Playpredict use average betaI, gammaU, gammaI when item or user has not been seen before
This commit is contained in:
ltcptgeneral
parent
aa423db398
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0a096b2af3
@ -9,7 +9,7 @@
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"cell_type": "code",
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"execution_count": 38,
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"execution_count": 12,
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"cell_type": "code",
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"execution_count": 41,
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"execution_count": 15,
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"cell_type": "code",
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"execution_count": 42,
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"execution_count": 16,
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@ -116,7 +116,7 @@
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"cell_type": "code",
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"execution_count": 43,
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -132,7 +132,15 @@
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"\n",
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" # Prediction for a single instance\n",
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" def predict(self, u, i):\n",
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" p = self.betaI[i] + tf.tensordot(self.gammaU[u], self.gammaI[i], 1)\n",
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" bi = self.bi\n",
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" gu = self.gu\n",
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" gi = self.gi\n",
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" if u != None:\n",
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" gu = self.gammaU[u]\n",
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" if i != None:\n",
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" bi = self.betaI[i]\n",
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" gi = self.gammaI[i]\n",
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" p = bi + tf.tensordot(gu, gi, 1)\n",
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" return p\n",
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"\n",
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" # Regularizer\n",
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@ -153,7 +161,12 @@
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" def call(self, sampleU, sampleI, sampleJ):\n",
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" x_ui = self.score(sampleU, sampleI)\n",
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" x_uj = self.score(sampleU, sampleJ)\n",
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" return -tf.reduce_mean(tf.math.log(tf.math.sigmoid(x_ui - x_uj)))"
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" return -tf.reduce_mean(tf.math.log(tf.math.sigmoid(x_ui - x_uj)))\n",
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" \n",
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" def finalize(self):\n",
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" self.bi = np.average(self.betaI, axis=0)\n",
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" self.gu = np.average(self.gammaU, axis=0)\n",
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" self.gi = np.average(self.gammaI, axis=0)"
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]
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},
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@ -165,7 +178,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -175,8 +188,6 @@
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" pass\n",
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"\n",
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" def fit(self, data, threshold=0.6, K=5, iters=100): # data is an array of (user, game, review) tuples\n",
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" self.topGames = self.getTopGames(threshold)\n",
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"\n",
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" self.userIDs = {}\n",
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" self.itemIDs = {}\n",
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" interactions = []\n",
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@ -222,84 +233,85 @@
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" obj = trainingStepBPR(self.modelBPR, interactions)\n",
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" if (i % 10 == 9): print(\"iteration \" + str(i+1) + \", objective = \" + str(obj))\n",
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"\n",
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" self.modelBPR.finalize()\n",
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" \n",
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" def predict(self, user, game, threshold=0.5):\n",
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" if user in self.userIDs and game in self.itemIDs:\n",
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" pred = self.modelBPR.predict(self.userIDs[user], self.itemIDs[game]).numpy()\n",
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" return int(pred > threshold)\n",
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" else:\n",
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" return int(game in self.topGames)\n",
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"\n",
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" def getTopGames (self, threshold):\n",
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" gameCount = defaultdict(int)\n",
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" totalPlayed = 0\n",
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"\n",
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" for user,game,_ in readJSON(\"train.json.gz\"):\n",
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" gameCount[game] += 1\n",
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" totalPlayed += 1\n",
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"\n",
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" mostPopular = [(gameCount[x], x) for x in gameCount]\n",
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" mostPopular.sort()\n",
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" mostPopular.reverse()\n",
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"\n",
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" return1 = set()\n",
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" count = 0\n",
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" for ic, i in mostPopular:\n",
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" count += ic\n",
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" return1.add(i)\n",
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" if count > totalPlayed * threshold: break\n",
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" return return1\n"
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" uid = None\n",
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" gid = None\n",
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" if user in self.userIDs:\n",
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" uid = self.userIDs[user]\n",
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" if game in self.itemIDs:\n",
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" gid = self.itemIDs[game]\n",
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" pred = self.modelBPR.predict(uid, gid).numpy()\n",
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" return int(pred > threshold)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"iteration 10, objective = 0.51180786\n",
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"iteration 20, objective = 0.48082852\n",
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"iteration 30, objective = 0.47100148\n",
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"iteration 40, objective = 0.45862892\n",
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"iteration 50, objective = 0.45290428\n",
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"iteration 60, objective = 0.44695023\n",
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"iteration 70, objective = 0.4453482\n",
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"iteration 80, objective = 0.444919\n",
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"iteration 90, objective = 0.4451945\n",
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"iteration 100, objective = 0.44311014\n",
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"iteration 110, objective = 0.44101325\n",
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"iteration 120, objective = 0.43727913\n",
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"iteration 130, objective = 0.43938398\n",
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"iteration 140, objective = 0.43788543\n",
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"iteration 150, objective = 0.43573555\n",
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"iteration 160, objective = 0.4379884\n",
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"iteration 170, objective = 0.43852594\n",
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"iteration 180, objective = 0.4391472\n",
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"iteration 190, objective = 0.4318109\n",
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"iteration 200, objective = 0.4389726\n",
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"PlayPredictor accuracy: 0.7234723472347235\n"
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"iteration 10, objective = 0.5121787\n",
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"iteration 20, objective = 0.4860348\n",
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"iteration 30, objective = 0.4675451\n",
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"iteration 40, objective = 0.46167055\n",
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"iteration 50, objective = 0.45701832\n",
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"iteration 60, objective = 0.44749424\n",
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"iteration 70, objective = 0.44757926\n",
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"iteration 80, objective = 0.4452785\n",
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"iteration 90, objective = 0.4446122\n",
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"iteration 100, objective = 0.44039646\n",
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"iteration 110, objective = 0.44507992\n",
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"iteration 120, objective = 0.44116876\n",
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"iteration 130, objective = 0.4395796\n",
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"iteration 140, objective = 0.4408364\n",
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"iteration 150, objective = 0.44295114\n",
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"iteration 160, objective = 0.43921968\n",
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"iteration 170, objective = 0.44189137\n",
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"iteration 180, objective = 0.43661243\n",
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"iteration 190, objective = 0.43899748\n",
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"iteration 200, objective = 0.4371943\n"
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]
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}
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],
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"source": [
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"model = PlayPredictor()\n",
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"model.fit(train, K=6, iters=200)\n",
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"\n",
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"error = 0\n",
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"balanced_valid = get_balanced_set(dataset, valid)\n",
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"for user, game, review in balanced_valid:\n",
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" pred = model.predict(user, game, threshold=0.5)\n",
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" if pred != review[\"played\"]:\n",
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" error += 1\n",
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"\n",
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"print(f\"PlayPredictor accuracy: \", 1 - error / len(balanced_valid))"
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"model.fit(train, K=6, iters=200)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 46,
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"execution_count": 23,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[7044 2955]\n",
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" [2598 7401]]\n",
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"PlayPredictor accuracy: 0.7223222322232223\n"
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]
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}
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],
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"source": [
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"CM = np.array([[0,0], [0,0]])\n",
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"balanced_valid = get_balanced_set(dataset, valid)\n",
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"for user, game, review in balanced_valid:\n",
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" pred = model.predict(user, game, threshold=0.5)\n",
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" CM[review[\"played\"]][pred] += 1\n",
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"\n",
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"print(CM)\n",
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"print(f\"PlayPredictor accuracy: \", 1 - (CM[1][0] + CM[0][1]) / len(balanced_valid))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 48,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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{
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"cell_type": "code",
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"execution_count": 49,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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