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Fix KDB algorithm argsort

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This commit is contained in:
Ricardo Montañana Gómez
2023-07-13 16:59:37 +02:00
parent 64f1500176
commit 99083ceede
11 changed files with 603 additions and 257 deletions
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93
bayesclass/BaseClassifier.cc Normal file
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@@ -0,0 +1,93 @@
#include "BaseClassifier.h"
namespace bayesnet {
using namespace std;
using namespace torch;
BaseClassifier::BaseClassifier(Network model) : model(model), m(0), n(0) {}
BaseClassifier& BaseClassifier::build(vector<string>& features, string className, map<string, vector<int>>& states)
{
dataset = torch::cat({ X, y.view({150, 1}) }, 1);
this->features = features;
this->className = className;
this->states = states;
checkFitParameters();
train();
return *this;
}
BaseClassifier& BaseClassifier::fit(Tensor& X, Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states)
{
this->X = X;
this->y = y;
return build(features, className, states);
}
BaseClassifier& BaseClassifier::fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states)
{
this->X = torch::zeros({ static_cast<int64_t>(X[0].size()), static_cast<int64_t>(X.size()) }, kInt64);
for (int i = 0; i < X.size(); ++i) {
this->X.index_put_({ "...", i }, torch::tensor(X[i], kInt64));
}
this->y = torch::tensor(y, kInt64);
return build(features, className, states);
}
void BaseClassifier::checkFitParameters()
{
auto sizes = X.sizes();
m = sizes[0];
n = sizes[1];
if (m != y.size(0)) {
throw invalid_argument("X and y must have the same number of samples");
}
if (n != features.size()) {
throw invalid_argument("X and features must have the same number of features");
}
if (states.find(className) == states.end()) {
throw invalid_argument("className not found in states");
}
for (auto feature : features) {
if (states.find(feature) == states.end()) {
throw invalid_argument("feature [" + feature + "] not found in states");
}
}
}
vector<vector<int>> tensorToVector(const torch::Tensor& tensor)
{
// convert mxn tensor to nxm vector
vector<vector<int>> result;
auto tensor_accessor = tensor.accessor<int, 2>();
// Iterate over columns and rows of the tensor
for (int j = 0; j < tensor.size(1); ++j) {
vector<int> column;
for (int i = 0; i < tensor.size(0); ++i) {
column.push_back(tensor_accessor[i][j]);
}
result.push_back(column);
}
return result;
}
Tensor BaseClassifier::predict(Tensor& X)
{
auto m_ = X.size(0);
auto n_ = X.size(1);
vector<vector<int>> Xd(n_, vector<int>(m_, 0));
for (auto i = 0; i < n_; i++) {
auto temp = X.index({ "...", i });
Xd[i] = vector<int>(temp.data_ptr<int>(), temp.data_ptr<int>() + m_);
}
auto yp = model.predict(Xd);
auto ypred = torch::tensor(yp, torch::kInt64);
return ypred;
}
float BaseClassifier::score(Tensor& X, Tensor& y)
{
Tensor y_pred = predict(X);
return (y_pred == y).sum().item<float>() / y.size(0);
}
void BaseClassifier::show()
{
model.show();
}
}

40
bayesclass/BaseClassifier.h Normal file
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#ifndef CLASSIFIERS_H
#include <torch/torch.h>
#include "Network.h"
using namespace std;
using namespace torch;
namespace bayesnet {
class BaseClassifier {
private:
BaseClassifier& build(vector<string>& features, string className, map<string, vector<int>>& states);
protected:
Network model;
int m, n; // m: number of samples, n: number of features
Tensor X;
Tensor y;
Tensor dataset;
vector<string> features;
string className;
map<string, vector<int>> states;
void checkFitParameters();
virtual void train() = 0;
public:
BaseClassifier(Network model);
Tensor& getX();
vector<string>& getFeatures();
string& getClassName();
BaseClassifier& fit(Tensor& X, Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states);
BaseClassifier& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states);
Tensor predict(Tensor& X);
float score(Tensor& X, Tensor& y);
void show();
};
}
#endif

