skrobot.feature_selection package¶
Submodules¶
skrobot.feature_selection.column_selector module¶
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class
skrobot.feature_selection.column_selector.ColumnSelector(cols, drop_axis=False)[source]¶ Bases:
sklearn.base.BaseEstimatorThe
ColumnSelectorclass is an implementation of a column selector for scikit-learn pipelines.It can be used for manual feature selection to select specific columns from an input data set.
It can select columns either by integer indices or by names.
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__init__(cols, drop_axis=False)[source]¶ This is the constructor method and can be used to create a new object instance of
ColumnSelectorclass.- Parameters
cols (list) – A non-empty list specifying the columns to be selected. For example, [1, 4, 5] to select the 2nd, 5th, and 6th columns, and [‘A’,’C’,’D’] to select the columns A, C and D.
drop_axis (bool, optional) – Can be used to reshape the output data set from (n_samples, 1) to (n_samples) by dropping the last axis. It defaults to False.
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fit_transform(X, y=None)[source]¶ Returns a slice of the input data set.
- Parameters
X ({NumPy array, pandas DataFrame, SciPy sparse matrix}) – Input vectors of shape (n_samples, n_features), where n_samples is the number of samples and n_features is the number of features.
y (None) – Ignored.
- Returns
Subset of the input data set of shape (n_samples, k_features), where n_samples is the number of samples and k_features <= n_features.
- Return type
{NumPy array, SciPy sparse matrix}
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transform(X, y=None)[source]¶ Returns a slice of the input data set.
- Parameters
X ({NumPy array, pandas DataFrame, SciPy sparse matrix}) – Input vectors of shape (n_samples, n_features), where n_samples is the number of samples and n_features is the number of features.
y (None) – Ignored.
- Returns
Subset of the input data set of shape (n_samples, k_features), where n_samples is the number of samples and k_features <= n_features.
- Return type
{NumPy array, SciPy sparse matrix}
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