GNN_refactor_template.ipynb

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      "/home/will/anaconda3/envs/pytorch/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/home/will/anaconda3/envs/pytorch/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "import torch\n",
    "\n",
    "from torch_geometric.data import Data,DataLoader\n",
    "from functions_refactor import *\n",
    "from pytorch_util import *\n",
    "from torch.optim import Adam\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# model parameters\n",
    "reuse = False\n",
    "block = schnet_block\n",
    "head = cat3Head\n",
    "data = '../Data/{}_data_ACSF.pickle'\n",
    "batch_size = 32\n",
    "dim = 128\n",
    "epochs = 5\n",
    "clip = 2\n",
    "layer1 = 2\n",
    "layer2 = 2\n",
    "factor = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dl,val_dl,test_dl = get_data(data,batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model = GNN(reuse,block,head,dim,layer1,layer2,factor,**data_dict[data]).to('cuda:0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "paras = trainable_parameter(model)\n",
    "opt = Adam(paras,lr=1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:0, train_loss: +1.097, val_loss: +0.776, \n",
      "train_vector: +4.47|+1.55|+0.59|+0.40|+0.14|+0.84|+0.89|-0.10, \n",
      "val_vector  : +4.35|+0.87|+0.40|-0.12|-0.37|+0.82|+0.66|-0.39\n",
      "\n",
      "epoch:1, train_loss: +0.613, val_loss: +0.593, \n",
      "train_vector: +4.07|+0.77|+0.20|-0.25|-0.52|+0.69|+0.53|-0.59, \n",
      "val_vector  : +3.81|+0.69|+0.25|-0.34|-0.57|+0.87|+0.54|-0.50\n",
      "\n",
      "epoch:2, train_loss: +0.333, val_loss: +0.047, \n",
      "train_vector: +3.00|+0.53|+0.05|-0.45|-0.73|+0.62|+0.40|-0.76, \n",
      "val_vector  : +1.58|+0.08|-0.03|-0.55|-0.83|+0.61|+0.33|-0.81\n",
      "\n",
      "epoch:3, train_loss: +0.022, val_loss: -0.017, \n",
      "train_vector: +1.25|+0.35|-0.04|-0.57|-0.85|+0.58|+0.33|-0.88, \n",
      "val_vector  : +1.15|+0.19|-0.03|-0.68|-0.89|+0.68|+0.33|-0.88\n",
      "\n",
      "epoch:4, train_loss: -0.091, val_loss: -0.172, \n",
      "train_vector: +0.96|+0.24|-0.12|-0.69|-0.96|+0.54|+0.26|-0.97, \n",
      "val_vector  : +1.01|+0.03|-0.20|-0.83|-1.07|+0.53|+0.21|-1.06\n",
      "\n",
      "Training completed in 186.4336326122284s\n"
     ]
    }
   ],
   "source": [
    "model,train_loss_perType,val_loss_perType = train(opt,model,epochs,train_dl,val_dl,paras,clip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": 170,
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   "outputs": [],
   "source": [
    "save_results(train_loss_perType,val_loss_perType,reuse,block,\\\n",
    "             head,data,batch_size,dim,clip,layer1,layer2,factor,epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_model(model,opt,reuse,block,head,data,batch_size,dim,clip,layer1,layer2,factor,epochs)"
   ]
  },
  {
   "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": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint = torch.load('../Model/gnn_int_logloss_2Edges.tar')\n",
    "model.load_state_dict(checkpoint['model_state_dict'])\n",
    "opt.load_state_dict(checkpoint['optimizer_state_dict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save({'model_state_dict': model.state_dict(),\n",
    "            'optimizer_state_dict': opt.state_dict(),\n",
    "            'epochs':epochs\n",
    "            }, '../Model/gnn_int_logloss_2Edges_ACSF.tar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### make submissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.eval()\n",
    "yhat_list = []\n",
    "with torch.no_grad():\n",
    "    for data in test_dl:\n",
    "        data = data.to('cuda:0')\n",
    "        yhat_list.append(model(data,False))\n",
    "\n",
    "yhat = torch.cat(yhat_list).cpu().detach().numpy()\n",
    "submission = pd.read_csv('../Data/sample_submission.csv')\n",
    "submission['scalar_coupling_constant'] = yhat\n",
    "submission.to_csv('../Submission/gnn_int_logloss_2Edges_ACSF.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  }
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