# -*- coding: utf-8 -*-
"""
Created on Mon Nov 23 08:59:00 2015
@author: efouche
"""
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
from __future__ import absolute_import
from future import standard_library
standard_library.install_aliases()
from collections import OrderedDict
from ibmdbpy.internals import idadf_state
from ibmdbpy.utils import timed
import pandas as pd
from numpy import log
import numpy as np
from ibmdbpy.feature_selection.entropy import entropy
from ibmdbpy.feature_selection.private import _check_input
@idadf_state
@timed
[docs]def info_gain(idadf, target = None, features = None, ignore_indexer=True):
"""
Compute the information gain / mutual information 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")
>>> info_gain(idadf)
"""
# Check input
target, features = _check_input(idadf, target, features, ignore_indexer)
entropy_dict = OrderedDict()
length = len(idadf)
loglength = log(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")
value = ((entropy_dict[t] + entropy_dict[feature] - join_entropy)/length + loglength)/log(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_values(inplace=True, ascending=False)
return result