediction")
```
If you are not satisfied with the model, try to increase the training time,
adjust the hyperparameters (https://auto.gluon.ai/stable/tutorials/multimodal/advanced_topics/customization.html),
or post issues on GitHub (https://github.com/autogluon/autogluon/issues).
<autogluon.multimodal.predictor.MultiModalPredictor at 0x7fc68cb4a3e0>
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:
------------
1 462.031036
31 462.761292
19 1002.259888
45 637.660217
82 826.627747
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': 0.7459707559175868}
embeddings = predictor.extract_embedding(test_data)
embeddings.shape
(5, 128)
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.