27. Text Classification with AutoGluon#
https://auto.gluon.ai/stable/index.html
This is an excellent library from AWS that may be used for multimodal machine learning in an automatic manner. It uses stack-ensembling and beats most kaggle competition winners. See the papers in the Guthub repo: awslabs/autogluon
from google.colab import drive
drive.mount('/content/drive') # Add My Drive/<>
import os
os.chdir('drive/My Drive')
os.chdir('Books_Writings/NLPBook/')
Mounted at /content/drive
%%capture
# %pylab inline
import pandas as pd
import os
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
27.1. Use AutoGluon Tabular on News Dataset#
We need to first install Meta’s PyTorch framework and then install AutoGluon, which runs on top of PyTorch. This is an extensive installation, and will take some time.
AutoGluon installation instructions: https://auto.gluon.ai/stable/install.html
%%time
!pip install -U pip
!pip install -U setuptools wheel
# !pip install -U uv
# CPU version of pytorch has smaller footprint - see installation instructions in
# pytorch documentation - https://pytorch.org/get-started/locally/
# !uv pip install torch==2.3.1 torchvision==0.18.1 --index-url https://download.pytorch.org/whl/cpu --system
# !uv pip install autogluon --system
# !pip install autogluon
!pip install autogluon --extra-index-url https://download.pytorch.org/whl/cpu
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Building wheels for collected packages: nvidia-ml-py3, seqeval
DEPRECATION: Building 'nvidia-ml-py3' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'nvidia-ml-py3'. Discussion can be found at https://github.com/pypa/pip/issues/6334
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Installing collected packages: py-spy, opencensus-context, nvidia-ml-py3, distlib, colorful, appdirs, virtualenv, tensorboardX, pytesseract, pycryptodome, pdf2image, ordered-set, openxlab, loguru, lightning-utilities, jmespath, fs, coreforecast, colorlog, colorama, window-ops, torch, model-index, einx, botocore, utilsforecast, triad, torchvision, torchmetrics, tokenizers, seqeval, s3transfer, pytorch-metric-learning, optuna, opendatalab, jsonschema, gluonts, catboost, aiohttp_cors, transformers, timm, ray, pytorch-lightning, openmim, opencensus, nlpaug, mlforecast, boto3, adagio, lightning, fugue, evaluate, autogluon.common, statsforecast, autogluon.features, autogluon.core, autogluon.tabular, autogluon.multimodal, autogluon.timeseries, autogluon
Attempting uninstall: torch
Found existing installation: torch 2.8.0+cu126
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Found existing installation: transformers 4.56.1
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Found existing installation: timm 1.0.19
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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 60/60 [autogluon]
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
torchaudio 2.8.0+cu126 requires torch==2.8.0, but you have torch 2.7.1+cpu which is incompatible.
Successfully installed adagio-0.2.6 aiohttp_cors-0.8.1 appdirs-1.4.4 autogluon-1.4.0 autogluon.common-1.4.0 autogluon.core-1.4.0 autogluon.features-1.4.0 autogluon.multimodal-1.4.0 autogluon.tabular-1.4.0 autogluon.timeseries-1.4.0 boto3-1.40.44 botocore-1.40.44 catboost-1.2.8 colorama-0.4.6 colorful-0.5.7 colorlog-6.9.0 coreforecast-0.0.16 distlib-0.4.0 einx-0.3.0 evaluate-0.4.6 fs-2.4.16 fugue-0.9.1 gluonts-0.16.2 jmespath-1.0.1 jsonschema-4.23.0 lightning-2.5.5 lightning-utilities-0.15.2 loguru-0.7.3 mlforecast-0.14.0 model-index-0.1.11 nlpaug-1.1.11 nvidia-ml-py3-7.352.0 opencensus-0.11.4 opencensus-context-0.1.3 opendatalab-0.0.10 openmim-0.3.9 openxlab-0.0.11 optuna-4.5.0 ordered-set-4.1.0 pdf2image-1.17.0 py-spy-0.4.1 pycryptodome-3.23.0 pytesseract-0.3.13 pytorch-lightning-2.5.5 pytorch-metric-learning-2.8.1 ray-2.44.1 s3transfer-0.14.0 seqeval-1.2.2 statsforecast-2.0.1 tensorboardX-2.6.4 timm-1.0.3 tokenizers-0.21.4 torch-2.7.1+cpu torchmetrics-1.7.4 torchvision-0.22.1+cpu transformers-4.49.0 triad-0.9.8 utilsforecast-0.2.11 virtualenv-20.34.0 window-ops-0.0.15
CPU times: user 5.39 s, sys: 1.27 s, total: 6.67 s
Wall time: 2min 24s
from autogluon.tabular import TabularPredictor
import pandas as pd
from sklearn.model_selection import train_test_split
# Read data
df = pd.read_csv('NLP_data/Sentences_AllAgree.txt', sep=".@", header=None, engine='python', encoding = "ISO-8859-1") # Finbert data
# df = pd.read_csv('NLP_data/Sentences_AllAgree.txt', sep=".@", header=None, engine='python', encoding = "utf-8") # Finbert data
# tmp = pd.read_csv('NLP_data/Sentences_75Agree.txt', sep=".@", header=None, engine='python')
# df = pd.concat([df,tmp])
# tmp = pd.read_csv('NLP_data/Sentences_66Agree.txt', sep=".@", header=None, engine='python')
# df = pd.concat([df,tmp])
# tmp = pd.read_csv('NLP_data/Sentences_50Agree.txt', sep=".@", header=None, engine='python')
# df = pd.concat([df,tmp])
df.columns = ["Text","Label"]
print(df.shape)
df.head()
(2264, 2)
| Text | Label | |
|---|---|---|
| 0 | According to Gran , the company has no plans t... | neutral |
| 1 | For the last quarter of 2010 , Componenta 's n... | positive |
| 2 | In the third quarter of 2010 , net sales incre... | positive |
| 3 | Operating profit rose to EUR 13.1 mn from EUR ... | positive |
| 4 | Operating profit totalled EUR 21.1 mn , up fro... | positive |
import seaborn as sns
import matplotlib.pyplot as plt
sns.countplot(x='Label', data=df)
plt.show()
27.2. Fit the model#
The next few lines of code are all that are needed to train the model. It is remarkable in its parsimony!
