from tsai.data.external import get_UCR_dataInference
Code required for inference.
Learner.get_X_preds
Learner.get_X_preds (X, y=None, bs=64, with_input=False, with_decoded=True, with_loss=False, act=None)
| Type | Default | Details | |
|---|---|---|---|
| X | |||
| y | NoneType | None | |
| bs | int | 64 | |
| with_input | bool | False | returns the input as well |
| with_decoded | bool | True | returns decoded predictions as well |
| with_loss | bool | False | returns the loss per item as well |
| act | NoneType | None | Apply activation to predictions, defaults to self.loss_func’s activation |
Get the predictions and targets, optionally with_input and with_loss.
with_decoded will also return the decoded predictions (it reverses the transforms applied).
The order of the output is the following:
- input (optional): if with_input is True
- probabiblities (for classification) or predictions (for regression)
- target: if y is provided. Otherwise None.
- predictions: predicted labels. Predictions will be decoded if with_decoded=True.
- loss (optional): if with_loss is set to True and y is not None.
dsid = 'OliveOil'
X, y, splits = get_UCR_data(dsid, split_data=False)
X_test = X[splits[1]]
y_test = y[splits[1]]learn = load_learner("./models/test.pth")⚠️ Warning: load_learner (from fastai) requires all your custom code be in the exact same place as when exporting your Learner (the main script, or the module you imported it from).
test_probas, test_targets, test_preds = learn.get_X_preds(X_test, with_decoded=True)
test_probas, test_targets, test_preds(tensor([[0.2574, 0.2421, 0.2364, 0.2641],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2421, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2421, 0.2364, 0.2641],
[0.2574, 0.2422, 0.2364, 0.2639],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2641],
[0.2574, 0.2421, 0.2364, 0.2641],
[0.2574, 0.2421, 0.2364, 0.2640],
[0.2574, 0.2421, 0.2364, 0.2641],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2421, 0.2364, 0.2641],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2421, 0.2364, 0.2641],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640]]),
None,
array(['4', '4', '4', '4', '4', '4', '4', '4', '4', '4', '4', '4', '4',
'4', '4', '4', '4', '4', '4', '4', '4', '4', '4', '4', '4', '4',
'4', '4', '4', '4'], dtype='<U1'))import torch
from fastcore.test import test_closetorch_test_probas, torch_test_targets, torch_test_preds = learn.get_X_preds(torch.from_numpy(X_test), with_decoded=True)
torch_test_probas, torch_test_targets, torch_test_preds
test_close(test_probas, torch_test_probas)test_probas2, test_targets2, test_preds2 = learn.get_X_preds(X_test, y_test, with_decoded=True)
test_probas2, test_targets2, test_preds2(tensor([[0.2574, 0.2421, 0.2364, 0.2641],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2421, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2421, 0.2364, 0.2641],
[0.2574, 0.2422, 0.2364, 0.2639],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2641],
[0.2574, 0.2421, 0.2364, 0.2641],
[0.2574, 0.2421, 0.2364, 0.2640],
[0.2574, 0.2421, 0.2364, 0.2641],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2421, 0.2364, 0.2641],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2421, 0.2364, 0.2641],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640]]),
tensor([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3]),
array(['4', '4', '4', '4', '4', '4', '4', '4', '4', '4', '4', '4', '4',
'4', '4', '4', '4', '4', '4', '4', '4', '4', '4', '4', '4', '4',
'4', '4', '4', '4'], dtype='<U1'))test_probas3, test_targets3, test_preds3, test_losses3 = learn.get_X_preds(X_test, y_test, with_loss=True, with_decoded=True)
test_probas3, test_targets3, test_preds3, test_losses3(tensor([[0.2574, 0.2421, 0.2364, 0.2641],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2421, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2421, 0.2364, 0.2641],
[0.2574, 0.2422, 0.2364, 0.2639],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2641],
[0.2574, 0.2421, 0.2364, 0.2641],
[0.2574, 0.2421, 0.2364, 0.2640],
[0.2574, 0.2421, 0.2364, 0.2641],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2421, 0.2364, 0.2641],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2421, 0.2364, 0.2641],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640],
[0.2574, 0.2422, 0.2364, 0.2640]]),
tensor([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3]),
array(['4', '4', '4', '4', '4', '4', '4', '4', '4', '4', '4', '4', '4',
'4', '4', '4', '4', '4', '4', '4', '4', '4', '4', '4', '4', '4',
'4', '4', '4', '4'], dtype='<U1'),
TensorBase([1.3572, 1.3572, 1.3572, 1.3571, 1.3572, 1.4181, 1.4181, 1.4181,
1.4181, 1.4181, 1.4181, 1.4181, 1.4181, 1.4181, 1.4423, 1.4422,
1.4422, 1.4422, 1.3316, 1.3316, 1.3316, 1.3316, 1.3316, 1.3316,
1.3316, 1.3316, 1.3316, 1.3316, 1.3317, 1.3317]))from fastcore.test import test_eqtest_eq(test_probas, test_probas2)
test_eq(test_preds, test_preds2)
test_eq(test_probas, test_probas3)
test_eq(test_preds, test_preds3)