{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "import array\n", "import gzip\n", "import random\n", "from tensorflow.keras import Model\n", "from collections import defaultdict" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def parse(path):\n", " g = gzip.open(path, 'r')\n", " for l in g:\n", " yield eval(l)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "userIDs = {}\n", "itemIDs = {}\n", "interactions = []\n", "\n", "for d in parse(\"goodreads_reviews_comics_graphic.json.gz\"):\n", " u = d['user_id']\n", " i = d['book_id']\n", " r = d['rating']\n", " if not u in userIDs: userIDs[u] = len(userIDs)\n", " if not i in itemIDs: itemIDs[i] = len(itemIDs)\n", " interactions.append((u,i,r))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "542338" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(interactions)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "mu = sum([x[2] for x in interactions]) / len(interactions)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "optimizer = tf.keras.optimizers.Adam(0.01)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "class LatentFactorModel(tf.keras.Model):\n", " def __init__(self, mu, K, lamb):\n", " super(LatentFactorModel, self).__init__()\n", " self.alpha = tf.Variable(mu)\n", " self.betaU = tf.Variable(tf.random.normal([len(userIDs)],stddev=0.001))\n", " self.betaI = tf.Variable(tf.random.normal([len(itemIDs)],stddev=0.001))\n", " self.gammaU = tf.Variable(tf.random.normal([len(userIDs),K],stddev=0.001))\n", " self.gammaI = tf.Variable(tf.random.normal([len(itemIDs),K],stddev=0.001))\n", " self.lamb = lamb\n", "\n", " def predict(self, u, i):\n", " p = self.alpha + self.betaU[u] + self.betaI[i] +\\\n", " tf.tensordot(self.gammaU[u], self.gammaI[i], 1)\n", " return p\n", "\n", " def reg(self):\n", " return self.lamb * (tf.reduce_sum(self.betaU**2) +\\\n", " tf.reduce_sum(self.betaI**2) +\\\n", " tf.reduce_sum(self.gammaU**2) +\\\n", " tf.reduce_sum(self.gammaI**2))\n", "\n", " def call(self, u, i, r):\n", " return (self.predict(u,i) - r)**2\n", "\n", "model = LatentFactorModel(mu, 5, 0.00001)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "def trainingStep(interactions):\n", " with tf.GradientTape() as tape:\n", " sample = random.sample(interactions, 100)\n", " losses = [model(userIDs[u],itemIDs[i],r) for (u,i,r) in sample]\n", " losses.append(model.reg())\n", " gradients = tape.gradient(losses, model.trainable_variables)\n", " optimizer.apply_gradients((grad, var) for\n", " (grad, var) in zip(gradients, model.trainable_variables)\n", " if grad is not None)\n", " losses = [l.numpy().flatten()[0] for l in losses]\n", " return sum(losses)/len(losses)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "iteration 0, objective = 1.003250898922434\n", "3.7940786\n", "iteration 1, objective = 2.055088543794164\n", "3.8151507\n", "iteration 2, objective = 1.8758883254707055\n", "3.8375888\n", "iteration 3, objective = 2.1403769497704315\n", "3.8535335\n", "iteration 4, objective = 2.1997593027327467\n", "3.8594613\n", "iteration 5, objective = 2.0430366800530897\n", "3.8684816\n", "iteration 6, objective = 2.0631628962215713\n", "3.8631065\n", "iteration 7, objective = 2.1383294237524444\n", "3.852622\n", "iteration 8, objective = 2.113640423009133\n", "3.8396275\n", "iteration 9, objective = 2.078660896708405\n", "3.8254569\n", "iteration 10, objective = 2.0437873573317824\n", "3.8107479\n", "iteration 11, objective = 2.059274099758563\n", "3.794509\n", "iteration 12, objective = 2.055733612745033\n", "3.7745717\n", "iteration 13, objective = 2.025617622854768\n", "3.7527993\n", "iteration 14, objective = 2.0009010525046182\n", "3.7372653\n", "iteration 15, objective = 1.9791319826125178\n", "3.7267566\n", "iteration 16, objective = 1.9444395438136142\n", "3.7219098\n", "iteration 17, objective = 1.9472874784609127\n", "3.716126\n", "iteration 18, objective = 1.9251154267317545\n", "3.7171922\n", "iteration 19, objective = 1.9175847847085195\n", "3.7165096\n", "iteration 20, objective = 1.8915846549208102\n", "3.719298\n", "iteration 21, objective = 1.8755508497558544\n", "3.716357\n", "iteration 22, objective = 1.862973529629603\n", "3.716226\n", "iteration 23, objective = 1.8503678263499357\n", "3.7204533\n", "iteration 24, objective = 1.8464343695090293\n", "3.725771\n", "iteration 25, objective = 1.8413233317514455\n", "3.7284305\n", "iteration 26, objective = 1.8338220364119298\n", "3.7287042\n", "iteration 27, objective = 1.8305295546807732\n", "3.7304442\n", "iteration 28, objective = 1.8173108950150267\n", "3.73387\n", "iteration 29, objective = 1.7992263700412365\n", "3.7410955\n", "iteration 30, objective = 1.787204615459906\n", "3.7492929\n", "iteration 31, objective = 1.7767980067330995\n", "3.7532325\n", "iteration 32, objective = 1.7583024333102626\n", "3.7560413\n", "iteration 33, objective = 1.740756074473952\n", "3.7635467\n", "iteration 34, objective = 1.7232113907693445\n", "3.7714498\n", "iteration 35, objective = 1.7146527001899325\n", "3.779834\n", "iteration 36, objective = 1.7102909910619555\n", "3.7879317\n", "iteration 37, objective = 1.7026374637543136\n", "3.7952247\n", "iteration 38, objective = 1.6924465929368868\n", "3.800264\n", "iteration 39, objective = 1.679280166622371\n", "3.8056254\n", "iteration 40, objective = 1.669396050302124\n", "3.8095539\n", "iteration 41, objective = 1.6705639483700079\n", "3.8095996\n", "iteration 42, objective = 1.6610061425512936\n", "3.8117738\n", "iteration 43, objective = 1.6538189803754129\n", "3.8165424\n", "iteration 44, objective = 1.6474223826678436\n", "3.8166308\n", "iteration 45, objective = 1.639817937647128\n", "3.8167732\n", "iteration 46, objective = 1.626529281658221\n", "3.8178637\n", "iteration 47, objective = 1.6199135309955712\n", "3.8185556\n", "iteration 48, objective = 1.6165834657978975\n", "3.8169231\n", "iteration 49, objective = 1.6053519671963803\n", "3.8165064\n", "iteration 50, objective = 1.6001122126976146\n", "3.8128717\n", "iteration 51, objective = 1.5962126831599934\n", "3.808097\n", "iteration 52, objective = 1.5895991851208982\n", "3.8047082\n", "iteration 53, objective = 1.5967505561988686\n", "3.7957447\n", "iteration 54, objective = 1.590056422468079\n", "3.7881572\n", "iteration 55, objective = 1.5854082195886579\n", "3.7793767\n", "iteration 56, objective = 1.5811930222095592\n", "3.7709484\n", "iteration 57, objective = 1.5679663353593232\n", "3.7673035\n", "iteration 58, objective = 1.5621179098930316\n", "3.763359\n", "iteration 59, objective = 1.5588279883755265\n", "3.7603655\n", "iteration 60, objective = 1.5532026989808696\n", "3.7571669\n", "iteration 61, objective = 1.5510115537649505\n", "3.756382\n", "iteration 62, objective = 1.5464027528651958\n", "3.7566833\n", "iteration 63, objective = 1.5374791648510409\n", "3.7587845\n", "iteration 64, objective = 1.5390736455549323\n", "3.7603862\n", "iteration 65, objective = 