lamindb.Collection

class lamindb.Collection(artifacts: Artifact | list[Artifact], key: str, description: str | None = None, meta: Any | None = None, reference: str | None = None, reference_type: str | None = None, run: Run | None = None, revises: Collection | None = None, skip_hash_lookup: bool = False)

Bases: SQLRecord, IsVersioned, TracksRun, TracksUpdates

Versioned collections of artifacts.

Parameters:

  • artifacts – Artifact | list[Artifact] One or several 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 by the order of the list that created the collection.

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.

is_locked: bool

Whether the record is locked for edits.

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 annotating 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.

blocks: CollectionBlock

Blocks that annotate this collection.

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:

docs: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

Examples

>>> ln.ULabel(name="my label").save()
>>> ln.ULabel.filter(name__startswith="my").to_dataframe()
classmethod to_dataframe(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.to_dataframe(include="created_by__name"])

Include display of features for Artifact:

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

Only include select features:

>>> df = ln.Artifact.to_dataframe(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 & stream 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 syncing logic: only downloads outdated artifacts.

Returns ordered paths to locally cached on-disk artifacts via .ordered_artifacts.all():

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

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)

View data lineage graph.

Return type:

Digraph | None

delete(permanent=None, **kwargs)

Delete record.

Parameters:

permanent (bool | None, default: None) – Whether to permanently delete the record (skips trash). If None, performs soft delete if the record is not already in the trash.

Return type:

None

Examples

For any SQLRecord object record, call:

>>> record.delete()
get_deferred_fields()

Return a set containing names of deferred fields for this instance.

refresh_from_db(using=None, fields=None, from_queryset=None)

Reload field values from the database.

By default, the reloading happens from the database this instance was loaded from, or by the read router if this instance wasn’t loaded from any database. The using parameter will override the default.

Fields can be used to specify which fields to reload. The fields should be an iterable of field attnames. If fields is None, then all non-deferred fields are reloaded.

When accessing deferred fields of an instance, the deferred loading of the field will call this method.

async arefresh_from_db(using=None, fields=None, from_queryset=None)
serializable_value(field_name)

Return the value of the field name for this instance. If the field is a foreign key, return the id value instead of the object. If there’s no Field object with this name on the model, return the model attribute’s value.

Used to serialize a field’s value (in the serializer, or form output, for example). Normally, you would just access the attribute directly and not use this method.

async asave(*args, force_insert=False, force_update=False, using=None, update_fields=None)
save_base(raw=False, force_insert=False, force_update=False, using=None, update_fields=None)

Handle the parts of saving which should be done only once per save, yet need to be done in raw saves, too. This includes some sanity checks and signal sending.

The ‘raw’ argument is telling save_base not to save any parent models and not to do any changes to the values before save. This is used by fixture loading.

async adelete(using=None, keep_parents=False)
prepare_database_save(field)
clean()

Hook for doing any extra model-wide validation after clean() has been called on every field by self.clean_fields. Any ValidationError raised by this method will not be associated with a particular field; it will have a special-case association with the field defined by NON_FIELD_ERRORS.

validate_unique(exclude=None)

Check unique constraints on the model and raise ValidationError if any failed.

date_error_message(lookup_type, field_name, unique_for)
unique_error_message(model_class, unique_check)
get_constraints()
validate_constraints(exclude=None)
clean_fields(exclude=None)

Clean all fields and raise a ValidationError containing a dict of all validation errors if any occur.