mojolearn.MinMaxScaler#

class mojolearn.MinMaxScaler(feature_range=(0, 1), *, copy=True, clip=False, numeric_mode=None)[source]#

Bases: _ScalerProtocol

Scale each feature to a given range.

Computes each feature’s minimum and maximum and maps them linearly onto feature_range. Input must be a dense, nonempty two-dimensional float32 array: a float64 or integer array raises TypeError (nested Python lists of numbers are converted). Sparse input is not supported. The endpoints of feature_range are rounded to float32. A feature whose range is below 10 float32 epsilons (including a constant feature) uses a denominator of one.

NaN is treated as missing: fit ignores it (a feature with no values gets NaN statistics) and transform keeps it. Infinity raises ValueError. The numeric mode is resolved at fit and kept for later transforms, including after pickling.

Parameters:

  • feature_range (tuple of two floats, default (0, 1)) – Target range (min, max); both finite in float32, with min < max after rounding.

  • copy (bool, default True) – If False, transform in place when X is already a writable C-contiguous float32 buffer.

  • clip (bool, default False) – Clip transformed values to feature_range. Values lost to clipping are not recovered by inverse_transform.

  • numeric_mode ({“fast”, “deterministic”, “identical”} or None, default None) – Numeric mode; None uses the library default.

Variables:

  • data_min_ (Array of shape (n_features,), float32) – Per-feature minimum seen.

  • data_max_ (Array of shape (n_features,), float32) – Per-feature maximum seen.

  • data_range_ (Array of shape (n_features,), float32) – data_max_ - data_min_.

  • scale_ (Array of shape (n_features,), float32) – Per-feature scale factor.

  • min_ (Array of shape (n_features,), float32) – Per-feature offset.

  • n_features_in_ (int) – Number of features seen during fit.

  • n_samples_seen_ (int) – Number of rows processed.

  • numeric_mode_ (str) – The numeric mode used by the fit.

  • feature_range_ (tuple of float) – feature_range as rounded to float32.

  • clip_ (bool) – The clip setting captured by the fit.

Examples

>>> import numpy as np, mojolearn
>>> X = np.random.default_rng(0).random((1000, 8), dtype=np.float32)
>>> scaler = mojolearn.MinMaxScaler().fit(X)
>>> Z = scaler.transform(X)
__init__(feature_range=(0, 1), *, copy=True, clip=False, numeric_mode=None)[source]#

Methods

fit(X[, y, sample_weight])

Compute the per-feature minimum and maximum.

fit_transform(X[, y])

get_params([deep])

inverse_transform(X)

Undo the scaling.

load(path)

Load a scaler written by save.

partial_fit(X[, y])

Update the running minimum and maximum with a batch of rows.

save(path)

Save the fitted scaler to path as an .npz file.

set_params(**params)

transform(X)

Scale X to feature_range.
fit(X, y=None, sample_weight=None)[source]#

Compute the per-feature minimum and maximum.

Parameters:

  • X (array-like of shape (n_samples, n_features), float32) – Training data; NaN is ignored, infinity is refused.

  • y (ignored)

  • sample_weight (None) – Not supported; any value raises NotImplementedError.

Returns:

self (MinMaxScaler) – The fitted scaler.

partial_fit(X, y=None)[source]#

Update the running minimum and maximum with a batch of rows.

On an unfitted scaler this is fit. A feature with NaN statistics stays NaN.

Parameters:

  • X (array-like of shape (n_samples, n_features), float32) – A batch of rows with the fitted feature count.

  • y (ignored)

Returns:

self (MinMaxScaler) – The updated scaler.

Raises:

ValueError – If feature_range changed since the first batch or the feature count differs.

transform(X)[source]#

Scale X to feature_range.

Parameters:

X (array-like of shape (n_samples, n_features), float32) – Data with the fitted feature count; NaN is kept.

Returns:

X_scaled (Array of shape (n_samples, n_features), float32) – The scaled data.

Raises:

  • NotFittedError – If the scaler is not fitted (a subclass of ValueError and AttributeError).

  • ValueError – If the feature count differs or the result overflows float32.

inverse_transform(X)[source]#

Undo the scaling.

Parameters:

X (array-like of shape (n_samples, n_features), float32) – Scaled data.

Returns:

X_original (Array of shape (n_samples, n_features), float32) – The data in its original units.

save(path)[source]#

Save the fitted scaler to path as an .npz file.

A scaler restored with MinMaxScaler.load or mojolearn.host_model(path) transforms, including on a CPU-only install.

Parameters:

path (str or path-like) – Destination file.

Raises:

RuntimeError – If the scaler is not fitted.

classmethod load(path)[source]#

Load a scaler written by save.

The loaded scaler transforms; arrays are read at their saved dtype and never cast.

Parameters:

path (str or path-like) – File written by MinMaxScaler.save.

Returns:

scaler (MinMaxScaler) – The restored scaler.

Raises:

ValueError – If the file is not a MinMaxScaler or its arrays are inconsistent.