CrabNet-global-type-disorder.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "865fd6bd-ed8c-427a-ab7d-9d3d1349a3ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import collections\n",
    "from collections import OrderedDict\n",
    "import pytorch_lightning as L\n",
    "import os\n",
    "import re\n",
    "import json\n",
    "import tqdm\n",
    "\n",
    "from sklearn.metrics import balanced_accuracy_score, accuracy_score, roc_auc_score, f1_score\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, matthews_corrcoef\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import StratifiedKFold, train_test_split\n",
    "\n",
    "from pytorch_lightning.loggers.csv_logs import CSVLogger\n",
    "from pytorch_lightning.loggers import WandbLogger\n",
    "from pytorch_lightning.callbacks import ModelCheckpoint, StochasticWeightAveraging\n",
    "# from pytorch_lightning.loggers.wandb import WandbLogger\n",
    "from pytorch_lightning.callbacks.early_stopping import EarlyStopping\n",
    "from pytorch_lightning import Trainer\n",
    "from torchmetrics.functional import mean_squared_error, mean_absolute_error\n",
    "\n",
    "from pymatgen.core.composition import Composition\n",
    "from crabnet.kingcrab import CrabNet\n",
    "\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.optim.lr_scheduler import CyclicLR, CosineAnnealingLR, StepLR\n",
    "\n",
    "from crabnet.utils.utils import (Lamb, Lookahead, RobustL1, CrossEntropyLoss,\n",
    "                         EDMDataset, get_edm, Scaler, DummyScaler, count_parameters)\n",
    "from crabnet.utils.get_compute_device import get_compute_device\n",
    "# from crabnet.utils.composition import _element_composition, get_sym_dict, parse_formula, CompositionError\n",
    "#from utils.optim import SWA\n",
    "\n",
    "data_type_np = np.float32\n",
    "data_type_torch = torch.float32\n",
    "\n",
    "import wandb\n",
    "\n",
    "\n",
    "class CrabNetDataModule(L.LightningDataModule):\n",
    "    def __init__(self, train_file: str , \n",
    "                 val_file: str, \n",
    "                 test_file: str,\n",
    "                 n_elements ='infer', \n",
    "                 classification = False,\n",
    "                 elem_prop='mat2vec',\n",
    "                 batch_size = 2**10,\n",
    "                 scale = True,\n",
    "                 pin_memory = True):\n",
    "        super().__init__()\n",
    "        self.train_path = train_file\n",
    "        self.val_path = val_file\n",
    "        self.test_path = test_file\n",
    "        self.batch_size = batch_size\n",
    "        self.n_elements=n_elements\n",
    "        self.pin_memory = pin_memory\n",
    "        self.scale = scale\n",
    "        self.classification = classification\n",
    "        self.elem_prop=elem_prop\n",
    "\n",
    "    def prepare_data(self):\n",
    "        ### loading and encoding trianing data\n",
    "        if(re.search('.json', self.train_path )):\n",
    "            self.data_train=pd.read_json(self.train_path)\n",
    "        elif(re.search('.csv', self.train_path)):\n",
    "            self.data_train=pd.read_csv(self.train_path)\n",
    "\n",
    "        self.train_main_data = list(get_edm(self.data_train, elem_prop=self.elem_prop,\n",
    "                                      n_elements=self.n_elements,\n",
    "                                      inference=False,\n",
    "                                      verbose=True,\n",
    "                                      drop_unary=False,\n",
    "                                      scale=self.scale))\n",
    "        \n",
    "        self.train_len_data = len(self.train_main_data[0])\n",
    "        self.train_n_elements = self.train_main_data[0].shape[1]//2\n",
    "\n",
    "        print(f'loading data with up to {self.train_n_elements:0.0f} '\n",
    "              f'elements in the formula for training')\n",
    "        \n",
    "        ### loading and encoding validation data\n",
    "        if(re.search('.json', self.val_path )):\n",
    "            self.data_val=pd.read_json(self.val_path)\n",
    "        elif(re.search('.csv', self.val_path)):\n",
    "            self.data_val=pd.read_csv(self.val_path)\n",
    "        \n",
    "        self.val_main_data = list(get_edm(self.data_val, elem_prop=self.elem_prop,\n",
    "                                      n_elements=self.n_elements,\n",
    "                                      inference=True,\n",
    "                                      verbose=True,\n",
    "                                      drop_unary=False,\n",
    "                                      scale=self.scale))\n",
    "        \n",
    "        self.val_len_data = len(self.val_main_data[0])\n",
    "        self.val_n_elements = self.val_main_data[0].shape[1]//2\n",
    "\n",
    "        print(f'loading data with up to {self.val_n_elements:0.0f} '\n",