The vectorization of the text adjusts the size of the vocabulary so that it uses the available memory efficiently.
!pip install dask[dataframe] --quiet
%%time
#TRAIN THE MODEL
train_data, test_data = train_test_split(df, test_size=0.3, random_state=42)
print("Train size =",train_data.shape," | Test size =",test_data.shape)
predictor = TabularPredictor(label='Label').fit(train_data=train_data) #, hyperparameters='multimodal')
# predictor = task.fit(train_data=train_data, label='Label')
performance = predictor.evaluate(train_data)
No path specified. Models will be saved in: "AutogluonModels/ag-20251002_222432"
Verbosity: 2 (Standard Logging)
=================== System Info ===================
AutoGluon Version: 1.4.0
Python Version: 3.12.11
Operating System: Linux
Platform Machine: x86_64
Platform Version: #1 SMP PREEMPT_DYNAMIC Sat Sep 6 09:54:41 UTC 2025
CPU Count: 2
Memory Avail: 11.37 GB / 12.67 GB (89.7%)
Disk Space Avail: 67.81 GB / 112.64 GB (60.2%)
===================================================
No presets specified! To achieve strong results with AutoGluon, it is recommended to use the available presets. Defaulting to `'medium'`...
Recommended Presets (For more details refer to https://auto.gluon.ai/stable/tutorials/tabular/tabular-essentials.html#presets):
presets='extreme' : New in v1.4: Massively better than 'best' on datasets <30000 samples by using new models meta-learned on https://tabarena.ai: TabPFNv2, TabICL, Mitra, and TabM. Absolute best accuracy. Requires a GPU. Recommended 64 GB CPU memory and 32+ GB GPU memory.
presets='best' : Maximize accuracy. Recommended for most users. Use in competitions and benchmarks.
presets='high' : Strong accuracy with fast inference speed.
presets='good' : Good accuracy with very fast inference speed.
presets='medium' : Fast training time, ideal for initial prototyping.
Using hyperparameters preset: hyperparameters='default'
Train size = (1584, 2) | Test size = (680, 2)
Beginning AutoGluon training ...
AutoGluon will save models to "/content/drive/MyDrive/Books_Writings/NLPBook/AutogluonModels/ag-20251002_222432"
Train Data Rows: 1584
Train Data Columns: 1
Label Column: Label
AutoGluon infers your prediction problem is: 'multiclass' (because dtype of label-column == object).
3 unique label values: ['neutral', 'positive', 'negative']
If 'multiclass' 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'])
Problem Type: multiclass
Preprocessing data ...
Train Data Class Count: 3
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
Available Memory: 11611.41 MB
Train Data (Original) Memory Usage: 0.26 MB (0.0% of available memory)
Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
Stage 1 Generators:
Fitting AsTypeFeatureGenerator...
Stage 2 Generators:
Fitting FillNaFeatureGenerator...
Stage 3 Generators:
Fitting CategoryFeatureGenerator...
Fitting CategoryMemoryMinimizeFeatureGenerator...
Fitting TextSpecialFeatureGenerator...
Fitting BinnedFeatureGenerator...
Fitting DropDuplicatesFeatureGenerator...
Fitting TextNgramFeatureGenerator...
Fitting CountVectorizer for text features: ['Text']
CountVectorizer fit with vocabulary size = 186
Stage 4 Generators:
Fitting DropUniqueFeatureGenerator...
Stage 5 Generators:
Fitting DropDuplicatesFeatureGenerator...
Types of features in original data (raw dtype, special dtypes):
('object', ['text']) : 1 | ['Text']
Types of features in processed data (raw dtype, special dtypes):
('category', ['text_as_category']) : 1 | ['Text']
('int', ['binned', 'text_special']) : 20 | ['Text.char_count', 'Text.word_count', 'Text.capital_ratio', 'Text.lower_ratio', 'Text.digit_ratio', ...]
('int', ['text_ngram']) : 180 | ['__nlp__.000', '__nlp__.10', '__nlp__.11', '__nlp__.12', '__nlp__.20', ...]
6.2s = Fit runtime
1 features in original data used to generate 201 features in processed data.
Train Data (Processed) Memory Usage: 0.58 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 6.24s ...
AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'
To change this, specify the eval_metric parameter of Predictor()
Automatically generating train/validation split with holdout_frac=0.2, Train Rows: 1267, Val Rows: 317
User-specified model hyperparameters to be fit:
{
'NN_TORCH': [{}],
'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, {'learning_rate': 0.03, 'num_leaves': 128, 'feature_fraction': 0.9, 'min_data_in_leaf': 3, 'ag_args': {'name_suffix': 'Large', 'priority': 0, 'hyperparameter_tune_kwargs': None}}],
'CAT': [{}],
'XGB': [{}],
'FASTAI': [{}],
'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
}
Fitting 11 L1 models, fit_strategy="sequential" ...
Fitting model: NeuralNetFastAI ...
Fitting with cpus=1, gpus=0, mem=0.0/11.2 GB
0.7445 = Validation score (accuracy)
11.55s = Training runtime
0.01s = Validation runtime
Fitting model: LightGBMXT ...
Fitting with cpus=1, gpus=0, mem=0.0/10.8 GB
0.8801 = Validation score (accuracy)
11.55s = Training runtime
0.02s = Validation runtime
Fitting model: LightGBM ...
Fitting with cpus=1, gpus=0, mem=0.0/10.5 GB
0.8549 = Validation score (accuracy)
1.76s = Training runtime
0.01s = Validation runtime
Fitting model: RandomForestGini ...
Fitting with cpus=2, gpus=0
0.8612 = Validation score (accuracy)
2.47s = Training runtime
0.1s = Validation runtime
Fitting model: RandomForestEntr ...
Fitting with cpus=2, gpus=0
0.8549 = Validation score (accuracy)
1.18s = Training runtime
0.1s = Validation runtime
Fitting model: CatBoost ...
Fitting with cpus=1, gpus=0
0.858 = Validation score (accuracy)
5.72s = Training runtime
0.01s = Validation runtime
Fitting model: ExtraTreesGini ...
Fitting with cpus=2, gpus=0
0.8549 = Validation score (accuracy)
1.1s = Training runtime
0.1s = Validation runtime
Fitting model: ExtraTreesEntr ...
Fitting with cpus=2, gpus=0
0.8612 = Validation score (accuracy)
1.27s = Training runtime
0.1s = Validation runtime
Fitting model: XGBoost ...
Fitting with cpus=1, gpus=0
0.858 = Validation score (accuracy)
2.68s = Training runtime
0.02s = Validation runtime
Fitting model: NeuralNetTorch ...
Fitting with cpus=1, gpus=0, mem=0.0/10.3 GB
0.7413 = Validation score (accuracy)
9.39s = Training runtime
0.01s = Validation runtime
Fitting model: LightGBMLarge ...
Fitting with cpus=1, gpus=0, mem=0.2/10.3 GB
0.8644 = Validation score (accuracy)
2.31s = Training runtime
0.01s = Validation runtime
Fitting model: WeightedEnsemble_L2 ...
Ensemble Weights: {'LightGBMXT': 1.0}
0.8801 = Validation score (accuracy)
0.1s = Training runtime
0.0s = Validation runtime
AutoGluon training complete, total runtime = 59.05s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 15697.3 rows/s (317 batch size)
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("/content/drive/MyDrive/Books_Writings/NLPBook/AutogluonModels/ag-20251002_222432")
CPU times: user 45.7 s, sys: 2.2 s, total: 47.9 s
Wall time: 1min 1s
# TEST OUT-OF-SAMPLE
y_test = test_data['Label']
test_data_nolabel = test_data.drop(labels=['Label'],axis=1)
y_pred = predictor.predict(test_data_nolabel)
y_prob = predictor.predict(test_data_nolabel)
perf = predictor.evaluate_predictions(y_true=y_test, y_pred=y_pred, auxiliary_metrics=True)
print(perf)
{'accuracy': 0.8720588235294118, 'balanced_accuracy': np.float64(0.7908290922121237), 'mcc': np.float64(0.7572923133729487)}
predictor.leaderboard(test_data, silent=True)
| model | score_test | score_val | eval_metric | pred_time_test | pred_time_val | fit_time | pred_time_test_marginal | pred_time_val_marginal | fit_time_marginal | stack_level | can_infer | fit_order | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LightGBMXT | 0.872059 | 0.880126 | accuracy | 0.036902 | 0.019261 | 11.553508 | 0.036902 | 0.019261 | 11.553508 | 1 | True | 2 |
| 1 | WeightedEnsemble_L2 | 0.872059 | 0.880126 | accuracy | 0.042148 | 0.020195 | 11.650759 | 0.005246 | 0.000933 | 0.097251 | 2 | True | 12 |
| 2 | LightGBM | 0.867647 | 0.854890 | accuracy | 0.021719 | 0.011121 | 1.761025 | 0.021719 | 0.011121 | 1.761025 | 1 | True | 3 |
| 3 | XGBoost | 0.854412 | 0.858044 | accuracy | 0.077250 | 0.022649 | 2.678355 | 0.077250 | 0.022649 | 2.678355 | 1 | True | 9 |
| 4 | CatBoost | 0.841176 | 0.858044 | accuracy | 0.017586 | 0.010405 | 5.719173 | 0.017586 | 0.010405 | 5.719173 | 1 | True | 6 |
| 5 | ExtraTreesEntr | 0.839706 | 0.861199 | accuracy | 0.146512 | 0.097935 | 1.272938 | 0.146512 | 0.097935 | 1.272938 | 1 | True | 8 |
| 6 | RandomForestGini | 0.838235 | 0.861199 | accuracy | 0.146591 | 0.098120 | 2.466754 | 0.146591 | 0.098120 | 2.466754 | 1 | True | 4 |
| 7 | ExtraTreesGini | 0.833824 | 0.854890 | accuracy | 0.145274 | 0.099282 | 1.095345 | 0.145274 | 0.099282 | 1.095345 | 1 | True | 7 |
| 8 | LightGBMLarge | 0.830882 | 0.864353 | accuracy | 0.027166 | 0.010562 | 2.308693 | 0.027166 | 0.010562 | 2.308693 | 1 | True | 11 |
| 9 | RandomForestEntr | 0.827941 | 0.854890 | accuracy | 0.149742 | 0.097670 | 1.181473 | 0.149742 | 0.097670 | 1.181473 | 1 | True | 5 |