569
bayesclass/BayesNetwork.cpp
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3
bayesclass/BayesNetwork.pyx
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@@ -18,6 +18,7 @@ cdef extern from "Network.h" namespace "bayesnet":
int getStates()
string getClassName()
string version()
void show()
cdef class BayesNetwork:
cdef Network *thisptr
@@ -52,6 +53,8 @@ cdef class BayesNetwork:
return self.thisptr.getClassName().decode()
def getClassNumStates(self):
return self.thisptr.getClassNumStates()
def show(self):
return self.thisptr.show()
def __reduce__(self):
return (BayesNetwork, ())

110
bayesclass/KDB.cc Normal file
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@@ -0,0 +1,110 @@
#include "KDB.h"
#include "Metrics.hpp"
namespace bayesnet {
using namespace std;
using namespace torch;
vector<int> argsort(vector<float>& nums)
{
int n = nums.size();
vector<int> indices(n);
iota(indices.begin(), indices.end(), 0);
sort(indices.begin(), indices.end(), [&nums](int i, int j) {return nums[i] > nums[j];});
return indices;
}
KDB::KDB(int k, float theta) : BaseClassifier(Network()), k(k), theta(theta) {}
void KDB::train()
{
/*
1. For each feature Xi, compute mutual information, I(X;C),
where C is the class.
2. Compute class conditional mutual information I(Xi;XjIC), f or each
pair of features Xi and Xj, where i#j.
3. Let the used variable list, S, be empty.
4. Let the DAG network being constructed, BN, begin with a single
class node, C.
5. Repeat until S includes all domain features
5.1. Select feature Xmax which is not in S and has the largest value
I(Xmax;C).
5.2. Add a node to BN representing Xmax.
5.3. Add an arc from C to Xmax in BN.
5.4. Add m = min(lSl,/c) arcs from m distinct features Xj in S with
the highest value for I(Xmax;X,jC).
5.5. Add Xmax to S.
Compute the conditional probabilility infered by the structure of BN by
using counts from DB, and output BN.
*/
// 1. For each feature Xi, compute mutual information, I(X;C),
// where C is the class.
cout << "Computing mutual information between features and class" << endl;
auto n_classes = states[className].size();
auto metrics = Metrics(dataset, features, className, n_classes);
vector <float> mi;
for (auto i = 0; i < features.size(); i++) {
Tensor firstFeature = X.index({ "...", i });
mi.push_back(metrics.mutualInformation(firstFeature, y));
cout << "Mutual information between " << features[i] << " and " << className << " is " << mi[i] << endl;
}
// 2. Compute class conditional mutual information I(Xi;XjIC), f or each
auto conditionalEdgeWeights = metrics.conditionalEdge();
cout << "Conditional edge weights" << endl;
cout << conditionalEdgeWeights << endl;
// 3. Let the used variable list, S, be empty.
vector<int> S;
// 4. Let the DAG network being constructed, BN, begin with a single
// class node, C.
model.addNode(className, states[className].size());
cout << "Adding node " << className << " to the network" << endl;
// 5. Repeat until S includes all domain features
// 5.1. Select feature Xmax which is not in S and has the largest value
// I(Xmax;C).
auto order = argsort(mi);
for (auto idx : order) {
cout << idx << " " << mi[idx] << endl;
// 5.2. Add a node to BN representing Xmax.
model.addNode(features[idx], states[features[idx]].size());
// 5.3. Add an arc from C to Xmax in BN.
model.addEdge(className, features[idx]);
// 5.4. Add m = min(lSl,/c) arcs from m distinct features Xj in S with
// the highest value for I(Xmax;X,jC).
add_m_edges(idx, S, conditionalEdgeWeights);
// 5.5. Add Xmax to S.
S.push_back(idx);
}
}
void KDB::add_m_edges(int idx, vector<int>& S, Tensor& weights)
{
auto n_edges = min(k, static_cast<int>(S.size()));
auto cond_w = clone(weights);
cout << "Conditional edge weights cloned for idx " << idx << endl;
cout << cond_w << endl;
bool exit_cond = k == 0;
int num = 0;
while (!exit_cond) {
auto max_minfo = argmax(cond_w.index({ idx, "..." })).item<int>();
auto belongs = find(S.begin(), S.end(), max_minfo) != S.end();
if (belongs && cond_w.index({ idx, max_minfo }).item<float>() > theta) {
try {
model.addEdge(features[max_minfo], features[idx]);
num++;
}
catch (const invalid_argument& e) {
// Loops are not allowed
}
}
cond_w.index_put_({ idx, max_minfo }, -1);
cout << "Conditional edge weights cloned for idx " << idx << " After -1" << endl;
cout << cond_w << endl;
cout << "cond_w.index({ idx, '...'})" << endl;
cout << cond_w.index({ idx, "..." }) << endl;
auto candidates_mask = cond_w.index({ idx, "..." }).gt(theta);
auto candidates = candidates_mask.nonzero();
cout << "Candidates mask" << endl;
cout << candidates_mask << endl;
cout << "Candidates: " << endl;
cout << candidates << endl;
cout << "Candidates size: " << candidates.size(0) << endl;
exit_cond = num == n_edges || candidates.size(0) == 0;
}
}
}

