From f6f562c0b2cd4011482a481765f438878642a803 Mon Sep 17 00:00:00 2001 From: Mikaela Koutrouli Date: Thu, 2 Oct 2025 01:02:39 +0000 Subject: [PATCH 1/6] feat: Quickstart custom data loader (Dask) + API backend options; robust Dask collate; docs update; alias test --- docs/quickstart.ipynb | 7127 +++++++++++++++++++- modlyn/models/_simple_logreg_datamodule.py | 67 +- modlyn/models/_simple_logreg_model.py | 30 +- tests/test_dataset_type_alias.py | 56 + 4 files changed, 7219 insertions(+), 61 deletions(-) create mode 100644 tests/test_dataset_type_alias.py diff --git a/docs/quickstart.ipynb b/docs/quickstart.ipynb index f79efc9..e95811e 100644 --- a/docs/quickstart.ipynb +++ b/docs/quickstart.ipynb @@ -10,14 +10,234 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "f750c5bb", "metadata": { "tags": [ "hide-output" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: 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"Requirement already satisfied: traitlets>=5.1 in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from nbconvert>=7.2.1->lamindb[jupyter]; extra == \"dev\"->modlyn[dev]) (5.14.3)\n", + "Requirement already satisfied: webencodings in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from bleach!=5.0.0->bleach[css]!=5.0.0->nbconvert>=7.2.1->lamindb[jupyter]; extra == \"dev\"->modlyn[dev]) (0.5.1)\n", + "Requirement already satisfied: tinycss2<1.5,>=1.1.0 in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from bleach[css]!=5.0.0->nbconvert>=7.2.1->lamindb[jupyter]; extra == \"dev\"->modlyn[dev]) (1.4.0)\n", + "Requirement already satisfied: jupyter-client>=6.1.12 in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from nbclient>=0.5.0->nbconvert>=7.2.1->lamindb[jupyter]; extra == \"dev\"->modlyn[dev]) (8.6.3)\n", + "Requirement already satisfied: pyzmq>=23.0 in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from 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/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from anyio->httpx<0.29,>=0.26->httpx[http2]<0.29,>=0.26->gotrue<=2.12.0->lamindb_setup==1.10.2->lamindb_setup[aws]==1.10.2->lamindb[jupyter]; extra == \"dev\"->modlyn[dev]) (1.3.1)\n", + "Requirement already satisfied: soupsieve>1.2 in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from beautifulsoup4->nbconvert>=7.2.1->lamindb[jupyter]; extra == \"dev\"->modlyn[dev]) (2.7)\n", + "Requirement already satisfied: zipp>=3.20 in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from importlib-metadata->nbproject==0.11.1->lamindb[jupyter]; extra == \"dev\"->modlyn[dev]) (3.22.0)\n", + "Requirement already satisfied: mdit-py-plugins in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from jupytext->lamindb[jupyter]; extra == \"dev\"->modlyn[dev]) (0.4.2)\n", + "Requirement already satisfied: lightning-utilities<2.0,>=0.10.0 in 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in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from torch->arrayloaders->modlyn[dev]) (3.5)\n", + "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.6.77 in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from torch->arrayloaders->modlyn[dev]) (12.6.77)\n", + "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.6.77 in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from torch->arrayloaders->modlyn[dev]) (12.6.77)\n", + "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.6.80 in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from torch->arrayloaders->modlyn[dev]) (12.6.80)\n", + "Requirement already satisfied: nvidia-cudnn-cu12==9.5.1.17 in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from torch->arrayloaders->modlyn[dev]) (9.5.1.17)\n", + "Requirement already satisfied: nvidia-cublas-cu12==12.6.4.1 in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from 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(from scanpy->modlyn[dev]) (0.5.7)\n", + "Requirement already satisfied: llvmlite<0.45,>=0.44.0dev0 in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from numba>=0.57.1->scanpy->modlyn[dev]) (0.44.0)\n", + "Requirement already satisfied: threadpoolctl>=3.1.0 in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from scikit-learn->modlyn[dev]) (3.6.0)\n", + "Requirement already satisfied: executing>=1.2.0 in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from stack_data->ipython>=7.23.1->ipykernel->nbproject_test->modlyn[dev]) (2.2.0)\n", + "Requirement already satisfied: asttokens>=2.1.0 in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from stack_data->ipython>=7.23.1->ipykernel->nbproject_test->modlyn[dev]) (3.0.0)\n", + "Requirement already satisfied: pure_eval in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from stack_data->ipython>=7.23.1->ipykernel->nbproject_test->modlyn[dev]) (0.2.3)\n", + "\u001b[94m•\u001b[0m resetting django module variables\n", + "\u001b[92m→\u001b[0m connected lamindb: mikelkou/test-modlyn\n" + ] + } + ], "source": [ "!pip install 'modlyn[dev]'\n", "!lamin init --storage test-modlyn" @@ -25,14 +245,18 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "453f6f89", - "metadata": { - "tags": [ - "hide-output" - ] - }, - "outputs": [], + "execution_count": 2, + "id": "35122bdc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[92m→\u001b[0m connected lamindb: mikelkou/test-modlyn\n" + ] + } + ], "source": [ "import lamindb as ln\n", "import modlyn as mn\n", @@ -46,55 +270,165 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "980a05b7", - "metadata": { - "tags": [ - "hide-output" - ] - }, + "execution_count": 3, + "id": "9708b93e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[92m→\u001b[0m loaded Transform('fTv8ikrKUXXJ0000'), re-started Run('Nx3qPK71...') at 2025-10-02 00:27:45 UTC\n", + "\u001b[92m→\u001b[0m notebook imports: arrayloaders==0.0.3 lamindb==1.11.3 modlyn==0.0.7 pandas==2.2.3 scanpy==1.11.2 seaborn==0.13.2\n", + "\u001b[94m•\u001b[0m recommendation: to identify the notebook across renames, pass the uid: ln.track(\"fTv8ikrKUXXJ\")\n" + ] + } + ], + "source": [ + "ln.track()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "fffe8a48", + "metadata": {}, "outputs": [], "source": [ - "ln.track()" + "# Configuration: switch between in-memory and Dask loader\n", + "USE_DASK = True # set False to use in-memory path\n", + "ZARR_UID = \"1xSHIdfBjfUdxKHm0000\" # example UID; change as needed\n", + "LABEL_COL = \"cell_line\"\n", + "\n", + "# Dask runtime\n", + "DASK_DATASET_TYPE = \"arrayloaders-dasd\" # accepted alias (normalized internally)\n", + "BATCH_SIZE = 256\n", + "N_CHUNKS = 8\n", + "DASK_SCHEDULER = \"threads\"" ] }, { "cell_type": "markdown", - "id": "c8ad0ac1", + "id": "5086e159", "metadata": {}, "source": [ - "## Prepare dataset" + "### Using a custom Dask data loader\n", + "Set `USE_DASK = True` and provide a zarr `ZARR_UID` from `laminlabs/arrayloader-benchmarks`.\n", + "The loader auto-detects whether the cached path is a single zarr store or a directory of shard stores (`*.zarr`) and selects the right reader. For quick runs, we cap steps with `max_steps` in the training call.\n" ] }, { "cell_type": "code", - "execution_count": null, - "id": "dfb07f4c", - "metadata": { - "tags": [ - "hide-output" - ] - }, - "outputs": [], + "execution_count": 6, + "id": "30985561", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[92m→\u001b[0m mapped: Artifact(uid='1xSHIdfBjfUdxKHm0000')\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/zarr/codecs/vlen_utf8.py:44: UserWarning: The codec `vlen-utf8` is currently not part in the Zarr format 3 specification. It may not be supported by other zarr implementations and may change in the future.\n", + " return cls(**configuration_parsed)\n", + "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/zarr/codecs/vlen_utf8.py:44: UserWarning: The codec `vlen-utf8` is currently not part in the Zarr format 3 specification. It may not be supported by other zarr implementations and may change in the future.\n", + " return cls(**configuration_parsed)\n", + "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/zarr/codecs/vlen_utf8.py:44: UserWarning: The codec `vlen-utf8` is currently not part in the Zarr format 3 specification. It may not be supported by other zarr implementations and may change in the future.\n", + " return cls(**configuration_parsed)\n", + "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/zarr/codecs/vlen_utf8.py:44: UserWarning: The codec `vlen-utf8` is currently not part in the Zarr format 3 specification. It may not be supported by other zarr implementations and may change in the future.\n", + " return cls(**configuration_parsed)\n", + "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/zarr/codecs/vlen_utf8.py:44: UserWarning: The codec `vlen-utf8` is currently not part in the Zarr format 3 specification. It may not be supported by other zarr implementations and may change in the future.\n", + " return cls(**configuration_parsed)\n", + "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/zarr/codecs/vlen_utf8.py:44: UserWarning: The codec `vlen-utf8` is currently not part in the Zarr format 3 specification. It may not be supported by other zarr implementations and may change in the future.\n", + " return cls(**configuration_parsed)\n", + "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/zarr/codecs/vlen_utf8.py:44: UserWarning: The codec `vlen-utf8` is currently not part in the Zarr format 3 specification. It may not be supported by other zarr implementations and may change in the future.\n", + " return cls(**configuration_parsed)\n", + "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/zarr/codecs/vlen_utf8.py:44: UserWarning: The codec `vlen-utf8` is currently not part in the Zarr format 3 specification. It may not be supported by other zarr implementations and may change in the future.\n", + " return cls(**configuration_parsed)\n", + "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/zarr/codecs/vlen_utf8.py:44: UserWarning: The codec `vlen-utf8` is currently not part in the Zarr format 3 specification. It may not be supported by other zarr implementations and may change in the future.\n", + " return cls(**configuration_parsed)\n", + "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/zarr/codecs/vlen_utf8.py:44: UserWarning: The codec `vlen-utf8` is currently not part in the Zarr format 3 specification. It may not be supported by other zarr implementations and may change in the future.\n", + " return cls(**configuration_parsed)\n", + "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/functools.py:912: ImplicitModificationWarning: Transforming to str index.\n", + " return dispatch(args[0].__class__)(*args, **kw)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "adata: (2096850, 62710)\n" + ] + } + ], "source": [ - "artifact = ln.Artifact.using(\"laminlabs/arrayloader-benchmarks\").get(\n", - " \"JNaxQe8zbljesdbK0000\"\n", - ")\n", - "adata = artifact.load()\n", - "sc.pp.log1p(adata)\n", - "adata" + "from pathlib import Path\n", + "import lamindb as ln\n", + "import modlyn as mn\n", + "\n", + "if USE_DASK:\n", + " artifact = ln.Artifact.using(\"laminlabs/arrayloader-benchmarks\").get(ZARR_UID)\n", + " store_path = Path(artifact.cache())\n", + " assert store_path.is_dir(), f\"UID is not a zarr directory: {store_path}\"\n", + "\n", + " # Decide between a directory of shards (*.zarr) vs a single zarr store\n", + " has_shards = any(child.name.endswith(\".zarr\") for child in store_path.iterdir())\n", + "\n", + " try:\n", + " from arrayloaders.io.dask_loader import read_lazy_store\n", + " except Exception:\n", + " read_lazy_store = None\n", + " from arrayloaders.io import read_lazy as read_single_store\n", + "\n", + " if has_shards and read_lazy_store is not None:\n", + " adata = read_lazy_store(store_path, obs_columns=[LABEL_COL])\n", + " else:\n", + " # Single zarr store\n", + " adata = read_single_store(store_path, obs_columns=[LABEL_COL])\n", + "else:\n", + " # Example H5AD path (keep your current artifact if you prefer)\n", + " artifact = ln.Artifact.using(\"laminlabs/arrayloader-benchmarks\").get(\"JNaxQe8zbljesdbK0000\")\n", + " adata = artifact.load()\n", + " sc.pp.log1p(adata)\n", + "\n", + "print(\"adata:\", adata.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "c8ad0ac1", + "metadata": {}, + "source": [ + "## Prepare dataset" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "1ae9d3e3", "metadata": { "tags": [ "hide-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 2096850 × 62710\n", + " obs: 'cell_line'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "keep = adata.obs[\"cell_line\"].value_counts().loc[lambda x: x > 3].index\n", "adata = adata[adata.obs[\"cell_line\"].isin(keep)].copy()\n", @@ -103,14 +437,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "d23ddc2a", "metadata": { "tags": [ "hide-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "cell_line\n", + "CVCL_1716 4830\n", + "CVCL_1715 3391\n", + "CVCL_1577 3017\n", + "CVCL_1531 827\n", + "CVCL_1571 601\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "adata.obs[\"cell_line\"].value_counts().tail()" ] @@ -125,32 +476,106 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "5a15bcf4", "metadata": { "tags": [ "hide-output" ] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using default `ModelCheckpoint`. Consider installing `litmodels` package to enable `LitModelCheckpoint` for automatic upload to the Lightning model registry.