From 11368b5215a7625b3b8aeabe04fb5d9f4dd04b4f Mon Sep 17 00:00:00 2001
From: Riza <42672299+zaRizk7@users.noreply.github.com>
Date: Sat, 23 Aug 2025 15:33:11 +0100
Subject: [PATCH 1/4] update cancer tutorial post-pykale restructuring
---
.../multiomics-cancer-classification/model.py | 2 +-
.../tutorial-cancer.ipynb | 246 +++++++++---------
2 files changed, 131 insertions(+), 117 deletions(-)
diff --git a/tutorials/multiomics-cancer-classification/model.py b/tutorials/multiomics-cancer-classification/model.py
index 75b3102..aa91afa 100644
--- a/tutorials/multiomics-cancer-classification/model.py
+++ b/tutorials/multiomics-cancer-classification/model.py
@@ -3,7 +3,7 @@
from torch.nn import CrossEntropyLoss
from yacs.config import CfgNode
-from kale.embed.mogonet import MogonetGCN
+from kale.embed.model_lib.mogonet import MogonetGCN
from kale.loaddata.multiomics_datasets import SparseMultiomicsDataset
from kale.pipeline.multiomics_trainer import MultiomicsTrainer
from kale.predict.decode import LinearClassifier, VCDN
diff --git a/tutorials/multiomics-cancer-classification/tutorial-cancer.ipynb b/tutorials/multiomics-cancer-classification/tutorial-cancer.ipynb
index ce84a16..cd7375b 100644
--- a/tutorials/multiomics-cancer-classification/tutorial-cancer.ipynb
+++ b/tutorials/multiomics-cancer-classification/tutorial-cancer.ipynb
@@ -43,7 +43,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"id": "551867b5",
"metadata": {
"tags": [
@@ -86,7 +86,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"id": "6050d5b4",
"metadata": {
"tags": [
@@ -112,7 +112,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"id": "1c9c4856",
"metadata": {
"tags": [
@@ -142,7 +142,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"id": "20700eaf",
"metadata": {},
"outputs": [],
@@ -172,7 +172,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"id": "f1f8bb7c",
"metadata": {},
"outputs": [],
@@ -192,7 +192,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"id": "f85914b1",
"metadata": {},
"outputs": [
@@ -250,7 +250,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"id": "2ecd6082",
"metadata": {},
"outputs": [],
@@ -268,7 +268,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"id": "1352ea41",
"metadata": {},
"outputs": [],
@@ -294,7 +294,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"id": "9041fabd",
"metadata": {},
"outputs": [
@@ -338,7 +338,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"id": "676ebd93",
"metadata": {},
"outputs": [
@@ -386,7 +386,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"id": "1537ce26",
"metadata": {},
"outputs": [],
@@ -406,7 +406,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"id": "da221bd6",
"metadata": {},
"outputs": [
@@ -477,7 +477,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"id": "7383c5c1",
"metadata": {
"tags": [
@@ -489,10 +489,10 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:pytorch_lightning.utilities.rank_zero:π‘ Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n",
- "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
- "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
- "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
+ "π‘ Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n",
+ "GPU available: True (cuda), used: True\n",
+ "TPU available: False, using: 0 TPU cores\n",
+ "HPU available: False, using: 0 HPUs\n"
]
}
],
@@ -522,7 +522,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"id": "2b42b719",
"metadata": {},
"outputs": [
@@ -530,28 +530,29 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
+ "You are using a CUDA device ('NVIDIA GeForce RTX 4090') 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",
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
]
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "6e6a2f06736a477c811c4a0608970adc",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Training: | | 0/? [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 99: 100%|ββββββββββ| 1/1 [00:00<00:00, 57.69it/s, v_num=0]"
+ ]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=100` reached.\n"
+ "`Trainer.fit` stopped: `max_epochs=100` reached.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 99: 100%|ββββββββββ| 1/1 [00:00<00:00, 19.96it/s, v_num=0]\n"
]
}
],
@@ -572,7 +573,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"id": "e94b710d",
"metadata": {},
"outputs": [
@@ -580,10 +581,16 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:pytorch_lightning.utilities.rank_zero:π‘ Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n",
- "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
- "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
- "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
+ "π‘ Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "GPU available: True (cuda), used: True\n",
+ "TPU available: False, using: 0 TPU cores\n",
+ "HPU available: False, using: 0 HPUs\n"
]
}
],
