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sgebreeg
RACER
Commits
d02744cc
Commit
d02744cc
authored
Mar 25, 2021
by
ahmedaj
Browse files
prediction for encoding
parent
3b24b859
Changes
3
Hide whitespace changes
Inline
Side-by-side
src/encoder.cpp
View file @
d02744cc
#include "encoder.hpp"
#include <iostream>
#include "util.hpp"
void
encodeData
(
vector
<
vector
<
string
>>
datasetAsString
,
vector
<
vector
<
string
>>&
encodedDatasetAsString
,
vector
<
string
>
features
,
vector
<
string
>&
encodedFeatures
,
vector
<
int
>
featuresToEncode
){
vector
<
string
>
features
,
vector
<
string
>&
encodedFeatures
,
vector
<
int
>
featuresToEncode
,
vector
<
FeatureType
>
featureTypes
,
vector
<
FeatureType
>&
encodedFeatureTypes
){
map
<
string
,
int
>
uniqueValues
;
map
<
int
,
map
<
string
,
int
>>
featureUniqueValues
;
map
<
int
,
map
<
string
,
int
>
,
greater
<
int
>>
featureUniqueValues
;
for
(
int
encodeidx
:
featuresToEncode
){
int
uniqueCounter
=
0
;
for
(
int
dataidx
=
0
;
dataidx
<
datasetAsString
[
encodeidx
].
size
();
dataidx
++
){
...
...
@@ -27,13 +29,16 @@ void encodeData(vector <vector<string>> datasetAsString, vector <vector<string>>
for
(
featUniqueItr
=
featureUniqueValues
.
begin
();
featUniqueItr
!=
featureUniqueValues
.
end
();
featUniqueItr
++
)
{
int
featIdx
=
featUniqueItr
->
first
;
cout
<<
featIdx
<<
endl
;
encodedFeatures
.
erase
(
encodedFeatures
.
begin
()
+
featIdx
);
encodedFeatureTypes
.
erase
(
encodedFeatureTypes
.
begin
()
+
featIdx
);
map
<
string
,
int
>
unique
=
featUniqueItr
->
second
;
map
<
string
,
int
>::
iterator
uniqueItr
;
vector
<
string
>
emptyRow
(
datasetAsString
[
0
].
size
());
for
(
uniqueItr
=
unique
.
begin
();
uniqueItr
!=
unique
.
end
();
uniqueItr
++
){
encodedFeatures
.
push_back
(
uniqueItr
->
first
);
encodedFeatureTypes
.
push_back
(
CATEGORICAL
);
encodedDatasetAsString
.
push_back
(
emptyRow
);
}
...
...
src/encoder.hpp
View file @
d02744cc
...
...
@@ -2,6 +2,7 @@
#include <vector>
#include <map>
#include <iterator>
#include "util.hpp"
#ifndef CODE_ENCODER_H
#define CODE_ENCODER_H
...
...
@@ -9,7 +10,7 @@
using
namespace
std
;
void
encodeData
(
vector
<
vector
<
string
>>
datasetAsString
,
vector
<
vector
<
string
>>&
encodedDatasetAsString
,
vector
<
string
>
features
,
vector
<
string
>&
encodedFeatures
,
vector
<
int
>
featuresToEncode
);
vector
<
string
>
features
,
vector
<
string
>&
encodedFeatures
,
vector
<
int
>
featuresToEncode
,
vector
<
FeatureType
>
featureTypes
,
vector
<
FeatureType
>&
encodedFeatureTypes
);
vector
<
int
>
binaryShift
(
int
size
,
int
value
);
...
...
src/main.cpp
View file @
d02744cc
...
...
@@ -43,7 +43,7 @@ int main(int argc, char *argv[]) {
// cout << featWeight << "\n";
vector
<
vector
<
string
>>
datasetAsString
,
encodedDatasetAsString
;
vector
<
FeatureType
>
featureTypes
;
vector
<
FeatureType
>
featureTypes
,
encodedFeatureTypes
;
vector
<
string
>
features
,
encodedfeatures
;
if
(
argc
==
4
)
{
cout
<<
"Dataset: "
<<
argv
[
3
]
<<
endl
;
...
...
@@ -53,101 +53,113 @@ int main(int argc, char *argv[]) {
}
else
{
cout
<<
"WARNING: No dataset provided as an argument!"
