TableTransforms.jl
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JuliaML
Transforms and pipelines with tabular data in Julia
A nice feature of the DataFrames.jl select function is that it allows in-place renaming: ```julia select(df, :x => :y, :a => :b) ``` Currently, we have to write a `Select(:x,...
Currently we provide a few basic checks as assertion functions that can be used before the transform is applied. For example, some transforms like EigenAnalysis only make sense with continuous...
Add a transform that takes a table with possibly generic column eltypes and returns a new table with specific column types.
The Factor Analysis (FA) transform is a useful probabilistic variation of PCA that identifies latent factors in a data set. We could provide a transform that returns a new table...
Looking at bit into MLJ integration. For better or worse, hyper-parameter optimization (eg, grid search) in MLJ generally works by mutating the field values of the model struct. I wonder...
I am not sure whether this is intentional, but when the input table has missing values, `assert_continuous` function throws `AssertionError: columns must hold continuous variables`, even though all other values...
Hope support Roll a function over data, run a statistic along a [weighted] data window ref: https://github.com/JeffreySarnoff/RollingFunctions.jl
