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statistics_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.
#
import re
from federatedml.param.base_param import BaseParam
from federatedml.util import consts
import copy
class StatisticsParam(BaseParam):
"""
Define statistics params
Parameters
----------
statistics: list, string, default "summary"
Specify the statistic types to be computed.
"summary" represents list: [consts.SUM, consts.MEAN, consts.STANDARD_DEVIATION,
consts.MEDIAN, consts.MIN, consts.MAX,
consts.MISSING_COUNT, consts.SKEWNESS, consts.KURTOSIS]
column_names: list of string, default []
Specify columns to be used for statistic computation by column names in header
column_indexes: list of int, default -1
Specify columns to be used for statistic computation by column order in header
-1 indicates to compute statistics over all columns
bias: bool, default: True
If False, the calculations of skewness and kurtosis are corrected for statistical bias.
need_run: bool, default True
Indicate whether to run this modules
"""
LEGAL_STAT = [consts.COUNT, consts.SUM, consts.MEAN, consts.STANDARD_DEVIATION,
consts.MEDIAN, consts.MIN, consts.MAX, consts.VARIANCE,
consts.COEFFICIENT_OF_VARIATION, consts.MISSING_COUNT,
consts.MISSING_RATIO,
consts.SKEWNESS, consts.KURTOSIS]
BASIC_STAT = [consts.SUM, consts.MEAN, consts.STANDARD_DEVIATION,
consts.MEDIAN, consts.MIN, consts.MAX, consts.MISSING_RATIO,
consts.MISSING_COUNT, consts.SKEWNESS, consts.KURTOSIS,
consts.COEFFICIENT_OF_VARIATION]
LEGAL_QUANTILE = re.compile("^(100)|([1-9]?[0-9])%$")
def __init__(self, statistics="summary", column_names=None,
column_indexes=-1, need_run=True, abnormal_list=None,
quantile_error=consts.DEFAULT_RELATIVE_ERROR, bias=True):
super().__init__()
self.statistics = statistics
self.column_names = column_names
self.column_indexes = column_indexes
self.abnormal_list = abnormal_list
self.need_run = need_run
self.quantile_error = quantile_error
self.bias = bias
# @staticmethod
# def extend_statistics(statistic_name):
# basic_metrics = [consts.SUM, consts.MEAN, consts.STANDARD_DEVIATION,
# consts.MEDIAN, consts.MIN, consts.MAX, consts.MISSING_RATIO,
# consts.MISSING_COUNT, consts.SKEWNESS, consts.KURTOSIS,
# consts.COEFFICIENT_OF_VARIATION]
# if statistic_name == "summary":
# return basic_metrics
#
# if statistic_name == "describe":
# return [consts.COUNT, consts.MEAN, consts.STANDARD_DEVIATION,
# consts.MIN, consts.MAX]
@staticmethod
def find_stat_name_match(stat_name):
if stat_name in StatisticsParam.LEGAL_STAT or StatisticsParam.LEGAL_QUANTILE.match(stat_name):
return True
return False
# match_result = [legal_name == stat_name for legal_name in StatisticsParam.LEGAL_STAT]
# match_result.append(0 if LEGAL_QUANTILE.match(stat_name) is None else True)
# match_found = sum(match_result) > 0
# return match_found
def check(self):
model_param_descr = "Statistics's param statistics"
BaseParam.check_boolean(self.need_run, model_param_descr)
statistics = copy.copy(self.BASIC_STAT)
if not isinstance(self.statistics, list):
if self.statistics in [consts.SUMMARY]:
self.statistics = statistics
else:
if self.statistics not in statistics:
statistics.append(self.statistics)
self.statistics = statistics
else:
for s in self.statistics:
if s not in statistics:
statistics.append(s)
self.statistics = statistics
for stat_name in self.statistics:
match_found = StatisticsParam.find_stat_name_match(stat_name)
if not match_found:
raise ValueError(f"Illegal statistics name provided: {stat_name}.")
self.column_names = [] if self.column_names is None else self.column_names
self.column_indexes = [] if self.column_indexes is None else self.column_indexes
self.abnormal_list = [] if self.abnormal_list is None else self.abnormal_list
model_param_descr = "Statistics's param column_names"
if not isinstance(self.column_names, list):
raise ValueError(f"column_names should be list of string.")
for col_name in self.column_names:
BaseParam.check_string(col_name, model_param_descr)
model_param_descr = "Statistics's param column_indexes"
if not isinstance(self.column_indexes, list) and self.column_indexes != -1:
raise ValueError(f"column_indexes should be list of int or -1.")
if self.column_indexes != -1:
for col_index in self.column_indexes:
if not isinstance(col_index, int):
raise ValueError(f"{model_param_descr} should be int or list of int")
if col_index < -consts.FLOAT_ZERO:
raise ValueError(f"{model_param_descr} should be non-negative int value(s)")
if not isinstance(self.abnormal_list, list):
raise ValueError(f"abnormal_list should be list of int or string.")
self.check_decimal_float(self.quantile_error, "Statistics's param quantile_error ")
self.check_boolean(self.bias, "Statistics's param bias ")
return True