lamindb.Collection¶

Bases: SQLRecord, IsVersioned, TracksRun, TracksUpdates

Versioned collections of artifacts.

Parameters:

artifacts – list[Artifact] A list of artifacts.
key – str A file-path like key, analogous to the key parameter of Artifact and Transform.
description – str | None = None A description.
revises – Collection | None = None An old version of the collection.
run – Run | None = None The run that creates the collection.
meta – Artifact | None = None An artifact that defines metadata for the collection.
reference – str | None = None A simple reference, e.g. an external ID or a URL.
reference_type – str | None = None A way to indicate to indicate the type of the simple reference "url".

See also

Artifact

Examples

Create a collection from a list of Artifact objects:

>>> collection = ln.Collection([artifact1, artifact2], key="my_project/my_collection")

Create a collection that groups a data & a metadata artifact (e.g., here RxRx: cell imaging):

>>> collection = ln.Collection(data_artifact, key="my_project/my_collection", meta=metadata_artifact)

Attributes¶

property data_artifact: Artifact | None¶

Access to a single data artifact.

If the collection has a single data & metadata artifact, this allows access via:

collection.data_artifact  # first & only element of collection.artifacts
collection.meta_artifact  # metadata

property name: str¶

Name of the collection.

Splits key on / and returns the last element.

property ordered_artifacts: QuerySet¶

Ordered QuerySet of .artifacts.

Accessing the many-to-many field collection.artifacts directly gives you non-deterministic order.

Using the property .ordered_artifacts allows to iterate through a set that’s ordered in the order of creation.

property stem_uid: str¶

Universal id characterizing the version family.

The full uid of a record is obtained via concatenating the stem uid and version information:

stem_uid = random_base62(n_char)  # a random base62 sequence of length 12 (transform) or 16 (artifact, collection)
version_uid = "0000"  # an auto-incrementing 4-digit base62 number
uid = f"{stem_uid}{version_uid}"  # concatenate the stem_uid & version_uid

property transform: Transform | None¶: Transform whose run created the collection.

property versions: QuerySet¶

Lists all records of the same version family.

>>> new_artifact = ln.Artifact(df2, revises=artifact).save()
>>> new_artifact.versions()

Simple fields¶

uid: str¶: Universal id, valid across DB instances.

key: str¶: Name or path-like key.

description: str | None¶: A description or title.

hash: str | None¶: Hash of collection content.

reference: str | None¶: A reference like URL or external ID.

reference_type: str | None¶: Type of reference, e.g., cellxgene Census collection_id.

meta_artifact: Artifact | None¶

An artifact that stores metadata that indexes a collection.

It has a 1:1 correspondence with an artifact. If needed, you can access the collection from the artifact via a private field: artifact._meta_of_collection.

version: str | None¶

Version (default None).

Defines version of a family of records characterized by the same stem_uid.

Consider using semantic versioning with Python versioning.

is_latest: bool¶: Boolean flag that indicates whether a record is the latest in its version family.

created_at: datetime¶: Time of creation of record.

updated_at: datetime¶: Time of last update to record.

Relational fields¶

branch: Branch¶: Whether record is on a branch or in another “special state”.

space: Space¶: The space in which the record lives.

created_by: User¶: Creator of record.

run: Run | None¶: Run that created the collection.

ulabels: ULabel¶: ULabels sampled in the collection (see Feature).

input_of_runs: Run¶: Runs that use this collection as an input.

artifacts: Artifact¶: Artifacts in collection.

references: Reference¶: Linked references.

projects: Project¶: Linked projects.

Class methods¶

classmethod get(idlike=None, *, is_run_input=False, **expressions)¶

Get a single collection.

Parameters:

idlike (int | str | None, default: None) – Either a uid stub, uid or an integer id.
is_run_input (bool | Run, default: False) – Whether to track this collection as run input.
expressions – Fields and values passed as Django query expressions.

Raises:

lamindb.errors.DoesNotExist – In case no matching record is found.

Return type:

Artifact

See also

Method in SQLRecord base class: get()

Examples

collection = ln.Collection.get("okxPW6GIKBfRBE3B0000")
collection = ln.Collection.get(key="scrna/collection1")

classmethod filter(*queries, **expressions)¶

Query records.

Parameters:

queries – One or multiple Q objects.
expressions – Fields and values passed as Django query expressions.

Return type:

QuerySet

Returns:

A QuerySet.