1.5402082881037547\n", "3.7585897\n", "iteration 66, objective = 1.5412985836372055\n", "3.7566326\n", "iteration 67, objective = 1.5388461668766085\n", "3.7546713\n", "iteration 68, objective = 1.5369681548816518\n", "3.751323\n", "iteration 69, objective = 1.534757966364322\n", "3.7465234\n", "iteration 70, objective = 1.5346183582103508\n", "3.7422273\n", "iteration 71, objective = 1.533021452027567\n", "3.7400026\n", "iteration 72, objective = 1.5281768704436436\n", "3.7419813\n", "iteration 73, objective = 1.5252403041981915\n", "3.742877\n", "iteration 74, objective = 1.5260838792644393\n", "3.7403653\n", "iteration 75, objective = 1.5202550800199839\n", "3.7423608\n", "iteration 76, objective = 1.5167706191481838\n", "3.744864\n", "iteration 77, objective = 1.5092696529042375\n", "3.7520628\n", "iteration 78, objective = 1.5087814466741658\n", "3.7579317\n", "iteration 79, objective = 1.5090901507842778\n", "3.762563\n", "iteration 80, objective = 1.5054996684378423\n", "3.7668967\n", "iteration 81, objective = 1.5042677538339266\n", "3.769727\n", "iteration 82, objective = 1.4977207545072393\n", "3.774652\n", "iteration 83, objective = 1.4976308324311185\n", "3.7761037\n", "iteration 84, objective = 1.4964565832627352\n", "3.777988\n", "iteration 85, objective = 1.4956038302264125\n", "3.780792\n", "iteration 86, objective = 1.4958459840073448\n", "3.7815642\n", "iteration 87, objective = 1.4922957374663524\n", "3.7825403\n", "iteration 88, objective = 1.4920019876510193\n", "3.782092\n", "iteration 89, objective = 1.4899339954205655\n", "3.7834356\n", "iteration 90, objective = 1.4922812864248094\n", "3.7815223\n", "iteration 91, objective = 1.491062304929267\n", "3.7767992\n", "iteration 92, objective = 1.4935056490325458\n", "3.7699838\n", "iteration 93, objective = 1.4944270153649701\n", "3.763561\n", "iteration 94, objective = 1.4940832245468605\n", "3.7552013\n", "iteration 95, objective = 1.490943756815849\n", "3.7482965\n", "iteration 96, objective = 1.489358208446273\n", "3.7418509\n", "iteration 97, objective = 1.4886627298488317\n", "3.7361283\n", "iteration 98, objective = 1.4870931812283077\n", "3.7321405\n", "iteration 99, objective = 1.483268274873276\n", "3.7306972\n", "iteration 100, objective = 1.483117396004056\n", "3.7277956\n", "iteration 101, objective = 1.4827823994971567\n", "3.7262762\n", "iteration 102, objective = 1.4808318381559982\n", "3.7265403\n", "iteration 103, objective = 1.4770719494356777\n", "3.7256525\n", "iteration 104, objective = 1.4764209687858392\n", "3.724015\n", "iteration 105, objective = 1.4751112851624282\n", "3.7226536\n", "iteration 106, objective = 1.4699890699226765\n", "3.7238154\n", "iteration 107, objective = 1.470104384478914\n", "3.723395\n", "iteration 108, objective = 1.4659606157596135\n", "3.7240825\n", "iteration 109, objective = 1.4634083679011705\n", "3.7260263\n", "iteration 110, objective = 1.4632652860177564\n", "3.7257\n", "iteration 111, objective = 1.4606958397469911\n", "3.725089\n", "iteration 112, objective = 1.45732326391641\n", "3.723994\n", "iteration 113, objective = 1.4540668543805242\n", "3.7240825\n", "iteration 114, objective = 1.454320151239924\n", "3.7220538\n", "iteration 115, objective = 1.4559424917909876\n", "3.7173584\n", "iteration 116, objective = 1.452053529858694\n", "3.7171483\n", "iteration 117, objective = 1.453578107346193\n", "3.7151773\n", "iteration 118, objective = 1.454055881775745\n", "3.7127478\n", "iteration 119, objective = 1.4518405222338762\n", "3.7101192\n", "iteration 120, objective = 1.4537773077722937\n", "3.7079904\n", "iteration 121, objective = 1.4496616739200907\n", "3.706253\n", "iteration 122, objective = 1.445347795013887\n", "3.709144\n", "iteration 123, objective = 1.443300973376991\n", "3.713333\n", "iteration 124, objective = 1.4434719379224397\n", "3.7186844\n", "iteration 125, objective = 1.4408787965709364\n", "3.726045\n", "iteration 126, objective = 1.4400868780751774\n", "3.7328575\n", "iteration 127, objective = 1.4372592271095133\n", "3.7399292\n", "iteration 128, objective = 1.4341098815947428\n", "3.749006\n", "iteration 129, objective = 1.4327727127193288\n", "3.7549655\n", "iteration 130, objective = 1.4323974202028216\n", "3.7620416\n", "iteration 131, objective = 1.4334642944915352\n", "3.7668903\n", "iteration 132, objective = 1.431687235840196\n", "3.7714517\n", "iteration 133, objective = 1.4279286593865639\n", "3.7740653\n", "iteration 134, objective = 1.4259149768240056\n", "3.7755096\n", "iteration 135, objective = 1.4280856961556103\n", "3.7748258\n", "iteration 136, objective = 1.4265539892929806\n", "3.7775424\n", "iteration 137, objective = 1.423403387823058\n", "3.7797756\n", "iteration 138, objective = 1.4228289385495805\n", "3.7804515\n", "iteration 139, objective = 1.4211453006026808\n", "3.7817729\n", "iteration 140, objective = 1.4202949831953458\n", "3.7828689\n", "iteration 141, objective = 1.4199270744600077\n", "3.784014\n", "iteration 142, objective = 1.421041354675548\n", "3.7825751\n", "iteration 143, objective = 1.4222686864463236\n", "3.7800689\n", "iteration 144, objective = 1.422552781983847\n", "3.776313\n", "iteration 145, objective = 1.4208241262790098\n", "3.7739818\n", "iteration 146, objective = 1.419886385870006\n", "3.7717147\n", "iteration 147, objective = 1.420508163580795\n", "3.7694626\n", "iteration 148, objective = 1.417101613240188\n", "3.7682447\n", "iteration 149, objective = 1.4151708781147276\n", "3.7668548\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "iteration 150, objective = 1.4150482742201083\n", "3.7645822\n", "iteration 151, objective = 1.4154148576223007\n", "3.7638109\n", "iteration 152, objective = 1.4127523474998613\n", "3.7647285\n", "iteration 153, objective = 1.4136084231699233\n", "3.767915\n", "iteration 154, objective = 1.414316532300552\n", "3.7697465\n", "iteration 155, objective = 1.4125326648768397\n", "3.7727113\n", "iteration 156, objective = 1.4128852587213705\n", "3.7728772\n", "iteration 157, objective = 1.4129021817928487\n", "3.7709951\n", "iteration 158, objective = 1.4132266935647284\n", "3.7683084\n", "iteration 159, objective = 1.410647669429374\n", "3.7649\n", "iteration 160, objective = 1.4089151267802802\n", "3.7624018\n", "iteration 161, objective = 1.4061388342413836\n", "3.7594604\n", "iteration 162, objective = 1.4097138627308476\n", "3.7553616\n", "iteration 163, objective = 1.408845798057241\n", "3.7513986\n", "iteration 164, objective = 1.4060089357558403\n", "3.7477355\n", "iteration 165, objective = 1.407102442855054\n", "3.743871\n", "iteration 166, objective = 1.4083993205117018\n", "3.7382247\n", "iteration 167, objective = 1.4079719812658882\n", "3.733392\n", "iteration 168, objective = 1.409549728150001\n", "3.7262642\n", "iteration 169, objective = 1.4078224323696564\n", "3.7208078\n", "iteration 170, objective = 1.4083533300269948\n", "3.7148852\n", "iteration 