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Initial cleanup pass on MLPPLogReg.
This commit is contained in:
parent
f5bd46c211
commit
2a5c278f40
@ -5,6 +5,7 @@
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//
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#include "log_reg.h"
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#include "../activation/activation.h"
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#include "../cost/cost.h"
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#include "../lin_alg/lin_alg.h"
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@ -14,52 +15,94 @@
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#include <iostream>
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#include <random>
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MLPPLogReg::MLPPLogReg(std::vector<std::vector<real_t>> pinputSet, std::vector<real_t> poutputSet, std::string preg, real_t plambda, real_t palpha) {
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inputSet = pinputSet;
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outputSet = poutputSet;
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n = pinputSet.size();
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k = pinputSet[0].size();
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reg = preg;
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lambda = plambda;
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alpha = palpha;
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/*
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Ref<MLPPMatrix> MLPPLogReg::get_input_set() {
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return _input_set;
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}
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void MLPPLogReg::set_input_set(const Ref<MLPPMatrix> &val) {
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_input_set = val;
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y_hat.resize(n);
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weights = MLPPUtilities::weightInitialization(k);
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bias = MLPPUtilities::biasInitialization();
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_initialized = false;
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}
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std::vector<real_t> MLPPLogReg::modelSetTest(std::vector<std::vector<real_t>> X) {
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return Evaluate(X);
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Ref<MLPPVector> MLPPLogReg::get_output_set() {
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return _output_set;
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}
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void MLPPLogReg::set_output_set(const Ref<MLPPVector> &val) {
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_output_set = val;
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_initialized = false;
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}
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real_t MLPPLogReg::modelTest(std::vector<real_t> x) {
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return Evaluate(x);
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MLPPReg::RegularizationType MLPPLogReg::get_reg() {
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return _reg;
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}
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void MLPPLogReg::set_reg(const MLPPReg::RegularizationType val) {
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_reg = val;
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_initialized = false;
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}
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void MLPPLogReg::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
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real_t MLPPLogReg::get_lambda() {
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return _lambda;
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}
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void MLPPLogReg::set_lambda(const real_t val) {
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_lambda = val;
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_initialized = false;
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}
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real_t MLPPLogReg::get_alpha() {
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return _alpha;
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}
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void MLPPLogReg::set_alpha(const real_t val) {
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_alpha = val;
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_initialized = false;
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}
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*/
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std::vector<real_t> MLPPLogReg::model_set_test(std::vector<std::vector<real_t>> X) {
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ERR_FAIL_COND_V(!_initialized, std::vector<real_t>());
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return evaluatem(X);
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}
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real_t MLPPLogReg::model_test(std::vector<real_t> x) {
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ERR_FAIL_COND_V(!_initialized, 0);
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return evaluatev(x);
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}
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void MLPPLogReg::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(!_initialized);
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MLPPLinAlg alg;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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forwardPass();
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forward_pass();
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while (true) {
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cost_prev = Cost(y_hat, outputSet);
