wehub-resource-sync
83 lines
3.8 KiB
Python
83 lines
3.8 KiB
Python
"""A basic model that can be used on tabular data
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Docs: https://docs.fast.ai/tabular.model.html.md"""
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# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/42_tabular.model.ipynb.
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# %% auto #0
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__all__ = ['emb_sz_rule', 'get_emb_sz', 'TabularModel', 'tabular_config']
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# %% ../../nbs/42_tabular.model.ipynb #b8fb8b4d
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from ..torch_basics import *
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from .core import *
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# %% ../../nbs/42_tabular.model.ipynb #393080e2
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def emb_sz_rule(
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n_cat:int # Cardinality of a category
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) -> int:
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"Rule of thumb to pick embedding size corresponding to `n_cat`"
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return min(600, round(1.6 * n_cat**0.56))
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# %% ../../nbs/42_tabular.model.ipynb #aaf47c66
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def _one_emb_sz(classes, n, sz_dict=None):
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"Pick an embedding size for `n` depending on `classes` if not given in `sz_dict`."
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sz_dict = ifnone(sz_dict, {})
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n_cat = len(classes[n])
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sz = sz_dict.get(n, int(emb_sz_rule(n_cat))) # rule of thumb
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return n_cat,sz
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# %% ../../nbs/42_tabular.model.ipynb #777807fb
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def get_emb_sz(
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to:Tabular|TabularPandas,
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sz_dict:dict=None # Dictionary of {'class_name' : size, ...} to override default `emb_sz_rule`
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) -> list: # List of embedding sizes for each category
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"Get embedding size for each cat_name in `Tabular` or `TabularPandas`, or populate embedding size manually using sz_dict"
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return [_one_emb_sz(to.classes, n, sz_dict) for n in to.cat_names]
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# %% ../../nbs/42_tabular.model.ipynb #75c40873
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class TabularModel(Module):
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"Basic model for tabular data."
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def __init__(self,
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emb_szs:list, # Sequence of (num_embeddings, embedding_dim) for each categorical variable
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n_cont:int, # Number of continuous variables
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out_sz:int, # Number of outputs for final `LinBnDrop` layer
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layers:list, # Sequence of ints used to specify the input and output size of each `LinBnDrop` layer
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ps:float|MutableSequence=None, # Sequence of dropout probabilities for `LinBnDrop`
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embed_p:float=0., # Dropout probability for `Embedding` layer
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y_range=None, # Low and high for `SigmoidRange` activation
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use_bn:bool=True, # Use `BatchNorm1d` in `LinBnDrop` layers
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bn_final:bool=False, # Use `BatchNorm1d` on final layer
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bn_cont:bool=True, # Use `BatchNorm1d` on continuous variables
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act_cls=nn.ReLU(inplace=True), # Activation type for `LinBnDrop` layers
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lin_first:bool=True # Linear layer is first or last in `LinBnDrop` layers
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):
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ps = ifnone(ps, [0]*len(layers))
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if not is_listy(ps): ps = [ps]*len(layers)
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self.embeds = nn.ModuleList([Embedding(ni, nf) for ni,nf in emb_szs])
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self.emb_drop = nn.Dropout(embed_p)
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self.bn_cont = nn.BatchNorm1d(n_cont) if bn_cont else None
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n_emb = sum(e.embedding_dim for e in self.embeds)
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self.n_emb,self.n_cont = n_emb,n_cont
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sizes = [n_emb + n_cont] + layers + [out_sz]
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actns = [act_cls for _ in range(len(sizes)-2)] + [None]
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_layers = [LinBnDrop(sizes[i], sizes[i+1], bn=use_bn and (i!=len(actns)-1 or bn_final), p=p, act=a, lin_first=lin_first)
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for i,(p,a) in enumerate(zip(ps+[0.],actns))]
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if y_range is not None: _layers.append(SigmoidRange(*y_range))
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self.layers = nn.Sequential(*_layers)
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def forward(self, x_cat, x_cont=None):
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if self.n_emb != 0:
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x = [e(x_cat[:,i]) for i,e in enumerate(self.embeds)]
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x = torch.cat(x, 1)
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x = self.emb_drop(x)
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if self.n_cont != 0:
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if self.bn_cont is not None: x_cont = self.bn_cont(x_cont)
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x = torch.cat([x, x_cont], 1) if self.n_emb != 0 else x_cont
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return self.layers(x)
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# %% ../../nbs/42_tabular.model.ipynb #b7283da3
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@delegates(TabularModel.__init__)
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def tabular_config(**kwargs):
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"Convenience function to easily create a config for `TabularModel`"
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return kwargs
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