pearson_param¶
pearson_param
¶
Classes¶
PearsonParam (BaseParam)
¶
param for pearson correlation
Parameters:
Name | Type | Description | Default |
---|---|---|---|
column_names |
list of string |
list of column names |
None |
column_index |
list of int |
list of column index |
required |
cross_parties |
bool, default: True |
if True, calculate correlation of columns from both party |
True |
need_run |
bool |
set False to skip this party |
True |
use_mix_rand |
bool, defalut: False |
mix system random and pseudo random for quicker calculation |
False |
calc_loca_vif |
bool, default True |
calculate VIF for columns in local |
required |
Source code in federatedml/param/pearson_param.py
class PearsonParam(BaseParam):
"""
param for pearson correlation
Parameters
----------
column_names : list of string
list of column names
column_index : list of int
list of column index
cross_parties : bool, default: True
if True, calculate correlation of columns from both party
need_run : bool
set False to skip this party
use_mix_rand : bool, defalut: False
mix system random and pseudo random for quicker calculation
calc_loca_vif : bool, default True
calculate VIF for columns in local
"""
def __init__(
self,
column_names=None,
column_indexes=None,
cross_parties=True,
need_run=True,
use_mix_rand=False,
calc_local_vif=True,
):
super().__init__()
self.column_names = column_names
self.column_indexes = column_indexes
self.cross_parties = cross_parties
self.need_run = need_run
self.use_mix_rand = use_mix_rand
if column_names is None:
self.column_names = []
if column_indexes is None:
self.column_indexes = []
self.calc_local_vif = calc_local_vif
def check(self):
if not isinstance(self.use_mix_rand, bool):
raise ValueError(
f"use_mix_rand accept bool type only, {type(self.use_mix_rand)} got"
)
if self.cross_parties and (not self.need_run):
raise ValueError(
f"need_run should be True(which is default) when cross_parties is True."
)
if not isinstance(self.column_names, list):
raise ValueError(
f"type mismatch, column_names with type {type(self.column_names)}"
)
for name in self.column_names:
if not isinstance(name, str):
raise ValueError(
f"type mismatch, column_names with element {name}(type is {type(name)})"
)
if isinstance(self.column_indexes, list):
for idx in self.column_indexes:
if not isinstance(idx, int):
raise ValueError(
f"type mismatch, column_indexes with element {idx}(type is {type(idx)})"
)
if isinstance(self.column_indexes, int) and self.column_indexes != -1:
raise ValueError(
f"column_indexes with type int and value {self.column_indexes}(only -1 allowed)"
)
if self.need_run:
if isinstance(self.column_indexes, list) and isinstance(
self.column_names, list
):
if len(self.column_indexes) == 0 and len(self.column_names) == 0:
raise ValueError(f"provide at least one column")
__init__(self, column_names=None, column_indexes=None, cross_parties=True, need_run=True, use_mix_rand=False, calc_local_vif=True)
special
¶
Source code in federatedml/param/pearson_param.py
def __init__(
self,
column_names=None,
column_indexes=None,
cross_parties=True,
need_run=True,
use_mix_rand=False,
calc_local_vif=True,
):
super().__init__()
self.column_names = column_names
self.column_indexes = column_indexes
self.cross_parties = cross_parties
self.need_run = need_run
self.use_mix_rand = use_mix_rand
if column_names is None:
self.column_names = []
if column_indexes is None:
self.column_indexes = []
self.calc_local_vif = calc_local_vif
check(self)
¶
Source code in federatedml/param/pearson_param.py
def check(self):
if not isinstance(self.use_mix_rand, bool):
raise ValueError(
f"use_mix_rand accept bool type only, {type(self.use_mix_rand)} got"
)
if self.cross_parties and (not self.need_run):
raise ValueError(
f"need_run should be True(which is default) when cross_parties is True."
)
if not isinstance(self.column_names, list):
raise ValueError(
f"type mismatch, column_names with type {type(self.column_names)}"
)
for name in self.column_names:
if not isinstance(name, str):
raise ValueError(
f"type mismatch, column_names with element {name}(type is {type(name)})"
)
if isinstance(self.column_indexes, list):
for idx in self.column_indexes:
if not isinstance(idx, int):
raise ValueError(
f"type mismatch, column_indexes with element {idx}(type is {type(idx)})"
)
if isinstance(self.column_indexes, int) and self.column_indexes != -1:
raise ValueError(
f"column_indexes with type int and value {self.column_indexes}(only -1 allowed)"
)
if self.need_run:
if isinstance(self.column_indexes, list) and isinstance(
self.column_names, list
):
if len(self.column_indexes) == 0 and len(self.column_names) == 0:
raise ValueError(f"provide at least one column")
Last update: 2021-11-24