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import json |
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from pathlib import Path |
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from textwrap import dedent |
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import datetime |
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import functools |
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import types |
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import typing |
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import pydantic |
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import mlcroissant as mlc |
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from . import validate |
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_distribution: list[mlc.FileObject | mlc.FileSet] = [ |
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mlc.FileObject( |
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id="repo", |
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name="repo", |
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content_url="https://huggingface.co/datasets/bboldt/elcc/", |
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encoding_format="git+https", |
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sha256="main", |
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), |
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mlc.FileSet( |
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id="system-metadata-files", |
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name="system-metadata-files", |
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contained_in=["repo"], |
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includes=["systems/*/system.json"], |
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encoding_format="application/json", |
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), |
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mlc.FileSet( |
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id="system-metadata-files-raw", |
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name="system-metadata-files-raw", |
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contained_in=["repo"], |
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includes=["systems/*/system.json"], |
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encoding_format="text/plain", |
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), |
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mlc.FileSet( |
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id="corpus-metadata-files", |
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name="corpus-metadata-files", |
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contained_in=["repo"], |
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includes=["systems/*/data/*/metadata.json"], |
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encoding_format="application/json", |
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), |
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mlc.FileSet( |
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id="corpus-metadata-files-raw", |
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name="corpus-metadata-files-raw", |
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contained_in=["repo"], |
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includes=["systems/*/data/*/metadata.json"], |
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encoding_format="text/plain", |
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), |
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] |
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_metadata = mlc.Metadata( |
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name="ELCC", |
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description="ELCC is a collection of emergent language corpora with accompanying metadata and analyses.", |
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license=["https://creativecommons.org/licenses/by/4.0/"], |
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url="https://huggingface.co/datasets/bboldt/elcc/", |
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date_published=datetime.datetime.now(datetime.UTC), |
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cite_as=dedent( |
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"""\ |
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@misc{boldt2024elcc, |
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title = "{ELCC}: the Emergent Language Corpus Collection", |
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author = "Brendon Boldt and David Mortensen", |
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year = 2024, |
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url = "https://huggingface.co/datasets/bboldt/elcc", |
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}""" |
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), |
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version="0.1.0", |
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keywords=["emergent communication", "emergent language", "corpus"], |
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distribution=_distribution, |
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record_sets=[], |
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) |
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def insert_corpora(metadata: mlc.Metadata) -> None: |
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paths = sorted(list(Path("systems").glob("*/data/*/corpus.json"))) |
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for path in paths: |
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comps = str(path).split("/") |
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name = f"{comps[-4]}/{comps[-2]}" |
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metadata.distribution.append( |
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mlc.FileObject( |
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id=str(path), |
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name=str(path), |
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content_url=str(path), |
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encoding_format="application/json", |
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contained_in=["repo"], |
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) |
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) |
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metadata.record_sets.append( |
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mlc.RecordSet( |
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id=name, |
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name=name, |
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fields=[ |
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mlc.Field( |
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id=f"{name}/line", |
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name="line", |
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data_types=mlc.DataType.INTEGER, |
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repeated=True, |
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source=mlc.Source( |
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file_object=str(path), |
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extract=mlc.Extract(json_path="$[*]"), |
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), |
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), |
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], |
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) |
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) |
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def make_system_field(base_name: str, typ: type | None) -> mlc.Field: |
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name = f"system-metadata/{base_name}" |
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jp = f"$.{base_name}" |
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type_map = [ |
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(int, mlc.DataType.INTEGER), |
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(float, mlc.DataType.FLOAT), |
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(str, mlc.DataType.TEXT), |
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(bool, mlc.DataType.BOOL), |
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] |
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mlc_typ = None |
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for x, y in type_map: |
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if isinstance(typ, types.UnionType): |
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not_none = [x for x in typ.__args__ if not x == type(None)] |
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if len(typ.__args__) > 2 or len(not_none) != 1: |
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raise ValueError(f"Cannot handle {typ}") |
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typ = not_none[0] |
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elif isinstance(typ, typing._LiteralGenericAlias): |
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typ = type(typ.__args__[0]) |
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elif base_name == "system.data_source": |
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typ = str |
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if issubclass(typ, x): |
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mlc_typ = y |
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break |
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if mlc_typ is None: |
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mlc_typ = mlc.DataType.TEXT |
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return mlc.Field( |
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id=name, |
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name=base_name, |
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data_types=[mlc_typ], |
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source=mlc.Source( |
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file_set="system-metadata-files", |
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extract=mlc.Extract(json_path=jp), |
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), |
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) |
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def insert_system_md(metadata: mlc.Metadata) -> None: |
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metadata.record_sets.append( |
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mlc.RecordSet( |
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id="system-metadata-raw", |
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name="system-metadata-raw", |
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fields=[ |
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mlc.Field( |
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id="system-metadata-raw/path", |
