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Initialize Sharing Ranker with a pre-trained model.

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Test: manual shared images in Photos; webpages in Chrome; contacts in
Contacts, and ran unit tests.

Change-Id: If8c724a4085f1436b3e1e5d62754c6563f756915
This commit is contained in:
Kang Li
2017-01-18 13:24:06 -08:00
parent 57c45562cd
commit 9988f36827
1 changed files with 38 additions and 29 deletions
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67
core/java/com/android/internal/app/ResolverComparator.java
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@@ -343,53 +343,42 @@ class ResolverComparator implements Comparator<ResolvedComponentInfo> {
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 static final String VERSION_PREF_KEY = "version";
// parameters for a pre-trained model, to initialize the app ranker. When updating the
// pre-trained model, please update these params, as well as initModel().
private static final int CURRENT_VERSION = 1;
private static final float LEARNING_RATE = 0.0001f;
private static final float REGULARIZER_PARAM = 0.0001f;
private SharedPreferences mParamSharedPref;
private ArrayMap<String, Float> mFeatureWeights;
private float mBias;
public LogisticRegressionAppRanker(Context context) {
mParamSharedPref = getParamSharedPref(context);
initModel();
}
public float predict(ArrayMap<String, Float> target) {
if (target == null || mParamSharedPref == null) {
if (target == 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);
}
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) {
if (target == null) {
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);
@@ -405,15 +394,13 @@ class ResolverComparator implements Comparator<ResolvedComponentInfo> {
}
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.putInt(VERSION_PREF_KEY, CURRENT_VERSION);
editor.apply();
}
@@ -431,5 +418,27 @@ class ResolverComparator implements Comparator<ResolvedComponentInfo> {
PARAM_SHARED_PREF_NAME + ".xml");
return context.getSharedPreferences(prefsFile, Context.MODE_PRIVATE);
}
private void initModel() {
mFeatureWeights = new ArrayMap<>(4);
if (mParamSharedPref == null ||
mParamSharedPref.getInt(VERSION_PREF_KEY, 0) < CURRENT_VERSION) {
// Initializing the app ranker to a pre-trained model. When updating the pre-trained
// model, please increment CURRENT_VERSION, and update LEARNING_RATE and
// REGULARIZER_PARAM.
mBias = -1.6568f;
mFeatureWeights.put(LAUNCH_SCORE, 2.5543f);
mFeatureWeights.put(TIME_SPENT_SCORE, 2.8412f);
mFeatureWeights.put(RECENCY_SCORE, 0.269f);
mFeatureWeights.put(CHOOSER_SCORE, 4.2222f);
} else {
mBias = mParamSharedPref.getFloat(BIAS_PREF_KEY, 0.0f);
mFeatureWeights.put(LAUNCH_SCORE, mParamSharedPref.getFloat(LAUNCH_SCORE, 0.0f));
mFeatureWeights.put(
TIME_SPENT_SCORE, mParamSharedPref.getFloat(TIME_SPENT_SCORE, 0.0f));
mFeatureWeights.put(RECENCY_SCORE, mParamSharedPref.getFloat(RECENCY_SCORE, 0.0f));
mFeatureWeights.put(CHOOSER_SCORE, mParamSharedPref.getFloat(CHOOSER_SCORE, 0.0f));
}
}
}
}