@@ -612,7 +619,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"id": "b3e66c8f",
"metadata": {},
"outputs": [
@@ -620,28 +627,28 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
]
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "8f4a6d9c28ce462d8bf982a19abe807c",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Training: | | 0/? [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 499: 100%|ββββββββββ| 1/1 [00:00<00:00, 42.63it/s, v_num=1]"
+ ]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=500` reached.\n"
+ "`Trainer.fit` stopped: `max_epochs=500` reached.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 499: 100%|ββββββββββ| 1/1 [00:00<00:00, 12.46it/s, v_num=1]\n"
]
}
],
@@ -660,7 +667,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 16,
"id": "019e2e7b",
"metadata": {},
"outputs": [
@@ -668,22 +675,15 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
]
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "99cb87830ed24a25b0b527d1cc0686d8",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Testing: | | 0/? [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Testing DataLoader 0: 100%|ββββββββββ| 1/1 [00:00<00:00, 37.12it/s]\n"
+ ]
},
{
"data": {
@@ -691,9 +691,9 @@
"
βββββββββββββββββββββββββββββ³ββββββββββββββββββββββββββββ\n",
"β Test metric β DataLoader 0 β\n",
"β‘ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ©\n",
- "β Accuracy β 0.8330000042915344 β\n",
- "β F1 macro β 0.7850000262260437 β\n",
- "β F1 weighted β 0.8299999833106995 β\n",
+ "β Accuracy β 0.824999988079071 β\n",
+ "β F1 macro β 0.75 β\n",
+ "β F1 weighted β 0.8050000071525574 β\n",
"βββββββββββββββββββββββββββββ΄ββββββββββββββββββββββββββββ\n",
"\n"
],
@@ -701,9 +701,9 @@
"βββββββββββββββββββββββββββββ³ββββββββββββββββββββββββββββ\n",
"β\u001b[1m \u001b[0m\u001b[1m Test metric \u001b[0m\u001b[1m \u001b[0mβ\u001b[1m \u001b[0m\u001b[1m DataLoader 0 \u001b[0m\u001b[1m \u001b[0mβ\n",
"β‘ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ©\n",
- "β\u001b[36m \u001b[0m\u001b[36m Accuracy \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.8330000042915344 \u001b[0m\u001b[35m \u001b[0mβ\n",
- "β\u001b[36m \u001b[0m\u001b[36m F1 macro \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.7850000262260437 \u001b[0m\u001b[35m \u001b[0mβ\n",
- "β\u001b[36m \u001b[0m\u001b[36m F1 weighted \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.8299999833106995 \u001b[0m\u001b[35m \u001b[0mβ\n",
+ "β\u001b[36m \u001b[0m\u001b[36m Accuracy \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.824999988079071 \u001b[0m\u001b[35m \u001b[0mβ\n",
+ "β\u001b[36m \u001b[0m\u001b[36m F1 macro \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.75 \u001b[0m\u001b[35m \u001b[0mβ\n",
+ "β\u001b[36m \u001b[0m\u001b[36m F1 weighted \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.8050000071525574 \u001b[0m\u001b[35m \u001b[0mβ\n",
"βββββββββββββββββββββββββββββ΄ββββββββββββββββββββββββββββ\n"
]
},
@@ -713,12 +713,12 @@
{
"data": {
"text/plain": [
- "[{'Accuracy': 0.8330000042915344,\n",
- " 'F1 weighted': 0.8299999833106995,\n",
- " 'F1 macro': 0.7850000262260437}]"
+ "[{'Accuracy': 0.824999988079071,\n",
+ " 'F1 weighted': 0.8050000071525574,\n",
+ " 'F1 macro': 0.75}]"
]
},
- "execution_count": 17,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -742,7 +742,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 17,
"id": "f061dd93",
"metadata": {
"tags": [
@@ -754,10 +754,10 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:pytorch_lightning.utilities.rank_zero:π‘ Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n",
- "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
- "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
- "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
+ "π‘ Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\n",
+ "GPU available: True (cuda), used: True\n",
+ "TPU available: False, using: 0 TPU cores\n",
+ "HPU available: False, using: 0 HPUs\n"
]
}
],
@@ -791,7 +791,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 18,
"id": "e428229c",
"metadata": {},
"outputs": [],
@@ -813,14 +813,16 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 19,
"id": "2dd9e5e3",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
- "text": []
+ "text": [
+ " \r"
+ ]
}
],
"source": [
@@ -845,7 +847,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 20,
"id": "c984bdb1",
"metadata": {},
"outputs": [
@@ -854,36 +856,36 @@
"output_type": "stream",
"text": [
"Rank\tFeature name \tOmics\tImportance\n",
- " 1\thsa-mir-205 \t 2\t32.6950\n",
- " 2\thsa-mir-378 \t 2\t28.1680\n",
- " 3\thsa-mir-452 \t 2\t25.1500\n",
- " 4\thsa-mir-9-2 \t 2\t23.6410\n",
- " 5\tHPDL|84842 \t 0\t23.0000\n",
- " 6\tMSLN|10232 \t 0\t23.0000\n",
- " 7\tKRT6B|3854 \t 0\t23.0000\n",
- " 8\tGPR37L1 \t 1\t22.0000\n",
- " 9\thsa-mir-204 \t 2\t21.1260\n",
- " 10\tMT1DP \t 1\t21.0000\n",
- " 11\tGAL|51083 \t 0\t20.0000\n",
