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merged 14 commits into from
Apr 7, 2025
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feat: Model Competitions - Competition Model Services #279

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bc21656
base changes to split CompetitionSpecification into prediction and mo…
roselynh100 Apr 4, 2025
8a2bd6f
clean up the competition classes
roselynh100 Apr 4, 2025
ea6b137
get_competition returns predictionbased or modelbased
roselynh100 Apr 4, 2025
620544d
submit_competition_model
roselynh100 Apr 4, 2025
6ef91b8
list_competition_models ?????
roselynh100 Apr 4, 2025
2e28fe4
competition model creator is not necessarily owner of the model?
roselynh100 Apr 4, 2025
594b346
competition model must specify owner (not creator)
roselynh100 Apr 7, 2025
5f261d6
replace all CompetitionSpecification with PredictionBasedCompetition
roselynh100 Apr 7, 2025
e9c0f42
update tutorial for submitting to competition
roselynh100 Apr 7, 2025
d9c1e4a
Update polaris/hub/client.py
roselynh100 Apr 7, 2025
7373c39
address PR comments
roselynh100 Apr 7, 2025
29cd11b
remove model existence check in submit_competition_model
roselynh100 Apr 7, 2025
0c1cfc6
can only submit a pre-existing model to a competition
roselynh100 Apr 7, 2025
4ec9423
we don't need artifact id on model upload anymore
roselynh100 Apr 7, 2025
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34 changes: 29 additions & 5 deletions docs/tutorials/submit_to_competition.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -92,11 +92,13 @@
"id": "36f3e829",
"metadata": {},
"source": [
"## The Competition API\n",
"Similar to the benchmark API, the competition exposes two main API endpoints:\n",
"## Competition Methods\n",
"Competitions are differentiated by what type of submissions they accept. There are two types of competitions: prediction-based and model-based.\n",
"\n",
"- `get_train_test_split()`, which does exactly the same as for benchmarks. \n",
"- `submit_predictions()`, which is used to submit your predictions to a competition.\n",
"Similar to the benchmark API, the competition exposes two main methods:\n",
"\n",
"- `get_train_test_split()`, which does exactly the same as for benchmarks.\n",
"- `submit_entry()`, which is used to submit to a competition.\n",
"\n",
"Note that different from regular benchmarks, competitions don't have an `evaluate()` endpoint. \n",
"\n",
Expand All @@ -117,7 +119,7 @@
" predictions=predictions,\n",
" prediction_name=\"my-first-predictions\",\n",
" prediction_owner=\"my-username\",\n",
" report_url=\"https://www.example.com\", \n",
" report_url=\"https://www.example.com\",\n",
" # The below metadata is optional, but recommended.\n",
" github_url=\"https://github.com/polaris-hub/polaris\",\n",
" description=\"Just testing the Polaris API here!\",\n",
Expand All @@ -126,6 +128,28 @@
")"
]
},
{
"cell_type": "markdown",
"id": "d289a82a",
"metadata": {},
"source": [
"### Submit your _model_\n",
"To submit a model to a competition, specify the artifact id of a model that already exists in the Hub."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3b21ed0f",
"metadata": {},
"outputs": [],
"source": [
"competition.submit_entry(\n",
" model=\"your-username/model-name\",\n",
" owner=\"your-username\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "44973556",
Expand Down
233 changes: 9 additions & 224 deletions polaris/competition/__init__.py
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@@ -1,224 +1,9 @@
from collections import defaultdict
from datetime import datetime
from itertools import chain
from typing import Callable

from pydantic import Field, computed_field, model_validator
from typing_extensions import Self

from polaris.benchmark._split import SplitSpecificationV1Mixin
from polaris.benchmark._task import PredictiveTaskSpecificationMixin
from polaris.dataset import DatasetV2, Subset
from polaris.evaluate import CompetitionPredictions
from polaris.utils.dict2html import dict2html
from polaris.utils.errors import InvalidCompetitionError
from polaris.utils.types import (
ColumnName,
HttpUrlString,
HubOwner,
HubUser,
IncomingPredictionsType,
SlugCompatibleStringType,
)


class CompetitionSpecification(DatasetV2, PredictiveTaskSpecificationMixin, SplitSpecificationV1Mixin):
"""An instance of this class represents a Polaris competition.

Examples:
Basic API usage:
```python
import polaris as po

# Load the benchmark from the Hub
competition = po.load_competition("dummy-user/dummy-name")

# Get the train and test data-loaders
train, test = competition.get_train_test_split()

# Use the training data to train your model
# Get the input as an array with 'train.inputs' and 'train.targets'
# Or simply iterate over the train object.
for x, y in train:
...

# Work your magic to accurately predict the test set
prediction_values = np.array([0.0 for x in test])

# Submit your predictions
competition.submit_predictions(
prediction_name="first-prediction",
prediction_owner="dummy-user",
report_url="REPORT_URL",
predictions=prediction_values,
)
```

Attributes:
start_time: The time at which the competition starts accepting prediction submissions.
end_time: The time at which the competition stops accepting prediction submissions.
n_classes: The number of classes within each target column that defines a classification task.