171, objective = 1.4088679685741152\n", "3.7096782\n", "iteration 172, objective = 1.4067224435758363\n", "3.704926\n", "iteration 173, objective = 1.4054412996302659\n", "3.700089\n", "iteration 174, objective = 1.4034668902349228\n", "3.697791\n", "iteration 175, objective = 1.4038736380837447\n", "3.6966355\n", "iteration 176, objective = 1.4049319209961235\n", "3.6942937\n", "iteration 177, objective = 1.4038648752766465\n", "3.6960516\n", "iteration 178, objective = 1.4037688954227445\n", "3.6967363\n", "iteration 179, objective = 1.4038272588574852\n", "3.696308\n", "iteration 180, objective = 1.4029046224140498\n", "3.696913\n", "iteration 181, objective = 1.402480471741983\n", "3.6976728\n", "iteration 182, objective = 1.4041766220380276\n", "3.6976893\n", "iteration 183, objective = 1.405254270952253\n", "3.6984599\n", "iteration 184, objective = 1.4057651173038401\n", "3.6991088\n", "iteration 185, objective = 1.406110043276805\n", "3.6996977\n", "iteration 186, objective = 1.407680498089589\n", "3.698315\n", "iteration 187, objective = 1.4068972005374332\n", "3.697276\n", "iteration 188, objective = 1.4066039351876782\n", "3.6978488\n", "iteration 189, objective = 1.407086001333501\n", "3.6963391\n", "iteration 190, objective = 1.4063483799831524\n", "3.695303\n", "iteration 191, objective = 1.40538713738659\n", "3.6965234\n", "iteration 192, objective = 1.404327429236826\n", "3.6998858\n", "iteration 193, objective = 1.4055016227788264\n", "3.7015278\n", "iteration 194, objective = 1.4045800387474665\n", "3.704483\n", "iteration 195, objective = 1.4044507072328687\n", "3.7066221\n", "iteration 196, objective = 1.4053748297583488\n", "3.7066755\n", "iteration 197, objective = 1.4048323910628409\n", "3.7071466\n", "iteration 198, objective = 1.4048138078402506\n", "3.7089818\n", "iteration 199, objective = 1.4031467157390214\n", "3.71207\n", "iteration 200, objective = 1.4014482382577307\n", "3.713398\n", "iteration 201, objective = 1.402158260079718\n", "3.7142665\n", "iteration 202, objective = 1.4013822318956395\n", "3.714479\n", "iteration 203, objective = 1.40125365128357\n", "3.713038\n", "iteration 204, objective = 1.3996274130018478\n", "3.7134647\n", "iteration 205, objective = 1.3989316300502546\n", "3.7143764\n", "iteration 206, objective = 1.3971896767201435\n", "3.714928\n", "iteration 207, objective = 1.398972977792923\n", "3.7133892\n", "iteration 208, objective = 1.3984891125666805\n", "3.7121484\n", "iteration 209, objective = 1.3993144626954757\n", "3.7109075\n", "iteration 210, objective = 1.3982023720695624\n", "3.7090538\n", "iteration 211, objective = 1.3981582113062598\n", "3.7086957\n", "iteration 212, objective = 1.397409737339074\n", "3.7074986\n", "iteration 213, objective = 1.3974821540972744\n", "3.7074988\n", "iteration 214, objective = 1.396176981440125\n", "3.709886\n", "iteration 215, objective = 1.394987900406572\n", "3.7110252\n", "iteration 216, objective = 1.394659732122541\n", "3.7116146\n", "iteration 217, objective = 1.3934716207220474\n", "3.7131922\n", "iteration 218, objective = 1.3924629246576665\n", "3.7152076\n", "iteration 219, objective = 1.3922002202866264\n", "3.7170246\n", "iteration 220, objective = 1.3898122122094232\n", "3.719993\n", "iteration 221, objective = 1.388783368804882\n", "3.723361\n", "iteration 222, objective = 1.3878561645131833\n", "3.7266068\n", "iteration 223, objective = 1.3887466069473688\n", "3.727975\n", "iteration 224, objective = 1.389086394488717\n", "3.7302167\n", "iteration 225, objective = 1.388081494929084\n", "3.7336783\n", "iteration 226, objective = 1.3874603084289883\n", "3.7386353\n", "iteration 227, objective = 1.386436795094308\n", "3.744917\n", "iteration 228, objective = 1.3854726749762647\n", "3.7508628\n", "iteration 229, objective = 1.3842783016481797\n", "3.7582846\n", "iteration 230, objective = 1.3845976960684359\n", "3.764814\n", "iteration 231, objective = 1.3858858746578606\n", "3.7693386\n", "iteration 232, objective = 1.3847418177407163\n", "3.7739875\n", "iteration 233, objective = 1.3830423585854776\n", "3.7776892\n", "iteration 234, objective = 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1.192644062893528\n", "3.7412047\n", "iteration 1225, objective = 1.1932907411606077\n", "3.7386234\n", "iteration 1226, objective = 1.1933934882390111\n", "3.7352483\n", "iteration 1227, objective = 1.1934274619956498\n", "3.7321124\n", "iteration 1228, objective = 1.1933876040540423\n", "3.7312644\n", "iteration 1229, objective = 1.193607345603864\n", "3.7290354\n", "iteration 1230, objective = 1.193486224843702\n", "3.7246845\n", "iteration 1231, objective = 1.1926782404779281\n", "3.7223222\n", "iteration 1232, objective = 1.1925352662904414\n", "3.721147\n", "iteration 1233, objective = 1.192800818431534\n", "3.7198544\n", "iteration 1234, objective = 1.192460918335444\n", "3.7191129\n", "iteration 1235, objective = 1.1929470401701807\n", "3.718778\n", "iteration 1236, objective = 1.1929162907621582\n", "3.7198436\n", "iteration 1237, objective = 1.193207006846579\n", "3.7224107\n", "iteration 1238, objective = 1.1926221319266785\n", "3.7237368\n", "iteration 1239, objective = 1.1923631024830914\n", "3.7257957\n", "iteration 1240, objective = 1.1925433855418257\n", "3.7274783\n", "iteration 1241, objective = 1.1927430546286664\n", "3.729429\n", "iteration 1242, objective = 1.1925460837492674\n", "3.730839\n", "iteration 1243, objective = 1.1926307991674723\n", "3.7318223\n", "iteration 1244, objective = 1.1926772131068346\n", "3.733742\n", "iteration 1245, objective = 1.1929778190350828\n", "3.7366142\n", "iteration 1246, objective = 1.1929701574785216\n", "3.7398105\n", "iteration 1247, objective = 1.1926340280011527\n", "3.7433085\n", "iteration 1248, objective = 1.1928371291715796\n", "3.7447581\n", "iteration 1249, objective = 1.1926720854318626\n", "3.7458599\n", "iteration 1250, objective = 1.192294413063786\n", "3.7460341\n", "iteration 1251, objective = 1.19264039129975\n", "3.746871\n", "iteration 1252, objective = 1.1929017116752334\n", "3.747698\n", "iteration 1253, objective = 1.192826188976285\n", "3.7482228\n", "iteration 1254, objective = 1.1928823949978715\n", "3.7485185\n", "iteration 1255, objective = 1.1926248979021583\n", "3.7482798\n", "iteration 1256, objective = 1.192741690352473\n", "3.7475522\n", "iteration 1257, objective = 1.1927266717161973\n", "3.7458704\n", "iteration 1258, objective = 1.1926049897245268\n", "3.7449896\n", "iteration 1259, objective = 1.1931281658862347\n", "3.7437572\n", "iteration 1260, objective = 1.193007251791989\n", "3.7424433\n", "iteration 1261, objective = 1.1932003111276648\n", "3.7401652\n", "iteration 1262, objective = 1.19297877947337\n", "3.73856\n", "iteration 1263, objective = 1.1928174593812433\n", "3.7365255\n", "iteration 1264, objective = 1.1922218932878677\n", "3.735183\n", "iteration 1265, objective = 1.1917316306546453\n", "3.7334132\n", "iteration 