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Merge "Rank apps by Logistic Regression for Smart-Sharing."

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This commit is contained in:
Kang Li
2017-01-04 16:08:23 +00:00
committed by Android (Google) Code Review
parent 525aa0d99f a2c7774d21
commit 97a8d70386
3 changed files with 156 additions and 22 deletions
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1
core/java/com/android/internal/app/ChooserActivity.java
View File

@@ -565,6 +565,7 @@ public class ChooserActivity extends ResolverActivity {
if (ri != null && ri.activityInfo != null) {
usageStatsManager.reportChooserSelection(ri.activityInfo.packageName, getUserId(),
annotation, null, info.getResolvedIntent().getAction());
mResolverComparator.updateModel(info.getResolvedComponentName());
if (DEBUG) {
Log.d(TAG, "ResolveInfo Package is" + ri.activityInfo.packageName);
}

1
core/java/com/android/internal/app/ResolverActivity.java
View File

@@ -107,6 +107,7 @@ public class ResolverActivity extends Activity {
private PickTargetOptionRequest mPickOptionRequest;
private String mReferrerPackage;
protected ResolverComparator mResolverComparator;
protected ResolverDrawerLayout mResolverDrawerLayout;
protected String mContentType;
protected PackageManager mPm;

176
core/java/com/android/internal/app/ResolverComparator.java
View File

@@ -27,11 +27,16 @@ import android.content.pm.ApplicationInfo;
import android.content.pm.ComponentInfo;
import android.content.pm.PackageManager;
import android.content.pm.ResolveInfo;
import android.content.SharedPreferences;
import android.os.Environment;
import android.os.storage.StorageManager;
import android.os.UserHandle;
import android.text.TextUtils;
import android.util.ArrayMap;
import android.util.Log;
import com.android.internal.app.ResolverActivity.ResolvedComponentInfo;
import java.io.File;
import java.text.Collator;
import java.util.ArrayList;
import java.util.Comparator;
@@ -54,6 +59,12 @@ class ResolverComparator implements Comparator<ResolvedComponentInfo> {
private static final float RECENCY_MULTIPLIER = 2.f;
// feature names used in ranking.
private static final String LAUNCH_SCORE = "launch";
private static final String TIME_SPENT_SCORE = "timeSpent";
private static final String RECENCY_SCORE = "recency";
private static final String CHOOSER_SCORE = "chooser";
private final Collator mCollator;
private final boolean mHttp;
private final PackageManager mPm;
@@ -65,6 +76,7 @@ class ResolverComparator implements Comparator<ResolvedComponentInfo> {
private final String mReferrerPackage;
public String mContentType;
private String mAction;
private LogisticRegressionAppRanker mRanker;
public ResolverComparator(Context context, Intent intent, String referrerPackage) {
mCollator = Collator.getInstance(context.getResources().getConfiguration().locale);
@@ -80,6 +92,7 @@ class ResolverComparator implements Comparator<ResolvedComponentInfo> {
mStats = mUsm.queryAndAggregateUsageStats(mSinceTime, mCurrentTime);
mContentType = intent.getType();
mAction = intent.getAction();
mRanker = new LogisticRegressionAppRanker(context);
}
public void compute(List<ResolvedComponentInfo> targets) {
@@ -152,16 +165,13 @@ class ResolverComparator implements Comparator<ResolvedComponentInfo> {
for (ScoredTarget target : mScoredTargets.values()) {
final float recency = (float) Math.max(target.lastTimeUsed - recentSinceTime, 0)
/ (mostRecentlyUsedTime - recentSinceTime);
final float recencyScore = recency * recency * RECENCY_MULTIPLIER;
final float usageTimeScore = (float) target.timeSpent / mostTimeSpent;
final float launchCountScore = (float) target.launchCount / mostLaunched;
target.score = recencyScore + usageTimeScore + launchCountScore;
target.setFeatures((float) target.launchCount / mostLaunched,
(float) target.timeSpent / mostTimeSpent,
recency * recency * RECENCY_MULTIPLIER,
(float) target.chooserCount / mostSelected);
target.selectProb = mRanker.predict(target.getFeatures());
if (DEBUG) {
Log.d(TAG, "Scores: recencyScore: " + recencyScore
+ " usageTimeScore: " + usageTimeScore
+ " launchCountScore: " + launchCountScore
+ " - " + target);
Log.d(TAG, "Scores: " + target);
}
}
}
@@ -215,17 +225,11 @@ class ResolverComparator implements Comparator<ResolvedComponentInfo> {
final ScoredTarget rhsTarget = mScoredTargets.get(new ComponentName(
rhs.activityInfo.packageName, rhs.activityInfo.name));
final int chooserCountDiff = Long.compare(
rhsTarget.chooserCount, lhsTarget.chooserCount);
final int selectProbDiff = Float.compare(
rhsTarget.selectProb, lhsTarget.selectProb);
if (chooserCountDiff != 0) {
return chooserCountDiff > 0 ? 1 : -1;
}
final int diff = Float.compare(rhsTarget.score, lhsTarget.score);
if (diff != 0) {
return diff > 0 ? 1 : -1;
if (selectProbDiff != 0) {
return selectProbDiff > 0 ? 1 : -1;
}
}
}
@@ -241,32 +245,160 @@ class ResolverComparator implements Comparator<ResolvedComponentInfo> {
public float getScore(ComponentName name) {