| 10 | NeuralNetTorch | 0.683824 | 0.741325 | accuracy | 0.023454 | 0.014523 | 9.388918 | 0.023454 | 0.014523 | 9.388918 | 1 | True | 10 |
| 11 | NeuralNetFastAI | 0.675000 | 0.744479 | accuracy | 0.025833 | 0.014433 | 11.552325 | 0.025833 | 0.014433 | 11.552325 | 1 | True | 1 |
27.3. Metrics#
https://en.wikipedia.org/wiki/Receiver_operating_characteristic
https://srdas.github.io/MLBook2/3_MachineLearningOverview.html
https://srdas.github.io/MLBook2/3_MachineLearningOverview.html#ROC-and-AUC
27.4. Movie Reviews, one more time, with AG-Tabular#
train_data = pd.read_csv("NLP_data/movie_review_train.txt", sep = " ", header=None)
test_data = pd.read_csv("NLP_data/movie_review_test.txt", sep = " ", header=None)
train_data.columns = ['Label','Text']
test_data.columns = ['Label','Text']
print(train_data.shape, test_data.shape)
train_data.head()
(4001, 2) (1000, 2)
| Label | Text | |
|---|---|---|
| 0 | __label__0 | Homelessness (or Houselessness as George Carlin stated) has been an issue for years but never a plan to help those on the street that were once considered human who did everything from going to school, work, or vote for the matter. Most people think of the homeless as just a lost cause while worrying about things such as racism, the war on Iraq, pressuring kids to succeed, technology, the elections, inflation, or worrying if they'll be next to end up on the streets.<br /><br />But what if you were given a bet to live on the streets for a month without the luxuries you once had from a home,... |
| 1 | __label__1 | This film lacked something I couldn't put my finger on at first: charisma on the part of the leading actress. This inevitably translated to lack of chemistry when she shared the screen with her leading man. Even the romantic scenes came across as being merely the actors at play. It could very well have been the director who miscalculated what he needed from the actors. I just don't know.<br /><br />But could it have been the screenplay? Just exactly who was the chef in love with? He seemed more enamored of his culinary skills and restaurant, and ultimately of himself and his youthful explo... |
| 2 | __label__1 | \"It appears that many critics find the idea of a Woody Allen drama unpalatable.\" And for good reason: they are unbearably wooden and pretentious imitations of Bergman. And let's not kid ourselves: critics were mostly supportive of Allen's Bergman pretensions, Allen's whining accusations to the contrary notwithstanding. What I don't get is this: why was Allen generally applauded for his originality in imitating Bergman, but the contemporaneous Brian DePalma was excoriated for \"ripping off\" Hitchcock in his suspense/horror films? In Robin Wood's view, it's a strange form of cultural snob... |
| 3 | __label__0 | This isn't the comedic Robin Williams, nor is it the quirky/insane Robin Williams of recent thriller fame. This is a hybrid of the classic drama without over-dramatization, mixed with Robin's new love of the thriller. But this isn't a thriller, per se. This is more a mystery/suspense vehicle through which Williams attempts to locate a sick boy and his keeper.<br /><br />Also starring Sandra Oh and Rory Culkin, this Suspense Drama plays pretty much like a news report, until William's character gets close to achieving his goal.<br /><br />I must say that I was highly entertained, though this... |
| 4 | __label__1 | I don't know who to blame, the timid writers or the clueless director. It seemed to be one of those movies where so much was paid to the stars (Angie, Charlie, Denise, Rosanna and Jon) that there wasn't enough left to really make a movie. This could have been very entertaining, but there was a veil of timidity, even cowardice, that hung over each scene. Since it got an R rating anyway why was the ubiquitous bubble bath scene shot with a 70-year-old woman and not Angie Harmon? Why does Sheen sleepwalk through potentially hot relationships WITH TWO OF THE MOST BEAUTIFUL AND SEXY ACTRESSES in... |
%%time
#TRAIN THE MODEL
print("Train size =",train_data.shape," | Test size =",test_data.shape)
predictor = TabularPredictor(label='Label').fit(train_data=train_data) #, hyperparameters='multimodal')
performance = predictor.evaluate(train_data)
No path specified. Models will be saved in: "AutogluonModels/ag-20251002_222607"
Verbosity: 2 (Standard Logging)
=================== System Info ===================
AutoGluon Version: 1.4.0
Python Version: 3.12.11
Operating System: Linux
Platform Machine: x86_64
Platform Version: #1 SMP PREEMPT_DYNAMIC Sat Sep 6 09:54:41 UTC 2025
CPU Count: 2
Memory Avail: 9.97 GB / 12.67 GB (78.7%)
Disk Space Avail: 67.71 GB / 112.64 GB (60.1%)
===================================================
No presets specified! To achieve strong results with AutoGluon, it is recommended to use the available presets. Defaulting to `'medium'`...
