How to use AutoGluon for Kaggle competitions¶
This tutorial will teach you how to use AutoGluon to become a serious Kaggle competitor without writing lots of code. We first outline the general steps to use AutoGluon in Kaggle contests. Here, we assume the competition involves tabular data which are stored in one (or more) CSV files.
Run Bash command: pip install kaggle
Navigate to: https://www.kaggle.com/account and create an account (if necessary). Then , click on “Create New API Token” and move downloaded file to this location on your machine:
~/.kaggle/kaggle.json. For troubleshooting, see Kaggle API instructions.
To download data programmatically: Execute this Bash command in your terminal:
kaggle competitions download -c [COMPETITION]
Here, [COMPETITION] should be replaced by the name of the competition you wish to enter. Alternatively, you can download data manually: Just navigate to website of the Kaggle competition you wish to enter, click “Download All”, and accept the competition’s terms.
If the competition’s training data is comprised of multiple CSV files, use pandas to properly merge/join them into a single data table where rows = training examples, columns = features.
fit()on the resulting data table.
Load the test dataset from competition (again making the necessary merges/joins to ensure it is in the exact same format as the training data table), and then call autogluon
predict(). Subsequently use pandas.read_csv to load the competition’s
sample_submission.csvfile into a Dataframe, put the AutoGluon predictions in the right column of this Dataframe, and finally save it as a CSV file via pandas.to_csv. If the competition does not offer a sample submission file, you will need to create the submission file yourself by appropriately reformatting AutoGluon’s test predictions.
Submit your predictions via Bash command:
kaggle competitions submit -c [COMPETITION] -f [FILE] -m ["MESSAGE"]
Here, [COMPETITION] again is the competition’s name, [FILE] is the name of the CSV file you created with your predictions, and [“MESSAGE”] is a string message you want to record with this submitted entry. Alternatively, you can manually upload your file of predictions on the competition website.
Finally, navigate to competition leaderboard website to see how well your submission performed! It may take time for your submission to appear.
Below, we demonstrate how to do steps (4)-(6) in Python for a specific
This means you’ll need to run the above steps with
ieee-fraud-detection in each command. Here, we assume
you’ve already completed steps (1)-(3) and the data CSV files are
available on your computer. To begin step (4), we first load the
competition’s training data into Python:
import pandas as pd import numpy as np from autogluon.tabular import TabularPrediction as task from autogluon.tabular.metrics import roc_auc directory = '~/IEEEfraud/' # directory where you have downloaded the data CSV files from the competition label_column = 'isFraud' # name of target variable to predict in this competition eval_metric = 'roc_auc' # Optional: specify that competition evaluation metric is AUC output_directory = directory + 'AutoGluonModels/' # where to store trained models train_identity = pd.read_csv(directory+'train_identity.csv') train_transaction = pd.read_csv(directory+'train_transaction.csv')
Since the training data for this competition is comprised of multiple CSV files, we just first join them into a single large table (with rows = examples, columns = features) before applying AutoGluon:
train = pd.merge(train_transaction, train_identity, on='TransactionID', how='left') train_data = task.Dataset(df = train) # convert to AutoGluon dataset del train_identity, train_transaction, train # free unused memory
Note that a left-join on the
TransactionID key happened to be most
appropriate for this Kaggle competition, but for others involving
multiple training data files, you will likely need to use a different
join strategy (always consider this very carefully). Now that all our
training data resides within a single table, we can apply AutoGluon.
Below, we specify the
presets argument to maximize AutoGluon’s
predictive accuracy which usually requires that you run
longer time limits (3600s below should likely be increased in your run):
predictor = task.fit(train_data=train_data, label=label_column, output_directory=output_directory, eval_metric=eval_metric, presets='best_quality', verbosity=3, time_limits=3600) results = predictor.fit_summary()
Now, we use the trained AutoGluon Predictor to make predictions on the
competition’s test data. It is imperative that multiple test data files
are joined together in the exact same manner as the training data.
Because this competition is evaluated based on the AUC (Area under the
ROC curve) metric, we ask AutoGluon for predicted class-probabilities
rather than class predictions. In general, when to use
predict_proba will depend on the particular competition.
test_identity = pd.read_csv(directory+'test_identity.csv') test_transaction = pd.read_csv(directory+'test_transaction.csv') test = pd.merge(test_transaction, test_identity, on='TransactionID', how='left') # same join applied to training files test_data = task.Dataset(df = test) # convert to AutoGluon dataset del test_identity, test_transaction, test # free unused memory y_predproba = predictor.predict_proba(test_data) print(y_predproba[:5]) # some example predicted fraud-probabilities
When submitting predicted probabilities for classification competitions, it is imperative these correspond to the same class expected by Kaggle. For binary classification tasks, you can see which class AutoGluon’s predicted probabilities correspond to via:
positive_class = [label for label in predictor.class_labels if predictor.class_labels_internal_map[label]==1]
For multiclass classification tasks, you can see which classes AutoGluon’s predicted probabilities correspond to via:
predictor.class_labels # classes in this list correspond to columns of predict_proba() output
Alternatively, the following command should clarify which predicted-probability corresponds to which class:
y_predproba = predictor.predict_proba(test_data, as_pandas=True)
Now that we have made a prediction for each row in the test dataset, we can submit these predictions to Kaggle. Most Kaggle competitions provide a sample submission file, in which you can simply overwrite the sample predictions with your own as we do below:
submission = pd.read_csv(directory+'sample_submission.csv') submission['isFraud'] = y_predproba submission.head() submission.to_csv(directory+'my_submission.csv', index=False)
We have now completed steps (4)-(6) from the top of this tutorial. To submit your predictions to Kaggle, you can run the following command in your terminal (from the appropriate directory):
kaggle competitions submit -c ieee-fraud-detection -f sample_submission.csv -m "my first submission"
You can now play with different
fit() arguments and
feature-engineering techniques to try and maximize the rank of your
submissions in the Kaggle Leaderboard!
Tips to maximize predictive performance:
Be sure to specify the appropriate evaluation metric if one is specified on the competition website! If you are unsure which metric is best, then simply do not specify this argument when invoking
fit(); AutoGluon should still produce high-quality models by automatically inferring which metric to use.
If the training examples are time-based and the competition test examples come from future data, we recommend you reserve the most recently-collected training examples as a separate validation dataset passed to
fit(). Otherwise, you do not need to specify a validation set yourself and AutoGluon will automatically partition the competition training data into its own training/validation sets.
Beyond simply specifying
presets = 'best_quality', you may play with more advanced
fit()arguments such as:
refit_full. However we recommend spending most of your time on feature-engineering and just specifying
presets = 'best_quality'inside the call to
Check that you have the right user-permissions on your computer to access the data files downloaded from Kaggle.
For issues downloading Kaggle data or submitting predictions, check your Kaggle account setup and the Kaggle FAQ.