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data_transform_param

data_transform_param

Classes

DataTransformParam (BaseParam)

Define data transform parameters that used in federated ml.

Parameters:

Name Type Description Default
input_format {'dense', 'sparse', 'tag'}

please have a look at this tutorial at "DataTransform" section of federatedml/util/README.md. Formally, dense input format data should be set to "dense", svm-light input format data should be set to "sparse", tag or tag:value input format data should be set to "tag".

'dense'
delimitor str

the delimitor of data input, default: ','

','
data_type int

{'float64','float','int','int64','str','long'} the data type of data input

'float64'
exclusive_data_type dict

the key of dict is col_name, the value is data_type, use to specified special data type of some features.

None
tag_with_value bool

use if input_format is 'tag', if tag_with_value is True, input column data format should be tag[delimitor]value, otherwise is tag only

False
tag_value_delimitor str

use if input_format is 'tag' and 'tag_with_value' is True, delimitor of tag[delimitor]value column value.

':'
missing_fill bool

need to fill missing value or not, accepted only True/False, default: False

False
default_value None or object or list

the value to replace missing value. if None, it will use default value define in federatedml/feature/imputer.py, if single object, will fill missing value with this object, if list, it's length should be the sample of input data' feature dimension, means that if some column happens to have missing values, it will replace it the value by element in the identical position of this list.

0
missing_fill_method None or str

the method to replace missing value, should be one of [None, 'min', 'max', 'mean', 'designated']

None
missing_impute None or list

element of list can be any type, or auto generated if value is None, define which values to be consider as missing

None
outlier_replace bool

need to replace outlier value or not, accepted only True/False, default: True

False
outlier_replace_method None or str

the method to replace missing value, should be one of [None, 'min', 'max', 'mean', 'designated']

None
outlier_impute None or list

element of list can be any type, which values should be regard as missing value

None
outlier_replace_value None or object or list

the value to replace outlier. if None, it will use default value define in federatedml/feature/imputer.py, if single object, will replace outlier with this object, if list, it's length should be the sample of input data' feature dimension, means that if some column happens to have outliers, it will replace it the value by element in the identical position of this list.

0
with_label bool

True if input data consist of label, False otherwise. default: 'false'

False
label_name str

column_name of the column where label locates, only use in dense-inputformat. default: 'y'

'y'
label_type {'int','int64','float','float64','long','str'}

use when with_label is True

'int'
output_format {'dense', 'sparse'}

output format

'dense'
with_match_id bool

True if dataset has match_id, default: False

False
Source code in federatedml/param/data_transform_param.py
class DataTransformParam(BaseParam):
    """
    Define data transform parameters that used in federated ml.

    Parameters
    ----------
    input_format : {'dense', 'sparse', 'tag'}
        please have a look at this tutorial at "DataTransform" section of federatedml/util/README.md.
        Formally,
            dense input format data should be set to "dense",
            svm-light input format data should be set to "sparse",
            tag or tag:value input format data should be set to "tag".

    delimitor : str 
        the delimitor of data input, default: ','

    data_type : int
        {'float64','float','int','int64','str','long'}
        the data type of data input

    exclusive_data_type : dict 
        the key of dict is col_name, the value is data_type, use to specified special data type
        of some features.

    tag_with_value: bool
        use if input_format is 'tag', if tag_with_value is True,
        input column data format should be tag[delimitor]value, otherwise is tag only

    tag_value_delimitor: str
        use if input_format is 'tag' and 'tag_with_value' is True,
        delimitor of tag[delimitor]value column value.

    missing_fill : bool
        need to fill missing value or not, accepted only True/False, default: False

    default_value : None or object or list
        the value to replace missing value.
        if None, it will use default value define in federatedml/feature/imputer.py,
        if single object, will fill missing value with this object,
        if list, it's length should be the sample of input data' feature dimension,
        means that if some column happens to have missing values, it will replace it
        the value by element in the identical position of this list.