    "              f'elements in the formula for validation')\n",
    "        \n",
    "        ### loading and encoding testing data\n",
    "        if(re.search('.json', self.test_path )):\n",
    "            self.data_test=pd.read_json(self.test_path)\n",
    "        elif(re.search('.csv', self.test_path)):\n",
    "            self.data_test=pd.read_csv(self.test_path)\n",
    "        \n",
    "        self.test_main_data = list(get_edm(self.data_test, elem_prop=self.elem_prop,\n",
    "                                      n_elements=self.n_elements,\n",
    "                                      inference=True,\n",
    "                                      verbose=True,\n",
    "                                      drop_unary=False,\n",
    "                                      scale=self.scale))\n",
    "        \n",
    "        self.test_len_data = len(self.test_main_data[0])\n",
    "        self.test_n_elements = self.test_main_data[0].shape[1]//2\n",
    "\n",
    "        print(f'loading data with up to {self.test_n_elements:0.0f} '\n",
    "              f'elements in the formula for testing')\n",
    "\n",
    "        self.train_dataset = EDMDataset(self.train_main_data, self.train_n_elements)\n",
    "        self.val_dataset = EDMDataset(self.val_main_data, self.val_n_elements)\n",
    "        self.test_dataset = EDMDataset(self.test_main_data, self.test_n_elements)\n",
    "\n",
    "    def train_dataloader(self):\n",
    "        return DataLoader(self.train_dataset, batch_size=self.batch_size,\n",
    "                          pin_memory=self.pin_memory, shuffle=True)\n",
    "    def val_dataloader(self):\n",
    "        return DataLoader(self.val_dataset, batch_size=self.batch_size,\n",
    "                        pin_memory=self.pin_memory, shuffle=False)\n",
    "    def test_dataloader(self):\n",
    "        return DataLoader(self.test_dataset, batch_size=self.test_len_data,\n",
    "                        pin_memory=self.pin_memory, shuffle=False)\n",
    "    def predict_dataloader(self):\n",
    "        return DataLoader(self.test_dataset, batch_size=self.test_len_data,\n",
    "                        pin_memory=self.pin_memory, shuffle=False)\n",
    "\n",
    "\n",
    "class CrabNetLightning(L.LightningModule):\n",
    "    def __init__(self, **config):\n",
    "        super().__init__()\n",
    "        # Saving hyperparameters\n",
    "        self.save_hyperparameters()\n",
    "\n",
    "        self.model = CrabNet(out_dims=config['out_dims'],\n",
    "                             d_model=config['d_model'],\n",
    "                             N=config['N'],\n",
    "                             heads=config['heads'])\n",
    "        print('\\nModel architecture: out_dims, d_model, N, heads')\n",
    "        print(f'{self.model.out_dims}, {self.model.d_model}, '\n",
    "                  f'{self.model.N}, {self.model.heads}')\n",
    "        print(f'Model size: {count_parameters(self.model)} parameters\\n')\n",
    "\n",
    "        ### here we define some important parameters\n",
    "        self.fudge=config['fudge']\n",
    "        self.batch_size=config['batch_size']\n",
    "        self.classification = config['classification']\n",
    "        self.base_lr=config['base_lr']\n",
    "        self.max_lr=config['max_lr']\n",
    "        \n",
    "        self.criterion = CrossEntropyLoss\n",
    "        \n",
    "        if(re.search('.json', config['train_path'] )):\n",
    "            train_data=pd.read_json(config['train_path'])\n",
    "        elif(re.search('.csv', config['train_path'])):\n",
    "            train_data=pd.read_csv(config['train_path'])\n",
    "        \n",
    "        y=train_data['target'].values\n",
    "        self.step_size = len(y)\n",
    "\n",
    "    def forward(self, src, frac):\n",
    "        out=self.model(src, frac)\n",
    "        return out\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        base_optim = Lamb(params=self.model.parameters(),lr=0.001)\n",
    "        optimizer = Lookahead(base_optimizer=base_optim)\n",
    "        lr_scheduler = CyclicLR(optimizer,\n",
    "                                base_lr=self.base_lr,\n",
    "                                max_lr=self.max_lr,\n",
    "                                cycle_momentum=False,\n",
    "                                step_size_up=self.step_size)\n",
    "        # lr_scheduler=StepLR(optimizer,\n",
    "        #                     step_size=3,\n",
    "        #                     gamma=0.5)\n",
    "        return [optimizer], [lr_scheduler]\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        X, y, formula = batch\n",
    "        src, frac = X.squeeze(-1).chunk(2, dim=1)\n",
    "        frac = frac * (1 + (torch.randn_like(frac))*self.fudge)\n",
    "        frac = torch.clamp(frac, 0, 1)\n",
    "        frac[src == 0] = 0\n",
    "        frac = frac / frac.sum(dim=1).unsqueeze(1).repeat(1, frac.shape[-1])\n",
