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data_transform_param.py
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data_transform_param.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright 2019 The FATE Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from federatedml.param.base_param import BaseParam
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