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cost_prev = cost(_y_hat, _output_set);
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std::vector<real_t> error = alg.subtraction(y_hat, outputSet);
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std::vector<real_t> error = alg.subtraction(_y_hat, _output_set);
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// Calculating the weight gradients
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weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate / n, alg.mat_vec_mult(alg.transpose(inputSet), error)));
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate / _n, alg.mat_vec_mult(alg.transpose(_input_set), error)));
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_weights = regularization.regWeights(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradients
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bias -= learning_rate * alg.sum_elements(error) / n;
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forwardPass();
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_bias -= learning_rate * alg.sum_elements(error) / _n;
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forward_pass();
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
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MLPPUtilities::UI(weights, bias);
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(_y_hat, _output_set));
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MLPPUtilities::UI(_weights, _bias);
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}
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epoch++;
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if (epoch > max_epoch) {
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@ -68,145 +111,242 @@ void MLPPLogReg::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
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}
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}
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void MLPPLogReg::MLE(real_t learning_rate, int max_epoch, bool UI) {
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void MLPPLogReg::mle(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(!_initialized);
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MLPPLinAlg alg;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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forwardPass();
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forward_pass();
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while (true) {
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cost_prev = Cost(y_hat, outputSet);
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cost_prev = cost(_y_hat, _output_set);
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std::vector<real_t> error = alg.subtraction(outputSet, y_hat);
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std::vector<real_t> error = alg.subtraction(_output_set, _y_hat);
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// Calculating the weight gradients
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weights = alg.addition(weights, alg.scalarMultiply(learning_rate / n, alg.mat_vec_mult(alg.transpose(inputSet), error)));
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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_weights = alg.addition(_weights, alg.scalarMultiply(learning_rate / _n, alg.mat_vec_mult(alg.transpose(_input_set), error)));
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_weights = regularization.regWeights(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradients
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bias += learning_rate * alg.sum_elements(error) / n;
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forwardPass();
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_bias += learning_rate * alg.sum_elements(error) / _n;
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
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MLPPUtilities::UI(weights, bias);
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forward_pass();
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(_y_hat, _output_set));
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MLPPUtilities::UI(_weights, _bias);
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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}
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void MLPPLogReg::SGD(real_t learning_rate, int max_epoch, bool UI) {
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void MLPPLogReg::sgd(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(!_initialized);
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MLPPLinAlg alg;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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std::random_device rd;
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std::default_random_engine generator(rd());
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std::uniform_int_distribution<int> distribution(0, int(_n - 1));
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while (true) {
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std::random_device rd;
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std::default_random_engine generator(rd());
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std::uniform_int_distribution<int> distribution(0, int(n - 1));
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int outputIndex = distribution(generator);
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real_t y_hat = Evaluate(inputSet[outputIndex]);
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cost_prev = Cost({ y_hat }, { outputSet[outputIndex] });
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real_t y_hat = evaluatev(_input_set[outputIndex]);
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cost_prev = cost({ y_hat }, { _output_set[outputIndex] });
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real_t error = y_hat - outputSet[outputIndex];
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real_t error = y_hat - _output_set[outputIndex];
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// Weight updation
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weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate * error, inputSet[outputIndex]));
