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[regression] Implement R2 computation using evaluated values

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Change-Id: Iecf8cb84f092c2ec8a9bc17bc0265d7dddaac36c
This commit is contained in:
Hugo Saint-Vignes
2020-05-20 14:04:42 +02:00
committed by Émilie Feral
parent 5c95cbd1d3
commit db69f6a6f5
5 changed files with 69 additions and 27 deletions
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83
apps/regression/store.cpp
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@@ -149,8 +149,8 @@ void Store::setDefault() {
}
}
float range = maxX - minX;
setXMin(minX - k_displayHorizontalMarginRatio*range);
setXMax(maxX + k_displayHorizontalMarginRatio*range);
setXMin(minX - k_displayHorizontalMarginRatio * range);
setXMax(maxX + k_displayHorizontalMarginRatio * range);
}
/* Series */
@@ -161,7 +161,7 @@ bool Store::seriesIsEmpty(int series) const {
/* Calculations */
double * Store::coefficientsForSeries(int series, Poincare::Context * globalContext) {
void Store::updateCoefficients(int series, Poincare::Context * globalContext) {
assert(series >= 0 && series <= k_numberOfSeries);
assert(!seriesIsEmpty(series));
uint32_t storeChecksumSeries = storeChecksumForSeries(series);
@@ -179,10 +179,25 @@ double * Store::coefficientsForSeries(int series, Poincare::Context * globalCont
seriesModel->fit(this, series, m_regressionCoefficients[series], globalContext);
m_regressionChanged[series] = false;
m_seriesChecksum[series] = storeChecksumSeries;
/* m_determinationCoefficient must be updated after m_seriesChecksum and m_regressionChanged
* updates to avoid infinite recursive calls as computeDeterminationCoefficient calls
* yValueForXValue which calls coefficientsForSeries which calls updateCoefficients */
m_determinationCoefficient[series] = computeDeterminationCoefficient(series, globalContext);
}
}
double * Store::coefficientsForSeries(int series, Poincare::Context * globalContext) {
updateCoefficients(series, globalContext);
return m_regressionCoefficients[series];
}
double Store::determinationCoefficientForSeries(int series, Poincare::Context * globalContext) {
/* Returns the Determination coefficient (R2).
* It will be updated if the regression has been updated */
updateCoefficients(series, globalContext);
return m_determinationCoefficient[series];
}
double Store::doubleCastedNumberOfPairsOfSeries(int series) const {
return DoublePairStore::numberOfPairsOfSeries(series);
}
@@ -212,12 +227,13 @@ float Store::minValueOfColumn(int series, int i) const {
double Store::squaredValueSumOfColumn(int series, int i, bool lnOfSeries) const {
double result = 0;
for (int k = 0; k < numberOfPairsOfSeries(series); k++) {
const int numberOfPairs = numberOfPairsOfSeries(series);
for (int k = 0; k < numberOfPairs; k++) {
double value = m_data[series][i][k];
if (lnOfSeries) {
result += log(m_data[series][i][k]) * log(m_data[series][i][k]);
} else {
result += m_data[series][i][k]*m_data[series][i][k];
value = log(value);
}
result += value * value;
}
return result;
}
@@ -225,22 +241,24 @@ double Store::squaredValueSumOfColumn(int series, int i, bool lnOfSeries) const
double Store::columnProductSum(int series, bool lnOfSeries) const {
double result = 0;
for (int k = 0; k < numberOfPairsOfSeries(series); k++) {
double value0 = m_data[series][0][k];
double value1 = m_data[series][1][k];
if (lnOfSeries) {
result += log(m_data[series][0][k]) * log(m_data[series][1][k]);
} else {
result += m_data[series][0][k] * m_data[series][1][k];
value0 = log(value0);
value1 = log(value1);
}
result += value0 * value1;
}
return result;
}
double Store::meanOfColumn(int series, int i, bool lnOfSeries) const {
