From 9860e7562e58a4adb2d2fa21623a5eb06f095b6d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A9d=C3=A9ric=20Hurier=20=28Fmind=29?= Date: Sat, 24 Feb 2024 10:12:17 +0100 Subject: [PATCH] V0.3.0 (#3) --- .github/ISSUE_TEMPLATE.md | 17 + .github/PULL_REQUEST_TEMPLATE.md | 7 + .github/actions/setup/action.yml | 11 + .github/workflows/on-pull-request.yml | 15 + .github/workflows/on-release-published.yml | 41 + .gitignore | 6 +- confs/inference.yaml | 3 - confs/training.yaml | 3 - data/Readme.txt | 0 data/hour.csv | 0 docker-compose.yml | 9 + mlops-python-package.code-workspace | 1 + models/.gitkeep | 0 poetry.lock | 2651 ++++++++++++++++++++ pyproject.toml | 25 +- src/bikes/__init__.py | 1 + src/bikes/configs.py | 56 +- src/bikes/datasets.py | 54 +- src/bikes/jobs.py | 291 ++- src/bikes/metrics.py | 52 +- src/bikes/models.py | 61 +- src/bikes/registers.py | 177 ++ src/bikes/schemas.py | 27 +- src/bikes/scripts.py | 46 +- src/bikes/searchers.py | 45 +- src/bikes/serializers.py | 74 - src/bikes/services.py | 107 +- src/bikes/splitters.py | 57 +- tasks/__init__.py | 1 - tasks/checks.py | 12 +- tasks/cleans.py | 3 +- tasks/containers.py | 8 +- tasks/docs.py | 8 +- tasks/packages.py | 6 +- tests/confs/valid/1. tuning.yaml | 4 +- tests/confs/valid/2. training.yaml | 4 +- tests/confs/valid/3. inference.yaml | 2 +- tests/conftest.py | 130 +- tests/test_configs.py | 40 +- tests/test_datasets.py | 16 +- tests/test_jobs.py | 93 +- tests/test_metrics.py | 2 - tests/test_models.py | 2 - tests/test_registers.py | 77 + tests/test_schemas.py | 2 - tests/test_scripts.py | 22 +- tests/test_searchers.py | 4 +- tests/test_serializers.py | 30 - tests/test_services.py | 23 +- tests/test_splitters.py | 2 - 50 files changed, 3902 insertions(+), 426 deletions(-) create mode 100644 .github/ISSUE_TEMPLATE.md create mode 100644 .github/PULL_REQUEST_TEMPLATE.md create mode 100644 .github/actions/setup/action.yml create mode 100644 .github/workflows/on-pull-request.yml create mode 100644 .github/workflows/on-release-published.yml mode change 100755 => 100644 data/Readme.txt mode change 100755 => 100644 data/hour.csv create mode 100644 docker-compose.yml delete mode 100644 models/.gitkeep create mode 100644 poetry.lock create mode 100644 src/bikes/registers.py delete mode 100644 src/bikes/serializers.py create mode 100644 tests/test_registers.py delete mode 100644 tests/test_serializers.py diff --git a/.github/ISSUE_TEMPLATE.md b/.github/ISSUE_TEMPLATE.md new file mode 100644 index 0000000..610808e --- /dev/null +++ b/.github/ISSUE_TEMPLATE.md @@ -0,0 +1,17 @@ +## Expected Behavior + + +## Actual Behavior + + +## Steps to Reproduce the Problem + +1. +1. +1. + +## Specifications + +- Version: +- Platform: +- Subsystem: \ No newline at end of file diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 0000000..0dd2e9d --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,7 @@ +Fixes # + +## Proposed Changes + +- +- +- \ No newline at end of file diff --git a/.github/actions/setup/action.yml b/.github/actions/setup/action.yml new file mode 100644 index 0000000..97486a6 --- /dev/null +++ b/.github/actions/setup/action.yml @@ -0,0 +1,11 @@ +name: Setup +description: Setup for project workflows +runs: + using: composite + steps: + - run: pipx install invoke poetry + shell: bash + - uses: actions/setup-python@v5 + with: + python-version: 3.12 + cache: poetry diff --git a/.github/workflows/on-pull-request.yml b/.github/workflows/on-pull-request.yml new file mode 100644 index 0000000..dd69569 --- /dev/null +++ b/.github/workflows/on-pull-request.yml @@ -0,0 +1,15 @@ +on: + pull_request: + branches: + - main +concurrency: + cancel-in-progress: true + group: ${{ github.workflow }}-${{ github.ref }} +jobs: + checks: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: ./.github/actions/setup + - run: poetry install --with dev,checks,formats + - run: poetry run invoke checks diff --git a/.github/workflows/on-release-published.yml b/.github/workflows/on-release-published.yml new file mode 100644 index 0000000..01984c9 --- /dev/null +++ b/.github/workflows/on-release-published.yml @@ -0,0 +1,41 @@ +on: + release: + types: [published] +env: + DOCKER_IMAGE: ghcr.io/fmind/mlops-python-package:latest +concurrency: + cancel-in-progress: true + group: on-release-published +jobs: + pages: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: ./.github/actions/setup + - run: poetry install --with docs + - run: poetry run invoke docs + - uses: JamesIves/github-pages-deploy-action@v4 + with: + folder: docs/ + branch: gh-pages + packages: + permissions: + packages: write + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: ./.github/actions/setup + - run: poetry run invoke packages + - uses: docker/login-action@v3 + with: + registry: ghcr.io + username: ${{ github.actor }} + password: ${{ secrets.GITHUB_TOKEN }} + - uses: docker/setup-buildx-action@v3 + - uses: docker/build-push-action@v5 + with: + push: true + context: . + cache-to: type=gha + cache-from: type=gha + tags: ${{ env.DOCKER_IMAGE }} diff --git a/.gitignore b/.gitignore index c0c2d2a..a78d83e 100644 --- a/.gitignore +++ b/.gitignore @@ -12,17 +12,13 @@ # Editor /.idea/ /.vscode/ +.ipynb_checkpoints/ # Environs /.venv/ -poetry.lock - -# Notebooks -.ipynb_checkpoints/ # Project /docs/* -/models/* /outputs/* !**/.gitkeep diff --git a/confs/inference.yaml b/confs/inference.yaml index b17a3ee..0aa6c2f 100644 --- a/confs/inference.yaml +++ b/confs/inference.yaml @@ -6,6 +6,3 @@ job: outputs: KIND: ParquetWriter path: outputs/predictions.parquet - deserializer: - KIND: JoblibModelDeserializer - path: models/model.joblib diff --git a/confs/training.yaml b/confs/training.yaml index 3801ee9..ca80fdc 100644 --- a/confs/training.yaml +++ b/confs/training.yaml @@ -6,6 +6,3 @@ job: targets: KIND: ParquetReader path: data/targets.parquet - serializer: - KIND: JoblibModelSerializer - path: models/model.joblib diff --git a/data/Readme.txt b/data/Readme.txt old mode 100755 new mode 100644 diff --git a/data/hour.csv b/data/hour.csv old mode 100755 new mode 100644 diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..7baece6 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,9 @@ +# https://docs.docker.com/compose/compose-file/ +services: + mlflow: + image: ghcr.io/mlflow/mlflow:v2.10.2 + ports: + - 5000:5000 + environment: + - MLFLOW_HOST=0.0.0.0 + command: mlflow server diff --git a/mlops-python-package.code-workspace b/mlops-python-package.code-workspace index 3395494..f551ecd 100644 --- a/mlops-python-package.code-workspace +++ b/mlops-python-package.code-workspace @@ -23,6 +23,7 @@ "dchanco.vsc-invoke", "ms-python.black-formatter", "ms-python.isort", + "ms-python.mypy-type-checker", "ms-python.pylint", "ms-python.python", "ms-python.vscode-pylance", diff --git a/models/.gitkeep b/models/.gitkeep 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available." repository = "https://github.com/fmind/mlops-python-package" authors = ["Médéric HURIER "] @@ -32,30 +32,32 @@ pyarrow = "^15.0.0" pydantic = "^2.5.3" pydantic-settings = "^2.1.0" scikit-learn = "^1.4.0" +mlflow-skinny = "^2.10.2" [tool.poetry.group.dev.dependencies] invoke = "^2.2.0" -ipykernel = "^6.29.0" pre-commit = "^3.6.0" -[tool.poetry.group.checkers.dependencies] +[tool.poetry.group.docs.dependencies] +pdoc = "^13.1.1" + +[tool.poetry.group.checks.dependencies] coverage = "^7.4.1" mypy = "^1.8.0" pylint = "^3.0.3" pytest = "^8.0.0" pytest-cov = "^4.1.0" pytest-xdist = "^3.5.0" +pandera = { extras = ["mypy"], version = "^0.18.0" } -[tool.poetry.group.notebooks.dependencies] -nbformat = "^5.9.2" - -[tool.poetry.group.documenters.dependencies] -pdoc = "^13.1.1" - -[tool.poetry.group.formatters.dependencies] +[tool.poetry.group.formats.dependencies] black = "^24.1.1" isort = "^5.13.2" +[tool.poetry.group.notebooks.dependencies] +ipykernel = "^6.29.0" +nbformat = "^5.9.2" + # CONFIGURATIONS [tool.black] @@ -63,6 +65,7 @@ line-length = 120 [tool.coverage.run] branch = true +source = ["src"] [tool.isort] line_length = 120 @@ -74,7 +77,7 @@ ignore_missing_imports = true plugins = ["pandera.mypy", "pydantic.mypy"] [tool.pylint.MASTER] -disable = ['too-few-public-methods', 'too-many-arguments'] +disable = ['too-few-public-methods', 'too-many-arguments', 'too-many-locals'] max-line-length = 120 extension-pkg-whitelist = "pydantic" diff --git a/src/bikes/__init__.py b/src/bikes/__init__.py index e69de29..afe8041 100644 --- a/src/bikes/__init__.py +++ b/src/bikes/__init__.py @@ -0,0 +1 @@ +"""Predict the number of bikes available.""" diff --git a/src/bikes/configs.py b/src/bikes/configs.py index 2e8142b..695fa7f 100644 --- a/src/bikes/configs.py +++ b/src/bikes/configs.py @@ -1,7 +1,9 @@ -"""Parse and merge config files.""" +"""Parse, merge, and convert YAML configs.""" # %% IMPORTS +import typing as T + from cloudpathlib import AnyPath from omegaconf import DictConfig, ListConfig, OmegaConf @@ -9,11 +11,18 @@ Config = ListConfig | DictConfig -# %% LOADERS +# %% PARSERS + +def parse_file(path: str) -> Config: + """Parse a config file from a path. -def parse_config(path: str) -> Config: - """Load a config file.""" + Args: + path (str): local or remote path. + + Returns: + Config: representation of the config file. + """ any_path = AnyPath(path) # pylint: disable=no-member text = any_path.read_text() # type: ignore @@ -21,16 +30,43 @@ def parse_config(path: str) -> Config: return config -def parse_configs(paths: list[str]) -> Config: - """Load and merge config files.""" - configs = map(parse_config, paths) - config = OmegaConf.merge(*configs) - return config +def parse_string(string: str) -> Config: + """Parse the given config string. + + Args: + string (str): configuration string. + + Returns: + Config: representation of the config string. + """ + return OmegaConf.create(string) + + +# %% MERGERS + + +def merge_configs(configs: T.Sequence[Config]) -> Config: + """Merge a list of config objects into one. + + Args: + configs (list[Config]): list of config objects. + + Returns: + Config: representation of