    "        \n",
    "        prediction = self(src, frac)\n",
    "        loss = self.criterion(prediction,y)\n",
    "        \n",
    "        self.log(\"train_loss\", loss, on_step=False, on_epoch=True, prog_bar=True, logger=True, batch_size=self.batch_size)\n",
    "        \n",
    "        prediction = torch.nn.functional.softmax(prediction,dim=1)\n",
    "        y_pred = torch.argmax(prediction,dim=1)\n",
    "        y=y.detach().cpu().numpy()\n",
    "        y_pred=y_pred.detach().cpu().numpy()\n",
    "        acc=balanced_accuracy_score(y,y_pred)\n",
    "        f1=f1_score(y,y_pred,average='macro')\n",
    "        mc=matthews_corrcoef(y,y_pred)\n",
    "            \n",
    "        self.log(\"train_acc\", acc, on_step=False, on_epoch=True, prog_bar=True, logger=True, batch_size=self.batch_size)\n",
    "        self.log(\"train_f1\", f1, on_step=False, on_epoch=True, prog_bar=False, logger=True, batch_size=self.batch_size)\n",
    "        self.log(\"train_mc\", mc, on_step=False, on_epoch=True, prog_bar=False, logger=True, batch_size=self.batch_size)\n",
    "        \n",
    "        return loss\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        X, y, formula = batch\n",
    "        src, frac = X.squeeze(-1).chunk(2, dim=1)\n",
    "        frac = frac * (1 + (torch.randn_like(frac))*self.fudge)\n",
    "        frac = torch.clamp(frac, 0, 1)\n",
    "        frac[src == 0] = 0\n",
    "        frac = frac / frac.sum(dim=1).unsqueeze(1).repeat(1, frac.shape[-1])\n",
    "        \n",
    "        prediction = self(src, frac)\n",
    "        val_loss = self.criterion(prediction,y)\n",
    "        \n",
    "        self.log(\"val_loss\", val_loss, on_step=False, on_epoch=True, prog_bar=True, logger=True, batch_size=self.batch_size)\n",
    "        \n",
    "        prediction = torch.nn.functional.softmax(prediction,dim=1)\n",
    "        y_pred = torch.argmax(prediction,dim=1)\n",
    "        y=y.detach().cpu().numpy()\n",
    "        y_pred=y_pred.detach().cpu().numpy()\n",
    "        acc=balanced_accuracy_score(y,y_pred)\n",
    "        f1=f1_score(y,y_pred,average='macro')\n",
    "        mc=matthews_corrcoef(y,y_pred)\n",
    "            \n",
    "        self.log(\"val_acc\", acc, on_step=False, on_epoch=True, prog_bar=True, logger=True, batch_size=self.batch_size)\n",
    "        self.log(\"val_f1\", f1, on_step=False, on_epoch=True, prog_bar=False, logger=True, batch_size=self.batch_size)\n",
    "        self.log(\"val_mc\", mc, on_step=False, on_epoch=True, prog_bar=False, logger=True, batch_size=self.batch_size)\n",
    "        return val_loss\n",
    "     \n",
    "    def test_step(self, batch, batch_idx):\n",
    "        X, y, formula = batch\n",
    "        src, frac = X.squeeze(-1).chunk(2, dim=1)\n",
    "        frac = frac * (1 + (torch.randn_like(frac))*self.fudge)\n",
    "        frac = torch.clamp(frac, 0, 1)\n",
    "        frac[src == 0] = 0\n",
    "        frac = frac / frac.sum(dim=1).unsqueeze(1).repeat(1, frac.shape[-1])\n",
    "        \n",
    "        prediction = self(src, frac)\n",
    "        \n",
    "        prediction = torch.nn.functional.softmax(prediction,dim=1)\n",
    "        y_pred = torch.argmax(prediction,dim=1)\n",
    "        y=y.detach().cpu().numpy()\n",
    "        y_pred=y_pred.detach().cpu().numpy()\n",
    "        acc=balanced_accuracy_score(y,y_pred)\n",
    "        f1=f1_score(y,y_pred,average='macro')\n",
    "        mc=matthews_corrcoef(y,y_pred)\n",
    "            \n",
    "        self.log(\"test_acc\", acc, on_step=False, on_epoch=True, prog_bar=True, logger=True, batch_size=self.batch_size)\n",
    "        self.log(\"test_f1\", f1, on_step=False, on_epoch=True, prog_bar=False, logger=True, batch_size=self.batch_size)\n",
    "        self.log(\"test_mc\", mc, on_step=False, on_epoch=True, prog_bar=False, logger=True, batch_size=self.batch_size)\n",
    "        return \n",
    "    \n",
    "    def predict_step(self, batch, batch_idx, dataloader_idx=0):\n",
    "        X, y, formula = batch\n",
    "        src, frac = X.squeeze(-1).chunk(2, dim=1)\n",
    "        frac = frac * (1 + (torch.randn_like(frac))*self.fudge)\n",
    "        frac = torch.clamp(frac, 0, 1)\n",
    "        frac[src == 0] = 0\n",
    "        frac = frac / frac.sum(dim=1).unsqueeze(1).repeat(1, frac.shape[-1])\n",
    "        \n",
    "        prediction = self(src, frac)\n",
    "        prediction = torch.nn.functional.softmax(prediction,dim=1)\n",
    "        y_pred = torch.argmax(prediction,dim=1)\n",
    "        y=y.detach().cpu().numpy()\n",
    "        y_pred=y_pred.detach().cpu().numpy()\n",
    "        acc=balanced_accuracy_score(y,y_pred)\n",
    "        f1=f1_score(y,y_pred,average='macro')\n",
    "        mc=matthews_corrcoef(y,y_pred)        \n",
