Input and Output interface for the LightGBM 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 LightGBM supports CSV for training and inference:
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For Training ContentType, valid inputs must be text/csv.
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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 LightGBM
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
train and validation channels to provide your input data.
Alternatively, you can use only the train channel.
Note
Both train and training are valid channel names for LightGBM training.
Use both the train and validation
channels
You can provide your input data by way of two S3 paths, one for the train
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 train or validation channels, the
LightGBM 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 train
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 train
channel:
You can alternatively provide your input data by way of a single S3 path for the
train channel. This S3 path should point to a directory with a
subdirectory named train/ 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 LightGBM uses the Python Joblib module to serialize or deserialize the model, which can be used for saving or loading the model.
To use a model trained with SageMaker AI LightGBM with the JobLib module
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Use the following Python code:
import joblib import tarfile t = tarfile.open('model.tar.gz', 'r:gz') t.extractall() model = joblib.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)