- " 12\tFGFBP1|9982 \t 0\t20.0000\n",
- " 13\tPI3|5266 \t 0\t20.0000\n",
- " 14\tBBOX1|8424 \t 0\t20.0000\n",
- " 15\thsa-mir-106b \t 2\t19.6170\n",
- " 16\thsa-let-7c \t 2\t19.6170\n",
- " 17\tFABP7|2173 \t 0\t18.0000\n",
- " 18\thsa-mir-2355 \t 2\t17.6050\n",
- " 19\thsa-mir-203 \t 2\t17.1020\n",
- " 20\thsa-mir-125b-2 \t 2\t17.1020\n",
- " 21\thsa-mir-584 \t 2\t17.1020\n",
- " 22\tSOX11|6664 \t 0\t17.0000\n",
- " 23\thsa-mir-511-1 \t 2\t16.0960\n",
- " 24\thsa-mir-148a \t 2\t16.0960\n",
- " 25\thsa-mir-187 \t 2\t16.0960\n",
- " 26\thsa-mir-184 \t 2\t15.5930\n",
- " 27\thsa-mir-1180 \t 2\t15.5930\n",
- " 28\thsa-mir-142 \t 2\t15.0900\n",
- " 29\thsa-mir-20b \t 2\t14.5870\n",
- " 30\thsa-mir-200b \t 2\t14.5870\n"
+ " 1\tFABP7|2173 \t 0\t28.0000\n",
+ " 2\tWNT6|7475 \t 0\t28.0000\n",
+ " 3\tMIA|8190 \t 0\t28.0000\n",
+ " 4\tCRHR1|1394 \t 0\t27.0000\n",
+ " 5\tSOX10|6663 \t 0\t27.0000\n",
+ " 6\tSERPINB5|5268 \t 0\t27.0000\n",
+ " 7\tGABRP|2568 \t 0\t27.0000\n",
+ " 8\tTMEM207 \t 1\t25.0000\n",
+ " 9\tKRT6B|3854 \t 0\t25.0000\n",
+ " 10\tPTX3|5806 \t 0\t25.0000\n",
+ " 11\tSCRG1|11341 \t 0\t24.0000\n",
+ " 12\tFLJ41941 \t 1\t23.0000\n",
+ " 13\tOR1J4 \t 1\t23.0000\n",
+ " 14\tGPR37L1 \t 1\t23.0000\n",
+ " 15\tSLC7A8|23428 \t 0\t22.0000\n",
+ " 16\tLEMD1|93273 \t 0\t20.0000\n",
+ " 17\tPGBD5|79605 \t 0\t19.0000\n",
+ " 18\tBPI|671 \t 0\t19.0000\n",
+ " 19\tCRIP1|1396 \t 0\t19.0000\n",
+ " 20\tATP1B3|483 \t 0\t19.0000\n",
+ " 21\tBBOX1|8424 \t 0\t18.0000\n",
+ " 22\tFOXC1|2296 \t 0\t17.0000\n",
+ " 23\tSOX21 \t 1\t17.0000\n",
+ " 24\tC10orf116|10974 \t 0\t17.0000\n",
+ " 25\thsa-mir-29b-1 \t 2\t16.0960\n",
+ " 26\tCXCL3|2921 \t 0\t16.0000\n",
+ " 27\tFGFBP1|9982 \t 0\t16.0000\n",
+ " 28\thsa-mir-944 \t 2\t14.0840\n",
+ " 29\thsa-mir-9-2 \t 2\t14.0840\n",
+ " 30\thsa-mir-9-1 \t 2\t14.0840\n"
]
}
],
@@ -913,9 +915,21 @@
],
"metadata": {
"kernelspec": {
- "display_name": "mmai-omics-tutorial",
+ "display_name": "embc25",
"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": "3.10.16"
}
},
"nbformat": 4,
From ef3c19766c17d624be4912ee1dea8d1757122681 Mon Sep 17 00:00:00 2001
From: Riza <42672299+zaRizk7@users.noreply.github.com>
Date: Sat, 23 Aug 2025 15:33:38 +0100
Subject: [PATCH 2/4] update drug tutorial post-pykale structuring
---
.../tutorial-drug.ipynb | 615 +++++++++---------
1 file changed, 308 insertions(+), 307 deletions(-)
diff --git a/tutorials/drug-target-interaction/tutorial-drug.ipynb b/tutorials/drug-target-interaction/tutorial-drug.ipynb
index c916906..23868b5 100644
--- a/tutorials/drug-target-interaction/tutorial-drug.ipynb
+++ b/tutorials/drug-target-interaction/tutorial-drug.ipynb
@@ -46,7 +46,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"id": "a6028209",
"metadata": {
"tags": [
@@ -83,7 +83,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"id": "53e3b14e",
"metadata": {
"tags": [
@@ -110,7 +110,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"id": "6e871c63",
"metadata": {},
"outputs": [],
@@ -118,8 +118,12 @@
"import os\n",
"import warnings\n",
"\n",
+ "from rdkit import RDLogger\n",
+ "\n",
"warnings.filterwarnings(\"ignore\")\n",
- "os.environ[\"PYTHONWARNINGS\"] = \"ignore\""
+ "os.environ[\"PYTHONWARNINGS\"] = \"ignore\"\n",
+ "\n",
+ "RDLogger.DisableLog(\"rdApp.*\")"
]
},
{
@@ -132,7 +136,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"id": "0d384020",
"metadata": {},
"outputs": [
@@ -140,7 +144,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "NumPy version: 2.0.2\n"
+ "NumPy version: 2.2.6\n"
]
}
],
@@ -162,7 +166,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"id": "55c13b48",
"metadata": {},
"outputs": [],
@@ -197,7 +201,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"id": "424c7286",
"metadata": {},
"outputs": [],
@@ -215,7 +219,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"id": "c69376fa",
"metadata": {},
"outputs": [],
@@ -233,7 +237,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"id": "45874296",
"metadata": {},
"outputs": [
@@ -285,7 +289,7 @@
" BATCH_SIZE: 32\n",
" DA_LEARNING_RATE: 5e-05\n",
" LEARNING_RATE: 0.0001\n",
- " MAX_EPOCH: 2\n",
+ " MAX_EPOCH: 5\n",
" NUM_WORKERS: 0\n",
" SEED: 20\n"
]
@@ -315,7 +319,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"id": "56f9f58e",
"metadata": {
"tags": [
@@ -330,9 +334,9 @@
"text": [
"Downloading...\n",
"From (original): https://drive.google.com/uc?id=1ogOcxZn-1q418LOT-gQ94aHQV0Y1sOmk\n",
- "From (redirected): https://drive.google.com/uc?id=1ogOcxZn-1q418LOT-gQ94aHQV0Y1sOmk&confirm=t&uuid=2840f509-90c2-4c19-9458-dd54256f6088\n",
- "To: /content/data/drug-target-interaction.zip\n",
- "100% 78.4M/78.4M [00:00<00:00, 168MB/s]\n"
+ "From (redirected): https://drive.google.com/uc?id=1ogOcxZn-1q418LOT-gQ94aHQV0Y1sOmk&confirm=t&uuid=d01bab20-b2f9-41cb-a5ea-259903d5854c\n",
+ "To: /home/zarizky/projects/mmai-tutorials/tutorials/drug-target-interaction/data/drug-target-interaction.zip\n",