1266, objective = 1.191620952791327\n", "3.7305553\n", "iteration 1267, objective = 1.1919690470455653\n", "3.7278805\n", "iteration 1268, objective = 1.1920672495048585\n", "3.724198\n", "iteration 1269, objective = 1.1919891229866917\n", "3.7207432\n", "iteration 1270, objective = 1.191565787185775\n", "3.7170339\n", "iteration 1271, objective = 1.1915395901998933\n", "3.7134893\n", "iteration 1272, objective = 1.191592154054512\n", "3.7097204\n", "iteration 1273, objective = 1.191453661512622\n", "3.7077851\n", "iteration 1274, objective = 1.1912961999885945\n", "3.706092\n", "iteration 1275, objective = 1.1907664756375371\n", "3.7059116\n", "iteration 1276, objective = 1.1912567741207138\n", "3.7035334\n", "iteration 1277, objective = 1.1907699364932556\n", "3.702199\n", "iteration 1278, objective = 1.1905783259870326\n", "3.7017694\n", "iteration 1279, objective = 1.190157244162771\n", "3.702594\n", "iteration 1280, objective = 1.1907086880311546\n", "3.7040427\n", "iteration 1281, objective = 1.1906030700056\n", "3.7054222\n", "iteration 1282, objective = 1.190400545756645\n", "3.7064462\n", "iteration 1283, objective = 1.190667578446457\n", "3.7078605\n", "iteration 1284, objective = 1.1905864729901856\n", "3.7093105\n", "iteration 1285, objective = 1.189964723508591\n", "3.7125132\n", "iteration 1286, objective = 1.1891223873326817\n", "3.7155194\n", "iteration 1287, objective = 1.1893279623579538\n", "3.7163334\n", "iteration 1288, objective = 1.1892682966655685\n", "3.7169404\n", "iteration 1289, objective = 1.1895827566226163\n", "3.7157645\n", "iteration 1290, objective = 1.189060355294309\n", "3.7143004\n", "iteration 1291, objective = 1.1890007492657457\n", "3.7145677\n", "iteration 1292, objective = 1.1895145803539884\n", "3.714881\n", "iteration 1293, objective = 1.189587043932003\n", "3.716093\n", "iteration 1294, objective = 1.1893818637457414\n", "3.7175984\n", "iteration 1295, objective = 1.1894648761482791\n", "3.7196925\n", "iteration 1296, objective = 1.1889638022129432\n", "3.7212358\n", "iteration 1297, objective = 1.1889096090157214\n", "3.7234936\n", "iteration 1298, objective = 1.1886158785057308\n", "3.7259474\n", "iteration 1299, objective = 1.1892421089067953\n", "3.7273796\n", "iteration 1300, objective = 1.1891973268605687\n", "3.7291844\n", "iteration 1301, objective = 1.1887807315658276\n", "3.7325165\n", "iteration 1302, objective = 1.1888764345829896\n", "3.7342408\n", "iteration 1303, objective = 1.1890701000407902\n", "3.7353973\n", "iteration 1304, objective = 1.1888909165386075\n", "3.7363634\n", "iteration 1305, objective = 1.1890301514901056\n", "3.7376375\n", "iteration 1306, objective = 1.187978529790501\n", "3.739193\n", "iteration 1307, objective = 1.187701640125993\n", "3.7406998\n", "iteration 1308, objective = 1.1873682055487325\n", "3.7418342\n", "iteration 1309, objective = 1.18729654389435\n", "3.742836\n", "iteration 1310, objective = 1.1866160013499605\n", "3.744491\n", "iteration 1311, objective = 1.1866640546079394\n", "3.746874\n", "iteration 1312, objective = 1.186563655093043\n", "3.7489927\n", "iteration 1313, objective = 1.1862182287004808\n", "3.7505891\n", "iteration 1314, objective = 1.1865150084198255\n", "3.7521095\n", "iteration 1315, objective = 1.1857465970709762\n", "3.7545323\n", "iteration 1316, objective = 1.185891409970668\n", "3.7557487\n", "iteration 1317, objective = 1.186084912147849\n", "3.7558472\n", "iteration 1318, objective = 1.1860994165438927\n", "3.7537656\n", "iteration 1319, objective = 1.1862583217916338\n", "3.7509668\n", "iteration 1320, objective = 1.1859566632769096\n", "3.7491477\n", "iteration 1321, objective = 1.1857560548653907\n", "3.7469308\n", "iteration 1322, objective = 1.1859225835256735\n", "3.7453792\n", "iteration 1323, objective = 1.1856478542582989\n", "3.7432716\n", "iteration 1324, objective = 1.1853648489152826\n", "3.74364\n", "iteration 1325, objective = 1.1846754364501033\n", "3.744304\n", "iteration 1326, objective = 1.1845841267690709\n", "3.745582\n", "iteration 1327, objective = 1.184667586737251\n", "3.7464821\n", "iteration 1328, objective = 1.1843592365915858\n", "3.7491415\n", "iteration 1329, objective = 1.1839001312542774\n", "3.7518647\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "iteration 1330, objective = 1.1839261702675814\n", "3.7555618\n", "iteration 1331, objective = 1.1839501779888348\n", "3.7583847\n", "iteration 1332, objective = 1.1840200744067448\n", "3.7605968\n", "iteration 1333, objective = 1.1835999584559636\n", "3.7632258\n", "iteration 1334, objective = 1.1835114759128134\n", "3.7646217\n", "iteration 1335, objective = 1.183434080897142\n", "3.7666078\n", "iteration 1336, objective = 1.1833080466812973\n", "3.768668\n", "iteration 1337, objective = 1.182661656626074\n", "3.7705493\n", "iteration 1338, objective = 1.1825597607250173\n", "3.773652\n", "iteration 1339, objective = 1.1825077181545578\n", "3.7780204\n", "iteration 1340, objective = 1.1825443146261845\n", "3.7824907\n", "iteration 1341, objective = 1.1822078390663104\n", "3.7879608\n", "iteration 1342, objective = 1.1824726590075474\n", "3.7926266\n", "iteration 1343, objective = 1.1824552915230984\n", "3.7959526\n", "iteration 1344, objective = 1.1823031721242363\n", "3.798866\n", "iteration 1345, objective = 1.1820972153392215\n", "3.8018808\n", "iteration 1346, objective = 1.1817708059087808\n", "3.8044662\n", "iteration 1347, objective = 1.1821001605536392\n", "3.8062046\n", "iteration 1348, objective = 1.1821154680689288\n", "3.8067825\n", "iteration 1349, objective = 1.182102136716974\n", "3.8058903\n", "iteration 1350, objective = 1.1825234204066668\n", "3.8039763\n", "iteration 1351, objective = 1.1824344063460215\n", "3.8025753\n", "iteration 1352, objective = 1.182464664195105\n", "3.8016336\n", "iteration 1353, objective = 1.1822069248648346\n", "3.7998989\n", "iteration 1354, objective = 1.1824790654209092\n", "3.7988424\n", "iteration 1355, objective = 1.182550897397124\n", "3.7963755\n", "iteration 1356, objective = 1.1826130404067798\n", "3.793058\n", "iteration 1357, objective = 1.1825222132757773\n", "3.7889588\n", "iteration 1358, objective = 1.1822582240482775\n", "3.786299\n", "iteration 1359, objective = 1.1826965425650788\n", "3.783846\n", "iteration 1360, objective = 1.1825783649406953\n", "3.7808776\n", "iteration 1361, objective = 1.182271047782907\n", "3.7783005\n", "iteration 1362, objective = 1.1822999755848902\n", "3.7754514\n", "iteration 1363, objective = 1.1819288175247806\n", "3.7737033\n", "iteration 1364, objective = 1.1822758798584532\n", "3.7713172\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow_core/python/distribute/distribution_strategy_context.py\u001b[0m in \u001b[0;36m_get_per_thread_mode\u001b[0;34m()\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_distribution_strategy_stack\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 