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I'm trying to introduce lightgbm for text multiclassification When a function or operation is applied to an object of the wrong type, a type error is raised. 2 columns in pandas dataframe, where 'category' and 'contents' are set as follows
And it looks like i can create the dataset with np.float32 data with no errors, but not really sure if that may cause any other issues somewhere else ‘numpy.float64’ object cannot be interpreted as an integer Please refer to my previous comment.
All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647)
Large values could be memory consuming Consider using consecutive integers starting from zero All negative values in categorical features will be treated as missing values. lightgbm はデフォルトで numpy のデータ型である float32 または float64 を受け付けますが、エラーが発生しているのは int64 型のデータが含まれているためです。 lightgbm に渡すデータが float32 または float64 に変換される必要があります。
1 i figured out that the error Series.dtypes must be int, float or bool refers in my case to the label, thus to the only passed series to the lgb.train () method My label had the type category which can not be handled by lgb.train () I had to change the dtype from 'category' to 'int'
When executing this, the first iterations always work, but most of the times the program crashes after ~10 iterations
It sometimes runs for much longer than others (it once managed to go to 100 iterations, but the size of the dataset was reduced each time). It's intentionally done here, as early as possible, to avoid issues like# libgomp.so.1 Cannot allocate memory in static tls block on aarch64 linux.## I set up the dataset and model and those work fine, but for some reason when i try to train the model, it gives me a type error, even though all of the values in the dataset are 32 bit floats.
We usually use python api of lightgbm, and use pandas dataframe reserve data Sometimes, we specify data in int8 or float16, but when convert into lightgbm dataset, they all convert to float64 or float32, it can cause more memory usage. In this article, we are going to see how to fix
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