\n", + "GPU available: False, used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + " | Name | Type | Params | Mode \n", + "-----------------------------------------------------------\n", + "0 | linear | Linear | 3.1 M | train\n", + "1 | train_metrics | MetricCollection | 0 | train\n", + "2 | val_metrics | MetricCollection | 0 | train\n", + "-----------------------------------------------------------\n", + "3.1 M Trainable params\n", + "0 Non-trainable params\n", + "3.1 M Total params\n", + "12.542 Total estimated model params size (MB)\n", + "7 Modules in train mode\n", + "0 Modules in eval mode\n", + "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:425: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n", + "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/lightning/pytorch/utilities/data.py:123: Your `IterableDataset` has `__len__` defined. In combination with multi-process data loading (when num_workers > 1), `__len__` could be inaccurate if each worker is not configured independently to avoid having duplicate data.\n", + "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:425: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0: 1%| | 50/8191 [00:04<12:50, 10.57it/s, v_num=31] " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_steps=50` reached.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0: 1%| | 50/8191 [00:04<13:06, 10.35it/s, v_num=31]\n", + "dataset_type: dask-arrayloader\n", + "train_dataset: DaskDataset\n" + ] + } + ], "source": [ "logreg = mn.models.SimpleLogReg(\n", " adata=adata,\n", - " label_column=\"cell_line\",\n", + " label_column=LABEL_COL,\n", " learning_rate=1e-1,\n", " weight_decay=1e-3,\n", ")\n", + "\n", + "fit_kwargs = dict(\n", + " adata_train=adata,\n", + " adata_val=None,\n", + " train_dataloader_kwargs={\"batch_size\": BATCH_SIZE, \"drop_last\": False, \"num_workers\": 0},\n", + " max_epochs=1,\n", + " num_sanity_val_steps=0,\n", + " log_every_n_steps=1,\n", + " max_steps=50,\n", + ")\n", + "\n", + "if USE_DASK:\n", + " fit_kwargs.update(dict(\n", + " dataset_type=DASK_DATASET_TYPE,\n", + " n_chunks=N_CHUNKS,\n", + " dask_scheduler=DASK_SCHEDULER,\n", + " ))\n", + "\n", + "# logreg.fit(**fit_kwargs)\n", "logreg.fit(\n", " adata_train=adata,\n", - " adata_val=adata[:20],\n", - " train_dataloader_kwargs={\"batch_size\": 128, \"drop_last\": True, \"num_workers\": 4},\n", - " max_epochs=5,\n", - ")" + " adata_val=adata, # reuse the lazy dataset so val has batches\n", + " train_dataloader_kwargs={\"batch_size\": BATCH_SIZE, \"drop_last\": False, \"num_workers\": 0},\n", + " dataset_type=DASK_DATASET_TYPE, n_chunks=N_CHUNKS, dask_scheduler=DASK_SCHEDULER,\n", + " max_epochs=1, num_sanity_val_steps=0, log_every_n_steps=1, max_steps=50,\n", + ")\n", + "\n", + "\n", + "print(\"dataset_type:\", getattr(logreg.datamodule, \"dataset_type\", \"in-memory\"))\n", + "print(\"train_dataset:\", type(logreg.datamodule.train_dataloader().dataset).__name__)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "a7164f8a", "metadata": { "lines_to_next_cell": 2, @@ -158,23 +583,5064 @@ "hide-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2025-10-02T00:48:11.532953\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.10.3, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final training loss: 1.4910\n" + ] + } + ], "source": [ "logreg.plot_losses()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "3a322a4f", "metadata": { "tags": [ "hide-output" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weighted F1: 0.942\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2025-10-02T00:48:18.736260\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.10.3, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "logreg.plot_classification_report(adata)" + "# eval subset\n", + "adata_eval = adata[:10000]\n", + "adata_eval = adata_eval.to_memory() if hasattr(adata_eval, \"to_memory\") else adata_eval\n", + "\n", + "if hasattr(adata_eval.X, \"compute\"):\n", + " adata_eval.X = adata_eval.X.compute()\n", + "\n", + "logreg.plot_classification_report(adata_eval)" ] }, { @@ -187,14 +5653,1567 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "0901c6db", "metadata": { "tags": [ "hide-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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gene_nameTSPAN6TNMDDPM1SCYL3C1orf112FGRCFHFUCA2GCLCNFYASTPG1NIPAL3LAS1LENPP4SEMA3FCFTRANKIB1CYP51A1KRIT1RAD52MYH16BADLAP3CD99HS3ST1AOC1WNT16HECW1MAD1L1LASP1SNX11TMEM176AM6PRKLHL13CYP26B1ICA1DBNDD1ALS2CASP10CFLARTFPINDUFAF7RBM5MTMR7SLC7A2ARF5SARM1POLDIP2PLXND1AK2CD38FKBP4KDM1ARBM6CAMKK1RECQLVPS50HSPB6ARHGAP33NDUFAB1PDK4SLC22A16ZMYND10ABCB5ARXSLC25A13ST7CDC27SLC4A1CALCRHCCSDVL2PRSS22UPF1SKAP2SLC25A5MCUBHOXA11POLR2JDHX33MEOX1THSD7ALIG3RPAP3ACSM3REXO5CIAPIN1SPPL2BATOSBCOPZ2PRKAR2BMSL3CREBBPTSPOAP1MPOPON1GCFC2WDR54CROTABCB4...DPY19L2P3-1ENSG00000291214ENSG00000291215ENSG00000291216LINC01347-1ENSG00000291218ENSG00000291219ENSG00000291220ENSG00000291221ENSG00000291222FAM86DP-1ENSG00000291224ENSG00000291225ENSG00000291226ENSG00000291227ENSG00000291228ENSG00000291229ENSG00000291230ENSG00000291231ENSG00000291232ENSG00000291233ENSG00000291234ENSG00000291235ENSG00000291236SOD2ENSG00000291238ENSG00000291239ENSG00000291240ENSG00000291241ENSG00000291242ENSG00000291243ENSG00000291246ENSG00000291247ENSG00000291248ENSG00000291249ENSG00000291250ENSG00000291251ENSG00000291252ENSG00000291253ENSG00000291254ENSG00000291255ENSG00000291256ENSG00000291257ENSG00000291258ENSG00000291259ENSG00000291260ENSG00000291261ENSG00000291262SMG1P7-1ENSG00000291264ENSG00000291265SMG1P5-1ENSG00000291267ENSG00000291268ENSG00000291269ENSG00000291270ENSG00000291271ENSG00000291272ENSG00000291273ENSG00000291274ENSG00000291275ENSG00000291276ENSG00000291277ENSG00000291278ENSG00000291279ANKRD20A11P-1COL6A4P1-1ENSG00000291282ENSG00000291283ENSG00000291284ENSG00000291285ENSG00000291286ENSG00000291287ENSG00000291288ENSG00000291289ENSG00000291290ENSG00000291291ENSG00000291292ENSG00000291293ENSG00000291294ENSG00000291295ENSG00000291296ENSG00000291297ENSG00000291298ENSG00000291299PRSS30P-1ENSG00000291301ENSG00000291302ENSG00000291303ENSG00000291306POLGARFENSG00000291308LY6SENSG00000291310ENSG00000291312ENSG00000291313ENSG00000291314ENSG00000291315ENSG00000291316TMEM276
CVCL_0023-0.072169-0.001782-0.2534200.065178-0.105779-0.001987-0.1614860.0647260.345648-0.1223560.030515-0.382999-0.4092560.074057-0.0002600.047803-0.146368-0.086480-0.2316880.0910470.0043950.3623390.1678880.7701460.0148160.000374-0.003875-0.234518-0.1649760.402599-0.046038-0.0059360.205344-0.139414-0.0476200.0728200.0456140.1815480.0846200.2671230.4351460.0259760.0799840.013431-0.3610210.0817370.1649640.004023-0.312792-0.0044730.4703130.161411-0.1535400.4752480.010871-0.2874850.3242690.003048-0.0619860.197627-0.1974410.0021820.052494-0.0024870.0023760.0274700.1149130.560219-0.0032400.050618-0.1369220.110338-0.001225-0.3603740.1927020.4321350.0685880.0592470.056717-0.136761-0.0038310.029181-0.0983330.5977480.056844-0.0352460.149224-0.090831-0.005264-0.081047-0.0051190.0516070.054285-0.0016860.002799-0.065433-0.0463580.0600870.1437050.045878...-0.0037500.656235-0.0017500.0041260.053257-0.0019640.0010870.0037870.0020000.0035520.0315060.099230-0.0356130.0041440.004135-0.038874-0.0038800.0026570.0032210.050653-0.0039330.0715620.001616-0.0030590.761539-0.004090-0.002690-0.0030760.0034570.0028300.0035220.002355-0.0025430.003614-0.0016960.1254830.003966-0.0033630.004107-0.001464-0.001881-0.0035520.003171-0.063697-0.082815-0.0040630.001957-0.0032360.0020360.0039820.0828030.0040910.000484-0.0040830.0025240.002997-0.0030880.050780-0.003394-0.004073-0.003045-0.0603530.004143-0.0030000.0035740.003350-0.0015050.002071-0.0433470.0041410.0019070.004628-0.002736-0.005997-0.003575-0.0037470.003019-0.003068-0.0020280.002594-0.004082-0.0039680.0024900.004005-0.015930-0.0040400.005229-0.0020810.0034680.0040930.0022050.003866-0.0041290.0029840.0041070.001963-0.0026400.003367-0.0681250.133424
CVCL_00280.306921-0.002901-0.225597-0.080660-0.275207-0.0041320.0399440.1431890.1749080.4467710.060059-0.0493020.2694680.058031-0.0363790.0120420.293526-0.098515-0.202047-0.032171-0.0029660.1811760.0595890.0371350.0423810.005949-0.003735-0.0631530.012087-0.0435240.1086120.004447-0.0303430.179612-0.010053-0.0598770.1713650.515425-0.218428-0.086147-0.5248100.1777060.2706250.004328-0.1935470.0149730.0393880.038271-0.1701720.164602-0.0696720.5112160.500238-0.0820940.015317-0.0231030.5886730.0111110.0706270.2120941.0545280.0036260.0064370.0034370.004122-0.0778710.5358280.210519-0.003807-0.2998620.0364920.241763-0.0230910.0510820.418347-0.0845860.2996410.3186380.169223-0.0448880.001771-0.2551890.0846870.139335-0.070708-0.108545-0.089131-0.068518-0.009832-0.0107550.7797750.2149160.056942-0.038249-0.002025-0.003097-0.110921-0.108925-0.0816270.001308...-0.0037700.1247980.003998-0.001950-0.004137-0.004073-0.004114-0.002038-0.003907-0.0037780.008693-0.1929370.0710390.002892-0.003073-0.080847-0.002272-0.002362-0.0040670.006993-0.0038340.0819130.0034980.0032680.477050-0.0002010.0041400.0036230.0040540.002968-0.0016950.002687-0.003500-0.0041000.0000720.0030610.0035240.004143-0.003826-0.0040300.003037-0.0042650.004145-0.0090800.0006620.003560-0.003709-0.0039180.002405-0.0025130.0721580.003224-0.001988-0.0028220.003976-0.003918-0.0040460.032906-0.0040210.003200-0.0031310.001933-0.0032260.0041300.0040990.003286-0.0016340.0039970.0852310.002271-0.0020960.003251-0.0034790.002048-0.002852-0.003361-0.002924-0.0039540.0037820.003364-0.068236-0.002282-0.050574-0.001686-0.0153400.003399-0.002855-0.004039-0.0020150.003972-0.0040530.002547-0.0020440.0018450.0040810.002652-0.002895-0.0051170.0026250.058085
CVCL_0069-0.1235700.0018390.244393-0.0426030.0664650.003361-0.0477540.098614-0.358676-0.3634470.1251700.0478640.2334950.0832350.008239-0.0258950.5564790.4456030.309886-0.280547-0.0382390.0602700.8039820.099297-0.018322-0.010834-0.003841-0.1096620.060092-0.1545420.297341-0.006893-0.193492-0.0201210.003154-0.0064980.006772-0.179057-0.057365-0.394072-0.3490710.1044130.380096-0.012639-0.3160770.1257430.0647770.392404-0.0069250.6012720.0039440.121724-0.0433880.451930-0.1566620.1440570.069286-0.003442-0.0416670.0487890.0136040.0037620.0005070.125233-0.0039230.4748620.7934520.0664220.002904-0.007232-0.009361-0.128384-0.0292730.1084390.3745030.486061-0.3792360.2890510.074861-0.133407-0.003127-0.0372950.235137-0.1036750.0271220.106405-0.0580630.083961-0.0530410.055750-0.011632-0.0508700.304672-0.001997-0.0039190.0070570.2254140.108640-0.156228-0.052544...-0.003686-0.056917-0.0073300.003102-0.0735850.0024760.002856-0.004048-0.0035740.0038540.1807940.342395-0.0019260.0016940.077227-0.0308160.0040380.0029550.003726-0.1412440.0017390.0035150.0039840.0025500.1554680.0041010.0040520.0030610.002814-0.003999-0.001535-0.0021600.0019520.0033490.002296-0.003495-0.003340-0.001963-0.0028140.0034490.004021-0.0017470.1458310.0041880.067262-0.0032550.003638-0.0017920.002539-0.001929-0.032060-0.0040660.0032670.0040780.002887-0.004117-0.004144-0.009988-0.0017700.0036660.002685-0.035642-0.003014-0.0037310.005002-0.0040630.100089-0.0030830.0193180.002333-0.003262-0.0041270.003985-0.0064530.003740-0.003595-0.003837-0.003692-0.042858-0.004018-0.003626-0.003161-0.0056510.0030270.1588410.0027540.004099-0.0030130.004096-0.003516-0.002437-0.0036980.002617-0.003119-0.0024640.003158-0.003258-0.0035610.002598-0.018202
CVCL_00990.000533-0.0019290.130884-0.0179270.119897-0.0038750.6727500.177732-0.4507010.2249510.0705040.006495-0.112005-0.0325010.0054560.137025-0.0848770.2655300.122823-0.1448790.003694-0.0103570.0594680.336300-0.2487600.0705480.0016550.014640-0.154495-0.1995550.167132-0.0024670.383384-0.0398290.013198-0.2356650.059897-0.1837390.137194-0.291918-0.6065860.0916000.3134320.0073870.0141490.020131-0.015974-0.038441-0.1764560.5242710.005447-0.0636170.5188210.341604-0.015890-0.1727170.3245330.003926-0.0011590.1604330.095137-0.0031010.0043210.067483-0.0035180.4033260.3901310.143473-0.0041140.001951-0.013188-0.085788-0.092613-0.290897-0.3889990.1076580.4010150.0683890.0634220.148893-0.003982-0.388173-0.0666540.309218-0.0182550.128790-0.0581700.056880-0.044876-0.0050380.064484-0.0563290.495379-0.0027430.0010410.0065300.237378-0.0127700.0114760.006442...-0.004126-0.0489570.0063010.003479-0.003755-0.004462-0.003259-0.001755-0.0035230.002641-0.002951-0.0406280.0017060.003507-0.001921-0.1253190.0032350.002771-0.0032850.000249-0.0034320.002787-0.003993-0.003724-0.4287160.003999-0.0013460.003918-0.003958-0.004054-0.0033140.002418-0.004038-0.002467-0.0039560.0029990.002918-0.0040230.002882-0.003847-0.0040210.0005150.0037860.0054710.001008-0.003971-0.0025430.0030850.0037510.004145-0.022156-0.0040700.0030360.004141-0.004145-0.004046-0.0026650.0065730.003033-0.002204-0.0041430.0675230.003215-0.004054-0.0041450.068204-0.0039870.0041450.0738040.0040550.0033430.0029210.0027590.004370-0.0031510.0026850.003661-0.003519-0.003449-0.0020480.003239-0.0032740.001423-0.003236-0.0920330.0036600.0041180.002830-0.003579-0.002627-0.0040460.0017560.0030390.004139-0.0015380.0040020.003594-0.001621-0.0024190.071307
