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scaler

TorchMinMaxScaler

A class for scaling data using min-max normalization with PyTorch tensors. This scaler calculates the minimum and maximum values in the dataset to scale the data within a given range.

Attributes:

Name Type Description
min Tensor

The minimum values computed over the fitted data.
max Tensor

The maximum values computed over the fitted data.

Examples:

>>> import torch
>>> from spotpython.utils.scaler import TorchMinMaxScaler
>>> scaler = TorchMinMaxScaler()
# Given a tensor
>>> tensor = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
# Fit and transform the tensor using the scaler
>>> scaled_tensor = scaler.fit_transform(tensor)
>>> print(scaled_tensor)
# The output will be a tensor with values scaled between 0 and 1.
Source code in spotpython/utils/scaler.py 
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class TorchMinMaxScaler:
    """
    A class for scaling data using min-max normalization with PyTorch tensors.
    This scaler calculates the minimum and maximum values in the dataset to scale the data within a given range.

    Attributes:
        min (torch.Tensor): The minimum values computed over the fitted data.
        max (torch.Tensor): The maximum values computed over the fitted data.

    Examples:
        >>> import torch
        >>> from spotpython.utils.scaler import TorchMinMaxScaler
        >>> scaler = TorchMinMaxScaler()
        # Given a tensor
        >>> tensor = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
        # Fit and transform the tensor using the scaler
        >>> scaled_tensor = scaler.fit_transform(tensor)
        >>> print(scaled_tensor)
        # The output will be a tensor with values scaled between 0 and 1.
    """

    def __init__(self):
        """
        Initializes the TorchMinMaxScaler class without any predefined min and max.
        """
        self.min = None
        self.max = None

    def fit(self, x: torch.Tensor) -> None:
        """
        Compute the minimum and maximum value of the input tensor.

        Args:
            x (torch.Tensor): The input tensor.

        Raises:
            TypeError: If the input is not a torch tensor.
        """
        if not torch.is_tensor(x):
            raise TypeError("Input should be a torch tensor")
        self.min = x.min(dim=0, keepdim=True).values
        self.max = x.max(dim=0, keepdim=True).values

    def transform(self, x: torch.Tensor) -> torch.Tensor:
        """
        Scale the input tensor using the computed minimum and maximum values.

        Args:
            x (torch.Tensor): The input tensor to be scaled.

        Returns:
            torch.Tensor: The scaled tensor.

        Raises:
            TypeError: If the input is not a torch tensor.
            RuntimeError: If the scaler has not been fitted before transforming data.
        """
        if not torch.is_tensor(x):
            raise TypeError("Input should be a torch tensor")
        if self.min is None or self.max is None:
            raise RuntimeError("Must fit scaler before transforming data")
        x = (x - self.min) / (self.max - self.min + 1e-7)
        return x

    def fit_transform(self, x: torch.Tensor) -> torch.Tensor:
        """
        Fit the scaler to the input tensor and then scale the tensor.

        Args:
            x (torch.Tensor): The input tensor.

        Returns:
            torch.Tensor: The scaled tensor.

        Raises:
            TypeError: If the input is not a torch tensor.
        """
        self.fit(x)
        return self.transform(x)

__init__()

Initializes the TorchMinMaxScaler class without any predefined min and max.

Source code in spotpython/utils/scaler.py 
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def __init__(self):
    """
    Initializes the TorchMinMaxScaler class without any predefined min and max.
    """
    self.min = None
    self.max = None

fit(x)

Compute the minimum and maximum value of the input tensor.

Parameters:

Name Type Description Default
x Tensor

The input tensor.

 required

Raises:

Type Description
TypeError

If the input is not a torch tensor.
Source code in spotpython/utils/scaler.py 
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def fit(self, x: torch.Tensor) -> None:
    """
    Compute the minimum and maximum value of the input tensor.

    Args:
        x (torch.Tensor): The input tensor.

    Raises:
        TypeError: If the input is not a torch tensor.
    """
    if not torch.is_tensor(x):
        raise TypeError("Input should be a torch tensor")
    self.min = x.min(dim=0, keepdim=True).values
    self.max = x.max(dim=0, keepdim=True).values

fit_transform(x)

Fit the scaler to the input tensor and then scale the tensor.