<<
endl
;
datasetAsString
=
parseDataToString
(
"../datasets/adult
1
.data"
);
datasetAsString
=
parseDataToString
(
"../datasets/adult.data"
);
featureTypes
=
parseFeatureTypes
(
"../datasets/adult.featureTypes"
);
features
=
parseFeatures
(
"../datasets/adult.features"
);
}
encodedDatasetAsString
=
datasetAsString
;
vector
<
string
>
finalLables
=
encodedDatasetAsString
.
back
();
encodedDatasetAsString
.
pop_back
();
encodedfeatures
=
features
;
encodedFeatureTypes
=
featureTypes
;
vector
<
int
>
featuresToEncode
;
featuresToEncode
.
push_back
(
5
);
featuresToEncode
.
push_back
(
6
);
encodeData
(
datasetAsString
,
encodedDatasetAsString
,
features
,
e
ncode
dfeatures
,
featuresToEncode
);
std
::
sort
(
featuresToEncode
.
begin
(),
features
ToE
ncode
.
end
(),
std
::
greater
<
int
>
()
);
for
(
string
feature
:
encodedfeatures
){
cout
<<
" "
<<
feature
<<
" "
;
}
cout
<<
endl
;
for
(
int
dataIdx
=
0
;
dataIdx
<
encodedDatasetAsString
[
0
].
size
();
dataIdx
++
){
for
(
int
i
=
0
;
i
<
encodedDatasetAsString
.
size
();
i
++
){
cout
<<
encodedDatasetAsString
[
i
][
dataIdx
]
<<
","
;
}
cout
<<
endl
;
}
encodeData
(
datasetAsString
,
encodedDatasetAsString
,
features
,
encodedfeatures
,
featuresToEncode
,
featureTypes
,
encodedFeatureTypes
);
encodedDatasetAsString
.
push_back
(
finalLables
);
// for(string feature:encodedfeatures){
// cout << " " << feature << " ";
// }
// cout<<endl;
// for(FeatureType ft:encodedFeatureTypes){
// cout<< " "<<ft<< " ";
// }
// cout<<endl;
// //pick number of features to select for random sub-spacing
// float featureWeight = sqrt(features.size())/features.size();
// for(int dataIdx=0; dataIdx < encodedDatasetAsString[0].size(); dataIdx++){
// for(int i = 0; i< encodedDatasetAsString.size();i++){
// cout << encodedDatasetAsString[i][dataIdx]<<",";
// }
// cout<<endl;
// }
// double accuracy = 0.0;
// double time = 0.0;
// for (int x = 0; x < 3; x++) {
// vector<int> trainingIdxs = randomSelect_WithoutReplacement(datasetAsString.at(0).size(), 0.7);
// //vector <vector<string>> trainingData;
// vector <int> testingIdxs = splitTrainingAndTesting(trainingIdxs, datasetAsString);
//pick number of features to select for random sub-spacing
float
featureWeight
=
sqrt
(
encodedfeatures
.
size
())
/
encodedfeatures
.
size
();
// cout << "Over sampling training data " << endl;
double
accuracy
=
0.0
;
double
time
=
0.0
;
for
(
int
x
=
0
;
x
<
3
;
x
++
)
{
vector
<
int
>
trainingIdxs
=
randomSelect_WithoutReplacement
(
encodedDatasetAsString
.
at
(
0
).
size
(),
0.7
);
//vector <vector<string>> trainingData;
vector
<
int
>
testingIdxs
=
splitTrainingAndTesting
(
trainingIdxs
,
encodedDatasetAsString
);
//
vector<int> oversampledData = o
versampl
e(datasetAsStr
ing
,
training
Idxs)
;
cout
<<
"O
ver
sampling training
data "
<<
endl
;
// trainingIdxs.insert(trainingIdxs.end(), oversampledData.begin(), oversampledData.end());
// // sort(trainingIdxs.begin(), trainingIdxs.end());
vector
<
int
>
oversampledData
=
oversample
(
encodedDatasetAsString
,
trainingIdxs
);
// vector <string> testData;
// string emptystring;
// for (int featIndex = 0; featIndex < datasetAsString.size(); featIndex++) {
// testData.push_back(emptystring);
// }
trainingIdxs
.
insert
(
trainingIdxs
.
end
(),
oversampledData
.