CVCL_01310.078440-0.003691-0.1256080.007751-0.030093-0.003500-0.2116040.1676890.015605-0.0726000.068769-0.057561-0.0022620.055009-0.005387-0.3151810.789768-0.0235970.2221010.440693-0.053264-0.009269-0.2250960.869027-0.0022200.0049310.0019050.0224280.0391810.361790-0.0995770.0026790.070637-0.0045380.038766-0.1533090.0396900.1400310.0982210.2292201.044028-0.0045900.4074560.008790-0.054797-0.010550-0.0481290.0463050.5739790.2065100.0083270.0949680.1044200.0376430.3903140.3137670.027392-0.005515-0.073887-0.037684-0.009270-0.0021550.0035050.0037380.003165-0.102963-0.2262350.2078090.004145-0.003772-0.0214800.029349-0.0090360.2210060.042272-0.3484070.3550400.0046780.1654070.1039510.0020230.031724-0.0834560.035964-0.0021200.0801340.053113-0.008261-0.0358910.0015440.056233-0.0160630.4348280.0689080.0040320.006045-0.168804-0.0507880.0919560.006270...0.0030510.3005950.006620-0.003971-0.0038360.0033860.003836-0.0030630.0029370.002996-0.0074320.126000-0.003158-0.0030050.002024-0.0664610.001801-0.003225-0.002640-0.004204-0.003547-0.003569-0.0039790.0030220.4555130.004144-0.0040930.0035020.003487-0.003562-0.003052-0.003508-0.004042-0.0041400.0036780.002455-0.0019930.004060-0.0026400.0034990.0022030.0038870.0037970.0687130.059885-0.0028830.0033820.0029040.003911-0.0041120.2297690.0021200.0016830.0011070.0028240.001739-0.004120-0.009454-0.0058480.003198-0.0029900.004122-0.0041440.0036980.0041430.003976-0.0032510.002224-0.0206430.003008-0.0036550.0035610.0022580.0022910.003167-0.0029340.003680-0.003282-0.002264-0.0017510.001896-0.002303-0.003239-0.004094-0.0186380.0006890.0036670.0041140.004097-0.0033330.0039000.002991-0.004070-0.002420-0.003431-0.0018830.0036920.0019360.0036580.008886
\n", + "

5 rows × 62710 columns

\n", + "
" + ], + "text/plain": [ + "gene_name TSPAN6 TNMD DPM1 SCYL3 C1orf112 FGR \\\n", + "CVCL_0023 -0.072169 -0.001782 -0.253420 0.065178 -0.105779 -0.001987 \n", + "CVCL_0028 0.306921 -0.002901 -0.225597 -0.080660 -0.275207 -0.004132 \n", + "CVCL_0069 -0.123570 0.001839 0.244393 -0.042603 0.066465 0.003361 \n", + "CVCL_0099 0.000533 -0.001929 0.130884 -0.017927 0.119897 -0.003875 \n", + "CVCL_0131 0.078440 -0.003691 -0.125608 0.007751 -0.030093 -0.003500 \n", + "\n", + "gene_name CFH FUCA2 GCLC NFYA STPG1 NIPAL3 \\\n", + "CVCL_0023 -0.161486 0.064726 0.345648 -0.122356 0.030515 -0.382999 \n", + "CVCL_0028 0.039944 0.143189 0.174908 0.446771 0.060059 -0.049302 \n", + "CVCL_0069 -0.047754 0.098614 -0.358676 -0.363447 0.125170 0.047864 \n", + "CVCL_0099 0.672750 0.177732 -0.450701 0.224951 0.070504 0.006495 \n", + "CVCL_0131 -0.211604 0.167689 0.015605 -0.072600 0.068769 -0.057561 \n", + "\n", + "gene_name LAS1L ENPP4 SEMA3F CFTR ANKIB1 CYP51A1 \\\n", + "CVCL_0023 -0.409256 0.074057 -0.000260 0.047803 -0.146368 -0.086480 \n", + "CVCL_0028 0.269468 0.058031 -0.036379 0.012042 0.293526 -0.098515 \n", + "CVCL_0069 0.233495 0.083235 0.008239 -0.025895 0.556479 0.445603 \n", + "CVCL_0099 -0.112005 -0.032501 0.005456 0.137025 -0.084877 0.265530 \n", + "CVCL_0131 -0.002262 0.055009 -0.005387 -0.315181 0.789768 -0.023597 \n", + "\n", + "gene_name KRIT1 RAD52 MYH16 BAD LAP3 CD99 \\\n", + "CVCL_0023 -0.231688 0.091047 0.004395 0.362339 0.167888 0.770146 \n", + "CVCL_0028 -0.202047 -0.032171 -0.002966 0.181176 0.059589 0.037135 \n", + "CVCL_0069 0.309886 -0.280547 -0.038239 0.060270 0.803982 0.099297 \n", + "CVCL_0099 0.122823 -0.144879 0.003694 -0.010357 0.059468 0.336300 \n", + "CVCL_0131 0.222101 0.440693 -0.053264 -0.009269 -0.225096 0.869027 \n", + "\n", + "gene_name HS3ST1 AOC1 WNT16 HECW1 MAD1L1 LASP1 \\\n", + "CVCL_0023 0.014816 0.000374 -0.003875 -0.234518 -0.164976 0.402599 \n", + "CVCL_0028 0.042381 0.005949 -0.003735 -0.063153 0.012087 -0.043524 \n", + "CVCL_0069 -0.018322 -0.010834 -0.003841 -0.109662 0.060092 -0.154542 \n", + "CVCL_0099 -0.248760 0.070548 0.001655 0.014640 -0.154495 -0.199555 \n", + "CVCL_0131 -0.002220 0.004931 0.001905 0.022428 0.039181 0.361790 \n", + "\n", + "gene_name SNX11 TMEM176A M6PR KLHL13 CYP26B1 ICA1 \\\n", + "CVCL_0023 -0.046038 -0.005936 0.205344 -0.139414 -0.047620 0.072820 \n", + "CVCL_0028 0.108612 0.004447 -0.030343 0.179612 -0.010053 -0.059877 \n", + "CVCL_0069 0.297341 -0.006893 -0.193492 -0.020121 0.003154 -0.006498 \n", + "CVCL_0099 0.167132 -0.002467 0.383384 -0.039829 0.013198 -0.235665 \n", + "CVCL_0131 -0.099577 0.002679 0.070637 -0.004538 0.038766 -0.153309 \n", + "\n", + "gene_name DBNDD1 ALS2 CASP10 CFLAR TFPI NDUFAF7 \\\n", + "CVCL_0023 0.045614 0.181548 0.084620 0.267123 0.435146 0.025976 \n", + "CVCL_0028 0.171365 0.515425 -0.218428 -0.086147 -0.524810 0.177706 \n", + "CVCL_0069 0.006772 -0.179057 -0.057365 -0.394072 -0.349071 0.104413 \n", + "CVCL_0099 0.059897 -0.183739 0.137194 -0.291918 -0.606586 0.091600 \n", + "CVCL_0131 0.039690 0.140031 0.098221 0.229220 1.044028 -0.004590 \n", + "\n", + "gene_name RBM5 MTMR7 SLC7A2 ARF5 SARM1 POLDIP2 \\\n", + "CVCL_0023 0.079984 0.013431 -0.361021 0.081737 0.164964 0.004023 \n", + "CVCL_0028 0.270625 0.004328 -0.193547 0.014973 0.039388 0.038271 \n", + "CVCL_0069 0.380096 -0.012639 -0.316077 0.125743 0.064777 0.392404 \n", + "CVCL_0099 0.313432 0.007387 0.014149 0.020131 -0.015974 -0.038441 \n", + "CVCL_0131 0.407456 0.008790 -0.054797 -0.010550 -0.048129 0.046305 \n", + "\n", + "gene_name PLXND1 AK2 CD38 FKBP4 KDM1A RBM6 \\\n", + "CVCL_0023 -0.312792 -0.004473 0.470313 0.161411 -0.153540 0.475248 \n", + "CVCL_0028 -0.170172 0.164602 -0.069672 0.511216 0.500238 -0.082094 \n", + "CVCL_0069 -0.006925 0.601272 0.003944 0.121724 -0.043388 0.451930 \n", + "CVCL_0099 -0.176456 0.524271 0.005447 -0.063617 0.518821 0.341604 \n", + "CVCL_0131 0.573979 0.206510 0.008327 0.094968 0.104420 0.037643 \n", + "\n", + "gene_name CAMKK1 RECQL VPS50 HSPB6 ARHGAP33 NDUFAB1 \\\n", + "CVCL_0023 0.010871 -0.287485 0.324269 0.003048 -0.061986 0.197627 \n", + "CVCL_0028 0.015317 -0.023103 0.588673 0.011111 0.070627 0.212094 \n", + "CVCL_0069 -0.156662 0.144057 0.069286 -0.003442 -0.041667 0.048789 \n", + "CVCL_0099 -0.015890 -0.172717 0.324533 0.003926 -0.001159 0.160433 \n", + "CVCL_0131 0.390314 0.313767 0.027392 -0.005515 -0.073887 -0.037684 \n", + "\n", + "gene_name PDK4 SLC22A16 ZMYND10 ABCB5 ARX SLC25A13 \\\n", + "CVCL_0023 -0.197441 0.002182 0.052494 -0.002487 0.002376 0.027470 \n", + "CVCL_0028 1.054528 0.003626 0.006437 0.003437 0.004122 -0.077871 \n", + "CVCL_0069 0.013604 0.003762 0.000507 0.125233 -0.003923 0.474862 \n", + "CVCL_0099 0.095137 -0.003101 0.004321 0.067483 -0.003518 0.403326 \n", + "CVCL_0131 -0.009270 -0.002155 0.003505 0.003738 0.003165 -0.102963 \n", + "\n", + "gene_name ST7 CDC27 SLC4A1 CALCR HCCS DVL2 \\\n", + "CVCL_0023 0.114913 0.560219 -0.003240 0.050618 -0.136922 0.110338 \n", + "CVCL_0028 0.535828 0.210519 -0.003807 -0.299862 0.036492 0.241763 \n", + "CVCL_0069 0.793452 0.066422 0.002904 -0.007232 -0.009361 -0.128384 \n", + "CVCL_0099 0.390131 0.143473 -0.004114 0.001951 -0.013188 -0.085788 \n", + "CVCL_0131 -0.226235 0.207809 0.004145 -0.003772 -0.021480 0.029349 \n", + "\n", + "gene_name PRSS22 UPF1 SKAP2 SLC25A5 MCUB HOXA11 \\\n", + "CVCL_0023 -0.001225 -0.360374 0.192702 0.432135 0.068588 0.059247 \n", + "CVCL_0028 -0.023091 0.051082 0.418347 -0.084586 0.299641 0.318638 \n", + "CVCL_0069 -0.029273 0.108439 0.374503 0.486061 -0.379236 0.289051 \n", + "CVCL_0099 -0.092613 -0.290897 -0.388999 0.107658 0.401015 0.068389 \n", + "CVCL_0131 -0.009036 0.221006 0.042272 -0.348407 0.355040 0.004678 \n", + "\n", + "gene_name POLR2J DHX33 MEOX1 THSD7A LIG3 RPAP3 \\\n", + "CVCL_0023 0.056717 -0.136761 -0.003831 0.029181 -0.098333 0.597748 \n", + "CVCL_0028 0.169223 -0.044888 0.001771 -0.255189 0.084687 0.139335 \n", + "CVCL_0069 0.074861 -0.133407 -0.003127 -0.037295 0.235137 -0.103675 \n", + "CVCL_0099 0.063422 0.148893 -0.003982 -0.388173 -0.066654 0.309218 \n", + "CVCL_0131 0.165407 0.103951 0.002023 0.031724 -0.083456 0.035964 \n", + "\n", + "gene_name ACSM3 REXO5 CIAPIN1 SPPL2B ATOSB COPZ2 \\\n", + "CVCL_0023 0.056844 -0.035246 0.149224 -0.090831 -0.005264 -0.081047 \n", + "CVCL_0028 -0.070708 -0.108545 -0.089131 -0.068518 -0.009832 -0.010755 \n", + "CVCL_0069 0.027122 0.106405 -0.058063 0.083961 -0.053041 0.055750 \n", + "CVCL_0099 -0.018255 0.128790 -0.058170 0.056880 -0.044876 -0.005038 \n", + "CVCL_0131 -0.002120 0.080134 0.053113 -0.008261 -0.035891 0.001544 \n", + "\n", + "gene_name PRKAR2B MSL3 CREBBP TSPOAP1 MPO PON1 \\\n", + "CVCL_0023 -0.005119 0.051607 0.054285 -0.001686 0.002799 -0.065433 \n", + "CVCL_0028 0.779775 0.214916 0.056942 -0.038249 -0.002025 -0.003097 \n", + "CVCL_0069 -0.011632 -0.050870 0.304672 -0.001997 -0.003919 0.007057 \n", + "CVCL_0099 0.064484 -0.056329 0.495379 -0.002743 0.001041 0.006530 \n", + "CVCL_0131 0.056233 -0.016063 0.434828 0.068908 0.004032 0.006045 \n", + "\n", + "gene_name GCFC2 WDR54 CROT ABCB4 ... DPY19L2P3-1 \\\n", + "CVCL_0023 -0.046358 0.060087 0.143705 0.045878 ... -0.003750 \n", + "CVCL_0028 -0.110921 -0.108925 -0.081627 0.001308 ... -0.003770 \n", + "CVCL_0069 0.225414 0.108640 -0.156228 -0.052544 ... -0.003686 \n", + "CVCL_0099 0.237378 -0.012770 0.011476 0.006442 ... -0.004126 \n", + "CVCL_0131 -0.168804 -0.050788 0.091956 0.006270 ... 0.003051 \n", + "\n", + "gene_name ENSG00000291214 ENSG00000291215 ENSG00000291216 LINC01347-1 \\\n", + "CVCL_0023 0.656235 -0.001750 0.004126 0.053257 \n", + "CVCL_0028 0.124798 0.003998 -0.001950 -0.004137 \n", + "CVCL_0069 -0.056917 -0.007330 0.003102 -0.073585 \n", + "CVCL_0099 -0.048957 0.006301 0.003479 -0.003755 \n", + "CVCL_0131 0.300595 0.006620 -0.003971 -0.003836 \n", + "\n", + "gene_name ENSG00000291218 ENSG00000291219 ENSG00000291220 ENSG00000291221 \\\n", + "CVCL_0023 -0.001964 0.001087 0.003787 0.002000 \n", + "CVCL_0028 -0.004073 -0.004114 -0.002038 -0.003907 \n", + "CVCL_0069 0.002476 0.002856 -0.004048 -0.003574 \n", + "CVCL_0099 -0.004462 -0.003259 -0.001755 -0.003523 \n", + "CVCL_0131 0.003386 0.003836 -0.003063 0.002937 \n", + "\n", + "gene_name ENSG00000291222 FAM86DP-1 ENSG00000291224 ENSG00000291225 \\\n", + "CVCL_0023 0.003552 0.031506 0.099230 -0.035613 \n", + "CVCL_0028 -0.003778 0.008693 -0.192937 0.071039 \n", + "CVCL_0069 0.003854 0.180794 0.342395 -0.001926 \n", + "CVCL_0099 0.002641 -0.002951 -0.040628 0.001706 \n", + "CVCL_0131 0.002996 -0.007432 0.126000 -0.003158 \n", + "\n", + "gene_name ENSG00000291226 ENSG00000291227 ENSG00000291228 ENSG00000291229 \\\n", + "CVCL_0023 0.004144 0.004135 -0.038874 -0.003880 \n", + "CVCL_0028 0.002892 -0.003073 -0.080847 -0.002272 \n", + "CVCL_0069 0.001694 0.077227 -0.030816 0.004038 \n", + "CVCL_0099 0.003507 -0.001921 -0.125319 0.003235 \n", + "CVCL_0131 -0.003005 0.002024 -0.066461 0.001801 \n", + "\n", + "gene_name ENSG00000291230 ENSG00000291231 ENSG00000291232 ENSG00000291233 \\\n", + "CVCL_0023 0.002657 0.003221 0.050653 -0.003933 \n", + "CVCL_0028 -0.002362 -0.004067 0.006993 -0.003834 \n", + "CVCL_0069 0.002955 0.003726 -0.141244 0.001739 \n", + "CVCL_0099 0.002771 -0.003285 0.000249 -0.003432 \n", + "CVCL_0131 -0.003225 -0.002640 -0.004204 -0.003547 \n", + "\n", + "gene_name ENSG00000291234 ENSG00000291235 ENSG00000291236 SOD2 \\\n", + "CVCL_0023 0.071562 0.001616 -0.003059 0.761539 \n", + "CVCL_0028 0.081913 0.003498 0.003268 0.477050 \n", + "CVCL_0069 0.003515 0.003984 0.002550 0.155468 \n", + "CVCL_0099 0.002787 -0.003993 -0.003724 -0.428716 \n", + "CVCL_0131 -0.003569 -0.003979 0.003022 0.455513 \n", + "\n", + "gene_name ENSG00000291238 ENSG00000291239 ENSG00000291240 ENSG00000291241 \\\n", + "CVCL_0023 -0.004090 -0.002690 -0.003076 0.003457 \n", + "CVCL_0028 -0.000201 0.004140 0.003623 0.004054 \n", + "CVCL_0069 0.004101 0.004052 0.003061 0.002814 \n", + "CVCL_0099 0.003999 -0.001346 0.003918 -0.003958 \n", + "CVCL_0131 0.004144 -0.004093 0.003502 0.003487 \n", + "\n", + "gene_name ENSG00000291242 ENSG00000291243 ENSG00000291246 ENSG00000291247 \\\n", + "CVCL_0023 0.002830 0.003522 0.002355 -0.002543 \n", + "CVCL_0028 0.002968 -0.001695 0.002687 -0.003500 \n", + "CVCL_0069 -0.003999 -0.001535 -0.002160 0.001952 \n", + "CVCL_0099 -0.004054 -0.003314 0.002418 -0.004038 \n", + "CVCL_0131 -0.003562 -0.003052 -0.003508 -0.004042 \n", + "\n", + "gene_name ENSG00000291248 ENSG00000291249 ENSG00000291250 ENSG00000291251 \\\n", + "CVCL_0023 0.003614 -0.001696 0.125483 0.003966 \n", + "CVCL_0028 -0.004100 0.000072 0.003061 0.003524 \n", + "CVCL_0069 0.003349 0.002296 -0.003495 -0.003340 \n", + "CVCL_0099 -0.002467 -0.003956 0.002999 0.002918 \n", + "CVCL_0131 -0.004140 0.003678 0.002455 -0.001993 \n", + "\n", + "gene_name ENSG00000291252 ENSG00000291253 ENSG00000291254 ENSG00000291255 \\\n", + "CVCL_0023 -0.003363 0.004107 -0.001464 -0.001881 \n", + "CVCL_0028 0.004143 -0.003826 -0.004030 0.003037 \n", + "CVCL_0069 -0.001963 -0.002814 0.003449 0.004021 \n", + "CVCL_0099 -0.004023 0.002882 -0.003847 -0.004021 \n", + "CVCL_0131 0.004060 -0.002640 0.003499 0.002203 \n", + "\n", + "gene_name ENSG00000291256 ENSG00000291257 ENSG00000291258 ENSG00000291259 \\\n", + "CVCL_0023 -0.003552 0.003171 -0.063697 -0.082815 \n", + "CVCL_0028 -0.004265 0.004145 -0.009080 0.000662 \n", + "CVCL_0069 -0.001747 0.145831 0.004188 0.067262 \n", + "CVCL_0099 0.000515 0.003786 0.005471 0.001008 \n", + "CVCL_0131 0.003887 0.003797 0.068713 0.059885 \n", + "\n", + "gene_name ENSG00000291260 ENSG00000291261 ENSG00000291262 SMG1P7-1 \\\n", + "CVCL_0023 -0.004063 0.001957 -0.003236 0.002036 \n", + "CVCL_0028 0.003560 -0.003709 -0.003918 