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feat: add augmentations inside the fit method
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,90 @@ | ||
| import torch | ||
| from pytorch_tabnet.utils import define_device | ||
| import numpy as np | ||
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| # TODO : change this so that p would be the proportion of rows that are changed | ||
| # add a beta argument (beta distribution) | ||
| class RegressionSMOTE(): | ||
| """ | ||
| Apply SMOTE | ||
| This will average a percentage p of the elements in the batch with other elements. | ||
| The target will be averaged as well (this might work with binary classification and certain loss), | ||
| following a beta distribution. | ||
| """ | ||
| def __init__(self, device_name="auto", p=0.8, alpha=0.5, beta=0.5, seed=0): | ||
| "" | ||
| self.seed = seed | ||
| self._set_seed() | ||
| self.device = define_device(device_name) | ||
| self.alpha = alpha | ||
| self.beta = beta | ||
| self.p = p | ||
| if (p < 0.) or (p > 1.0): | ||
| raise ValueError("Value of p should be between 0. and 1.") | ||
|
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| def _set_seed(self): | ||
| torch.manual_seed(self.seed) | ||
| np.random.seed(self.seed) | ||
| return | ||
|
|
||
| def __call__(self, X, y): | ||
| batch_size = X.shape[0] | ||
| random_values = torch.rand(batch_size, device=self.device) | ||
| idx_to_change = random_values < self.p | ||
|
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||
| # ensure that first element to switch has probability > 0.5 | ||
| np_betas = np.random.beta(self.alpha, self.beta, batch_size) / 2 + 0.5 | ||
| random_betas = torch.from_numpy(np_betas).to(self.device).float() | ||
| index_permute = torch.randperm(batch_size, device=self.device) | ||
|
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||
| X[idx_to_change] = random_betas[idx_to_change, None]*X[idx_to_change] + \ | ||
| (1 - random_betas[idx_to_change, None])*X[index_permute][idx_to_change].view(X[idx_to_change].size()) | ||
|
|
||
| y[idx_to_change] = random_betas[idx_to_change, None]*y[idx_to_change] + \ | ||
| (1 - random_betas[idx_to_change, None])*y[index_permute][idx_to_change].view(y[idx_to_change].size()) | ||
|
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| return X, y | ||
|
|
||
| class ClassificationSMOTE(): | ||
| """ | ||
| Apply SMOTE for classification tasks. | ||
| This will average a percentage p of the elements in the batch with other elements. | ||
| The target will stay unchanged and keep the value of the most important row in the mix. | ||
| """ | ||
| def __init__(self, device_name="auto", p=0.8, alpha=0.5, beta=0.5, seed=0): | ||
| "" | ||
| self.seed = seed | ||
| self._set_seed() | ||
| self.device = define_device(device_name) | ||
| self.alpha = alpha | ||
| self.beta = beta | ||
| self.p = p | ||
| if (p < 0.) or (p > 1.0): | ||
| raise ValueError("Value of p should be between 0. and 1.") | ||
|
|
||
| def _set_seed(self): | ||
| torch.manual_seed(self.seed) | ||
| np.random.seed(self.seed) | ||
| return | ||
|
|
||
| def __call__(self, X, y): | ||
| batch_size = X.shape[0] | ||
| random_values = torch.rand(batch_size, device=self.device) | ||
| idx_to_change = random_values < self.p | ||
|
|
||
| # ensure that first element to switch has probability > 0.5 | ||
| np_betas = np.random.beta(self.alpha, self.beta, batch_size) / 2 + 0.5 | ||
| random_betas = torch.from_numpy(np_betas).to(self.device).float() | ||
| index_permute = torch.randperm(batch_size, device=self.device) | ||
|
|
||
| X[idx_to_change] = random_betas[idx_to_change, None]*X[idx_to_change] + \ | ||
| (1 - random_betas[idx_to_change, None])*X[index_permute][idx_to_change].view(X[idx_to_change].size()) | ||
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| return X, y | ||
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