    missing_fill_method: None or str
        the method to replace missing value, should be one of [None, 'min', 'max', 'mean', 'designated']

    missing_impute: None or list
        element of list can be any type, or auto generated if value is None, define which values to be consider as missing

    outlier_replace: bool
        need to replace outlier value or not, accepted only True/False, default: True

    outlier_replace_method: None or str
        the method to replace missing value, should be one of [None, 'min', 'max', 'mean', 'designated']

    outlier_impute: None or list
        element of list can be any type, which values should be regard as missing value

    outlier_replace_value: None or object or list
        the value to replace outlier.
        if None, it will use default value define in federatedml/feature/imputer.py,
        if single object, will replace outlier with this object,
        if list, it's length should be the sample of input data' feature dimension,
        means that if some column happens to have outliers, it will replace it
        the value by element in the identical position of this list.

    with_label : bool
        True if input data consist of label, False otherwise. default: 'false'

    label_name : str
        column_name of the column where label locates, only use in dense-inputformat. default: 'y'

    label_type : {'int','int64','float','float64','long','str'}
        use when with_label is True

    output_format : {'dense', 'sparse'}
        output format

    with_match_id: bool
        True if dataset has match_id, default: False

    """
    def __init__(self, input_format="dense", delimitor=',', data_type='float64',
                 exclusive_data_type=None,
                 tag_with_value=False, tag_value_delimitor=":",
                 missing_fill=False, default_value=0, missing_fill_method=None,
                 missing_impute=None, outlier_replace=False, outlier_replace_method=None,
                 outlier_impute=None, outlier_replace_value=0,
                 with_label=False, label_name='y',
                 label_type='int', output_format='dense', need_run=True,
                 with_match_id=False):
        self.input_format = input_format
        self.delimitor = delimitor
        self.data_type = data_type
        self.exclusive_data_type = exclusive_data_type
        self.tag_with_value = tag_with_value
        self.tag_value_delimitor = tag_value_delimitor
        self.missing_fill = missing_fill
        self.default_value = default_value
        self.missing_fill_method = missing_fill_method
        self.missing_impute = missing_impute
        self.outlier_replace = outlier_replace
        self.outlier_replace_method = outlier_replace_method
        self.outlier_impute = outlier_impute
        self.outlier_replace_value = outlier_replace_value
        self.with_label = with_label
        self.label_name = label_name
        self.label_type = label_type
        self.output_format = output_format
        self.need_run = need_run
        self.with_match_id = with_match_id

    def check(self):

        descr = "data_transform param's"

        self.input_format = self.check_and_change_lower(self.input_format,
                                                        ["dense", "sparse", "tag"],
                                                        descr)

        self.output_format = self.check_and_change_lower(self.output_format,
                                                         ["dense", "sparse"],
                                                         descr)

        self.data_type = self.check_and_change_lower(self.data_type,
                                                     ["int", "int64", "float", "float64", "str", "long"],
                                                     descr)

        if type(self.missing_fill).__name__ != 'bool':
            raise ValueError("data_transform param's missing_fill {} not supported".format(self.missing_fill))

        if self.missing_fill_method is not None:
            self.missing_fill_method = self.check_and_change_lower(self.missing_fill_method,
                                                                   ['min', 'max', 'mean', 'designated'],
                                                                   descr)

        if self.outlier_replace_method is not None:
            self.outlier_replace_method = self.check_and_change_lower(self.outlier_replace_method,
                                                                      ['min', 'max', 'mean', 'designated'],
                                                                      descr)

        if type(self.with_label).__name__ != 'bool':
            raise ValueError("data_transform param's with_label {} not supported".format(self.with_label))

        if self.with_label:
            if not isinstance(self.label_name, str):
                raise ValueError("data transform param's label_name {} should be str".format(self.label_name))

            self.label_type = self.check_and_change_lower(self.label_type,
                                                          ["int", "int64", "float", "float64", "str", "long"],
                                                          descr)

