Importing information into the Catboost Pool entity

Currently, I am in the process of training a Catboost model and utilizing a Pool object in the following manner:

pool = Pool(data=x_train, label=y_train, cat_features=cat_cols)
eval_set = Pool(data=x_validation, label=y_validation['Label'], cat_features=cat_cols)

model.fit(pool, early_stopping_rounds=EARLY_STOPPING_ROUNDS, eval_set=eval_set)

The x_train, y_train, x_validation, and y_validation data are all coming from Pandas DataFrame types (The datasets are saved as Parquet files, and PyArrow is used to read them into the dataframes).

The model being utilized is a Catboost classifier/regressor.

My main goal is to optimize for large datasets, which leads me to the following questions:

  1. When converting the dataset to a Pandas DataFrame (using PyArrow) before creating the Pool object, am I essentially doubling the amount of memory being used to store the dataset? My understanding is that the data is copied to structure the Pool and it's not just a reference.
  2. Is there a more efficient method for creating the pool, such as loading it directly from a libsvm file? Reference:
  3. Are there any techniques available to load the data into the Pool in batches instead of loading everything into memory at once?
pythonpandasparquetcatboostcatboostregressor

Answer №1

  1. Regrettably, the amount of RAM used is essentially doubled when working with Catboost, so it's advisable to convert your data into a file format that Catboost recognizes before creating your pool. Catboost utilizes extra RAM in order to quantize the dataset. One approach is to prepare a Pool from a large Pandas dataframe (which must be loaded into RAM), delete the dataframe, quantize the pool, and save it if you anticipate needing to repeat training later on. Keep in mind that only a quantized pool can be saved. When doing so, always specify quantization borders, or else you will not be able to create auxiliary datasets (such as a validation set) since they require the same quantization. Simple file formats like csv/tsv can be read directly from disk by Catboost (and quantize using a helper function in the utils module).
  2. Absolutely, just as you mentioned.
  3. You have the option to manually load batches using batch training, or utilize training continuation. Both methods are effective for your needs, and I have tested them myself. Training continuation may seem simpler (only requiring init_model as input), but it does not support training on GPUs at this time. Additionally, it restricts you to symmetric trees only and imposes limitations on hyperparameters. On the other hand, batched training allows you to leverage GPUs for faster processing.

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