+ "100%|ββββββββββββββββββββββββββββββββββββββ| 88.3M/88.3M [00:00<00:00, 94.5MB/s]\n"
]
}
],
@@ -353,7 +357,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"id": "a6258d1f",
"metadata": {},
"outputs": [
@@ -362,8 +366,8 @@
"output_type": "stream",
"text": [
"Contents of the data folder:\n",
- "biosnap\n",
- "bindingdb\n"
+ "bindingdb\n",
+ "biosnap\n"
]
}
],
@@ -400,7 +404,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"id": "a93303c51c8b974e",
"metadata": {},
"outputs": [
@@ -409,8 +413,8 @@
"output_type": "stream",
"text": [
"Contents of bindingdb folder:\n",
- "random\n",
"interpretation_samples.csv\n",
+ "random\n",
"cluster\n",
"full.csv\n"
]
@@ -509,7 +513,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"id": "0c709e31",
"metadata": {},
"outputs": [
@@ -565,7 +569,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"id": "ae5af8eb",
"metadata": {},
"outputs": [],
@@ -601,7 +605,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"id": "94a15868",
"metadata": {},
"outputs": [
@@ -646,7 +650,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"id": "24ba12b5",
"metadata": {},
"outputs": [
@@ -686,7 +690,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 16,
"id": "b4cf543a",
"metadata": {},
"outputs": [],
@@ -711,7 +715,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 17,
"id": "31b8a93f",
"metadata": {},
"outputs": [
@@ -720,9 +724,9 @@
"output_type": "stream",
"text": [
"First sample from source batch:\n",
- "Drug graph: Data(x=[290, 7], edge_index=[2, 91], edge_attr=[91, 1], num_nodes=290)\n",
- "Protein sequence: tensor([11., 14., 16., ..., 0., 0., 0.], dtype=torch.float64)\n",
- "Label: tensor(0., dtype=torch.float64)\n"
+ "Drug graph: Data(x=[290, 7], edge_index=[2, 50], edge_attr=[50, 1], num_nodes=290)\n",
+ "Protein sequence: tensor([11., 4., 12., ..., 0., 0., 0.], dtype=torch.float64)\n",
+ "Label: tensor(1., dtype=torch.float64)\n"
]
}
],
@@ -782,7 +786,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 18,
"id": "1c8f3acc",
"metadata": {},
"outputs": [
@@ -791,7 +795,7 @@
"output_type": "stream",
"text": [
"DrugBAN(\n",
- " (drug_extractor): MolecularGCN(\n",
+ " (molecular_extractor): MolecularGCN(\n",
" (init_transform): Linear(in_features=7, out_features=128, bias=False)\n",
" (gcn_layers): ModuleList(\n",
" (0-2): 3 x GCNConv(128, 128)\n",
@@ -806,7 +810,7 @@
" (conv3): Conv1d(128, 128, kernel_size=(9,), stride=(1,))\n",
" (bn3): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
- " (bcn): BANLayer(\n",
+ " (bcn): ParametrizedBANLayer(\n",
" (v_net): FCNet(\n",
" (main): Sequential(\n",
" (0): Dropout(p=0.2, inplace=False)\n",
@@ -823,24 +827,30 @@
" )\n",
" (p_net): AvgPool1d(kernel_size=(3,), stride=(3,), padding=(0,))\n",
" (bn): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (parametrizations): ModuleDict(\n",
+ " (h_mat): ParametrizationList(\n",
+ " (0): _WeightNorm()\n",
+ " )\n",
+ " )\n",
" )\n",
" (mlp_classifier): MLPDecoder(\n",
- " (fc1): Linear(in_features=256, out_features=512, bias=True)\n",
- " (bn1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (fc2): Linear(in_features=512, out_features=512, bias=True)\n",
- " (bn2): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (fc3): Linear(in_features=512, out_features=128, bias=True)\n",
- " (bn3): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (fc4): Linear(in_features=128, out_features=2, bias=True)\n",
+ " (model): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): ReLU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=128, bias=True)\n",
+ " (4): ReLU()\n",
+ " (5): Linear(in_features=128, out_features=2, bias=True)\n",
+ " )\n",
" )\n",
")\n"
]
}
],
"source": [
- "from kale.embed.ban import DrugBAN\n",
+ "from kale.embed.model_lib.drugban import DrugBAN\n",
"\n",
- "model = DrugBAN(**cfg)\n",
+ "model = DrugBAN(cfg)\n",
"print(model)"
]
},
@@ -855,7 +865,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 19,
"id": "46e2b9b4",
"metadata": {},
"outputs": [],
@@ -863,7 +873,7 @@
"from kale.pipeline.drugban_trainer import DrugbanTrainer\n",
"\n",
"drugban_trainer = DrugbanTrainer(\n",
- " model=DrugBAN(**cfg),\n",
+ " model=model,\n",
" solver_lr=cfg.SOLVER.LEARNING_RATE,\n",
" num_classes=cfg.DECODER.BINARY,\n",
" batch_size=cfg.SOLVER.BATCH_SIZE,\n",
@@ -889,7 +899,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 20,
"id": "7754bd38",
"metadata": {},
"outputs": [],
@@ -898,8 +908,8 @@
"from pytorch_lightning.callbacks import ModelCheckpoint\n",
"\n",
"checkpoint_cb = ModelCheckpoint(\n",
- " filename=\"{epoch}-{step}-{val_BinaryAUROC:.4f}\",\n",
- " monitor=\"val_BinaryAUROC\",\n",
+ " filename=\"{epoch}-{step}-{valid_BinaryAUROC:.4f}\",\n",
+ " monitor=\"valid_BinaryAUROC\",\n",
" mode=\"max\",\n",
")"
]
@@ -914,7 +924,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 21,
"id": "e68e07bc",
"metadata": {
"tags": [
@@ -926,9 +936,9 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
