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package com.expleague.ml.models.gpf;
import com.expleague.commons.math.vectors.Mx;
import com.expleague.commons.math.vectors.MxTools;
import com.expleague.ml.models.gpf.weblogmodel.BlockV1;
import com.expleague.commons.math.vectors.impl.mx.VecBasedMx;
/**
* Created with IntelliJ IDEA.
* User: irlab
* Date: 08.07.14
* Time: 13:31
* To change this template use File | Settings | File Templates.
*/
public interface GPFModel extends AttractivenessModel {
String explainTheta();
String explainSessionProb(Session ses);
double getClickGivenViewProbability(Blk b);
/**
* @param ses - session
* @return transmx_0[(i, click_i), (j, click_j)] - вероятность перехода за один шаг из состояния (i, click_i) в (j, click_j)
*/
VecBasedMx evalSessionTransitionProbs(Session ses);
/**
* for each block in ses.getBlocks(), evaluates probability that a user has one or more clicks on the block, given the behavior model
* @param ses - viewport structure
* @return double[ses.getBlocks().length] - array of probabilities
*/
double[] evalHasClickProbabilities(Session ses);
/**
* for each block in ses.getBlocks(), evaluates probability that a user has one or more views on the block, given the behavior model
* @param ses - viewport structure
* @return double[ses.getBlocks().length] - array of probabilities
*/
double[] evalHasViewProbabilities(Session ses);
/**
* for each block in ses.getBlocks(), evaluates expected number of steps when a user looks at the block, given the behavior model
* this is not distribution, sum of values is not equal to 1
* @param ses - viewport structure
* @return double[ses.getBlocks().length] - array of expected number of steps
*/
double[] evalExpectedAttention(Session ses);
/**
* evaluates expected number of clicks on the SERP, given the behavior model
* @param ses - viewport structure
* @return double - array of expected number of steps
*/
double evalExpectedNumberOfClicks(Session ses);
abstract class Stub implements GPFModel {
public int MAX_PATH_LENGTH = 15;
/**
* @param ses - session
* @return transmx_0[(i, click_i), (j, click_j)] - вероятность перехода за один шаг из состояния (i, click_i) в (j, click_j)
*/
@Override
public VecBasedMx evalSessionTransitionProbs(final Session ses) {
final Blk[] blocks = ses.getBlocks();
// 1 & для каждой пары блоков $i$, $j$ вычислить $f(i,j)$; третья координата - наличие клика c_i
final Tensor3 f = new Tensor3(blocks.length, blocks.length, 2);
for (int i = 0; i < blocks.length; i++) {
for (final int j: ses.getEdgesFrom(i)) {
for (int click_i = 0; click_i < 2; click_i++) {
f.set(i, j, click_i, eval_f(ses, i, j, click_i));
}
}
}
f.set(Session.E_INDEX, Session.E_INDEX, 0, 1.);
f.set(Session.E_INDEX, Session.E_INDEX, 1, 1.);
// transmx[(i, click_i), (j, click_j)] - вероятность перехода за один шаг из состояния (i, click_i) в (j, click_j)
final VecBasedMx transmx_0 = new VecBasedMx(blocks.length * 2, blocks.length * 2);
for (int i = 0; i < blocks.length; i++) {
for (int click_i = 0; click_i < 2; click_i++) {
double sum_f_i_j = 0.;
for (final int j: ses.getEdgesFrom(i))
sum_f_i_j += f.get(i, j, click_i);
for (final int j: ses.getEdgesFrom(i)) {
final double trans_prob = f.get(i, j, click_i) / sum_f_i_j;
final double click_prob = getClickGivenViewProbability(blocks[j]);
transmx_0.set(click_i * blocks.length + i, 0 * blocks.length + j, trans_prob * (1. - click_prob));
transmx_0.set(click_i * blocks.length + i, 1 * blocks.length + j, trans_prob * click_prob);
}
}
}
return transmx_0;
}
/**
* for each block in ses.getBlocks(), evaluates probability that a user has one or more clicks on the block, given the behavior model
* @param ses - viewport structure
* @return double[ses.getBlocks().length] - array of probabilities
*/
@Override
public double[] evalHasClickProbabilities(final Session ses) {
final Session.Block[] blocks = ses.getBlocks();
// transmx_0[(i, click_i), (j, click_j)] - вероятность перехода за один шаг из состояния (i, click_i) в (j, click_j)