    "        return formula, y_pred, prediction\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "960839e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 27\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('crabnet/crabnet_config.json','r') as f:\n",
    "        config=json.load(f)\n",
    "\n",
    "L.seed_everything(config['random_seed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2ba25ece",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model architecture: out_dims, d_model, N, heads\n",
      "4, 512, 3, 4\n",
      "Model size: 11987335 parameters\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 68141/68141 [00:00<00:00, 214564.88formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 16 elements in the formula for training\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 7572/7572 [00:00<00:00, 210117.56formulae/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 13 elements in the formula for validation"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 18929/18929 [00:00<00:00, 213622.22formulae/s]\n",
      "You are using a CUDA device ('NVIDIA GeForce RTX 3080') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\callbacks\\model_checkpoint.py:613: UserWarning: Checkpoint directory crabnet_models/crabnet_trained_models/ exists and is not empty.\n",
      "  rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name  | Type    | Params\n",
      "----------------------------------\n",
      "0 | model | CrabNet | 12.0 M\n",
      "----------------------------------\n",
      "12.0 M    Trainable params\n",
      "23.8 K    Non-trainable params\n",
      "12.0 M    Total params\n",
      "48.045    Total estimated model params size (MB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 15 elements in the formula for testing\n"
     ]
    },
    {
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       "model_id": "",
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       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:224: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 16 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n",
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:224: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 16 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
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      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
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       "Validation: 0it [00:00, ?it/s]"
      ]
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    {
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       "Validation: 0it [00:00, ?it/s]"
      ]
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       "Validation: 0it [00:00, ?it/s]"
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       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Swapping scheduler `CyclicLR` for `SWALR`\n"
     ]
    },
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    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 68141/68141 [00:00<00:00, 208917.90formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 16 elements in the formula for training\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 7572/7572 [00:00<00:00, 196481.50formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 13 elements in the formula for validation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 18929/18929 [00:00<00:00, 206462.13formulae/s]\n",
      "You are using a CUDA device ('NVIDIA GeForce RTX 3080') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
      "Restoring states from the checkpoint path at crabnet_models/crabnet_trained_models/disorder-epoch=40-val_acc=0.78.ckpt\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "Loaded model weights from checkpoint at crabnet_models/crabnet_trained_models/disorder-epoch=40-val_acc=0.78.ckpt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 15 elements in the formula for testing\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:224: PossibleUserWarning: The dataloader, test_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 16 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e5ada5699b94d3183b73f0ec53dde44",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\r\n",
      "       Test metric             DataLoader 0\r\n",
      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\r\n",
      "        test_acc            0.7757253178090717\r\n",
      "         test_f1            0.7796478968013522\r\n",
      "         test_mc            0.7487202552310397\r\n",
      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'test_acc': 0.7757253178090717,\n",
       "  'test_f1': 0.7796478968013522,\n",
       "  'test_mc': 0.7487202552310397}]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = CrabNetLightning(**config)\n",
    "# wandb_logger = WandbLogger(project=\"Crabnet-global-disorder-new\", config=config, log_model=\"all\")\n",
    "trainer = Trainer(max_epochs=100,accelerator='gpu', devices=1, \n",