- "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
- "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
+ "GPU available: True (cuda), used: True\n",
+ "TPU available: False, using: 0 TPU cores\n",
+ "HPU available: False, using: 0 HPUs\n"
]
}
],
@@ -974,7 +984,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 22,
"id": "0624b0c6",
"metadata": {},
"outputs": [
@@ -982,101 +992,50 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
- "INFO:pytorch_lightning.callbacks.model_summary:\n",
+ "You are using a CUDA device ('NVIDIA GeForce RTX 4090') 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"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+ "\n",
" | Name | Type | Params | Mode \n",
"---------------------------------------------------------------------\n",
- "0 | model | DrugBAN | 1.0 M | train\n",
+ "0 | model | DrugBAN | 747 K | train\n",
"1 | domain_discriminator | DomainNetSmallImage | 133 K | train\n",
"2 | random_layer | RandomLayer | 66.0 K | train\n",
"3 | valid_metrics | MetricCollection | 0 | train\n",
"4 | test_metrics | MetricCollection | 0 | train\n",
"---------------------------------------------------------------------\n",
- "1.2 M Trainable params\n",
+ "946 K Trainable params\n",
"0 Non-trainable params\n",
- "1.2 M Total params\n",
- "4.847 Total estimated model params size (MB)\n",
- "64 Modules in train mode\n",
+ "946 K Total params\n",
+ "3.787 Total estimated model params size (MB)\n",
+ "67 Modules in train mode\n",
"0 Modules in eval mode\n"
]
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "9901815e548947d8b664a7be70ed0fe9",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Sanity Checking: | | 0/? [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "c93c19136b484821926f59888165e195",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Training: | | 0/? [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
+ "name": "stdout",
"output_type": "stream",
"text": [
- "[17:56:37] Unusual charge on atom 0 number of radical electrons set to zero\n",
- "[17:56:52] Unusual charge on atom 0 number of radical electrons set to zero\n"
+ "Epoch 4: 100%|ββββββββββ| 305/305 [00:19<00:00, 15.61it/s, v_num=1] "
]
},
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "96b7d503fbca4094ac847a36d3c50a2d",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Validation: | | 0/? [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
{
"name": "stderr",
"output_type": "stream",
"text": [
- "[17:57:26] Unusual charge on atom 0 number of radical electrons set to zero\n",
- "[17:57:48] Unusual charge on atom 0 number of radical electrons set to zero\n"
+ "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
]
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "1d02467474a149a2892e78dc35f08742",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Validation: | | 0/? [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
+ "name": "stdout",
"output_type": "stream",
"text": [
- "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=2` reached.\n"
+ "Epoch 4: 100%|ββββββββββ| 305/305 [00:19<00:00, 15.61it/s, v_num=1]\n"
]
}
],
@@ -1107,7 +1066,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 23,
"id": "c1415c02",
"metadata": {},
"outputs": [
@@ -1115,24 +1074,17 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:pytorch_lightning.utilities.rank_zero:Restoring states from the checkpoint path at /content/lightning_logs/version_3/checkpoints/epoch=1-step=1220-val_BinaryAUROC=0.5209.ckpt\n",
- "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
- "INFO:pytorch_lightning.utilities.rank_zero:Loaded model weights from the checkpoint at /content/lightning_logs/version_3/checkpoints/epoch=1-step=1220-val_BinaryAUROC=0.5209.ckpt\n"
+ "Restoring states from the checkpoint path at /home/zarizky/projects/mmai-tutorials/tutorials/drug-target-interaction/lightning_logs/version_1/checkpoints/epoch=0-step=610-valid_BinaryAUROC=0.5565.ckpt\n",
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+ "Loaded model weights from the checkpoint at /home/zarizky/projects/mmai-tutorials/tutorials/drug-target-interaction/lightning_logs/version_1/checkpoints/epoch=0-step=610-valid_BinaryAUROC=0.5565.ckpt\n"
]
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "40c354c897b74e07b50023a18c57b134",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Testing: | | 0/? [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Testing DataLoader 0: 100%|ββββββββββ| 29/29 [00:00<00:00, 33.86it/s]\n"
+ ]
},
{
"data": {
@@ -1140,18 +1092,18 @@
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- "β test_BinaryAUROC β 0.5208548307418823 β\n",
- "β test_BinaryAccuracy β 0.49503859877586365 β\n",
- "β test_BinaryF1Score β 0.008658008649945259 β\n",
- "β test_BinaryRecall β 0.004395604599267244 β\n",
- "β test_BinarySpecificity β 0.98893803358078 β\n",
- "β test_accuracy_sklearn β 0.5148842334747314 β\n",