return numberOfPairsOfSeries(series) == 0 ? 0 : sumOfColumn(series, i, lnOfSeries)/numberOfPairsOfSeries(series);
return numberOfPairsOfSeries(series) == 0 ? 0 : sumOfColumn(series, i, lnOfSeries) / numberOfPairsOfSeries(series);
}
double Store::varianceOfColumn(int series, int i, bool lnOfSeries) const {
double mean = meanOfColumn(series, i, lnOfSeries);
return squaredValueSumOfColumn(series, i, lnOfSeries)/numberOfPairsOfSeries(series) - mean*mean;
return squaredValueSumOfColumn(series, i, lnOfSeries)/numberOfPairsOfSeries(series) - mean * mean;
}
double Store::standardDeviationOfColumn(int series, int i, bool lnOfSeries) const {
@@ -248,7 +266,9 @@ double Store::standardDeviationOfColumn(int series, int i, bool lnOfSeries) cons
}
double Store::covariance(int series, bool lnOfSeries) const {
return columnProductSum(series, lnOfSeries)/numberOfPairsOfSeries(series) - meanOfColumn(series, 0, lnOfSeries)*meanOfColumn(series, 1, lnOfSeries);
double mean0 = meanOfColumn(series, 0, lnOfSeries);
double mean1 = meanOfColumn(series, 1, lnOfSeries);
return columnProductSum(series, lnOfSeries)/numberOfPairsOfSeries(series) - mean0 * mean1;
}
double Store::slope(int series, bool lnOfSeries) const {
@@ -268,20 +288,39 @@ double Store::yValueForXValue(int series, double x, Poincare::Context * globalCo
double Store::xValueForYValue(int series, double y, Poincare::Context * globalContext) {
Model * model = regressionModel((int)m_regressionTypes[series]);
double * coefficients = coefficientsForSeries(series, globalContext);
return model->levelSet(coefficients, xMin(), xGridUnit()/10.0, xMax(), y, globalContext);
return model->levelSet(coefficients, xMin(), xGridUnit() / 10.0, xMax(), y, globalContext);
}
double Store::correlationCoefficient(int series) const {
double sd0 = standardDeviationOfColumn(series, 0);
double sd1 = standardDeviationOfColumn(series, 1);
return (sd0 == 0.0 || sd1 == 0.0) ? 1.0 : covariance(series)/(sd0*sd1);
}
double Store::squaredCorrelationCoefficient(int series) const {
double cov = covariance(series);
/* Returns the correlation coefficient (R) between the series X and Y.
* In non-linear regressions, its square is different from the determinationCoefficient
* It is usually displayed in linear regressions only to avoid confusion */
double v0 = varianceOfColumn(series, 0);
double v1 = varianceOfColumn(series, 1);
return (v0 == 0.0 || v1 == 0.0) ? 1.0 : cov*cov/(v0*v1);
return (v0 == 0.0 || v1 == 0.0) ? 1.0 : covariance(series) / std::sqrt(v0 * v1);
}
double Store::computeDeterminationCoefficient(int series, Poincare::Context * globalContext) {
/* Computes and returns the determination coefficient (R2) of the regression.
* For regressions, it is equal to the square of the correlation coefficient between
* the series Y and the evaluated values from the series X and the selected model
* Computing the coefficient using the latter equality would require more calls to the evaluated
* values and would be less precise. */
// Residual sum of squares
double ssr = 0;
// Total sum of squares
double sst = 0;
double mean = meanOfColumn(series, 1);
const int numberOfPairs = numberOfPairsOfSeries(series);
for (int k = 0; k < numberOfPairs; k++) {
// Difference between the observation and the estimated value of the model
double residual = m_data[series][1][k] - yValueForXValue(series, m_data[series][0][k], globalContext);
ssr += residual * residual;
// Difference between the observation and the overall observations mean
double difference = m_data[series][1][k] - mean;
sst += difference * difference;
}
return sst == 0.0 ? 1.0 : 1.0 - ssr / sst;
}
Model * Store::regressionModel(int index) {