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import torch
import numpy as np
from scipy.special import softmax
from pytorch_tabnet.utils import SparsePredictDataset, PredictDataset, filter_weights
from pytorch_tabnet.abstract_model import TabModel
from pytorch_tabnet.multiclass_utils import infer_output_dim, check_output_dim
from torch.utils.data import DataLoader
import scipy
class TabNetClassifier(TabModel):
def __post_init__(self):
super(TabNetClassifier, self).__post_init__()
self._task = 'classification'
self._default_loss = torch.nn.functional.cross_entropy
self._default_metric = 'accuracy'
def weight_updater(self, weights):
"""
Updates weights dictionary according to target_mapper.
Parameters
----------
weights : bool or dict
Given weights for balancing training.
Returns
-------
bool or dict
Same bool if weights are bool, updated dict otherwise.
"""
if isinstance(weights, int):
return weights
elif isinstance(weights, dict):
return {self.target_mapper[key]: value for key, value in weights.items()}
else:
return weights
def prepare_target(self, y):
return np.vectorize(self.target_mapper.get)(y)
def compute_loss(self, y_pred, y_true):
return self.loss_fn(y_pred, y_true.long())
def update_fit_params(
self,
X_train,
y_train,
eval_set,
weights,
):
output_dim, train_labels = infer_output_dim(y_train)
for X, y in eval_set:
check_output_dim(train_labels, y)
self.output_dim = output_dim
self._default_metric = ('auc' if self.output_dim == 2 else 'accuracy')
self.classes_ = train_labels
self.target_mapper = {
class_label: index for index, class_label in enumerate(self.classes_)
}
self.preds_mapper = {
str(index): class_label for index, class_label in enumerate(self.classes_)
}
self.updated_weights = self.weight_updater(weights)
def stack_batches(self, list_y_true, list_y_score):
y_true = np.hstack(list_y_true)
y_score = np.vstack(list_y_score)
y_score = softmax(y_score, axis=1)
return y_true, y_score
def predict_func(self, outputs):
outputs = np.argmax(outputs, axis=1)
return np.vectorize(self.preds_mapper.get)(outputs.astype(str))
def predict_proba(self, X):
"""
Make predictions for classification on a batch (valid)
Parameters
----------
X : a :tensor: `torch.Tensor` or matrix: `scipy.sparse.csr_matrix`
Input data
Returns
-------
res : np.ndarray
"""
self.network.eval()
if scipy.sparse.issparse(X):
dataloader = DataLoader(
SparsePredictDataset(X),
batch_size=self.batch_size,
shuffle=False,
)
else:
dataloader = DataLoader(
PredictDataset(X),
batch_size=self.batch_size,
shuffle=False,
)
results = []
for batch_nb, data in enumerate(dataloader):
data = data.to(self.device).float()
output, M_loss = self.network(data)
predictions = torch.nn.Softmax(dim=1)(output).cpu().detach().numpy()
results.append(predictions)
res = np.vstack(results)
return res
class TabNetRegressor(TabModel):
def __post_init__(self):
super(TabNetRegressor, self).__post_init__()
self._task = 'regression'
self._default_loss = torch.nn.functional.mse_loss
self._default_metric = 'mse'
def prepare_target(self, y):
return y
def compute_loss(self, y_pred, y_true):
return self.loss_fn(y_pred, y_true)
def update_fit_params(
self,
X_train,
y_train,
eval_set,
weights
):
if len(y_train.shape) != 2:
msg = "Targets should be 2D : (n_samples, n_regression) " + \
f"but y_train.shape={y_train.shape} given.\n" + \
"Use reshape(-1, 1) for single regression."
raise ValueError(msg)
self.output_dim = y_train.shape[1]
self.preds_mapper = None
self.updated_weights = weights
filter_weights(self.updated_weights)
def predict_func(self, outputs):
return outputs
def stack_batches(self, list_y_true, list_y_score):
y_true = np.vstack(list_y_true)
y_score = np.vstack(list_y_score)
return y_true, y_score
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