- "β test_auroc_sklearn β 0.5208548307418823 β\n",
- "β test_f1_sklearn β 0.6687231063842773 β\n",
- "β test_loss β 1.135701298713684 β\n",
- "β test_optim_threshold β 0.048901185393333435 β\n",
- "β test_sensitivity β 0.044247787445783615 β\n",
- "β test_specificity β 0.9824175834655762 β\n",
+ "β test_BinaryAUROC β 0.556501030921936 β\n",
+ "β test_BinaryAccuracy β 0.5380374789237976 β\n",
+ "β test_BinaryF1Score β 0.44355911016464233 β\n",
+ "β test_BinaryRecall β 0.3670329749584198 β\n",
+ "β test_BinarySpecificity β 0.7101770043373108 β\n",
+ "β test_accuracy_sklearn β 0.5049614310264587 β\n",
+ "β test_auroc_sklearn β 0.556501030921936 β\n",
+ "β test_f1_sklearn β 0.6676556468009949 β\n",
+ "β test_loss β 0.7176582217216492 β\n",
+ "β test_optim_threshold β 0.25262197852134705 β\n",
+ "β test_sensitivity β 0.008849557489156723 β\n",
+ "β test_specificity β 0.997802197933197 β\n",
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"\n"
],
@@ -1159,18 +1111,18 @@
"βββββββββββββββββββββββββββββ³ββββββββββββββββββββββββββββ\n",
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- "β\u001b[36m \u001b[0m\u001b[36m test_BinaryAUROC \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.5208548307418823 \u001b[0m\u001b[35m \u001b[0mβ\n",
- "β\u001b[36m \u001b[0m\u001b[36m test_BinaryAccuracy \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.49503859877586365 \u001b[0m\u001b[35m \u001b[0mβ\n",
- "β\u001b[36m \u001b[0m\u001b[36m test_BinaryF1Score \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.008658008649945259 \u001b[0m\u001b[35m \u001b[0mβ\n",
- "β\u001b[36m \u001b[0m\u001b[36m test_BinaryRecall \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.004395604599267244 \u001b[0m\u001b[35m \u001b[0mβ\n",
- "β\u001b[36m \u001b[0m\u001b[36m test_BinarySpecificity \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.98893803358078 \u001b[0m\u001b[35m \u001b[0mβ\n",
- "β\u001b[36m \u001b[0m\u001b[36m test_accuracy_sklearn \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.5148842334747314 \u001b[0m\u001b[35m \u001b[0mβ\n",
- "β\u001b[36m \u001b[0m\u001b[36m test_auroc_sklearn \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.5208548307418823 \u001b[0m\u001b[35m \u001b[0mβ\n",
- "β\u001b[36m \u001b[0m\u001b[36m test_f1_sklearn \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.6687231063842773 \u001b[0m\u001b[35m \u001b[0mβ\n",
- "β\u001b[36m \u001b[0m\u001b[36m test_loss \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 1.135701298713684 \u001b[0m\u001b[35m \u001b[0mβ\n",
- "β\u001b[36m \u001b[0m\u001b[36m test_optim_threshold \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.048901185393333435 \u001b[0m\u001b[35m \u001b[0mβ\n",
- "β\u001b[36m \u001b[0m\u001b[36m test_sensitivity \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.044247787445783615 \u001b[0m\u001b[35m \u001b[0mβ\n",
- "β\u001b[36m \u001b[0m\u001b[36m test_specificity \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.9824175834655762 \u001b[0m\u001b[35m \u001b[0mβ\n",
+ "β\u001b[36m \u001b[0m\u001b[36m test_BinaryAUROC \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.556501030921936 \u001b[0m\u001b[35m \u001b[0mβ\n",
+ "β\u001b[36m \u001b[0m\u001b[36m test_BinaryAccuracy \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.5380374789237976 \u001b[0m\u001b[35m \u001b[0mβ\n",
+ "β\u001b[36m \u001b[0m\u001b[36m test_BinaryF1Score \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.44355911016464233 \u001b[0m\u001b[35m \u001b[0mβ\n",
+ "β\u001b[36m \u001b[0m\u001b[36m test_BinaryRecall \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.3670329749584198 \u001b[0m\u001b[35m \u001b[0mβ\n",
+ "β\u001b[36m \u001b[0m\u001b[36m test_BinarySpecificity \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.7101770043373108 \u001b[0m\u001b[35m \u001b[0mβ\n",
+ "β\u001b[36m \u001b[0m\u001b[36m test_accuracy_sklearn \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.5049614310264587 \u001b[0m\u001b[35m \u001b[0mβ\n",
+ "β\u001b[36m \u001b[0m\u001b[36m test_auroc_sklearn \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.556501030921936 \u001b[0m\u001b[35m \u001b[0mβ\n",
+ "β\u001b[36m \u001b[0m\u001b[36m test_f1_sklearn \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.6676556468009949 \u001b[0m\u001b[35m \u001b[0mβ\n",
+ "β\u001b[36m \u001b[0m\u001b[36m test_loss \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.7176582217216492 \u001b[0m\u001b[35m \u001b[0mβ\n",
+ "β\u001b[36m \u001b[0m\u001b[36m test_optim_threshold \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.25262197852134705 \u001b[0m\u001b[35m \u001b[0mβ\n",
+ "β\u001b[36m \u001b[0m\u001b[36m test_sensitivity \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.008849557489156723 \u001b[0m\u001b[35m \u001b[0mβ\n",
+ "β\u001b[36m \u001b[0m\u001b[36m test_specificity \u001b[0m\u001b[36m \u001b[0mβ\u001b[35m \u001b[0m\u001b[35m 0.997802197933197 \u001b[0m\u001b[35m \u001b[0mβ\n",
