skoot.feature_selection.FeatureFilter

class skoot.feature_selection.FeatureFilter(cols=None, as_df=True)

A simple feature-dropping transformer class.

A very simple class to be used at the beginning or any stage of a Pipeline that will drop the given features from the remainder of the pipe. This is useful if a transformer or encoder creates variables that you’re disinterested in and would like to exclude from your modeling process.

Parameters:

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

The features to drop. Note that FeatureFilter behaves slightly differently from all other BaseFeatureSelector classes in the sense that it will drop all of the features prescribed in this parameter. However, if cols is None, it will not drop any (which is counter to other classes, which will operate on all columns in the absence of an explicit cols parameter).

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.

Examples

An example using the FeatureFilter:

>>> import numpy as np
>>> import pandas as pd
>>>
>>> X = pd.DataFrame.from_records(data=np.random.rand(3,3),
...                               columns=['a','b','c'])
>>> dropper = FeatureFilter(cols=['a','b'])
>>> X_transform = dropper.fit_transform(X)
>>> assert X_transform.shape[1] == 1 # drop out first two columns

Methods

fit(X[, y])	Fit the 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)

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

fit(X, y=None)

Fit the transformer.

Default behavior is not to fit any parameters and return self. This is useful for transformers which do not require parameterization, but need to fit into a pipeline.

Parameters:

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

The Pandas frame to fit.

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

Pass-through for sklearn.pipeline.Pipeline.

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.