Catboost Error when Loading Pool from Disk
In this article, we will explore the error message “library/cpp/string_utils/csv/csv.cpp:30: RFC4180 violation: quotation mark must be in the escaped string only” produced by CatBoost while loading a pool from disk. This error is caused by the way the data was saved and loaded using quantize() and save() functions.
Understanding Quantization
quantize() function converts the data to a binary format, which is useful for saving memory when working with large datasets. However, this process also requires that the data be loaded correctly to ensure accuracy in the model. When you call quantize() on your dataset, it will transform the data into a format that can be saved to disk.
Saving Data with Quantization
To save the data to disk using save(), we need to prefix the file path with “quantized://”. This tells CatBoost to expect binary data and load it correctly. The correct syntax for saving data with quantization is:
pool.save('path_to_file')
In this example, replace 'path_to_file' with the actual file path where you want to save your dataset.
Loading Data from Disk
To load the data back into a Pool object, we use the following syntax:
pool = cb.Pool(f"quantized://path_to_dir/{'file_name'}")
In this example, replace 'path_to_dir' with the actual directory path where your dataset is saved and 'file_name' with the name of the file you want to load.
The Error Message
When CatBoost encounters an error while loading the data from disk, it will produce an error message that includes a specific line number and filename. The error message we are interested in looks like this:
CatBoostError: library/cpp/string_utils/csv/csv.cpp:30: RFC4180 violation: quotation mark must be in the escaped string only
In this error message, “RFC4180” refers to the standard for quoting strings in CSV files. The issue here is that CatBoost expects the quotation marks to be used correctly when loading data from disk.
The Problem
The problem lies in the way we are saving and loading the data using quantize() and save(). When we call quantize(), it transforms the data into a binary format, but it does not handle the quoting of strings correctly. When we save the data to disk, CatBoost expects the quotation marks to be used in a specific way.
However, when we load the data back from disk using Pool(), CatBoost is expecting the quoting to be done differently than what was saved. This mismatch between how the data was saved and loaded causes the error message.
The Solution
To solve this problem, you need to load the file with a prefix quantized. When you want to load, the “quantized://” prefix tells CatBoost to expect binary data. Here is an example of how to do it:
# Load from Drive
pool2 = cb.Pool(f"quantized://path_to_dir/{'cbpool'}")
In this example, replace 'path_to_dir' with the actual directory path where your dataset is saved and 'cbpool' with the name of the file you want to load.
Verification
To verify that the loaded pool is correct, you can use the following methods:
# Print the number of features in the pool
print("Number of features:", pool2.num_col())
# Print the number of samples in the pool
print("Number of samples:", pool2.num_row())
In this example, num_col() returns the total number of columns (features) in the dataset and num_row() returns the total number of rows (samples) in the dataset.
Conclusion
The error message produced by CatBoost when loading a pool from disk is caused by a mismatch between how the data was saved and loaded. To solve this problem, you need to load the file with a prefix quantized. By following the steps outlined in this article, you can ensure that your data is loaded correctly into a Pool object and that your model trains accurately.
Additional Tips
- Make sure to check the documentation for CatBoost for more information on how to work with pools.
- Consider using the
csvmodule in Python to convert your dataset to CSV format before loading it into CatBoost. - Always verify that the loaded pool is correct by printing out the number of features and samples.
Last modified on 2023-11-01