the merged config objects. + """ + return OmegaConf.merge(*configs) # %% CONVERTERS def to_object(config: Config) -> object: - """Convert a config to a python object.""" + """Convert a config object to a python object. + + Args: + config (Config): representation of the config. + + Returns: + object: conversion of the config to a python object. + """ return OmegaConf.to_container(config, resolve=True) diff --git a/src/bikes/datasets.py b/src/bikes/datasets.py index abd6285..3db6be4 100644 --- a/src/bikes/datasets.py +++ b/src/bikes/datasets.py @@ -11,11 +11,15 @@ # %% READERS -class Reader(abc.ABC, pdt.BaseModel): - """Base class for a dataset reader.""" +class Reader(abc.ABC, pdt.BaseModel, strict=True): + """Base class for a dataset reader. - # note: use reader to load data in memory - # e.g., to read file, database, cloud storage, ... + Use a reader to load a dataset in memory. + e.g., to read file, database, cloud storage, ... + + Attributes: + limit (int, optional): maximum number of rows to read from dataset. + """ KIND: str @@ -23,19 +27,30 @@ class Reader(abc.ABC, pdt.BaseModel): @abc.abstractmethod def read(self) -> pd.DataFrame: - """Read a dataframe from a dataset.""" + """Read a dataframe from a dataset. + + Returns: + pd.DataFrame: dataframe representation. + """ class ParquetReader(Reader): - """Read a dataframe from a parquet file.""" + """Read a dataframe from a parquet file. + + Attributes: + path (str): local or remote path to a dataset. + """ KIND: T.Literal["ParquetReader"] = "ParquetReader" path: str + @T.override def read(self) -> pd.DataFrame: - """Read a dataframe from a parquet dataset.""" - return pd.read_parquet(self.path).head(self.limit) + data = pd.read_parquet(self.path) + if self.limit is not None: + data = data.head(self.limit) + return data ReaderKind = ParquetReader @@ -43,28 +58,37 @@ def read(self) -> pd.DataFrame: # %% WRITERS -class Writer(abc.ABC, pdt.BaseModel): - """Base class for a dataset writer.""" +class Writer(abc.ABC, pdt.BaseModel, strict=True): + """Base class for a dataset writer. - # note: use writer to save data from memory - # e.g., to write file, database, cloud storage, ... + Use a writer to save a dataset from memory. + e.g., to write file, database, cloud storage, ... + """ KIND: str @abc.abstractmethod def write(self, data: pd.DataFrame) -> None: - """Write a dataframe to a dataset.""" + """Write a dataframe to a dataset. + + Args: + data (pd.DataFrame): dataframe representation. + """ class ParquetWriter(Writer): - """Writer a dataframe to a parquet file.""" + """Writer a dataframe to a parquet file. + + Attributes: + path (str): local or remote file to a dataset. + """ KIND: T.Literal["ParquetWriter"] = "ParquetWriter" path: str + @T.override def write(self, data: pd.DataFrame) -> None: - """Write a dataframe to a parquet dataset.""" pd.DataFrame.to_parquet(data, self.path) diff --git a/src/bikes/jobs.py b/src/bikes/jobs.py index b47cfe5..f65fac5 100644 --- a/src/bikes/jobs.py +++ b/src/bikes/jobs.py @@ -5,10 +5,11 @@ import abc import typing as T +import mlflow import pydantic as pdt from loguru import logger -from bikes import datasets, metrics, models, schemas, searchers, serializers, services, splitters +from bikes import datasets, metrics, models, registers, schemas, searchers, services, splitters # %% TYPES @@ -18,41 +19,76 @@ # %% JOBS -class Job(abc.ABC, pdt.BaseModel): - """Base class for a job.""" +class Job(abc.ABC, pdt.BaseModel, strict=True): + """Base class for a job. - # note: use jobs to provide run contexts - # e.g., to define common services like logger + use a job to execute runs in context. + e.g., to define common services like logger + + Attributes: + logger_service (services.LoggerService): manage the logging system. + mlflow_service (services.MLflowService): manage the mlflow system. + """ KIND: str - # services - logger_service: services.Service = services.LoggerService() + logger_service: services.LoggerService = services.LoggerService() + mlflow_service: services.MLflowService = services.MLflowService() + + def __enter__(self) -> T.Self: + """Enter the job context. - def __enter__(self) -> "Job": - """Enter the context.""" - # services + Returns: + T.Self: return the current object. + """ self.logger_service.start() - # return + logger.debug("[START] MLflow service: {}", self.mlflow_service) + self.mlflow_service.start() return self def __exit__(self, exc_type, exc_value, traceback) -> T.Literal[False]: - """Exit the context.""" - # services + """Exit the job context. + + Args: + exc_type: ignored. + exc_value: ignored. + traceback: ignored. + + Returns: + T.Literal[False]: always propagate exceptions. + """ + logger.debug("[STOP] MLflow service: {}", self.mlflow_service) + self.mlflow_service.stop() self.logger_service.stop() - # return - return False # do not suppress exceptions + return False @abc.abstractmethod def run(self) -> Locals: - """Run the job in context.""" + """Run the job in context. + + Returns: + Locals: local job variables. + """ class TuningJob(Job): - """Find the best hyperparameters for a model.""" + """Find the best hyperparameters for a model. + + Attributes: + run_name (str): name of the MLflow experiment run. + inputs (datasets.ReaderKind): dataset reader with inputs variables. + targets (datasets.ReaderKind): dataset reader with targets variables. + results (datasets.WriterKind): dataset writer for searcher results. + model (models.ModelKind): machine learning model to tune. + metric (metrics.MetricKind): main metric for evaluation. + splitter (splitters.SplitterKind): splitter for datasets. + searcher (searchers.SearcherKind): searcher algorithm. + """ KIND: T.Literal["TuningJob"] = "TuningJob" + # run + run_name: str = "Tuning" # read inputs: datasets.ReaderKind targets: datasets.ReaderKind @@ -69,132 +105,179 @@ class TuningJob(Job): param_grid={"max_depth": [3, 5, 7]}, ) + @T.override def run(self) -> Locals: - """Run the tuning job in context.""" - # read - # - inputs - logger.info("Read inputs: {}", self.inputs) - inputs = schemas.InputsSchema.check(self.inputs.read()) - logger.info("- Inputs shape: {}", inputs.shape) - # - targets - logger.info("Read targets: {}", self.targets) - targets = schemas.TargetsSchema.check(self.targets.read()) - logger.info("- Targets shape: {}", targets.shape) - # - asserts - assert len(inputs) == len(targets), "Inputs and targets should have the same length!" - # model - logger.info("With model: {}", self.model) - # metric - logger.info("With metric: {}", self.metric) - # splitter - logger.info("With splitter: {}", self.splitter) - # searcher - logger.info("Run searcher: {}", self.searcher) - results, best_score, best_params = self.searcher.search( - model=self.model, metric=self.metric, cv=self.splitter, inputs=inputs, targets=targets - ) - logger.info("- Results: {}", len(results)) - logger.info("- Best Score: {}", best_score) - logger.info("- Best Params: {}", best_params) - # write - logger.info("Write results: {}", self.results) - self.results.write(results) + # run + logger.info("Start run: {} ", self.run_name) + with mlflow.start_run(run_name=self.run_name) as run: + logger.info("- Run ID: {}", run.info.run_id) + # read + # - inputs + logger.info("Read inputs: {}", self.inputs) + inputs = schemas.InputsSchema.check(self.inputs.read()) + logger.info("- Inputs shape: {}", inputs.shape) + # - targets + logger.info("Read targets: {}", self.targets) + targets = schemas.TargetsSchema.check(self.targets.read()) + logger.info("- Targets shape: {}", targets.shape) + # - asserts + assert len(inputs) == len(targets), "Inputs and targets should have the same length!" + # model + logger.info("With model: {}", self.model) + # metric + logger.info("With metric: {}", self.metric) + # splitter + logger.info("With splitter: {}", self.splitter) + # searcher + logger.info("Execute searcher: {}", self.searcher) + results, best_score, best_params = self.searcher.search( + model=self.model, metric=self.metric, cv=self.splitter, inputs=inputs, targets=targets + ) + logger.info("- # Results: {}", len(results)) + logger.info("- Best Score: {}", best_score) + logger.info("- Best Params: {}", best_params) + # write + logger.info("Write results: {}", self.results) + self.results.write(results) return locals() class TrainingJob(Job): - """Train and register a single AI/ML model""" + """Train and register a single AI/ML model + + Attributes: + run_name (str): name of the MLflow experiment run. + inputs (datasets.ReaderKind): dataset reader with inputs variables. + targets (datasets.ReaderKind): dataset reader with targets variables. + saver (registers.SaverKind): save the trained model in registry. + model (models.ModelKind): machine learning model to tune. + signer (registers.SignerKind): signer for the trained model. + scorers (list[metrics.MetricKind]): metrics for the evaluation. + splitter (splitters.SplitterKind): splitter for datasets. + registry_alias (str): alias of model. + """ KIND: T.Literal["TrainingJob"] = "TrainingJob" + # run + run_name: str = "Training" # read inputs: datasets.ReaderKind targets: datasets.ReaderKind # write - serializer: serializers.ModelSerializerKind + saver: registers.SaverKind = registers.CustomSaver() # model model: models.ModelKind = models.BaselineSklearnModel() + # signer + signer: registers.SignerKind = registers.InferSigner() # scorers scorers: list[metrics.MetricKind] = [metrics.SklearnMetric()] # splitter splitter: splitters.SplitterKind = splitters.TrainTestSplitter() + # register + registry_alias: str = "Champion" + @T.override def run(self) -> Locals: - """Run the training job in context.""" - # read - # - inputs - logger.info("Read inputs: {}", self.inputs) - inputs = schemas.InputsSchema.check(self.inputs.read()) - logger.info("- Inputs shape: {}", inputs.shape) - # - targets - logger.info("Read targets: {}", self.targets) - targets = schemas.TargetsSchema.check(self.targets.read()) - logger.info("- Targets shape: {}", targets.shape) - # - asserts - assert len(inputs) == len(targets), "Inputs