maxframe.learn.preprocessing.StandardScaler#

class maxframe.learn.preprocessing.StandardScaler(*, copy=True, with_mean=True, with_std=True, validate=True)[source]#

Standardize features by removing the mean and scaling to unit variance.

The standard score of a sample x is calculated as:

z = (x - u) / s

where u is the mean of the training samples or zero if with_mean=False, and s is the standard deviation of the training samples or one if with_std=False.

Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set. Mean and standard deviation are then stored to be used on later data using transform().

Standardization of a dataset is a common requirement for many machine learning estimators: they might behave badly if the individual features do not more or less look like standard normally distributed data (e.g. Gaussian with 0 mean and unit variance).

For instance many elements used in the objective function of a learning algorithm (such as the RBF kernel of Support Vector Machines or the L1 and L2 regularizers of linear models) assume that all features are centered around 0 and have variance in the same order. If a feature has a variance that is orders of magnitude larger than others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected.

StandardScaler is sensitive to outliers, and the features may scale differently from each other in the presence of outliers. For an example visualization, refer to Compare StandardScaler with other scalers.

This scaler can also be applied to sparse CSR or CSC matrices by passing with_mean=False to avoid breaking the sparsity structure of the data.

Read more in the User Guide.

Parameters:

  • copy (bool, default=True) – If False, try to avoid a copy and do inplace scaling instead. This is not guaranteed to always work inplace; e.g. if the data is not a NumPy array or scipy.sparse CSR matrix, a copy may still be returned.

  • with_mean (bool, default=True) – If True, center the data before scaling. This does not work (and will raise an exception) when attempted on sparse matrices, because centering them entails building a dense matrix which in common use cases is likely to be too large to fit in memory.

  • with_std (bool, default=True) – If True, scale the data to unit variance (or equivalently, unit standard deviation).

scale_#

Per feature relative scaling of the data to achieve zero mean and unit variance. Generally this is calculated using np.sqrt(var_). If a variance is zero, we can’t achieve unit variance, and the data is left as-is, giving a scaling factor of 1. scale_ is equal to None when with_std=False.

Type:

ndarray of shape (n_features,) or None

mean_#

The mean value for each feature in the training set. Equal to None when with_mean=False and with_std=False.

Type:

ndarray of shape (n_features,) or None

var_#

The variance for each feature in the training set. Used to compute scale_. Equal to None when with_mean=False and with_std=False.

Type:

ndarray of shape (n_features,) or None

n_features_in_#

Number of features seen during fit.

Type:

int

feature_names_in_#

Names of features seen during fit. Defined only when X has feature names that are all strings.

Type:

ndarray of shape (n_features_in_,)

n_samples_seen_#

The number of samples processed by the estimator for each feature. If there are no missing samples, the n_samples_seen will be an integer, otherwise it will be an array of dtype int. If sample_weights are used it will be a float (if no missing data) or an array of dtype float that sums the weights seen so far. Will be reset on new calls to fit, but increments across partial_fit calls.

Type:

int or ndarray of shape (n_features,)

See also

scale

Equivalent function without the estimator API.

PCA

Further removes the linear correlation across features with ‘whiten=True’.

Notes

NaNs are treated as missing values: disregarded in fit, and maintained in transform.

We use a biased estimator for the standard deviation, equivalent to numpy.std(x, ddof=0). Note that the choice of ddof is unlikely to affect model performance.

Examples

>>> from maxframe.learn.preprocessing import StandardScaler
>>> data = [[0, 0], [0, 0], [1, 1], [1, 1]]
>>> scaler = StandardScaler()
>>> print(scaler.fit(data))
StandardScaler()
>>> print(scaler.mean_.execute())
[0.5 0.5]
>>> print(scaler.transform(data).execute())
[[-1. -1.]
 [-1. -1.]
 [ 1.  1.]
 [ 1.  1.]]
>>> print(scaler.transform([[2, 2]]).execute())
[[3. 3.]]
__init__(*, copy=True, with_mean=True, with_std=True, validate=True)[source]#

Methods

__init__(*[, copy, with_mean, with_std, ...])

execute([session, run_kwargs, extra_tileables])

fetch([session, run_kwargs])

fit(X[, y, sample_weight, execute, session, ...])

Compute the mean and std to be used for later scaling.

fit_transform(X[, y])

Fit to data, then transform it.

inverse_transform(X[, copy, execute, ...])

Scale back the data to the original representation.

partial_fit(X[, y, sample_weight, execute, ...])

Online computation of mean and std on X for later scaling.

transform(X[, copy, execute, session, ...])

Perform standardization by centering and scaling.