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate * error, _input_set[outputIndex]));
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_weights = regularization.regWeights(_weights, _lambda, _alpha, _reg);
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// Bias updation
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bias -= learning_rate * error;
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_bias -= learning_rate * error;
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y_hat = Evaluate({ inputSet[outputIndex] });
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y_hat = evaluatev(_input_set[outputIndex]);
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost({ y_hat }, { outputSet[outputIndex] }));
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MLPPUtilities::UI(weights, bias);
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost({ y_hat }, { _output_set[outputIndex] }));
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MLPPUtilities::UI(_weights, _bias);
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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forwardPass();
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forward_pass();
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}
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void MLPPLogReg::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) {
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void MLPPLogReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) {
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ERR_FAIL_COND(!_initialized);
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MLPPLinAlg alg;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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// Creating the mini-batches
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int n_mini_batch = n / mini_batch_size;
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auto bacthes = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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int n_mini_batch = _n / mini_batch_size;
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auto bacthes = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
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auto inputMiniBatches = std::get<0>(bacthes);
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auto outputMiniBatches = std::get<1>(bacthes);
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while (true) {
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for (int i = 0; i < n_mini_batch; i++) {
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std::vector<real_t> y_hat = Evaluate(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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std::vector<real_t> y_hat = evaluatem(inputMiniBatches[i]);
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cost_prev = cost(y_hat, outputMiniBatches[i]);
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std::vector<real_t> error = alg.subtraction(y_hat, outputMiniBatches[i]);
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// Calculating the weight gradients
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weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate / outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error)));
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate / outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error)));
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_weights = regularization.regWeights(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradients
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bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size();
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y_hat = Evaluate(inputMiniBatches[i]);
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_bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size();
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y_hat = evaluatem(inputMiniBatches[i]);
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i]));
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MLPPUtilities::UI(weights, bias);
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputMiniBatches[i]));
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MLPPUtilities::UI(_weights, _bias);
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}
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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forwardPass();
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forward_pass();
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}
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real_t MLPPLogReg::score() {
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ERR_FAIL_COND_V(!_initialized, 0);
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MLPPUtilities util;
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return util.performance(y_hat, outputSet);
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return util.performance(_y_hat, _output_set);
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}
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void MLPPLogReg::save(std::string fileName) {
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void MLPPLogReg::save(std::string file_name) {
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ERR_FAIL_COND(!_initialized);
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MLPPUtilities util;
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util.saveParameters(fileName, weights, bias);
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util.saveParameters(file_name, _weights, _bias);
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}
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real_t MLPPLogReg::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
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bool MLPPLogReg::is_initialized() {
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return _initialized;
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}
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void MLPPLogReg::initialize() {
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if (_initialized) {
