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:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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 andn_features
is the number of features.
- self : Imputer
- Returns self.
- A parameter
-
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:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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.
- A parameter
-
transform
(X)[source]¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Impute all missing values in X.
- X : {array-like, sparse matrix}, shape = [n_samples, n_features]
- The input data to complete.
- A parameter
- A parameter