wehub-resource-sync
58 lines
2.4 KiB
Python
58 lines
2.4 KiB
Python
"""The function to immediately get a `Learner` ready to train for tabular data
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Docs: https://docs.fast.ai/tabular.learner.html.md"""
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# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/43_tabular.learner.ipynb.
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# %% auto #0
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__all__ = ['TabularLearner', 'tabular_learner', 'show_results']
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# %% ../../nbs/43_tabular.learner.ipynb #154c3eb2
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from ..basics import *
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from .core import *
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from .model import *
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from .data import *
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# %% ../../nbs/43_tabular.learner.ipynb #91c77401
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class TabularLearner(Learner):
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"`Learner` for tabular data"
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def predict(self,
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row:pd.Series, # Features to be predicted
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):
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"Predict on a single sample"
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dl = self.dls.test_dl(row.to_frame().T)
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dl.dataset.conts = dl.dataset.conts.astype(np.float32)
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inp,preds,_,dec_preds = self.get_preds(dl=dl, with_input=True, with_decoded=True)
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b = (*tuplify(inp),*tuplify(dec_preds))
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full_dec = self.dls.decode(b)
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return full_dec,dec_preds[0],preds[0]
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# %% ../../nbs/43_tabular.learner.ipynb #087411eb
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@delegates(Learner.__init__)
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def tabular_learner(
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dls:TabularDataLoaders,
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layers:list=None, # Size of the layers generated by `LinBnDrop`
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emb_szs:list=None, # Tuples of `n_unique, embedding_size` for all categorical features
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config:dict=None, # Config params for TabularModel from `tabular_config`
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n_out:int=None, # Final output size of the model
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y_range:Tuple=None, # Low and high for the final sigmoid function
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**kwargs
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):
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"Get a `Learner` using `dls`, with `metrics`, including a `TabularModel` created using the remaining params."
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if config is None: config = tabular_config()
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if layers is None: layers = [200,100]
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to = dls.train_ds
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emb_szs = get_emb_sz(dls.train_ds, {} if emb_szs is None else emb_szs)
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if n_out is None: n_out = get_c(dls)
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assert n_out, "`n_out` is not defined, and could not be inferred from data, set `dls.c` or pass `n_out`"
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if y_range is None and 'y_range' in config: y_range = config.pop('y_range')
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model = TabularModel(emb_szs, len(dls.cont_names), n_out, layers, y_range=y_range, **config)
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return TabularLearner(dls, model, **kwargs)
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# %% ../../nbs/43_tabular.learner.ipynb #e853caa0
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@dispatch
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def show_results(x:Tabular, y:Tabular, samples, outs, ctxs=None, max_n=10, **kwargs):
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df = x.all_cols[:max_n]
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for n in x.y_names: df[n+'_pred'] = y[n][:max_n].values
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display_df(df)
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