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Chcked mutual_info with sklearn

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Ricardo Montañana Gómez
2023-06-23 01:21:24 +02:00
parent 0094d500d4
commit 30cc744033
7 changed files with 982 additions and 386 deletions
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194
bayesclass/FeatureSelect.cpp
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@@ -1,113 +1,121 @@
#include "FeatureSelect.h" #include "FeatureSelect.h"
#include <iostream> #include <iostream>
namespace features { namespace features {
// SelectKBestWeighted::SelectKBestWeighted(samples_t& samples, labels_t& labels, weights_t& weights, int k) SelectKBestWeighted::SelectKBestWeighted(samples_t& samples, labels_t& labels, weights_t& weights, int k, bool nat)
// : samples(samples), labels(labels), weights(weights), k(k) : samples(samples), labels(labels), weights(weights), k(k), nat(nat)
// { {
// // if (samples.size() == 0 || samples[0].size() == 0) if (samples.size() == 0 || samples[0].size() == 0)
// // throw invalid_argument("features must be a non-empty matrix"); throw invalid_argument("features must be a non-empty matrix");
// // if (samples.size() != labels.size()) if (samples.size() != labels.size())
// // throw invalid_argument("number of samples and labels must be equal"); throw invalid_argument("number of samples and labels must be equal");
// // if (samples.size() != weights.size()) if (samples.size() != weights.size())
// // throw invalid_argument("number of samples and weights must be equal"); throw invalid_argument("number of samples and weights must be equal");
// // if (k < 1 || k > static_cast<int>(samples[0].size())) if (k < 1 || k > static_cast<int>(samples[0].size()))
// // throw invalid_argument("k must be between 1 and number of features"); throw invalid_argument("k must be between 1 and number of features");
// numFeatures = 0; numFeatures = 0;
// numClasses = 0; numClasses = 0;
// numSamples = 0; numSamples = 0;
// fitted = false; fitted = false;
// } }
SelectKBestWeighted::SelectKBestWeighted(samples_t& samples) : samples(samples) {}
void SelectKBestWeighted::SelectKBestWeighted::fit() void SelectKBestWeighted::SelectKBestWeighted::fit()
{ {
// auto labelsCopy = labels; auto labelsCopy = labels;
numFeatures = 0;//samples[0].size(); numFeatures = samples[0].size();
numSamples = samples.size(); numSamples = samples.size();
// sort(labelsCopy.begin(), labelsCopy.end()); sort(labelsCopy.begin(), labelsCopy.end());
// auto last = unique(labelsCopy.begin(), labelsCopy.end()); auto last = unique(labelsCopy.begin(), labelsCopy.end());
// labelsCopy.erase(last, labelsCopy.end()); labelsCopy.erase(last, labelsCopy.end());
// numClasses = labelsCopy.size(); numClasses = labelsCopy.size();
// score.reserve(numFeatures); score.reserve(numFeatures);
// for (int i = 0; i < numFeatures; ++i) { for (int i = 0; i < numFeatures; ++i) {
// score.push_back(MutualInformation(i)); score.push_back(MutualInformation(i));
// } }
outputValues(); outputValues();
fitted = true; fitted = true;
} }
void SelectKBestWeighted::outputValues() void SelectKBestWeighted::outputValues()
{ {
cout << "numFeatures: " << numFeatures << endl; cout << "numFeatures: " << numFeatures << endl;
// cout << "numClasses: " << numClasses << endl; cout << "numClasses: " << numClasses << endl;
cout << "numSamples: " << numSamples << endl; cout << "numSamples: " << numSamples << endl;
// cout << "k: " << k << endl; cout << "k: " << k << endl;
// cout << "weights: "; cout << "weights: ";
// for (auto item : weights) for (auto item : weights)
// cout << item << ", ";
// cout << "end." << endl;
// cout << "labels: ";
// for (auto item : labels)
// cout << item << ", ";
// cout << "end." << endl;
cout << "samples: ";
for (auto item : samples) {
// for (auto item2 : item)
// cout << item2 << ", ";
// cout << "end." << endl;
cout << item << ", "; cout << item << ", ";
cout << "end." << endl;
cout << "labels: ";
for (auto item : labels)
cout << item << ", ";
cout << "end." << endl;
cout << "samples: " << endl;
for (auto item : samples) {
for (auto item2 : item)
cout << item2 << ", ";
cout << "end." << endl;
} }
cout << "end." << endl; cout << "end." << endl;
} }
// precision_t SelectKBestWeighted::entropyLabel() precision_t SelectKBestWeighted::entropyLabel()
// { {
// return entropy(labels); return entropy(labels);
// } }
// precision_t SelectKBestWeighted::entropy(const sample_t& data) precision_t SelectKBestWeighted::entropy(const sample_t& data)
// { {
// precision_t p; precision_t p;
// precision_t ventropy = 0, totalWeight = 0; precision_t ventropy = 0, totalWeight = 0;
// score_t counts(numClasses + 1, 0); score_t counts(numClasses + 1, 0);
// for (auto i = 0; i < data.size(); ++i) { for (auto i = 0; i < data.size(); ++i) {
// counts[data[i]] += weights[i]; counts[data[i]] += weights[i];
// totalWeight += weights[i]; totalWeight += weights[i];
// } }
// for (auto count : counts) { for (auto count : counts) {
// p = count / totalWeight; p = count / totalWeight;
// ventropy -= p * log2(p); if (p > 0)
// } if (nat)
// return ventropy; ventropy -= p * log(p);
// } else
// // H(Y|X) = sum_{x in X} p(x) H(Y|X=x) ventropy -= p * log2(p);
// precision_t SelectKBestWeighted::conditionalEntropy(const int feature) }
// { return ventropy;
// unordered_map<value_t, precision_t> featureCounts; }
// unordered_map<value_t, unordered_map<value_t, precision_t>> jointCounts; // H(Y|X) = sum_{x in X} p(x) H(Y|X=x)
// featureCounts.clear(); precision_t SelectKBestWeighted::conditionalEntropy(const int feature)
// jointCounts.clear(); {
// auto totalWeight = 0; unordered_map<value_t, precision_t> featureCounts;
// for (auto i = 0; i < numSamples; i++) { unordered_map<value_t, unordered_map<value_t, precision_t>> jointCounts;
// featureCounts[samples[feature][i]] += weights[i]; featureCounts.clear();
// jointCounts[samples[feature][i]][labels[i]] += weights[i]; jointCounts.clear();
// totalWeight += weights[i]; precision_t totalWeight = 0;
// } for (auto i = 0; i < numSamples; i++) {
// precision_t entropy = 0; featureCounts[samples[i][feature]] += weights[i];
// for (auto& [f, count] : featureCounts) { jointCounts[samples[i][feature]][labels[i]] += weights[i];
// auto p_f = count / totalWeight; totalWeight += weights[i];
// precision_t entropy_f = 0; }
// for (auto& [l, jointCount] : jointCounts[f]) { if (totalWeight == 0)
// auto p_l_f = jointCount / totalWeight; throw invalid_argument("Total weight should not be zero");
// entropy_f -= p_l_f * log2(p_l_f); precision_t entropy = 0;
// } for (auto& [feat, count] : featureCounts) {
// entropy += p_f * entropy_f; auto p_f = count / totalWeight;
// } precision_t entropy_f = 0;
// return entropy; for (auto& [label, jointCount] : jointCounts[feat]) {
// } auto p_l_f = jointCount / count;
if (p_l_f > 0) {
// // I(X;Y) = H(Y) - H(Y|X) double epsilon = 1e-9;
// precision_t SelectKBestWeighted::MutualInformation(const int i) if (nat)
// { entropy_f -= p_l_f * log(p_l_f + epsilon);
// // return entropyLabel() - conditionalEntropy(i); else
// return 25 / (i + 1); entropy_f -= p_l_f * log2(p_l_f + epsilon);
// } }
}
entropy += p_f * entropy_f;
}
return entropy;
}
// I(X;Y) = H(Y) - H(Y|X)
precision_t SelectKBestWeighted::MutualInformation(const int i)
{
return entropyLabel() - conditionalEntropy(i);
}
score_t SelectKBestWeighted::getScore() const score_t SelectKBestWeighted::getScore() const
{ {
if (!fitted) if (!fitted)

