skoot.feature_selection.MultiCorrFilter

class skoot.feature_selection.MultiCorrFilter(cols=None, threshold=0.85, method='pearson', as_df=True)

Remove highly correlated features.

Multi-collinear data (features which are not independent from one another) can pose problems in coefficient stability for parametric models, or feature importance scores for non-parametric models.

This class filters out features with a correlation greater than the provided threshold. When a pair of correlated features is identified, the mean absolute correlation (MAC) of each feature is considered, and the feature with the highest MAC is discarded.

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 transformation.

threshold : float, optional (default=0.85)

The threshold above which to filter correlated features

method : str, optional (default=’pearson’)

The method used to compute the correlation, one of (‘pearson’, ‘kendall’, ‘spearman’).

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.

Attributes

drop_	(array-like, shape=(n_features,)) Assigned after calling fit. These are the features that are designated as “bad” and will be dropped in the transform method.
mean_abs_correlations_	(list, float) The corresponding mean absolute correlations of each drop_ name

Examples

The following demonstrates a simple multi-correlation filter applied to the iris dataset.

>>> from skoot.datasets import load_iris_df
>>>
>>> X = load_iris_df(include_tgt=False)
>>> mcf = MultiCorrFilter(threshold=0.85)
>>> mcf.fit_transform(X).head()
   sepal length (cm)  sepal width (cm)  petal width (cm)
0                5.1               3.5               0.2
1                4.9               3.0               0.2
2                4.7               3.2               0.2
3                4.6               3.1               0.2
4                5.0               3.6               0.2

Methods

fit(X[, y])	Fit the multi-collinearity filter.
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, threshold=0.85, method='pearson', as_df=True)

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

fit(X, y=None)

Fit the multi-collinearity filter.

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)

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)

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)

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)

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

The selected columns from X.