Donate to e Foundation | Murena handsets with /e/OS | Own a part of Murena! Learn more

Commit 9988f368 authored by Kang Li's avatar Kang Li
Browse files

Initialize Sharing Ranker with a pre-trained model. 

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

Change-Id: If8c724a4085f1436b3e1e5d62754c6563f756915
parent 57c45562

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));

            }

        }

    }

}