awarebayes/RecNN

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reinforce with recnn\n",
    "\n",
    "The following code contains an implementation of the REINFORCE algorithm, **without Off Policy Correction, LSTM state encoder, and Noise Contrastive Estimation**. Look for these in other notebooks.\n",
    "\n",
    "Also, I am not google staff, and unlike the paper authors, I cannot have online feedback concerning the recommendations.\n",
    "\n",
    "**I use actor-critic for reward assigning.** In a real-world scenario that would be done through interactive user feedback, but here I use a neural network (critic) that aims to emulate it.\n",
    "\n",
    "note: due to implementation details, this algorithm currently doesn't support testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm.auto import tqdm\n",
    "from time import gmtime, strftime\n",
    "\n",
    "from IPython.display import clear_output\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "# == recnn ==\n",
    "import sys\n",
    "sys.path.append(\"../../\")\n",
    "import recnn\n",
    "\n",
    "cuda = torch.device('cuda')\n",
    "\n",
    "# ---\n",
    "frame_size = 10\n",
    "batch_size = 10\n",
    "n_epochs   = 100\n",
    "plot_every = 30\n",
    "num_items    = 5000 # n items to recommend. Can be adjusted for your vram \n",
    "# --- \n",
    "\n",
    "tqdm.pandas()\n",
    "\n",
    "\n",
    "from jupyterthemes import jtplot\n",
    "jtplot.style(theme='grade3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "0%|          | 0/18946308 [00:00<?, ?it/s]4999\naction space is reduced to 26744 - 21744 = 5000\n4999\nNone\n100%|██████████| 18946308/18946308 [00:12<00:00, 1483580.75it/s]\n100%|██████████| 18946308/18946308 [00:14<00:00, 1298942.64it/s]\nmax movie_thingy 4999\n100%|██████████| 138493/138493 [00:06<00:00, 19850.04it/s]\n"
    }
   ],
   "source": [
    "def embed_batch(batch, item_embeddings_tensor, *args, **kwargs):\n",
    "    return recnn.data.batch_contstate_discaction(batch, item_embeddings_tensor,\n",
    "                                                 frame_size=frame_size, num_items=num_items)\n",
    "\n",
    "    \n",
    "def prepare_dataset(args_mut, kwargs):\n",
    "    kwargs.set('reduce_items_to', num_items) # set kwargs for your functions here!\n",
    "    pipeline = [recnn.data.truncate_dataset, recnn.data.prepare_dataset]\n",
    "    recnn.data.build_data_pipeline(pipeline, kwargs, args_mut)\n",
    "    \n",
    "\n",
    "\n",
    "# embeddgings: https://drive.google.com/open?id=1EQ_zXBR3DKpmJR3jBgLvt-xoOvArGMsL\n",
    "dirs = recnn.data.env.DataPath(\n",
    "    base=\"../../data/\",\n",
    "    embeddings=\"embeddings/ml20_pca128.pkl\",\n",
    "    ratings=\"ml-20m/ratings.csv\",\n",
    "    # IMPORTANT! I am using a different name for cache\n",
    "    # If you change your pipeline, change the name as well!\n",
    "    # Different pipelines must have different names!\n",
    "    cache=\"cache/frame_env_truncated.pkl\", \n",
    "    use_cache=True\n",
    ")\n",
    "\n",
    "env = recnn.data.env.FrameEnv(\n",
    "    dirs, frame_size,\n",
    "    batch_size,\n",
    "    embed_batch=embed_batch,\n",
    "    prepare_dataset=prepare_dataset,\n",
    "    num_workers=0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "value_net  = recnn.nn.Critic(1290, num_items, 2048, 54e-2).to(cuda)\n",
    "policy_net = recnn.nn.DiscreteActor(1290, num_items, 2048).to(cuda)\n",
    "\n",
    "reinforce = recnn.nn.Reinforce(policy_net, value_net)\n",
    "reinforce = reinforce.to(cuda)\n",
    "\n",
    "reinforce.writer = SummaryWriter(log_dir='../../runs/Reinforce{}/'.format(strftime(\"%H_%M\", gmtime())))\n",
    "plotter = recnn.utils.Plotter(reinforce.loss_layout, [['value', 'policy']],)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "step 990\n"
    },
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "8%|▊         | 1000/13156 [01:29<18:05, 11.20it/s]\n"
    },
    {
     "output_type": "error",
     "ename": "AssertionError",
     "evalue": "",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-6baa5d2be417>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mreinforce\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_step\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     12\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m             \u001b[0;32massert\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for epoch in range(n_epochs):\n",
    "    for batch in tqdm(env.train_dataloader):\n",
    "        loss = reinforce.update(batch)\n",
    "        reinforce.step()\n",
    "        if loss:\n",
    "            plotter.log_losses(loss)\n",
    "        if reinforce._step % plot_every == 0:\n",
    "            clear_output(True)\n",
    "            print('step', reinforce._step)\n",
    "            plotter.plot_loss()\n",
    "        if reinforce._step > 1000:\n",
    "            pass\n",
    "            assert False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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