25
bayesclass/FeatureSelect.h
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@@ -5,31 +5,30 @@
#include <string> #include <string>
using namespace std; using namespace std;
namespace features { namespace features {
typedef float precision_t; typedef double precision_t;
typedef int value_t; typedef int value_t;
typedef vector<value_t> sample_t; typedef vector<value_t> sample_t;
// typedef vector<sample_t> samples_t; typedef vector<sample_t> samples_t;
typedef vector<value_t> samples_t;
typedef vector<value_t> labels_t; typedef vector<value_t> labels_t;
typedef vector<precision_t> score_t, weights_t; typedef vector<precision_t> score_t, weights_t;
class SelectKBestWeighted { class SelectKBestWeighted {
private: private:
samples_t& samples; const samples_t samples;
// const labels_t& labels; const labels_t labels;
// const weights_t& weights; const weights_t weights;
// const int k; const int k;
bool nat; // use natural log or log2
int numFeatures, numClasses, numSamples; int numFeatures, numClasses, numSamples;
bool fitted; bool fitted;
score_t score; score_t score;
// precision_t entropyLabel(); precision_t entropyLabel();
// precision_t entropy(const sample_t&); precision_t entropy(const sample_t&);
// precision_t conditionalEntropy(const int); precision_t conditionalEntropy(const int);
// precision_t MutualInformation(const int); precision_t MutualInformation(const int);
void outputValues(); void outputValues();
public: public:
// SelectKBestWeighted(samples_t&, labels_t&, weights_t&, int); SelectKBestWeighted(samples_t&, labels_t&, weights_t&, int, bool);
SelectKBestWeighted(samples_t&);
void fit(); void fit();
score_t getScore() const; score_t getScore() const;
static inline string version() { return "0.1.0"; }; static inline string version() { return "0.1.0"; };

