Train an ML model on a dataset#
In the previous tutorial, we loaded an entire dataset into memory to perform a simple analysis.
Here, we’ll iterate over the files within the dataset to train an ML model.
import lamindb as ln
import anndata as ad
import numpy as np
💡 lamindb instance: testuser1/test-scrna
ln.track()
💡 notebook imports: anndata==0.9.2 lamindb==0.63.4 numpy==1.26.2 torch==2.1.1
💡 saved: Transform(uid='Qr1kIHvK506rz8', name='Train an ML model on a dataset', short_name='scrna5', version='0', type=notebook, updated_at=2023-12-08 11:40:25 UTC, created_by_id=1)
💡 saved: Run(uid='VIVeHOh22UCs06zn69VT', run_at=2023-12-08 11:40:25 UTC, transform_id=5, created_by_id=1)
Preprocessing#
Let us get our dataset:
dataset_v2 = ln.Dataset.filter(name="My versioned scRNA-seq dataset", version="2").one()
dataset_v2
Dataset(uid='CWQMw6Uyq6Ho5mlvHm4W', name='My versioned scRNA-seq dataset', version='2', hash='BOAf0T5UbN_iOe3fQDyq', visibility=1, updated_at=2023-12-08 11:40:03 UTC, transform_id=2, run_id=2, initial_version_id=1, created_by_id=1)
PyTorch DataLoader#
If you need to train your model on a list of files, you can use mapped() with the PyTorch DataLoader.
It only loads batches into memory and thus allows to work with very large datasets.
from torch.utils.data import DataLoader, WeightedRandomSampler
Make a MappedDataset object out of ln.Dataset. It also does virtual inner join of the variables of the underlying AnnData objects.
ds_mapped = dataset_v2.mapped(label_keys=["cell_type"])
The intersected variable names can be accessed:
len(ds_mapped.var_joint)
749
This is compatible with pytorch DataLoader because it implements __getitem__ over a list of AnnData files.
ds_mapped[5]
Show code cell output
[array([0. , 0. , 0. , 1.51316404, 0. ,
0. , 0. , 0. , 1.01904154, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 3.43718052, 0. , 0. ,
0. , 1.51316404, 0. , 0. , 1.51316404,
1.01904154, 0. , 0. , 1.01904154, 1.01904154,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 1.01904154, 0. ,
0. , 0. , 0. , 0. , 2.08965826,
0. , 1.51316404, 1.84239149, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
1.01904154, 0. , 0. , 0. , 0. ,
1.01904154, 0. , 0. , 0. , 0. ,
1.84239149, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
1.01904154, 0. , 0. , 1.01904154, 0. ,
1.84239149, 0. , 1.01904154, 0. , 0. ,
0. , 0. , 1.01904154, 0. , 0. ,
0. , 1.51316404, 0. , 0. , 2.28774452,
2.45300555, 0. , 0. , 1.01904154, 0. ,
0. , 0. , 0. , 1.01904154, 0. ,
0. , 0. , 1.01904154, 1.01904154, 1.01904154,
1.01904154, 1.84239149, 0. , 0. , 2.08965826,
1.01904154, 1.84239149, 0. , 0. , 0. ,
0. , 0. , 1.51316404, 0. , 1.51316404,
1.01904154, 0. , 0. , 1.01904154, 0. ,
0. , 0. , 1.01904154, 0. , 1.01904154,
0. , 0. , 1.01904154, 0. , 1.01904154,
1.51316404, 0. , 1.01904154, 0. , 0. ,
1.01904154, 1.01904154, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 2.28774452, 0. , 1.51316404, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 1.01904154, 0. ,
1.51316404, 1.01904154, 0. , 0. , 1.01904154,
1.84239149, 2.08965826, 1.01904154, 0. , 1.51316404,
1.01904154, 1.01904154, 1.01904154, 0. , 1.01904154,
0. , 0. , 0. , 0. , 0. ,
1.01904154, 1.51316404, 0. , 0. , 1.84239149,
0. , 1.01904154, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
5.14149761, 0. , 2.45300555, 1.01904154, 1.51316404,
0. , 0. , 1.01904154, 0. , 2.45300555,
1.01904154, 0. , 0. , 1.01904154, 0. ,
1.51316404, 0. , 1.84239149, 0. , 0. ,
1.51316404, 2.92881131, 0. , 0. , 0. ,
2.28774452, 1.01904154, 3.85087061, 1.51316404, 3.24989128,
2.45300555, 2.28774452, 0. , 1.84239149, 0. ,
2.71894431, 2.92881131, 0. , 1.01904154, 0. ,
1.01904154, 0. , 1.01904154, 1.01904154, 0. ,
2.28774452, 1.01904154, 0. , 1.84239149, 1.01904154,