        if self.exclusive_data_type is not None and not isinstance(self.exclusive_data_type, dict):
            raise ValueError("exclusive_data_type is should be None or a dict")

        if not isinstance(self.with_match_id, bool):
            raise ValueError("with_match_id should be boolean variable, but {} find".format(self.with_match_id))

        return True
__init__(self, input_format='dense', delimitor=',', data_type='float64', exclusive_data_type=None, tag_with_value=False, tag_value_delimitor=':', missing_fill=False, default_value=0, missing_fill_method=None, missing_impute=None, outlier_replace=False, outlier_replace_method=None, outlier_impute=None, outlier_replace_value=0, with_label=False, label_name='y', label_type='int', output_format='dense', need_run=True, with_match_id=False) special
Source code in federatedml/param/data_transform_param.py
def __init__(self, input_format="dense", delimitor=',', data_type='float64',
             exclusive_data_type=None,
             tag_with_value=False, tag_value_delimitor=":",
             missing_fill=False, default_value=0, missing_fill_method=None,
             missing_impute=None, outlier_replace=False, outlier_replace_method=None,
             outlier_impute=None, outlier_replace_value=0,
             with_label=False, label_name='y',
             label_type='int', output_format='dense', need_run=True,
             with_match_id=False):
    self.input_format = input_format
    self.delimitor = delimitor
    self.data_type = data_type
    self.exclusive_data_type = exclusive_data_type
    self.tag_with_value = tag_with_value
    self.tag_value_delimitor = tag_value_delimitor
    self.missing_fill = missing_fill
    self.default_value = default_value
    self.missing_fill_method = missing_fill_method
    self.missing_impute = missing_impute
    self.outlier_replace = outlier_replace
    self.outlier_replace_method = outlier_replace_method
    self.outlier_impute = outlier_impute
    self.outlier_replace_value = outlier_replace_value
    self.with_label = with_label
    self.label_name = label_name
    self.label_type = label_type
    self.output_format = output_format
    self.need_run = need_run
    self.with_match_id = with_match_id
check(self)
Source code in federatedml/param/data_transform_param.py
def check(self):

    descr = "data_transform param's"

    self.input_format = self.check_and_change_lower(self.input_format,
                                                    ["dense", "sparse", "tag"],
                                                    descr)

    self.output_format = self.check_and_change_lower(self.output_format,
                                                     ["dense", "sparse"],
                                                     descr)

    self.data_type = self.check_and_change_lower(self.data_type,
                                                 ["int", "int64", "float", "float64", "str", "long"],
                                                 descr)

    if type(self.missing_fill).__name__ != 'bool':
        raise ValueError("data_transform param's missing_fill {} not supported".format(self.missing_fill))

    if self.missing_fill_method is not None:
        self.missing_fill_method = self.check_and_change_lower(self.missing_fill_method,
                                                               ['min', 'max', 'mean', 'designated'],
                                                               descr)

    if self.outlier_replace_method is not None:
        self.outlier_replace_method = self.check_and_change_lower(self.outlier_replace_method,
                                                                  ['min', 'max', 'mean', 'designated'],
                                                                  descr)

    if type(self.with_label).__name__ != 'bool':
        raise ValueError("data_transform param's with_label {} not supported".format(self.with_label))

    if self.with_label:
        if not isinstance(self.label_name, str):
            raise ValueError("data transform param's label_name {} should be str".format(self.label_name))

        self.label_type = self.check_and_change_lower(self.label_type,
                                                      ["int", "int64", "float", "float64", "str", "long"],
                                                      descr)

    if self.exclusive_data_type is not None and not isinstance(self.exclusive_data_type, dict):
        raise ValueError("exclusive_data_type is should be None or a dict")

    if not isinstance(self.with_match_id, bool):
        raise ValueError("with_match_id should be boolean variable, but {} find".format(self.with_match_id))

    return True

Last update: 2021-12-03
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