    "                      callbacks=[StochasticWeightAveraging(swa_epoch_start=config['swa_epoch_start'],swa_lrs=config['swa_lrs']),\n",
    "                                EarlyStopping(monitor='val_loss', patience=config['patience']), ModelCheckpoint(monitor='val_acc', mode=\"max\", \n",
    "                                dirpath='crabnet_models/crabnet_trained_models/', filename='disorder-{epoch:02d}-{val_acc:.2f}')])\n",
    "disorder_data = CrabNetDataModule(config['train_path'],\n",
    "                                   config['val_path'],\n",
    "                                   config['test_path'],\n",
    "                                   classification = config['classification'])\n",
    "trainer.fit(model, datamodule=disorder_data)\n",
    "trainer.test(ckpt_path='best',datamodule=disorder_data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d63e676e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model architecture: out_dims, d_model, N, heads\n",
      "4, 512, 3, 4\n",
      "Model size: 11987335 parameters\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\setup.py:176: PossibleUserWarning: GPU available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='gpu', devices=1)`.\n",
      "  rank_zero_warn(\n",
      "Generating EDM: 100%|██████████| 68141/68141 [00:00<00:00, 201109.72formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 16 elements in the formula for training\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 7572/7572 [00:00<00:00, 185740.85formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 13 elements in the formula for validation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 18929/18929 [00:00<00:00, 186759.71formulae/s]\n",
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:224: PossibleUserWarning: The dataloader, predict_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 16 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 15 elements in the formula for testing\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "29335dfec0224369b6b7270e42698d21",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score,average_precision_score, confusion_matrix,ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "for x in disorder_data.predict_dataloader():\n",
    "        _, y_true, _ = x\n",
    "model=CrabNetLightning.load_from_checkpoint('crabnet_models/crabnet_trained_models/crabnet-type-disorder-0.ckpt')\n",
    "trainer=Trainer()\n",
    "formula, y_pred, prediction=trainer.predict(model=model, datamodule=disorder_data)[0]\n",
    "    \n",
    "metrics={}\n",
    "metrics['acc']=balanced_accuracy_score(y_true,y_pred)\n",
    "metrics['f1']=f1_score(y_true,y_pred,average='macro')\n",
    "metrics['precision']=average_precision_score(y_true,y_pred.reshape(-1, 1),average='macro')\n",
    "metrics['recall']=recall_score(y_true,y_pred,average='macro')\n",
    "metrics['mc']=matthews_corrcoef(y_true,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "00c174cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'acc': 0.775638331031062,\n",
       " 'f1': 0.7795809319798652,\n",
       " 'precision': 0.3506962667213484,\n",
       " 'recall': 0.775638331031062,\n",
       " 'mc': 0.748639494554961}"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d7879521",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm=confusion_matrix(y_true,y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['O','S','V','M'])\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9c95ddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "6362/(6362+371+388+92)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3368e5ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm=confusion_matrix(y_true,y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['O','S','V','M'])\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bed88bd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 68141/68141 [00:00<00:00, 216105.51formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 16 elements in the formula for training\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 7572/7572 [00:00<00:00, 213082.25formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 13 elements in the formula for validation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 18929/18929 [00:00<00:00, 213596.93formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 15 elements in the formula for testing\n"
     ]
    }
   ],
   "source": [
    "with open('crabnet/crabnet_config.json','r') as f:\n",
    "        config=json.load(f)\n",
    "disorder_data = CrabNetDataModule(config['train_path'],\n",
    "                                   config['val_path'],\n",
    "                                   config['test_path'],\n",
    "                                   classification = config['classification'])\n",
    "disorder_data.prepare_data()\n",
    "predictloader=disorder_data.predict_dataloader()\n",
    "for x in predictloader:\n",
    "    _,y_true,_=x\n",
    "    datalength=len(y_true)\n",
