Source code for ibmdbpy.feature_selection.symmetric_uncertainty

# -*- coding: utf-8 -*-
Created on Mon Nov 23 09:53:19 2015

@author: efouche
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
from __future__ import absolute_import
from builtins import dict
from future import standard_library

from collections import OrderedDict

import ibmdbpy

from ibmdbpy.feature_selection import entropy
from ibmdbpy.feature_selection.private import _check_input

from ibmdbpy.internals import idadf_state
from ibmdbpy.utils import timed

import pandas as pd
import numpy as np

[docs]def su(idadf, target = None, features = None, ignore_indexer=True): """ Compute the symmetric uncertainty coefficients between a set of features and a set of target in an IdaDataFrame. Parameters ---------- idadf : IdaDataFrame target : str or list of str, optional A column or list of columns against to be used as target. Per default, consider all columns features : str or list of str, optional A column or list of columns to be used as features. Per default, consider all columns. ignore_indexer : bool, default: True Per default, ignore the column declared as indexer in idadf Returns ------- Pandas.DataFrame or Pandas.Series if only one target Notes ----- Input columns as target and features should be categorical, otherwise this measure does not make much sense. Examples -------- >>> idadf = IdaDataFrame(idadb, "IRIS") >>> su(idadf) """ # Check input target, features = _check_input(idadf, target, features, ignore_indexer) entropy_dict = dict() length = len(idadf) corrector = np.log(length)*length values = OrderedDict() for t in target: if t not in values: values[t] = OrderedDict() features_notarget = [x for x in features if (x != t)] for feature in features_notarget: if feature not in values: values[feature] = OrderedDict() if t not in values[feature]: if t not in entropy_dict: entropy_dict[t] = entropy(idadf, t, mode = "raw") if feature not in entropy_dict: entropy_dict[feature] = entropy(idadf, feature, mode = "raw") join_entropy = entropy(idadf, [t] + [feature], mode = "raw") disjoin_entropy = entropy_dict[t] + entropy_dict[feature] value = (2.0*(disjoin_entropy - join_entropy + corrector)/(disjoin_entropy + corrector*2)) values[t][feature] = value if feature in target: values[feature][t] = value result = pd.DataFrame(values).fillna(np.nan) result = result.dropna(axis=1, how="all") if len(result.columns) > 1: order = [x for x in result.columns if x in features] + [x for x in features if x not in result.columns] result = result.reindex(order) if len(result.columns) == 1: if len(result) == 1: result = result.iloc[0,0] else: result = result[result.columns[0]].copy() result.sort(ascending = True) else: result = result.fillna(1) return result
@idadf_state @timed def outer_su(idadf1, key1, idadf2, key2, target = None, features1 = None, features2 = None): """ Compute the symmetric uncertainty coefficients between a set of features and a set of target from two different IdaDataFrames on a particular key. This is experimental """ target1, features1 = _check_input(idadf1, target, features1) target2, features2 = _check_input(idadf2, None, features2) if key1 not in idadf1.columns: raise ValueError("%s is not a column in idadf1") if key2 not in idadf2.columns: raise ValueError("%s is not a column in idadf2") condition = "a.\"%s\" = b.\"%s\""%(key1,key2) if key2 in features2: features2.remove(key2) afeaturesas = ", ".join(["a.\"%s\" as \"a.%s\" "%(feature, feature) for feature in features1]) bfeaturesas = ", ".join(["b.\"%s\" as \"b.%s\" "%(feature, feature) for feature in features2]) selectlist = [afeaturesas, bfeaturesas] if target1 is not None: atargetas = ", ".join(["a.\"%s\" as \"a.%s\" "%(tar, tar) for tar in [target1]]) selectlist.append(atargetas) atarget = "a." + target1 else: atarget = None abfeatures = ["a." + feature for feature in features1] + ["b." + feature for feature in features2] selectstr = ", ".join(selectlist) expression = "SELECT %s FROM %s as a FULL OUTER JOIN %s as b ON %s"%(selectstr,,, condition) viewname = idadf1._idadb._create_view_from_expression(expression) try: idadf_join = ibmdbpy.IdaDataFrame(idadf1._idadb, viewname) return su(idadf_join, target = atarget, features = abfeatures) except: raise finally: idadf1._idadb.drop_view(viewname)