0.002405 \n", + "CVCL_0069 -0.003255 0.003638 -0.001792 0.002539 \n", + "CVCL_0099 -0.003971 -0.002543 0.003085 0.003751 \n", + "CVCL_0131 -0.002883 0.003382 0.002904 0.003911 \n", + "\n", + "gene_name ENSG00000291264 ENSG00000291265 SMG1P5-1 ENSG00000291267 \\\n", + "CVCL_0023 0.003982 0.082803 0.004091 0.000484 \n", + "CVCL_0028 -0.002513 0.072158 0.003224 -0.001988 \n", + "CVCL_0069 -0.001929 -0.032060 -0.004066 0.003267 \n", + "CVCL_0099 0.004145 -0.022156 -0.004070 0.003036 \n", + "CVCL_0131 -0.004112 0.229769 0.002120 0.001683 \n", + "\n", + "gene_name ENSG00000291268 ENSG00000291269 ENSG00000291270 ENSG00000291271 \\\n", + "CVCL_0023 -0.004083 0.002524 0.002997 -0.003088 \n", + "CVCL_0028 -0.002822 0.003976 -0.003918 -0.004046 \n", + "CVCL_0069 0.004078 0.002887 -0.004117 -0.004144 \n", + "CVCL_0099 0.004141 -0.004145 -0.004046 -0.002665 \n", + "CVCL_0131 0.001107 0.002824 0.001739 -0.004120 \n", + "\n", + "gene_name ENSG00000291272 ENSG00000291273 ENSG00000291274 ENSG00000291275 \\\n", + "CVCL_0023 0.050780 -0.003394 -0.004073 -0.003045 \n", + "CVCL_0028 0.032906 -0.004021 0.003200 -0.003131 \n", + "CVCL_0069 -0.009988 -0.001770 0.003666 0.002685 \n", + "CVCL_0099 0.006573 0.003033 -0.002204 -0.004143 \n", + "CVCL_0131 -0.009454 -0.005848 0.003198 -0.002990 \n", + "\n", + "gene_name ENSG00000291276 ENSG00000291277 ENSG00000291278 ENSG00000291279 \\\n", + "CVCL_0023 -0.060353 0.004143 -0.003000 0.003574 \n", + "CVCL_0028 0.001933 -0.003226 0.004130 0.004099 \n", + "CVCL_0069 -0.035642 -0.003014 -0.003731 0.005002 \n", + "CVCL_0099 0.067523 0.003215 -0.004054 -0.004145 \n", + "CVCL_0131 0.004122 -0.004144 0.003698 0.004143 \n", + "\n", + "gene_name ANKRD20A11P-1 COL6A4P1-1 ENSG00000291282 ENSG00000291283 \\\n", + "CVCL_0023 0.003350 -0.001505 0.002071 -0.043347 \n", + "CVCL_0028 0.003286 -0.001634 0.003997 0.085231 \n", + "CVCL_0069 -0.004063 0.100089 -0.003083 0.019318 \n", + "CVCL_0099 0.068204 -0.003987 0.004145 0.073804 \n", + "CVCL_0131 0.003976 -0.003251 0.002224 -0.020643 \n", + "\n", + "gene_name ENSG00000291284 ENSG00000291285 ENSG00000291286 ENSG00000291287 \\\n", + "CVCL_0023 0.004141 0.001907 0.004628 -0.002736 \n", + "CVCL_0028 0.002271 -0.002096 0.003251 -0.003479 \n", + "CVCL_0069 0.002333 -0.003262 -0.004127 0.003985 \n", + "CVCL_0099 0.004055 0.003343 0.002921 0.002759 \n", + "CVCL_0131 0.003008 -0.003655 0.003561 0.002258 \n", + "\n", + "gene_name ENSG00000291288 ENSG00000291289 ENSG00000291290 ENSG00000291291 \\\n", + "CVCL_0023 -0.005997 -0.003575 -0.003747 0.003019 \n", + "CVCL_0028 0.002048 -0.002852 -0.003361 -0.002924 \n", + "CVCL_0069 -0.006453 0.003740 -0.003595 -0.003837 \n", + "CVCL_0099 0.004370 -0.003151 0.002685 0.003661 \n", + "CVCL_0131 0.002291 0.003167 -0.002934 0.003680 \n", + "\n", + "gene_name ENSG00000291292 ENSG00000291293 ENSG00000291294 ENSG00000291295 \\\n", + "CVCL_0023 -0.003068 -0.002028 0.002594 -0.004082 \n", + "CVCL_0028 -0.003954 0.003782 0.003364 -0.068236 \n", + "CVCL_0069 -0.003692 -0.042858 -0.004018 -0.003626 \n", + "CVCL_0099 -0.003519 -0.003449 -0.002048 0.003239 \n", + "CVCL_0131 -0.003282 -0.002264 -0.001751 0.001896 \n", + "\n", + "gene_name ENSG00000291296 ENSG00000291297 ENSG00000291298 ENSG00000291299 \\\n", + "CVCL_0023 -0.003968 0.002490 0.004005 -0.015930 \n", + "CVCL_0028 -0.002282 -0.050574 -0.001686 -0.015340 \n", + "CVCL_0069 -0.003161 -0.005651 0.003027 0.158841 \n", + "CVCL_0099 -0.003274 0.001423 -0.003236 -0.092033 \n", + "CVCL_0131 -0.002303 -0.003239 -0.004094 -0.018638 \n", + "\n", + "gene_name PRSS30P-1 ENSG00000291301 ENSG00000291302 ENSG00000291303 \\\n", + "CVCL_0023 -0.004040 0.005229 -0.002081 0.003468 \n", + "CVCL_0028 0.003399 -0.002855 -0.004039 -0.002015 \n", + "CVCL_0069 0.002754 0.004099 -0.003013 0.004096 \n", + "CVCL_0099 0.003660 0.004118 0.002830 -0.003579 \n", + "CVCL_0131 0.000689 0.003667 0.004114 0.004097 \n", + "\n", + "gene_name ENSG00000291306 POLGARF ENSG00000291308 LY6S \\\n", + "CVCL_0023 0.004093 0.002205 0.003866 -0.004129 \n", + "CVCL_0028 0.003972 -0.004053 0.002547 -0.002044 \n", + "CVCL_0069 -0.003516 -0.002437 -0.003698 0.002617 \n", + "CVCL_0099 -0.002627 -0.004046 0.001756 0.003039 \n", + "CVCL_0131 -0.003333 0.003900 0.002991 -0.004070 \n", + "\n", + "gene_name ENSG00000291310 ENSG00000291312 ENSG00000291313 ENSG00000291314 \\\n", + "CVCL_0023 0.002984 0.004107 0.001963 -0.002640 \n", + "CVCL_0028 0.001845 0.004081 0.002652 -0.002895 \n", + "CVCL_0069 -0.003119 -0.002464 0.003158 -0.003258 \n", + "CVCL_0099 0.004139 -0.001538 0.004002 0.003594 \n", + "CVCL_0131 -0.002420 -0.003431 -0.001883 0.003692 \n", + "\n", + "gene_name ENSG00000291315 ENSG00000291316 TMEM276 \n", + "CVCL_0023 0.003367 -0.068125 0.133424 \n", + "CVCL_0028 -0.005117 0.002625 0.058085 \n", + "CVCL_0069 -0.003561 0.002598 -0.018202 \n", + "CVCL_0099 -0.001621 -0.002419 0.071307 \n", + "CVCL_0131 0.001936 0.003658 0.008886 \n", + "\n", + "[5 rows x 62710 columns]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_modlyn_logreg = logreg.get_weights()\n", "df_modlyn_logreg.head()" @@ -313,7 +7332,7 @@ "notebook_metadata_filter": "-all" }, "kernelspec": { - "display_name": "py312", + "display_name": "lamin_env", "language": "python", "name": "python3" }, @@ -327,7 +7346,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.8" + "version": "3.12.10" } }, "nbformat": 4, diff --git a/modlyn/models/_simple_logreg_datamodule.py b/modlyn/models/_simple_logreg_datamodule.py index 0e2d392..921bf4b 100644 --- a/modlyn/models/_simple_logreg_datamodule.py +++ b/modlyn/models/_simple_logreg_datamodule.py @@ -4,7 +4,6 @@ import lightning as L import torch -from arrayloaders.io.dask_loader import DaskDataset from sklearn.preprocessing import LabelEncoder from torch.utils.data import DataLoader, TensorDataset @@ -82,8 +81,9 @@ def __init__( self.n_chunks = n_chunks self.dask_scheduler = dask_scheduler - # Fit label encoder on training data (only needed for tensor datasets) - if self.dataset_type == "in-memory" and self.adata_train is not None: + # Fit label encoder on training data (used by both backends) + self.label_encoder = None + if self.adata_train is not None: self.label_encoder = LabelEncoder() self.label_encoder.fit(self.adata_train.obs[self.label_col]) @@ -107,6 +107,13 @@ def _create_tensor_dataset(self, adata): def _create_dask_dataset(self, adata, shuffle=True): """Create a DaskDataset from AnnData.""" + try: + from arrayloaders.io.dask_loader import DaskDataset # lazy import + except Exception as e: + raise ImportError( + "arrayloaders is required for dataset_type='dask-arrayloader'. Install with `pip install arrayloaders`." + ) from e + return DaskDataset( adata, label_column=self.label_col, @@ -115,28 +122,76 @@ def _create_dask_dataset(self, adata, shuffle=True): dask_scheduler=self.dask_scheduler, ) + def _collate_dask_batch(self, batch): + """Collate function for DaskDataset batches -> (x_tensor, y_tensor).""" + import numpy as np + import torch + try: + import scipy.sparse as sp + except Exception: # pragma: no cover - optional + sp = None + + if not batch: + return torch.empty(0), torch.empty(0, dtype=torch.long) + first = batch[0] + if isinstance(first, tuple) and len(first) == 3: + xs, ys, _ = zip(*batch) + else: + xs, ys = zip(*batch) + if self.label_encoder is None: + raise RuntimeError("label_encoder not initialized") + y_enc = self.label_encoder.transform(list(ys)) + # ensure each row is a contiguous 1D float32 array; handle sparse and object types + xs_arr = [] + for x in xs: + # densify sparse rows + if sp is not None and getattr(sp, "issparse", None) and sp.issparse(x): + arr = x.toarray() + else: + arr = np.asarray(x) + # flatten any 2D shapes (e.g., 1 x n_vars) + if arr.ndim > 1: + arr = arr.ravel() + # robust dtype conversion + if arr.dtype == object: + # last-resort element-wise float coercion + try: + arr = arr.astype(np.float32, copy=False) + except Exception: + arr = np.array([float(v) for v in arr], dtype=np.float32) + else: + arr = arr.astype(np.float32, copy=False) + xs_arr.append(arr) + x_tensor = torch.as_tensor(np.stack(xs_arr, axis=0), dtype=torch.float32) + y_tensor = torch.as_tensor(y_enc, dtype=torch.long) + return x_tensor, y_tensor + def train_dataloader(self): if self.adata_train is None: raise ValueError("adata_train is None") + kwargs = dict(self.train_dataloader_kwargs) if self.dataset_type == "in-memory": train_dataset = self._create_tensor_dataset(self.adata_train) elif self.dataset_type == "dask-arrayloader": train_dataset = self._create_dask_dataset(self.adata_train, shuffle=True) + kwargs.setdefault("collate_fn", self._collate_dask_batch) else: raise ValueError(f"Unknown dataset_type: {self.dataset_type}") - return DataLoader(train_dataset, **self.train_dataloader_kwargs) + return DataLoader(train_dataset, **kwargs) def val_dataloader(self): if self.adata_val is None: - return None + return [] + kwargs = dict(self.val_dataloader_kwargs) if self.dataset_type == "in-memory": val_dataset = self._create_tensor_dataset(self.adata_val) elif self.dataset_type == "dask-arrayloader": val_dataset = self._create_dask_dataset(self.adata_val, shuffle=False) + kwargs.setdefault("collate_fn", self._collate_dask_batch) else: raise ValueError(f"Unknown dataset_type: {self.dataset_type}") - return DataLoader(val_dataset, **self.val_dataloader_kwargs) + return DataLoader(val_dataset, **kwargs) diff --git a/modlyn/models/_simple_logreg_model.py b/modlyn/models/_simple_logreg_model.py index bea735c..135118d 100644 --- a/modlyn/models/_simple_logreg_model.py +++ b/modlyn/models/_simple_logreg_model.py @@ -113,6 +113,10 @@ def fit( adata_val: ad.AnnData | None, train_dataloader_kwargs=None, val_dataloader_kwargs=None, + # dataset backend configuration + dataset_type: str = "in-memory", + n_chunks: int = 8, + dask_scheduler: str = "threads", max_epochs: int = 4, log_every_n_steps: int = 1, num_sanity_val_steps: int = 0, @@ -131,12 +135,26 @@ def fit( max_steps: Maximum number of training steps. """ + # normalize dataset_type aliases (robust to common typos and synonyms) + normalized_dataset_type = { + "in_memory": "in-memory", + "in-memory": "in-memory", + "memory": "in-memory", + "dask": "dask-arrayloader", + "arrayloaders-dask": "dask-arrayloader", + "arrayloaders-dasd": "dask-arrayloader", # common typo / requested alias + "dask-arrayloader": "dask-arrayloader", + }.get(dataset_type, dataset_type) + self.datamodule = SimpleLogRegDataModule( adata_train=adata_train, adata_val=adata_val, label_column=self.label_column, + dataset_type=normalized_dataset_type, # type: ignore[arg-type] train_dataloader_kwargs=train_dataloader_kwargs, val_dataloader_kwargs=val_dataloader_kwargs, + n_chunks=n_chunks, + dask_scheduler=dask_scheduler, # type: ignore[arg-type] ) self.trainer = L.Trainer( max_epochs=max_epochs, @@ -149,10 +167,20 @@ def fit( def get_weights(self) -> pd.DataFrame: """Get the weights of the linear layer as a DataFrame.""" weights = self.linear.weight.detach().numpy() # shape: (n_classes, n_genes) + # Prefer label encoder classes if available, otherwise fall back to labels + try: + class_index = self.datamodule.label_encoder.classes_ # type: ignore[attr-defined] + except Exception: + labels = self._adata.obs[self.label_column] + if hasattr(labels, "cat") and getattr(labels.dtype, "name", "") == "category": + class_index = list(labels.cat.categories) + else: + class_index = list(pd.unique(labels)) + df = pd.DataFrame( weights, columns=self._adata.var_names, - index=self.datamodule.label_encoder.classes_, + index=class_index, ) df.attrs["method_name"] = "modlyn_logreg" return df diff --git a/tests/test_dataset_type_alias.py b/tests/test_dataset_type_alias.py new file mode 100644 index 0000000..61dddfd --- /dev/null +++ b/tests/test_dataset_type_alias.py @@ -0,0 +1,56 @@ +from __future__ import annotations + +import sys +import types + +import numpy as np +import pandas as pd +import anndata as ad +import torch +from torch.utils.data import IterableDataset + + +def test_dataset_type_alias_normalizes_and_trains(): + # Inject a fake DaskDataset into the expected import path + fake_pkg = types.ModuleType("arrayloaders") + fake_io = types.ModuleType("arrayloaders.io") + fake_dl = types.ModuleType("arrayloaders.io.dask_loader") + + class FakeDaskDataset(IterableDataset): + def __init__(self, adata, label_column: str, shuffle: bool, n_chunks: int, dask_scheduler: str): + X = adata.X.toarray() if hasattr(adata.X, "toarray") else adata.X + self.X = X.astype("float32") + self.y = pd.Categorical(adata.obs[label_column]).codes.astype("int64") + + def __iter__(self): + for i in range(self.X.shape[0]): + yield self.X[i], int(self.y[i]) + + fake_dl.DaskDataset = FakeDaskDataset + sys.modules["arrayloaders"] = fake_pkg + sys.modules["arrayloaders.io"] = fake_io + sys.modules["arrayloaders.io.dask_loader"] = fake_dl + + # Small synthetic dataset + X = np.random.rand(64, 8).astype("float32") + obs = pd.DataFrame({"cell_line": np.random.choice(["A", "B", "C"], size=64)}) + adata = ad.AnnData(X=X, obs=obs) + + from modlyn.models import SimpleLogReg + + model = SimpleLogReg(adata=adata, label_column="cell_line") + model.fit( + adata_train=adata, + adata_val=None, + train_dataloader_kwargs={"batch_size": 16, "num_workers": 0}, + dataset_type="arrayloaders-dasd", # alias to be normalized + n_chunks=2, + dask_scheduler="threads", + max_epochs=1, + num_sanity_val_steps=0, + max_steps=5, + ) + + assert model.datamodule is not None + assert model.datamodule.dataset_type == "dask-arrayloader" + From 5af90c48d73aa83b076295691d74f39db5011f01 Mon Sep 17 00:00:00 2001 From: Mikaela Koutrouli Date: Fri, 3 Oct 2025 17:45:17 +0000 Subject: [PATCH 2/6] chore: quickstart ValueError; fit doc args; numpy Generator in test --- docs/quickstart.ipynb | 19 +++++++++++++++---- modlyn/models/_simple_logreg_model.py | 3 +++ tests/test_dataset_type_alias.py | 7 ++++--- 3 files changed, 22 insertions(+), 7 deletions(-) diff --git a/docs/quickstart.ipynb b/docs/quickstart.ipynb index e95811e..c22ee06 100644 --- a/docs/quickstart.ipynb +++ b/docs/quickstart.ipynb @@ -319,7 +319,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "30985561", "metadata": {}, "outputs": [ @@ -374,7 +374,8 @@ "if USE_DASK:\n", " artifact = ln.Artifact.using(\"laminlabs/arrayloader-benchmarks\").get(ZARR_UID)\n", " store_path = Path(artifact.cache())\n", - " assert store_path.is_dir(), f\"UID is not a zarr directory: {store_path}\"\n", + " if not store_path.is_dir():\n", + " raise ValueError(f\"ZARR_UID must cache to a directory, got: {store_path}\")\n", "\n", " # Decide between a directory of shards (*.zarr) vs a single zarr store\n", " has_shards = any(child.name.endswith(\".zarr\") for child in store_path.iterdir())\n", @@ -7312,14 +7313,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "id": "83d187a5", "metadata": { "tags": [ "hide-output" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[94m•\u001b[0m please hit CTRL + s to save the notebook in your editor .... still waiting \u001b[92m✓\u001b[0m\n", + "\u001b[93m!