CML_comparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import implicit\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from implicit.cml import CollaborativeMetricLearning\n",
    "from implicit.als import AlternatingLeastSquares\n",
    "from implicit.lmf import LogisticMatrixFactorization\n",
    "from implicit.evaluation import *\n",
    "from implicit.datasets.sketchfab import get_sketchfab\n",
    "from implicit.datasets.movielens import get_movielens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#_, _, mat = get_sketchfab()\n",
    "_, mat = get_movielens(variant='1m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed=1541"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat.data[:] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tr, te = train_test_split(mat, 0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "m2 = CollaborativeMetricLearning(factors=64, \n",
    "                                 threshold=1.0,\n",
    "                                 learning_rate=0.1, \n",
    "                                 iterations=15, \n",
    "                                 num_threads=8, \n",
    "                                 regularization=0.00,\n",
    "                                 neg_sampling=100,\n",
    "                                 random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [00:40<00:00,  2.79s/it]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=3000), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "{'precision': 0.2958226060958392, 'map': 0.20293504045410232, 'ndcg': 0.28618070893351455, 'auc': 0.5181144038465575}\n"
     ]
    }
   ],
   "source": [
    "m2.fit(tr.T, True)\n",
    "print(ranking_metrics_at_k(m2, tr[:3000], te[:3000], 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "m2 = CollaborativeMetricLearning(factors=64, \n",
    "                                 threshold=1.0,\n",
    "                                 learning_rate=0.1, \n",
    "                                 iterations=15, \n",
    "                                 num_threads=8, \n",
    "                                 regularization=0.01,\n",
    "                                 neg_sampling=100,\n",
    "                                 random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [00:43<00:00,  3.08s/it]\n"
     ]
    },
    {
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       "HBox(children=(IntProgress(value=0, max=3000), HTML(value='')))"
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     },
     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "{'precision': 0.33095257868947764, 'map': 0.23899023594696514, 'ndcg': 0.32266404435662915, 'auc': 0.5204181639238991}\n"
     ]
    }
   ],
   "source": [
    "m2.fit(tr.T, True)\n",
    "print(ranking_metrics_at_k(m2, tr[:3000], te[:3000], 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "m2 = CollaborativeMetricLearning(factors=64, \n",
    "                                 threshold=1.0,\n",
    "                                 learning_rate=0.3, \n",
    "                                 iterations=15, \n",
    "                                 num_threads=8, \n",
    "                                 regularization=0.03,\n",
    "                                 neg_sampling=100,\n",
    "                                 random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [01:03<00:00,  4.25s/it]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "74a1d29d18034cdfbcd2d1292e5de7a7",
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       "HBox(children=(IntProgress(value=0, max=3000), HTML(value='')))"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "{'precision': 0.37646374885806827, 'map': 0.28215497521921507, 'ndcg': 0.3662162875887344, 'auc': 0.5236010609909335}\n"
     ]
    }
   ],
   "source": [
    "m2.fit(tr.T, True)\n",
    "print(ranking_metrics_at_k(m2, tr[:3000], te[:3000], 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "m2 = CollaborativeMetricLearning(factors=64, \n",
    "                                 threshold=1.0,\n",
    "                                 learning_rate=0.1, \n",
    "                                 iterations=15, \n",
    "                                 num_threads=8, \n",
    "                                 regularization=0.05,\n",
    "                                 neg_sampling=100,\n",
    "                                 random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [00:54<00:00,  3.70s/it]\n"
     ]
    },
    {
     "data": {
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       "HBox(children=(IntProgress(value=0, max=3000), HTML(value='')))"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "{'precision': 0.37380616227888047, 'map': 0.2818614182234088, 'ndcg': 0.36362742743417237, 'auc': 0.5222198795750684}\n"
     ]
    }
   ],
   "source": [
    "m2.fit(tr.T, True)\n",
    "print(ranking_metrics_at_k(m2, tr[:3000], te[:3000], 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "m2 = CollaborativeMetricLearning(factors=64, \n",
    "                                 threshold=1.0,\n",
    "                                 learning_rate=0.1, \n",
    "                                 iterations=15, \n",
    "                                 num_threads=8, \n",
    "                                 regularization=0.1,\n",
    "                                 neg_sampling=100,\n",
    "                                 random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [00:55<00:00,  3.85s/it]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "acc87622b9974a4493f5257591f6ec11",
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       "version_minor": 0
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      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=3000), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "{'precision': 0.3812806245328461, 'map': 0.28596698013301086, 'ndcg': 0.36791157480005765, 'auc': 0.5231909268136861}\n"
     ]
    }
   ],
   "source": [
    "m2.fit(tr.T, True)\n",
    "print(ranking_metrics_at_k(m2, tr[:3000], te[:3000], 5))"
   ]
  }
 ],
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