Merge "Initialize Sharing Ranker with a pre-trained model." (00269391) · Commits · e / os / android_frameworks_base

core/java/com/android/internal/app/ResolverComparator.java

+38 −29

Original line number	Diff line number	Diff line
		@@ -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);
		}
		}
		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));
		}
		}
		}
		}