(De)Normalizer

normalizer.py

import dataclasses
from typing import Self

import numpy as np

__all__ = (
    "MinMaxNormalizer",
)


@dataclasses.dataclass(frozen=True)
class MinMaxNormalizer:
    """
    Dataclass for normalizing and denormalizing data using min-max normalization scaling to 0..1.

    Attributes:
        min_val (float): The minimum value of the data before normalization.
        max_val (float): The maximum value of the data before normalization.

    Usage Examples:
        >>> import numpy as np
        >>> from normalizer import MinMaxNormalizer  # replace with the actual module name

        >>> # Create a numpy array
        >>> data = np.array([1, 2, 3, 4, 5])

        >>> # Create a normalizer and normalize the data
        >>> normalizer, normalized_data = MinMaxNormalizer.from_regular(data)
        >>> print(normalized_data)
        [0.   0.25 0.5  0.75 1.  ]

        >>> # Denormalize the data
        >>> denormalized_data = normalizer.denormalize(normalized_data)
        >>> print(denormalized_data)
        [1. 2. 3. 4. 5.]

    Note:
        The `from_regular` class method is used to create a `_MinMaxNormalizer` instance and normalize the data in one step.
        The `normalize` and `denormalize` methods are used to normalize and denormalize the data, respectively.
    """
    min_val: float
    max_val: float

    @classmethod
    def from_regular(cls, regular: np.ndarray) -> (Self, np.ndarray):
        obj = cls(
            min_val=regular.min(),
            max_val=regular.max(),
        )
        return obj, obj.normalize(regular=regular)

    def normalize(self, regular: np.ndarray) -> np.ndarray:
        return (regular - self.min_val) / (self.max_val - self.min_val)

    def denormalize(self, normalized: np.ndarray) -> np.ndarray:
        return normalized * (self.max_val - self.min_val) + self.min_val

test_normalizer

import numpy as np
import pytest

from normalizer import MinMaxNormalizer


class TestMinMaxNormalizer:
    @pytest.fixture
    def normalizer_and_data(self) -> (MinMaxNormalizer, np.ndarray, np.ndarray):
        data = np.array([1, 2, 3, 4, 5])
        normalizer, normalized = MinMaxNormalizer.from_regular(data)
        return normalizer, data, normalized

    def test_acceptance(self, normalizer_and_data):
        normalizer, data, normalized = normalizer_and_data
        denormalized = normalizer.denormalize(normalized)
        assert np.allclose(denormalized, data)

    @pytest.mark.parametrize("regular, normalized", [
        (np.array([1, 2, 3, 4, 5]), np.array([0., 0.25, 0.5, 0.75, 1.])),
        (np.array([10, 20, 30, 40, 50]), np.array([0., 0.25, 0.5, 0.75, 1.])),
        (np.array([[1, 2], [3, 4]]), np.array([[0., 0.3333], [0.6666, 1.]])),
        (np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]), np.array([[[0., 0.1428], [0.2857, 0.4285]], [[0.5714, 0.7142], [0.8571, 1.]]])),
    ])
    def test_normalize_and_back(self, regular, normalized):
        normalizer, actual_normalized = MinMaxNormalizer.from_regular(regular)
        assert np.allclose(actual_normalized, normalized, rtol=1e-3)
        actual_denormalized = normalizer.denormalize(normalized)
        assert np.allclose(actual_denormalized, regular, rtol=1e-3)

    def test_from_regular(self, normalizer_and_data):
        normalizer, data, normalized = normalizer_and_data
        assert normalizer.min_val == 1
        assert normalizer.max_val == 5
        assert np.allclose(normalized, np.array([0., 0.25, 0.5, 0.75, 1.]))

    def test_empty_array(self):
        with pytest.raises(ValueError):
            MinMaxNormalizer.from_regular(np.array([]))

... Or just use https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html