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name="path", |
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data_types=[mlc.DataType.TEXT], |
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source=mlc.Source( |
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file_set="system-metadata-files-raw", |
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extract=mlc.Extract(file_property=mlc.FileProperty.fullpath), |
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), |
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), |
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mlc.Field( |
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id="system-metadata-raw/raw", |
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name="raw", |
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data_types=[mlc.DataType.TEXT], |
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source=mlc.Source( |
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file_set="system-metadata-files-raw", |
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extract=mlc.Extract(file_property=mlc.FileProperty.content), |
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), |
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), |
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], |
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) |
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) |
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fields = [ |
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mlc.Field( |
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id="system-metadata/path", |
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name="path", |
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data_types=[mlc.DataType.TEXT], |
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source=mlc.Source( |
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file_set="system-metadata-files", |
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extract=mlc.Extract(file_property=mlc.FileProperty.fullpath), |
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), |
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), |
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] |
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for k0, v0 in validate.SystemMetadata.model_fields.items(): |
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assert v0.annotation is not None |
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if isinstance(v0.annotation, type) and issubclass( |
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v0.annotation, pydantic.BaseModel |
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): |
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for k1, v1 in v0.annotation.model_fields.items(): |
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if k1 == "variants": |
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continue |
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fields.append(make_system_field(f"{k0}.{k1}", v1.annotation)) |
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else: |
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if k0 == "notes": |
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continue |
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fields.append(make_system_field(k0, v0.annotation)) |
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metadata.record_sets.append( |
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mlc.RecordSet( |
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id="system-metadata", |
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name="system-metadata", |
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fields=fields, |
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) |
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) |
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def insert_corpus_md(metadata: mlc.Metadata) -> None: |
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metadata.record_sets.append( |
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mlc.RecordSet( |
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id="corpus-metadata-raw", |
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name="corpus-metadata-raw", |
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fields=[ |
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mlc.Field( |
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id="corpus-metadata-raw/path", |
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name="path", |
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data_types=[mlc.DataType.TEXT], |
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source=mlc.Source( |
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file_set="corpus-metadata-files-raw", |
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extract=mlc.Extract(file_property=mlc.FileProperty.fullpath), |
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), |
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), |
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mlc.Field( |
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id="corpus-metadata-raw/raw", |
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name="raw", |
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data_types=[mlc.DataType.TEXT], |
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source=mlc.Source( |
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file_set="corpus-metadata-files-raw", |
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extract=mlc.Extract(file_property=mlc.FileProperty.content), |
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), |
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), |
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], |
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) |
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) |
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exemplar_path = "systems/nav-to-center/data/temperature_10/metadata.json" |
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with open(exemplar_path) as fo: |
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exemplar_data = json.load(fo) |
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fields = [ |
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mlc.Field( |
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id="corpus-metadata/path", |
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name="path", |
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data_types=[mlc.DataType.TEXT], |
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source=mlc.Source( |
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file_set="corpus-metadata-files", |
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extract=mlc.Extract(file_property=mlc.FileProperty.fullpath), |
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), |
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), |
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] |
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items = exemplar_data["metrics"]["analysis"].items() |
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for k, v in items: |
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name = f"metrics.analysis.{k}".replace(" ", "_").lower() |
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if isinstance(v, int): |
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typ = mlc.DataType.INTEGER |
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elif isinstance(v, bool): |
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typ = mlc.DataType.BOOL |
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else: |
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typ = mlc.DataType.FLOAT |
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jp = f'$.metrics.analysis["{k}"]' |
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fields.append( |
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mlc.Field( |
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id=f"corpus-metadata/{name}", |
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name=name, |
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data_types=[typ], |
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source=mlc.Source( |
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file_set="corpus-metadata-files", |
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extract=mlc.Extract(json_path=jp), |
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), |
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) |
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) |
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metadata.record_sets.append( |
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mlc.RecordSet( |
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id="corpus-metadata", |
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name="corpus-metadata", |
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fields=fields, |
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) |
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) |
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@functools.cache |
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def get_metadata() -> mlc.Metadata: |
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insert_system_md(_metadata) |
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insert_corpus_md(_metadata) |
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insert_corpora(_metadata) |
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return _metadata |
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def save_metadata() -> None: |
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metadata = get_metadata() |
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print(_metadata.issues.report()) |
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with open("croissant.json", "w") as fo: |
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d = metadata.to_json() |
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d["datePublished"] = str(d["datePublished"]) |
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json.dump(d, fo, indent=2) |
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def test() -> None: |
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dataset = mlc.Dataset(jsonld="croissant.json") |
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records = dataset.records(record_set="corpus-metadata-raw") |
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for i, x in enumerate(records): |
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print(i, x) |
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if i > 10: |
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break |
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pass |
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if __name__ == "__main__": |
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save_metadata() |
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