Recommended Presets (For more details refer to https://auto.gluon.ai/stable/tutorials/tabular/tabular-essentials.html#presets):
presets='extreme' : New in v1.4: Massively better than 'best' on datasets <30000 samples by using new models meta-learned on https://tabarena.ai: TabPFNv2, TabICL, Mitra, and TabM. Absolute best accuracy. Requires a GPU. Recommended 64 GB CPU memory and 32+ GB GPU memory.
presets='best' : Maximize accuracy. Recommended for most users. Use in competitions and benchmarks.
presets='high' : Strong accuracy with fast inference speed.
presets='good' : Good accuracy with very fast inference speed.
presets='medium' : Fast training time, ideal for initial prototyping.
Using hyperparameters preset: hyperparameters='default'
Train size = (4001, 2) | Test size = (1000, 2)
Beginning AutoGluon training ...
AutoGluon will save models to "/content/drive/MyDrive/Books_Writings/NLPBook/AutogluonModels/ag-20251002_222607"
Train Data Rows: 4001
Train Data Columns: 1
Label Column: Label
AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).
2 unique label values: ['__label__0', '__label__1']
If 'binary' 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'])
Problem Type: binary
Preprocessing data ...
Selected class <--> label mapping: class 1 = __label__1, class 0 = __label__0
Note: For your binary classification, AutoGluon arbitrarily selected which label-value represents positive (__label__1) vs negative (__label__0) class.
To explicitly set the positive_class, either rename classes to 1 and 0, or specify positive_class in Predictor init.
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
Available Memory: 10218.01 MB
Train Data (Original) Memory Usage: 5.22 MB (0.1% of available memory)
Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
Stage 1 Generators:
Fitting AsTypeFeatureGenerator...
Stage 2 Generators:
Fitting FillNaFeatureGenerator...
Stage 3 Generators:
Fitting CategoryFeatureGenerator...
Fitting CategoryMemoryMinimizeFeatureGenerator...
Fitting TextSpecialFeatureGenerator...
Fitting BinnedFeatureGenerator...
Fitting DropDuplicatesFeatureGenerator...
Fitting TextNgramFeatureGenerator...
Fitting CountVectorizer for text features: ['Text']
CountVectorizer fit with vocabulary size = 5515
Stage 4 Generators:
Fitting DropUniqueFeatureGenerator...
Stage 5 Generators:
Fitting DropDuplicatesFeatureGenerator...
Types of features in original data (raw dtype, special dtypes):
('object', ['text']) : 1 | ['Text']
Types of features in processed data (raw dtype, special dtypes):
('category', ['text_as_category']) : 1 | ['Text']
('int', ['binned', 'text_special']) : 30 | ['Text.char_count', 'Text.word_count', 'Text.capital_ratio', 'Text.lower_ratio', 'Text.digit_ratio', ...]
('int', ['text_ngram']) : 5437 | ['__nlp__.000', '__nlp__.10', '__nlp__.10 10', '__nlp__.100', '__nlp__.11', ...]
39.9s = Fit runtime
1 features in original data used to generate 5468 features in processed data.
Train Data (Processed) Memory Usage: 41.61 MB (0.4% of available memory)
Data preprocessing and feature engineering runtime = 41.41s ...
AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'
To change this, specify the eval_metric parameter of Predictor()
Automatically generating train/validation split with holdout_frac=0.12496875781054737, Train Rows: 3501, Val Rows: 500
User-specified model hyperparameters to be fit:
{
'NN_TORCH': [{}],
'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, {'learning_rate': 0.03, 'num_leaves': 128, 'feature_fraction': 0.9, 'min_data_in_leaf': 3, 'ag_args': {'name_suffix': 'Large', 'priority': 0, 'hyperparameter_tune_kwargs': None}}],
'CAT': [{}],
'XGB': [{}],
'FASTAI': [{}],
'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
}
Fitting 11 L1 models, fit_strategy="sequential" ...
Fitting model: LightGBMXT ...
Fitting with cpus=1, gpus=0, mem=1.2/10.0 GB
0.88 = Validation score (accuracy)
13.57s = Training runtime
0.05s = Validation runtime
Fitting model: LightGBM ...
Fitting with cpus=1, gpus=0, mem=1.2/10.0 GB
0.868 = Validation score (accuracy)
15.27s = Training runtime
0.07s = Validation runtime
Fitting model: RandomForestGini ...
Fitting with cpus=2, gpus=0
0.856 = Validation score (accuracy)
16.56s = Training runtime
0.09s = Validation runtime
Fitting model: RandomForestEntr ...
Fitting with cpus=2, gpus=0
0.846 = Validation score (accuracy)
14.79s = Training runtime
0.11s = Validation runtime
Fitting model: CatBoost ...
Fitting with cpus=1, gpus=0, mem=5.3/10.0 GB
0.858 = Validation score (accuracy)
48.8s = Training runtime
0.19s = Validation runtime
Fitting model: ExtraTreesGini ...
