examples/2. REINFORCE TopK Off Policy Correction/3. TopK Reinforce Off Policy Correction.ipynb
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"cell_type": "markdown",
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"## TopK Reinforce Off Policy Correction\n",
"\n",
"### (TopK is here!)\n",
"\n",
"The following code contains an implementation of the REINFORCE algorithm, **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."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"from torch.utils.tensorboard import SummaryWriter\n",
"import torch_optimizer as optim\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": 9,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": "0%| | 0/18946308 [00:00<?, ?it/s]action space is reduced to 26744 - 21744 = 5000\n100%|██████████| 18946308/18946308 [00:12<00:00, 1491220.28it/s]\n100%|██████████| 18946308/18946308 [00:14<00:00, 1300204.77it/s]\n100%|██████████| 138493/138493 [00:07<00:00, 19291.96it/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",
"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=4\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"class Beta(nn.Module):\n",
" def __init__(self):\n",
" super(Beta, self).__init__()\n",
" self.net = nn.Sequential(\n",
" nn.Linear(1290, num_items),\n",
" nn.Softmax()\n",
" )\n",
" self.optim = optim.RAdam(self.net.parameters(), lr=1e-5, weight_decay=1e-5)\n",
" self.criterion = nn.CrossEntropyLoss()\n",
" \n",
" def forward(self, state, action):\n",
" \n",
" predicted_action = self.net(state)\n",
" \n",
" loss = self.criterion(predicted_action, action.argmax(1))\n",
" \n",
" self.optim.zero_grad()\n",
" loss.backward()\n",
" self.optim.step()\n",
" \n",
" return predicted_action.detach()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"beta_net = Beta().to(cuda)\n",
"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",
"# as miracle24 has suggested https://github.com/awarebayes/RecNN/issues/7\n",
"# you can enable this to be more like the paper authors meant it to\n",
"policy_net.action_source = {'pi': 'beta', 'beta': 'beta'}\n",
"\n",
"reinforce = recnn.nn.Reinforce(policy_net, value_net)\n",
"reinforce = reinforce.to(cuda)\n",
"\n",
"reinforce.writer = SummaryWriter(log_dir='../../runs/ReinforceTopKoffPolicy{}/'.format(strftime(\"%H_%M\", gmtime())))\n",
"plotter = recnn.utils.Plotter(reinforce.loss_layout, [['value', 'policy']],)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"from recnn.nn import ChooseREINFORCE\n",
"\n",
"def select_action_corr(state, action, K, writer, step, **kwargs):\n",
" # note here I provide beta_net forward in the arguments\n",
" return reinforce.nets['policy_net']._select_action_with_TopK_correction(state, beta_net.forward, action,\n",
" K=K, writer=writer,\n",
" step=step)\n",
"\n",
"reinforce.nets['policy_net'].select_action = select_action_corr\n",
"reinforce.params['reinforce'] = ChooseREINFORCE(ChooseREINFORCE.reinforce_with_TopK_correction)\n",
"reinforce.params['K'] = 10"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "step 1110\n"
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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": "8%|▊ | 1115/13156 [01:57<21:09, 9.48it/s]\n"
},
{
"output_type": "error",
"ename": "KeyboardInterrupt",
"evalue": "",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-13-f6f7cdd9927a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_epochs\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 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_dataloader\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----> 4\u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreinforce\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\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 5\u001b[0m \u001b[0mreinforce\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\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[0;32mif\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/Documents/programming/python/RecNN/recnn/nn/algo.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, batch, learn)\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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---> 49\u001b[0;31m return self.algorithm(batch, self.params, self.nets, self.optimizers,\n\u001b[0m\u001b[1;32m 50\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwriter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwriter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m learn=learn, step=self._step)\n",
"\u001b[0;32m~/Documents/programming/python/RecNN/recnn/nn/update/reinforce.py\u001b[0m in \u001b[0;36mreinforce_update\u001b[0;34m(batch, params, nets, optimizer, device, debug, writer, learn, step)\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0mnets\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'policy_net'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrewards\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreward\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\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 83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m value_loss = value_update(batch, params, nets, optimizer,\n\u001b[0m\u001b[1;32m 85\u001b[0m \u001b[0mwriter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwriter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 86\u001b[0m debug=debug, learn=True, step=step)\n",
"\u001b[0;32m~/Documents/programming/python/RecNN/recnn/nn/update/misc.py\u001b[0m in \u001b[0;36mvalue_update\u001b[0;34m(batch, params, nets, optimizer, device, debug, writer, learn, step)\u001b[0m\n\u001b[1;32m 17\u001b[0m \"\"\"\n\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnext_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_base_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\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 20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\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~/Documents/programming/python/RecNN/recnn/data/utils.py\u001b[0m in \u001b[0;36mget_base_batch\u001b[0;34m(batch, device, done)\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'reward'\u001b[0m\u001b[0;34m]\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--> 221\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mb\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 222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/Documents/programming/python/RecNN/recnn/data/utils.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'reward'\u001b[0m\u001b[0;34m]\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--> 221\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mb\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 222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"from tqdm.auto import tqdm\n",
"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": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5-final"
}
},
"nbformat": 4,
"nbformat_minor": 2
}