\u001b[0m cells [(4, 6), (6, 8), (9, 30), (33, None), (None, None), (None, None), (None, None)] were not run consecutively\n", + "\u001b[92m→\u001b[0m finished Run('Nx3qPK71') after 29m at 2025-10-02 00:57:29 UTC\n" + ] + } + ], "source": [ "ln.finish()" ] diff --git a/modlyn/models/_simple_logreg_model.py b/modlyn/models/_simple_logreg_model.py index 135118d..9926876 100644 --- a/modlyn/models/_simple_logreg_model.py +++ b/modlyn/models/_simple_logreg_model.py @@ -129,6 +129,9 @@ def fit( adata_val: `AnnData` object containing the validation data. train_dataloader_kwargs: Additional keyword arguments passed to the torch DataLoader for the training dataset. val_dataloader_kwargs: Additional keyword arguments passed to the torch DataLoader for the validation dataset. + dataset_type: Backend to use: "in-memory" or "dask-arrayloader" (aliases accepted). + n_chunks: Number of dask chunks to combine per iteration (Dask backend only). + dask_scheduler: Dask scheduler to use, e.g., "threads" or "synchronous" (Dask backend only). max_epochs: Maximum number of epochs to train. log_every_n_steps: Log training metrics every n steps. num_sanity_val_steps: Number of sanity validation steps to run before training. diff --git a/tests/test_dataset_type_alias.py b/tests/test_dataset_type_alias.py index 61dddfd..1a79e94 100644 --- a/tests/test_dataset_type_alias.py +++ b/tests/test_dataset_type_alias.py @@ -31,9 +31,10 @@ def __iter__(self): sys.modules["arrayloaders.io"] = fake_io sys.modules["arrayloaders.io.dask_loader"] = fake_dl - # Small synthetic dataset - X = np.random.rand(64, 8).astype("float32") - obs = pd.DataFrame({"cell_line": np.random.choice(["A", "B", "C"], size=64)}) + # Small synthetic dataset (Generator API per NPY002) + rng = np.random.default_rng(0) + X = rng.random((64, 8)).astype("float32") + obs = pd.DataFrame({"cell_line": rng.choice(["A", "B", "C"], size=64)}) adata = ad.AnnData(X=X, obs=obs) from modlyn.models import SimpleLogReg From 0974fb1d1f9dd1d286fb50ea7967b7f115b70462 Mon Sep 17 00:00:00 2001 From: Mikaela Koutrouli Date: Fri, 3 Oct 2025 17:51:05 +0000 Subject: [PATCH 3/6] chore: apply nbstripout + ruff + ruff-format auto-fixes --- docs/quickstart.ipynb | 7057 +----------- .../.lamindb/DCZ2tnW2WRdK2Kwr0000.html | 9576 +++++++++++++++++ .../.lamindb/lDo3JXKSXbk3OZNn0000.txt | 324 + docs/test-modlyn/.lamindb/lamin.db | Bin 0 -> 2134016 bytes docs/test-modlyn/.lamindb/storage_uid.txt | 5 + .../version_0/checkpoints/epoch=0-step=4.ckpt | Bin 0 -> 5948 bytes lightning_logs/version_0/metrics.csv | 6 + modlyn/models/_simple_logreg_datamodule.py | 5 +- modlyn/models/_simple_logreg_model.py | 5 +- tests/test_dataset_type_alias.py | 12 +- 10 files changed, 9985 insertions(+), 7005 deletions(-) create mode 100644 docs/test-modlyn/.lamindb/DCZ2tnW2WRdK2Kwr0000.html create mode 100644 docs/test-modlyn/.lamindb/lDo3JXKSXbk3OZNn0000.txt create mode 100644 docs/test-modlyn/.lamindb/lamin.db create mode 100644 docs/test-modlyn/.lamindb/storage_uid.txt create mode 100644 lightning_logs/version_0/checkpoints/epoch=0-step=4.ckpt create mode 100644 lightning_logs/version_0/metrics.csv diff --git a/docs/quickstart.ipynb b/docs/quickstart.ipynb index c22ee06..b773cb8 100644 --- a/docs/quickstart.ipynb +++ b/docs/quickstart.ipynb @@ -10,234 +10,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "f750c5bb", "metadata": { "tags": [ "hide-output" ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: modlyn[dev] in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (0.0.7)\n", - "Requirement already satisfied: anndata>=0.12.0rc1 in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from modlyn[dev]) (0.12.0rc3)\n", - "Requirement already satisfied: scikit-learn in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from modlyn[dev]) (1.6.1)\n", - "Requirement already satisfied: matplotlib in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from modlyn[dev]) (3.10.3)\n", - "Requirement already satisfied: arrayloaders in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from modlyn[dev]) (0.0.3)\n", - "Requirement already satisfied: lightning in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from modlyn[dev]) (2.5.1.post0)\n", - 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"Requirement already satisfied: asttokens>=2.1.0 in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from stack_data->ipython>=7.23.1->ipykernel->nbproject_test->modlyn[dev]) (3.0.0)\n", - "Requirement already satisfied: pure_eval in /home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages (from stack_data->ipython>=7.23.1->ipykernel->nbproject_test->modlyn[dev]) (0.2.3)\n", - "\u001b[94m•\u001b[0m resetting django module variables\n", - "\u001b[92m→\u001b[0m connected lamindb: mikelkou/test-modlyn\n" - ] - } - ], + "outputs": [], "source": [ "!pip install 'modlyn[dev]'\n", "!lamin init --storage test-modlyn" @@ -245,18 +25,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "35122bdc", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[92m→\u001b[0m connected lamindb: mikelkou/test-modlyn\n" - ] - } - ], + "outputs": [], "source": [ "import lamindb as ln\n", "import modlyn as mn\n", @@ -270,27 +42,17 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "9708b93e", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[92m→\u001b[0m loaded Transform('fTv8ikrKUXXJ0000'), re-started Run('Nx3qPK71...') at 2025-10-02 00:27:45 UTC\n", - "\u001b[92m→\u001b[0m notebook imports: arrayloaders==0.0.3 lamindb==1.11.3 modlyn==0.0.7 pandas==2.2.3 scanpy==1.11.2 seaborn==0.13.2\n", - "\u001b[94m•\u001b[0m recommendation: to identify the notebook across renames, pass the uid: ln.track(\"fTv8ikrKUXXJ\")\n" - ] - } - ], + "outputs": [], "source": [ - "ln.track()\n" + "ln.track()" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "fffe8a48", "metadata": {}, "outputs": [], @@ -322,54 +84,10 @@ "execution_count": null, "id": "30985561", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[92m→\u001b[0m mapped: Artifact(uid='1xSHIdfBjfUdxKHm0000')\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/zarr/codecs/vlen_utf8.py:44: UserWarning: The codec `vlen-utf8` is currently not part in the Zarr format 3 specification. It may not be supported by other zarr implementations and may change in the future.\n", - " return cls(**configuration_parsed)\n", - "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/zarr/codecs/vlen_utf8.py:44: UserWarning: The codec `vlen-utf8` is currently not part in the Zarr format 3 specification. It may not be supported by other zarr implementations and may change in the future.\n", - " return cls(**configuration_parsed)\n", - "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/zarr/codecs/vlen_utf8.py:44: UserWarning: The codec `vlen-utf8` is currently not part in the Zarr format 3 specification. It may not be supported by other zarr implementations and may change in the future.\n", - " return cls(**configuration_parsed)\n", - "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/zarr/codecs/vlen_utf8.py:44: UserWarning: The codec `vlen-utf8` is currently not part in the Zarr format 3 specification. It may not be supported by other zarr implementations and may change in the future.\n", - " return cls(**configuration_parsed)\n", - "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/zarr/codecs/vlen_utf8.py:44: UserWarning: The codec `vlen-utf8` is currently not part in the Zarr format 3 specification. It may not be supported by other zarr implementations and may change in the future.\n", - " return cls(**configuration_parsed)\n", - "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/zarr/codecs/vlen_utf8.py:44: UserWarning: The codec `vlen-utf8` is currently not part in the Zarr format 3 specification. It may not be supported by other zarr implementations and may change in the future.\n", - " return cls(**configuration_parsed)\n", - "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/zarr/codecs/vlen_utf8.py:44: UserWarning: The codec `vlen-utf8` is currently not part in the Zarr format 3 specification. It may not be supported by other zarr implementations and may change in the future.\n", - " return cls(**configuration_parsed)\n", - "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/zarr/codecs/vlen_utf8.py:44: UserWarning: The codec `vlen-utf8` is currently not part in the Zarr format 3 specification. It may not be supported by other zarr implementations and may change in the future.\n", - " return cls(**configuration_parsed)\n", - "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/zarr/codecs/vlen_utf8.py:44: UserWarning: The codec `vlen-utf8` is currently not part in the Zarr format 3 specification. It may not be supported by other zarr implementations and may change in the future.\n", - " return cls(**configuration_parsed)\n", - "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/zarr/codecs/vlen_utf8.py:44: UserWarning: The codec `vlen-utf8` is currently not part in the Zarr format 3 specification. It may not be supported by other zarr implementations and may change in the future.\n", - " return cls(**configuration_parsed)\n", - "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/functools.py:912: ImplicitModificationWarning: Transforming to str index.\n", - " return dispatch(args[0].__class__)(*args, **kw)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "adata: (2096850, 62710)\n" - ] - } - ], + "outputs": [], "source": [ "from pathlib import Path\n", "import lamindb as ln\n", - "import modlyn as mn\n", "\n", "if USE_DASK:\n", " artifact = ln.Artifact.using(\"laminlabs/arrayloader-benchmarks\").get(ZARR_UID)\n", @@ -393,7 +111,9 @@ " adata = read_single_store(store_path, obs_columns=[LABEL_COL])\n", "else:\n", " # Example H5AD path (keep your current artifact if you prefer)\n", - " artifact = ln.Artifact.using(\"laminlabs/arrayloader-benchmarks\").get(\"JNaxQe8zbljesdbK0000\")\n", + " artifact = ln.Artifact.using(\"laminlabs/arrayloader-benchmarks\").get(\n", + " \"JNaxQe8zbljesdbK0000\"\n", + " )\n", " adata = artifact.load()\n", " sc.pp.log1p(adata)\n", "\n", @@ -410,26 +130,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "1ae9d3e3", "metadata": { "tags": [ "hide-output" ] }, - "outputs": [ - { - "data": { - "text/plain": [ - "AnnData object with n_obs × n_vars = 2096850 × 62710\n", - " obs: 'cell_line'" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "keep = adata.obs[\"cell_line\"].value_counts().loc[lambda x: x > 3].index\n", "adata = adata[adata.obs[\"cell_line\"].isin(keep)].copy()\n", @@ -438,31 +146,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "d23ddc2a", "metadata": { "tags": [ "hide-output" ] }, - "outputs": [ - { - "data": { - "text/plain": [ - "cell_line\n", - "CVCL_1716 4830\n", - "CVCL_1715 3391\n", - "CVCL_1577 3017\n", - "CVCL_1531 827\n", - "CVCL_1571 601\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "adata.obs[\"cell_line\"].value_counts().tail()" ] @@ -477,64 +168,14 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "5a15bcf4", "metadata": { "tags": [ "hide-output" ] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using default `ModelCheckpoint`. Consider installing `litmodels` package to enable `LitModelCheckpoint` for automatic upload to the Lightning model registry.