For additional metadata attributes, see the base classes.
"""

_artifact_type = "competition"

dataset: None = None

start_time: datetime
end_time: datetime
n_classes: dict[ColumnName, int | None] = Field(..., default_factory=lambda: defaultdict(None))

@model_validator(mode="after")
def _validate_split_in_dataset(self) -> Self:
"""
All indices are valid given the dataset. We check the len of `self` here because a
competition entity includes both the dataset and benchmark in one artifact.
"""
max_i = len(self)
if any(i < 0 or i >= max_i for i in chain(self.split[0], *self.split[1].values())):
raise InvalidCompetitionError("The predefined split contains invalid indices")

return self

@model_validator(mode="after")
def _validate_cols_in_dataset(self) -> Self:
"""
Verifies that all specified columns are present in the dataset.
"""
columns = self.target_cols | self.input_cols
dataset_columns = set(self.columns)
if not columns.issubset(dataset_columns):
raise InvalidCompetitionError("Not all target or input columns were found in the dataset.")

return self

@model_validator(mode="after")
def _validate_n_classes(self) -> Self:
"""
The number of classes for each of the target columns.
"""
columns = set(self.n_classes.keys())
if not columns.issubset(self.target_cols):
raise InvalidCompetitionError("Not all target class members were found in the target columns.")

return self

@computed_field
@property
def dataset_artifact_id(self) -> str:
return self.artifact_id

def _get_subset(self, indices, hide_targets=True, featurization_fn=None) -> Subset:
"""Returns a [`Subset`][polaris.dataset.Subset] using the given indices. Used
internally to construct the train and test sets."""
return Subset(
dataset=self,
indices=indices,
input_cols=self.input_cols,
target_cols=self.target_cols,
hide_targets=hide_targets,
featurization_fn=featurization_fn,
)

def _get_test_sets(
self, hide_targets=True, featurization_fn: Callable | None = None
) -> dict[str, Subset]:
"""
Construct the test set(s), given the split in the competition specification. Used
internally to construct the test set for client use and evaluation.
"""
test_split = self.split[1]
return {
k: self._get_subset(v, hide_targets=hide_targets, featurization_fn=featurization_fn)
for k, v in test_split.items()
}

def get_train_test_split(
self, featurization_fn: Callable | None = None
) -> tuple[Subset, Subset | dict[str, Subset]]:
"""Construct the train and test sets, given the split in the competition specification.

Returns [`Subset`][polaris.dataset.Subset] objects, which offer several ways of accessing the data
and can thus easily serve as a basis to build framework-specific (e.g. PyTorch, Tensorflow)
data-loaders on top of.

Args:
featurization_fn: A function to apply to the input data. If a multi-input benchmark, this function
expects an input in the format specified by the `input_format` parameter.

Returns:
A tuple with the train `Subset` and test `Subset` objects.
If there are multiple test sets, these are returned in a dictionary and each test set has
an associated name. The targets of the test set can not be accessed.
"""

train = self._get_subset(self.split[0], hide_targets=False, featurization_fn=featurization_fn)
test = self._get_test_sets(hide_targets=True, featurization_fn=featurization_fn)

# For improved UX, we return the object instead of the dictionary if there is only one test set.
# Internally, however, assume that the test set is always a dictionary simplifies the code.
if len(test) == 1:
test = test["test"]
return train, test

def submit_predictions(
self,
predictions: IncomingPredictionsType,
prediction_name: SlugCompatibleStringType,
prediction_owner: str,
report_url: HttpUrlString,
contributors: list[HubUser] | None = None,
github_url: HttpUrlString | None = None,
description: str = "",
tags: list[str] | None = None,
user_attributes: dict[str, str] | None = None,
) -> None:
"""
Convenient wrapper around the
[`PolarisHubClient.submit_competition_predictions`][polaris.hub.client.PolarisHubClient.submit_competition_predictions] method.
It handles the creation of a standardized predictions object, which is expected by the Hub, automatically.

Args:
prediction_name: The name of the prediction.
prediction_owner: The slug of the user/organization which owns the prediction.
predictions: The predictions for each test set defined in the competition.
report_url: A URL to a report/paper/write-up which describes the methods used to generate the predictions.
contributors: The users credited with generating these predictions.
github_url: An optional URL to a code repository containing the code used to generated these predictions.
description: An optional and short description of the predictions.
tags: An optional list of tags to categorize the prediction by.
user_attributes: An optional dict with additional, textual user attributes.
"""
from polaris.hub.client import PolarisHubClient

standardized_predictions = CompetitionPredictions(
name=prediction_name,
owner=HubOwner(slug=prediction_owner),
predictions=predictions,
report_url=report_url,
contributors=contributors or [],
github_url=github_url,
description=description,
tags=tags or [],
user_attributes=user_attributes or {},
target_labels=self.target_cols,
test_set_labels=self.test_set_labels,
test_set_sizes=self.test_set_sizes,
)

with PolarisHubClient() as client:
client.submit_competition_predictions(
competition=self, competition_predictions=standardized_predictions
)

def _repr_html_(self):
"""For pretty printing in Jupyter."""
return dict2html(self.model_dump(exclude={"zarr_manifest_path", "zarr_manifest_md5sum", "split"}))

def __repr__(self):
return self.model_dump_json(exclude={"zarr_manifest_path", "zarr_manifest_md5sum", "split"}, indent=2)

def __str__(self):
return self.__repr__()
from polaris.competition._base import CompetitionSpecification
from polaris.competition._model_based import ModelBasedCompetition
from polaris.competition._prediction_based import PredictionBasedCompetition

__all__ = [
"CompetitionSpecification",
"ModelBasedCompetition",
"PredictionBasedCompetition",
]
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