"βββββββββββββββββββββββββββββ΄ββββββββββββββββββββββββββββ\n"
]
},
@@ -1180,18 +1132,18 @@
{
"data": {
"text/plain": [
- "[{'test_loss': 1.135701298713684,\n",
- " 'test_auroc_sklearn': 0.5208548307418823,\n",
- " 'test_accuracy_sklearn': 0.5148842334747314,\n",
- " 'test_f1_sklearn': 0.6687231063842773,\n",
- " 'test_optim_threshold': 0.048901185393333435,\n",
- " 'test_sensitivity': 0.044247787445783615,\n",
- " 'test_specificity': 0.9824175834655762,\n",
- " 'test_BinaryAUROC': 0.5208548307418823,\n",
- " 'test_BinaryF1Score': 0.008658008649945259,\n",
- " 'test_BinaryRecall': 0.004395604599267244,\n",
- " 'test_BinarySpecificity': 0.98893803358078,\n",
- " 'test_BinaryAccuracy': 0.49503859877586365}]"
+ "[{'test_loss': 0.7176582217216492,\n",
+ " 'test_sensitivity': 0.008849557489156723,\n",
+ " 'test_specificity': 0.997802197933197,\n",
+ " 'test_auroc_sklearn': 0.556501030921936,\n",
+ " 'test_accuracy_sklearn': 0.5049614310264587,\n",
+ " 'test_f1_sklearn': 0.6676556468009949,\n",
+ " 'test_optim_threshold': 0.25262197852134705,\n",
+ " 'test_BinaryAUROC': 0.556501030921936,\n",
+ " 'test_BinaryF1Score': 0.44355911016464233,\n",
+ " 'test_BinaryRecall': 0.3670329749584198,\n",
+ " 'test_BinarySpecificity': 0.7101770043373108,\n",
+ " 'test_BinaryAccuracy': 0.5380374789237976}]"
]
},
"execution_count": 23,
@@ -1254,7 +1206,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 24,
"id": "2c67553408592b2",
"metadata": {},
"outputs": [],
@@ -1287,7 +1239,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 25,
"id": "7ef1867541d2577a",
"metadata": {},
"outputs": [],
@@ -1305,7 +1257,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 26,
"id": "c99a558c96a1ffd",
"metadata": {},
"outputs": [],
@@ -1329,7 +1281,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 27,
"id": "3b7f12b12b139799",
"metadata": {},
"outputs": [],
@@ -1337,21 +1289,19 @@
"def get_model_from_ckpt(ckpt_path, config):\n",
" return DrugbanTrainer.load_from_checkpoint(\n",
" checkpoint_path=ckpt_path,\n",
- " model=DrugBAN(**config),\n",
- " solver_lr=config.SOLVER.LEARNING_RATE,\n",
- " num_classes=config.DECODER.BINARY,\n",
- " batch_size=config.SOLVER.BATCH_SIZE,\n",
- " # --- domain adaptation parameters ---\n",
- " is_da=config.DA.USE,\n",
- " solver_da_lr=config.SOLVER.DA_LEARNING_RATE,\n",
- " da_init_epoch=config.DA.INIT_EPOCH,\n",
- " da_method=config.DA.METHOD,\n",
- " original_random=config.DA.ORIGINAL_RANDOM,\n",
- " use_da_entropy=config.DA.USE_ENTROPY,\n",
- " da_random_layer=config.DA.RANDOM_LAYER,\n",
- " # --- discriminator parameters ---\n",
- " da_random_dim=config.DA.RANDOM_DIM,\n",
- " decoder_in_dim=config.DECODER.IN_DIM,\n",
+ " model=DrugBAN(config),\n",
+ " solver_lr=cfg.SOLVER.LEARNING_RATE,\n",
+ " num_classes=cfg.DECODER.BINARY,\n",
+ " batch_size=cfg.SOLVER.BATCH_SIZE,\n",
+ " is_da=cfg.DA.USE,\n",
+ " solver_da_lr=cfg.SOLVER.DA_LEARNING_RATE,\n",
+ " da_init_epoch=cfg.DA.INIT_EPOCH,\n",
+ " da_method=cfg.DA.METHOD,\n",
+ " original_random=cfg.DA.ORIGINAL_RANDOM,\n",
+ " use_da_entropy=cfg.DA.USE_ENTROPY,\n",
+ " da_random_layer=cfg.DA.RANDOM_LAYER,\n",
+ " da_random_dim=cfg.DA.RANDOM_DIM,\n",
+ " decoder_in_dim=cfg.DECODER.IN_DIM,\n",
" )"
]
},
@@ -1365,55 +1315,94 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 28,
"id": "d2a8931099b73c01",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "DrugBAN(\n",
- " (drug_extractor): MolecularGCN(\n",
- " (init_transform): Linear(in_features=7, out_features=128, bias=False)\n",
- " (gcn_layers): ModuleList(\n",
- " (0-2): 3 x GCNConv(128, 128)\n",
+ "DrugbanTrainer(\n",
+ " (model): DrugBAN(\n",
+ " (molecular_extractor): MolecularGCN(\n",
+ " (init_transform): Linear(in_features=7, out_features=128, bias=False)\n",
+ " (gcn_layers): ModuleList(\n",
+ " (0-2): 3 x GCNConv(128, 128)\n",
+ " )\n",
" )\n",
- " )\n",
- " (protein_extractor): ProteinCNN(\n",
- " (embedding): Embedding(26, 128, padding_idx=0)\n",
- " (conv1): Conv1d(128, 128, kernel_size=(3,), stride=(1,))\n",
- " (bn1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (conv2): Conv1d(128, 128, kernel_size=(6,), stride=(1,))\n",
- " (bn2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (conv3): Conv1d(128, 128, kernel_size=(9,), stride=(1,))\n",
- " (bn3): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " )\n",
- " (bcn): BANLayer(\n",
- " (v_net): FCNet(\n",
- " (main): Sequential(\n",
- " (0): Dropout(p=0.2, inplace=False)\n",
- " (1): Linear(in_features=128, out_features=768, bias=True)\n",
- " (2): ReLU()\n",
+ " (protein_extractor): ProteinCNN(\n",
+ " (embedding): Embedding(26, 128, padding_idx=0)\n",
+ " (conv1): Conv1d(128, 128, kernel_size=(3,), stride=(1,))\n",
+ " (bn1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (conv2): Conv1d(128, 128, kernel_size=(6,), stride=(1,))\n",
+ " (bn2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (conv3): Conv1d(128, 128, kernel_size=(9,), stride=(1,))\n",