begin
(),
oversampledData
.
end
());
// sort(trainingIdxs.begin(), trainingIdxs.end());
vector
<
string
>
testData
;
string
emptystring
;
for
(
int
featIndex
=
0
;
featIndex
<
encodedDatasetAsString
.
size
();
featIndex
++
)
{
testData
.
push_back
(
emptystring
);
}
// auto start = high_resolution_clock::now();
// RandomForest *randomForest = new RandomForest(datasetAsString, trainingIdxs, featureTypes, numTrees,
// baggingWeight, featureWeight, depth);
auto
start
=
high_resolution_clock
::
now
();
RandomForest
*
randomForest
=
new
RandomForest
(
encodedDatasetAsString
,
trainingIdxs
,
encodedFeatureTypes
,
numTrees
,
baggingWeight
,
featureWeight
,
depth
);
// time += (high_resolution_clock::now() - start).count() / 1000000000.0;
time
+=
(
high_resolution_clock
::
now
()
-
start
).
count
()
/
1000000000.0
;
// cout << endl;
// cout << "********************* Forest accuracy *****************" << endl;
// accuracyReport report = randomForest->getAccuracy(datasetAsString,testingIdxs);
// accuracy += report.accuracy;
// randomForest->printAccuracyReportFile(report);
cout
<<
endl
;
cout
<<
"********************* Forest accuracy *****************"
<<
endl
;
accuracyReport
report
=
randomForest
->
getAccuracy
(
encodedDatasetAsString
,
testingIdxs
);
accuracy
+=
report
.
accuracy
;
randomForest
->
printAccuracyReportFile
(
report
);
// cout << "**************** prediction with explanation ********** " << endl;
// for (int featIndex = 0; featIndex < datasetAsString.size(); featIndex++) {
// testData.at(featIndex) = datasetAsString.at(featIndex)[testingIdxs[0]];
// cout << datasetAsString.at(featIndex)[testingIdxs[0]] << ", ";
// }
// cout << endl;
cout
<<
"**************** prediction with explanation ********** "
<<
endl
;
for
(
int
featIndex
=
0
;
featIndex
<
encodedDatasetAsString
.
size
();
featIndex
++
)
{
testData
.
at
(
featIndex
)
=
encodedDatasetAsString
.
at
(
featIndex
)[
testingIdxs
[
0
]];
cout
<<
encodedDatasetAsString
.
at
(
featIndex
)[
testingIdxs
[
0
]]
<<
", "
;
}
cout
<<
endl
;
// randomForest->predict("HARD",testData, randomForest, features);
//
for (int i = 0; i<
randomForest->
t
re
es.size(); i++){
randomForest
->
p
re
dict
(
"HARD"
,
testData
,
randomForest
,
features
);
// cleanTree(randomForest->trees[i]->root);
// delete randomForest->trees[i];
// }
// delete randomForest;
for
(
int
i
=
0
;
i
<
randomForest
->
trees
.
size
();
i
++
){
cleanTree
(
randomForest
->
trees
[
i
]
->
root
);
delete
randomForest
->
trees
[
i
];
}
delete
randomForest
;
// }
// ofstream outfile;
// outfile.open("avg.txt", ios::app);
// outfile << "------ Report ------ " << endl;
// outfile << numTrees << "\t" << depth << "\t" << featureWeight << "\t" << baggingWeight << "\t" << accuracy / 3
// << "\t" << time / 3 << endl;
// // outfile<< numTrees<<"\t"<<10<<"\t"<<0.7<<"\t"<<baggingWeight<<"\t"<<accuracy/3<<"\t"<<time/3<<endl;
// outfile.close();
}
ofstream
outfile
;
outfile
.
open
(
"avg.txt"
,
ios
::
app
);
outfile
<<
"------ Report ------ "
<<
endl
;
outfile
<<
numTrees
<<
"
\t
"
<<
depth
<<
"
\t
"
<<
featureWeight
<<
"
\t
"
<<
baggingWeight
<<
"
\t
"
<<
accuracy
/
3
<<
"
\t
"
<<
time
/
3
<<
endl
;
// outfile<< numTrees<<"\t"<<10<<"\t"<<0.7<<"\t"<<baggingWeight<<"\t"<<accuracy/3<<"\t"<<time/3<<endl;
outfile
.
close
();
return
0
;
...
...
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