final VecBasedMx transmx_0 = evalSessionTransitionProbs(ses);
final double[] hasClickProbabilities = new double[blocks.length];
for (int ci = Session.R0_INDEX; ci < blocks.length; ci++) {
final VecBasedMx transmx_ci = new VecBasedMx(transmx_0);
// модифицируем стохастическую матрицу transmx_ci так, чтобы после клика на ci пользователь оставался в том же состоянии
for (int j = 0; j < transmx_ci.columns; j++)
transmx_ci.set(1 * blocks.length + ci, j, 0.);
transmx_ci.set(1 * blocks.length + ci, 1 * blocks.length + ci, 1.);
// сначала пользователь в состоянии (Q, no_click)
Mx state_probabilities = new VecBasedMx(1, transmx_0.columns);
state_probabilities.set(0, Session.Q_INDEX, 1.);
for (int t = 0; t < MAX_PATH_LENGTH; t++)
state_probabilities = MxTools.multiply(state_probabilities, transmx_ci);
// вероятность через MAX_PATH_LENGTH шагов остаться в состоянии (ci, click)
hasClickProbabilities[ci] = state_probabilities.get(1 * blocks.length + ci);
}
return hasClickProbabilities;
}
/**
* for each block in ses.getBlocks(), evaluates probability that a user has one or more views on the block, given the behavior model
* @param ses - viewport structure
* @return double[ses.getBlocks().length] - array of probabilities
*/
@Override
public double[] evalHasViewProbabilities(final Session ses) {
final Session.Block[] blocks = ses.getBlocks();
// transmx_0[(i, click_i), (j, click_j)] - вероятность перехода за один шаг из состояния (i, click_i) в (j, click_j)
final VecBasedMx transmx_0 = evalSessionTransitionProbs(ses);
final double[] hasViewProbabilities = new double[blocks.length];
for (int ci = Session.R0_INDEX; ci < blocks.length; ci++) {
final VecBasedMx transmx_ci = new VecBasedMx(transmx_0);
// модифицируем стохастическую матрицу transmx_ci так, чтобы после view на ci пользователь оставался в том же состоянии
for (int j = 0; j < transmx_ci.columns; j++) {
transmx_ci.set(0 * blocks.length + ci, j, 0.);
transmx_ci.set(1 * blocks.length + ci, j, 0.);
}
transmx_ci.set(0 * blocks.length + ci, 0 * blocks.length + ci, 1.);
transmx_ci.set(1 * blocks.length + ci, 1 * blocks.length + ci, 1.);
// сначала пользователь в состоянии (Q, no_click)
Mx state_probabilities = new VecBasedMx(1, transmx_0.columns);
state_probabilities.set(0, Session.Q_INDEX, 1.);
for (int t = 0; t < MAX_PATH_LENGTH; t++)
state_probabilities = MxTools.multiply(state_probabilities, transmx_ci);
// вероятность через MAX_PATH_LENGTH шагов остаться в состоянии (ci, click) или (ci, noclick)
hasViewProbabilities[ci] = state_probabilities.get(0 * blocks.length + ci) + state_probabilities.get(1 * blocks.length + ci);
}
return hasViewProbabilities;
}
/**
* for each block in ses.getBlocks(), evaluates expected number of steps when a user looks at the block, given the behavior model
* this is not distribution, sum of values is not equal to 1
* @param ses - viewport structure
* @return double[ses.getBlocks().length] - array of expected number of steps
*/
@Override
public double[] evalExpectedAttention(final Session ses) {
final Session.Block[] blocks = ses.getBlocks();
// transmx_0[(i, click_i), (j, click_j)] - вероятность перехода за один шаг из состояния (i, click_i) в (j, click_j)
final VecBasedMx transmx_0 = evalSessionTransitionProbs(ses);
final double[] expectedAttention = new double[blocks.length];
// сначала пользователь в состоянии (Q, no_click)
Mx state_probabilities = new VecBasedMx(1, transmx_0.columns);
state_probabilities.set(0, Session.Q_INDEX, 1.);
for (int t = 0; t < MAX_PATH_LENGTH; t++) {
state_probabilities = MxTools.multiply(state_probabilities, transmx_0);
for (int i = Session.R0_INDEX; i < blocks.length; i++)
expectedAttention[i] += state_probabilities.get(0 * blocks.length + i) + state_probabilities.get(1 * blocks.length + i);
}
return expectedAttention;
}
/**
* for each block in ses.getBlocks(), evaluates expected number of steps when a user looks at the block, given the behavior model
* this is not distribution, sum of values is not equal to 1