    "\n",
    "prediction_crabnet=np.zeros((10,datalength,4))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "68301684",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 18929, 4)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(prediction_crabnet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "653cc84a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model architecture: out_dims, d_model, N, heads\n",
      "4, 512, 3, 4\n",
      "Model size: 11987335 parameters\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\setup.py:176: PossibleUserWarning: GPU available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='gpu', devices=1)`.\n",
      "  rank_zero_warn(\n",
      "Generating EDM: 100%|██████████| 68141/68141 [00:00<00:00, 214131.48formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 16 elements in the formula for training\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 7572/7572 [00:00<00:00, 199021.60formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 13 elements in the formula for validation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 18929/18929 [00:00<00:00, 208463.58formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 15 elements in the formula for testing\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:224: PossibleUserWarning: The dataloader, predict_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 16 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c40a4add6e1442df90aefe9c91df770f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model architecture: out_dims, d_model, N, heads\n",
      "4, 512, 3, 4\n",
      "Model size: 11987335 parameters\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\setup.py:176: PossibleUserWarning: GPU available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='gpu', devices=1)`.\n",
      "  rank_zero_warn(\n",
      "Generating EDM: 100%|██████████| 68141/68141 [00:00<00:00, 211718.95formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 16 elements in the formula for training\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 7572/7572 [00:00<00:00, 207228.84formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 13 elements in the formula for validation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 18929/18929 [00:00<00:00, 203342.29formulae/s]\n",
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:224: PossibleUserWarning: The dataloader, predict_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 16 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 15 elements in the formula for testing\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "90d182f9c2154aacbbf426ed8397ba91",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model architecture: out_dims, d_model, N, heads\n",
      "4, 512, 3, 4\n",
      "Model size: 11987335 parameters\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\setup.py:176: PossibleUserWarning: GPU available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='gpu', devices=1)`.\n",
      "  rank_zero_warn(\n",
      "Generating EDM: 100%|██████████| 68141/68141 [00:00<00:00, 211710.17formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 16 elements in the formula for training\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 7572/7572 [00:00<00:00, 207236.95formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 13 elements in the formula for validation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 18929/18929 [00:00<00:00, 210123.70formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 15 elements in the formula for testing\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:224: PossibleUserWarning: The dataloader, predict_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 16 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0ed4b1fb991f4e44b8dd56a8031a631f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model architecture: out_dims, d_model, N, heads\n",
      "4, 512, 3, 4\n",
      "Model size: 11987335 parameters\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\setup.py:176: PossibleUserWarning: GPU available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='gpu', devices=1)`.\n",
      "  rank_zero_warn(\n",
      "Generating EDM: 100%|██████████| 68141/68141 [00:00<00:00, 216234.84formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 16 elements in the formula for training\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 7572/7572 [00:00<00:00, 209984.20formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 13 elements in the formula for validation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 18929/18929 [00:00<00:00, 205550.76formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 15 elements in the formula for testing\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:224: PossibleUserWarning: The dataloader, predict_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 16 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "10a3a94973664433a626a2a8bc28ef20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model architecture: out_dims, d_model, N, heads\n",
