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Modular toolkit for Data Processing MDP
Package mdp :: Package nodes :: Class ImputerScikitsLearnNode
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Class ImputerScikitsLearnNode


Imputation transformer for completing missing values.

This node has been automatically generated by wrapping the ``sklearn.preprocessing.imputation.Imputer`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

Read more in the :ref:`User Guide <imputation>`.

**Parameters**

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.

**Attributes**

``statistics_`` : array of shape (n_features,)
    The imputation fill value for each feature if axis == 0.

**Notes**

- 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).
Instance Methods [hide private]
 
__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
Imputation transformer for completing missing values.
 
_execute(self, x)
 
_get_supported_dtypes(self)
Return the list of dtypes supported by this node. The types can be specified in any format allowed by numpy.dtype.
 
_stop_training(self, **kwargs)
Concatenate the collected data in a single array.
 
execute(self, x)
Impute all missing values in X.
 
stop_training(self, **kwargs)
Fit the imputer on X.

Inherited from unreachable.newobject: __long__, __native__, __nonzero__, __unicode__, next

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __setattr__, __sizeof__, __subclasshook__

    Inherited from Cumulator
 
_train(self, *args)
Collect all input data in a list.
 
train(self, *args)
Collect all input data in a list.
    Inherited from Node
 
__add__(self, other)
 
__call__(self, x, *args, **kwargs)
Calling an instance of Node is equivalent to calling its execute method.
 
__repr__(self)
repr(x)
 
__str__(self)
str(x)
 
_check_input(self, x)
 
_check_output(self, y)
 
_check_train_args(self, x, *args, **kwargs)
 
_get_train_seq(self)
 
_if_training_stop_training(self)
 
_inverse(self, x)
 
_pre_execution_checks(self, x)
This method contains all pre-execution checks.
 
_pre_inversion_checks(self, y)
This method contains all pre-inversion checks.
 
_refcast(self, x)
Helper function to cast arrays to the internal dtype.
 
_set_dtype(self, t)
 
_set_input_dim(self, n)
 
_set_output_dim(self, n)
 
copy(self, protocol=None)
Return a deep copy of the node.
 
get_current_train_phase(self)
Return the index of the current training phase.
 
get_dtype(self)
Return dtype.
 
get_input_dim(self)
Return input dimensions.
 
get_output_dim(self)
Return output dimensions.
 
get_remaining_train_phase(self)
Return the number of training phases still to accomplish.
 
get_supported_dtypes(self)
Return dtypes supported by the node as a list of dtype objects.
 
has_multiple_training_phases(self)
Return True if the node has multiple training phases.
 
inverse(self, y, *args, **kwargs)
Invert y.
 
is_training(self)
Return True if the node is in the training phase, False otherwise.
 
save(self, filename, protocol=-1)
Save a pickled serialization of the node to filename. If filename is None, return a string.
 
set_dtype(self, t)
Set internal structures' dtype.
 
set_input_dim(self, n)
Set input dimensions.
 
set_output_dim(self, n)
Set output dimensions.
Static Methods [hide private]
 
is_invertible()
Return True if the node can be inverted, False otherwise.
 
is_trainable()
Return True if the node can be trained, False otherwise.
Properties [hide private]

Inherited from object: __class__

    Inherited from Node
  _train_seq
List of tuples:
  dtype
dtype
  input_dim
Input dimensions
  output_dim
Output dimensions
  supported_dtypes
Supported dtypes
Method Details [hide private]

__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
(Constructor)

  

Imputation transformer for completing missing values.

This node has been automatically generated by wrapping the ``sklearn.preprocessing.imputation.Imputer`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

Read more in the :ref:`User Guide <imputation>`.

**Parameters**

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.

**Attributes**

``statistics_`` : array of shape (n_features,)
    The imputation fill value for each feature if axis == 0.

**Notes**

- 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).
Overrides: object.__init__

_execute(self, x)

 
Overrides: Node._execute

_get_supported_dtypes(self)

 
Return the list of dtypes supported by this node. The types can be specified in any format allowed by numpy.dtype.
Overrides: Node._get_supported_dtypes

_stop_training(self, **kwargs)

 
Concatenate the collected data in a single array.
Overrides: Node._stop_training

execute(self, x)

 

Impute all missing values in X.

This node has been automatically generated by wrapping the sklearn.preprocessing.imputation.Imputer class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

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

is_invertible()
Static Method

 
Return True if the node can be inverted, False otherwise.
Overrides: Node.is_invertible
(inherited documentation)

is_trainable()
Static Method

 
Return True if the node can be trained, False otherwise.
Overrides: Node.is_trainable

stop_training(self, **kwargs)

 

Fit the imputer on X.

This node has been automatically generated by wrapping the sklearn.preprocessing.imputation.Imputer class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

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.

Returns

self : object
Returns self.
Overrides: Node.stop_training

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Modular toolkit for Data Processing MDP
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