Train a machine learning model on a collection

Here, we iterate over the artifacts within a collection to train a machine learning model at scale.

import lamindb as ln

💡 connected lamindb: testuser1/test-scrna

ln.settings.transform.stem_uid = "Qr1kIHvK506r"
ln.settings.transform.version = "1"
ln.track()

💡 notebook imports: lamindb==0.72.0 torch==2.3.0

💡 saved: Transform(version='1', uid='Qr1kIHvK506r5zKv', name='Train a machine learning model on a collection', key='scrna5', type='notebook', updated_at=2024-05-20 08:34:35 UTC, created_by_id=1)

💡 saved: Run(uid='f8CRJlKYc3z2dfPvj5y3', transform_id=5, created_by_id=1)

Query our collection:

collection = ln.Collection.filter(
    name="My versioned scRNA-seq collection", version="2"
).one()
collection.describe()

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Collection(version='2', updated_at=2024-05-20 08:34:14 UTC, uid='h97PCjzwqbbQTdGPTxh4', name='My versioned scRNA-seq collection', hash='HNR3VFV60_yqRnUka11E', visibility=1)

Provenance:
  📎 created_by: User(uid='DzTjkKse', handle='testuser1', name='Test User1')
  📎 transform: Transform(version='1', uid='ManDYgmftZ8C5zKv', name='Standardize and append a batch of data', key='scrna2', type='notebook')
  📎 run: Run(uid='ka9LM9UnxfRAbPbJ2vRI', started_at=2024-05-20 08:33:50 UTC, is_consecutive=True)
  📎 input_of (core.Run): ['2024-05-20 08:34:23 UTC']
Features:
  obs: FeatureSet(uid='nMzktuMdWrMztdGcNVQ9', n=4, registry='Feature')
    🔗 donor (4, cat[ULabel]): 
    🔗 tissue (cat[bionty.Tissue])
    🔗 cell_type (cat[bionty.CellType])
    🔗 assay (cat[bionty.ExperimentalFactor])
  var: FeatureSet(uid='m2n31KWfgmw4UL1IGSdm', n=36508, dtype='float', registry='bionty.Gene')
    'MIR1302-2HG', 'FAM138A', 'OR4F5', 'None', 'OR4F29', 'OR4F16', 'LINC01409', 'FAM87B', 'LINC01128', 'LINC00115', 'FAM41C', 'LINC02593', 'SAMD11', 'NOC2L', 'KLHL17', 'PLEKHN1', 'PERM1', 'HES4'

Create a map-style dataset

Let us create a map-style dataset using using mapped(): a MappedCollection. This is what, for example, the PyTorch DataLoader expects as an input.

Under-the-hood, it performs a virtual inner join of the features of the underlying AnnData objects and thus allows to work with very large collections.

You can either perform a virtual inner join:

with collection.mapped(obs_keys=["cell_type"], join="inner") as dataset:
    print(len(dataset.var_joint))

749

Or a virtual outer join:

dataset = collection.mapped(obs_keys=["cell_type"], join="outer")

len(dataset.var_joint)

36508

This is compatible with a PyTorch DataLoader because it implements __getitem__ over a list of backed AnnData objects. The 5th cell in the collection can be accessed like:

dataset[5]

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{'X': array([ 0.   ,  0.   ,  0.   , ...,  0.   ,  0.   , -0.456], dtype=float32),
 '_store_idx': 0,
 'cell_type': 27}

The labels are encoded into integers:

dataset.encoders

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{'cell_type': {'B cell, CD19-positive': 0,
  'animal cell': 1,
  'CD8-positive, alpha-beta memory T cell, CD45RO-positive': 2,
  'conventional dendritic cell': 3,
  'mast cell': 4,
  'CD4-positive, alpha-beta T cell': 5,
  'megakaryocyte': 6,
  'naive thymus-derived CD4-positive, alpha-beta T cell': 7,
  'macrophage': 8,
  'CD16-negative, CD56-bright natural killer cell, human': 9,
  'lymphocyte': 10,
  'CD4-positive helper T cell': 11,
  'naive thymus-derived CD8-positive, alpha-beta T cell': 12,
  'CD38-positive naive B cell': 13,
  'germinal center B cell': 14,
  'T follicular helper cell': 15,
  'memory B cell': 16,
  'CD8-positive, CD25-positive, alpha-beta regulatory T cell': 17,
  'alveolar macrophage': 18,
  'gamma-delta T cell': 19,
  'plasmacytoid dendritic cell': 20,
  'effector memory CD8-positive, alpha-beta T cell, terminally differentiated': 21,
  'alpha-beta T cell': 22,
  'group 3 innate lymphoid cell': 23,
  'mucosal invariant T cell': 24,
  'regulatory T cell': 25,
  'CD14-positive, CD16-negative classical monocyte': 26,
  'cytotoxic T cell': 27,
  'progenitor cell': 28,
  'naive B cell': 29,
  'plasma cell': 30,
  'non-classical monocyte': 31,
  'classical monocyte': 32,
  'CD8-positive, alpha-beta memory T cell': 33,
  'dendritic cell, human': 34,
  'effector memory CD4-positive, alpha-beta T cell': 35,
  'dendritic cell': 36,
  'plasmablast': 37,
  'CD16-positive, CD56-dim natural killer cell, human': 38,
  'effector memory CD4-positive, alpha-beta T cell, terminally differentiated': 39}}

Create a pytorch DataLoader

Let us use a weighted sampler:

from torch.utils.data import DataLoader, WeightedRandomSampler

# label_key for weight doesn't have to be in labels on init
sampler = WeightedRandomSampler(
    weights=dataset.get_label_weights("cell_type"), num_samples=len(dataset)
)
dataloader = DataLoader(dataset, batch_size=128, sampler=sampler)

We can now iterate through the data loader:

for batch in dataloader:
    pass

Close the connections in MappedCollection:

dataset.close()

In practice, use a context manager

with collection.mapped(obs_keys=["cell_type"]) as dataset:
    sampler = WeightedRandomSampler(
        weights=dataset.get_label_weights("cell_type"), num_samples=len(dataset)
    )
    dataloader = DataLoader(dataset, batch_size=128, sampler=sampler)
    for batch in dataloader:
        pass