skoot.preprocessing.SelectiveStandardScaler

class skoot.preprocessing.SelectiveStandardScaler(cols=None, as_df=True, trans_col_name=None, **kwargs)[source][source]

Standardize features by removing the mean and scaling to unit variance (applied to selected columns).

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 the transform method.

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 that others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected.

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.

This class wraps scikit-learn’s StandardScaler. When a pd.DataFrame is passed to the fit method, the transformation is applied to the selected columns, which are subsequently dropped from the frame. All remaining columns are left alone.

Parameters:

cols : array-like, shape=(n_features,), optional (default=None)

The names of the columns on which to apply the transformation. If no column names are provided, the transformer will be fit on the entire frame. Note that the transformation will also only apply to the specified columns, and any other non-specified columns will still be present after the transformation.

as_df : bool, optional (default=True)

Whether to return a Pandas DataFrame in the transform method. If False, will return a Numpy ndarray instead. Since most skoot transformers depend on explicitly-named DataFrame features, the as_df parameter is True by default.

trans_col_name : str, unicode or iterable, optional

The name or list of names to apply to the transformed column(s). If a string is provided, it is used as a prefix for new columns. If an iterable is provided, its dimensions must match the number of produced columns. If None (default), will use the estimator class name as the prefix.

copy : boolean, optional, 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 : boolean, True by default

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 : boolean, True by default

If True, scale the data to unit variance (or equivalently, unit standard deviation).

Attributes

scale_ (ndarray or None, shape (n_features,)) Per feature relative scaling of the data. This is calculated using np.sqrt(var_). Equal to None when with_std=False. .. versionadded:: 0.17 scale_
mean_ (ndarray or None, shape (n_features,)) The mean value for each feature in the training set. Equal to None when with_mean=False.
var_ (ndarray or None, shape (n_features,)) The variance for each feature in the training set. Used to compute scale_. Equal to None when with_std=False.
n_samples_seen_ (int or array, shape (n_features,)) The number of samples processed by the estimator for each feature. If there are not missing samples, the n_samples_seen will be an integer, otherwise it will be an array. Will be reset on new calls to fit, but increments across partial_fit calls.

See also

scale
Equivalent function without the estimator API.
sklearn.decomposition.PCA
Further removes the linear correlation across features with ‘whiten=True’.

Examples

>>> from sklearn.preprocessing import StandardScaler
>>> data = [[0, 0], [0, 0], [1, 1], [1, 1]]
>>> scaler = StandardScaler()
>>> print(scaler.fit(data))
StandardScaler(copy=True, with_mean=True, with_std=True)
>>> print(scaler.mean_)
[0.5 0.5]
>>> print(scaler.transform(data))
[[-1. -1.]
 [-1. -1.]
 [ 1.  1.]
 [ 1.  1.]]
>>> print(scaler.transform([[2, 2]]))
[[3. 3.]]

Methods

fit(X[, y]) Fit the wrapped transformer.
fit_transform(X[, y]) Fit to data, then transform it.
get_params([deep]) Get parameters for this estimator.
set_params(**params) Set the parameters of this estimator.
transform(X) Transform a test dataframe.
__init__(cols=None, as_df=True, trans_col_name=None, **kwargs)[source]

Initialize self. See help(type(self)) for accurate signature.

fit(X, y=None, **fit_kwargs)[source]

Fit the wrapped transformer.

This method will fit the wrapped sklearn transformer on the selected columns, leaving other columns alone.

Parameters:

X : pd.DataFrame, shape=(n_samples, n_features)

The Pandas frame to fit. The frame will only be fit on the prescribed cols (see __init__) or all of them if cols is None. Furthermore, X will not be altered in the process of the fit.

y : array-like or None, shape=(n_samples,), optional (default=None)

Pass-through for sklearn.pipeline.Pipeline. Even if explicitly set, will not change behavior of fit.
fit_transform(X, y=None, **fit_params)[source]

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.
Returns:

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.
get_params(deep=True)[source]

Get parameters for this estimator.

Parameters:

deep : boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:

params : mapping of string to any

Parameter names mapped to their values.
set_params(**params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self
transform(X)[source]

Transform a test dataframe.

Parameters:

X : pd.DataFrame, shape=(n_samples, n_features)

The Pandas frame to transform. The operation will be applied to a copy of the input data, and the result will be returned.
Returns:

X : pd.DataFrame, shape=(n_samples, n_features)

The operation is applied to a copy of X, and the result set is returned.

Examples using skoot.preprocessing.SelectiveStandardScaler