and targets should have the same length!" - # split - logger.info("With splitter: {}", self.splitter) - # - index - train_index, test_index = next(self.splitter.split(inputs=inputs, targets=targets)) - # - inputs - inputs_train, inputs_test = inputs.iloc[train_index], inputs.iloc[test_index] - logger.info("- Inputs train shape: {}", inputs_train.shape) - logger.info("- Inputs test shape: {}", inputs_test.shape) - # - targets - targets_train, targets_test = targets.iloc[train_index], targets.iloc[test_index] - logger.info("- Targets train shape: {}", targets_train.shape) - logger.info("- Targets test shape: {}", targets_test.shape) - # - asserts - assert len(inputs_train) == len(targets_train), "Inputs and targets train should have the same length!" - assert len(inputs_test) == len(targets_test), "Inputs and targets test should have the same length!" - # model - logger.info("Fit model: {}", self.model) - self.model.fit(inputs=inputs_train, targets=targets_train) - # outputs - logger.info("Predict outputs: {}", len(inputs_test)) - outputs_test = self.model.predict(inputs=inputs_test) - logger.info("- Outputs test shape: {}", outputs_test.shape) - assert len(inputs_test) == len(outputs_test), "Inputs and outputs test should have the same length!" - # metric - for i, scorer in enumerate(self.scorers, start=1): - logger.info("{}. Run scorer: {}", i, scorer) - score = scorer.score(targets=targets_test, outputs=outputs_test) - logger.info("- Metric score: {}", score) - # write - logger.info("Write model: {}", self.serializer) - self.serializer.save(model=self.model) + # run + logger.info("Start run: {} ", self.run_name) + with mlflow.start_run(run_name=self.run_name) as run: + logger.info("- Run ID: {}", run.info.run_id) + # read + # - inputs + logger.info("Read inputs: {}", self.inputs) + inputs = schemas.InputsSchema.check(self.inputs.read()) + logger.info("- Inputs shape: {}", inputs.shape) + # - targets + logger.info("Read targets: {}", self.targets) + targets = schemas.TargetsSchema.check(self.targets.read()) + logger.info("- Targets shape: {}", targets.shape) + # - asserts + assert len(inputs) == len(targets), "Inputs and targets should have the same length!" + # split + logger.info("With splitter: {}", self.splitter) + # - index + train_index, test_index = next(self.splitter.split(inputs=inputs, targets=targets)) + # - inputs + inputs_train, inputs_test = inputs.iloc[train_index], inputs.iloc[test_index] + logger.info("- Inputs train shape: {}", inputs_train.shape) + logger.info("- Inputs test shape: {}", inputs_test.shape) + # - targets + targets_train, targets_test = targets.iloc[train_index], targets.iloc[test_index] + logger.info("- Targets train shape: {}", targets_train.shape) + logger.info("- Targets test shape: {}", targets_test.shape) + # - asserts + assert len(inputs_train) == len(targets_train), "Inputs and targets train should have the same length!" + assert len(inputs_test) == len(targets_test), "Inputs and targets test should have the same length!" + # model + logger.info("Fit model: {}", self.model) + self.model.fit(inputs=inputs_train, targets=targets_train) + # outputs + logger.info("Predict outputs: {}", len(inputs_test)) + outputs_test = self.model.predict(inputs=inputs_test) + logger.info("- Outputs test shape: {}", outputs_test.shape) + assert len(inputs_test) == len(outputs_test), "Inputs and outputs test should have the same length!" + # scorers + for i, scorer in enumerate(self.scorers, start=1): + logger.info("{}. Run scorer: {}", i, scorer) + score = scorer.score(targets=targets_test, outputs=outputs_test) + mlflow.log_metric(key=scorer.name, value=score) + logger.info("- Metric score: {}", score) + # sign + logger.info("Sign model: {}", self.signer) + signature = self.signer.sign(inputs=inputs, outputs=outputs_test) + logger.info("- Model signature: {}", signature.to_dict()) + # save + logger.info("Save model: {}", self.saver) + info = self.saver.save(model=self.model, signature=signature, input_example=inputs) + logger.info("- Model URI: {}", info.model_uri) + # register + logger.info("Register model: {}", self.registry_alias) + version = self.mlflow_service.register( + run_id=run.info.run_id, path=self.saver.path, alias=self.registry_alias + ) + logger.info("- Model version: {}", version.version) return locals() class InferenceJob(Job): - """Load a model and generate predictions.""" + """Load a model and generate predictions. + + Attributes: + inputs (datasets.ReaderKind): dataset reader with inputs variables. + outputs (datasets.WriterKind): dataset writer for the model outputs. + registry_alias (str): alias of the model to load. + loader (registers.LoaderKind): load the model from registry. + """ KIND: T.Literal["InferenceJob"] = "InferenceJob" - # inputs + # data inputs: datasets.ReaderKind - # outputs outputs: datasets.WriterKind # model - deserializer: serializers.ModelDeserializerKind + registry_alias: str = "Champion" + loader: registers.LoaderKind = registers.CustomLoader() + @T.override def run(self) -> Locals: - """Run the inference job in context.""" - # inputs + # read logger.info("Read inputs: {}", self.inputs) inputs = self.inputs.read() inputs = schemas.InputsSchema.check(inputs) logger.info("- Inputs shape: {}", inputs.shape) - # model - logger.info("With deserializer: {}", self.deserializer) - model = self.deserializer.load() - logger.info("- Model loaded: {}", model) + # uri + uri = f"models:/{self.mlflow_service.registry_name}@{self.registry_alias}" + logger.info("With URI: {}", uri) + # load + logger.info("Load model: {}", self.loader) + model = self.loader.load(uri=uri) + logger.info("- Model: {}", model) # predict logger.info("Predict outputs: {}", len(inputs)) - outputs = model.predict(inputs=inputs) + outputs = model.predict(data=inputs) logger.info("- Outputs shape: {}", outputs.shape) - assert len(inputs) == len(outputs), "Inputs and outputs should have the same length!" - # outputs + # write logger.info("Write outputs: {}", self.outputs) self.outputs.write(data=outputs) return locals() diff --git a/src/bikes/metrics.py b/src/bikes/metrics.py index f81ae9a..8e6dfe1 100644 --- a/src/bikes/metrics.py +++ b/src/bikes/metrics.py @@ -13,41 +13,69 @@ # %% METRICS -class Metric(abc.ABC, pdt.BaseModel): - """Base class for a metric.""" +class Metric(abc.ABC, pdt.BaseModel, strict=True): + """Base class for a metric. - # note: use metrics to evaluate models - # e.g., accuracy, precision, recall, f1, ... + Use metrics to evaluate model performance. + e.g., accuracy, precision, recall, mae, f1, ... + + Attributes: + name (str): name of the metric. + """ KIND: str + name: str + @abc.abstractmethod def score(self, targets: schemas.Targets, outputs: schemas.Outputs) -> float: - """Score the outputs against the targets.""" + """Score the outputs against the targets. + + Args: + targets (schemas.Targets): expected values. + outputs (schemas.Outputs): predicted values. + + Returns: + float: metric result. + """ def scorer(self, model: models.Model, inputs: schemas.Inputs, targets: schemas.Targets) -> float: - """Score the model outputs against the targets.""" + """Score the model outputs against the targets. + + Args: + model (models.Model): model to evaluate. + inputs (schemas.Inputs): model inputs values. + targets (schemas.Targets): model expected values. + + Returns: + float: metric result. + """ outputs = model.predict(inputs=inputs) # prediction score = self.score(targets=targets, outputs=outputs) return score class SklearnMetric(Metric): - """Compute metrics with sklearn.""" + """Compute metrics with sklearn. + + Attributes: + name (str): name of the sklearn metric. + greater_is_better (bool): maximize or minimize. + """ KIND: T.Literal["SklearnMetric"] = "SklearnMetric" name: str = "mean_squared_error" greater_is_better: bool = False + @T.override def score(self, targets: schemas.Targets, outputs: schemas.Outputs) -> float: - """Score the outputs against the targets with sklearn.""" metric = getattr(metrics, self.name) sign = 1 if self.greater_is_better else -1 - targets = targets[schemas.TargetsSchema.cnt] - outputs = outputs[schemas.OutputsSchema.prediction] - score = metric(y_pred=outputs, y_true=targets) - return score * sign + y_true = targets[schemas.TargetsSchema.cnt] + y_pred = outputs[schemas.OutputsSchema.prediction] + score = metric(y_pred=y_pred, y_true=y_true) * sign + return score MetricKind = SklearnMetric diff --git a/src/bikes/models.py b/src/bikes/models.py index 0c2e704..be3dc87 100644 --- a/src/bikes/models.py +++ b/src/bikes/models.py @@ -1,4 +1,4 @@ -"""Define machine learning models for the project.""" +"""Define trainable machine learning models.""" # %% IMPORTS @@ -19,40 +19,73 @@ # %% MODELS -class Model(abc.ABC, pdt.BaseModel): - """Base class for a model.""" +class Model(abc.ABC, pdt.BaseModel, strict=True): + """Base class for a model. - # note: use models to adapt AI/ML frameworks - # e.g., to swap easily one model with another + Use a model to adapt AI/ML frameworks. + e.g., to swap easily one model with another. + """ KIND: str # pylint: disable=unused-argument def get_params(self, deep: bool = True) -> Params: - """Get the model params.""" + """Get the model params. + + Args: + deep (bool, optional): ignored. Defaults to True. + + Returns: + Params: internal model parameters. + """ params: Params = {} for key, value in self.model_dump().items(): if not key.startswith("_") and not key.isupper(): params[key] = value return params - def set_params(self, **params: ParamValue) -> "Model": - """Set the model params in place.""" + def set_params(self, **params: ParamValue) -> T.Self: + """Set the model params in place. + + Returns: + T.Self: instance of the model. + """ for key, value in params.items(): setattr(self, key, value) return self @abc.abstractmethod - def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> "Model": - """Fit the model on the given inputs and targets.""" + def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> T.Self: + """Fit the model on the given