      "4, 512, 3, 4\n",
      "Model size: 11987335 parameters\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\setup.py:176: PossibleUserWarning: GPU available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='gpu', devices=1)`.\n",
      "  rank_zero_warn(\n",
      "Generating EDM: 100%|██████████| 68141/68141 [00:00<00:00, 206909.03formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 16 elements in the formula for training\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 7572/7572 [00:00<00:00, 204463.21formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 13 elements in the formula for validation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 18929/18929 [00:00<00:00, 209755.65formulae/s]\n",
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:224: PossibleUserWarning: The dataloader, predict_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 16 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 15 elements in the formula for testing\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cb7083b6560541d4974fe19ebaa32621",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model architecture: out_dims, d_model, N, heads\n",
      "4, 512, 3, 4\n",
      "Model size: 11987335 parameters\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\setup.py:176: PossibleUserWarning: GPU available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='gpu', devices=1)`.\n",
      "  rank_zero_warn(\n",
      "Generating EDM: 100%|██████████| 68141/68141 [00:00<00:00, 207212.06formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 16 elements in the formula for training\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 7572/7572 [00:00<00:00, 207234.25formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 13 elements in the formula for validation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 18929/18929 [00:00<00:00, 202107.22formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 15 elements in the formula for testing\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:224: PossibleUserWarning: The dataloader, predict_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 16 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81b8cf6abc4b487da48d55510326237b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model architecture: out_dims, d_model, N, heads\n",
      "4, 512, 3, 8\n",
      "Model size: 11987335 parameters\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\setup.py:176: PossibleUserWarning: GPU available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='gpu', devices=1)`.\n",
      "  rank_zero_warn(\n",
      "Generating EDM: 100%|██████████| 68141/68141 [00:00<00:00, 205877.78formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 16 elements in the formula for training\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 7572/7572 [00:00<00:00, 210125.91formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 13 elements in the formula for validation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 18929/18929 [00:00<00:00, 207812.62formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 15 elements in the formula for testing\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:224: PossibleUserWarning: The dataloader, predict_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 16 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cf09787a4396460f9e5b5fca03563843",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model architecture: out_dims, d_model, N, heads\n",
      "4, 512, 3, 4\n",
      "Model size: 11987335 parameters\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\setup.py:176: PossibleUserWarning: GPU available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='gpu', devices=1)`.\n",
      "  rank_zero_warn(\n",
      "Generating EDM: 100%|██████████| 68141/68141 [00:00<00:00, 210091.35formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 16 elements in the formula for training\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 7572/7572 [00:00<00:00, 213033.65formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 13 elements in the formula for validation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 18929/18929 [00:00<00:00, 207818.06formulae/s]\n",
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:224: PossibleUserWarning: The dataloader, predict_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 16 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 15 elements in the formula for testing\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5197295ddafd4c15b2da1174f32afdf8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model architecture: out_dims, d_model, N, heads\n",
      "4, 512, 3, 4\n",
      "Model size: 11987335 parameters\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\setup.py:176: PossibleUserWarning: GPU available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='gpu', devices=1)`.\n",