\n", - "GPU available: False, used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "HPU available: False, using: 0 HPUs\n", - "\n", - " | Name | Type | Params | Mode \n", - "-----------------------------------------------------------\n", - "0 | linear | Linear | 3.1 M | train\n", - "1 | train_metrics | MetricCollection | 0 | train\n", - "2 | val_metrics | MetricCollection | 0 | train\n", - "-----------------------------------------------------------\n", - "3.1 M Trainable params\n", - "0 Non-trainable params\n", - "3.1 M Total params\n", - "12.542 Total estimated model params size (MB)\n", - "7 Modules in train mode\n", - "0 Modules in eval mode\n", - "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:425: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n", - "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/lightning/pytorch/utilities/data.py:123: Your `IterableDataset` has `__len__` defined. In combination with multi-process data loading (when num_workers > 1), `__len__` could be inaccurate if each worker is not configured independently to avoid having duplicate data.\n", - "/home/ubuntu/conda/envs/lamin_env/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:425: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=31` in the `DataLoader` to improve performance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 0: 1%| | 50/8191 [00:04<12:50, 10.57it/s, v_num=31] " - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_steps=50` reached.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 0: 1%| | 50/8191 [00:04<13:06, 10.35it/s, v_num=31]\n", - "dataset_type: dask-arrayloader\n", - "train_dataset: DaskDataset\n" - ] - } - ], + "outputs": [], "source": [ "logreg = mn.models.SimpleLogReg(\n", " adata=adata,\n", @@ -543,30 +184,45 @@ " weight_decay=1e-3,\n", ")\n", "\n", - "fit_kwargs = dict(\n", - " adata_train=adata,\n", - " adata_val=None,\n", - " train_dataloader_kwargs={\"batch_size\": BATCH_SIZE, \"drop_last\": False, \"num_workers\": 0},\n", - " max_epochs=1,\n", - " num_sanity_val_steps=0,\n", - " log_every_n_steps=1,\n", - " max_steps=50,\n", - ")\n", + "fit_kwargs = {\n", + " \"adata_train\": adata,\n", + " \"adata_val\": None,\n", + " \"train_dataloader_kwargs\": {\n", + " \"batch_size\": BATCH_SIZE,\n", + " \"drop_last\": False,\n", + " \"num_workers\": 0,\n", + " },\n", + " \"max_epochs\": 1,\n", + " \"num_sanity_val_steps\": 0,\n", + " \"log_every_n_steps\": 1,\n", + " \"max_steps\": 50,\n", + "}\n", "\n", "if USE_DASK:\n", - " fit_kwargs.update(dict(\n", - " dataset_type=DASK_DATASET_TYPE,\n", - " n_chunks=N_CHUNKS,\n", - " dask_scheduler=DASK_SCHEDULER,\n", - " ))\n", + " fit_kwargs.update(\n", + " {\n", + " \"dataset_type\": DASK_DATASET_TYPE,\n", + " \"n_chunks\": N_CHUNKS,\n", + " \"dask_scheduler\": DASK_SCHEDULER,\n", + " }\n", + " )\n", "\n", "# logreg.fit(**fit_kwargs)\n", "logreg.fit(\n", " adata_train=adata,\n", " adata_val=adata, # reuse the lazy dataset so val has batches\n", - " train_dataloader_kwargs={\"batch_size\": BATCH_SIZE, \"drop_last\": False, \"num_workers\": 0},\n", - " dataset_type=DASK_DATASET_TYPE, n_chunks=N_CHUNKS, dask_scheduler=DASK_SCHEDULER,\n", - " max_epochs=1, num_sanity_val_steps=0, log_every_n_steps=1, max_steps=50,\n", + " train_dataloader_kwargs={\n", + " \"batch_size\": BATCH_SIZE,\n", + " \"drop_last\": False,\n", + " \"num_workers\": 0,\n", + " },\n", + " dataset_type=DASK_DATASET_TYPE,\n", + " n_chunks=N_CHUNKS,\n", + " dask_scheduler=DASK_SCHEDULER,\n", + " max_epochs=1,\n", + " num_sanity_val_steps=0,\n", + " log_every_n_steps=1,\n", + " max_steps=50,\n", ")\n", "\n", "\n", @@ -576,7 +232,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "a7164f8a", "metadata": { "lines_to_next_cell": 2, @@ -584,5055 +240,21 @@ "hide-output" ] }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2025-10-02T00:48:11.532953\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.10.3, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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CVCL_0023-0.072169-0.001782-0.2534200.065178-0.105779-0.001987-0.1614860.0647260.345648-0.1223560.030515-0.382999-0.4092560.074057-0.0002600.047803-0.146368-0.086480-0.2316880.0910470.0043950.3623390.1678880.7701460.0148160.000374-0.003875-0.234518-0.1649760.402599-0.046038-0.0059360.205344-0.139414-0.0476200.0728200.0456140.1815480.0846200.2671230.4351460.0259760.0799840.013431-0.3610210.0817370.1649640.004023-0.312792-0.0044730.4703130.161411-0.1535400.4752480.010871-0.2874850.3242690.003048-0.0619860.197627-0.1974410.0021820.052494-0.0024870.0023760.0274700.1149130.560219-0.0032400.050618-0.1369220.110338-0.001225-0.3603740.1927020.4321350.0685880.0592470.056717-0.136761-0.0038310.029181-0.0983330.5977480.056844-0.0352460.149224-0.090831-0.005264-0.081047-0.0051190.0516070.054285-0.0016860.002799-0.065433-0.0463580.0600870.1437050.045878...-0.0037500.656235-0.0017500.0041260.053257-0.0019640.0010870.0037870.0020000.0035520.0315060.099230-0.0356130.0041440.004135-0.038874-0.0038800.0026570.0032210.050653-0.0039330.0715620.001616-0.0030590.761539-0.004090-0.002690-0.0030760.0034570.0028300.0035220.002355-0.0025430.003614-0.0016960.1254830.003966-0.0033630.004107-0.001464-0.001881-0.0035520.003171-0.063697-0.082815-0.0040630.001957-0.0032360.0020360.0039820.0828030.0040910.000484-0.0040830.0025240.002997-0.0030880.050780-0.003394-0.004073-0.003045-0.0603530.004143-0.0030000.0035740.003350-0.0015050.002071-0.0433470.0041410.0019070.004628-0.002736-0.005997-0.003575-0.0037470.003019-0.003068-0.0020280.002594-0.004082-0.0039680.0024900.004005-0.015930-0.0040400.005229-0.0020810.0034680.0040930.0022050.003866-0.0041290.0029840.0041070.001963-0.0026400.003367-0.0681250.133424
CVCL_00280.306921-0.002901-0.225597-0.080660-0.275207-0.0041320.0399440.1431890.1749080.4467710.060059-0.0493020.2694680.058031-0.0363790.0120420.293526-0.098515-0.202047-0.032171-0.0029660.1811760.0595890.0371350.0423810.005949-0.003735-0.0631530.012087-0.0435240.1086120.004447-0.0303430.179612-0.010053-0.0598770.1713650.515425-0.218428-0.086147-0.5248100.1777060.2706250.004328-0.1935470.0149730.0393880.038271-0.1701720.164602-0.0696720.5112160.500238-0.0820940.015317-0.0231030.5886730.0111110.0706270.2120941.0545280.0036260.0064370.0034370.004122-0.0778710.5358280.210519-0.003807-0.2998620.0364920.241763-0.0230910.0510820.418347-0.0845860.2996410.3186380.169223-0.0448880.001771-0.2551890.0846870.139335-0.070708-0.108545-0.089131-0.068518-0.009832-0.0107550.7797750.2149160.056942-0.038249-0.002025-0.003097-0.110921-0.108925-0.0816270.001308...-0.0037700.1247980.003998-0.001950-0.004137-0.004073-0.004114-0.002038-0.003907-0.0037780.008693-0.1929370.0710390.002892-0.003073-0.080847-0.002272-0.002362-0.0040670.006993-0.0038340.0819130.0034980.0032680.477050-0.0002010.0041400.0036230.0040540.002968-0.0016950.002687-0.003500-0.0041000.0000720.0030610.0035240.004143-0.003826-0.0040300.003037-0.0042650.004145-0.0090800.0006620.003560-0.003709-0.0039180.002405-0.0025130.0721580.003224-0.001988-0.0028220.003976-0.003918-0.0040460.032906-0.0040210.003200-0.0031310.001933-0.0032260.0041300.0040990.003286-0.0016340.0039970.0852310.002271-0.0020960.003251-0.0034790.002048-0.002852-0.003361-0.002924-0.0039540.0037820.003364-0.068236-0.002282-0.050574-0.001686-0.0153400.003399-0.002855-0.004039-0.0020150.003972-0.0040530.002547-0.0020440.0018450.0040810.002652-0.002895-0.0051170.0026250.058085
CVCL_0069-0.1235700.0018390.244393-0.0426030.0664650.003361-0.0477540.098614-0.358676-0.3634470.1251700.0478640.2334950.0832350.008239-0.0258950.5564790.4456030.309886-0.280547-0.0382390.0602700.8039820.099297-0.018322-0.010834-0.003841-0.1096620.060092-0.1545420.297341-0.006893-0.193492-0.0201210.003154-0.0064980.006772-0.179057-0.057365-0.394072-0.3490710.1044130.380096-0.012639-0.3160770.1257430.0647770.392404-0.0069250.6012720.0039440.121724-0.0433880.451930-0.1566620.1440570.069286-0.003442-0.0416670.0487890.0136040.0037620.0005070.125233-0.0039230.4748620.7934520.0664220.002904-0.007232-0.009361-0.128384-0.0292730.1084390.3745030.486061-0.3792360.2890510.074861-0.133407-0.003127-0.0372950.235137-0.1036750.0271220.106405-0.0580630.083961-0.0530410.055750-0.011632-0.0508700.304672-0.001997-0.0039190.0070570.2254140.108640-0.156228-0.052544...-0.003686-0.056917-0.0073300.003102-0.0735850.0024760.002856-0.004048-0.0035740.0038540.1807940.342395-0.0019260.0016940.077227-0.0308160.0040380.0029550.003726-0.1412440.0017390.0035150.0039840.0025500.1554680.0041010.0040520.0030610.002814-0.003999-0.001535-0.0021600.0019520.0033490.002296-0.003495-0.003340-0.001963-0.0028140.0034490.004021-0.0017470.1458310.0041880.067262-0.0032550.003638-0.0017920.002539-0.001929-0.032060-0.0040660.0032670.0040780.002887-0.004117-0.004144-0.009988-0.0017700.0036660.002685-0.035642-0.003014-0.0037310.005002-0.0040630.100089-0.0030830.0193180.002333-0.003262-0.0041270.003985-0.0064530.003740-0.003595-0.003837-0.003692-0.042858-0.004018-0.003626-0.003161-0.0056510.0030270.1588410.0027540.004099-0.0030130.004096-0.003516-0.002437-0.0036980.002617-0.003119-0.0024640.003158-0.003258-0.0035610.002598-0.018202
CVCL_00990.000533-0.0019290.130884-0.0179270.119897-0.0038750.6727500.177732-0.4507010.2249510.0705040.006495-0.112005-0.0325010.0054560.137025-0.0848770.2655300.122823-0.1448790.003694-0.0103570.0594680.336300-0.2487600.0705480.0016550.014640-0.154495-0.1995550.167132-0.0024670.383384-0.0398290.013198-0.2356650.059897-0.1837390.137194-0.291918-0.6065860.0916000.3134320.0073870.0141490.020131-0.015974-0.038441-0.1764560.5242710.005447-0.0636170.5188210.341604-0.015890-0.1727170.3245330.003926-0.0011590.1604330.095137-0.0031010.0043210.067483-0.0035180.4033260.3901310.143473-0.0041140.001951-0.013188-0.085788-0.092613-0.290897-0.3889990.1076580.4010150.0683890.0634220.148893-0.003982-0.388173-0.0666540.309218-0.0182550.128790-0.0581700.056880-0.044876-0.0050380.064484-0.0563290.495379-0.0027430.0010410.0065300.237378-0.0127700.0114760.006442...-0.004126-0.0489570.0063010.003479-0.003755-0.004462-0.003259-0.001755-0.0035230.002641-0.002951-0.0406280.0017060.003507-0.001921-0.1253190.0032350.002771-0.0032850.000249-0.0034320.002787-0.003993-0.003724-0.4287160.003999-0.0013460.003918-0.003958-0.004054-0.0033140.002418-0.004038-0.002467-0.0039560.0029990.002918-0.0040230.002882-0.003847-0.0040210.0005150.0037860.0054710.001008-0.003971-0.0025430.0030850.0037510.004145-0.022156-0.0040700.0030360.004141-0.004145-0.004046-0.0026650.0065730.003033-0.002204-0.0041430.0675230.003215-0.004054-0.0041450.068204-0.0039870.0041450.0738040.0040550.0033430.0029210.0027590.004370-0.0031510.0026850.003661-0.003519-0.003449-0.0020480.003239-0.0032740.001423-0.003236-0.0920330.0036600.0041180.002830-0.003579-0.002627-0.0040460.0017560.0030390.004139-0.0015380.0040020.003594-0.001621-0.0024190.071307
CVCL_01310.078440-0.003691-0.1256080.007751-0.030093-0.003500-0.2116040.1676890.015605-0.0726000.068769-0.057561-0.0022620.055009-0.005387-0.3151810.789768-0.0235970.2221010.440693-0.053264-0.009269-0.2250960.869027-0.0022200.0049310.0019050.0224280.0391810.361790-0.0995770.0026790.070637-0.0045380.038766-0.1533090.0396900.1400310.0982210.2292201.044028-0.0045900.4074560.008790-0.054797-0.010550-0.0481290.0463050.5739790.2065100.0083270.0949680.1044200.0376430.3903140.3137670.027392-0.005515-0.073887-0.037684-0.009270-0.0021550.0035050.0037380.003165-0.102963-0.2262350.2078090.004145-0.003772-0.0214800.029349-0.0090360.2210060.042272-0.3484070.3550400.0046780.1654070.1039510.0020230.031724-0.0834560.035964-0.0021200.0801340.053113-0.008261-0.0358910.0015440.056233-0.0160630.4348280.0689080.0040320.006045-0.168804-0.0507880.0919560.006270...0.0030510.3005950.006620-0.003971-0.0038360.0033860.003836-0.0030630.0029370.002996-0.0074320.126000-0.003158-0.0030050.002024-0.0664610.001801-0.003225-0.002640-0.004204-0.003547-0.003569-0.0039790.0030220.4555130.004144-0.0040930.0035020.003487-0.003562-0.003052-0.003508-0.004042-0.0041400.0036780.002455-0.0019930.004060-0.0026400.0034990.0022030.0038870.0037970.0687130.059885-0.0028830.0033820.0029040.003911-0.0041120.2297690.0021200.0016830.0011070.0028240.001739-0.004120-0.009454-0.0058480.003198-0.0029900.004122-0.0041440.0036980.0041430.003976-0.0032510.002224-0.0206430.003008-0.0036550.0035610.0022580.0022910.003167-0.0029340.003680-0.003282-0.002264-0.0017510.001896-0.002303-0.003239-0.004094-0.0186380.0006890.0036670.0041140.004097-0.0033330.0039000.002991-0.004070-0.002420-0.003431-0.0018830.0036920.0019360.0036580.008886
\n", - "

5 rows × 62710 columns

\n", - "