See also

Guide: Query & search registries
Django documentation: Queries

Examples

>>> ln.ULabel(name="my label").save()
>>> ln.ULabel.filter(name__startswith="my").df()

classmethod df(include=None, features=False, limit=100)¶

Convert to pd.DataFrame.

By default, shows all direct fields, except updated_at.

Use arguments include or feature to include other data.

Parameters:

include (str | list[str] | None, default: None) – Related fields to include as columns. Takes strings of form "ulabels__name", "cell_types__name", etc. or a list of such strings.
features (bool | list[str], default: False) – If a list of feature names, filters Feature down to these features. If True, prints all features with dtypes in the core schema module. If "queryset", infers the features used within the set of artifacts or records. Only available for Artifact and Record.
limit (int, default: 100) – Maximum number of rows to display from a Pandas DataFrame. Defaults to 100 to reduce database load.

Return type:

DataFrame

Examples

Include the name of the creator in the DataFrame:

>>> ln.ULabel.df(include="created_by__name"])

Include display of features for Artifact:

>>> df = ln.Artifact.df(features=True)
>>> ln.view(df)  # visualize with type annotations

Only include select features:

>>> df = ln.Artifact.df(features=["cell_type_by_expert", "cell_type_by_model"])

classmethod search(string, *, field=None, limit=20, case_sensitive=False)¶

Search.

Parameters:

string (str) – The input string to match against the field ontology values.
field (str | DeferredAttribute | None, default: None) – The field or fields to search. Search all string fields by default.
limit (int | None, default: 20) – Maximum amount of top results to return.
case_sensitive (bool, default: False) – Whether the match is case sensitive.

Return type:

QuerySet

Returns:

A sorted DataFrame of search results with a score in column score. If return_queryset is True. QuerySet.

See also

filter() lookup()

Examples

>>> ulabels = ln.ULabel.from_values(["ULabel1", "ULabel2", "ULabel3"], field="name")
>>> ln.save(ulabels)
>>> ln.ULabel.search("ULabel2")

classmethod lookup(field=None, return_field=None)¶

Return an auto-complete object for a field.

Parameters:

field (str | DeferredAttribute | None, default: None) – The field to look up the values for. Defaults to first string field.
return_field (str | DeferredAttribute | None, default: None) – The field to return. If None, returns the whole record.
keep – When multiple records are found for a lookup, how to return the records. - "first": return the first record. - "last": return the last record. - False: return all records.

Return type:

NamedTuple

Returns:

A NamedTuple of lookup information of the field values with a dictionary converter.

See also

search()

Examples

>>> import bionty as bt
>>> bt.settings.organism = "human"
>>> bt.Gene.from_source(symbol="ADGB-DT").save()
>>> lookup = bt.Gene.lookup()
>>> lookup.adgb_dt
>>> lookup_dict = lookup.dict()
>>> lookup_dict['ADGB-DT']
>>> lookup_by_ensembl_id = bt.Gene.lookup(field="ensembl_gene_id")
>>> genes.ensg00000002745
>>> lookup_return_symbols = bt.Gene.lookup(field="ensembl_gene_id", return_field="symbol")

classmethod using(instance)¶

Use a non-default LaminDB instance.

Parameters:: instance (str | None) – An instance identifier of form “account_handle/instance_name”.
Return type:: QuerySet

Examples

>>> ln.ULabel.using("account_handle/instance_name").search("ULabel7", field="name")
            uid    score
name
ULabel7  g7Hk9b2v  100.0
ULabel5  t4Jm6s0q   75.0
ULabel6  r2Xw8p1z   75.0

Methods¶

append(artifact, run=None)¶

Append an artifact to the collection.

This does not modify the original collection in-place, but returns a new version of the original collection with the appended artifact.

Parameters:

artifact (Artifact) – An artifact to add to the collection.
run (Run | None, default: None) – The run that creates the new version of the collection.

Return type:

Collection

Examples

collection_v1 = ln.Collection(artifact, key="My collection").save()
collection_v2 = collection.append(another_artifact)  # returns a new version of the collection
collection_v2.save()  # save the new version

open(engine='pyarrow', is_run_input=None, **kwargs)¶

Open a dataset for streaming.

Works for pyarrow and polars compatible formats (.parquet, .csv, .ipc etc. files or directories with such files).

Parameters:

engine (Literal['pyarrow', 'polars'], default: 'pyarrow') – Which module to use for lazy loading of a dataframe from pyarrow or polars compatible formats.
is_run_input (bool | None, default: None) – Whether to track this artifact as run input.
**kwargs – Keyword arguments for pyarrow.dataset.dataset or polars.scan_* functions.