1.01904154, 1.01904154, 0. , 0. , 0. ,
1.51316404, 1.01904154, 1.84239149, 0. , 0. ,
1.01904154, 1.84239149, 1.84239149, 0. , 0. ,
1.51316404, 0. , 1.01904154, 0. , 2.08965826,
1.01904154, 0. , 0. , 1.01904154, 0. ,
0. , 1.01904154, 1.01904154, 2.59478951, 1.01904154,
0. , 0. , 0. , 0. , 0. ,
0. , 1.51316404, 0. , 1.01904154, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 2.08965826, 0. , 0. ,
0. , 0. , 1.01904154, 0. , 1.01904154,
0. , 0. , 0. , 1.01904154, 0. ,
0. , 0. , 0. , 0. , 1.01904154,
0. , 1.51316404, 0. , 0. , 0. ,
0. , 0. , 0. , 1.51316404, 0. ,
1.01904154, 1.51316404, 0. , 1.01904154, 0. ,
1.51316404, 0. , 1.01904154, 0. , 1.01904154,
0. , 0. , 1.01904154, 0. , 0. ,
0. , 3.43718052, 0. , 0. , 0. ,
0. , 2.08965826, 0. , 0. , 1.01904154,
0. , 1.01904154, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 1.51316404,
1.01904154, 3.17876172, 0. , 1.01904154, 1.01904154,
1.01904154, 0. , 0. , 1.01904154, 0. ,
1.01904154, 0. , 0. , 0. , 0. ,
1.51316404, 1.51316404, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 1.51316404, 1.51316404, 1.01904154,
0. , 0. , 0. , 2.45300555, 0. ,
1.51316404, 0. , 0. , 0. , 0. ,
1.01904154, 1.01904154, 0. , 1.01904154, 0. ,
1.51316404, 0. , 0. , 1.01904154, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 1.01904154, 0. , 1.01904154, 0. ,
1.51316404, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
1.51316404, 0. , 0. , 0. , 1.01904154,
1.01904154, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 2.08965826, 0. ,
2.45300555, 1.01904154, 0. , 0. , 0. ,
1.51316404, 0. , 1.51316404, 1.01904154, 0. ,
0. , 0. , 0. , 1.01904154, 1.01904154,
0. , 0. , 0. , 1.01904154, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 2.28774452,
0. , 2.59478951, 1.01904154, 0. , 0. ,
0. , 0. , 1.01904154, 2.92881131, 1.01904154,
2.45300555, 0. , 0. , 1.01904154, 2.08965826,
0. , 0. , 0. , 0. , 0. ,
1.51316404, 0. , 1.01904154, 0. , 1.01904154,
0. , 0. , 1.01904154, 0. , 1.01904154,
0. , 0. , 0. , 0. , 0. ,
0. , 1.01904154, 1.51316404, 0. , 0. ,
0. , 0. , 1.01904154, 0. , 0. ,
0. , 0. , 1.01904154, 1.51316404, 1.01904154,
0. , 0. , 0. , 2.08965826, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 1.84239149,
0. , 0. , 0. , 0. , 0. ,
0. , 1.51316404, 0. , 0. , 0. ,
1.84239149, 0. , 0. , 1.84239149, 0. ,
0. , 0. , 0. , 0. , 1.01904154,
0. , 0. , 0. , 0. , 0. ,
0. , 1.51316404, 0. , 0. , 1.51316404,
0. , 2.45300555, 0. , 1.51316404, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 1.51316404, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 1.01904154,
1.01904154, 1.51316404, 0. , 0. , 0. ,
1.01904154, 1.01904154, 1.01904154, 0. , 0. ,
0. , 0. , 0. , 1.01904154, 1.01904154,
0. , 0. , 0. , 1.51316404, 0. ,
0. , 0. , 0. , 0. , 1.01904154,
1.84239149, 2.08965826, 0. , 0. , 0. ,
1.01904154, 0. , 2.92881131, 0. , 1.01904154,
0. , 1.01904154, 1.01904154, 0. , 1.84239149,
0. , 0. , 0. , 0. , 0. ,
1.01904154, 0. , 0. , 2.08965826, 0. ,
0. , 0. , 0. , 0. , 3.31629562,
0. , 2.45300555, 0. , 1.01904154, 0. ,
1.01904154, 0. , 0. , 0. , 0. ,
0. , 0. , 1.01904154, 0. , 0. ,
0. , 0. , 1.01904154, 0. , 0. ,
0. , 1.01904154, 0. , 0. , 1.01904154,
1.51316404, 1.84239149, 0. , 2.45300555, 1.01904154,
1.01904154, 0. , 1.01904154, 1.01904154, 0. ,
1.51316404, 0. , 1.01904154, 0. , 0. ,
0. , 1.01904154, 1.01904154, 0. , 0. ,
1.01904154, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 1.01904154,
0. , 2.08965826, 0. , 1.01904154, 1.01904154,
0. , 0. , 0. , 1.51316404, 0. ,
0. , 0. , 1.51316404, 1.01904154, 1.01904154,
0. , 0. , 0. , 0. , 1.01904154,
1.01904154, 1.01904154, 1.51316404, 0. , 1.01904154,
0. , 0. , 0. , 4.55217552, 1.01904154,
0. , 1.01904154, 0. , 4.14264011]),
13]
The labels are encoded into integers.