Parameters:

Name Type Description Default
x Tensor

The input tensor.

 required

Returns:

Type Description
Tensor

torch.Tensor: The scaled tensor.

Raises:

Type Description
TypeError

If the input is not a torch tensor.
Source code in spotpython/utils/scaler.py 
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def fit_transform(self, x: torch.Tensor) -> torch.Tensor:
    """
    Fit the scaler to the input tensor and then scale the tensor.

    Args:
        x (torch.Tensor): The input tensor.

    Returns:
        torch.Tensor: The scaled tensor.

    Raises:
        TypeError: If the input is not a torch tensor.
    """
    self.fit(x)
    return self.transform(x)

transform(x)

Scale the input tensor using the computed minimum and maximum values.

Parameters:

Name Type Description Default
x Tensor

The input tensor to be scaled.

 required

Returns:

Type Description
Tensor

torch.Tensor: The scaled tensor.

Raises:

Type Description
TypeError

If the input is not a torch tensor.
RuntimeError

If the scaler has not been fitted before transforming data.
Source code in spotpython/utils/scaler.py 
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def transform(self, x: torch.Tensor) -> torch.Tensor:
    """
    Scale the input tensor using the computed minimum and maximum values.

    Args:
        x (torch.Tensor): The input tensor to be scaled.

    Returns:
        torch.Tensor: The scaled tensor.

    Raises:
        TypeError: If the input is not a torch tensor.
        RuntimeError: If the scaler has not been fitted before transforming data.
    """
    if not torch.is_tensor(x):
        raise TypeError("Input should be a torch tensor")
    if self.min is None or self.max is None:
        raise RuntimeError("Must fit scaler before transforming data")
    x = (x - self.min) / (self.max - self.min + 1e-7)
    return x

TorchStandardScaler

A class for scaling data using standardization with torch tensors. This scaler computes the mean and standard deviation on a dataset so that it can later be used to scale the data using the computed mean and standard deviation.

Attributes:

Name Type Description
mean Tensor

The mean value computed over the fitted data.
std Tensor

The standard deviation computed over the fitted data.

Examples:

>>> import torch
>>> from spotpython.utils.scaler import TorchStandardScaler
# Create a sample tensor
>>> tensor = torch.rand((10, 3))  # Random tensor with shape (10, 3)
>>> scaler = TorchStandardScaler()
# Fit the scaler to the data
>>> scaler.fit(tensor)
# Transform the data using the fitted scaler
>>> transformed_tensor = scaler.transform(tensor)
>>> print(transformed_tensor)
# Using fit_transform method to fit and transform in one step
>>> another_tensor = torch.rand((10, 3))
>>> scaled_tensor = scaler.fit_transform(another_tensor)
>>> print(scaled_tensor)
Source code in spotpython/utils/scaler.py 
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class TorchStandardScaler:
    """
    A class for scaling data using standardization with torch tensors.
    This scaler computes the mean and standard deviation on a dataset so that
    it can later be used to scale the data using the computed mean and standard deviation.

    Attributes:
        mean (torch.Tensor): The mean value computed over the fitted data.
        std (torch.Tensor): The standard deviation computed over the fitted data.

    Examples:
        >>> import torch
        >>> from spotpython.utils.scaler import TorchStandardScaler
        # Create a sample tensor
        >>> tensor = torch.rand((10, 3))  # Random tensor with shape (10, 3)
        >>> scaler = TorchStandardScaler()
        # Fit the scaler to the data
        >>> scaler.fit(tensor)
        # Transform the data using the fitted scaler
        >>> transformed_tensor = scaler.transform(tensor)
        >>> print(transformed_tensor)
        # Using fit_transform method to fit and transform in one step
        >>> another_tensor = torch.rand((10, 3))
        >>> scaled_tensor = scaler.fit_transform(another_tensor)
        >>> print(scaled_tensor)
    """

    def __init__(self):
        """
        Initializes the TorchStandardScaler class without any pre-defined mean and std.
        """
        self.mean = None
        self.std = None

    def fit(self, x: torch.Tensor) -> None:
        """
        Compute the mean and standard deviation of the input tensor.

