wehub-resource-sync
62 lines
2.9 KiB
Python
62 lines
2.9 KiB
Python
"""Helper functions to get data in a `DataLoaders` in the tabular application and higher class `TabularDataLoaders`
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Docs: https://docs.fast.ai/tabular.data.html.md"""
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# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/41_tabular.data.ipynb.
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# %% auto #0
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__all__ = ['TabularDataLoaders']
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# %% ../../nbs/41_tabular.data.ipynb #8dc4e497
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from ..torch_basics import *
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from ..data.all import *
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from .core import *
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# %% ../../nbs/41_tabular.data.ipynb #d7cd6053
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class TabularDataLoaders(DataLoaders):
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"Basic wrapper around several `DataLoader`s with factory methods for tabular data"
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@classmethod
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@delegates(Tabular.dataloaders, but=["dl_type", "dl_kwargs"])
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def from_df(cls,
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df:pd.DataFrame,
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path:str|Path='.', # Location of `df`, defaults to current working directory
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procs:list=None, # List of `TabularProc`s
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cat_names:list=None, # Column names pertaining to categorical variables
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cont_names:list=None, # Column names pertaining to continuous variables
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y_names:list=None, # Names of the dependent variables
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y_block:TransformBlock=None, # `TransformBlock` to use for the target(s)
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valid_idx:list=None, # List of indices to use for the validation set, defaults to a random split
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**kwargs
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):
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"Create `TabularDataLoaders` from `df` in `path` using `procs`"
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if cat_names is None: cat_names = []
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if cont_names is None: cont_names = list(set(df)-set(L(cat_names))-set(L(y_names)))
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splits = RandomSplitter()(df) if valid_idx is None else IndexSplitter(valid_idx)(df)
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to = TabularPandas(df, procs, cat_names, cont_names, y_names, splits=splits, y_block=y_block)
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return to.dataloaders(path=path, **kwargs)
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@classmethod
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def from_csv(cls,
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csv:str|Path|io.BufferedReader, # A csv of training data
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skipinitialspace:bool=True, # Skip spaces after delimiter
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**kwargs
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):
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"Create `TabularDataLoaders` from `csv` file in `path` using `procs`"
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return cls.from_df(pd.read_csv(csv, skipinitialspace=skipinitialspace), **kwargs)
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@delegates(TabDataLoader.__init__)
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def test_dl(self,
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test_items, # Items to create new test `TabDataLoader` formatted the same as the training data
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rm_type_tfms=None, # Number of `Transform`s to be removed from `procs`
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process:bool=True, # Apply validation `TabularProc`s to `test_items` immediately
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inplace:bool=False, # Keep separate copy of original `test_items` in memory if `False`
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**kwargs
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):
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"Create test `TabDataLoader` from `test_items` using validation `procs`"
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to = self.train_ds.new(test_items, inplace=inplace)
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if process: to.process()
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return self.valid.new(to, **kwargs)
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Tabular._dbunch_type = TabularDataLoaders
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TabularDataLoaders.from_csv = delegates(to=TabularDataLoaders.from_df)(TabularDataLoaders.from_csv)
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