37
bayesclass/FeatureTest.cpp
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@@ -1,37 +0,0 @@
#include "FeatureTest.h"
#include <iostream>
namespace featuresTest {
SelectKBest::SelectKBest(vector<int>& samples) : samples(samples) {}
SelectKBest::SelectKBest() = default;
SelectKBest::~SelectKBest() = default;
void SelectKBest::SelectKBest::fit()
{
numFeatures = 0;
numSamples = samples.size();
outputValues();
fitted = true;
}
void SelectKBest::outputValues()
{
cout << "numFeatures: " << numFeatures << endl;
// cout << "numClasses: " << numClasses << endl;
cout << "numSamples: " << numSamples << endl;
// cout << "k: " << k << endl;
// cout << "weights: ";
// for (auto item : weights)
// cout << item << ", ";
// cout << "end." << endl;
// cout << "labels: ";
// for (auto item : labels)
// cout << item << ", ";
// cout << "end." << endl;
cout << "samples: ";
for (auto item : samples) {
// for (auto item2 : item)
// cout << item2 << ", ";
// cout << "end." << endl;
cout << item << ", ";
}
cout << "end." << endl;
}
}

30
bayesclass/FeatureTest.h
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@@ -1,30 +0,0 @@
#ifndef SELECT_K_BEST_TEST_H
#define SELECT_K_BEST_TEST_H
#include <map>
#include <vector>
#include <string>
using namespace std;
namespace featuresTest {
typedef float precision_t;
typedef int value_t;
typedef vector<value_t> sample_t;
// typedef vector<sample_t> samples_t;
typedef vector<value_t> samples_t;
typedef vector<value_t> labels_t;
typedef vector<precision_t> score_t, weights_t;
class SelectKBest {
private:
vector<int>& samples;
int numFeatures, numClasses, numSamples;
bool fitted;
void outputValues();
public:
SelectKBest();
SelectKBest(vector<int>&);
~SelectKBest();
void fit();
static inline string version() { return "0.1.0"; };
};
}
#endif

1023
bayesclass/cSelectFeatures.cpp
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49
bayesclass/cSelectFeatures.pyx
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@@ -2,50 +2,29 @@
# cython: language_level = 3 # cython: language_level = 3
from libcpp.vector cimport vector from libcpp.vector cimport vector
from libcpp.string cimport string from libcpp.string cimport string
from libcpp cimport bool
cdef extern from "FeatureTest.h" namespace "featuresTest":
ctypedef float precision_t cdef extern from "FeatureSelect.h" namespace "features":
cdef cppclass SelectKBest: ctypedef double precision_t
SelectKBest(vector[int]&) except + cdef cppclass SelectKBestWeighted:
SelectKBestWeighted(vector[vector[int]]&, vector[int]&, vector[precision_t]&, int, bool) except +
void fit() void fit()
string version() string version()
vector[precision_t] getScore()
cdef class CSelectKBest: cdef class CSelectKBestWeighted:
cdef SelectKBest *thisptr cdef SelectKBestWeighted *thisptr
def __cinit__(self, X): def __cinit__(self, X, y, weights, k, natural=False): # log or log2
self.thisptr = new SelectKBest(X) self.thisptr = new SelectKBestWeighted(X, y, weights, k, natural)
def __dealloc__(self): def __dealloc__(self):
del self.thisptr del self.thisptr
def fit(self,): def fit(self,):
self.thisptr.fit() self.thisptr.fit()
return self return self
def get_score(self):
return self.thisptr.getScore()
def get_version(self): def get_version(self):
return self.thisptr.version() return self.thisptr.version()
def __reduce__(self): def __reduce__(self):
return (CSelectKBest, ()) return (CSelectKBestWeighted, ())
# cdef extern from "FeatureSelect.h" namespace "features":
# ctypedef float precision_t
# cdef cppclass SelectKBestWeighted:
# SelectKBestWeighted(vector[int]&) except +
# # SelectKBestWeighted(vector[int]&, vector[int]&, vector[precision_t]&, int) except +
# void fit()
# string version()
# vector[precision_t] getScore()
# cdef class CSelectKBestWeighted:
# cdef SelectKBestWeighted *thisptr
# def __cinit__(self, X, y, weights, k):
# # self.thisptr = new SelectKBestWeighted(X, y, weights, k)
# self.thisptr = new SelectKBestWeighted(X)
# def __dealloc__(self):
# del self.thisptr
# def fit(self,):
# self.thisptr.fit()
# return self
# def get_score(self):
# return self.thisptr.getScore()
# def get_version(self):
# return self.thisptr.version()
# def __reduce__(self):
# return (CSelectKBestWeighted, ())

10
test.py Normal file
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@@ -0,0 +1,10 @@
from bayesclass.cppSelectFeatures import CSelectKBestWeighted
X = [[x for x in range(i, i + 3)] for i in range(1, 30, 3)]
weights = [25 / (i + 1) for i in range(10)]
labels = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
test = CSelectKBestWeighted(X, labels, weights, 3)
test.fit()
for item in test.get_score():
print(item)