ds_mapped.encoders
Show code cell output
[{'naive thymus-derived CD4-positive, alpha-beta T cell': 0,
'conventional dendritic cell': 1,
'effector memory CD8-positive, alpha-beta T cell, terminally differentiated': 2,
'group 3 innate lymphoid cell': 3,
'dendritic cell, human': 4,
'CD8-positive, alpha-beta memory T cell, CD45RO-positive': 5,
'plasmablast': 6,
'gamma-delta T cell': 7,
'CD8-positive, CD25-positive, alpha-beta regulatory T cell': 8,
'effector memory CD4-positive, alpha-beta T cell, terminally differentiated': 9,
'T follicular helper cell': 10,
'plasmacytoid dendritic cell': 11,
'CD16-negative, CD56-bright natural killer cell, human': 12,
'memory B cell': 13,
'dendritic cell': 14,
'macrophage': 15,
'mucosal invariant T cell': 16,
'germinal center B cell': 17,
'animal cell': 18,
'naive thymus-derived CD8-positive, alpha-beta T cell': 19,
'non-classical monocyte': 20,
'alpha-beta T cell': 21,
'CD38-positive naive B cell': 22,
'plasma cell': 23,
'B cell, CD19-positive': 24,
'naive B cell': 25,
'alveolar macrophage': 26,
'CD16-positive, CD56-dim natural killer cell, human': 27,
'megakaryocyte': 28,
'progenitor cell': 29,
'CD4-positive helper T cell': 30,
'effector memory CD4-positive, alpha-beta T cell': 31,
'mast cell': 32,
'cytotoxic T cell': 33,
'CD4-positive, alpha-beta T cell': 34,
'lymphocyte': 35,
'CD14-positive, CD16-negative classical monocyte': 36,
'regulatory T cell': 37,
'CD8-positive, alpha-beta memory T cell': 38,
'classical monocyte': 39}]
Let us use a weighted sampler:
# label_key for weight doesn't have to be in labels on init
sampler = WeightedRandomSampler(
weights=ds_mapped.get_label_weights("cell_type"), num_samples=len(ds_mapped)
)
dl = DataLoader(ds_mapped, batch_size=128, sampler=sampler)
We can now iterate through the data loader:
for batch in dl:
pass
Close the connections in MappedDataset:
ds_mapped.close()
In practice, use a context manager
with ds_train.mapped(label_keys=["cell_type"]) as ds_mapped:
sampler = WeightedRandomSampler(
weights=ds_mapped.get_label_weights("cell_type"), num_samples=len(ds_mapped)
)
dl = DataLoader(ds_mapped, batch_size=128, sampler=sampler)
for batch in dl:
pass
Show code cell content
# clean up test instance
!lamin delete --force test-scrna
!rm -r ./test-scrna
💡 deleting instance testuser1/test-scrna
✅ deleted instance settings file: /home/runner/.lamin/instance--testuser1--test-scrna.env
✅ instance cache deleted
✅ deleted '.lndb' sqlite file
❗ consider manually deleting your stored data: /home/runner/work/lamin-usecases/lamin-usecases/docs/test-scrna