| from ..torch_core import * |
| from ..layers import * |
| from ..basic_data import * |
| from ..basic_train import * |
| from ..train import ClassificationInterpretation |
|
|
| __all__ = ['TabularModel'] |
|
|
| class TabularModel(Module): |
| "Basic model for tabular data." |
| def __init__(self, emb_szs:ListSizes, n_cont:int, out_sz:int, layers:Collection[int], ps:Collection[float]=None, |
| emb_drop:float=0., y_range:OptRange=None, use_bn:bool=True, bn_final:bool=False): |
| super().__init__() |
| ps = ifnone(ps, [0]*len(layers)) |
| ps = listify(ps, layers) |
| self.embeds = nn.ModuleList([embedding(ni, nf) for ni,nf in emb_szs]) |
| self.emb_drop = nn.Dropout(emb_drop) |
| self.bn_cont = nn.BatchNorm1d(n_cont) |
| n_emb = sum(e.embedding_dim for e in self.embeds) |
| self.n_emb,self.n_cont,self.y_range = n_emb,n_cont,y_range |
| sizes = self.get_sizes(layers, out_sz) |
| actns = [nn.ReLU(inplace=True) for _ in range(len(sizes)-2)] + [None] |
| layers = [] |
| for i,(n_in,n_out,dp,act) in enumerate(zip(sizes[:-1],sizes[1:],[0.]+ps,actns)): |
| layers += bn_drop_lin(n_in, n_out, bn=use_bn and i!=0, p=dp, actn=act) |
| if bn_final: layers.append(nn.BatchNorm1d(sizes[-1])) |
| self.layers = nn.Sequential(*layers) |
|
|
| def get_sizes(self, layers, out_sz): |
| return [self.n_emb + self.n_cont] + layers + [out_sz] |
|
|
| def forward(self, x_cat:Tensor, x_cont:Tensor) -> Tensor: |
| if self.n_emb != 0: |
| x = [e(x_cat[:,i]) for i,e in enumerate(self.embeds)] |
| x = torch.cat(x, 1) |
| x = self.emb_drop(x) |
| if self.n_cont != 0: |
| x_cont = self.bn_cont(x_cont) |
| x = torch.cat([x, x_cont], 1) if self.n_emb != 0 else x_cont |
| x = self.layers(x) |
| if self.y_range is not None: |
| x = (self.y_range[1]-self.y_range[0]) * torch.sigmoid(x) + self.y_range[0] |
| return x |
|
|
| @classmethod |
| def _cl_int_from_learner(cls, learn:Learner, ds_type=DatasetType.Valid, activ:nn.Module=None): |
| "Creates an instance of 'ClassificationInterpretation" |
| preds = learn.get_preds(ds_type=ds_type, activ=activ, with_loss=True) |
| return cls(learn, *preds, ds_type=ds_type) |
|
|
| def _cl_int_plot_top_losses(self, k, largest:bool=True, return_table:bool=False)->Optional[plt.Figure]: |
| "Generates a dataframe of 'top_losses' along with their prediction, actual, loss, and probability of the actual class." |
| tl_val, tl_idx = self.top_losses(k, largest) |
| classes = self.data.classes |
| cat_names = self.data.x.cat_names |
| cont_names = self.data.x.cont_names |
| df = pd.DataFrame(columns=[['Prediction', 'Actual', 'Loss', 'Probability'] + cat_names + cont_names]) |
| for i, idx in enumerate(tl_idx): |
| da, cl = self.data.dl(self.ds_type).dataset[idx] |
| cl = int(cl) |
| t1 = str(da) |
| t1 = t1.split(';') |
| arr = [] |
| arr.extend([classes[self.pred_class[idx]], classes[cl], f'{self.losses[idx]:.2f}', |
| f'{self.preds[idx][cl]:.2f}']) |
| for x in range(len(t1)-1): |
| _, value = t1[x].rsplit(' ', 1) |
| arr.append(value) |
| df.loc[i] = arr |
| display(df) |
| return_fig = return_table |
| if ifnone(return_fig, defaults.return_fig): return df |
|
|
|
|
| ClassificationInterpretation.from_learner = _cl_int_from_learner |
| ClassificationInterpretation.plot_top_losses = _cl_int_plot_top_losses |
|
|
| def _learner_interpret(learn:Learner, ds_type:DatasetType = DatasetType.Valid): |
| "Create a 'ClassificationInterpretation' object from 'learner' on 'ds_type'." |
| return ClassificationInterpretation.from_learner(learn, ds_type=ds_type) |
|
|
| Learner.interpret = _learner_interpret |
|
|