inputs and targets. + + Args: + inputs (schemas.Inputs): model training inputs. + targets (schemas.Targets): model training targets. + + Returns: + Model: instance of the model. + """ @abc.abstractmethod def predict(self, inputs: schemas.Inputs) -> schemas.Outputs: - """Generate outputs with the model for the given inputs.""" + """Generate outputs with the model for the given inputs. + + Args: + inputs (schemas.Inputs): model prediction inputs. + + Returns: + schemas.Outputs: model prediction outputs. + """ class BaselineSklearnModel(Model): - """Simple baseline model built on top of sklearn.""" + """Simple baseline model built on top of sklearn. + + Attributes: + max_depth (int): maximum depth of the random forest. + n_estimators (int): number of estimators in the random forest. + random_state (int, optional): random state of the machine learning pipeline. + """ KIND: T.Literal["BaselineSklearnModel"] = "BaselineSklearnModel" @@ -81,8 +114,8 @@ class BaselineSklearnModel(Model): "weathersit", ] + @T.override def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> "BaselineSklearnModel": - """Fit the baseline sklearn model on the given inputs and targets.""" # subcomponents categoricals_transformer = preprocessing.OneHotEncoder(sparse_output=False, handle_unknown="ignore") # components @@ -106,8 +139,8 @@ def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> "BaselineSkle self._pipeline.fit(X=inputs, y=targets[schemas.TargetsSchema.cnt]) return self + @T.override def predict(self, inputs: schemas.Inputs) -> schemas.Outputs: - """Generate outputs with the baseline sklearn model for the given inputs.""" assert self._pipeline is not None, "Model should be fitted first!" prediction = self._pipeline.predict(inputs) # return an np.ndarray, not a dataframe! outputs = schemas.Outputs({schemas.OutputsSchema.prediction: prediction}, index=inputs.index) diff --git a/src/bikes/registers.py b/src/bikes/registers.py new file mode 100644 index 0000000..5b1fca1 --- /dev/null +++ b/src/bikes/registers.py @@ -0,0 +1,177 @@ +"""Adapters, signers, savers, and loaders for model registries.""" + +# %% IMPORTS + +import abc +import typing as T + +import mlflow +import pydantic as pdt + +from bikes import models, schemas + +# %% TYPES + +Info: T.TypeAlias = mlflow.models.model.ModelInfo +Version: T.TypeAlias = mlflow.entities.model_registry.ModelVersion +Signature: T.TypeAlias = mlflow.models.ModelSignature +CustomModel: T.TypeAlias = mlflow.pyfunc.PythonModel + +# %% ADAPTERS + + +class CustomAdapter(mlflow.pyfunc.PythonModel): + """Adapt a custom model to the MLflow PyFunc flavor. + + https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html + """ + + def __init__(self, model: models.Model): + """Initialize the custom adapter. + + Args: + model (models.Model): project model. + """ + self.model = model + + # pylint: disable=arguments-differ, unused-argument + def predict(self, context: mlflow.pyfunc.PythonModelContext, inputs: schemas.Inputs) -> schemas.Outputs: + """Generate predictions from a custom model. + + Args: + context (mlflow.pyfunc.PythonModelContext): ignored. + inputs (schemas.Inputs): inputs for the model. + + Returns: + schemas.Outputs: outputs of the model. + """ + return self.model.predict(inputs=inputs) + + +# %% SIGNERS + + +class Signer(abc.ABC, pdt.BaseModel, strict=True): + """Base class for making signatures. + + Allow to switch between signing approaches. + e.g., automatic inference vs manual signatures + https://mlflow.org/docs/latest/models.html#model-signature-and-input-example + """ + + KIND: str + + @abc.abstractmethod + def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature: + """Make a model signature from inputs/outputs. + + Args: + inputs (schemas.Inputs): inputs of the model. + outputs (schemas.Outputs): ouputs of the model. + + Returns: + ModelSignature: generated signature for the model. + """ + + +class InferSigner(Signer): + """Generate model signatures from data inference.""" + + KIND: T.Literal["InferModelSigner"] = "InferModelSigner" + + @T.override + def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature: + return mlflow.models.infer_signature(model_input=inputs, model_output=outputs) + + +SignerKind = InferSigner + + +# %% SAVERS + + +class Saver(abc.ABC, pdt.BaseModel, strict=True): + """Base class for saving models in registry. + + Separate model definition from serialization. + e.g., to switch between serialization flavors. + + Attributes: + path (str): model path inside the MLflow artifact store. + """ + + KIND: str + + path: str = "model" + + @abc.abstractmethod + def save(self, model: models.Model, signature: Signature, input_example: schemas.Inputs) -> Info: + """Save a model in the model registry. + + Args: + model (models.Model): model to save. + signature (Signature): model signature. + input_example (schemas.Inputs): inputs sample. + + Returns: + Info: model saving information. + """ + + +class CustomSaver(Saver): + """Saver for custom models using the MLflow PyFunc module. + + https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html + """ + + KIND: T.Literal["CustomSaver"] = "CustomSaver" + + def save(self, model: models.Model, signature: Signature, input_example: schemas.Inputs) -> Info: + """Save a custom model to the MLflow Model Registry.""" + custom = CustomAdapter(model=model) # adapt model + return mlflow.pyfunc.log_model( + artifact_path=self.path, python_model=custom, signature=signature, input_example=input_example + ) + + +SaverKind = CustomSaver + + +# %% LOADERS + + +class Loader(abc.ABC, pdt.BaseModel, strict=True): + """Base class for loading models from registry. + + Separate model definition from deserialization. + e.g., to switch between deserialization flavors. + """ + + KIND: str + + @abc.abstractmethod + def load(self, uri: str) -> T.Any: + """Load a model from the model registry. + + Args: + uri (str): URI of the model to load. + + Returns: + T.Any: model loaded from registry. + """ + + +class CustomLoader(Loader): + """Loader for custom models using the MLflow PyFunc module. + + https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html + """ + + KIND: T.Literal["CustomLoader"] = "CustomLoader" + + @T.override + def load(self, uri: str) -> CustomModel: + return mlflow.pyfunc.load_model(model_uri=uri) + + +LoaderKind = CustomLoader diff --git a/src/bikes/schemas.py b/src/bikes/schemas.py index 20e04c0..856d85f 100644 --- a/src/bikes/schemas.py +++ b/src/bikes/schemas.py @@ -10,20 +10,33 @@ class Schema(pa.DataFrameModel): - """Base class for a dataframe schema.""" + """Base class for a dataframe schema. - # note: use schemas to type your dataframes - # e.g., to communicate and validate its fields + Use a schema to type your dataframe object. + e.g., to communicate and validate its fields. + """ class Config: - """Default configuration.""" + """Default configuration. - coerce = True - strict = True + Attributes: + coerce (bool): convert data type if possible. + strict (bool): ensure the data type is correct. + """ + + coerce: bool = True + strict: bool = True @classmethod def check(cls, data: pd.DataFrame, **kwargs): - """Check the data with this schema.""" + """Check the data with this schema. + + Args: + data (pd.DataFrame): dataframe to check. + + Returns: + pd.DataFrame: validated dataframe with schema. + """ return cls.validate(data, **kwargs) diff --git a/src/bikes/scripts.py b/src/bikes/scripts.py index 2a3a023..5ae1476 100644 --- a/src/bikes/scripts.py +++ b/src/bikes/scripts.py @@ -1,8 +1,9 @@ -"""CLI Scripts for the program.""" +"""Entry point of the program.""" # %% IMPORTS import argparse +import json import pydantic as pdt import pydantic_settings as pdts @@ -12,26 +13,45 @@ # %% SETTINGS -class Settings(pdts.BaseSettings): - """Settings for the program.""" +class Settings(pdts.BaseSettings, strict=True): + """Settings for the program. + + Attributes: + job (jobs.JobKind): job associated with the settings. + """ job: jobs.JobKind = pdt.Field(..., discriminator="KIND") # %% PARSERS -parser = argparse.ArgumentParser(description="Run a job with configs.") -parser.add_argument("configs", nargs="+", help="Config files for a single job.") +parser = argparse.ArgumentParser(description="Run a single job from external settings.") +parser.add_argument("configs", nargs="+", help="Config files for the job (local or remote).") +parser.add_argument("-e", "--extras", nargs="+", default=[], help="Config strings for the job.") +parser.add_argument("-s", "--schema", action="store_true", help="Print settings schema and exit.") # %% SCRIPTS def main(argv: list[str] | None = None) -> int: - """Main function of the script.""" - args = parser.parse_args(argv) # argv or sys.argv - config = configs.parse_configs(args.configs) - object_ = configs.to_object(config) # dict - settings = Settings.model_validate(object_) - with settings.job as runner: # context - runner.run() # run in the context - return 0 # 0 = success + """Main function of the program. + + Args: + argv (list[str] | None, optional): program arguments. Defaults to None for sys.argv. + + Returns: + int: status code of the program. + """ + args = parser.parse_args(argv) + if args.schema is True: + schema = Settings.model_json_schema() + print(json.dumps(schema, indent=2)) + return 0 # success + files = map(configs.parse_file, args.configs) + strings = map(configs.parse_string, args.extras) + config = configs.merge_configs([*files, *strings]) + object_ = configs.to_object(config) # convert to dict + settings = Settings.model_validate(object_) # to pydantic + with settings.job as runner: + runner.run() # execute job + return 0 # success diff --git a/src/bikes/searchers.py b/src/bikes/searchers.py index a6096d4..19f3e70 100644 --- a/src/bikes/searchers.py +++ b/src/bikes/searchers.py @@ -13,6 +13,10 @@ # %% TYPES +# Grid of model params +# {param name -> param values} +Grid = dict[str, list[T.Any]] + # results of a model search # (results, best score, best params) Results = tuple[pd.DataFrame, float, models.Params] @@ -23,11 +27,12 @@ # %% SEARCHERS -class Searcher(abc.ABC, pdt.BaseModel): - """Base class for a searcher.""" +class Searcher(abc.ABC, pdt.BaseModel, strict=True): + """Base class for a searcher. - # note: use searcher to tune models - # e.g., to find the best model params + note: use searcher to tune models. + e.g., to find the best model params. + """ KIND: str @@ -40,22 +45,43 @@ def search( inputs: schemas.Inputs, targets: schemas.Targets, ) -> Results: - """Search the best model for the given inputs and targets.""" + """Search the best model for the given inputs and targets. + + Args: + model (models.Model): machine learning model to tune. + metric (metrics.Metric): main metric to optimize. + cv (CrossValidation): structure for cross-fold. + inputs (schemas.Inputs): model inputs for tuning. + targets (schemas.Targets): model targets for tuning. + + Returns: + Results: all the results of the tuning process. + """ class GridCVSearcher(Searcher): - """Grid searcher with cross-folds.""" + """Grid searcher with cross-folds. + + Attributes: + param_grid (Grid): mapping of param key -> values. + n_jobs (int, optional): number of jobs to run in parallel. + refit (bool): refit the model after the tuning. + verbose (int): set the search verbosity level. + error_score (str | float): strategy or value on error. + return_train_score (bool): include train scores. + """ KIND: T.Literal["GridCVSearcher"] = "GridCVSearcher" # public - param_grid: dict[str, list] + param_grid: Grid n_jobs: int | None = None - refit: bool = True + refit: bool = False verbose: int = 3 error_score: str | float = "raise" - return_train_score: bool = True + return_train_score: bool = False + @T.override def search( self, model: models.Model, @@ -64,7 +90,6 @@ def search( inputs: schemas.Inputs, targets: schemas.Targets, ) -> Results: - """Search the best model for the given inputs and targets using a grid search.""" searcher = model_selection.GridSearchCV( estimator=model, scoring=metric.scorer, diff --git a/src/bikes/serializers.py b/src/bikes/serializers.py deleted file mode 100644 index 104100f..0000000 --- a/src/bikes/serializers.py +++ /dev/null @@ -1,74 +0,0 @@ -"""Define objects serializers and deserializers.""" - -# %% IMPORTS - -import abc -import typing as T - -import joblib as jl -import pydantic as pdt - -from bikes import models - -# %% MODELS - -# %% - Serializers - - -class ModelSerializer(abc.ABC, pdt.BaseModel): - """Base class for a model serializer.""" - - # note: use serializer to save model objects - # e.g., to export the model to a pickle file - - KIND: str - - @abc.abstractmethod - def save(self, model: models.Model) -> None: - """Save the model to a given destination.""" - - -class JoblibModelSerializer(ModelSerializer): - """Model serializer based on joblib.""" - - KIND: T.Literal["JoblibModelSerializer"] = "JoblibModelSerializer" - - path: str - - def save(self, model: models.Model) -> None: - """Save the model to the given path.""" - jl.dump(model, self.path) - - -ModelSerializerKind = JoblibModelSerializer - - -# %% - Deserializers - - -class ModelDeserializer(abc.ABC, pdt.BaseModel): - """Base class for a model deserializer.""" - - # note: use serializer to load model objects - # e.g., to import the model from a pickle file - - KIND: str - - @abc.abstractmethod - def load(self) -> models.Model: - """Load the model from a given source.""" - - -class JoblibModelDeserializer(ModelDeserializer): - """Model deserializer based on joblib.""" - - KIND: T.Literal["JoblibModelDeserializer"] = "JoblibModelDeserializer" - - path: str - - def load(self) -> models.Model: - """Load the model from the given path.""" - return jl.load(self.path) - - -ModelDeserializerKind = JoblibModelDeserializer diff --git a/src/bikes/services.py b/src/bikes/services.py index ff9e87d..4493c1e 100644 --- a/src/bikes/services.py +++ b/src/bikes/services.py @@ -1,21 +1,25 @@ -"""Manage global contexts during execution.""" +"""Manage global context during execution.""" # %% IMPORTS import abc import sys +import typing as T +import mlflow import pydantic as pdt from loguru import logger +from mlflow.tracking import MlflowClient # %% SERVICES -class Service(abc.ABC, pdt.BaseModel): - """Base class for a global service.""" +class Service(abc.ABC, pdt.BaseModel, strict=True): + """Base class for a global service. - # Note: use services to manage global contexts - # e.g., logger object, mlflow client, spark context, ... + Use services to manage global contexts. + e.g., logger object, mlflow client, spark context, ... + """ @abc.abstractmethod def start(self) -> None: @@ -23,13 +27,24 @@ def start(self) -> None: def stop(self) -> None: """Stop the service.""" - # do nothing by default + # does nothing by default class LoggerService(Service): - """Service for logging messages.""" + """Service for logging messages. - # reference: https://loguru.readthedocs.io/en/stable/api/logger.html + https://loguru.readthedocs.io/en/stable/api/logger.html + + Attributes: + sink (str): logging output. + level (str): logging level. + format (str): logging format. + colorize (bool): colorize output. + serialize (bool): convert to JSON. + backtrace (bool): enable exception trace. + diagnose (bool): enable variable display. + catch (bool): catch errors during log handling. + """ sink: str = "stderr" level: str = "INFO" @@ -45,8 +60,8 @@ class LoggerService(Service): diagnose: bool = False catch: bool = True + @T.override def start(self) -> None: - """Start the logger service.""" # sinks sinks = { "stderr": sys.stderr, @@ -61,3 +76,77 @@ def start(self) -> None: config["sink"] = sinks.get(config["sink"], config["sink"]) # config logger.add(**config) + + +class MLflowService(Service): + """Service for MLflow tracking and registry. + + Attributes: + autolog_disable (bool): disable autologging. + autolog_disable_for_unsupported_versions (bool): disable autologging for unsupported versions. + autolog_exclusive (bool): If True, enables exclusive autologging. + autolog_log_input_examples (bool): If True, logs input examples during autologging. + autolog_log_model_signatures (bool): If True, logs model signatures during autologging. + autolog_log_models (bool): If True, enables logging of models during autologging. + autolog_log_datasets (bool): If True, logs datasets used during autologging. + autolog_silent (bool): If True, suppresses all MLflow warnings during autologging. + tracking_uri (str): The URI for the MLflow tracking server. + experiment_name (str): The name of the experiment to log runs under. + registry_uri (str): The URI for the MLflow model registry. + registry_name (str): The name of the registry. + """ + + # autolog + autolog_disable: bool = False + autolog_disable_for_unsupported_versions: bool = False + autolog_exclusive: bool = False + autolog_log_input_examples: bool = True + autolog_log_model_signatures: bool = True + autolog_log_models: bool = False + autolog_log_datasets: bool = True + autolog_silent: bool = False + # tracking + tracking_uri: str = "http://localhost:5000" + experiment_name: str = "bikes" + # registry + registry_uri: str = "http://localhost:5000" + registry_name: str = "bikes" + + def start(self): + """Start the mlflow service.""" + # uri + mlflow.set_tracking_uri(uri=self.tracking_uri) + mlflow.set_registry_uri(uri=self.registry_uri) + # experiment + mlflow.set_experiment(experiment_name=self.experiment_name) + # autologging + mlflow.autolog( + disable=self.autolog_disable, + disable_for_unsupported_versions=self.autolog_disable_for_unsupported_versions, + exclusive=self.autolog_exclusive, + log_input_examples=self.autolog_log_input_examples, + log_model_signatures=self.autolog_log_model_signatures, + log_models=self.autolog_log_models, + silent=self.autolog_silent, + ) + + def client(self) -> MlflowClient: + """Get an instance of MLflow client.""" + return MlflowClient(tracking_uri=self.tracking_uri, registry_uri=self.registry_uri) + + def register(self, run_id: str, path: str, alias: str) -> mlflow.entities.model_registry.ModelVersion: + """Register a model to mlflow registry. + + Args: + run_id (str): id of mlflow run. + path (str): path of artifact. + alias (str): model alias. + + Returns: + mlflow.entities.model_registry.ModelVersion: registered version. + """ + client = self.client() + model_uri = f"runs:/{run_id}/{path}" + version = mlflow.register_model(model_uri=model_uri, name=self.registry_name) + client.set_registered_model_alias(name=self.registry_name, alias=alias, version=version.version) + return version diff --git a/src/bikes/splitters.py b/src/bikes/splitters.py index d978ce3..4c03774 100644 --- a/src/bikes/splitters.py +++ b/src/bikes/splitters.py @@ -1,4 +1,4 @@ -"""Split dataframes into subsets.""" +"""Split dataframes into subsets (e.g., train/valid/test).""" # %% IMPORTS @@ -20,11 +20,12 @@ # %% SPLITTERS -class Splitter(abc.ABC, pdt.BaseModel): - """Base class for a splitter.""" +class Splitter(abc.ABC, pdt.BaseModel, strict=True): + """Base class for a splitter. - # note: use splitters to split datasets - # e.g., split between a train and test subsets + Use splitters to split datasets. + e.g., split between a train/test subsets. + """ # https://scikit-learn.org/stable/glossary.html#term-CV-splitter @@ -32,15 +33,39 @@ class Splitter(abc.ABC, pdt.BaseModel): @abc.abstractmethod def split(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> Splits: - """Split a dataframe into subsets.""" + """Split a dataframe into subsets. + + Args: + inputs (schemas.Inputs): model inputs. + targets (schemas.Targets): model targets. + groups (list | None, optional): group labels. Defaults to None. + + Returns: + Splits: iterator over the dataframe splits. + """ @abc.abstractmethod def get_n_splits(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> int: - """Get the number of splits generated.""" + """Get the number of splits generated. + + Args: + inputs (schemas.Inputs): models inputs. + targets (schemas.Targets): model targets. + groups (list | None, optional): group labels. Defaults to