81\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mAttributeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mIndexError\u001b[0m: list index out of range", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mlosses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1000000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrainingStep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minteractions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mlosses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlosses\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m print(\"iteration \" + str(i) + \", objective = \" +\n", "\u001b[0;32m\u001b[0m in \u001b[0;36mtrainingStep\u001b[0;34m(interactions)\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGradientTape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtape\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0msample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minteractions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mlosses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muserIDs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mitemIDs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mlosses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mgradients\u001b[0m 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\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 531\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_as_graph_element\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py\u001b[0m in \u001b[0;36m_read_variable_op\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 610\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_read_variable_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 611\u001b[0;31m \u001b[0mvariable_accessed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 612\u001b[0m result = gen_resource_variable_ops.read_variable_op(self._handle,\n\u001b[1;32m 613\u001b[0m self._dtype)\n", "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py\u001b[0m in \u001b[0;36mvariable_accessed\u001b[0;34m(variable)\u001b[0m\n\u001b[1;32m 318\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwatch_variable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvariable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 319\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mvariable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 320\u001b[0;31m \u001b[0mtape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariable_accessed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvariable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 321\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 322\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow_core/python/eager/tape.py\u001b[0m in \u001b[0;36mvariable_accessed\u001b[0;34m(variable)\u001b[0m\n\u001b[1;32m 79\u001b[0m \"\"\"\n\u001b[1;32m 80\u001b[0m strategy, context = (\n\u001b[0;32m---> 81\u001b[0;31m distribution_strategy_context.get_strategy_and_replica_context())\n\u001b[0m\u001b[1;32m 82\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[0mvariables\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mstrategy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextended\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_container\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvariable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow_core/python/distribute/distribution_strategy_context.py\u001b[0m in \u001b[0;36mget_strategy_and_replica_context\u001b[0;34m()\u001b[0m\n\u001b[1;32m 214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_strategy_and_replica_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m \u001b[0mper_thread_mode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_per_thread_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 217\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mper_thread_mode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrategy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mper_thread_mode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplica_context\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow_core/python/distribute/distribution_strategy_context.py\u001b[0m in \u001b[0;36m_get_per_thread_mode\u001b[0;34m()\u001b[0m\n\u001b[1;32m 78\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_per_thread_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_distribution_strategy_stack\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 81\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mAttributeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m_get_default_replica_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "losses = []\n", "for i in range(1000000):\n", " obj = trainingStep(interactions)\n", " losses = (losses + [obj])[-1000:]\n", " print(\"iteration \" + str(i) + \", objective = \" +\n", " str(sum(losses) / len(losses)))\n", " print(model.alpha.numpy())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "itemsPerUser = defaultdict(set)\n", "for u,i,_ in interactions:\n", " itemsPerUser[u].add(i)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "items = list(itemIDs.keys())" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [], "source": [ "optimizer = tf.keras.optimizers.Adam(0.01)" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [], "source": [ "class BPR(tf.keras.Model):\n", " def __init__(self, K, lamb):\n", " super(BPR, self).__init__()\n", " self.betaI = tf.Variable(tf.random.normal([len(itemIDs)],stddev=0.001))\n", " self.gammaU = tf.Variable(tf.random.normal([len(userIDs),K],stddev=0.001))\n", " self.gammaI = tf.Variable(tf.random.normal([len(itemIDs),K],stddev=0.001))\n", " self.lamb = lamb\n", "\n", " def predict(self, u, i):\n", " p = self.betaI[i] + tf.tensordot(self.gammaU[u], self.gammaI[i], 1)\n", " return p\n", "\n", " def reg(self):\n", " return self.lamb * (tf.reduce_sum(self.betaI**2) +\\\n", " tf.reduce_sum(self.gammaU**2) +\\\n", " tf.reduce_sum(self.gammaI**2))\n", "\n", " def call(self, u, i, j):\n", " return -tf.math.log(tf.math.sigmoid(self.predict(u,i) - self.predict(u,j)))\n", "\n", "model = BPR(5, 0.00001)" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [], "source": [ "def trainingStep(interactions):\n", " with tf.GradientTape() as tape:\n", " sample = []\n", " for _ in range(100):\n", " u,i,_ = random.choice(interactions) # positive sample\n", " j = random.choice(items) # negative sample\n", " while j in itemsPerUser[u]:\n", " j = random.choice(items)\n", " sample.append((u,i,j))\n", " loss = tf.reduce_mean([model(userIDs[u],itemIDs[i],itemIDs[j]) for (u,i,j) in sample])\n", " loss += model.reg()\n", " gradients = tape.gradient(loss, model.trainable_variables)\n", " optimizer.apply_gradients((grad, var) for\n", " (grad, var) in zip(gradients, model.trainable_variables)\n", " if grad is not None)\n", " return