Fitting with cpus=2, gpus=0
0.842 = Validation score (accuracy)
16.65s = Training runtime
0.12s = Validation runtime
Fitting model: ExtraTreesEntr ...
Fitting with cpus=2, gpus=0
0.858 = Validation score (accuracy)
16.75s = Training runtime
0.11s = Validation runtime
Fitting model: NeuralNetFastAI ...
Fitting with cpus=1, gpus=0, mem=0.4/9.9 GB
0.62 = Validation score (accuracy)
5.1s = Training runtime
0.01s = Validation runtime
Fitting model: XGBoost ...
Fitting with cpus=1, gpus=0, mem=2.3/9.9 GB
0.85 = Validation score (accuracy)
143.61s = Training runtime
0.05s = Validation runtime
Fitting model: NeuralNetTorch ...
Fitting with cpus=1, gpus=0, mem=0.2/9.9 GB
0.63 = Validation score (accuracy)
6.4s = Training runtime
0.03s = Validation runtime
Fitting model: LightGBMLarge ...
Fitting with cpus=1, gpus=0, mem=4.2/9.9 GB
0.842 = Validation score (accuracy)
46.52s = Training runtime
0.06s = Validation runtime
Fitting model: WeightedEnsemble_L2 ...
Ensemble Weights: {'LightGBMXT': 1.0}
0.88 = Validation score (accuracy)
0.09s = Training runtime
0.0s = Validation runtime
AutoGluon training complete, total runtime = 388.3s ... Best model: WeightedEnsemble_L2 | Estimated inference throughput: 9391.6 rows/s (500 batch size)
Disabling decision threshold calibration for metric `accuracy` due to having fewer than 10000 rows of validation data for calibration, to avoid overfitting (500 rows).
`accuracy` is generally not improved through threshold calibration. Force calibration via specifying `calibrate_decision_threshold=True`.
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("/content/drive/MyDrive/Books_Writings/NLPBook/AutogluonModels/ag-20251002_222607")
CPU times: user 6min 55s, sys: 3.49 s, total: 6min 58s
Wall time: 6min 33s
%%time
# TEST OUT-OF-SAMPLE
y_test = test_data['Label']
test_data_nolabel = test_data.drop(labels=['Label'],axis=1)
y_pred = predictor.predict(test_data_nolabel)
y_prob = predictor.predict_proba(test_data_nolabel)
perf = predictor.evaluate_predictions(y_true=y_test, y_pred=y_pred, auxiliary_metrics=True)
print(perf)
{'accuracy': 0.842, 'balanced_accuracy': np.float64(0.8420313681254725), 'mcc': np.float64(0.6843694036793613), 'f1': 0.8397565922920892, 'precision': 0.8536082474226804, 'recall': 0.8263473053892215}
CPU times: user 2.63 s, sys: 8.64 ms, total: 2.64 s
Wall time: 2.75 s
#ROC, AUC
import numpy as np
from sklearn.metrics import roc_curve, auc
y_score = [1 if y_prob.loc[i][1]>y_prob.loc[i][0] else 0 for i in range(len(y_prob)) ]
y_true = np.array([1 if j=="__label__1" else 0 for j in y_test])
fpr, tpr, _ = roc_curve(y_true, y_score)
plt.title('ROC curve')
plt.xlabel('FPR (Precision)')
plt.ylabel('TPR (Recall)')
plt.plot(fpr,tpr)
plt.plot((0,1), ls='dashed',color='black')
plt.show()
print('Area under curve (AUC): ', auc(fpr,tpr))
/tmp/ipython-input-3953508519.py:4: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
y_score = [1 if y_prob.loc[i][1]>y_prob.loc[i][0] else 0 for i in range(len(y_prob)) ]
Area under curve (AUC): 0.8420313681254725
predictor.leaderboard(test_data, silent=True)
| model | score_test | score_val | eval_metric | pred_time_test | pred_time_val | fit_time | pred_time_test_marginal | pred_time_val_marginal | fit_time_marginal | stack_level | can_infer | fit_order | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ExtraTreesGini | 0.848 | 0.842 | accuracy | 0.196469 | 0.115682 | 16.648332 | 0.196469 | 0.115682 | 16.648332 | 1 | True | 6 |
| 1 | LightGBMXT | 0.842 | 0.880 | accuracy | 0.149253 | 0.052402 | 13.573560 | 0.149253 | 0.052402 | 13.573560 | 1 | True | 1 |
| 2 | WeightedEnsemble_L2 | 0.842 | 0.880 | accuracy | 0.155291 | 0.053239 | 13.666825 | 0.006038 | 0.000837 | 0.093265 | 2 | True | 12 |
| 3 | ExtraTreesEntr | 0.841 | 0.858 | accuracy | 0.199272 | 0.106071 | 16.748843 | 0.199272 | 0.106071 | 16.748843 | 1 | True | 7 |
| 4 | LightGBM | 0.833 | 0.868 | accuracy | 0.156100 | 0.072036 | 15.272505 | 0.156100 | 0.072036 | 15.272505 | 1 | True | 2 |
| 5 | RandomForestGini | 0.828 | 0.856 | accuracy | 0.218809 | 0.092718 | 16.560973 | 0.218809 | 0.092718 | 16.560973 | 1 | True | 3 |
| 6 | XGBoost | 0.819 | 0.850 | accuracy | 0.145802 | 0.045433 | 143.609277 | 0.145802 | 0.045433 | 143.609277 | 1 | True | 9 |
| 7 | CatBoost | 0.819 | 0.858 | accuracy | 0.211468 | 0.185300 | 48.795847 | 0.211468 | 0.185300 | 48.795847 | 1 | True | 5 |
| 8 | RandomForestEntr | 0.815 | 0.846 | accuracy | 0.187284 | 0.105884 | 14.788632 | 0.187284 | 0.105884 | 14.788632 | 1 | True | 4 |
| 9 | LightGBMLarge | 0.805 | 0.842 | accuracy | 0.150905 | 0.056236 | 46.520023 | 0.150905 | 0.056236 | 46.520023 | 1 | True | 11 |
| 10 | NeuralNetFastAI | 0.578 | 0.620 | accuracy | 0.028617 | 0.014310 | 5.095802 | 0.028617 | 0.014310 | 5.095802 | 1 | True | 8 |
| 11 | NeuralNetTorch | 0.564 | 0.630 | accuracy | 0.027634 | 0.027619 | 6.403214 | 0.027634 | 0.027619 | 6.403214 | 1 | True | 10 |
The hyperparameter option multimodal below will also do hyperparameter tuning, which takes considerable time, so wait for it to finish. Maybe best to skip over this code segment.
