Input and Output interface for the CatBoost algorithm - Amazon SageMaker AI

Input and Output interface for the CatBoost algorithm

Gradient boosting operates on tabular data, with the rows representing observations, one column representing the target variable or label, and the remaining columns representing features.

The SageMaker AI implementation of CatBoost supports CSV for training and inference:

  • For Training ContentType, valid inputs must be text/csv.

  • For Inference ContentType, valid inputs must be text/csv.

Note

For CSV training, the algorithm assumes that the target variable is in the first column and that the CSV does not have a header record.

For CSV inference, the algorithm assumes that CSV input does not have the label column.

Input format for training data, validation data, and categorical features

Be mindful of how to format your training data for input to the CatBoost model. You must provide the path to an Amazon S3 bucket that contains your training and validation data. You can also include a list of categorical features. Use both the training and validation channels to provide your input data. Alternatively, you can use only the training channel.

Use both the training and validation channels

You can provide your input data by way of two S3 paths, one for the training channel and one for the validation channel. Each S3 path can either be an S3 prefix that points to one or more CSV files or a full S3 path pointing to one specific CSV file. The target variables should be in the first column of your CSV file. The predictor variables (features) should be in the remaining columns. If multiple CSV files are provided for the training or validation channels, the CatBoost algorithm concatenates the files. The validation data is used to compute a validation score at the end of each boosting iteration. Early stopping is applied when the validation score stops improving.

If your predictors include categorical features, you can provide a JSON file named categorical_index.json in the same location as your training data file or files. If you provide a JSON file for categorical features, your training channel must point to an S3 prefix and not a specific CSV file. This file should contain a Python dictionary where the key is the string "cat_index_list" and the value is a list of unique integers. Each integer in the value list should indicate the column index of the corresponding categorical features in your training data CSV file. Each value should be a positive integer (greater than zero because zero represents the target value), less than the Int32.MaxValue (2147483647), and less than the total number of columns. There should only be one categorical index JSON file.

Use only the training channel:

You can alternatively provide your input data by way of a single S3 path for the training channel. This S3 path should point to a directory with a subdirectory named training/ that contains one or more CSV files. You can optionally include another subdirectory in the same location called validation/ that also has one or more CSV files. If the validation data is not provided, then 20% of your training data is randomly sampled to serve as the validation data. If your predictors include categorical features, you can provide a JSON file named categorical_index.json in the same location as your data subdirectories.

Note

For CSV training input mode, the total memory available to the algorithm (instance count multiplied by the memory available in the InstanceType) must be able to hold the training dataset.

SageMaker AI CatBoost uses the catboost.CatBoostClassifier and catboost.CatBoostRegressor modules to serialize or deserialize the model, which can be used for saving or loading the model.

To use a model trained with SageMaker AI CatBoost with catboost

  • Use the following Python code:

    import tarfile
from catboost import CatBoostClassifier

t = tarfile.open('model.tar.gz', 'r:gz')
t.extractall()

file_path = os.path.join(model_file_path, "model")
model = CatBoostClassifier()
model.load_model(file_path)

# prediction with test data
# dtest should be a pandas DataFrame with column names feature_0, feature_1, ..., feature_d
pred = model.predict(dtest)