loss.numpy()" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "iteration 0, objective = 1.4376771450042725\n", "iteration 1, objective = 32.11431014537811\n", "iteration 2, objective = 23.021116018295288\n", "iteration 3, objective = 20.9239142537117\n", "iteration 4, objective = 23.936439752578735\n", "iteration 5, objective = 24.098796725273132\n", "iteration 6, objective = 21.500775371279037\n", "iteration 7, objective = 19.011106729507446\n", "iteration 8, objective = 18.157492637634277\n", "iteration 9, objective = 18.19003267288208\n", "iteration 10, objective = 17.85881588675759\n", "iteration 11, objective = 16.842211763064068\n", "iteration 12, objective = 15.636714678544264\n", "iteration 13, objective = 14.796602623803276\n", "iteration 14, objective = 14.394345696767171\n", "iteration 15, objective = 14.105401486158371\n", "iteration 16, objective = 13.645142414990593\n", "iteration 17, objective = 13.014042920536465\n", "iteration 18, objective = 12.402221610671596\n", "iteration 19, objective = 11.954346233606339\n", "iteration 20, objective = 11.644453213328408\n", "iteration 21, objective = 11.34793416478417\n", "iteration 22, objective = 10.986181243606236\n", "iteration 23, objective = 10.58624334136645\n", "iteration 24, objective = 10.222976641654968\n", "iteration 25, objective = 9.932935948555286\n", "iteration 26, objective = 9.688411319697344\n", "iteration 27, objective = 9.440059402159282\n", "iteration 28, objective = 9.170494457771039\n", "iteration 29, objective = 8.9017365415891\n", "iteration 30, objective = 8.661752012468153\n", "iteration 31, objective = 8.454140949994326\n", "iteration 32, objective = 8.260137836138407\n", "iteration 33, objective = 8.06242515409694\n", "iteration 34, objective = 7.862287524768284\n", "iteration 35, objective = 7.672740360101064\n", "iteration 36, objective = 7.501205708529498\n", "iteration 37, objective = 7.3429211754547925\n", "iteration 38, objective = 7.187887097016359\n", "iteration 39, objective = 7.033089379966259\n", "iteration 40, objective = 6.883218605343888\n", "iteration 41, objective = 6.743290648573921\n", "iteration 42, objective = 6.612802078557569\n", "iteration 43, objective = 6.487064217979258\n", "iteration 44, objective = 6.363205371962653\n", "iteration 45, objective = 6.242686076008755\n", "iteration 46, objective = 6.128189909965434\n", "iteration 47, objective = 6.019960422068834\n", "iteration 48, objective = 5.915791123497243\n", "iteration 49, objective = 5.813893393278122\n", "iteration 50, objective = 5.71497258719276\n", "iteration 51, objective = 5.62028146133973\n", "iteration 52, objective = 5.5298804573293\n", "iteration 53, objective = 5.442607241648215\n", "iteration 54, objective = 5.357606029510498\n", "iteration 55, objective = 5.274984353354999\n", "iteration 56, objective = 5.195452239429741\n", "iteration 57, objective = 5.119073920208832\n", "iteration 58, objective = 5.045195488606469\n", "iteration 59, objective = 4.9732685983181\n", "iteration 60, objective = 4.903294742107391\n", "iteration 61, objective = 4.835828763823355\n", "iteration 62, objective = 4.770593506003183\n", "iteration 63, objective = 4.707261617295444\n", "iteration 64, objective = 4.64560099198268\n", "iteration 65, objective = 4.585666175141479\n", "iteration 66, objective = 4.5276471154013676\n", "iteration 67, objective = 4.471305047764497\n", "iteration 68, objective = 4.416529956935109\n", "iteration 69, objective = 4.363179435900279\n", "iteration 70, objective = 4.311377609279794\n", "iteration 71, objective = 4.260999599264728\n", "iteration 72, objective = 4.211966020603702\n", "iteration 73, objective = 4.164328281943862\n", "iteration 74, objective = 4.117786174615224\n", "iteration 75, objective = 4.072523716248964\n", "iteration 76, objective = 4.028463591228832\n", "iteration 77, objective = 3.9855186396684403\n", "iteration 78, objective = 3.9436167455926725\n", "iteration 79, objective = 3.90274513438344\n", "iteration 80, objective = 3.8627916007866094\n", "iteration 81, objective = 3.823851549043888\n", "iteration 82, objective = 3.785865923008287\n", "iteration 83, objective = 3.7487308773256482\n", "iteration 84, objective = 3.7125019283855663\n", "iteration 85, objective = 3.6771003638589104\n", "iteration 86, objective = 3.6424907911782975\n", "iteration 87, objective = 3.608642130412839\n", "iteration 88, objective = 3.5755323914999373\n", "iteration 89, objective = 3.5431965947151185\n", "iteration 90, objective = 3.5115129325416063\n", "iteration 91, objective = 3.4806269485017527\n", "iteration 92, objective = 3.4503531552130178\n", "iteration 93, objective = 3.420758986092628\n", "iteration 94, objective = 3.3916950872069913\n", "iteration 95, objective = 3.363314556578795\n", "iteration 96, objective = 3.335521610127282\n", "iteration 97, objective = 3.3082046490542742\n", "iteration 98, objective = 3.281456130923647\n", "iteration 99, objective = 3.255226867198944\n", "iteration 100, objective = 3.229611632257405\n", "iteration 101, objective = 3.204398980327681\n", "iteration 102, objective = 3.1796504455862693\n", "iteration 103, objective = 3.155371167911933\n", "iteration 104, objective = 3.131481625352587\n", "iteration 105, objective = 3.1080991102839417\n", "iteration 106, objective = 3.0852750472933335\n", "iteration 107, objective = 3.0626844195304095\n", "iteration 108, objective = 3.0405206838879018\n", "iteration 109, objective = 3.018832729621367\n", "iteration 110, objective = 2.9974841647319965\n", "iteration 111, objective = 2.9765032652233328\n", "iteration 112, objective = 2.9560128307975497\n", "iteration 113, objective = 2.935819641539925\n", "iteration 114, objective = 2.916053889627042\n", "iteration 115, objective = 2.8965033215695413\n", "iteration 116, objective = 2.877372117124052\n", "iteration 117, objective = 2.858495291511891\n", "iteration 118, objective = 2.8399260534959683\n", "iteration 119, objective = 2.8215998431046803\n", "iteration 120, objective = 2.803747384508779\n", "iteration 121, objective = 2.7861076397973985\n", "iteration 122, objective = 2.7687616740784993\n", "iteration 123, objective = 2.7517124169295832\n", "iteration 124, objective = 2.734882227897644\n", "iteration 125, objective = 2.7183301198104073\n", "iteration 126, objective = 2.701943214484087\n", "iteration 127, objective = 2.6858566300943494\n", "iteration 128, objective = 2.6699821602466494\n", "iteration 129, objective = 2.654370730198347\n", "iteration 130, objective = 2.639047977578549\n", "iteration 131, objective = 2.6239289767814404\n", "iteration 132, objective = 2.609141691734916\n", "iteration 133, objective = 2.594479303306608\n", "iteration 134, objective = 2.579917312551428\n", "iteration 135, objective = 2.5656983624486362\n", "iteration 136, objective = 2.551760539521266\n", "iteration 137, objective = 2.537932414939438\n", "iteration 138, objective = 2.5242967313999753\n", "iteration 139, objective = 