Datasets
Models
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"id": "56ecab01-a110-440f-a111-c132dd1fa26c",
"metadata": {
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"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"import sys\n",
"sys.path.append(\"../../models/ss/\")\n",
"import train"
]
},
{
"cell_type": "markdown",
"id": "3f7594c8-e7f0-4aaa-8d8e-4606b31d183f",
"metadata": {},
"source": [
"# Dataset"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "34110148-8ee5-44ec-9bee-0066f1620654",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" removed labels ssE|ssL|ssT|ssB\n"
]
},
{
"data": {
"text/plain": [
"(411, 500, 21)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_filename = '../../data/raw_0/test.csv'\n",
"use_labels=['ssH','ssG','ssI','ssS']\n",
"data = train.load_data(data_filename,use_labels)#'ssI'\n",
"data[0].shape"
]
},
{
"cell_type": "markdown",
"id": "7b258ae6-e493-41e9-8ccc-18d64ccd3cca",
"metadata": {
"tags": []
},
"source": [
"# Model - Helix"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "7b73117e-fa24-46f7-8fc8-251f617d8ff6",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Hyper Parameters\n",
"\n",
"Learning Rate: 0.0001\n",
"Dropout: 0.15\n",
"Batch Dimension: 320\n",
"Number of Epochs: 2000\n",
"\n",
"Loss: Custom Loss\n",
"\n",
"CNNModel(\n",
" (model): Sequential(\n",
" (0): Conv1d(21, 256, kernel_size=(21,), stride=(1,), padding=(10,))\n",
" (1): ReLU()\n",
" (2): Dropout(p=0.15, inplace=False)\n",
" (3): Conv1d(256, 128, kernel_size=(21,), stride=(1,), padding=(10,))\n",
" (4): ReLU()\n",
" (5): Dropout(p=0.15, inplace=False)\n",
" (6): Conv1d(128, 64, kernel_size=(21,), stride=(1,), padding=(10,))\n",
" (7): ReLU()\n",
" (8): Dropout(p=0.15, inplace=False)\n",
" (9): Conv1d(64, 4, kernel_size=(21,), stride=(1,), padding=(10,))\n",
" )\n",
" (activation): Sigmoid()\n",
")\n",
"Epoch 0: Loss: 0.125625, Average Test Accuracy: 0.00%\n",
"Epoch 100: Loss: 0.006360, Average Test Accuracy: 12.02%\n",
"Epoch 200: Loss: 0.005251, Average Test Accuracy: 36.06%\n",
"Epoch 300: Loss: 0.004695, Average Test Accuracy: 42.31%\n",
"Epoch 400: Loss: 0.004553, Average Test Accuracy: 43.27%\n",
"Epoch 500: Loss: 0.004176, Average Test Accuracy: 43.75%\n",
"Epoch 600: Loss: 0.003515, Average Test Accuracy: 54.33%\n",
"Epoch 700: Loss: 0.003549, Average Test Accuracy: 63.46%\n",
"Epoch 800: Loss: 0.003311, Average Test Accuracy: 67.31%\n",
"Epoch 900: Loss: 0.002835, Average Test Accuracy: 69.71%\n",
"Epoch 1000: Loss: 0.002266, Average Test Accuracy: 70.67%\n",
"Epoch 1100: Loss: 0.001848, Average Test Accuracy: 71.15%\n",
"Epoch 1200: Loss: 0.001513, Average Test Accuracy: 71.63%\n",
"Epoch 1300: Loss: 0.001247, Average Test Accuracy: 71.63%\n",
"Epoch 1400: Loss: 0.001204, Average Test Accuracy: 72.12%\n",
"Epoch 1500: Loss: 0.001046, Average Test Accuracy: 72.12%\n",
"Epoch 1600: Loss: 0.000754, Average Test Accuracy: 72.12%\n",
"Epoch 1700: Loss: 0.000724, Average Test Accuracy: 72.12%\n",
"Epoch 1800: Loss: 0.000646, Average Test Accuracy: 72.12%\n",
"Epoch 1900: Loss: 0.000452, Average Test Accuracy: 72.12%\n",
"Complete.\n",
"\n"
]
}
],
"source": [
"import architecture as arch #PyTorch model\n",
"dropout_probability = 0.15\n",
"output_classes=len(use_labels)\n",
"model = arch.CNNModel (input_channels=data[0].shape[-1], #pssm\n",
" output_classes=output_classes, \n",
" window_size=21,\n",
" padding=10,\n",
" dropout_probability=dropout_probability)\n",
"train.nn_epochs = 2000\n",
"train.epoch_eval =100\n",
"\n",
