"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"run = wandb.init(\n",
" entity='grelu', project=project_name, job_type='evaluate', name='evaluate',\n",
" settings=wandb.Settings(\n",
" program_relpath='3_evaluate_model.ipynb',\n",
" program_abspath='/code/github/gReLU-applications/VEP_benchmark/3_evaluate_model.ipynb')\n",
")"
]
},
{
"cell_type": "markdown",
"id": "769cc3a3-dd2e-49be-942f-fd0b8aa40872",
"metadata": {},
"source": [
"## Load dataset"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6e44a90e-a551-4148-89e5-2d359529a7b4",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mwandb\u001b[0m: 1 of 1 files downloaded. \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Selecting training samples\n",
"Keeping 390473 intervals\n",
"\n",
"\n",
"Selecting validation samples\n",
"Keeping 21987 intervals\n",
"\n",
"\n",
"Selecting test samples\n",
"Keeping 22595 intervals\n",
"Final sizes: train: (390473, 3), val: (21987, 3), test: (22595, 3)\n"
]
}
],
"source": [
"artifact = run.use_artifact('dataset:latest')\n",
"dir = artifact.download()\n",
"intervals = read_bed(os.path.join(dir, \"intervals.bed\"))\n",
"\n",
"train, val, test = split(intervals, val_chroms=[\"chr10\"], test_chroms=[\"chr11\"])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "1708a890-0a1b-4475-a6a9-2d01a65223de",
"metadata": {},
"outputs": [],
"source": [
"test_dataset = BigWigSeqDataset(\n",
" intervals = test, \n",
" bw_files=[\"ENCFF093VXI.bigWig\"],\n",
" label_len=1000, label_aggfunc=\"sum\", \n",
" label_transform_func=np.log1p, genome='hg38'\n",
")"
]
},
{
"cell_type": "markdown",
"id": "b64ff302-0636-4e94-bb23-bb7d4b8ec01a",
"metadata": {},
"source": [
"## Load model"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9f3acd4b-7fc9-4c86-9a77-d1080dd19b18",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact model-rotgarkd:v0, 78.49MB. 1 files... \n",
"\u001b[34m\u001b[1mwandb\u001b[0m: 1 of 1 files downloaded. \n",
"Done. 0:0:0.2\n",
"/opt/conda/lib/python3.11/site-packages/lightning_fabric/utilities/cloud_io.py:57: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
"/opt/conda/lib/python3.11/site-packages/pytorch_lightning/utilities/migration/utils.py:56: The loaded checkpoint was produced with Lightning v2.5.0.post0, which is newer than your current Lightning version: v2.3.3\n"
]
}
],
"source": [
"artifact = run.use_artifact('model-rotgarkd:v0')\n",
"dir = artifact.download()\n",
"model = LightningModel.load_from_checkpoint(os.path.join(dir, \"model.ckpt\"))"
]
},
{
"cell_type": "markdown",
"id": "d7b427a0-2a66-45c7-97f9-56236442f62e",
"metadata": {},
"source": [
"## Calculate test metrics"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b9383629-c2da-471f-b1a4-b66e6bb9649e",
"metadata": {},
"outputs": [
{
"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",
"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing DataLoader 0: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 45/45 [00:21<00:00, 2.08it/s]\n"
]
},
{
"data": {
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃ Test metric ┃ DataLoader 0 ┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│ test_loss │ 0.4121752977371216 │\n",
"│ test_mse │ 0.41129639744758606 │\n",
"│ test_pearson │ 0.8056452870368958 │\n",
"└───────────────────────────┴───────────────────────────┘\n",
"\n"
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃\u001b[1m \u001b[0m\u001b[1m Test metric \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m DataLoader 0 \u001b[0m\u001b[1m \u001b[0m┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│\u001b[36m \u001b[0m\u001b[36m test_loss \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.4121752977371216 \u001b[0m\u001b[35m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36m test_mse \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.41129639744758606 \u001b[0m\u001b[35m \u001b[0m│\n",
"│\u001b[36m \u001b[0m\u001b[36m test_pearson \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.8056452870368958 \u001b[0m\u001b[35m \u001b[0m│\n",
"└───────────────────────────┴───────────────────────────┘\n"
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"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" test_mse | \n",
" test_pearson | \n",
"
\n",
" \n",
" \n",
" \n",
" | ENCFF093VXI | \n",
" 0.411296 | \n",
" 0.805645 | \n",
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\n",
" \n",
"
\n",
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View project at: https://wandb.ai/grelu/GM12878_dnase
Synced 6 W&B file(s), 0 media file(s), 2 artifact file(s) and 0 other file(s)"
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"source": [
"run.finish()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d770010c-adc4-4238-b3f8-4c903f39c288",
"metadata": {},
"outputs": [],
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}
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