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return;
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}
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//ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
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_initialized = true;
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}
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MLPPLogReg::MLPPLogReg(std::vector<std::vector<real_t>> p_input_set, std::vector<real_t> p_output_set, std::string p_reg, real_t p_lambda, real_t p_alpha) {
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_input_set = p_input_set;
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_output_set = p_output_set;
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_n = p_input_set.size();
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_k = p_input_set[0].size();
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_reg = p_reg;
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_lambda = p_lambda;
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_alpha = p_alpha;
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_y_hat.resize(_n);
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_weights = MLPPUtilities::weightInitialization(_k);
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_bias = MLPPUtilities::biasInitialization();
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_initialized = true;
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}
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MLPPLogReg::MLPPLogReg() {
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_initialized = false;
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}
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MLPPLogReg::~MLPPLogReg() {
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}
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real_t MLPPLogReg::cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
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MLPPReg regularization;
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class MLPPCost cost;
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return cost.LogLoss(y_hat, y) + regularization.regTerm(weights, lambda, alpha, reg);
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return cost.LogLoss(y_hat, y) + regularization.regTerm(_weights, _lambda, _alpha, _reg);
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}
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std::vector<real_t> MLPPLogReg::Evaluate(std::vector<std::vector<real_t>> X) {
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real_t MLPPLogReg::evaluatev(std::vector<real_t> x) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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return avn.sigmoid(alg.scalarAdd(bias, alg.mat_vec_mult(X, weights)));
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return avn.sigmoid(alg.dot(_weights, x) + _bias);
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}
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real_t MLPPLogReg::Evaluate(std::vector<real_t> x) {
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std::vector<real_t> MLPPLogReg::evaluatem(std::vector<std::vector<real_t>> X) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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return avn.sigmoid(alg.dot(weights, x) + bias);
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return avn.sigmoid(alg.scalarAdd(_bias, alg.mat_vec_mult(X, _weights)));
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}
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// sigmoid ( wTx + b )
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void MLPPLogReg::forwardPass() {
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y_hat = Evaluate(inputSet);
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void MLPPLogReg::forward_pass() {
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_y_hat = evaluatem(_input_set);
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}
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void MLPPLogReg::_bind_methods() {
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/*
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ClassDB::bind_method(D_METHOD("get_input_set"), &MLPPLogReg::get_input_set);
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ClassDB::bind_method(D_METHOD("set_input_set", "val"), &MLPPLogReg::set_input_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "input_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_input_set", "get_input_set");
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ClassDB::bind_method(D_METHOD("get_output_set"), &MLPPLogReg::get_output_set);
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ClassDB::bind_method(D_METHOD("set_output_set", "val"), &MLPPLogReg::set_output_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "output_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "set_output_set", "get_output_set");
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ClassDB::bind_method(D_METHOD("get_reg"), &MLPPLogReg::get_reg);
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ClassDB::bind_method(D_METHOD("set_reg", "val"), &MLPPLogReg::set_reg);
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ADD_PROPERTY(PropertyInfo(Variant::INT, "reg"), "set_reg", "get_reg");
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ClassDB::bind_method(D_METHOD("get_lambda"), &MLPPLogReg::get_lambda);
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ClassDB::bind_method(D_METHOD("set_lambda", "val"), &MLPPLogReg::set_lambda);
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ADD_PROPERTY(PropertyInfo(Variant::REAL, "lambda"), "set_lambda", "get_lambda");
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ClassDB::bind_method(D_METHOD("get_alpha"), &MLPPLogReg::get_alpha);
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ClassDB::bind_method(D_METHOD("set_alpha", "val"), &MLPPLogReg::set_alpha);
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ADD_PROPERTY(PropertyInfo(Variant::REAL, "alpha"), "set_alpha", "get_alpha");
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ClassDB::bind_method(D_METHOD("model_test", "x"), &MLPPLogReg::model_test);
