66 lines
2.4 KiB
Python
66 lines
2.4 KiB
Python
import torch
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class NumericCalibration(torch.nn.Module):
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"""
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Numeric calibration module for adjusting probability scores.
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This module scales probability scores to correct for imbalanced datasets, where positive and negative samples
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may be underrepresented or have different ratios. It is designed to be used as a component in a neural network
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for tasks such as binary classification.
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Args:
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pos_downsampling_rate (float): The downsampling rate for positive samples.
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neg_downsampling_rate (float): The downsampling rate for negative samples.
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Example:
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To use `NumericCalibration` in a PyTorch model, you can create an instance of it and apply it to probability
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scores like this:
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```python
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# Create a NumericCalibration instance with downsampling rates
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calibration = NumericCalibration(pos_downsampling_rate=0.1, neg_downsampling_rate=0.2)
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# Generate probability scores (e.g., from a neural network)
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raw_probs = torch.tensor([0.8, 0.6, 0.2, 0.9])
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# Apply numeric calibration to adjust the probabilities
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calibrated_probs = calibration(raw_probs)
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# The `calibrated_probs` now contains the adjusted probability scores
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```
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Note:
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The `NumericCalibration` module is used to adjust probability scores to account for differences in
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the number of positive and negative samples in a dataset. It can help improve the calibration of
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probability estimates in imbalanced classification problems.
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Warning:
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This class is intended for internal use within neural network architectures and should not be
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directly accessed or modified by external code.
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"""
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def __init__(
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self,
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pos_downsampling_rate: float,
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neg_downsampling_rate: float,
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):
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"""
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Apply numeric calibration to probability scores.
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Args:
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probs (torch.Tensor): Probability scores to be calibrated.
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Returns:
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torch.Tensor: Calibrated probability scores.
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"""
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super().__init__()
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# Using buffer to make sure they are on correct device (and not moved every time).
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# Will also be part of state_dict.
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self.register_buffer(
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"ratio", torch.as_tensor(neg_downsampling_rate / pos_downsampling_rate), persistent=True
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)
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def forward(self, probs: torch.Tensor):
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return probs * self.ratio / (1.0 - probs + (self.ratio * probs))
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