* @param ses - viewport structure
* @return double[ses.getBlocks().length] - array of expected number of steps
*/
@Override
public double evalExpectedNumberOfClicks(final Session ses) {
final Session.Block[] blocks = ses.getBlocks();
// transmx_0[(i, click_i), (j, click_j)] - вероятность перехода за один шаг из состояния (i, click_i) в (j, click_j)
final VecBasedMx transmx_0 = evalSessionTransitionProbs(ses);
double expectedNumberOfClicks = 0.;
// сначала пользователь в состоянии (Q, no_click)
Mx state_probabilities = new VecBasedMx(1, transmx_0.columns);
state_probabilities.set(0, Session.Q_INDEX, 1.);
for (int t = 0; t < MAX_PATH_LENGTH; t++) {
state_probabilities = MxTools.multiply(state_probabilities, transmx_0);
for (int i = Session.R0_INDEX; i < blocks.length; i++)
expectedNumberOfClicks += state_probabilities.get(blocks.length + i);
}
return expectedNumberOfClicks;
}
@Override
public String explainSessionProb(final Session ses) {
final VecBasedMx sum_f = new VecBasedMx(ses.getBlocks().length, 2);
for (int i = 0; i < ses.getBlocks().length; i++) {
for (final int j: ses.getEdgesFrom(i)) {
sum_f.adjust(i, 0, eval_f(ses, i, j, 0));
sum_f.adjust(i, 1, eval_f(ses, i, j, 1));
}
}
final double[] hasClickProbabilities = evalHasClickProbabilities(ses);
final double[] hasViewProbabilities = evalHasViewProbabilities(ses);
final double[] attentionExpectation = evalExpectedAttention(ses);
// ArrayVec attentionDistribution = new ArrayVec(attentionExpectation);
// attentionDistribution.scale(1. / VecTools.sum(attentionDistribution));
final StringBuffer ret = new StringBuffer();
ret.append("pos\tsntype\trel\tclick\t");
ret.append("P(has_click)\tP(has_view)\tE(Att)\t\t");
ret.append("P(click|V)\tP(Q->i)\tP(S->i)\t\t");
ret.append("P(i->i+1|c=0)\tP(i->i-1|c=0)\tP(i->E|c=0)\tP(i->S|c=0)\t\t");
ret.append("P(i->i+1|c=1)\tP(i->i-1|c=1)\tP(i->E|c=1)\tP(i->S|c=1)\n");
for (int i = Session.R0_INDEX; i < ses.getBlocks().length; i++) {
final Blk bi = ses.getBlock(i);
int click_position = -1;
for (int ci = 0; ci < ses.getClick_indexes().length; ci++) {
if (ses.getClick_indexes()[ci] == i) {
click_position = ci+1;
break;
}
}
//ret.append("pos\tsntype\trel\tclick\t");
ret.append("" + bi.position + "\t" +
(bi instanceof BlockV1 ? ((BlockV1)bi).resultType.name() : "?") + "\t" +
(bi instanceof BlockV1 ? ((BlockV1)bi).resultGrade.name() : "?") + "\t" +
(click_position >= 0 ? click_position : "-") + "\t");
//ret.append("P(has_click)\tP(has_view)\tAtt\t\t");
ret.append("" + hasClickProbabilities[i] + "\t" + hasViewProbabilities[i] + "\t" + attentionExpectation[i] + "\t\t");
//ret.append("P(click|V)\tP(Q->i)\tP(S->i)\t\t");
final double P_Q_i = eval_f(ses, Session.Q_INDEX, i, 0) / sum_f.get(Session.Q_INDEX, 0);
final double P_S_i = eval_f(ses, Session.S_INDEX, i, 0) / sum_f.get(Session.S_INDEX, 0);
ret.append("" + getClickGivenViewProbability(bi) + "\t" + P_Q_i + "\t" + P_S_i + "\t\t");
//ret.append("P(i->i+1|c=0)\tP(i->i-1|c=0)\tP(i->E|c=0)\tP(i->S|c=0)\t\t");
int click_i = 0;
double P_down = i + 1 < ses.getBlocks().length ? eval_f(ses, i, i+1, click_i) / sum_f.get(i, click_i) : 0;
double P_up = i - 1 >= Session.R0_INDEX ? eval_f(ses, i, i-1, click_i) / sum_f.get(i, click_i) : 0;
double P_i_E = eval_f(ses, i, Session.E_INDEX, click_i) / sum_f.get(i, click_i);
double P_i_S = eval_f(ses, i, Session.S_INDEX, click_i) / sum_f.get(i, click_i);
ret.append("" + P_down + "\t" + P_up + "\t" + P_i_E + "\t" + P_i_S + "\t\t");
//ret.append("P(i->i+1|c=1)\tP(i->i-1|c=1)\tP(i->E|c=1)\tP(i->S|c=1)\n");
click_i = 1;
P_down = i + 1 < ses.getBlocks().length ? eval_f(ses, i, i+1, click_i) / sum_f.get(i, click_i) : 0;
P_up = i - 1 >= Session.R0_INDEX ? eval_f(ses, i, i-1, click_i) / sum_f.get(i, click_i) : 0;
P_i_E = eval_f(ses, i, Session.E_INDEX, click_i) / sum_f.get(i, click_i);
P_i_S = eval_f(ses, i, Session.S_INDEX, click_i) / sum_f.get(i, click_i);
ret.append("" + P_down + "\t" + P_up + "\t" + P_i_E + "\t" + P_i_S + "\n");
}
return ret.toString();
}
}
}