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ClassDB::bind_method(D_METHOD("model_set_test", "X"), &MLPPLogReg::model_set_test);
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ClassDB::bind_method(D_METHOD("gradient_descent", "learning_rate", "max_epoch", "ui"), &MLPPLogReg::gradient_descent, false);
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ClassDB::bind_method(D_METHOD("sgd", "learning_rate", "max_epoch", "ui"), &MLPPLogReg::sgd, false);
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ClassDB::bind_method(D_METHOD("mbgd", "learning_rate", "max_epoch", "mini_batch_size", "ui"), &MLPPLogReg::mbgd, false);
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ClassDB::bind_method(D_METHOD("score"), &MLPPLogReg::score);
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ClassDB::bind_method(D_METHOD("save", "file_name"), &MLPPLogReg::save);
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ClassDB::bind_method(D_METHOD("is_initialized"), &MLPPLogReg::is_initialized);
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ClassDB::bind_method(D_METHOD("initialize"), &MLPPLogReg::initialize);
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*/
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||||
}
|
||||
|
||||
@ -10,42 +10,85 @@
|
||||
|
||||
#include "core/math/math_defs.h"
|
||||
|
||||
#include "core/object/reference.h"
|
||||
|
||||
#include "../lin_alg/mlpp_matrix.h"
|
||||
#include "../lin_alg/mlpp_vector.h"
|
||||
|
||||
#include "../regularization/reg.h"
|
||||
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
class MLPPLogReg {
|
||||
class MLPPLogReg : public Reference {
|
||||
GDCLASS(MLPPLogReg, Reference);
|
||||
|
||||
public:
|
||||
MLPPLogReg(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
|
||||
std::vector<real_t> modelSetTest(std::vector<std::vector<real_t>> X);
|
||||
real_t modelTest(std::vector<real_t> x);
|
||||
void gradientDescent(real_t learning_rate, int max_epoch, bool UI = false);
|
||||
void MLE(real_t learning_rate, int max_epoch, bool UI = false);
|
||||
void SGD(real_t learning_rate, int max_epoch, bool UI = false);
|
||||
void MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI = false);
|
||||
/*
|
||||
Ref<MLPPMatrix> get_input_set();
|
||||
void set_input_set(const Ref<MLPPMatrix> &val);
|
||||
|
||||
Ref<MLPPVector> get_output_set();
|
||||
void set_output_set(const Ref<MLPPVector> &val);
|
||||
|
||||
MLPPReg::RegularizationType get_reg();
|
||||
void set_reg(const MLPPReg::RegularizationType val);
|
||||
|
||||
real_t get_lambda();
|
||||
void set_lambda(const real_t val);
|
||||
|
||||
real_t get_alpha();
|
||||
void set_alpha(const real_t val);
|
||||
*/
|
||||
|
||||
std::vector<real_t> model_set_test(std::vector<std::vector<real_t>> X);
|
||||
real_t model_test(std::vector<real_t> x);
|
||||
|
||||
void gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
|
||||
void mle(real_t learning_rate, int max_epoch, bool ui = false);
|
||||
void sgd(real_t learning_rate, int max_epoch, bool ui = false);
|
||||
void mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui = false);
|
||||
|
||||
real_t score();
|
||||
void save(std::string fileName);
|
||||
|
||||
private:
|
||||
real_t Cost(std::vector<real_t> y_hat, std::vector<real_t> y);
|
||||
void save(std::string file_name);
|
||||
|
||||
std::vector<real_t> Evaluate(std::vector<std::vector<real_t>> X);
|
||||
real_t Evaluate(std::vector<real_t> x);
|
||||
void forwardPass();
|
||||
bool is_initialized();
|
||||
void initialize();
|
||||
|
||||
std::vector<std::vector<real_t>> inputSet;
|
||||
std::vector<real_t> outputSet;
|
||||
std::vector<real_t> y_hat;
|
||||
std::vector<real_t> weights;
|
||||
real_t bias;
|
||||
//MLPPSoftmaxReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPMatrix> &p_output_set, MLPPReg::RegularizationType p_reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t p_lambda = 0.5, real_t p_alpha = 0.5);
|
||||
|
||||
int n;
|
||||
int k;
|
||||
real_t learning_rate;
|
||||
MLPPLogReg(std::vector<std::vector<real_t>> p_input_set, std::vector<real_t> p_output_set, std::string p_reg = "None", real_t p_lambda = 0.5, real_t p_alpha = 0.5);
|
||||
|
||||
MLPPLogReg();
|
||||
~MLPPLogReg();
|
||||
|
||||
protected:
|
||||
real_t cost(std::vector<real_t> y_hat, std::vector<real_t> y);
|
||||
|
||||
real_t evaluatev(std::vector<real_t> x);
|
||||
std::vector<real_t> evaluatem(std::vector<std::vector<real_t>> X);
|
||||
|
||||
void forward_pass();
|
||||
|
||||
static void _bind_methods();
|
||||
|
||||
std::vector<std::vector<real_t>> _input_set;
|
||||
std::vector<real_t> _output_set;
|
||||
std::vector<real_t> _y_hat;
|
||||
std::vector<real_t> _weights;
|
||||
real_t _bias;
|
||||
|
||||
int _n;
|
||||
int _k;
|
||||
real_t _learning_rate;
|
||||
|
||||
// Regularization Params
|
||||
std::string reg;
|
||||
real_t lambda; /* Regularization Parameter */
|
||||
real_t alpha; /* This is the controlling param for Elastic Net*/
|
||||
std::string _reg;
|
||||
real_t _lambda; /* Regularization Parameter */
|
||||
real_t _alpha; /* This is the controlling param for Elastic Net*/
|
||||
|
||||
bool _initialized;
|
||||
};
|
||||
|
||||
#endif /* LogReg_hpp */
|
||||
|
||||
@ -354,11 +354,18 @@ void MLPPTests::test_logistic_regression(bool ui) {
|
||||
MLPPLinAlg alg;
|
||||
MLPPData data;
|
||||
|
||||
// LOGISTIC REGRESSION
|
||||
Ref<MLPPDataSimple> dt = data.load_breast_cancer(_breast_cancer_data_path);
|
||||
|
||||
// LOGISTIC REGRESSION
|
||||
|
||||
MLPPLogRegOld model_old(dt->get_input()->to_std_vector(), dt->get_output()->to_std_vector());
|
||||
model_old.SGD(0.001, 100000, ui);
|
||||
alg.printVector(model_old.modelSetTest(dt->get_input()->to_std_vector()));
|
||||
std::cout << "ACCURACY (Old): " << 100 * model_old.score() << "%" << std::endl;
|
||||
|
||||
MLPPLogReg model(dt->get_input()->to_std_vector(), dt->get_output()->to_std_vector());
|
||||
model.SGD(0.001, 100000, ui);
|
||||
alg.printVector(model.modelSetTest(dt->get_input()->to_std_vector()));
|
||||
model.sgd(0.001, 100000, ui);
|
||||
alg.printVector(model.model_set_test(dt->get_input()->to_std_vector()));
|
||||
std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
|
||||
}
|
||||
void MLPPTests::test_probit_regression(bool ui) {
|
||||
|
||||
Loading…
Reference in New Issue
Block a user