None. + + Returns: + int: number of splits generated. + """ class TrainTestSplitter(Splitter): - """Split a dataframe into a train and test subsets.""" + """Split a dataframe into a train and test subsets. + + Attributes: + shuffle (bool): shuffle dataset before splitting. + test_size (int | float): number or ratio for the test dataset. + random_state (int): random state for the splitter object. + """ KIND: T.Literal["TrainTestSplitter"] = "TrainTestSplitter" @@ -48,21 +73,27 @@ class TrainTestSplitter(Splitter): test_size: int | float = 24 * 30 * 2 # 2 months random_state: int = 42 + @T.override def split(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> Splits: - """Split a dataframe into a train and test subsets.""" index = np.arange(len(inputs)) # return integer position train_index, test_index = model_selection.train_test_split( index, shuffle=self.shuffle, test_size=self.test_size, random_state=self.random_state ) yield train_index, test_index + @T.override def get_n_splits(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> int: - """Get the unique train and test split.""" return 1 class TimeSeriesSplitter(Splitter): - """Split a dataframe into fixed time series subsets.""" + """Split a dataframe into fixed time series subsets. + + Attributes: + gap (int): gap between splits. + n_splits (int): number of split to generate. + test_size (int | float): number or ratio for the test dataset. + """ KIND: T.Literal["TimeSeriesSplitter"] = "TimeSeriesSplitter" @@ -70,13 +101,13 @@ class TimeSeriesSplitter(Splitter): n_splits: int = 4 test_size: int | float = 24 * 30 * 2 # 2 months + @T.override def split(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> Splits: - """Split a dataframe into fixed time series subsets.""" splitter = model_selection.TimeSeriesSplit(n_splits=self.n_splits, test_size=self.test_size) yield from splitter.split(inputs) + @T.override def get_n_splits(self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None) -> int: - """Get the number time series splits.""" return self.n_splits diff --git a/tasks/__init__.py b/tasks/__init__.py index a568255..4cc1e1b 100644 --- a/tasks/__init__.py +++ b/tasks/__init__.py @@ -1,6 +1,5 @@ """Task collections for the project.""" -# pylint: disable=redefined-builtin # mypy: ignore-errors # %% IMPORTS diff --git a/tasks/checks.py b/tasks/checks.py index 70c4b81..7dff32f 100644 --- a/tasks/checks.py +++ b/tasks/checks.py @@ -7,6 +7,11 @@ from invoke import task from invoke.context import Context +# %% CONFIGS + +COVERAGE_FAIL_UNDER = 80 +PYTEST_N_PROCESSES = "auto" + # %% TASKS @@ -19,7 +24,10 @@ def code(ctx: Context) -> None: @task def coverage(ctx: Context) -> None: """Check the coverage with coverage.""" - ctx.run("poetry run pytest -n auto --cov src/ --cov-fail-under=80 tests/") + ctx.run( + f"poetry run pytest --numprocesses={PYTEST_N_PROCESSES}" + f" --cov=src/ --cov-fail-under={COVERAGE_FAIL_UNDER} tests/" + ) @task @@ -38,7 +46,7 @@ def poetry(ctx: Context) -> None: @task def test(ctx: Context) -> None: """Check the tests with pytest.""" - ctx.run("poetry run pytest -n auto tests/") + ctx.run("poetry run pytest --numprocesses={PYTEST_N_PROCESSES} tests/") @task diff --git a/tasks/cleans.py b/tasks/cleans.py index d543202..446fe2c 100644 --- a/tasks/cleans.py +++ b/tasks/cleans.py @@ -13,7 +13,6 @@ @task def coverage(ctx: Context) -> None: """Clean coverage files.""" - ctx.run("rm -f .coverage") ctx.run("rm -f .coverage*") @@ -58,7 +57,7 @@ def pytest(ctx: Context) -> None: def python(ctx: Context) -> None: """Clean python files and folders.""" ctx.run("find . -type f -name '*.py[co]' -delete") - ctx.run("find . -type d -name __pycache__ -delete") + ctx.run(r"find . -type d -name __pycache__ -exec rm -r {} \+") @task(pre=[coverage, dist, docs, mypy, pytest, python], default=True) diff --git a/tasks/containers.py b/tasks/containers.py index ac9f537..012d6a4 100644 --- a/tasks/containers.py +++ b/tasks/containers.py @@ -12,6 +12,12 @@ # %% TASKS +@task +def compose(ctx: Context) -> None: + """Start docker compose.""" + ctx.run("docker compose up") + + @task(pre=[packages.build]) def build(ctx: Context) -> None: """Build the container image.""" @@ -24,6 +30,6 @@ def run(ctx: Context) -> None: ctx.run(f"docker run --rm {ctx.project.name}:latest") -@task(pre=[build, run], default=True) +@task(pre=[build], default=True) def all(_: Context) -> None: """Run all container tasks.""" diff --git a/tasks/docs.py b/tasks/docs.py index 7f2337f..91ad2be 100644 --- a/tasks/docs.py +++ b/tasks/docs.py @@ -9,19 +9,23 @@ from . import cleans +# %% CONFIGS + +DOC_FORMAT = "google" + # %% TASKS @task def api(ctx: Context) -> None: """Document the API with pdoc.""" - ctx.run(f"poetry run pdoc -o docs/api src/{ctx.project.name}") + ctx.run(f"poetry run pdoc --docformat={DOC_FORMAT} --output-directory=docs/ src/{ctx.project.name}") @task def serve(ctx: Context) -> None: """Document the API with pdoc.""" - ctx.run(f"poetry run pdoc src/{ctx.project.name}") + ctx.run(f"poetry run pdoc --docformat={DOC_FORMAT} src/{ctx.project.name}") @task(pre=[cleans.docs, api], default=True) diff --git a/tasks/packages.py b/tasks/packages.py index 509252a..19b85ff 100644 --- a/tasks/packages.py +++ b/tasks/packages.py @@ -9,13 +9,17 @@ from . import cleans +# %% CONFIGS + +BUILD_FORMAT = "wheel" + # %% TASKS @task(pre=[cleans.dist]) def build(ctx: Context) -> None: """Build a wheel package.""" - ctx.run("poetry build -f wheel") + ctx.run(f"poetry build --format={BUILD_FORMAT}") @task(pre=[build], default=True) diff --git a/tests/confs/valid/1. tuning.yaml b/tests/confs/valid/1. tuning.yaml index 26e0149..58ec32e 100644 --- a/tests/confs/valid/1. tuning.yaml +++ b/tests/confs/valid/1. tuning.yaml @@ -2,11 +2,11 @@ job: KIND: TuningJob inputs: KIND: ParquetReader - path: tests/data/inputs.parquet + path: ${test_data_path:}/inputs.parquet limit: 1500 targets: KIND: ParquetReader - path: tests/data/targets.parquet + path: ${test_data_path:}/targets.parquet limit: 1500 results: KIND: ParquetWriter diff --git a/tests/confs/valid/2. training.yaml b/tests/confs/valid/2. training.yaml index 7314c33..5e5d92b 100644 --- a/tests/confs/valid/2. training.yaml +++ b/tests/confs/valid/2. training.yaml @@ -2,11 +2,11 @@ job: KIND: TrainingJob inputs: KIND: ParquetReader - path: tests/data/inputs.parquet + path: ${test_data_path:}/inputs.parquet limit: 1500 targets: KIND: ParquetReader - path: tests/data/targets.parquet + path: ${test_data_path:}/targets.parquet limit: 1500 serializer: KIND: JoblibModelSerializer diff --git a/tests/confs/valid/3. inference.yaml b/tests/confs/valid/3. inference.yaml index 2200989..ffe366e 100644 --- a/tests/confs/valid/3. inference.yaml +++ b/tests/confs/valid/3. inference.yaml @@ -2,7 +2,7 @@ job: KIND: InferenceJob inputs: KIND: ParquetReader - path: tests/data/inputs.parquet + path: ${test_data_path:}/inputs.parquet limit: 1500 outputs: KIND: ParquetWriter diff --git a/tests/conftest.py b/tests/conftest.py index 6a30021..86653cb 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -5,11 +5,13 @@ # %% IMPORTS import os +import typing as T +import mlflow import omegaconf import pytest -from bikes import datasets, metrics, models, schemas, serializers, services, splitters +from bikes import datasets, metrics, models, registers, schemas, searchers, services, splitters # %% CONFIGS @@ -72,10 +74,24 @@ def tmp_results_path(tmp_path: str) -> str: return os.path.join(tmp_path, "results.parquet") -@pytest.fixture(scope="function") -def tmp_model_path(tmp_path: str) -> str: - """Return a tmp path of the model object.""" - return os.path.join(tmp_path, "model.joblib") +# %% - Configs + + +@pytest.fixture(scope="session") +def extra_config() -> str: + """Extra config for scripts.""" + # use OmegaConf resolver for ${tmp_path:} + string = """ + { + "job": { + "mlflow_service": { + "tracking_uri": "${tmp_path:}/experiments/", + "registry_uri": "${tmp_path:}/models/", + } + } + } + """ + return string # %% - Datasets @@ -166,7 +182,22 @@ def train_test_sets( train_index, test_index = train_test_split inputs_train, inputs_test = inputs.iloc[train_index], inputs.iloc[test_index] targets_train, targets_test = targets.iloc[train_index], targets.iloc[test_index] - return inputs_train, targets_train, inputs_test, targets_test + return ( + T.cast(schemas.Inputs, inputs_train), + T.cast(schemas.Targets, targets_train), + T.cast(schemas.Inputs, inputs_test), + T.cast(schemas.Targets, targets_test), + ) + + +# %% - Searchers + + +@pytest.fixture(scope="session") +def default_searcher() -> searchers.GridCVSearcher: + """Return the default searcher.""" + param_grid = {"max_depth": [2, 4], "n_estimators": [10, 15]} + return searchers.GridCVSearcher(param_grid=param_grid) # %% - Models @@ -208,27 +239,65 @@ def logger_service(): return service -# %% Serializers +@pytest.fixture(scope="function", autouse=True) +def mlflow_service(tmp_path: str) -> services.MLflowService: + """Return and start the mlflow service.""" + tracking_uri = f"{tmp_path}/experiments/" + registry_uri = f"{tmp_path}/models/" + service = services.MLflowService( + tracking_uri=tracking_uri, + registry_uri=registry_uri, + experiment_name="Testing", + registry_name="Testing", + ) + service.start() # ready to be used + return service -@pytest.fixture(scope="function") -def model_serializer(tmp_model_path: str) -> serializers.JoblibModelSerializer: - """Return the default model serializer.""" - return serializers.JoblibModelSerializer(path=tmp_model_path) +# %% - Registers -@pytest.fixture(scope="function") -def model_deserializer( - default_model: models.Model, model_serializer: serializers.JoblibModelSerializer -) -> serializers.JoblibModelDeserializer: - """Return the default model deserializer.""" - model_serializer.save(model=default_model) # refresh model - return serializers.JoblibModelDeserializer(path=model_serializer.path) +@pytest.fixture(scope="session") +def default_signer() -> registers.InferSigner: + """Return the default infer signer.""" + return