Return type:

Dataset | Iterator[LazyFrame]

Notes

For more info, see guide: Slice arrays.

mapped(layers_keys=None, obs_keys=None, obsm_keys=None, obs_filter=None, join='inner', encode_labels=True, unknown_label=None, cache_categories=True, parallel=False, dtype=None, stream=False, is_run_input=None)¶

Return a map-style dataset.

Returns a pytorch map-style dataset by virtually concatenating AnnData arrays.

By default (stream=False) AnnData arrays are moved into a local cache first.

__getitem__ of the MappedCollection object takes a single integer index and returns a dictionary with the observation data sample for this index from the AnnData objects in the collection. The dictionary has keys for layers_keys (.X is in "X"), obs_keys, obsm_keys (under f"obsm_{key}") and also "_store_idx" for the index of the AnnData object containing this observation sample.

Note

For a guide, see Train a machine learning model on a collection.

This method currently only works for collections or query sets of AnnData artifacts.

Parameters:

layers_keys (str | list[str] | None, default: None) – Keys from the .layers slot. layers_keys=None or "X" in the list retrieves .X.
obs_keys (str | list[str] | None, default: None) – Keys from the .obs slots.
obsm_keys (str | list[str] | None, default: None) – Keys from the .obsm slots.
obs_filter (dict[str, str | list[str]] | None, default: None) – Select only observations with these values for the given obs columns. Should be a dictionary with obs column names as keys and filtering values (a string or a list of strings) as values.
join (Literal['inner', 'outer'] | None, default: 'inner') – "inner" or "outer" virtual joins. If None is passed, does not join.
encode_labels (bool | list[str], default: True) – Encode labels into integers. Can be a list with elements from obs_keys.
unknown_label (str | dict[str, str] | None, default: None) – Encode this label to -1. Can be a dictionary with keys from obs_keys if encode_labels=True or from encode_labels if it is a list.
cache_categories (bool, default: True) – Enable caching categories of obs_keys for faster access.
parallel (bool, default: False) – Enable sampling with multiple processes.
dtype (str | None, default: None) – Convert numpy arrays from .X, .layers and .obsm
stream (bool, default: False) – Whether to stream data from the array backend.
is_run_input (bool | None, default: None) – Whether to track this collection as run input.

Return type:

MappedCollection

Examples

>>> import lamindb as ln
>>> from torch.utils.data import DataLoader
>>> ds = ln.Collection.get(description="my collection")
>>> mapped = collection.mapped(obs_keys=["cell_type", "batch"])
>>> dl = DataLoader(mapped, batch_size=128, shuffle=True)
>>> # also works for query sets of artifacts, '...' represents some filtering condition
>>> # additional filtering on artifacts of the collection
>>> mapped = collection.artifacts.all().filter(...).order_by("-created_at").mapped()
>>> # or directly from a query set of artifacts
>>> mapped = ln.Artifact.filter(..., otype="AnnData").order_by("-created_at").mapped()

cache(is_run_input=None)¶

Download cloud artifacts in collection to local cache.

Follows synching logic: only caches outdated artifacts.

Returns paths to locally cached on-disk artifacts.

Parameters:: is_run_input (bool | None, default: None) – Whether to track this collection as run input.
Return type:: list[UPath]

load(join='outer', is_run_input=None, **kwargs)¶

Cache and load to memory.

Returns an in-memory concatenated DataFrame or AnnData object.

Return type:: DataFrame | AnnData

delete(permanent=None)¶

Delete collection.

Parameters:: permanent (bool | None, default: None) – Whether to permanently delete the collection record (skips trash).
Return type:: None

Examples

For any Collection object collection, call:

>>> collection.delete()

save(using=None)¶

Save the collection and underlying artifacts to database & storage.

Parameters:: using (str | None, default: None) – The database to which you want to save.
Return type:: Collection

Examples

>>> collection = ln.Collection("./myfile.csv", name="myfile")

restore()¶

Restore collection record from trash.

Return type:: None

Examples

For any Collection object collection, call:

>>> collection.restore()

describe()¶

Describe relations of record.

Return type:: None

Examples

>>> artifact.describe()

view_lineage(with_children=True, return_graph=False)¶

Graph of data flow.

Return type:: Digraph | None

Notes

For more info, see use cases: Data lineage.

Examples

>>> collection.view_lineage()
>>> artifact.view_lineage()