      "  rank_zero_warn(\n",
      "Generating EDM: 100%|██████████| 68141/68141 [00:00<00:00, 211595.28formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 16 elements in the formula for training\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 7572/7572 [00:00<00:00, 211304.45formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 13 elements in the formula for validation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 18929/18929 [00:00<00:00, 206583.54formulae/s]\n",
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:224: PossibleUserWarning: The dataloader, predict_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 16 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 15 elements in the formula for testing\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af6d5c86d05d42808bea6d9843f18b18",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model architecture: out_dims, d_model, N, heads\n",
      "4, 512, 3, 4\n",
      "Model size: 11987335 parameters\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\setup.py:176: PossibleUserWarning: GPU available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='gpu', devices=1)`.\n",
      "  rank_zero_warn(\n",
      "Generating EDM: 100%|██████████| 68141/68141 [00:00<00:00, 205849.76formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 16 elements in the formula for training\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 7572/7572 [00:00<00:00, 204457.94formulae/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 13 elements in the formula for validation\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating EDM: 100%|██████████| 18929/18929 [00:00<00:00, 209328.15formulae/s]\n",
      "C:\\Users\\patykova\\.conda\\envs\\roost\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:224: PossibleUserWarning: The dataloader, predict_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 16 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data with up to 15 elements in the formula for testing\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d08dddd2e5b2439d82e784e805ee33bd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    name='crabnet-type-disorder-'+str(i)\n",
    "    model = CrabNetLightning.load_from_checkpoint('crabnet_models/crabnet_trained_models/'+name+'.ckpt')\n",
    "    trainer = Trainer()\n",
    "    formula, ypred, prediction=trainer.predict(model=model,datamodule=disorder_data)[0]\n",
    "    prediction_crabnet[i,:,:]=prediction.detach().cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5e9abfb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_ens=np.zeros((10,datalength))\n",
    "for i in range(10):\n",
    "    y_pred=np.argmax(prediction_crabnet[i,:,:],axis=-1)\n",
    "    y_pred_ens[i,:]=y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b7d1a3af",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels=[]\n",
    "for i in range(datalength):\n",
    "    a=y_pred_ens[:,i].astype(int)\n",
    "    counts = np.bincount(a)\n",
    "    labels.append(np.argmax(counts))\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6d13ec7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c0232f4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics={}\n",
    "metrics['acc']=balanced_accuracy_score(y_true,y_pred)\n",
    "metrics['f1']=f1_score(y_true,y_pred,average='macro')\n",
    "metrics['recall']=recall_score(y_true,y_pred,average='macro')\n",
    "metrics['mc']=matthews_corrcoef(y_true,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bfb14f60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'acc': 0.8026207887528969,\n",
       " 'f1': 0.8115935759711704,\n",
       " 'recall': 0.8026207887528969,\n",
       " 'mc': 0.7885254348161043}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "58bcff56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm=confusion_matrix(y_true,y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['O','S','V','M'])\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e20b443",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c4704e90",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=torch.randn(12,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0bd8c842",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=np.zeros((10,12,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dce1580f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y[0,:,:]=x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a4744d27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
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       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "y[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e0cc919",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fcf3e58",
   "metadata": {},
   "outputs": [],
   "source": []
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