" - ], - "text/plain": [ - "gene_name TSPAN6 TNMD DPM1 SCYL3 C1orf112 FGR \\\n", - "CVCL_0023 -0.072169 -0.001782 -0.253420 0.065178 -0.105779 -0.001987 \n", - "CVCL_0028 0.306921 -0.002901 -0.225597 -0.080660 -0.275207 -0.004132 \n", - "CVCL_0069 -0.123570 0.001839 0.244393 -0.042603 0.066465 0.003361 \n", - "CVCL_0099 0.000533 -0.001929 0.130884 -0.017927 0.119897 -0.003875 \n", - "CVCL_0131 0.078440 -0.003691 -0.125608 0.007751 -0.030093 -0.003500 \n", - "\n", - "gene_name CFH FUCA2 GCLC NFYA STPG1 NIPAL3 \\\n", - "CVCL_0023 -0.161486 0.064726 0.345648 -0.122356 0.030515 -0.382999 \n", - "CVCL_0028 0.039944 0.143189 0.174908 0.446771 0.060059 -0.049302 \n", - "CVCL_0069 -0.047754 0.098614 -0.358676 -0.363447 0.125170 0.047864 \n", - "CVCL_0099 0.672750 0.177732 -0.450701 0.224951 0.070504 0.006495 \n", - "CVCL_0131 -0.211604 0.167689 0.015605 -0.072600 0.068769 -0.057561 \n", - "\n", - "gene_name LAS1L ENPP4 SEMA3F CFTR ANKIB1 CYP51A1 \\\n", - "CVCL_0023 -0.409256 0.074057 -0.000260 0.047803 -0.146368 -0.086480 \n", - "CVCL_0028 0.269468 0.058031 -0.036379 0.012042 0.293526 -0.098515 \n", - "CVCL_0069 0.233495 0.083235 0.008239 -0.025895 0.556479 0.445603 \n", - "CVCL_0099 -0.112005 -0.032501 0.005456 0.137025 -0.084877 0.265530 \n", - "CVCL_0131 -0.002262 0.055009 -0.005387 -0.315181 0.789768 -0.023597 \n", - "\n", - "gene_name KRIT1 RAD52 MYH16 BAD LAP3 CD99 \\\n", - "CVCL_0023 -0.231688 0.091047 0.004395 0.362339 0.167888 0.770146 \n", - "CVCL_0028 -0.202047 -0.032171 -0.002966 0.181176 0.059589 0.037135 \n", - "CVCL_0069 0.309886 -0.280547 -0.038239 0.060270 0.803982 0.099297 \n", - "CVCL_0099 0.122823 -0.144879 0.003694 -0.010357 0.059468 0.336300 \n", - "CVCL_0131 0.222101 0.440693 -0.053264 -0.009269 -0.225096 0.869027 \n", - "\n", - "gene_name HS3ST1 AOC1 WNT16 HECW1 MAD1L1 LASP1 \\\n", - "CVCL_0023 0.014816 0.000374 -0.003875 -0.234518 -0.164976 0.402599 \n", - "CVCL_0028 0.042381 0.005949 -0.003735 -0.063153 0.012087 -0.043524 \n", - "CVCL_0069 -0.018322 -0.010834 -0.003841 -0.109662 0.060092 -0.154542 \n", - "CVCL_0099 -0.248760 0.070548 0.001655 0.014640 -0.154495 -0.199555 \n", - "CVCL_0131 -0.002220 0.004931 0.001905 0.022428 0.039181 0.361790 \n", - "\n", - "gene_name SNX11 TMEM176A M6PR KLHL13 CYP26B1 ICA1 \\\n", - "CVCL_0023 -0.046038 -0.005936 0.205344 -0.139414 -0.047620 0.072820 \n", - "CVCL_0028 0.108612 0.004447 -0.030343 0.179612 -0.010053 -0.059877 \n", - "CVCL_0069 0.297341 -0.006893 -0.193492 -0.020121 0.003154 -0.006498 \n", - "CVCL_0099 0.167132 -0.002467 0.383384 -0.039829 0.013198 -0.235665 \n", - "CVCL_0131 -0.099577 0.002679 0.070637 -0.004538 0.038766 -0.153309 \n", - "\n", - "gene_name DBNDD1 ALS2 CASP10 CFLAR TFPI NDUFAF7 \\\n", - "CVCL_0023 0.045614 0.181548 0.084620 0.267123 0.435146 0.025976 \n", - "CVCL_0028 0.171365 0.515425 -0.218428 -0.086147 -0.524810 0.177706 \n", - "CVCL_0069 0.006772 -0.179057 -0.057365 -0.394072 -0.349071 0.104413 \n", - "CVCL_0099 0.059897 -0.183739 0.137194 -0.291918 -0.606586 0.091600 \n", - "CVCL_0131 0.039690 0.140031 0.098221 0.229220 1.044028 -0.004590 \n", - "\n", - "gene_name RBM5 MTMR7 SLC7A2 ARF5 SARM1 POLDIP2 \\\n", - "CVCL_0023 0.079984 0.013431 -0.361021 0.081737 0.164964 0.004023 \n", - "CVCL_0028 0.270625 0.004328 -0.193547 0.014973 0.039388 0.038271 \n", - "CVCL_0069 0.380096 -0.012639 -0.316077 0.125743 0.064777 0.392404 \n", - "CVCL_0099 0.313432 0.007387 0.014149 0.020131 -0.015974 -0.038441 \n", - "CVCL_0131 0.407456 0.008790 -0.054797 -0.010550 -0.048129 0.046305 \n", - "\n", - "gene_name PLXND1 AK2 CD38 FKBP4 KDM1A RBM6 \\\n", - "CVCL_0023 -0.312792 -0.004473 0.470313 0.161411 -0.153540 0.475248 \n", - "CVCL_0028 -0.170172 0.164602 -0.069672 0.511216 0.500238 -0.082094 \n", - "CVCL_0069 -0.006925 0.601272 0.003944 0.121724 -0.043388 0.451930 \n", - "CVCL_0099 -0.176456 0.524271 0.005447 -0.063617 0.518821 0.341604 \n", - "CVCL_0131 0.573979 0.206510 0.008327 0.094968 0.104420 0.037643 \n", - "\n", - "gene_name CAMKK1 RECQL VPS50 HSPB6 ARHGAP33 NDUFAB1 \\\n", - "CVCL_0023 0.010871 -0.287485 0.324269 0.003048 -0.061986 0.197627 \n", - "CVCL_0028 0.015317 -0.023103 0.588673 0.011111 0.070627 0.212094 \n", - "CVCL_0069 -0.156662 0.144057 0.069286 -0.003442 -0.041667 0.048789 \n", - "CVCL_0099 -0.015890 -0.172717 0.324533 0.003926 -0.001159 0.160433 \n", - "CVCL_0131 0.390314 0.313767 0.027392 -0.005515 -0.073887 -0.037684 \n", - "\n", - "gene_name PDK4 SLC22A16 ZMYND10 ABCB5 ARX SLC25A13 \\\n", - "CVCL_0023 -0.197441 0.002182 0.052494 -0.002487 0.002376 0.027470 \n", - "CVCL_0028 1.054528 0.003626 0.006437 0.003437 0.004122 -0.077871 \n", - "CVCL_0069 0.013604 0.003762 0.000507 0.125233 -0.003923 0.474862 \n", - "CVCL_0099 0.095137 -0.003101 0.004321 0.067483 -0.003518 0.403326 \n", - "CVCL_0131 -0.009270 -0.002155 0.003505 0.003738 0.003165 -0.102963 \n", - "\n", - "gene_name ST7 CDC27 SLC4A1 CALCR HCCS DVL2 \\\n", - "CVCL_0023 0.114913 0.560219 -0.003240 0.050618 -0.136922 0.110338 \n", - "CVCL_0028 0.535828 0.210519 -0.003807 -0.299862 0.036492 0.241763 \n", - "CVCL_0069 0.793452 0.066422 0.002904 -0.007232 -0.009361 -0.128384 \n", - "CVCL_0099 0.390131 0.143473 -0.004114 0.001951 -0.013188 -0.085788 \n", - "CVCL_0131 -0.226235 0.207809 0.004145 -0.003772 -0.021480 0.029349 \n", - "\n", - "gene_name PRSS22 UPF1 SKAP2 SLC25A5 MCUB HOXA11 \\\n", - "CVCL_0023 -0.001225 -0.360374 0.192702 0.432135 0.068588 0.059247 \n", - "CVCL_0028 -0.023091 0.051082 0.418347 -0.084586 0.299641 0.318638 \n", - "CVCL_0069 -0.029273 0.108439 0.374503 0.486061 -0.379236 0.289051 \n", - "CVCL_0099 -0.092613 -0.290897 -0.388999 0.107658 0.401015 0.068389 \n", - "CVCL_0131 -0.009036 0.221006 0.042272 -0.348407 0.355040 0.004678 \n", - "\n", - "gene_name POLR2J DHX33 MEOX1 THSD7A LIG3 RPAP3 \\\n", - "CVCL_0023 0.056717 -0.136761 -0.003831 0.029181 -0.098333 0.597748 \n", - "CVCL_0028 0.169223 -0.044888 0.001771 -0.255189 0.084687 0.139335 \n", - "CVCL_0069 0.074861 -0.133407 -0.003127 -0.037295 0.235137 -0.103675 \n", - "CVCL_0099 0.063422 0.148893 -0.003982 -0.388173 -0.066654 0.309218 \n", - "CVCL_0131 0.165407 0.103951 0.002023 0.031724 -0.083456 0.035964 \n", - "\n", - "gene_name ACSM3 REXO5 CIAPIN1 SPPL2B ATOSB COPZ2 \\\n", - "CVCL_0023 0.056844 -0.035246 0.149224 -0.090831 -0.005264 -0.081047 \n", - "CVCL_0028 -0.070708 -0.108545 -0.089131 -0.068518 -0.009832 -0.010755 \n", - "CVCL_0069 0.027122 0.106405 -0.058063 0.083961 -0.053041 0.055750 \n", - "CVCL_0099 -0.018255 0.128790 -0.058170 0.056880 -0.044876 -0.005038 \n", - "CVCL_0131 -0.002120 0.080134 0.053113 -0.008261 -0.035891 0.001544 \n", - "\n", - "gene_name PRKAR2B MSL3 CREBBP TSPOAP1 MPO PON1 \\\n", - "CVCL_0023 -0.005119 0.051607 0.054285 -0.001686 0.002799 -0.065433 \n", - "CVCL_0028 0.779775 0.214916 0.056942 -0.038249 -0.002025 -0.003097 \n", - "CVCL_0069 -0.011632 -0.050870 0.304672 -0.001997 -0.003919 0.007057 \n", - "CVCL_0099 0.064484 -0.056329 0.495379 -0.002743 0.001041 0.006530 \n", - "CVCL_0131 0.056233 -0.016063 0.434828 0.068908 0.004032 0.006045 \n", - "\n", - "gene_name GCFC2 WDR54 CROT ABCB4 ... DPY19L2P3-1 \\\n", - "CVCL_0023 -0.046358 0.060087 0.143705 0.045878 ... -0.003750 \n", - "CVCL_0028 -0.110921 -0.108925 -0.081627 0.001308 ... -0.003770 \n", - "CVCL_0069 0.225414 0.108640 -0.156228 -0.052544 ... -0.003686 \n", - "CVCL_0099 0.237378 -0.012770 0.011476 0.006442 ... -0.004126 \n", - "CVCL_0131 -0.168804 -0.050788 0.091956 0.006270 ... 0.003051 \n", - "\n", - "gene_name ENSG00000291214 ENSG00000291215 ENSG00000291216 LINC01347-1 \\\n", - "CVCL_0023 0.656235 -0.001750 0.004126 0.053257 \n", - "CVCL_0028 0.124798 0.003998 -0.001950 -0.004137 \n", - "CVCL_0069 -0.056917 -0.007330 0.003102 -0.073585 \n", - "CVCL_0099 -0.048957 0.006301 0.003479 -0.003755 \n", - "CVCL_0131 0.300595 0.006620 -0.003971 -0.003836 \n", - "\n", - "gene_name ENSG00000291218 ENSG00000291219 ENSG00000291220 ENSG00000291221 \\\n", - "CVCL_0023 -0.001964 0.001087 0.003787 0.002000 \n", - "CVCL_0028 -0.004073 -0.004114 -0.002038 -0.003907 \n", - "CVCL_0069 0.002476 0.002856 -0.004048 -0.003574 \n", - "CVCL_0099 -0.004462 -0.003259 -0.001755 -0.003523 \n", - "CVCL_0131 0.003386 0.003836 -0.003063 0.002937 \n", - "\n", - "gene_name ENSG00000291222 FAM86DP-1 ENSG00000291224 ENSG00000291225 \\\n", - "CVCL_0023 0.003552 0.031506 0.099230 -0.035613 \n", - "CVCL_0028 -0.003778 0.008693 -0.192937 0.071039 \n", - "CVCL_0069 0.003854 0.180794 0.342395 -0.001926 \n", - "CVCL_0099 0.002641 -0.002951 -0.040628 0.001706 \n", - "CVCL_0131 0.002996 -0.007432 0.126000 -0.003158 \n", - "\n", - "gene_name ENSG00000291226 ENSG00000291227 ENSG00000291228 ENSG00000291229 \\\n", - "CVCL_0023 0.004144 0.004135 -0.038874 -0.003880 \n", - "CVCL_0028 0.002892 -0.003073 -0.080847 -0.002272 \n", - "CVCL_0069 0.001694 0.077227 -0.030816 0.004038 \n", - "CVCL_0099 0.003507 -0.001921 -0.125319 0.003235 \n", - "CVCL_0131 -0.003005 0.002024 -0.066461 0.001801 \n", - "\n", - "gene_name ENSG00000291230 ENSG00000291231 ENSG00000291232 ENSG00000291233 \\\n", - "CVCL_0023 0.002657 0.003221 0.050653 -0.003933 \n", - "CVCL_0028 -0.002362 -0.004067 0.006993 -0.003834 \n", - "CVCL_0069 0.002955 0.003726 -0.141244 0.001739 \n", - "CVCL_0099 0.002771 -0.003285 0.000249 -0.003432 \n", - "CVCL_0131 -0.003225 -0.002640 -0.004204 -0.003547 \n", - "\n", - "gene_name ENSG00000291234 ENSG00000291235 ENSG00000291236 SOD2 \\\n", - "CVCL_0023 0.071562 0.001616 -0.003059 0.761539 \n", - "CVCL_0028 0.081913 0.003498 0.003268 0.477050 \n", - "CVCL_0069 0.003515 0.003984 0.002550 0.155468 \n", - "CVCL_0099 0.002787 -0.003993 -0.003724 -0.428716 \n", - "CVCL_0131 -0.003569 -0.003979 0.003022 0.455513 \n", - "\n", - "gene_name ENSG00000291238 ENSG00000291239 ENSG00000291240 ENSG00000291241 \\\n", - "CVCL_0023 -0.004090 -0.002690 -0.003076 0.003457 \n", - "CVCL_0028 -0.000201 0.004140 0.003623 0.004054 \n", - "CVCL_0069 0.004101 0.004052 0.003061 0.002814 \n", - "CVCL_0099 0.003999 -0.001346 0.003918 -0.003958 \n", - "CVCL_0131 0.004144 -0.004093 0.003502 0.003487 \n", - "\n", - "gene_name ENSG00000291242 ENSG00000291243 ENSG00000291246 ENSG00000291247 \\\n", - "CVCL_0023 0.002830 0.003522 0.002355 -0.002543 \n", - "CVCL_0028 0.002968 -0.001695 0.002687 -0.003500 \n", - "CVCL_0069 -0.003999 -0.001535 -0.002160 0.001952 \n", - "CVCL_0099 -0.004054 -0.003314 0.002418 -0.004038 \n", - "CVCL_0131 -0.003562 -0.003052 -0.003508 -0.004042 \n", - "\n", - "gene_name ENSG00000291248 ENSG00000291249 ENSG00000291250 ENSG00000291251 \\\n", - "CVCL_0023 0.003614 -0.001696 0.125483 0.003966 \n", - "CVCL_0028 -0.004100 0.000072 0.003061 0.003524 \n", - "CVCL_0069 0.003349 0.002296 -0.003495 -0.003340 \n", - "CVCL_0099 -0.002467 -0.003956 0.002999 0.002918 \n", - "CVCL_0131 -0.004140 0.003678 0.002455 -0.001993 \n", - "\n", - "gene_name ENSG00000291252 ENSG00000291253 ENSG00000291254 ENSG00000291255 \\\n", - "CVCL_0023 -0.003363 0.004107 -0.001464 -0.001881 \n", - "CVCL_0028 0.004143 -0.003826 -0.004030 0.003037 \n", - "CVCL_0069 -0.001963 -0.002814 0.003449 0.004021 \n", - "CVCL_0099 -0.004023 0.002882 -0.003847 -0.004021 \n", - "CVCL_0131 0.004060 -0.002640 0.003499 0.002203 \n", - "\n", - "gene_name ENSG00000291256 ENSG00000291257 ENSG00000291258 ENSG00000291259 \\\n", - "CVCL_0023 -0.003552 0.003171 -0.063697 -0.082815 \n", - "CVCL_0028 -0.004265 0.004145 -0.009080 0.000662 \n", - "CVCL_0069 -0.001747 0.145831 0.004188 0.067262 \n", - "CVCL_0099 0.000515 0.003786 0.005471 0.001008 \n", - "CVCL_0131 0.003887 0.003797 0.068713 0.059885 \n", - "\n", - "gene_name ENSG00000291260 ENSG00000291261 ENSG00000291262 SMG1P7-1 \\\n", - "CVCL_0023 -0.004063 0.001957 -0.003236 0.002036 \n", - "CVCL_0028 0.003560 -0.003709 -0.003918 0.002405 \n", - "CVCL_0069 -0.003255 0.003638 -0.001792 0.002539 \n", - "CVCL_0099 -0.003971 -0.002543 0.003085 0.003751 \n", - "CVCL_0131 -0.002883 0.003382 0.002904 0.003911 \n", - "\n", - "gene_name ENSG00000291264 ENSG00000291265 SMG1P5-1 ENSG00000291267 \\\n", - "CVCL_0023 0.003982 0.082803 0.004091 0.000484 \n", - "CVCL_0028 -0.002513 0.072158 0.003224 -0.001988 \n", - "CVCL_0069 -0.001929 -0.032060 -0.004066 0.003267 \n", - "CVCL_0099 0.004145 -0.022156 -0.004070 0.003036 \n", - "CVCL_0131 -0.004112 0.229769 0.002120 0.001683 \n", - "\n", - "gene_name ENSG00000291268 ENSG00000291269 ENSG00000291270 ENSG00000291271 \\\n", - "CVCL_0023 -0.004083 0.002524 0.002997 -0.003088 \n", - "CVCL_0028 -0.002822 0.003976 -0.003918 -0.004046 \n", - "CVCL_0069 0.004078 0.002887 -0.004117 -0.004144 \n", - "CVCL_0099 0.004141 -0.004145 -0.004046 -0.002665 \n", - "CVCL_0131 0.001107 0.002824 0.001739 -0.004120 \n", - "\n", - "gene_name ENSG00000291272 ENSG00000291273 ENSG00000291274 ENSG00000291275 \\\n", - "CVCL_0023 0.050780 -0.003394 -0.004073 -0.003045 \n", - "CVCL_0028 0.032906 -0.004021 0.003200 -0.003131 \n", - "CVCL_0069 -0.009988 -0.001770 0.003666 0.002685 \n", - "CVCL_0099 0.006573 0.003033 -0.002204 -0.004143 \n", - "CVCL_0131 -0.009454 -0.005848 0.003198 -0.002990 \n", - "\n", - "gene_name ENSG00000291276 ENSG00000291277 ENSG00000291278 ENSG00000291279 \\\n", - "CVCL_0023 -0.060353 0.004143 -0.003000 0.003574 \n", - "CVCL_0028 0.001933 -0.003226 0.004130 0.004099 \n", - "CVCL_0069 -0.035642 -0.003014 -0.003731 0.005002 \n", - "CVCL_0099 0.067523 0.003215 -0.004054 -0.004145 \n", - "CVCL_0131 0.004122 -0.004144 0.003698 0.004143 \n", - "\n", - "gene_name ANKRD20A11P-1 COL6A4P1-1 ENSG00000291282 ENSG00000291283 \\\n", - "CVCL_0023 0.003350 -0.001505 0.002071 -0.043347 \n", - "CVCL_0028 0.003286 -0.001634 0.003997 0.085231 \n", - "CVCL_0069 -0.004063 0.100089 -0.003083 0.019318 \n", - "CVCL_0099 0.068204 -0.003987 0.004145 0.073804 \n", - "CVCL_0131 0.003976 -0.003251 0.002224 -0.020643 \n", - "\n", - "gene_name ENSG00000291284 ENSG00000291285 ENSG00000291286 ENSG00000291287 \\\n", - "CVCL_0023 0.004141 0.001907 0.004628 -0.002736 \n", - "CVCL_0028 0.002271 -0.002096 0.003251 -0.003479 \n", - "CVCL_0069 0.002333 -0.003262 -0.004127 0.003985 \n", - "CVCL_0099 0.004055 0.003343 0.002921 0.002759 \n", - "CVCL_0131 0.003008 -0.003655 0.003561 0.002258 \n", - "\n", - "gene_name ENSG00000291288 ENSG00000291289 ENSG00000291290 ENSG00000291291 \\\n", - "CVCL_0023 -0.005997 -0.003575 -0.003747 0.003019 \n", - "CVCL_0028 0.002048 -0.002852 -0.003361 -0.002924 \n", - "CVCL_0069 -0.006453 0.003740 -0.003595 -0.003837 \n", - "CVCL_0099 0.004370 -0.003151 0.002685 0.003661 \n", - "CVCL_0131 0.002291 0.003167 -0.002934 0.003680 \n", - "\n", - "gene_name ENSG00000291292 ENSG00000291293 ENSG00000291294 ENSG00000291295 \\\n", - "CVCL_0023 -0.003068 -0.002028 0.002594 -0.004082 \n", - "CVCL_0028 -0.003954 0.003782 0.003364 -0.068236 \n", - "CVCL_0069 -0.003692 -0.042858 -0.004018 -0.003626 \n", - "CVCL_0099 -0.003519 -0.003449 -0.002048 0.003239 \n", - "CVCL_0131 -0.003282 -0.002264 -0.001751 0.001896 \n", - "\n", - "gene_name ENSG00000291296 ENSG00000291297 ENSG00000291298 ENSG00000291299 \\\n", - "CVCL_0023 -0.003968 0.002490 0.004005 -0.015930 \n", - "CVCL_0028 -0.002282 -0.050574 -0.001686 -0.015340 \n", - "CVCL_0069 -0.003161 -0.005651 0.003027 0.158841 \n", - "CVCL_0099 -0.003274 0.001423 -0.003236 -0.092033 \n", - "CVCL_0131 -0.002303 -0.003239 -0.004094 -0.018638 \n", - "\n", - "gene_name PRSS30P-1 ENSG00000291301 ENSG00000291302 ENSG00000291303 \\\n", - "CVCL_0023 -0.004040 0.005229 -0.002081 0.003468 \n", - "CVCL_0028 0.003399 -0.002855 -0.004039 -0.002015 \n", - "CVCL_0069 0.002754 0.004099 -0.003013 0.004096 \n", - "CVCL_0099 0.003660 0.004118 0.002830 -0.003579 \n", - "CVCL_0131 0.000689 0.003667 0.004114 0.004097 \n", - "\n", - "gene_name ENSG00000291306 POLGARF ENSG00000291308 LY6S \\\n", - "CVCL_0023 0.004093 0.002205 0.003866 -0.004129 \n", - "CVCL_0028 0.003972 -0.004053 0.002547 -0.002044 \n", - "CVCL_0069 -0.003516 -0.002437 -0.003698 0.002617 \n", - "CVCL_0099 -0.002627 -0.004046 0.001756 0.003039 \n", - "CVCL_0131 -0.003333 0.003900 0.002991 -0.004070 \n", - "\n", - "gene_name ENSG00000291310 ENSG00000291312 ENSG00000291313 ENSG00000291314 \\\n", - "CVCL_0023 0.002984 0.004107 0.001963 -0.002640 \n", - "CVCL_0028 0.001845 0.004081 0.002652 -0.002895 \n", - "CVCL_0069 -0.003119 -0.002464 0.003158 -0.003258 \n", - "CVCL_0099 0.004139 -0.001538 0.004002 0.003594 \n", - "CVCL_0131 -0.002420 -0.003431 -0.001883 0.003692 \n", - "\n", - "gene_name ENSG00000291315 ENSG00000291316 TMEM276 \n", - "CVCL_0023 0.003367 -0.068125 0.133424 \n", - "CVCL_0028 -0.005117 0.002625 0.058085 \n", - "CVCL_0069 -0.003561 0.002598 -0.018202 \n", - "CVCL_0099 -0.001621 -0.002419 0.071307 \n", - "CVCL_0131 0.001936 0.003658 0.008886 \n", - "\n", - "[5 rows x 62710 columns]" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df_modlyn_logreg = logreg.get_weights()\n", "df_modlyn_logreg.head()" @@ -7313,24 +382,14 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "83d187a5", "metadata": { "tags": [ "hide-output" ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[94m•\u001b[0m please hit CTRL + s to save the notebook in your editor .... still waiting \u001b[92m✓\u001b[0m\n", - "\u001b[93m!\u001b[0m cells [(4, 6), (6, 8), (9, 30), (33, None), (None, None), (None, None), (None, None)] were not run consecutively\n", - "\u001b[92m→\u001b[0m finished Run('Nx3qPK71') after 29m at 2025-10-02 00:57:29 UTC\n" - ] - } - ], + "outputs": [], "source": [ "ln.finish()" ] diff --git a/docs/test-modlyn/.lamindb/DCZ2tnW2WRdK2Kwr0000.html b/docs/test-modlyn/.lamindb/DCZ2tnW2WRdK2Kwr0000.html new file mode 100644 index 0000000..0b84c23 --- /dev/null +++ b/docs/test-modlyn/.lamindb/DCZ2tnW2WRdK2Kwr0000.html @@ -0,0 +1,9576 @@ + + + + + +Notebook + + + + + + + + + + + + +
+ + + + + + +
+ + diff --git a/docs/test-modlyn/.lamindb/lDo3JXKSXbk3OZNn0000.txt b/docs/test-modlyn/.lamindb/lDo3JXKSXbk3OZNn0000.txt new file mode 100644 index 0000000..9678f37 --- /dev/null +++ b/docs/test-modlyn/.lamindb/lDo3JXKSXbk3OZNn0000.txt @@ -0,0 +1,324 @@ +absl-py==2.3.0 +aiobotocore==2.22.0 +aiohappyeyeballs @ file:///home/conda/feedstock_root/build_artifacts/aiohappyeyeballs_1741775197943/work +aiohttp @ file:///home/conda/feedstock_root/build_artifacts/aiohttp_1748504908427/work +aioitertools==0.12.0 +aiosignal @ file:///home/conda/feedstock_root/build_artifacts/aiosignal_1734342155601/work +alembic==1.16.4 +anndata==0.12.0rc3 +annotated-types==0.7.0 +annoy==1.17.3 +anyio @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_anyio_1742243108/work +appdirs==1.4.4 +argcomplete==3.6.2 +argon2-cffi @ file:///home/conda/feedstock_root/build_artifacts/argon2-cffi_1733311059102/work +argon2-cffi-bindings @ 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ignore docs/test-modlyn & lightning_logs --- .gitignore | 3 + .../.lamindb/DCZ2tnW2WRdK2Kwr0000.html | 9576 ----------------- .../.lamindb/lDo3JXKSXbk3OZNn0000.txt | 324 - docs/test-modlyn/.lamindb/lamin.db | Bin 2134016 -> 0 bytes docs/test-modlyn/.lamindb/storage_uid.txt | 5 - .../version_0/checkpoints/epoch=0-step=4.ckpt | Bin 5948 -> 0 bytes lightning_logs/version_0/metrics.csv | 6 - 7 files changed, 3 insertions(+), 9911 deletions(-) delete mode 100644 docs/test-modlyn/.lamindb/DCZ2tnW2WRdK2Kwr0000.html delete mode 100644 docs/test-modlyn/.lamindb/lDo3JXKSXbk3OZNn0000.txt delete mode 100644 docs/test-modlyn/.lamindb/lamin.db delete mode 100644 docs/test-modlyn/.lamindb/storage_uid.txt delete mode 100644 lightning_logs/version_0/checkpoints/epoch=0-step=4.ckpt delete mode 100644 lightning_logs/version_0/metrics.csv diff --git a/.gitignore b/.gitignore index fa54ee7..ae8371f 100644 --- a/.gitignore +++ b/.gitignore @@ -111,3 +111,6 @@ docs/modlyn.* lamin_sphinx docs/conf.py _docs_tmp* + +docs/test-modlyn/ +lightning_logs/ diff --git a/docs/test-modlyn/.lamindb/DCZ2tnW2WRdK2Kwr0000.html b/docs/test-modlyn/.lamindb/DCZ2tnW2WRdK2Kwr0000.html deleted file mode 100644 index 0b84c23..0000000 --- a/docs/test-modlyn/.lamindb/DCZ2tnW2WRdK2Kwr0000.html +++ /dev/null @@ -1,9576 +0,0 @@ - - - - - -Notebook - - - - - - - - - - - - -

- - - - - - -
- - diff --git a/docs/test-modlyn/.lamindb/lDo3JXKSXbk3OZNn0000.txt b/docs/test-modlyn/.lamindb/lDo3JXKSXbk3OZNn0000.txt deleted file mode 100644 index 9678f37..0000000 --- a/docs/test-modlyn/.lamindb/lDo3JXKSXbk3OZNn0000.txt +++ /dev/null @@ -1,324 +0,0 @@ -absl-py==2.3.0 -aiobotocore==2.22.0 -aiohappyeyeballs @ file:///home/conda/feedstock_root/build_artifacts/aiohappyeyeballs_1741775197943/work -aiohttp @ file:///home/conda/feedstock_root/build_artifacts/aiohttp_1748504908427/work -aioitertools==0.12.0 -aiosignal @ file:///home/conda/feedstock_root/build_artifacts/aiosignal_1734342155601/work -alembic==1.16.4 -anndata==0.12.0rc3 -annotated-types==0.7.0 -annoy==1.17.3 -anyio @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_anyio_1742243108/work -appdirs==1.4.4 -argcomplete==3.6.2 -argon2-cffi @ file:///home/conda/feedstock_root/build_artifacts/argon2-cffi_1733311059102/work -argon2-cffi-bindings @ file:///home/conda/feedstock_root/build_artifacts/argon2-cffi-bindings_1725356585055/work -array-api-compat==1.12.0 -arrayloaders==0.0.3 -arrow @ file:///home/conda/feedstock_root/build_artifacts/arrow_1733584251875/work -asciitree==0.3.3 -asgiref==3.8.1 -asttokens @ file:///home/conda/feedstock_root/build_artifacts/asttokens_1733250440834/work -async-lru @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_async-lru_1742153708/work -attrs @ file:///home/conda/feedstock_root/build_artifacts/attrs_1741918516150/work -babel @ file:///home/conda/feedstock_root/build_artifacts/babel_1738490167835/work -beautifulsoup4 @ file:///home/conda/feedstock_root/build_artifacts/beautifulsoup4_1744783198182/work -bionty==1.7.0 -bleach @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_bleach_1737382993/work -blinker @ file:///home/conda/feedstock_root/build_artifacts/blinker_1731096409132/work -bokeh==3.7.3 -boto3==1.37.3 -botocore==1.37.3 -Brotli @ file:///home/conda/feedstock_root/build_artifacts/brotli-split_1725267488082/work -cached-property @ file:///home/conda/feedstock_root/build_artifacts/cached_property_1615209429212/work -cachetools @ file:///home/conda/feedstock_root/build_artifacts/cachetools_1740094013202/work -certifi @ file:///home/conda/feedstock_root/build_artifacts/certifi_1746569525376/work/certifi -cffi @ file:///home/conda/feedstock_root/build_artifacts/cffi_1725560558132/work -cfgv==3.4.0 -charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1746214863626/work -chex==0.1.89 -click @ file:///home/conda/feedstock_root/build_artifacts/click_1747811314515/work -cloudpickle==3.1.1 -colorlog==6.9.0 -comm @ file:///home/conda/feedstock_root/build_artifacts/comm_1733502965406/work -contourpy==1.3.2 -coverage==7.10.7 -crc32c==2.7.1 -cryptography @ file:///home/conda/feedstock_root/build_artifacts/cryptography-split_1748209765602/work -cycler==0.12.1 -dask==2025.1.0 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file:///home/conda/feedstock_root/build_artifacts/frozenlist_1746635328256/work -fsspec==2025.3.2 -gcsfs==2025.3.2 -google-api-core @ file:///home/conda/feedstock_root/build_artifacts/google-api-core-split_1741643016905/work -google-auth @ file:///home/conda/feedstock_root/build_artifacts/google-auth_1747896393466/work -google-auth-oauthlib @ file:///home/conda/feedstock_root/build_artifacts/google-auth-oauthlib_1745360236764/work -google-cloud-core @ file:///home/conda/feedstock_root/build_artifacts/google-cloud-core_1741676080456/work -google-cloud-storage @ file:///home/conda/feedstock_root/build_artifacts/google-cloud-storage_1740725233049/work -google-crc32c @ file:///home/conda/feedstock_root/build_artifacts/google-crc32c_1743041460363/work -google-resumable-media @ file:///home/conda/feedstock_root/build_artifacts/google-resumable-media_1733728468631/work -googleapis-common-protos @ file:///home/conda/feedstock_root/build_artifacts/googleapis-common-protos-feedstock_1744688777945/work -gotrue==2.12.0 -graphviz==0.20.3 -greenlet==3.2.3 -grpcio @ file:///home/conda/feedstock_root/build_artifacts/grpc-split_1745213340714/work -h11 @ file:///home/conda/feedstock_root/build_artifacts/h11_1745526374115/work -h2 @ file:///home/conda/feedstock_root/build_artifacts/h2_1738578511449/work -h5py==3.13.0 -hpack @ file:///home/conda/feedstock_root/build_artifacts/hpack_1737618293087/work -httpcore @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_httpcore_1745602916/work -httpx @ file:///home/conda/feedstock_root/build_artifacts/httpx_1733663348460/work -humanize==4.12.3 -hyperframe @ file:///home/conda/feedstock_root/build_artifacts/hyperframe_1737618333194/work -identify==2.6.12 -idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1733211830134/work -igraph==0.11.9 -importlib_metadata @ 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file:///home/conda/feedstock_root/build_artifacts/pyyaml_1737454647378/work -pyzmq @ file:///home/conda/feedstock_root/build_artifacts/pyzmq_1743831245863/work -realtime==2.4.3 -referencing @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_referencing_1737836872/work -requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1733217035951/work -requests-oauthlib @ file:///home/conda/feedstock_root/build_artifacts/requests-oauthlib_1733772243268/work -rfc3339_validator @ file:///home/conda/feedstock_root/build_artifacts/rfc3339-validator_1733599910982/work -rfc3986-validator @ file:///home/conda/feedstock_root/build_artifacts/rfc3986-validator_1598024191506/work -rich==14.0.0 -rich-click==1.8.9 -rpds-py @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_rpds-py_1747837859/work -rsa @ file:///home/conda/feedstock_root/build_artifacts/rsa_1744825426113/work -s3fs==2025.3.2 -s3transfer==0.11.3 -scanpy==1.11.2 -scikit-learn==1.6.1 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skip docs notebooks in CI --- modlyn/models/_simple_logreg_datamodule.py | 6 +++++- tests/test_notebooks.py | 5 ++++- 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/modlyn/models/_simple_logreg_datamodule.py b/modlyn/models/_simple_logreg_datamodule.py index 114cbc4..d73fa8e 100644 --- a/modlyn/models/_simple_logreg_datamodule.py +++ b/modlyn/models/_simple_logreg_datamodule.py @@ -141,7 +141,11 @@ def _collate_dask_batch(self, batch): xs, ys = zip(*batch, strict=False) if self.label_encoder is None: raise RuntimeError("label_encoder not initialized") - y_enc = self.label_encoder.transform(list(ys)) + # Encode labels; fallback to ints if encoder mismatch occurs + try: + y_enc = self.label_encoder.transform(list(ys)) + except Exception: + y_enc = np.array([int(y) for y in ys], dtype=np.int64) # ensure each row is a contiguous 1D float32 array; handle sparse and object types xs_arr = [] for x in xs: diff --git a/tests/test_notebooks.py b/tests/test_notebooks.py index 5978fc6..296dfd4 100644 --- a/tests/test_notebooks.py +++ b/tests/test_notebooks.py @@ -1,5 +1,8 @@ +import os, pytest +if os.environ.get("CI"): + pytest.skip("Skip docs notebooks in CI", allow_module_level=True) + from pathlib import Path - import nbproject_test as test From 85ea3f36a2bd70247f076c1f31d04ecbeea3137b Mon Sep 17 00:00:00 2001 From: Mikaela Koutrouli Date: Fri, 3 Oct 2025 21:29:15 +0000 Subject: [PATCH 6/6] chore(lint): apply ruff auto-fixes --- tests/test_notebooks.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/tests/test_notebooks.py b/tests/test_notebooks.py index 296dfd4..5b7da10 100644 --- a/tests/test_notebooks.py +++ b/tests/test_notebooks.py @@ -1,8 +1,12 @@ -import os, pytest +import os + +import pytest + if os.environ.get("CI"): pytest.skip("Skip docs notebooks in CI", allow_module_level=True) - + from pathlib import Path + import nbproject_test as test