public final double lgrad(double u, double a, Loss loss) {
assert loss.isForNumeric() : "Loss function " + loss + " not applicable to numerics";
switch (loss) {
case Quadratic:
return 2 * (u - a);
case Absolute:
return Math.signum(u - a);
case Huber:
return Math.abs(u - a) <= 1 ? u - a : Math.signum(u - a);
case Poisson:
assert a >= 0 : "Poisson loss L(u,a) requires variable a >= 0";
return Math.exp(u) - a;
case Hinge:
// return a*u <= 1 ? -a : 0;
return a == 0
? (-u <= 1 ? 1 : 0)
: (u <= 1 ? -1 : 0); // Booleans are coded as {0,1} instead of {-1,1}
case Logistic:
// return -a/(1+Math.exp(a*u));
return a == 0
? 1 / (1 + Math.exp(-u))
: -1 / (1 + Math.exp(u)); // Booleans are coded as {0,1} instead of {-1,1}
case Periodic:
return ((2 * Math.PI) / _period) * Math.sin((a - u) * (2 * Math.PI) / _period);
default:
throw new RuntimeException("Unknown loss function " + loss);
}
}
public final double loss(double u, double a, Loss loss) {
assert loss.isForNumeric() : "Loss function " + loss + " not applicable to numerics";
switch (loss) {
case Quadratic:
return (u - a) * (u - a);
case Absolute:
return Math.abs(u - a);
case Huber:
return Math.abs(u - a) <= 1 ? 0.5 * (u - a) * (u - a) : Math.abs(u - a) - 0.5;
case Poisson:
assert a >= 0 : "Poisson loss L(u,a) requires variable a >= 0";
return Math.exp(u)
+ (a == 0 ? 0 : -a * u + a * Math.log(a) - a); // Since \lim_{a->0} a*log(a) = 0
case Hinge:
// return Math.max(1-a*u,0);
return Math.max(1 - (a == 0 ? -u : u), 0); // Booleans are coded {0,1} instead of {-1,1}
case Logistic:
// return Math.log(1 + Math.exp(-a * u));
return Math.log(
1 + Math.exp(a == 0 ? u : -u)); // Booleans are coded {0,1} instead of {-1,1}
case Periodic:
return 1 - Math.cos((a - u) * (2 * Math.PI) / _period);
default:
throw new RuntimeException("Unknown loss function " + loss);
}
}
// Read the 'tree' columns, do model-specific math and put the results in the
// fs[] array, and return the sum. Dividing any fs[] element by the sum
// turns the results into a probability distribution.
@Override
protected float score1(Chunk chks[], float fs[ /*nclass*/], int row) {
if (_nclass == 1) // Classification?
return (float) chk_tree(chks, 0).at0(row); // Regression.
if (_nclass == 2) { // The Boolean Optimization
// This optimization assumes the 2nd tree of a 2-class system is the
// inverse of the first. Fill in the missing tree
fs[1] = (float) Math.exp(chk_tree(chks, 0).at0(row));
fs[2] = 1.0f / fs[1]; // exp(-d) === 1/d
return fs[1] + fs[2];
}
float sum = 0;
for (int k = 0; k < _nclass; k++) // Sum across of likelyhoods
sum += (fs[k + 1] = (float) Math.exp(chk_tree(chks, k).at0(row)));
return sum;
}
protected void calcModelStats(
CoxPHModel model, final double[] newCoef, final double newLoglik) {
CoxPHModel.CoxPHParameters p = model._parms;
CoxPHModel.CoxPHOutput o = model._output;
final int n_coef = o.coef.length;
final Matrix inv_hessian = new Matrix(o.hessian).inverse();
for (int j = 0; j < n_coef; ++j) {
for (int k = 0; k <= j; ++k) {
final double elem = -inv_hessian.get(j, k);
o.var_coef[j][k] = elem;
o.var_coef[k][j] = elem;
}
}
for (int j = 0; j < n_coef; ++j) {
o.coef[j] = newCoef[j];
o.exp_coef[j] = Math.exp(o.coef[j]);
o.exp_neg_coef[j] = Math.exp(-o.coef[j]);
o.se_coef[j] = Math.sqrt(o.var_coef[j][j]);
o.z_coef[j] = o.coef[j] / o.se_coef[j];
}
if (o.iter == 0) {
o.null_loglik = newLoglik;
o.maxrsq = 1 - Math.exp(2 * o.null_loglik / o.n);
o.score_test = 0;
for (int j = 0; j < n_coef; ++j) {
double sum = 0;
for (int k = 0; k < n_coef; ++k) sum += o.var_coef[j][k] * o.gradient[k];
o.score_test += o.gradient[j] * sum;
}
}
o.loglik = newLoglik;
o.loglik_test = -2 * (o.null_loglik - o.loglik);
o.rsq = 1 - Math.exp(-o.loglik_test / o.n);
o.wald_test = 0;
for (int j = 0; j < n_coef; ++j) {
double sum = 0;
for (int k = 0; k < n_coef; ++k) sum -= o.hessian[j][k] * (o.coef[k] - p.init);
o.wald_test += (o.coef[j] - p.init) * sum;
}
}
public static double impute(double u, Loss loss) {
assert loss.isForNumeric() : "Loss function " + loss + " not applicable to numerics";
switch (loss) {
case Quadratic:
case Absolute:
case Huber:
case Periodic:
return u;
case Poisson:
return Math.exp(u) - 1;
case Hinge:
case Logistic:
return u > 0 ? 1 : 0; // Booleans are coded as {0,1} instead of {-1,1}
default:
throw new RuntimeException("Unknown loss function " + loss);
}
}
@Override
protected float[] score0(double[] data, float[] preds) {
float[] p = super.score0(data, preds);
if (nclasses() > 1) { // classification
// Because we call Math.exp, we have to be numerically stable or else
// we get Infinities, and then shortly NaN's. Rescale the data so the
// largest value is +/-1 and the other values are smaller.