"train.run(data, model, tag='hlx')"
]
},
{
"cell_type": "markdown",
"id": "fa2f6fb5-8308-448a-ac41-2caea08f7ad0",
"metadata": {},
"source": [
"# Model - Beta sheets"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "cc4bd5a7-21eb-4c75-83da-693bdf49b7b9",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" removed labels ssL|ssT|ssH|ssG|ssS|ssI\n"
]
}
],
"source": [
"use_labels=['ssB','ssE']#'ssL']#\n",
"data = train.load_data(data_filename,use_labels, only_pssm=True)\n",
"output_classes= len(use_labels)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "c921771f-e180-4443-93fa-2e2b1764b697",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Hyper Parameters\n",
"\n",
"Learning Rate: 0.01\n",
"Dropout: 0.1\n",
"Batch Dimension: 3200\n",
"Number of Epochs: 2000\n",
"\n",
"Loss: Custom Loss\n",
"\n",
"CNNModel(\n",
" (model): Sequential(\n",
" (0): Conv1d(21, 256, kernel_size=(11,), stride=(1,), padding=(5,))\n",
" (1): ReLU()\n",
" (2): Dropout(p=0.1, inplace=False)\n",
" (3): Conv1d(256, 128, kernel_size=(11,), stride=(1,), padding=(5,))\n",
" (4): ReLU()\n",
" (5): Dropout(p=0.1, inplace=False)\n",
" (6): Conv1d(128, 64, kernel_size=(11,), stride=(1,), padding=(5,))\n",
" (7): ReLU()\n",
" (8): Dropout(p=0.1, inplace=False)\n",
" (9): Conv1d(64, 2, kernel_size=(11,), stride=(1,), padding=(5,))\n",
" )\n",
" (activation): Sigmoid()\n",
")\n",
"Epoch 0: Loss: 0.124714, Average Test Accuracy: 6.73%\n",
"Epoch 100: Loss: 0.034316, Average Test Accuracy: 6.73%\n",
"Epoch 200: Loss: 0.034316, Average Test Accuracy: 6.73%\n",
"Epoch 300: Loss: 0.034316, Average Test Accuracy: 6.73%\n",
"Epoch 400: Loss: 0.034316, Average Test Accuracy: 6.73%\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[63], line 13\u001b[0m\n\u001b[1;32m 11\u001b[0m train\u001b[38;5;241m.\u001b[39mbatch_dim \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3200\u001b[39m\n\u001b[1;32m 12\u001b[0m train\u001b[38;5;241m.\u001b[39mLR \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.01\u001b[39m\n\u001b[0;32m---> 13\u001b[0m \u001b[43mtrain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtag\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbeta\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccuracy_threshold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/ReL/bpg/biophysics-protein-genomics/ProteinStructurePredictionCNN/experiments/experiment_1/../../models/train.py:81\u001b[0m, in \u001b[0;36mrun\u001b[0;34m(data, model, tag, accuracy_threshold)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m inputs, targets \u001b[38;5;129;01min\u001b[39;00m train_loader:\n\u001b[1;32m 80\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m---> 81\u001b[0m inputs, targets \u001b[38;5;241m=\u001b[39m \u001b[43minputs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m, targets\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m 82\u001b[0m outputs \u001b[38;5;241m=\u001b[39m model(inputs)\n\u001b[1;32m 83\u001b[0m loss \u001b[38;5;241m=\u001b[39m loss_fn2(outputs, targets)\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"import architecture as arch #PyTorch model\n",
"\n",
"\n",
"model = arch.CNNModel (input_channels=data[0].shape[-1], #pssm\n",
" output_classes=output_classes,\n",
" window_size=11,\n",
" padding=5,\n",
" dropout_probability=0.1)\n",
"train.nn_epochs = 2000\n",
"train.epoch_eval =100\n",
"train.batch_dim = 3200\n",
"train.LR = 0.01\n",
"train.run(data, model, tag='beta', accuracy_threshold=0.5)"
]
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