Imputer

class ibex.sklearn.preprocessing.Imputer(missing_values='NaN', strategy='mean', axis=0, verbose=0, copy=True)

Bases: sklearn.preprocessing.imputation.Imputer, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Imputation transformer for completing missing values.

Read more in the User Guide.

missing_values : integer or “NaN”, optional (default=”NaN”)
The placeholder for the missing values. All occurrences of missing_values will be imputed. For missing values encoded as np.nan, use the string value “NaN”.
strategy : string, optional (default=”mean”)

The imputation strategy.

  • If “mean”, then replace missing values using the mean along the axis.
  • If “median”, then replace missing values using the median along the axis.
  • If “most_frequent”, then replace missing using the most frequent value along the axis.
axis : integer, optional (default=0)

The axis along which to impute.

  • If axis=0, then impute along columns.
  • If axis=1, then impute along rows.
verbose : integer, optional (default=0)
Controls the verbosity of the imputer.
copy : boolean, optional (default=True)

If True, a copy of X will be created. If False, imputation will be done in-place whenever possible. Note that, in the following cases, a new copy will always be made, even if copy=False:

  • If X is not an array of floating values;
  • If X is sparse and missing_values=0;
  • If axis=0 and X is encoded as a CSR matrix;
  • If axis=1 and X is encoded as a CSC matrix.
statistics_ : array of shape (n_features,)
The imputation fill value for each feature if axis == 0.
  • When axis=0, columns which only contained missing values at fit are discarded upon transform.
  • When axis=1, an exception is raised if there are rows for which it is not possible to fill in the missing values (e.g., because they only contain missing values).
fit(X, y=None)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit the imputer on X.

X : {array-like, sparse matrix}, shape (n_samples, n_features)
Input data, where n_samples is the number of samples and n_features is the number of features.
self : Imputer
Returns self.
fit_transform(X, y=None, **fit_params)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit to data, then transform it.

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

X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
transform(X)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Impute all missing values in X.

X : {array-like, sparse matrix}, shape = [n_samples, n_features]
The input data to complete.