registers.InferSigner() + + +@pytest.fixture(scope="session") +def default_signature( + default_signer: registers.InferSigner, inputs: schemas.Inputs, outputs: schemas.Outputs +) -> registers.Signature: + """Return the default signature.""" + return default_signer.sign(inputs=inputs, outputs=outputs) + + +@pytest.fixture(scope="session") +def default_saver() -> registers.CustomSaver: + """Return the default model saver.""" + return registers.CustomSaver(path="test") + + +@pytest.fixture(scope="session") +def default_loader() -> registers.CustomLoader: + """Return the default model loader.""" + return registers.CustomLoader() # %% - Resolvers +@pytest.fixture(scope="session", autouse=True) +def test_data_path_resolver(data_path: str) -> str: + """Register test_data with OmegaConf.""" + + def test_data_resolver() -> str: + """Get data_path.""" + return data_path + + omegaconf.OmegaConf.register_new_resolver("test_data_path", test_data_resolver, use_cache=True, replace=True) + return data_path + + @pytest.fixture(scope="function", autouse=True) def tmp_path_resolver(tmp_path: str) -> str: """Register tmp_path with Omegaconf.""" @@ -239,3 +308,28 @@ def tmp_path_resolver() -> str: omegaconf.OmegaConf.register_new_resolver("tmp_path", tmp_path_resolver, replace=True) return tmp_path + + +# %% - Mlflow Registry + + +@pytest.fixture(scope="function") +def default_alias() -> str: + """Return the default model alias.""" + return "Default" + + +@pytest.fixture(scope="function") +def default_mlflow_model_version( + inputs: schemas.Inputs, + default_alias: str, + default_model: models.Model, + default_saver: registers.CustomSaver, + default_signature: registers.Signature, + mlflow_service: services.MLflowService, +) -> registers.Version: + """Return an MLflow version for the default model.""" + with mlflow.start_run(run_name="Default") as run: + default_saver.save(model=default_model, signature=default_signature, input_example=inputs) + version = mlflow_service.register(run_id=run.info.run_id, path=default_saver.path, alias=default_alias) + return version diff --git a/tests/test_configs.py b/tests/test_configs.py index 9092e3e..0ef2c7b 100644 --- a/tests/test_configs.py +++ b/tests/test_configs.py @@ -1,5 +1,3 @@ -"""Test the configs module.""" - # pylint: disable=missing-docstring # %% IMPORTS @@ -13,7 +11,7 @@ # %% LOADERS -def test_parse_config(tmp_path: str): +def test_parse_file(tmp_path: str): # given text = """ a: 1 @@ -24,38 +22,40 @@ def test_parse_config(tmp_path: str): with open(path, "w", encoding="utf-8") as writer: writer.write(text) # when - config = configs.parse_config(path) + config = configs.parse_file(path) # then assert config == { "a": 1, "b": True, "c": [3, 4], - }, "Config should be loaded correctly!" + }, "File config should be loaded correctly!" + + +def test_parse_string(): + # given + text = """{"a": 1, "b": 2, "data": [3, 4]}""" + # when + config = configs.parse_string(text) + # then + assert config == { + "a": 1, + "b": 2, + "data": [3, 4], + }, "String config should be loaded correctly!" -def test_parse_configs(tmp_path: str): +def test_merge_configs(): # given - paths = [] - for i in range(3): - text = f""" - x: {i} - {i}: {i} - """ - path = os.path.join(tmp_path, f"{i}.yml") - with open(path, "w", encoding="utf-8") as writer: - writer.write(text) - paths.append(path) + confs = [OmegaConf.create({"x": i, i: i}) for i in range(3)] # when - config = configs.parse_configs(paths) + config = configs.merge_configs(confs) # then assert config == { - # each file should have its key 0: 0, 1: 1, 2: 2, - # x should be the last value read "x": 2, - }, "Configs should be loaded correctly!" + }, "Configs should be merged correctly!" # %% CONVERTERS diff --git a/tests/test_datasets.py b/tests/test_datasets.py index a9188ed..42b927a 100644 --- a/tests/test_datasets.py +++ b/tests/test_datasets.py @@ -1,25 +1,29 @@ -"""Test the datasets module.""" - # pylint: disable=missing-docstring # %% IMPORTS import os +import pytest + from bikes import datasets, schemas -# %% DATASETS +# %% READERS -def test_parquet_reader(inputs_path: str): +@pytest.mark.parametrize("limit", [None, 50]) +def test_parquet_reader(limit: int | None, inputs_path: str): # given - limit = 50 reader = datasets.ParquetReader(path=inputs_path, limit=limit) # when data = reader.read() # then assert data.ndim == 2, "Data should be a dataframe!" - assert len(data) == limit, "Data should have the limit size!" + if limit is not None: + assert len(data) == limit, "Data should have the limit size!" + + +# %% WRITERS def test_parquet_writer(targets: schemas.Targets, tmp_outputs_path: str): diff --git a/tests/test_jobs.py b/tests/test_jobs.py index 99b1153..349f44a 100644 --- a/tests/test_jobs.py +++ b/tests/test_jobs.py @@ -1,12 +1,10 @@ -"""Test the jobs module.""" - # pylint: disable=missing-docstring # %% IMPORTS import os -from bikes import datasets, jobs, metrics, models, searchers, serializers, splitters +from bikes import datasets, jobs, metrics, models, registers, searchers, services, splitters # %% JOBS @@ -17,20 +15,27 @@ def test_tuning_job( tmp_results_writer: datasets.ParquetWriter, default_model: models.Model, default_metric: metrics.Metric, + default_searcher: searchers.GridCVSearcher, time_series_splitter: splitters.TimeSeriesSplitter, + logger_service: services.LoggerService, + mlflow_service: services.MLflowService, ): # given - param_grid = {"max_depth": [2, 4], "n_estimators": [10, 15]} - searcher = searchers.GridCVSearcher(param_grid=param_grid) + run_name = "TuningTest" job = jobs.TuningJob( + run_name=run_name, inputs=inputs_reader, targets=targets_reader, results=tmp_results_writer, model=default_model, metric=default_metric, + searcher=default_searcher, splitter=time_series_splitter, - searcher=searcher, + logger_service=logger_service, + mlflow_service=mlflow_service, ) + mlflow_client = mlflow_service.client() + n_trials = sum(len(vs) for vs in default_searcher.param_grid.values()) # when with job as runner: out = runner.run() @@ -38,41 +43,60 @@ def test_tuning_job( # - vars assert set(out) == { "self", + "run", "inputs", "targets", "results", - "best_params", "best_score", + "best_params", } + # - run + assert out["run"].info.run_name == run_name, "Run name should be the same!" # - read assert out["inputs"].ndim == 2, "Inputs should be a dataframe!" assert out["targets"].ndim == 2, "Target should be a dataframe!" # - search + assert len(out["results"]) == n_trials, "Results should one row per candidate!" assert float("-inf") <= out["best_score"] <= float("+inf"), "Best score should be a float!" - assert out["best_params"].keys() == param_grid.keys(), "Best params should have the same keys!" - assert len(out["results"]) == sum(map(len, param_grid.values())), "Results should one row per candidate!" + assert out["best_params"].keys() == default_searcher.param_grid.keys(), "Best params should have the same keys!" # - write assert os.path.exists(tmp_results_writer.path), "Results should be saved to the given path!" + # - mlflow tracking + mlflow_experiment = mlflow_client.get_experiment_by_name(name=mlflow_service.experiment_name) + assert mlflow_experiment.name == mlflow_service.experiment_name, "Mlflow experiment name should be the same!" + mlflow_runs = mlflow_client.search_runs(experiment_ids=mlflow_experiment.experiment_id) + assert len(mlflow_runs) == n_trials + 1, "There should be as many Mlflow runs as trials + 1 for parent!" def test_training_job( inputs_reader: datasets.Reader, targets_reader: datasets.Reader, + default_saver: registers.Saver, empty_model: models.Model, default_metric: metrics.Metric, + default_signer: registers.Signer, train_test_splitter: splitters.TrainTestSplitter, - model_serializer: serializers.JoblibModelSerializer, + logger_service: services.LoggerService, + mlflow_service: services.MLflowService, ): # given + run_name = "TrainingTest" scorers = [default_metric] + registry_alias = "Testing" job = jobs.TrainingJob( + run_name=run_name, inputs=inputs_reader, targets=targets_reader, + saver=default_saver, model=empty_model, + signer=default_signer, scorers=scorers, splitter=train_test_splitter, - serializer=model_serializer, + registry_alias=registry_alias, + logger_service=logger_service, + mlflow_service=mlflow_service, ) + mlflow_client = mlflow_service.client() # when with job as runner: out = runner.run() @@ -80,6 +104,7 @@ def test_training_job( # - vars assert set(out) == { "self", + "run", "inputs", "targets", "train_index", @@ -92,7 +117,12 @@ def test_training_job( "scorer", "score", "i", + "signature", + "info", + "version", } + # - run + assert out["run"].info.run_name == run_name, "Run name should be the same!" # - read assert out["inputs"].ndim == 2, "Inputs should be a dataframe!" assert out["targets"].ndim == 2, "Target should be a dataframe!" @@ -112,20 +142,44 @@ def test_training_job( # - score assert out["i"] == len(scorers), "i should have the same length as scorers!" assert float("-inf") <= out["score"] <= float("+inf"), "Score should be between a numeric value!" - # - write - assert os.path.exists(model_serializer.path), "Model should be saved to the given path!" + # - signature + assert out["signature"].inputs is not None, "Signature inputs should not be None!" + assert out["signature"].outputs is not None, "Signature outputs should not be None!" + # - info + assert out["info"].run_id == out["run"].info.run_id, "Info run id should be the same!" + assert out["info"].signature == out["signature"], "Info signatures should be the same!" + assert out["info"].artifact_path == default_saver.path, "Info path should be the same!" + # - version + assert out["version"].version == 1, "Version number should be 1!" + assert out["version"].run_id == out["run"].info.run_id, "Version run id should be the same!" + # - mlflow tracking + mlflow_experiment = mlflow_client.get_experiment_by_name(name=mlflow_service.experiment_name) + assert mlflow_experiment.name == mlflow_service.experiment_name, "MLflow Experiment name should be the same!" + mlflow_runs = mlflow_client.search_runs(experiment_ids=mlflow_experiment.experiment_id) + assert len(mlflow_runs) == 1, "There should be a single MLflow run for the training!" + # - mlflow registry + model_version = mlflow_client.get_model_version(name=mlflow_service.registry_name, version=out["version"].version) + assert model_version.run_id == out["run"].info.run_id, "MLFlow model version run id should be the same!" def test_inference_job( inputs_reader: datasets.ParquetReader, tmp_outputs_writer: datasets.ParquetWriter, - model_deserializer: serializers.JoblibModelDeserializer, + default_loader: registers.Loader, + default_alias: str, + default_mlflow_model_version: registers.Version, + logger_service: services.LoggerService, + mlflow_service: services.MLflowService, ): # given + registry_alias = "Testing" job = jobs.InferenceJob( inputs=inputs_reader, outputs=tmp_outputs_writer, - deserializer=model_deserializer, + registry_alias=default_alias, + loader=default_loader, + logger_service=logger_service, + mlflow_service=mlflow_service, ) # when with job as runner: @@ -135,13 +189,18 @@ def test_inference_job( assert set(out) == { "self", "inputs", + "uri", "model", "outputs", } # - inputs assert out["inputs"].ndim == 2, "Inputs should be a dataframe!" + # - uri + assert mlflow_service.registry_name in out["uri"], "URI should contain the registry name!" + assert registry_alias in out["uri"], "URI should contain the registry alias!" # - model - assert isinstance(out["model"], models.Model), "Model should be a valid instance!" + assert out["model"].metadata.signature is not None, "Model should have a valid signature!" + assert out["model"].metadata.flavors.get("python_function"), "Model should have a pyfunc flavor!" + assert out["model"].metadata.run_id == default_mlflow_model_version.run_id, "Model run id should be the same!" # - outputs - assert out["outputs"].ndim == 2, "Outputs should be a dataframe!" assert os.path.exists(tmp_outputs_writer.path), "Outputs should be saved to the given path!" diff --git a/tests/test_metrics.py b/tests/test_metrics.py index b8afe36..d782343 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -1,5 +1,3 @@ -"""Test the metrics module.""" - # pylint: disable=missing-docstring # %% IMPORTS diff --git a/tests/test_models.py b/tests/test_models.py index e081ace..9bd8fd0 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -1,5 +1,3 @@ -"""Test the models module.""" - # pylint: disable=missing-docstring # %% IMPORTS diff --git a/tests/test_registers.py b/tests/test_registers.py new file mode 100644 index 0000000..ad2e58f --- /dev/null +++ b/tests/test_registers.py @@ -0,0 +1,77 @@ +"""Test the registers module.""" + +# pylint: disable=missing-docstring + +# %% IMPORTS + +import mlflow + +from bikes import models, registers, schemas, services + +# %% ADAPTERS + + +def test_custom_adapter(default_model: models.Model, inputs: schemas.Inputs): + # given + adapter = registers.CustomAdapter(model=default_model) + # when + outputs = adapter.predict(context=None, inputs=inputs) + # then + assert schemas.OutputsSchema.check(outputs) is not None, "Outputs should be valid!" + + +# %% SIGNERS + + +def test_infer_signer(inputs: schemas.Inputs, outputs: schemas.Outputs): + # given + signer = registers.InferSigner() + # when + signature = signer.sign(inputs=inputs, outputs=outputs) + # then + assert set(signature.inputs.input_names()) == set( + inputs.columns + ), "Signature inputs should contain input column names." + assert set(signature.outputs.input_names()) == set( + outputs.columns + ), "Signature outputs should contain output column names." + + +# %% SAVERS + + +def test_custom_saver(inputs: schemas.Inputs, default_model: models.Model, default_signature: registers.Signature): + # given + path = "custom" + saver = registers.CustomSaver(path=path) + # when + with mlflow.start_run(run_name="Custom") as run: + info = saver.save(model=default_model, signature=default_signature, input_example=inputs) + # then + assert info.run_id == run.info.run_id, "The run id should be the same!" + assert info.artifact_path == path, "The artifact path should be the same!" + assert info.flavors.get("python_function"), "The model should have a pyfunc!" + assert info.signature == default_signature, "The model signature should be the same!" + + +# %% LOADERS + + +def test_custom_loader( + inputs: schemas.Inputs, mlflow_service: services.MLflowService, default_mlflow_model_version: registers.Version +): + # given + name = mlflow_service.registry_name + version = default_mlflow_model_version.version + uri = f"models:/{name}/{version}" + loader = registers.CustomLoader() + # when + model = loader.load(uri=uri) + outputs = model.predict(data=inputs) + # then + # # - model + assert model.metadata.signature is not None, "The model should have a valid signature!" + assert model.metadata.run_id == default_mlflow_model_version.run_id, "The model run id should be the same!" + assert model.metadata.flavors.get("python_function") is not None, "The model should have a python_function flavor!" + # - output + assert schemas.OutputsSchema.check(outputs) is not None, "Outputs should be valid!" diff --git a/tests/test_schemas.py b/tests/test_schemas.py index 7683434..4cbd588 100644 --- a/tests/test_schemas.py +++ b/tests/test_schemas.py @@ -1,5 +1,3 @@ -"""Test the schemas module.""" - # pylint: disable=missing-docstring # %% IMPORTS diff --git a/tests/test_scripts.py b/tests/test_scripts.py index dba4c7a..3516dbc 100644 --- a/tests/test_scripts.py +++ b/tests/test_scripts.py @@ -1,9 +1,8 @@ -"""Test the scripts module.""" - # pylint: disable=missing-docstring # %% IMPORTS +import json import os import pytest @@ -13,6 +12,17 @@ # %% SCRIPTS +def test_schema(capsys): + # given + argv = ["prog", "--schema"] + # when + scripts.main(argv) + captured = capsys.readouterr() + # then + assert captured.err == "", "Capture error should be empty!" + assert json.loads(captured.out), "Capture output should be a valid JSON!" + + @pytest.mark.parametrize( "scenario", [ @@ -22,12 +32,14 @@ pytest.param("invalid", marks=pytest.mark.xfail), ], ) -def test_main(scenario: str, confs_path: str): +def test_main(scenario: str, confs_path: str, extra_config: str): # given root = os.path.join(confs_path, scenario) # when for path in sorted(os.listdir(root)): - conf = os.path.join(root, path) - status = scripts.main(argv=[conf]) + config = os.path.join(root, path) + argv = [config, "-e", extra_config] + print("Running main with:", argv) + status = scripts.main(argv=argv) # then assert status == 0, "Main should return 0!" diff --git a/tests/test_searchers.py b/tests/test_searchers.py index ce6e0ba..91718e8 100644 --- a/tests/test_searchers.py +++ b/tests/test_searchers.py @@ -1,5 +1,3 @@ -"""Test the searchers module.""" - # pylint: disable=missing-docstring # %% IMPORTS @@ -26,4 +24,4 @@ def test_grid_cv_searcher( # then assert set(best_params) == set(param_grid), "Best params should have the same keys as grid!" assert float("-inf") <= best_score <= float("+inf"), "Best score should be a floating number!" - assert len(result) == len(param_grid["max_depth"]), "Results should have one row per candidate!" + assert len(result) == sum(map(len, param_grid.values())), "Results should have one row per candidate!" diff --git a/tests/test_serializers.py b/tests/test_serializers.py deleted file mode 100644 index 75eec45..0000000 --- a/tests/test_serializers.py +++ /dev/null @@ -1,30 +0,0 @@ -"""Test the serializers module.""" - -# pylint: disable=missing-docstring - -# %% IMPORTS - -import os - -from bikes import models, serializers - -# %% MODELS - - -def test_joblib_model_serializer(empty_model: models.Model, tmp_model_path: str): - # given - serializer = serializers.JoblibModelSerializer(path=tmp_model_path) - # when - serializer.save(model=empty_model) - # then - assert os.path.exists(tmp_model_path), "Model should be saved to the given path!" - - -def test_joblib_model_deserializer(default_model: models.Model, model_serializer: serializers.JoblibModelSerializer): - # given - model_serializer.save(model=default_model) # save the model to a temporary path - deserializer = serializers.JoblibModelDeserializer(path=model_serializer.path) - # when - model = deserializer.load() - # then - assert model.get_params() == default_model.get_params(), "Deserialized model should have the same params!" diff --git a/tests/test_services.py b/tests/test_services.py index 8ad77a7..0c14ae0 100644 --- a/tests/test_services.py +++ b/tests/test_services.py @@ -1,9 +1,8 @@ -"""Test the services module.""" - # pylint: disable=missing-docstring # %% IMPORTS +import mlflow from loguru import logger from bikes import services @@ -23,3 +22,23 @@ def test_logger_service(capsys): assert capture.err == "", "No output to stderr!" assert "INFO" in capture.out, "INFO should be logged!" assert "DEBUG" not in capture.out, "Debug should not be logged!" + + +def test_mlflow_service(mlflow_service: services.MLflowService): + # given + service = mlflow_service + # when + service.start() + client = service.client() + service.stop() # no effect + # then + # - mlflow + assert mlflow_service.tracking_uri == mlflow.get_tracking_uri(), "Tracking URI should be the same!" + assert mlflow_service.registry_uri == mlflow.get_registry_uri(), "Registry URI should be the same!" + assert mlflow.get_experiment_by_name(mlflow_service.experiment_name), "Experiment should be setup!" + # - client + assert mlflow_service.tracking_uri == client.tracking_uri, "Tracking URI should be the same!" + assert ( + mlflow_service.registry_uri == client._registry_uri # pylint: disable=protected-access + ), "Tracking URI should be the same!" + assert client.get_experiment_by_name(mlflow_service.experiment_name), "Experiment should be setup!" diff --git a/tests/test_splitters.py b/tests/test_splitters.py index f774dac..7acbe71 100644 --- a/tests/test_splitters.py +++ b/tests/test_splitters.py @@ -1,5 +1,3 @@ -"""Test the splitters module.""" - # pylint: disable=missing-docstring # %% IMPORTS