// See notes here: http://www.hongliangjie.com/2011/01/07/logsum/
float maxval = Float.NEGATIVE_INFINITY;
float dsum = 0;
if (nclasses() == 2) p[2] = -p[1];
// Find a max
for (int k = 1; k < p.length; k++) maxval = Math.max(maxval, p[k]);
assert !Float.isInfinite(maxval)
: "Something is wrong with GBM trees since returned prediction is "
+ Arrays.toString(p);
for (int k = 1; k < p.length; k++) dsum += (p[k] = (float) Math.exp(p[k] - maxval));
div(p, dsum);
p[0] = getPrediction(p, data);
} else { // regression
// do nothing for regression
}
return p;
}
@Override
protected void processRow(long gid, Row row) {
n++;
double[] response = row.response;
int ncats = row.nBins;
int[] cats = row.numIds;
double[] nums = row.numVals;
final double weight = _has_weights_column ? response[0] : 1.0;
if (weight <= 0) throw new IllegalArgumentException("weights must be positive values");
final long event = (long) response[response.length - 1];
final int t1 =
_has_start_column ? (int) (((long) response[response.length - 3] + 1) - _min_time) : -1;
final int t2 = (int) (((long) response[response.length - 2]) - _min_time);
if (t1 > t2)
throw new IllegalArgumentException("start times must be strictly less than stop times");
final int numStart = _dinfo.numStart();
sumWeights += weight;
for (int j = 0; j < ncats; ++j) sumWeightedCatX[cats[j]] += weight;
for (int j = 0; j < nums.length; ++j) sumWeightedNumX[j] += weight * nums[j];
double logRisk = 0;
for (int j = 0; j < ncats; ++j) logRisk += _beta[cats[j]];
for (int j = 0; j < nums.length - _n_offsets; ++j) logRisk += nums[j] * _beta[numStart + j];
for (int j = nums.length - _n_offsets; j < nums.length; ++j) logRisk += nums[j];
final double risk = weight * Math.exp(logRisk);
logRisk *= weight;
if (event > 0) {
countEvents[t2]++;
sizeEvents[t2] += weight;
sumLogRiskEvents[t2] += logRisk;
sumRiskEvents[t2] += risk;
} else sizeCensored[t2] += weight;
if (_has_start_column) {
for (int t = t1; t <= t2; ++t) sizeRiskSet[t] += weight;
for (int t = t1; t <= t2; ++t) rcumsumRisk[t] += risk;
} else {
sizeRiskSet[t2] += weight;
rcumsumRisk[t2] += risk;
}
final int ntotal = ncats + (nums.length - _n_offsets);
final int numStartIter = numStart - ncats;
for (int jit = 0; jit < ntotal; ++jit) {
final boolean jIsCat = jit < ncats;
final int j = jIsCat ? cats[jit] : numStartIter + jit;
final double x1 = jIsCat ? 1.0 : nums[jit - ncats];
final double xRisk = x1 * risk;
if (event > 0) {
sumXEvents[t2][j] += weight * x1;
sumXRiskEvents[t2][j] += xRisk;
}
if (_has_start_column) {
for (int t = t1; t <= t2; ++t) rcumsumXRisk[t][j] += xRisk;
} else {
rcumsumXRisk[t2][j] += xRisk;
}
for (int kit = 0; kit < ntotal; ++kit) {
final boolean kIsCat = kit < ncats;
final int k = kIsCat ? cats[kit] : numStartIter + kit;
final double x2 = kIsCat ? 1.0 : nums[kit - ncats];
final double xxRisk = x2 * xRisk;
if (event > 0) sumXXRiskEvents[t2][j][k] += xxRisk;
if (_has_start_column) {
for (int t = t1; t <= t2; ++t) rcumsumXXRisk[t][j][k] += xxRisk;
} else {
rcumsumXXRisk[t2][j][k] += xxRisk;
}
}
}
}