%%time
# predictor = TabularPredictor(label='Label').fit(train_data=train_data, hyperparameters='multimodal')
# y_test = test_data['Label']
# test_data_nolabel = test_data.drop(labels=['Label'],axis=1)
# y_pred = predictor.predict(test_data_nolabel)
# perf = predictor.evaluate_predictions(y_true=y_test, y_pred=y_pred, auxiliary_metrics=True)
CPU times: user 4 µs, sys: 0 ns, total: 4 µs
Wall time: 6.91 µs
27.5. Multimodal Extension#
AutoGluon can also handle images in addition to text and here is an example from their library from: https://auto.gluon.ai/stable/tutorials/multimodal/multimodal_prediction/beginner_multimodal.html
import os
import numpy as np
import warnings
warnings.filterwarnings('ignore')
np.random.seed(123)
%%time
download_dir = './ag_automm_tutorial'
zip_file = 'https://automl-mm-bench.s3.amazonaws.com/petfinder_for_tutorial.zip'
from autogluon.core.utils.loaders import load_zip
load_zip.unzip(zip_file, unzip_dir=download_dir)
Unzipping ./ag_automm_tutorial/file.zip to ./ag_automm_tutorial
CPU times: user 675 ms, sys: 258 ms, total: 932 ms
Wall time: 6min 53s
import pandas as pd
download_dir = './ag_automm_tutorial'
dataset_path = download_dir + '/petfinder_for_tutorial'
train_data = pd.read_csv(f'{dataset_path}/train.csv', index_col=0)
test_data = pd.read_csv(f'{dataset_path}/test.csv', index_col=0)
label_col = 'AdoptionSpeed'
train_data.head()
| Type | Name | Age | Breed1 | Breed2 | Gender | Color1 | Color2 | Color3 | MaturitySize | ... | Quantity | Fee | State | RescuerID | VideoAmt | Description | PetID | PhotoAmt | AdoptionSpeed | Images | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2 | Yumi Hamasaki | 4 | 292 | 265 | 2 | 1 | 5 | 7 | 2 | ... | 1 | 0 | 41326 | bcc4e1b9557a8b3aaf545ea8e6e86991 | 0 | I rescued Yumi Hamasaki at a food stall far away in Kelantan. At that time i was on my way back to KL, she was suffer from stomach problem and looking very2 sick.. I send her to vet & get the treatment + vaccinated and right now she's very2 healthy.. About yumi : - love to sleep with ppl - she will keep on meowing if she's hugry - very2 active, always seeking for people to accompany her playing - well trained (poo+pee in her own potty) - easy to bathing - I only feed her with these brands : IAMS, Kittenbites, Pro-formance Reason why i need someone to adopt Yumi: I just married and need to ... | 7d7a39d71 | 3.0 | 0 | images/7d7a39d71-1.jpg |
| 1 | 2 | Nene/ Kimie | 12 | 285 | 0 | 2 | 5 | 6 | 7 | 2 | ... | 1 | 0 | 41326 | f0450bf0efe0fa3ff9321d0b827b1237 | 0 | Has adopted by a friend with new pet name Kimie | 0e107c82f | 3.0 | 0 | images/0e107c82f-1.jpg |
| 2 | 2 | Mattie | 12 | 266 | 0 | 2 | 1 | 7 | 0 | 2 | ... | 1 | 0 | 41401 | 9b52af6d48a4521fd01d4028eb5879a3 | 0 | I rescued Mattie with a broken leg. After surgery with pin inserted in her leg, she's made a full recovery. | 1a8fd6707 | 5.0 | 0 | images/1a8fd6707-1.jpg |
| 3 | 1 | NaN | 1 | 189 | 307 | 2 | 1 | 2 | 0 | 2 | ... | 1 | 0 | 41401 | 88da1210e021a5cf43480b074778f3bc | 0 | She born on 30 September . I really hope the animal lovers can adopt her. | bca8b44ae | 3.0 | 0 | images/bca8b44ae-1.jpg |
| 4 | 2 | Coco | 6 | 276 | 285 | 2 | 2 | 4 | 7 | 2 | ... | 1 | 100 | 41326 | 227d7b1bcfaffb5f9882bf57b5ee8fab | 0 | Calico Tame and easy going Diet RC Kitten Supplement - brewer yeast + VCO *11.7.17 - Coco had found her new home. | 2def67952 | 1.0 | 0 | images/2def67952-1.jpg |
5 rows × 25 columns
# Expand image paths for loading in training
image_col = 'Images'
train_data[image_col] = train_data[image_col].apply(lambda ele: ele.split(';')[0]) # Use the first image for a quick tutorial
test_data[image_col] = test_data[image_col].apply(lambda ele: ele.split(';')[0])
def path_expander(path, base_folder):
path_l = path.split(';')
return ';'.join([os.path.abspath(os.path.join(base_folder, path)) for path in path_l])