2.510891758969852\n", "iteration 140, objective = 2.4975701380283275\n", "iteration 141, objective = 2.4845196537568537\n", "iteration 142, objective = 2.4715543510196927\n", "iteration 143, objective = 2.4588752827710576\n", "iteration 144, objective = 2.446358568503939\n", "iteration 145, objective = 2.4340680592680632\n", "iteration 146, objective = 2.4218941172774957\n", "iteration 147, objective = 2.409887153554607\n", "iteration 148, objective = 2.3980532924600895\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mlosses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1000000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrainingStep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minteractions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mlosses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlosses\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m print(\"iteration \" + str(i) + \", objective = \" +\n", "\u001b[0;32m\u001b[0m in \u001b[0;36mtrainingStep\u001b[0;34m(interactions)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce_mean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muserIDs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mitemIDs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mitemIDs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mgradients\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgradient\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainable_variables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m optimizer.apply_gradients((grad, var) for\n\u001b[1;32m 14\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgradients\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainable_variables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow_core/python/eager/backprop.py\u001b[0m in \u001b[0;36mgradient\u001b[0;34m(self, target, sources, output_gradients, unconnected_gradients)\u001b[0m\n\u001b[1;32m 1027\u001b[0m \u001b[0moutput_gradients\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_gradients\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1028\u001b[0m \u001b[0msources_raw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mflat_sources_raw\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1029\u001b[0;31m unconnected_gradients=unconnected_gradients)\n\u001b[0m\u001b[1;32m 1030\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1031\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_persistent\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow_core/python/eager/imperative_grad.py\u001b[0m in \u001b[0;36mimperative_grad\u001b[0;34m(tape, target, sources, output_gradients, sources_raw, unconnected_gradients)\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[0moutput_gradients\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0msources_raw\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m compat.as_str(unconnected_gradients.value))\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow_core/python/eager/backprop.py\u001b[0m in \u001b[0;36m_gradient_function\u001b[0;34m(op_name, attr_tuple, num_inputs, inputs, outputs, out_grads, skip_input_indices)\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnum_inputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mgrad_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmock_op\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mout_grads\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow_core/python/ops/math_grad.py\u001b[0m in \u001b[0;36m_SubGrad\u001b[0;34m(op, grad)\u001b[0m\n\u001b[1;32m 1185\u001b[0m if (isinstance(grad, ops.Tensor) and\n\u001b[1;32m 1186\u001b[0m _ShapesFullySpecifiedAndEqual(x, y, grad)):\n\u001b[0;32m-> 1187\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1188\u001b[0m (sx, rx, must_reduce_x), (sy, ry, must_reduce_y) = (\n\u001b[1;32m 1189\u001b[0m SmartBroadcastGradientArgs(x, y, grad))\n", "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow_core/python/ops/gen_math_ops.py\u001b[0m in \u001b[0;36mneg\u001b[0;34m(x, name)\u001b[0m\n\u001b[1;32m 6283\u001b[0m _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(\n\u001b[1;32m 6284\u001b[0m \u001b[0m_ctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_context_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Neg\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mop_callbacks\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6285\u001b[0;31m x)\n\u001b[0m\u001b[1;32m 6286\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6287\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_FallbackException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "losses = []\n", "for i in range(1000000):\n", " obj = trainingStep(interactions)\n", " losses = (losses + [obj])[-1000:]\n", " print(\"iteration \" + str(i) + \", objective = \" +\n", " str(sum(losses) / len(losses)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [], "source": [ "optimizer = tf.keras.optimizers.Adam(0.001)" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [], "source": [ "class BPRbatch(tf.keras.Model):\n", " def __init__(self, K, lamb):\n", " super(BPRbatch, self).__init__()\n", " # Initialize variables\n", " self.betaI = tf.Variable(tf.random.normal([len(itemIDs)],stddev=0.001))\n", " self.gammaU = tf.Variable(tf.random.normal([len(userIDs),K],stddev=0.001))\n", " self.gammaI = tf.Variable(tf.random.normal([len(itemIDs),K],stddev=0.001))\n", " # Regularization coefficient\n", " self.lamb = lamb\n", "\n", " # Prediction for a single instance\n", " def predict(self, u, i):\n", " p = self.betaI[i] + tf.tensordot(self.gammaU[u], self.gammaI[i], 1)\n", " return p\n", "\n", " # Regularizer\n", " def reg(self):\n", " return self.lamb * (tf.nn.l2_loss(self.betaI) +\\\n", " tf.nn.l2_loss(self.gammaU) +\\\n", " tf.nn.l2_loss(self.gammaI))\n", " \n", " def score(self, sampleU, sampleI):\n", " u = tf.convert_to_tensor(sampleU, dtype=tf.int32)\n", " i = tf.convert_to_tensor(sampleI, dtype=tf.int32)\n", " beta_i = tf.nn.embedding_lookup(self.betaI, i)\n", " gamma_u = tf.nn.embedding_lookup(self.gammaU, u)\n", " gamma_i = tf.nn.embedding_lookup(self.gammaI, i)\n", " x_ui = beta_i + tf.reduce_sum(tf.multiply(gamma_u, gamma_i), 1)\n", " return x_ui\n", "\n", " def call(self, sampleU, sampleI, sampleJ):\n", " x_ui = self.score(sampleU, sampleI)\n", " x_uj = self.score(sampleU, sampleJ)\n", " return -tf.reduce_mean(tf.math.log(tf.math.sigmoid(x_ui - x_uj)))\n", "\n", "model = BPRbatch(5, 0.00001)" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [], "source": [ "def trainingStep(interactions):\n", " with tf.GradientTape() as tape:\n", " sampleU, sampleI, sampleJ = [], [], []\n", " for _ in range(100000):\n", " u,i,_ = random.choice(interactions) # positive sample\n", " j = random.choice(items) # negative sample\n", " while j in itemsPerUser[u]:\n", " j = random.choice(items)\n", " sampleU.append(userIDs[u])\n", " sampleI.append(itemIDs[i])\n", " sampleJ.append(itemIDs[j])\n", "\n", " loss = model(sampleU,sampleI,sampleJ)\n", " loss += model.reg()\n", " gradients = tape.gradient(loss, model.trainable_variables)\n", " optimizer.apply_gradients((grad, var) for\n", " (grad, var) in zip(gradients, model.trainable_variables)\n", " if grad is not None)\n", " return loss.numpy()" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "iteration 0, objective = 1.4377176761627197\n", "iteration 1, objective = 1.2112258970737457\n", "iteration 2, objective = 1.0928723812103271\n", "iteration 3, objective = 1.049851879477501\n", "iteration 4, objective = 1.0337864995002746\n", "iteration 5, objective = 1.0133000612258911\n", "iteration 6, objective = 0.9868159294128418\n", "iteration 7, objective = 0.9628333523869514\n", "iteration 8, objective = 0.9454027745458815\n", "iteration 9, objective = 0.9316659271717072\n", "iteration 10, objective = 0.9182850610126149\n", "iteration 11, objective = 0.9050509730974833\n", "iteration 12, objective = 0.8934481189801142\n", "iteration 13, objective = 0.8840138401303973\n", "iteration 14, objective = 0.8757736523946126\n", "iteration 15, objective = 0.8676682896912098\n", "iteration 16, objective = 0.8596078227548039\n", "iteration 17, objective = 0.8521164357662201\n", "iteration 18, objective = 0.8454737506414715\n", "iteration 19, objective = 0.8394642174243927\n", "iteration 20, objective = 0.8337655379658654\n", "iteration 21, objective = 0.8282955614003268\n", "iteration 22, objective = 0.8231440005095109\n", "iteration 23, objective = 0.8183520684639612\n", "iteration 24, objective = 0.8138660478591919\n", "iteration 25, objective = 0.809625309247237\n", "iteration 26, objective = 0.805611769358317\n", "iteration 27, objective = 0.801803143961089\n", "iteration 28, objective = 0.7981718059243827\n", "iteration 29, objective = 0.7947135150432587\n", "iteration 30, objective = 0.7914450572383019\n", "iteration 31, objective = 0.7883593756705523\n", "iteration 32, objective = 0.7854148185614384\n", "iteration 33, objective = 0.7825768291950226\n", "iteration 34, objective = 0.7798531838825771\n", "iteration 35, objective = 0.7772700803147422\n", "iteration 36, objective = 0.7748167144285666\n", "iteration 37, objective = 0.7724551417325672\n", "iteration 38, objective = 0.7701767774728628\n", "iteration 39, objective = 0.7679892510175705\n", "iteration 40, objective = 0.7658968596923642\n", "iteration 41, objective = 0.7638907900878361\n", "iteration 42, objective = 0.7619547483532928\n", "iteration 43, objective = 0.7600866664539684\n", "iteration 44, objective = 0.7582888311809963\n", "iteration 45, objective = 0.7565593576949575\n", "iteration 46, objective = 0.7548875580442712\n", "iteration 47, objective = 0.7532714332143465\n", "iteration 48, objective = 0.7517099830569053\n", "iteration 49, objective = 0.7502002930641174\n", "iteration 50, objective = 0.7487378342478883\n", "iteration 51, objective = 0.7473248804991062\n", "iteration 52, objective = 0.7459557731196566\n", "iteration 53, objective = 0.7446259138760744\n", "iteration 54, objective = 0.7433364293792031\n", "iteration 55, objective = 0.7420834398695401\n", "iteration 56, objective = 0.7408682693514907\n", "iteration 57, objective = 0.7396889986663029\n", "iteration 58, objective = 0.7385393199274095\n", "iteration 59, objective = 0.7374192744493484\n", "iteration 60, objective = 0.7363320911516908\n", "iteration 61, objective = 0.7352725017455316\n", "iteration 62, objective = 0.7342419274269588\n", "iteration 63, objective = 0.733235701918602\n", "iteration 64, objective = 0.732253950375777\n", "iteration 65, objective = 0.7313001851240793\n", "iteration 66, objective = 0.7303672406210828\n", "iteration 67, objective = 0.7294564422439126\n", "iteration 68, objective = 0.7285648791686349\n", "iteration 69, objective = 0.7276940371309008\n", "iteration 70, objective = 0.7268427736322645\n", "iteration 71, objective = 0.72601125554906\n", "iteration 72, objective = 0.7251983031834641\n", "iteration 73, objective = 0.7244010358243376\n", "iteration 74, objective = 0.723620753288269\n", "iteration 75, objective = 0.7228572509790722\n", "iteration 76, objective = 0.7221074197199437\n", "iteration 77, objective = 0.7213737895855536\n", "iteration 78, objective = 0.7206527598296539\n", "iteration 79, objective = 0.7199452511966229\n", "iteration 80, objective = 0.7192528321419234\n", "iteration 81, objective = 0.7185704395538424\n", "iteration 82, objective = 0.7179024650389889\n", "iteration 83, objective = 0.7172463940722602\n", "iteration 84, objective = 0.7166028422467848\n", "iteration 85, objective = 0.7159675546856814\n", "iteration 86, objective = 0.7153454429801853\n", "iteration 87, objective = 0.7147328372706067\n", "iteration 88, objective = 0.7141314664583528\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mlosses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1000000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m 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"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow_core/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 668\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 669\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 670\u001b[0;31m \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_convert_non_tensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 671\u001b[0m \u001b[0minput_list\u001b[0m \u001b[0;34m=\u001b[0m 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expand_composites=expand_composites)\n\u001b[1;32m 570\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow_core/python/util/nest.py\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 566\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 567\u001b[0m return pack_sequence_as(\n\u001b[0;32m--> 568\u001b[0;31m \u001b[0mstructure\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 569\u001b[0m expand_composites=expand_composites)\n\u001b[1;32m 570\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", 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"\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow_core/python/framework/constant_op.py\u001b[0m in \u001b[0;36mconstant\u001b[0;34m(value, dtype, shape, name)\u001b[0m\n\u001b[1;32m 256\u001b[0m \"\"\"\n\u001b[1;32m 257\u001b[0m return _constant_impl(value, dtype, shape, name, verify_shape=False,\n\u001b[0;32m--> 258\u001b[0;31m allow_broadcast=True)\n\u001b[0m\u001b[1;32m 259\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 260\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow_core/python/framework/constant_op.py\u001b[0m in \u001b[0;36m_constant_impl\u001b[0;34m(value, dtype, shape, name, verify_shape, allow_broadcast)\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[0mctx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 265\u001b[0m \u001b[0;32mif\u001b[0m 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