18
bayesclass/KDB.h Normal file
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@@ -0,0 +1,18 @@
#ifndef KDB_H
#define KDB_H
#include "BaseClassifier.h"
namespace bayesnet {
using namespace std;
using namespace torch;
class KDB : public BaseClassifier {
private:
int k;
float theta;
void add_m_edges(int idx, vector<int>& S, Tensor& weights);
protected:
void train();
public:
KDB(int k, float theta=0.03);
};
}
#endif

9
bayesclass/Metrics.cc
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@@ -30,7 +30,7 @@ namespace bayesnet {
}
return result;
}
vector<float> Metrics::conditionalEdgeWeights()
torch::Tensor Metrics::conditionalEdge()
{
auto result = vector<double>();
auto source = vector<string>(features);
@@ -65,6 +65,11 @@ namespace bayesnet {
matrix[x][y] = result[i];
matrix[y][x] = result[i];
}
return matrix;
}
vector<float> Metrics::conditionalEdgeWeights()
{
auto matrix = conditionalEdge();
std::vector<float> v(matrix.data_ptr<float>(), matrix.data_ptr<float>() + matrix.numel());
return v;
}
@@ -89,7 +94,7 @@ namespace bayesnet {
totalWeight += 1;
}
if (totalWeight == 0)
return 0;
throw invalid_argument("Total weight should not be zero");
double entropyValue = 0;
for (int value = 0; value < featureCounts.sizes()[0]; ++value) {
double p_f = featureCounts[value].item<double>() / totalWeight;

3
bayesclass/Metrics.hpp
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@@ -14,11 +14,12 @@ namespace bayesnet {
vector<pair<string, string>> doCombinations(const vector<string>&);
double entropy(torch::Tensor&);
double conditionalEntropy(torch::Tensor&, torch::Tensor&);
double mutualInformation(torch::Tensor&, torch::Tensor&);
public:
double mutualInformation(torch::Tensor&, torch::Tensor&);
Metrics(torch::Tensor&, vector<string>&, string&, int);
Metrics(const vector<vector<int>>&, const vector<int>&, const vector<string>&, const string&, const int);
vector<float> conditionalEdgeWeights();
torch::Tensor conditionalEdge();
};
}
#endif

11
bayesclass/Network.cc
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@@ -245,5 +245,16 @@ namespace bayesnet {
}
return result;
}
void Network::show()
{
// Draw the network
for (auto node : nodes) {
cout << node.first << " -> ";
for (auto child : node.second->getChildren()) {
cout << child->getName() << ", ";
}
cout << endl;
}
}
}

2
bayesclass/Network.h
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@@ -4,7 +4,6 @@
#include <map>
#include <vector>
namespace bayesnet {
class Network {
private:
@@ -45,6 +44,7 @@ namespace bayesnet {
torch::Tensor conditionalEdgeWeight();
vector<vector<double>> predict_proba(const vector<vector<int>>&);
double score(const vector<vector<int>>&, const vector<int>&);
void show();
inline string version() { return "0.1.0"; }
};
}

2
bayesclass/clfs.py
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@@ -423,7 +423,7 @@ class KDB(BayesBase):
self.model_.addNode(self.class_name_, self.n_classes_)
# 5. Repeat until S includes all domain features
# 5.1 Select feature Xmax which is not in S and has the largest value
for idx in np.argsort(mutual):
for idx in np.argsort(-mutual):
# 5.2 Add a node to BN representing Xmax.
feature = self.feature_names_in_[idx]
self.model_.addNode(feature, num_states[feature])