final ScoredTarget target = mScoredTargets.get(name);
if (target != null) {
return target.score;
return target.selectProb;
}
return 0;
}
static class ScoredTarget {
public final ComponentInfo componentInfo;
public float score;
public long lastTimeUsed;
public long timeSpent;
public long launchCount;
public long chooserCount;
public ArrayMap<String, Float> features;
public float selectProb;
public ScoredTarget(ComponentInfo ci) {
componentInfo = ci;
features = new ArrayMap<>(5);
}
@Override
public String toString() {
return "ScoredTarget{" + componentInfo
+ " score: " + score
+ " lastTimeUsed: " + lastTimeUsed
+ " timeSpent: " + timeSpent
+ " launchCount: " + launchCount
+ " chooserCount: " + chooserCount
+ " selectProb: " + selectProb
+ "}";
}
public void setFeatures(float launchCountScore, float usageTimeScore, float recencyScore,
float chooserCountScore) {
features.put(LAUNCH_SCORE, launchCountScore);
features.put(TIME_SPENT_SCORE, usageTimeScore);
features.put(RECENCY_SCORE, recencyScore);
features.put(CHOOSER_SCORE, chooserCountScore);
}
public ArrayMap<String, Float> getFeatures() {
return features;
}
}
public void updateModel(ComponentName componentName) {
if (mScoredTargets == null || componentName == null ||
!mScoredTargets.containsKey(componentName)) {
return;
}
ScoredTarget selected = mScoredTargets.get(componentName);
for (ComponentName targetComponent : mScoredTargets.keySet()) {
if (targetComponent.equals(componentName)) {
continue;
}
ScoredTarget target = mScoredTargets.get(targetComponent);
// A potential point of optimization. Save updates or derive a closed form for the
// positive case, to avoid calculating them repeatedly.
if (target.selectProb >= selected.selectProb) {
mRanker.update(target.getFeatures(), target.selectProb, false);
mRanker.update(selected.getFeatures(), selected.selectProb, true);
}
}
mRanker.commitUpdate();
}
class LogisticRegressionAppRanker {
private static final String PARAM_SHARED_PREF_NAME = "resolver_ranker_params";
private static final String BIAS_PREF_KEY = "bias";
private static final float LEARNING_RATE = 0.02f;
private static final float REGULARIZER_PARAM = 0.1f;
private SharedPreferences mParamSharedPref;
private ArrayMap<String, Float> mFeatureWeights;
private float mBias;
public LogisticRegressionAppRanker(Context context) {
mParamSharedPref = getParamSharedPref(context);
}
public float predict(ArrayMap<String, Float> target) {
if (target == null || mParamSharedPref == null) {
return 0.0f;
}
final int featureSize = target.size();
if (featureSize == 0) {
return 0.0f;
}
float sum = 0.0f;
if (mFeatureWeights == null) {
mBias = mParamSharedPref.getFloat(BIAS_PREF_KEY, 0.0f);
mFeatureWeights = new ArrayMap<>(featureSize);
for (int i = 0; i < featureSize; i++) {
String featureName = target.keyAt(i);
float weight = mParamSharedPref.getFloat(featureName, 0.0f);
sum += weight * target.valueAt(i);
mFeatureWeights.put(featureName, weight);
}
} else {
for (int i = 0; i < featureSize; i++) {
String featureName = target.keyAt(i);
float weight = mFeatureWeights.getOrDefault(featureName, 0.0f);
sum += weight * target.valueAt(i);
}
}
return (float) (1.0 / (1.0 + Math.exp(-mBias - sum)));
}
public void update(ArrayMap<String, Float> target, float predict, boolean isSelected) {
if (target == null || target.size() == 0) {
return;
}
final int featureSize = target.size();
if (mFeatureWeights == null) {
mBias = 0.0f;
mFeatureWeights = new ArrayMap<>(featureSize);
}
float error = isSelected ? 1.0f - predict : -predict;
for (int i = 0; i < featureSize; i++) {
String featureName = target.keyAt(i);
float currentWeight = mFeatureWeights.getOrDefault(featureName, 0.0f);
mBias += LEARNING_RATE * error;
currentWeight = currentWeight - LEARNING_RATE * REGULARIZER_PARAM * currentWeight +
LEARNING_RATE * error * target.valueAt(i);
mFeatureWeights.put(featureName, currentWeight);
}
if (DEBUG) {
Log.d(TAG, "Weights: " + mFeatureWeights + " Bias: " + mBias);
}
}
public void commitUpdate() {
if (mFeatureWeights == null || mFeatureWeights.size() == 0) {
return;
}
SharedPreferences.Editor editor = mParamSharedPref.edit();
editor.putFloat(BIAS_PREF_KEY, mBias);
final int size = mFeatureWeights.size();
for (int i = 0; i < size; i++) {
editor.putFloat(mFeatureWeights.keyAt(i), mFeatureWeights.valueAt(i));
}
editor.apply();
}
private SharedPreferences getParamSharedPref(Context context) {
// The package info in the context isn't initialized in the way it is for normal apps,
// so the standard, name-based context.getSharedPreferences doesn't work. Instead, we
// build the path manually below using the same policy that appears in ContextImpl.
if (DEBUG) {
Log.d(TAG, "Context Package Name: " + context.getPackageName());
}
final File prefsFile = new File(new File(
Environment.getDataUserCePackageDirectory(StorageManager.UUID_PRIVATE_INTERNAL,
context.getUserId(), context.getPackageName()),
"shared_prefs"),
PARAM_SHARED_PREF_NAME + ".xml");
return context.getSharedPreferences(prefsFile, Context.MODE_PRIVATE);
}
}
}