def fit(self, y: Union[pd.Series, np.ndarray, torch.Tensor]):
"""
Fit transformer, i.e. determine center and scale of data
Args:
y (Union[pd.Series, np.ndarray, torch.Tensor]): input data
Returns:
TorchNormalizer: self
"""
y = self._preprocess_y(y)
if self.method == "standard":
if isinstance(y, torch.Tensor):
self.center_ = torch.mean(y)
self.scale_ = torch.std(y) / (self.center_ + self.eps)
else:
self.center_ = np.mean(y)
self.scale_ = np.std(y) / (self.center_ + self.eps)
elif self.method == "robust":
if isinstance(y, torch.Tensor):
self.center_ = torch.median(y)
q_75 = y.kthvalue(int(len(y) * 0.75)).values
q_25 = y.kthvalue(int(len(y) * 0.25)).values
else:
self.center_ = np.median(y)
q_75 = np.percentiley(y, 75)
q_25 = np.percentiley(y, 25)
self.scale_ = (q_75 - q_25) / (self.center_ + self.eps) / 2.0
return self
def fit(self, y: Union[pd.Series, np.ndarray, torch.Tensor]):
"""
Fit transformer, i.e. determine center and scale of data
Args:
y (Union[pd.Series, np.ndarray, torch.Tensor]): input data
Returns:
TorchNormalizer: self
"""
y = self.preprocess(y)
if self.method == "identity":
if isinstance(y, torch.Tensor):
self.center_ = torch.zeros(y.size()[:-1])
self.scale_ = torch.ones(y.size()[:-1])
elif isinstance(y, (np.ndarray, pd.Series, pd.DataFrame)):
self.center_ = np.zeros(y.shape[:-1])
self.scale_ = np.ones(y.shape[:-1])
else:
self.center_ = 0.0
self.scale_ = 1.0
elif self.method == "standard":
if isinstance(y, torch.Tensor):
self.center_ = torch.mean(y, dim=-1)
self.scale_ = torch.std(y, dim=-1) + self.eps
elif isinstance(y, np.ndarray):
self.center_ = np.mean(y, axis=-1)
self.scale_ = np.std(y, axis=-1) + self.eps
else:
self.center_ = np.mean(y)
self.scale_ = np.std(y) + self.eps
elif self.method == "robust":
if isinstance(y, torch.Tensor):
self.center_ = torch.median(y, dim=-1).values
q_75 = y.kthvalue(int(len(y) * 0.75), dim=-1).values
q_25 = y.kthvalue(int(len(y) * 0.25), dim=-1).values
elif isinstance(y, np.ndarray):
self.center_ = np.median(y, axis=-1)
q_75 = np.percentiley(y, 75, axis=-1)
q_25 = np.percentiley(y, 25, axis=-1)
else:
self.center_ = np.median(y)
q_75 = np.percentiley(y, 75)
q_25 = np.percentiley(y, 25)
self.scale_ = (q_75 - q_25) / 2.0 + self.eps
if not self.center:
self.scale_ = self.center_
if isinstance(y, torch.Tensor):
self.center_ = torch.zeros_like(self.center_)
else:
self.center_ = np.zeros_like(self.center_)
return self
def fit(self, y: Union[pd.Series, np.ndarray, torch.Tensor]):
"""
Fit transformer, i.e. determine center and scale of data
Args:
y (Union[pd.Series, np.ndarray, torch.Tensor]): input data
Returns:
TorchNormalizer: self
"""
if self.coerce_positive is None and not self.log_scale:
self.coerce_positive = (y >= 0).all()
y = self._preprocess_y(y)
if self.method == "standard":
if isinstance(y, torch.Tensor):
self.center_ = torch.mean(y, dim=-1) + self.eps
self.scale_ = torch.std(y, dim=-1) + self.eps
elif isinstance(y, np.ndarray):
self.center_ = np.mean(y, axis=-1) + self.eps
self.scale_ = np.std(y, axis=-1) + self.eps
else:
self.center_ = np.mean(y) + self.eps
self.scale_ = np.std(y) + self.eps
elif self.method == "robust":
if isinstance(y, torch.Tensor):
self.center_ = torch.median(y, dim=-1).values + self.eps
q_75 = y.kthvalue(int(len(y) * 0.75), dim=-1).values
q_25 = y.kthvalue(int(len(y) * 0.25), dim=-1).values
elif isinstance(y, np.ndarray):
self.center_ = np.median(y, axis=-1) + self.eps
q_75 = np.percentiley(y, 75, axis=-1)
q_25 = np.percentiley(y, 25, axis=-1)
else:
self.center_ = np.median(y) + self.eps
q_75 = np.percentiley(y, 75)
q_25 = np.percentiley(y, 25)
self.scale_ = (q_75 - q_25) / 2.0 + self.eps
if not self.center:
self.scale_ = self.center_
if isinstance(y, torch.Tensor):
self.center_ = torch.zeros_like(self.center_)
else:
self.center_ = np.zeros_like(self.center_)
return self