        Args:
            x (torch.Tensor): The input tensor, expected shape [n_samples, n_features]

        Raises:
            TypeError: If the input is not a torch tensor.
        """
        if not torch.is_tensor(x):
            raise TypeError("Input should be a torch tensor")
        self.mean = x.mean(dim=0, keepdim=True)
        self.std = x.std(dim=0, unbiased=False, keepdim=True)

    def transform(self, x: torch.Tensor) -> torch.Tensor:
        """
        Scale the input tensor using the computed mean and standard deviation.

        Args:
            x (torch.Tensor): The input tensor to be transformed, expected shape [n_samples, n_features]

        Returns:
            torch.Tensor: The scaled tensor.

        Raises:
            TypeError: If the input is not a torch tensor.
            RuntimeError: If the scaler has not been fitted before transforming data.
        """
        if not torch.is_tensor(x):
            raise TypeError("Input should be a torch tensor")
        if self.mean is None or self.std is None:
            raise RuntimeError("Must fit scaler before transforming data")
        x = (x - self.mean) / (self.std + 1e-7)
        return x

    def fit_transform(self, x: torch.Tensor) -> torch.Tensor:
        """
        Fit the scaler to the input tensor and then scale the tensor.

        Args:
            x (torch.Tensor): The input tensor, expected shape [n_samples, n_features]

        Returns:
            torch.Tensor: The scaled tensor.

        Raises:
            TypeError: If the input is not a torch tensor.
        """
        self.fit(x)
        return self.transform(x)

__init__()

Initializes the TorchStandardScaler class without any pre-defined mean and std.

Source code in spotpython/utils/scaler.py 
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def __init__(self):
    """
    Initializes the TorchStandardScaler class without any pre-defined mean and std.
    """
    self.mean = None
    self.std = None

fit(x)

Compute the mean and standard deviation of the input tensor.

Parameters:

Name Type Description Default
x Tensor

The input tensor, expected shape [n_samples, n_features]

 required

Raises:

Type Description
TypeError

If the input is not a torch tensor.
Source code in spotpython/utils/scaler.py 
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def fit(self, x: torch.Tensor) -> None:
    """
    Compute the mean and standard deviation of the input tensor.

    Args:
        x (torch.Tensor): The input tensor, expected shape [n_samples, n_features]

    Raises:
        TypeError: If the input is not a torch tensor.
    """
    if not torch.is_tensor(x):
        raise TypeError("Input should be a torch tensor")
    self.mean = x.mean(dim=0, keepdim=True)
    self.std = x.std(dim=0, unbiased=False, keepdim=True)

fit_transform(x)

Fit the scaler to the input tensor and then scale the tensor.

Parameters:

Name Type Description Default
x Tensor

The input tensor, expected shape [n_samples, n_features]

 required

Returns:

Type Description
Tensor

torch.Tensor: The scaled tensor.

Raises:

Type Description
TypeError

If the input is not a torch tensor.
Source code in spotpython/utils/scaler.py 
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def fit_transform(self, x: torch.Tensor) -> torch.Tensor:
    """
    Fit the scaler to the input tensor and then scale the tensor.

    Args:
        x (torch.Tensor): The input tensor, expected shape [n_samples, n_features]

    Returns:
        torch.Tensor: The scaled tensor.

    Raises:
        TypeError: If the input is not a torch tensor.
    """
    self.fit(x)
    return self.transform(x)

transform(x)

Scale the input tensor using the computed mean and standard deviation.

Parameters:

Name Type Description Default
x Tensor

The input tensor to be transformed, expected shape [n_samples, n_features]

 required

Returns:

Type Description
Tensor

torch.Tensor: The scaled tensor.

Raises:

Type Description
TypeError

If the input is not a torch tensor.
RuntimeError

If the scaler has not been fitted before transforming data.
Source code in spotpython/utils/scaler.py 
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def transform(self, x: torch.Tensor) -> torch.Tensor:
    """
    Scale the input tensor using the computed mean and standard deviation.

    Args:
        x (torch.Tensor): The input tensor to be transformed, expected shape [n_samples, n_features]

    Returns:
        torch.Tensor: The scaled tensor.

    Raises:
        TypeError: If the input is not a torch tensor.
        RuntimeError: If the scaler has not been fitted before transforming data.
    """
    if not torch.is_tensor(x):
        raise TypeError("Input should be a torch tensor")
    if self.mean is None or self.std is None:
        raise RuntimeError("Must fit scaler before transforming data")
    x = (x - self.mean) / (self.std + 1e-7)
    return x