train_data[image_col] = train_data[image_col].apply(lambda ele: path_expander(ele, base_folder=dataset_path))
test_data[image_col] = test_data[image_col].apply(lambda ele: path_expander(ele, base_folder=dataset_path))
train_data[image_col].iloc[0]
'/content/drive/MyDrive/Books_Writings/NLPBook/ag_automm_tutorial/petfinder_for_tutorial/images/7d7a39d71-1.jpg'
example_row = train_data.iloc[0]
example_row
| 0 | |
|---|---|
| Type | 2 |
| Name | Yumi Hamasaki |
| Age | 4 |
| Breed1 | 292 |
| Breed2 | 265 |
| Gender | 2 |
| Color1 | 1 |
| Color2 | 5 |
| Color3 | 7 |
| MaturitySize | 2 |
| FurLength | 2 |
| Vaccinated | 1 |
| Dewormed | 3 |
| Sterilized | 2 |
| Health | 1 |
| Quantity | 1 |
| Fee | 0 |
| State | 41326 |
| RescuerID | bcc4e1b9557a8b3aaf545ea8e6e86991 |
| VideoAmt | 0 |
| Description | I rescued Yumi Hamasaki at a food stall far away in Kelantan. At that time i was on my way back to KL, she was suffer from stomach problem and looking very2 sick.. I send her to vet & get the treatment + vaccinated and right now she's very2 healthy.. About yumi : - love to sleep with ppl - she will keep on meowing if she's hugry - very2 active, always seeking for people to accompany her playing - well trained (poo+pee in her own potty) - easy to bathing - I only feed her with these brands : IAMS, Kittenbites, Pro-formance Reason why i need someone to adopt Yumi: I just married and need to ... |
| PetID | 7d7a39d71 |
| PhotoAmt | 3.0 |
| AdoptionSpeed | 0 |
| Images | /content/drive/MyDrive/Books_Writings/NLPBook/ag_automm_tutorial/petfinder_for_tutorial/images/7d7a39d71-1.jpg |
example_image = example_row[image_col]
from IPython.display import Image, display
pil_img = Image(filename=example_image)
display(pil_img)
%%time
from autogluon.multimodal import MultiModalPredictor
predictor = MultiModalPredictor(label=label_col)
predictor.fit(
train_data=train_data,
time_limit=120, # seconds
)
No path specified. Models will be saved in: "AutogluonModels/ag-20251002_224319"
=================== System Info ===================
AutoGluon Version: 1.4.0
Python Version: 3.12.11
Operating System: Linux
Platform Machine: x86_64
Platform Version: #1 SMP PREEMPT_DYNAMIC Sat Sep 6 09:54:41 UTC 2025
CPU Count: 2
Pytorch Version: 2.7.1+cpu
CUDA Version: CUDA is not available
GPU Count: 0
Memory Avail: 9.82 GB / 12.67 GB (77.5%)
Disk Space Avail: 67.49 GB / 112.64 GB (59.9%)
===================================================
AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).
2 unique label values: [np.int64(0), np.int64(1)]
If 'binary' 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 /content/drive/MyDrive/Books_Writings/NLPBook/AutogluonModels/ag-20251002_224319
```
INFO: Seed set to 0
GPU Count: 0
GPU Count to be Used: 0
INFO: GPU available: False, used: False
INFO: TPU available: False, using: 0 TPU cores
INFO: HPU available: False, using: 0 HPUs
INFO:
| Name | Type | Params | Mode
------------------------------------------------------------------
0 | model | MultimodalFusionMLP | 207 M | train
1 | validation_metric | BinaryAUROC | 0 | train
2 | loss_func | CrossEntropyLoss | 0 | train
------------------------------------------------------------------
207 M Trainable params
0 Non-trainable params
207 M Total params
828.307 Total estimated model params size (MB)
1171 Modules in train mode
0 Modules in eval mode
INFO: Time limit reached. Elapsed time is 0:02:08. Signaling Trainer to stop.
scores = predictor.evaluate(test_data, metrics=["roc_auc"])
scores
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
/tmp/ipython-input-2894174086.py in <cell line: 0>()
----> 1 scores = predictor.evaluate(test_data, metrics=["roc_auc"])
2 scores
NameError: name 'predictor' is not defined
predictions = predictor.predict(test_data.drop(columns=label_col))
print(predictions[:5])
print(test_data[label_col][:5])
probas = predictor.predict_proba(test_data.drop(columns=label_col))
probas[:5]