+ " (bn3): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " )\n",
+ " (bcn): ParametrizedBANLayer(\n",
+ " (v_net): FCNet(\n",
+ " (main): Sequential(\n",
+ " (0): Dropout(p=0.2, inplace=False)\n",
+ " (1): Linear(in_features=128, out_features=768, bias=True)\n",
+ " (2): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (q_net): FCNet(\n",
+ " (main): Sequential(\n",
+ " (0): Dropout(p=0.2, inplace=False)\n",
+ " (1): Linear(in_features=128, out_features=768, bias=True)\n",
+ " (2): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (p_net): AvgPool1d(kernel_size=(3,), stride=(3,), padding=(0,))\n",
+ " (bn): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (parametrizations): ModuleDict(\n",
+ " (h_mat): ParametrizationList(\n",
+ " (0): _WeightNorm()\n",
+ " )\n",
" )\n",
" )\n",
- " (q_net): FCNet(\n",
- " (main): Sequential(\n",
- " (0): Dropout(p=0.2, inplace=False)\n",
- " (1): Linear(in_features=128, out_features=768, bias=True)\n",
- " (2): ReLU()\n",
+ " (mlp_classifier): MLPDecoder(\n",
+ " (model): Sequential(\n",
+ " (0): Linear(in_features=256, out_features=512, bias=True)\n",
+ " (1): ReLU()\n",
+ " (2): Dropout(p=0.1, inplace=False)\n",
+ " (3): Linear(in_features=512, out_features=128, bias=True)\n",
+ " (4): ReLU()\n",
+ " (5): Linear(in_features=128, out_features=2, bias=True)\n",
" )\n",
" )\n",
- " (p_net): AvgPool1d(kernel_size=(3,), stride=(3,), padding=(0,))\n",
- " (bn): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
- " (mlp_classifier): MLPDecoder(\n",
- " (fc1): Linear(in_features=256, out_features=512, bias=True)\n",
- " (bn1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (fc2): Linear(in_features=512, out_features=512, bias=True)\n",
- " (bn2): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (fc3): Linear(in_features=512, out_features=128, bias=True)\n",
- " (bn3): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
- " (fc4): Linear(in_features=128, out_features=2, bias=True)\n",
+ " (domain_discriminator): DomainNetSmallImage(\n",
+ " (fc1): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (bn1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu1): ReLU()\n",
+ " (fc2): Linear(in_features=256, out_features=256, bias=True)\n",
+ " (bn2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu2): ReLU()\n",
+ " (fc3): Linear(in_features=256, out_features=2, bias=True)\n",
+ " )\n",
+ " (random_layer): RandomLayer(\n",
+ " (random_matrix): ParameterList(\n",
+ " (0): Parameter containing: [torch.float32 of size 256x256 (cuda:0)]\n",
+ " (1): Parameter containing: [torch.float32 of size 2x256 (cuda:0)]\n",
+ " )\n",
+ " )\n",
+ " (valid_metrics): MetricCollection(\n",
+ " (BinaryAUROC): BinaryAUROC()\n",
+ " (BinaryF1Score): BinaryF1Score()\n",
+ " (BinaryRecall): BinaryRecall()\n",
+ " (BinarySpecificity): BinarySpecificity()\n",
+ " (BinaryAccuracy): BinaryAccuracy(),\n",
+ " prefix=valid_\n",
+ " )\n",
+ " (test_metrics): MetricCollection(\n",
+ " (BinaryAUROC): BinaryAUROC()\n",
+ " (BinaryF1Score): BinaryF1Score()\n",
+ " (BinaryRecall): BinaryRecall()\n",
+ " (BinarySpecificity): BinarySpecificity()\n",
+ " (BinaryAccuracy): BinaryAccuracy(),\n",
+ " prefix=test_\n",
" )\n",
")"
]
@@ -1426,7 +1415,7 @@
"source": [
"checkpoint_path = \"checkpoint/best.ckpt\"\n",
"model = get_model_from_ckpt(checkpoint_path, cfg)\n",
- "model.model.eval()"
+ "model.eval()"
]
},
{
@@ -1439,7 +1428,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 29,
"id": "781a7762c36c72be",
"metadata": {},
"outputs": [
@@ -1447,7 +1436,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|ββββββββββ| 6/6 [00:00<00:00, 44.70it/s]\n"
+ "100%|ββββββββββ| 6/6 [00:00<00:00, 106.43it/s]\n"
]
},
{
@@ -1536,7 +1525,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 30,
"id": "d3c1d2e4cab69107",
"metadata": {},
"outputs": [],
@@ -1558,7 +1547,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 31,
"id": "7f70a6810c1c5e60",
"metadata": {},
"outputs": [],
@@ -1579,7 +1568,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 32,
"id": "e808c255fe862925",
"metadata": {},
"outputs": [],
@@ -1599,7 +1588,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 33,
"id": "af15baa1c8caabc0",
"metadata": {},
"outputs": [],
@@ -1633,7 +1622,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 34,
"id": "403f77ada0ecc446",
"metadata": {},
"outputs": [],
@@ -1649,7 +1638,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 35,
"id": "b1003372361a66d6",
"metadata": {},
"outputs": [],
@@ -1659,7 +1648,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 36,
"id": "4mHWCbJmGMgG",
"metadata": {
"tags": [
@@ -1669,7 +1658,7 @@
"outputs": [
{
"data": {
- "image/png": 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",
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",
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""
]
@@ -1683,72 +1672,72 @@
"